the linguistic and reading skills of english language ... · language comprehension skills in...

THE LINGUISTIC AND READING SKILLS OF ENGLISH LANGUAGE LEARNERS AT-

RISK FOR POOR READING COMPREHENSION: PROFILES AND PREDICTORS

by

Christine M. J. Fraser

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Graduate Department of Applied Psychology and Human Development

Ontario Institute for Studies in Education

University of Toronto

© Copyright by Christine M. J. Fraser (2017)

THE LINGUISTIC AND READING SKILLS OF ELLS

ii

THE LINGUISTIC AND READING SKILLS OF ENGLISH LANGUAGE LEARNERS

AT-RISK FOR POOR READING COMPREHENSION: PROFILES AND PREDICTORS

Doctor of Philosophy 2017

Christine M. J. Fraser

Department of Applied Psychology and Human Development

Ontario Institute for Studies in Education

University of Toronto

Abstract

This dissertation concerns the linguistic and reading profiles and predictors of English language

learners (ELLs) classified as typically developing or at-risk for poor reading comprehension. The

ELLs in the studies came from Chinese, Portuguese, and Spanish home language backgrounds,

but had all begun formal schooling in English in kindergarten. An at-risk classification model

based on performance on components of the simple view of reading (Gough & Tunmer, 1986),

and using cut-off scores at the 30th percentile or below and the 40th percentile or above, was

employed for identification of poor and good readers, respectively. ELLs (n = 127) were

subtyped in grade 4 as either typically developing or at-risk based on their decoding and

language comprehension skills in relation to the ELL sample (and not to monolingual norms).

Reader subtypes used in the final analyses were: poor decoders (difficulties with word reading; n

= 17), poor language comprehenders (language impaired; n = 15), multi-deficit at-risker

(problems in decoding and language comprehension; n = 20), and typical developers (no deficits

in decoding or language comprehension; n = 57). Study 1 compared the grade 4 profiles of the

ELL reader subtypes on the following skills: word reading, reading fluency at the word- and text-

levels, vocabulary, inferencing strategy, and reading comprehension. To validate the at-risk

classification model, multivariate analysis of covariance (MANCOVA) results indicated that all

three at-risk reader subtypes were experiencing significant problems with their reading

comprehension in grade 4 when compared to typically developing ELLs. Different skill profiles


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were observed across the three at-risk reader groups in grade 4: poor decoders demonstrated

difficulties with various aspects of word reading (accuracy and fluency), and inferencing

strategy; poor language comprehenders demonstrated difficulties in word reading fluency; and

multi-deficit at-riskers demonstrated pervasive difficulties with all the reading and language

skills under study, including fluency and inferencing strategy. Study 2 identified longitudinal

(from grade 2) linguistic and reading predictors of later at-risk ELL reader subtype in grade 4.

Multinomial logistic regression models indicated that there were different predictors of later at-

risk status across the reading groups: word reading fluency for poor decoders; receptive

vocabulary for poor language comprehenders; and fluency and oral expression for multi-deficit

at-riskers. Similar to the findings of previous research with poor reading ELLs (e.g., Geva &

Herbert, 2012; Geva & Massey-Garrison, 2013; Li & Kirby, 2014), findings suggest that not all

ELL readers with poor reading comprehension are the same; there are different sources of

reading comprehension problems which point to different intervention foci. Furthermore, it

appears that readers struggling with reading comprehension due to poor language can be

successfully identified as early as grade 2, prior to the onset of their later difficulties in reading

comprehension. Findings provide support for an enhanced simple view of reading that also

includes fluency and inferencing strategy. Directions for future research and implications for

practice are presented.


iv

Acknowledgements

This dissertation would not have been possible without the commitment and dedication of

my supervisor, Dr. Esther Geva, and my committee members, Drs. Alexandra Gottardo and

Monique Herbert, all of whom were by my side every step of the way and who believed in me

when I couldn't believe in myself. Thank you. I would also like to thank the Geva Lab members,

ROP students, APHD administrative assistants, and many OISE friends and colleagues for their

continued support during this process.

The research reported on in this dissertation was funded by grants from the Canadian

Language and Literacy Research Network (CLLRNet) and the Social Sciences and Humanities

Research Council (SSHRC) of Canada, and is part of a larger project conducted in collaboration

with Dr. Esther Geva of the Ontario Institute for Studies in Education/University of Toronto, and

Dr. Alexandra Gottardo of Wilfrid Laurier University.


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Dedication

For my father, Thomas Fraser.

All of my successes in life are because of your unconditional love and support. Thank you.

And for my brother, Jamie T. J. Fraser (1974 – 2015).


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Table of Contents

Abstract ii

Acknowledgments iv

Dedication v

Table of Contents vi

List of Figures viii

List of Tables ix

Chapter 1: Theoretical Perspectives – Reading Comprehension in English

Language Learners (ELLs) 1

Introduction 1

The Simple View of Reading 2

The Cognitive Processes Involved in Word Reading 7

Sources of Difficulties in Word Reading 11

Oral Language Proficiency and Reading Comprehension 13

Language Impairment and Poor Comprehension 17

The Importance of Reading with Fluency 22

Inferencing and its Role in Comprehension 29

Chapter 2: The Current Research 36

Methodological Considerations 38

Chapter 3: General Method 42

Recruitment and Data Collection 42

Educational Context 43

Participants 44

Measures 45

Measures Used for Group Classification 48

Decoding 48

Language Comprehension 48

Cognitive Ability 49

Nonverbal Cognitive Ability 49

Phonological Awareness 49

Naming Speed 50

Working Memory 50

Oral Language Proficiency 50

Vocabulary Knowledge 50

Oral Expression 51

Reading Skills 51

Word Reading 52

Reading Fluency 53

Reading Comprehension 54


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Higher-order Processing 54

Inferencing 55

Chapter 4: Data Preparation 56

Amalgamation of Participants from Different Home Language Backgrounds 56

Classification of ELL Participants to Reading Subtypes 63

Summary of Data Preparation Techniques 75

Chapter 5: Study 1 –Linguistic and Reading Profiles of At-risk ELL Reader 78

Research Question and Hypotheses for Study 1 79

Relationships among the Grade 4 Variables Used in Study 1 82

Do children with compromised decoding and/or language comprehension

also have difficulties with their current word reading, fluency, vocabulary,

inferencing strategy, and reading comprehension? 84

Types of Inferencing Strategies Used by the ELL Reading Groups 91

Summary of Study 1 95

Chapter 6: Study 2 – Longitudinal Predictors of At-risk ELL Readers 98

Research Questions and Hypotheses for Study 2 98

Relationships Among the Grade 2 Variables Used in Study 2 101

Do ELL reader groups as classified in grade 4, differ on phonological processing,

oral language, and reading skills in grade 2? 103

What grade 2 cognitive, language, and reading skills best predict at-risk ELL

reading group status in grade 4? 106

Summary of Study 2 110

Chapter 7: Discussion 113

Profiles and Predictors of ELLs At-risk for Poor Reading Comprehension 113

Poor Decoder Profile 116

Poor Language Comprehender Profile 117

Children with a Combined Poor Decoding and Poor Language

Comprehension Profile 118

Fluency as an Index of Poor Reading in ELLs 120

The Role of Inferencing in Second Language Reading Comprehension 122

Limitations and Future Directions for Research 126

Implications for Theory and Practice 127

A Specialized View of Reading for Special Populations 127

Differentiated Instruction for Subtypes of At-risk ELL Readers 129

Conclusion 130

References 133


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List of Figures

Figure 1. The two-dimensional model for conceptualizing the relationship 37

between language comprehension and decoding in typical developing

and at-risk reader groups

Figure 2. Scatterplot showing home language background of the reader groups by 68

their decoding and language comprehension raw scores

Figure 3. Bar graph displaying the number of participants in each reader group by 69

home language group

Figure 4. Decoding and language comprehension profiles for the reader groups 71

under study: multi-deficit at-riskers, poor decoders, poor language

comprehenders, and typical developers

Figure 5. Word reading profiles (in isolation and in context) for the typically 88

developing and at-risk reader groups under study

Figure 6. Word- and text-level fluency profiles for the typically developing and 89

at-risk reader groups under study

Figure 7. Vocabulary, inferencing, and reading comprehension profiles for the 90

typically developing and at-risk reader groups under study


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List of Tables

Table 1. Summary of the variables used in analyses 47

Table 2. The effect of home language background on age and the cognitive, 58

linguistic, and reading skills in grade 2: Descriptive statistics and post

hoc comparisons

Table 3. The effect of home language background on age, the grouping measures, 62

and the cognitive, linguistic, higher-order, and reading skills in grade 4:

Descriptive statistics and post hoc comparisons

Table 4. The effect of ELL reader group on age, decoding, and language 74

comprehension: Descriptive statistics and post hoc comparisons

Table 5. Correlation matrix showing the relationships among the variables in 83

Study 1

Table 6. The effect of ELL reader group on word reading, fluency, vocabulary, 86

inferencing, and reading comprehension variables of interest in grade 4:

Descriptive statistics and post hoc comparisons

Table 7. Descriptive statistics and item analyses for different inferencing strategy 93

types

Table 8. The effect of ELL reader group on inference strategy type: Descriptive 94

statistics and post hoc comparisons

Table 9. Study 2 correlation matrix showing relationships among the grouping 102

variables in grade 4 and variables of interest in grade 2

Table 10. The effect of ELL reader group on the cognitive, linguistic, and reading 104

variables of interest in grade 2: Descriptive statistics and post hoc

comparisons

Table 11. The prediction of at-risk reader group in grade 4 using grade 2 variables: 108

Multinomial logistic regression summary

Table 12. Grade 4 skill set summary: Concurrent linguistic and reading profiles 114

of 114 ELL readers at-risk for poor reading comprehension when

compared with typically developing ELLs

Table 13. Grade 2 skill set summary: Retrospective cognitive, linguistic, and 115

reading profiles of ELL readers in grade 2 who by grade 4 are at-risk

for poor reading comprehension


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Chapter 1: Theoretical Perspectives – Reading Comprehension in English Language

Learners (ELLs)

Introduction

Skilled reading comprehension is comprised of an intricate interaction between various

aspects of oral language proficiency, decoding, reading fluency, higher-order processing, cultural

and background knowledge, and the ability to use metacognitive comprehension strategies, to

name a few. The following excerpt from Perfetti and Adlof (2012), captures eloquently this

complexity. This definition is also relevant to second language learners (L2). Yet, L2 learners

often develop their reading and language skills in tandem.

Reading comprehension is widely agreed to be not one, but many things. At the

least, it is agreed to entail cognitive processes that operate on many different

kinds of knowledge to achieve many different kinds of reading tasks.

Comprehension occurs as the reader builds one or more mental representations

of a text message (e.g., Kintsch & Rawson, 2005). Among these representations,

an accurate model of the situation described by the text (Van Dijk & Kintsch,

1983) is the product of successful deep comprehension. The comprehension

processes that bring about these mental representations occur at multiple levels

across units of language: word-level, sentence-level, and text-level. Across these

levels, processes of word identification, parsing, referential mapping, and

inference all contribute, interacting with the reader’s conceptual knowledge

(Perfetti and Adlof, 2012, p. 3).

English language learners (ELLs) are by definition less proficient in the English

language. The task of distinguishing typically developing ELLs from ELLs with reading

difficulties is complex and problematic because it is often unclear whether their poor reading


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ability stems from their developing language skill, or from an underlying problem with their

reading (Geva & Herbert, 2012; Mancilla-Martinez & Lesaux, 2010). Research regarding the

early and accurate identification of ELLs with differing types of poor reading, and in particular

with difficulties involving poor language comprehension, has only of late begun to receive

focused attention. The purpose of this dissertation is to gain insight into the reading difficulties

experienced by ELLs who struggle with their reading through two complementary lines of

investigation. Study 1 examines the concurrent grade 4 reading and language abilities of ELL

readers subtyped as typically developing, or at-risk for poor reading comprehension due to poor

decoding, poor language comprehension, or both. Study 2 investigates the extent to which it is

possible to predict different profiles of ELL poor readers in grade 4 by examining individual

differences on early language and literacy skills, namely, phonological processing, early oral

language proficiency, and reading skills in grade 2. Accurate identification, timely intervention,

and differentiated instruction that is informed by the type(s) of deficits observed, is critical for

ELL children with reading problems if they are to receive the support they need to be good

readers (Fraser, Adelson, & Geva, 2014).

The Simple View of Reading

Two foundational areas that are noted for being particularly important for reading

comprehension are word reading and language comprehension. These areas are parsimoniously

captured in the popular Simple View of Reading model (SVR; Gough & Tunmer 1986). A key

tenet of the SVR that is highly relevant for the present research is that reading comprehension is

the result of the interaction between decoding and language comprehension; adequate ability in

both skills is necessary for successful reading comprehension. Indeed, poor readers have been

identified with good decoding skills but poor language ability, as well as the reverse pattern,

poor decoding skills but good language ability (for review, see Kirby and Savage, 2008). This


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suggests not only the importance of decoding and language in reading comprehension, but also a

distinction between the skills; decoding and language comprehension are two separate abilities.

The SVR model then, is suggestive of three types of poor readers: those with poor decoding,

those with poor language comprehension, and those with varying profiles of poor skills in both

areas.

The specific type of interaction between decoding and language comprehension however,

has been less substantiated. Initial empirical work involving the SVR suggests that the

interaction between decoding and language is multiplicative in nature (e.g., Hoover & Gough,

1990). Hoover and Gough (1990) in their study of 254 English-Spanish bilingual children in

grades 1 through 4, found that the relationship between decoding and language comprehension

was best characterized in a product or multiplicative model, where reading comprehension was

the product of decoding and language comprehension, and that an absence of either skill would

result in difficulties in making meaning from text. That is, excellent decoding is not sufficient to

enable reading comprehension in the absence of language comprehension, and vice-versa,

excellent language comprehension is not sufficient to enable effective reading comprehension in

the absence of decoding skills. Findings of an additive interaction model are also prevalent in the

literature (e.g., Chen & Vellutino, 1997; Dreyer & Katz, 1992; Kershaw and Schatschneider

2012; Lee & Wheldall, 2009; Savage, 2006; for a further discussion see Salceda, Alonso, &

Castilla-Earls, 2014). There are even studies which show evidence that both models

(multiplicative and additive) work equally well (e.g., Joshi & Aaron, 2000). Regardless of the

model considered, it is clear that both skill sets are required for effective reading comprehension,

and that a deficit(s) in either will likely result in diminished reading comprehension capacity.

More recent research has added nuance to the SVR by acknowledging the contribution of

components skills: with decoding examples including naming speed and phonological


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awareness, and language comprehension examples including syntax and vocabulary (Braze et al.,

2016; Chen & Vellutino, 1997; Conners, 2009; Geva & Farnia, 2012; Johnston & Kirby, 2006;

Oakhill & Cain, 2012; Silverman et al., 2015). This nuance is supported by findings that the

cognitive process involved in word reading—which included visual, phonological, and

orthographic-phonological mapping skills required to derive meaning from the printed word—

correlate with reading comprehension concurrently and longitudinally (e.g. Geva & Farnia, 2012;

Gottardo & Mueller, 2009). Likewise, global measures of language comprehension, as well as a

gamut of language comprehension component skills (e.g., vocabulary, morphology, syntax,

semantics, and pragmatics) also contribute to reading comprehension (Babayigit, 2014; Braze et

al., 2016; Droop & Verhoeven, 2003; Farnia & Geva, 2011; Geva, 2006; Hutchinson, Whitely,

Smith, & Connors, 2003; Lam, Chen, Geva, Luo, & Li, 2012; Lesaux, Rupp, & Siegel, 2007;

Proctor, Carlo, August, & Snow, 2005; Verhoeven, 2000). This added nuance to the SVR model

may be of particular importance when considering how the model accounts for sources of

reading comprehension problems stemming from poor decoding, poor language, or both.

Much of the research supporting the SVR has been conducted with learners whose L1 is

English (e.g., Catts, Hogan, & Fey, 2003; Kendeou, van den Broek, White, & Lynch, 2009;

Oakhill & Cain, 2012; Oakhill, Cain, & Bryant, 2003; Tilstra, McMaster, van den Broek,

Kendeou, & Rapp, 2009). In more recent years the SVR has also received ample support with

learners of diverse orthographies, including Latin-based alphabetic orthographies (e.g., Kendeou,

Papadopoulos, & Kotzapoulou, 2013 in Greek; Tobia & Bonifacci, 2015 in Italian; Torppa et al.,

2016 in Finnish; for a review of Latin-based alphabetic orthographies, see Florit & Cain, 2011),

as well as, non-Latin-based orthographies (e.g., Joshi, Ji, Breznitz, Amiel, & Yulia, 2015 in

Hebrew; Joshi, Tao, Aaron, & Quiroz, 2012 in Greek). It has also received support with L2

learners (e.g., Geva & Farnia, 2012; Gottardo & Mueller, 2009; Nakamura, Koda, & Joshi, 2014;


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Manis, Lindsey, & Bailey, 2004; Proctor, Carlo, August, & Snow, 2005; Verhoeven, & van

Leeuwe, 2012; Yaghoub-Zadeh, Farnia, & Geva, 2012).

Where findings about the usefulness of the SVR in understanding reading in L1 and L2

diverge however, is the extent to which each decoding and language comprehension, contribute

to reading comprehension. L1 and L2 decoding skills (typically measured through word reading

accuracy and fluency measured using both words and nonwords) tend to correlate with each

other due to underlying common cognitive processes such as phonological awareness and

naming speed (for reviews, see Lervag, Braten, & Hulme; 2009; Melby-Lervag & Lervag, 2014).

Given adequate instruction and time-on-task, typically developing L1 and L2 learners will

perform similarly on word reading tasks. Thus, there is a stability in contribution to early reading

comprehension that can be observed across L1 and L2 learners. The contribution of word reading

skills to reading comprehension diminishes with age however (Whitehurst & Lonigan, 2002),

and other processes essential for reading comprehension, such as inferencing, begin to become

more important. One caveat to this observation though, is age of exposure for L2 learners.

Pasquarella, Gottardo, and Grant (2012) found that when L2 learners began their L2 learning

only in adolescence, both decoding and language played an equally important role in their

reading comprehension even though decoding skills typically do not play a role in adolescent

reading comprehension.

Despite these compelling findings, decoding and language comprehension do not account

for all of the variance observed in reading comprehension. In a recent systematic review

involving 56 studies where the language of focus was English (L1), Salceda et al. (2014)

reported that decoding and language comprehension accounted for an average of 50% of the

variance of reading comprehension, with measurement error explaining an additional 22% (no

similar review could be located involving L2 learners). At the same time, some researchers have


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called for a model of reading comprehension which includes additional factors known to

contribute to reading comprehension, such as working memory, reading fluency, and

metacognitive strategies (Cain, Oakhill, and Bryant, 2004; Hulme and Snowling, 2009; Kirby &

Savage, 2008). For example, Cain, Oakhill, and Bryant (2004) found in their research involving

the relationship between working memory and reading comprehension in English monolingual

children aged 8, 9, and 11, that at each time point working memory and higher level component

skills of reading comprehension such as inference making, predicted unique variance in reading

comprehension beyond decoding and language comprehension. In a similar vein, Hulme and

Snowling (2009) note the importance of inferencing making and metacognitive factors for

reading comprehension that account for variance beyond decoding and language.

In the literature it has also argued that an expanded SVR might also be warranted for L2

learners (Geva & Farnia, 2012; Pasquarella, Gottardo, & Grant, 2012; Yaghoub-Zadeh, Farnia,

& Geva, 2012). For example, Geva and Farnia (2012) conducted a longitudinal study with ELL

children from different linguistic backgrounds in grades 2 to 5. The SVR framework was helpful

in understanding which factors distinguished L2 learners with good English comprehension from

those who struggled; results indicated that word reading skills and language skills in English

predicted English reading comprehension concurrently and longitudinally. In support of an

expanded view however, their findings also indicated that word- and text-level reading fluency

assessed in grade 5 were significant predictors of English reading comprehension (in addition to

word reading and language skills). Yaghoub-Zadeh, Farnia, and Geva (2012) in their study

involving 308 ELLs from differing linguistic backgrounds arrived at similar conclusions

regarding the inclusion of fluency in an expanded view of the SVR. They examined the

mediating role of grade 2 word-level reading skills in the relationship between grade 1

phonological awareness, naming speed, and listening comprehension, and grade 3 reading


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comprehension and reading fluency. They concluded that the development of reading in ELLs

was better understood when reading fluency was added to the SVR equation. (See the section on

reading fluency for a more detailed discussion about the role of fluency in reading

comprehension). Overall, results suggest that the SVR framework, and even more so, an

expanded framework, works for monolinguals. SVR components are also applicable where L2

samples are concerned, but the relationships are not identical to those of L1 learners. When

applying the model to L2 populations additional factors play a role because L2 learners by

definition perform more poorly on various components of language comprehension (for review

see Jeon & Yamshita, 2014), as well as because depending on the onset of exposure, decoding

may continue to play an important role even into adolescence (e.g., Pasquarella et al., 2012).

More research is needed to clarify how the SVR model might be adapted in light of the unique

characteristics and differences observed in L2 learners, in terms of predictors of reading

comprehension.

The Cognitive Processes Involved in Word Reading

Several underlying cognitive processes are needed for efficient word reading. These

processes include phonological awareness (PA), rapid automatized naming (RAN), and working

memory (for review, see Wagner & Torgesen, 1987). PA is the ability to recognize and

manipulate the sound parts of language (Goswami & Bryant, 1990). Numerous studies have

shown PA to be one of the most powerful predictors of current and later reading success, directly

for word reading, and indirectly for reading comprehension through word reading (Bradley &

Bryant, 1983; Wagner et al., 1997; for reviews, see Kirby, Desrochers, Roth, & Lai, 2008;

Melby-Lervag, Lyster, & Hulme, 2012). In addition to being important for English word reading,

researchers have also observed the importance of PA for word reading in languages as diverse as

Greek, Hebrew, Czech, Cantonese, Mandarin, Turkish, French, Japanese, and Spanish (Aidinis


8

& Nunes, 2001; Ben-Dror, Bentin, & Frost, 1995; Caravolas & Bruck, 1993; Cheung, Chen, Lai,

Wong, & Hills, 2001; Durgunoglu & Oney, 1999; Morita & Tamaoka, 2002; for reviews, see

Geva & Wang, 2001; Ziegler & Goswami, 2005). It appears that regardless of type of

orthography—alphabetic (e.g., English, Spanish), syllabic (e.g., Japanese Katakana), or

logographic (e.g., Mandarin, Cantonese)—an understanding of the sound components of words

is a critical component of word reading. Accordingly, PA is also of great importance when

reading in an L2, and there is plenty of research supporting this finding that includes: L2 PA

predicting L2 word reading, but also L1 PA predicting L2 word reading (for review, see Melby-

Lervag & Lervag, 2011), with the degree of relationship varying dependent on the typological

similarities and differences observed between the L1 and L2 (to name one factor; Branum-

Martin, Tao, Garnaat, Bunta, & Francis, 2012 also note other mitigating factors). Generally,

stronger relationships can be observed on initial word reading learning when the L1 and L2 are

more typologically similar (e.g., English and Spanish, or English and French), than when they

are more typologically distant (e.g., English and Chinese) (Branum-Martin et al., 2012; Melby-

Lervag & Lervag, 2011). That said, with experience and practice in the L2, these differences

gradually diminish (Gottardo, Pasquarella, Chen, & Ramirez, 2015).

RAN (rapid automatized naming or naming speed) is the ability to name quickly and

accurately familiar stimuli such as objects, colors, digits, or letters. RAN is considered a

cognitive index of the speed of access of the phonological and orthographic information held in

memory, which is needed for the efficient decoding of words. Naming speed tasks assess how

quickly and effectively this type of information is retrieved from long-term memory. Because

efficient word recognition is a necessary prerequisite of reading comprehension, the speed at

which children decode words (i.e., their reading fluency) affects their reading comprehension.

Consequently, RAN is not only important for word reading, but also for text reading fluency, and


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reading comprehension. Similar to findings in the PA literature, naming speed is predictive of L1

word reading (where RAN and word reading are measured in the same L1; e.g., Johnston &

Kirby, 2006; Joshi & Aaron, 2000; Kirby, Parrila, & Pfeiffer, 2003), L2 word reading (where

RAN and word reading are measured in the same L2; e.g., Chung & Ho, 2010; Geva & Farnia,

2012; Zadeh, Farnia, & Geva, 2012), and across languages (where RAN is measure in L1 and

word reading in L2, or vice-versa; e.g., Keung & Ho, 2009; Pae, Sevcik, & Morris, 2010;

Pasquarella, Chen, Gottardo, & Geva, 2015; Shum, Ho, Siegel, & Au, 2016; for review, see

NELP, 2008).

Working memory is “the ability to store information while simultaneously carrying out

processing operations” (Nouwens, Groen, & Verhoeven, 2016, p. 2). Like RAN, working

memory has complex roles supporting reading at both the word- and text-levels (Christopher et

al., 2012). One popular interpretation of the importance of these roles is that working memory is

developmental in nature in that working memory begins to predict reading comprehension once

word reading has been mastered (Seigneuric & Ehrlich, 2005). Although an in-depth discussion

of these roles is beyond the scope of this review, a brief mention of both roles is worthy. In early

reading development, much effort is devoted to actively decoding the printed word. Working

memory is critical for analyzing and recalling the grapheme-phoneme units for each segment of

the word, and holding that information in memory until the whole word has been decoded

(Verhoeven, Reitsma, & Siegel, 2011). Research with L2 learners has shown that working

memory, although important, is less important for word reading in the early grades for ELLs

when compared to L1 learners. This difference decreases over time however and as ELLs

become more proficient in the English, and working memory becomes a more important

predictor of their word reading (Farnia & Geva, 2013; Lesaux & Siegel, 2003).


10

At the level of comprehension, working memory is additionally important for keeping

explicit and implicit information available while meaning-making occurs, and this appears to be

true of L1 learners (Alptekin & Ercetin, 2010; Cain, Oakhill, & Bryant, 2004; Christopher et al.,

2012) and also L2 learners (Farnia & Geva, 2013; Geva & Ryan, 1993; Geva & Siegel, 2000;

Nouwens, Groen, &Verhoeven, 2016). The contribution of working memory is particularly

evident when higher-order processing skills are needed, for example in inferencing (e.g.,

Alptekin & Ercetin, 2010; Cain, Oakhill, & Bryant, 2004; Harrington,1992), and when

processing syntactically complex sentences (Farnia & Geva, 2013). Recent research in the area

of working memory with L1 learners has focused on understanding the different roles of working

memory in word reading and reading comprehension. For example, in a study involving 483

English monolingual children of varying ages, Christopher et al. (2012) found that: (a) working

memory is a unique predictor of both word reading and comprehension; and (b) contrary to the

popular belief that working memory is more important for reading comprehension than word

reading (Christopher et al., 2012), working memory is equally important for both reading

abilities. The extent to which this distinction is also true for L2 learners has yet to be

investigated. Nonetheless, L1 and L2 findings related to working memory point to the

fundamental importance of working memory for reading (e.g., Geva & Ryan, 1993; Geva &

Siegel, 2000).

Relevant to the current research is the general finding that for each of these three

cognitive processes (PA, RAN, and working memory), typically developing L2 primary school

children tend to perform on par (or sometimes even better) than L1 learners despite lower

performance on language and reading comprehension measures (Chiappe, Siegel, & Gottardo,

2002; Chiappe, Siegel, & Wade-Woolley, 2002; D’Angiulli, Siegel, and Serra, 2001; Geva &

Farnia, 2012; Jongejan, Verhoeven, & Siegel, 2007; Lesaux & Siegel, 2003). Moreover, children


11

who receive adequate exposure, and systematic and explicit instruction in English language and

reading instruction can obtain accurate word reading skills that are similar to that of their L1

peers (Abu-Rabia and Siegel, 2002; Farnia & Geva, 2013; Lesaux & Siegel, 2003; see Geva,

2006 for a systematic review). Studies based on ELLs from multiple language groups (e.g.,

Punjabi, Urdu, Portuguese, Spanish, Italian, Chinese) have also demonstrated word reading and

decoding skills that do not differ from the skills of students whose L1 is English (e.g., Geva,

2006; Geva, Yaghoub-Zadeh, & Schuster, 2000).

These findings make the task of identifying ELL children with pure word reading

difficulties somewhat more straightforward, but this is not the case when considering ELL

children with language difficulties. Because of their developing language skill, it is difficult to

tease apart the decreased command of English language skills that is commensurate with typical

L2 development from developmental issues with language that are above and beyond what

would be considered typical (this topic is discussed in further detail in the section related to oral

language proficiency and reading comprehension in ELLs).

Sources of difficulties in word reading. Children who have persistent difficulties in

their word reading skills in spite of quality teaching and practice constitute a group of students

referred to as dyslexic or poor decoders. In addition to difficulties in developing accurate and

fluent word recognition and decoding skills, these children have difficulties with PA, RAN,

and/or phonological short-term memory (the ability to hold phonological/verbal information

temporarily in mind). L1- and L2-based research findings consistently reveal that these common

underlying cognitive processes play a direct role in their reading difficulties and acquisition of

word reading skills in L1 (Geva et al., 2000; Ndlovu, 2010; Lesaux & Geva, 2006; Lipka &

Siegel, 2012; McBride-Chang, Liu, Wong, Wong, & Shu, 2012; Ramus, 2013; Shaywitz &

Shaywitz, 2005; Swanson, Zheng, & Jerman, 2009; for reviews, see Melby-Lervag, Lyster, &


12

Hulme, 2012; Vellutino, Fletcher, Snowling, & Scanlon, 2004), and in L2 (Chung & Ho, 2009;

Chung, Lo, Ho, Xiao, & Chan, 2014; Geva & Herbert, 2012; Verhoeven et al., 2011). It appears

that the difficulties experienced by learners with dyslexia in their word reading performance and

on cognitive tasks measuring various aspects of phonological processing (i.e., PA, RAN,

phonological short term memory), reflect a basic deficit in their ability to represent accurately

and efficiently phonological information in the brain (Hulme & Snowling, 2009; Szenkovits &

Ramus, 2005). What is still not entirely agreed upon after years of research in the area however,

is what specific aspects of those phonological representations, are most important for word

reading learning (Melby-Lervag, Lyster, & Hulme, 2012; Vellutino et al. 2004).

Melby-Lervag, Lyster, and Hulme (2012) sought to explore this issue in their review of

235 L1 studies (predominantly L1 English) examining predictors of word reading. They found

that although phonemic awareness, rime awareness, and verbal short-term memory were reliable

correlates of individual differences in children’s word reading skills, that phonemic awareness

was the largest unique predictor (Melby-Lervag, Lyster, & Hulme, 2012). Melby-Lervag, Lyster,

and Hulme (2012) concluded that a “failure to develop such phonemically structured

phonological representations is a principal cause of the difficulties in learning to read

experienced by children with dyslexia” (Melby-Lervag, Lyster, & Hulme, 2012, p.341).

Interestingly, naming speed (i.e., RAN)—a usual suspect involved in difficulties with word

reading—was not included as a predictor of interest in this review.

Naming speed deficits are widely accepted for their role in the word reading difficulties

observed in children with dyslexia (e.g., Kirby, Parrila, & Pfeiffer, 2003; Steacy, Kirby, Parrila,

& Compton, 2014; Wolf & Bowers, 1999). The Double Deficit Hypothesis approach to

understanding dyslexia (Wolf & Bowers; 1999) suggests three distinct subtypes of poor word

readers: those with phonological deficits, those with naming speed deficits, and those with a


13

double deficit having both phonological and naming speed deficits. This inclusion of RAN in the

profile of dyslexic children is well supported in the research (Katzir, Kim, Wolf, Morris, and

Lovett, 2008; King, Giess, and Lombardino, 2007; Kirby, Parrila, & Pfeiffer, 2003; Lovett,

Steinbach, & Frijters, 2000; Steacy, Kirby, Parrila, & Compton, 2014). Indeed, children with

pure phonological problems (and no naming speed difficulties), as well as children with pure

naming speed problems (and no phonological problems) do exist, in addition to those with a

double-deficit in both skills. Naming speed as an indicator of word reading problems cannot

simply be disregarded. The current study aims to address the role of both of these predictors (i.e.,

PA and naming speed) in its investigation of potential sources of poor reading in ELL children.

Because the cognitive deficits associated with dyslexia are not language-specific (i.e., a

child with dyslexia will have difficulties in word reading regardless of the language(s) they

speak, and those difficulties will be observed in their L1, L2, etc.), similar cognitive profiles

between L1 and L2 students who have word reading problems can be expected. This means that

children with word reading problems can be assessed and accurately identified in the L2, and

without the need for information about their performance on diagnostic tasks in the L1 (Everatt,

Smythe, Adams, & Ocampo, 2000; Guron & Lundberg, 2003; Lesaux & Geva, 2006). This

supposition is particularly useful when considering children who live in multi-cultural and multi-

lingual contexts like those found in Canada, where L2 learners can come from many different

home language backgrounds, for which many, reliable L1 tasks are not available.

Oral Language Proficiency and Reading Comprehension

Oral language skills provide the foundation for learning to read and are critical for

reading comprehension (Chall, 1996). Listening comprehension, syntactic knowledge,

morphological skills, and vocabulary knowledge are all components of language proficiency that

are important for reading comprehension in L2 children (e.g., Au-Yeung, Hipfner-Boucher,


14

Chen, Pasquarella, & D’Angelo, 2015; Babayigit, 2014; Droop & Verhoeven, 2003; Farnia &

Geva, 2011; Geva, 2006; Geva & Farina, 2012; Hutchinson, Whitely, Smith, & Connors, 2003;

Kieffer, 2012; Lam, Chen, Geva, Luo, & Li, 2012; Lesaux, Rupp, & Siegel, 2007; Proctor,

Carlo, August, & Snow, 2005; Verhoeven, 2000). Yet, many typically developing L2 children

struggle with their L2 reading comprehension when compared to their monolingual counterparts

despite the fact that they can learn to read words with accuracy and fluency similar to that of L1

learners (August, Carlo, Dressler & Snow, 2005; Geva & Farnia, 2012; Farnia & Geva, 2013;

Lesaux & Geva, 2006). Individual differences observed in word reading are not enough to

explain the reading comprehension difficulties that L2 learners tend to experience. Consequently,

researchers have turned to the language comprehension component of reading to understand why

and how ELLs might be lagging. Two components of language relevant to the current research

are vocabulary knowledge and syntactic awareness.

Vocabulary knowledge is a key component of oral language proficiency, and some

research suggests that it may be one of the strongest predictors of reading comprehension for

ELLs (August et al., 2005; Farnia & Geva, 2013; Geva & Farnia, 2012; Proctor, Carlo, August,

& Snow, 2005). Vocabulary development is also an area where many ELLs show delays, and

these delays can impact their reading comprehension (Au-Yeung et al., 2015; Farnia & Geva,

2011; 2013; Hutchinson, Whitely, Smith, & Connors, 2003; Kieffer, 2012). For example, Farnia

and Geva (2011) in their research involving the vocabulary development of ELLs and English

monolinguals, found that ELLs consistently lagged in their vocabulary development when

compared to their monolingual peers. They observed a gap in vocabulary at each measurement

point from grades one through six, with the gap between ELLs and EL1s (English as a first

language) being larger in the early years. Specifically, vocabulary growth was steeper in the first

three years, and while the gap decreased over time, it did not close even by grade 6. Au-Yeung et


15

al. (2015) noted similar results in their work comparing English monolinguals and ELLs in a

French immersion context. In this context English monolinguals are receiving instruction in

English (their L1) and also French (L2), while ELLs are receiving instruction in English (their

L2) and French (L3). They observed that English monolinguals outperformed ELLs on English

vocabulary at four time points across grades 1 through 3, despite a more rapid vocabulary growth

by the ELLs. That said, this lag did not translate into poor reading comprehension, at least not

yet; the ELLs performed similarly to English monolinguals on reading comprehension in grades

2 and 3. In their examination of children in slightly older grades, Farnia and Geva (2013) found

that the gap in vocabulary development played a substantial role in reading comprehension

development in grades 4, 5, and 6 with ELLs performing below the EL1s on reading

comprehension tasks. This suggests that the impact of poorer vocabulary on reading

comprehension may not be evident until the later grades when decoding skills are mastered, texts

become more difficult and more demanding linguistically, and the instructional focus switches

from learning to read, to reading to learn (Chall, 1996). Kieffer’s (2012) longitudinal study,

which followed Spanish-speaking ELLs from kindergarten to grade 8, found that English

expressive vocabulary was a better predictor of later English reading comprehension for Spanish

ELLs than performance on listening comprehension or story retells. Vocabulary appears to be at

least one area of language that is especially important for L2 English reading comprehension. It

may also be a potential source of difficulty for ELL readers who are experiencing problems with

their language development (beyond what would be considered typical) that impacts their


Syntactic awareness, the ability to manipulate and reflect on the grammatical structure of

language, also appears to be an important skill for reading comprehension in ELLs (Babayigit,

2014; Geva & Farnia, 2012; Grabe, 2009; Lesaux, Lipka, & Siegle, 2006; for review, see Jeon &


16

Yamashita, 2016). Geva and Farnia (2012) compared longitudinally (from grade 2) and

concurrently (grade 5) the role of syntactical skill and vocabulary between ELLs and English

monolinguals. They found that syntactic skill in grade 5 was an important predictor for ELL

reading comprehension, as was vocabulary and listening comprehension. In contrast, only

vocabulary was a pivotal predictor for the English monolinguals where components of language

were concerned. They interpreted these findings to mean that ELLs may need to rely on a more

differentiated set of language component skills when reading for meaning, when compared to

English monolinguals. Babayigit (2014) examined the role of oral language (i.e., vocabulary and

morphosyntax) in the English reading comprehension of L1 English and L2 English children of

varying home language backgrounds who were 10 years old. They found that vocabulary and

morphosyntax most strongly predicted reading comprehension after accounting for age,

cognitive ability, working memory, word reading accuracy, and text reading fluency; and that

vocabulary and morphosyntax made similar sized contributions to reading comprehension

(Babayigit, 2014). In their meta-analysis of 59 studies which examined the roles of 10 key

reading component variables in L2 English reading comprehension, Jeon and Yamashita (2014)

observed that grammar (i.e. syntax), vocabulary knowledge, and decoding (all measured in L2)

were the three strongest correlates of L2 reading comprehension. In sum, it appears that syntactic

awareness and also vocabulary are, in the least, two important components of oral language

required for effective reading comprehension by L2 English learners. Performance on vocabulary

and syntax may be good indicators to consider when trying to establish which ELLs are

following a typical trajectory in their language development, and which ones are following a

trajectory that represents a real problem with language that is unrelated to their L2 status; there is

considerable evidence that difficulties with grammar are a prominent characteristic of children

with language impairment (e.g., Anderson & Lockowitz, 2009).


17

Language impairment and poor comprehension. Despite the fact that ELL children lag

in their language development in a number of areas like those discussed above, and that these

delays may impact their reading comprehension, there are also ELL children who have language

difficulties that are over and above what would be considered typically developing for second

language development. The extent to which ELLs with atypical language problems also struggle

in their reading comprehension due to their poor language skills is not a well-researched area. It

appears that these children are not a homogeneous group, and the variety of terminology used for

these at-risk learners reflects this lack of homogeneity. There are some ELL children who have

good decoding ability but difficulties in various components of language and higher-order

processing skills resulting in poor reading comprehension. In the L2 literature, one can find

reference to these children as: poor comprehenders, unexpected poor comprehenders (D-Angelo

& Chen, 2016; Geva & Massey-Garrison, 2013; Li & Kirby, 2014; Tong, Deacon, Kirby, Cain,

& Parrila, 2011), hyperlexic (Joshi, Padakannaya, & Nishanimath, 2010; Sparks, 2015), or

reading comprehension impaired (Snowling & Hulme, 2012). There also children who have

phonological processing problems in addition to their language deficits, and this compound

deficit has a serious impact on their word reading and reading comprehension skills (Farnia &

Geva, 2012, in preparation; Geva & Farnia, 2015; Peterson & Gillam, 2013); these at-risk

children are generally referred to as having a language impairment or specific language

impairment. In some of the literature, they are also referred to as garden-variety poor readers

(Bishop & Snowling, 2004; Stanovich, 1988). It would appear then that there are at least two

subtypes of children with language problems: those with language problems in the absence of

decoding problems (referred to as poor language comprehenders in the current research), and

those with language problems in addition to difficulties in decoding (referred to as multi-deficit

at-riskers in the current research).


18

Poor comprehenders are defined by their poor reading comprehension despite accurate

and fluent word reading and after accounting for age and cognitive ability (Li & Kirby, 2014;

Tong, Deacon, Kirby, Cain, & Parrila, 2011). When considering this definition through the lens

of the SVR (Gough & Tunmer, 1986), it would seem likely that the source(s) of their reading

comprehension problems are with the language component of reading. As discussed earlier

though, the SVR does not account for all of the variance associated with reading comprehension,

so it is possible that these at-risk learners may also experience difficulties that may include

components of reading comprehension beyond language. Li and Kirby (2014) found that in

addition to their problems with language, which included difficulties in vocabulary breadth and

depth, listening comprehension, morphological awareness; poor comprehenders also had

difficulties with coherence and elaborative inferences, and other reading strategies like finding

the main idea, predicting, and summarizing. Geva and Massey-Garrison (2013) also found that

their poor comprehenders had difficulties with various aspects of language and inferencing (this

and the Li & Kirby study are discussed in more depth in the inferencing section of this chapter).

More research is needed to examine the scope of the difficulties experienced by ELL poor

comprehenders, which appear to include a variety of issues with language but also problems with

higher-order skills and strategies. What is clear however (and what differentiates poor

comprehenders from children with language impairment), is that they appear to experience no

difficulties in naming speed, phonological awareness, or orthographic awareness and as a result,

have good word reading skills (Geva & Massey-Garrison; Tong et al., 2011).

Children with language impairment are defined by their pervasive difficulties in language

development. Asikainen (2005) defines language impairment as “a disorder characterized by

failure to acquire normal language at an appropriate age despite adequate hearing and normal

nonverbal intelligence (Holopainen, Korpilahti, Juottonen, Lang, & Sillanpaa, 1997)” (p. 17). In


19

addition to a few studies which focus solely on the language differences between ELLs with and

without language impairment (Paradis, Schneider, & Duncan, 2013; Verhoeven, Steenge & van

Balkom, 2012); there is a small but growing body of research which focuses on the reading

difficulties experienced by L2 learners with language impairment (Erdos, Genesee, Savage,

Haigh; 2013; Farnia and Geva, 2012, in preparation; Peterson and Gillam, 2013). Like findings

from the L1 (for reviews, see Bishop & Snowling, 2004; Snowling & Hulme, 2012), this L2

research suggests that ELL children with language impairment experience difficulties with both

the phonological and nonphonological components of language, and that this affects their

learning to read.

Erdos et al. (2013) explored the L1 and L2 language and reading skills of L1 English

children learning L2 French in kindergarten and grade 1. There were two primary goals of their

study: (1) to investigate the extent to which L1 abilities could effectively predict later L2

language and reading difficulties, and (2) to examine whether reading and language impairment

comprise different risk profiles in L2 children. They found that L1 PA, naming speed, and letter-

sound knowledge were significant predictors of risk in L2 reading (both word reading and

reading comprehension); while L1 PA and grammar (sentence repetition and tense marking)

were the best predictors of L1 and L2 oral language difficulties. They concluded that there are

distinct profiles for L2 children experiencing difficulties in language and reading, and that these

difficulties can be accurately predicted by relying on performance on L1 tasks (Erdos et al.,

2013). Peterson and Gillam (2013) also examined L1 and L2 predictors of reading

comprehension in Spanish-English bilinguals in kindergarten and grade 1. Contrary to Erdos et

al. (2013), they found that L1 Spanish measures did not predict reading difficulties in grade 1

over and above the English L2 measures. They did find however that, as expected, oral language

skills significantly predicted reading comprehension difficulties in grade 1. Farnia and Geva


20

(2012) examined a model for identifying early- (grade 1) and later-emerging (grade 3) language

impairment in L1 and L2 English learners. Generally, they found that there were different early

and later predictors of impairment status. Specifically of the grade 1 predictor variables, working

memory and naming speed reliably distinguished the groups with and without later-emerging

language impairment (in both L1 and L2). In addition, by grade 3, phonological short-term

memory and vocabulary reliably distinguished these groups. They concluded that regardless of

L1-L2 status, it is possible to identify children at-risk for developing later-emerging language

impairment as early as grade 1 (Farnia & Geva, 2012). Collectively, these studies suggest that it

is possible to discern between ELLs with and without language impairment, even while their L2

language proficiency is still developing. Further, it seems clear that L2 children with language

impairment experience a range of issues with their language and phonological processing, and

these difficulties are reflected in their lack of ability to develop good word reading and reading

comprehension skills.

It is important to note that children with dyslexia and language impairment typically

share a common risk for word reading problems that can be traced back to difficulties in

phonological processing (Snowling & Hayiou-Thomas, 2006). Historically, it has been suggested

that due to continuities in the cognition and behaviour among children with these diagnoses,

children with dyslexia and children with language impairment may constitute a similar group of

at-risk children; there are many dyslexic children that are also diagnosed as language impaired

(for a further discussion of this issue, see Bishop & Snowling, 2004; Snowling & Hulme, 2012).

A similar argument could be presented for children identified as poor comprehenders, and those

diagnosed as language impaired (based on similarities in their language difficulties). Currently, it

is generally accepted that dyslexia and language impairment are two distinct subtypes of learning

disorders (DMS-V), and that despite earlier arguments suggesting that these two groups are


21

located on one continuum (Catts, 1991; Kamhi & Catts, 1986), the two profiles cannot simply be

captured by a simple “gradient of severity” on phonological processing (Bishop & Snowling,

2004, p. 858). Snowling and Hulme (2012) make a similar argument for the inclusion of reading

comprehension impairment as a distinct reading disorder; presently, this distinction is not

included in the DSM-V. It is arguable that by differentiating children who are dyslexic, poor

comprehenders, or language impaired, and considering them as distinct groups of at-risk children

based on their decoding and language abilities, a better understanding of the sources of their

difficulties can be gained such that differentiated instruction can be provided to address their

deficits and support their reading development. Evidence indicates that remediation for children

with reading and/or language impairment is most effective when it targets the child’s specific

deficit(s) (Scammacca, Roberts, Vaughn & Stuebing; Snowling, 2013; Vaughn et al., 2008).

More is known about the predictors and profiles of ELL poor decoders (i.e., children with

dyslexia) and to some extent, poor comprehenders. Much less is known about ELL children with

compound difficulties in word reading and language comprehension (i.e., children with language

impairment). This dissertation adds to the research by also examining this multiple deficit group,

and contrasting them with children at-risk for poor reading comprehension due to pure decoding

problems, and children at-risk for poor reading comprehension due to pure language problems.

Given that decoding and language comprehension are perhaps the two most important

foundational skills for reading, it is expected that all three at-risk ELL subtypes (poor decoding,

poor language comprehension, and combined poor decoding and language comprehension)

would have serious and pervasive difficulties with their English reading comprehension. By

examining the profiles and predictors of children classified by their abilities in word reading and

language comprehension, we may better understand the components of those overarching skills

that are the source(s) of their reading comprehension difficulties. That said, there may be


22

additional skills on which these at-risk readers struggle that fall outside the umbrellas of

decoding and language, that are worthy of consideration. Two additional areas of potential

importance investigated in the current research are reading fluency and inferencing.

The Importance of Reading with Fluency

Fluency, the “ability to read orally with speed, accuracy and proper expression” (NICHD,

2000), is one of the defining features of good readers. Components of fluency include: accuracy,

rate, and prosody (i.e., stress, intonation, tone, pauses, and expression of a language). Fluent

readers recognize words automatically, read aloud effortlessly and with expression, and

understand what they read. Fluency plays a critical role in reading comprehension because being

able to read accurately and with speed, the two “hallmarks” of fluency, ensures that adequate

cognitive and processing resources are available for reading comprehension (Perfetti, 1985;

2007; Perfetti & Hart, 2002). When children begin to read, their oral reading is not smooth; it is

slow, effortful, and resource intensive (Schwanenflugel, Meisinger, Wisenbaker, Kuhn, Strauss,

& Morris, 2006). Early readers need to focus on using phonological and orthographic processing

routes to decode and recognize words (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001). With

time and practice however, word recognition becomes faster, and then automatic, such that the

attentional resources once used for word reading can be redirected to the complex processes

involved in reading comprehension. Indeed, automatic word recognition (i.e., word reading

fluency) is considered one of the limiting factors in reading comprehension (Schwanenflugel et

al., 2006).

Historically, fluency has been operationalized differently across the literature, with some

studies using a broad definition for fluency that considers fluency as one construct, and other

studies examining fluency by its potential constituent parts, for example word-level reading

fluency and fluency in reading connected text. As a result, research findings about the


23

contributions of reading fluency to reading comprehension are inconsistent (Veenendaal, Groen,

& Verhoeven, 2015). On the one hand, Adlof, Catts, and Little (2006) in their study with 604

English monolingual students in grades 2, 4, and 8 found that reading fluency did not account for

variance in reading comprehension after accounting for word reading accuracy and listening

comprehension. They concluded that: (1) word- and text-level reading fluency formed one

construct and were not different from each other in any meaningful way, and (2) reading fluency

did not account for variance observed in reading comprehension beyond the contributions of

decoding and language comprehension (Adlof, Catts, & Little, 2006). Schwanenflugel et al.

(2006) came to similar conclusions in their study with children in grades 1, 2, and 3. No

contribution was observed in text-level reading fluency to reading comprehension separate from

that already encompassed in word-level reading fluency. They did however acknowledge a

developmental perspective suggesting that the children in their study were perhaps not old

enough to observe the separation of fluency into two factors that might occur at an older age

(Schwanenflugel et al., 2006).

Conversely, more recent studies suggest that word- and text-level fluencies although

related, are different constructs with similar underlying processes, and as a result, they can

impact reading comprehension in different ways. For example, Kim and Wagner (2015) in their

longitudinal study with English monolinguals in grades 1 through 4, found that text-level reading

fluency contributed to reading comprehension over and above word-level reading fluency and

listening comprehension, and was facilitative in reading only once children had developed a

certain level of word reading proficiency. They also found that the role played by these

component skills of reading comprehension (word-level reading fluency, text-level reading

fluency, and listening comprehension) changed with time (Kim &Wagner, 2015); in grade 1

reading comprehension was largely explained by word-level reading fluency and listening


24

comprehension. In grade 2, listening comprehension became more strongly related to reading

comprehension and text-level reading fluency began to make an independent contribution. A

similar pattern was observed in grades 3 and 4 with listening comprehension and text-level

reading fluency becoming stronger over time (Kim &Wagner, 2015).

Interestingly, Geva and Farnia (2012) made complimentary observations in their study

with ELL and EL1 (English as a first language) students in grades 2 and 5. Regardless of

language status (ELL or EL1), grade 2 word-level and text-level reading fluency formed a single

factor, and by grade 5 two distinct factors could be observed, one involving word reading

fluency and another involving text reading fluency. Importantly, they reported that by grade 5,

unlike word-level reading fluency, text-level reading fluency was associated with language

comprehension, and accounted for unique variance in grade 5 reading comprehension after

accounting for grade 2 autoregressors, and in particular, after accounting for word-level reading

fluency. In other words, it appears that word-level reading fluency and connected-text reading

fluency overlap in their requirement for efficient word recognitions skills. Where these two

fluency skills diverge though, is in their reliance on language and when this reliance emerges. In

higher grades, text-level reading fluency processes rely on language comprehension skills such

as vocabulary, grammatical knowledge, and listening comprehension. These aspects of language

comprehension predict unique variance in reading comprehension beyond what word-level

fluency processes predict. In other words, the construct of text-level reading fluency changes

with its increased reliance on language demands, and that is when text-level reading fluency

becomes a unique predictor of reading comprehension. It should be noted that text-level reading

fluency, like reading comprehension, can only be assessed by requiring the reading of connected

discourse (and often the same texts), so there is an inherent confounding effect that needs to be


25

acknowledged in its measurement (Jenkins, Fuchs, van den Broek, Epsin, & Deno, 2003;

Yaghoub-Zadeh, Farnia, & Geva 2012).

Research suggests that reading fluency appears to be essential in, at least, three different

levels for reading: sublexical level (sublexical referring to the constituent parts of a word), word

level, and text level. First, at the sublexical level, rapid automatized naming (RAN) or naming

speed is the ability to rapidly and accurately name familiar stimuli such as objects, colors, digits,

or letters repeatedly. Naming speed, well established for its role in word reading, is regarded as a

predictor of fluency (Kirby, Parrila, & Pfeiffer, 2003; Wolf & Katzir-Cohen, 2001). RAN has

been found to contribute to young reader’s current and later reading comprehension directly, but

also indirectly through word reading (e.g., Johnston & Kirby, 2006; Joshi & Aaron, 2000; Kirby,

Parrila, & Pfeiffer, 2003). For example, Joshi and Aaron (2000) used naming speed as a proxy

for fluency in their study examining reading comprehension in 4th grade English monolinguals.

They found that letter naming speed accounted for 10% of the unique variance in fourth grade

reading comprehension after controlling for nonword reading accuracy and listening

comprehension. RAN has also been implicated as one of the primary sources of deficit in

children with word reading problems (i.e., dyslexics or poor decoders), the other being

phonological awareness. Kirby, Parrila, and Pfeiffer (2003) found that children with poor naming

speed in kindergarten had difficulties with comprehension (and word reading) that continued

throughout their elementary school years until grade 5. They concluded that the relationship

between naming speed and reading comprehension was likely through fluency. In short, naming

speed is required for fluency, and fluency is required for comprehension (Wolf & Katzir-Cohen,

2001).

Second, reading fluency is also necessary for reading at the word level. Word-level

reading fluency entails the automatic (accurate and quick) recognition of printed words. It is


26

often measured through the timed reading of lists of words. Automaticity in word reading is

thought to be critical in reading comprehension because the ability to recognize printed words

without much effort allows for cognitive resources to be allocated towards other goals, for

example higher-order processing skills (e.g. inferencing) and metacognitive strategies (e.g.,

comprehension monitoring). Several theories attempting to reconcile the relationships between

lower-level processes at the word level, and higher-level processes at the text level using notions

about fluency are noteworthy. Automaticity theory (LaBerge & Samuels, 1974) posits that

accuracy develops before speed, and that fluency or efficiency is gained when a reader can read

words both accurately and fast, that is, automatically. Verbal efficiency theory emphasizes the

importance of effective lexical retrieval processes and the impact on individual differences in

reading comprehension (Perfetti, 1985). More recently, the lexical quality hypothesis has added

to these theories by highlighting the quality of lexical representations. Perfetti argues that reading

comprehension depends not only on the quantity, but also the quality of the lexical

representations of words, and that it is this quality of representations that facilitates fluency. A

reader with a good breadth (quantity) and depth (quality) of vocabulary knowledge (that is, a

large lexicon) associated with many high-quality phonological and orthographic representations,

can read text with less effort and make faster word-to-text integrations (Perfetti, 2007; Perfetti &

Hart; 2002; Segers & Verhoeven, 2016).

As previously discussed, the third level of fluency is the text-level. Text-level reading

fluency is the fast and accurate reading of connected text. Less is known about the intricacies of

text-level reading fluency. It is thought to primarily develop from word-level reading fluency

(Ehri, 2002; Kim; NICHD, 2000), but as discussed above, language comprehension also plays a

role (Geva & Farnia, 2012; Kim & Wagner, 2015). Geva and Farnia (2012) found that in the

lower grades (grade 2) word- and text- levels of reading fluency were one construct, but later


27

(grade 5) observed a distinction between the two. Text reading fluency became more strongly

related to reading comprehension by grade 5, through its connection to oral language skills; the

relationship between text reading fluency and oral language also increased between grades 2 and

5. Kim and Wagner (2015) observed that text-level fluency, in addition to fully mediating the

relationship between word-level fluency and reading comprehension, also partially mediated the

relationship between listening comprehension and reading comprehension in grades 2 through 4.

Crosson and Lesaux (2010) found that text-level reading fluency was related to reading

comprehension in ELLs with good oral language proficiency, but not for ELLs with poor levels

of oral language proficiency. This is an interesting finding as it suggests that below a certain

level of language proficiency, the “action” is at the word level and not at the text level. Similar

results are reported in another study of ELLs (Geva & Yaghoub-Zadeh, 2006). Geva and

Yaghoub-Zadeh (2006) reported that no significant differences were observed between EL1s and

ELLs in grade 2, in their ability to read simple narrative texts with accuracy and speed (i.e., text-

level reading fluency) provided they had received good instruction in language and literacy in

English, and had effective word recognition skills (Geva & Yaghoub-Zadeh, 2006). Quirk and

Beam (2012) reported a significant relationship between text reading fluency and reading

comprehension in their study with ELLs and EL1s in grades 2, 3, and 5, but noted that the

relationship varied across grade and levels of English language proficiency with relationships

being weaker for younger children and ELLs with lower levels of oral language. This group of

studies underscores the importance of oral language proficiency not only for reading

comprehension, but also for text-level fluency (which in turn contributes to reading

comprehension).

Text-level reading fluency appears to be an outcome of word reading fluency, but is also

dependent on language comprehension given that if the order the words are presented makes


28

sense to the reader, they are able to read more efficiently. This notion is further supported by

findings suggesting that fluency in text-level reading is easier (i.e., faster) for readers than word-

level reading, with readers reading words in connected text more fluently than random words

presented in lists (Geva & Yaghoub-Zadeh, 2006; Kim and Wagner, 2015). In support of the

threshold hypothesis presented earlier, it seems that learners with a better command of the

language can anticipate the words and morphosyntactic structures presented in context, and this

in turn, facilitates the quick and accurate reading of words.

Findings from the aforementioned studies also highlight the developmental nature of

reading fluency. In the early stages of reading development (for L1 as well as L2 readers) when

the primary focus is decoding, word-level reading fluency and text-level reading fluency are

highly related and form one construct (e.g., Geva and Farnia, 2012). But as word reading

becomes automatized and efficient, the ability to read connected text becomes more important

and mediates the relationship between word reading fluency and language, to reading

comprehension. In this way, text-level reading fluency forms the “bridge” from word reading, to

reading comprehension (Hudson, Pullen, Lane, & Torgesen, 2008; Kim & Wagner, 2015; Kuhn,

Schwanenflugel, & Meisinger, 2010; Pikulski & Chard, 2005). What is not clear is at what age

and/or level of language proficiency children are able to take advantage of the benefits of text-

level reading fluency. Therefore, more research is needed.

In the context of monolingual research it has been suggested that dysfluent reading can be

the result of deficits in one or more component processes (Wolf & Katzir-Cohen, 2001).

Likewise, different profiles of dysfluency in ELL poor readers are likely to exist depending on

the source(s) of impairment (e.g., naming speed issues, delayed oral language development,

decoding difficulties). In the early elementary grades, as typically developing L2 children

advance their decoding skills, their ability to read isolated words with accuracy and fluency also


29

increases. Typically developing ELL children at this time can read words in isolation (i.e., lists

of words) and in context (i.e., words within passages of text) with similar fluency because they

tend to focus on decoding. In the middle elementary school grades (around grade 4), the texts

encountered in school become linguistically more demanding and require higher-order

processing skills, and a separation of these two levels of fluency can be observed. Some children

will be able to transition into reading connected texts with more fluency, others may continue to

have good word-level reading fluency but fail to progress in their text-level fluency, and some

will continue to struggle with decoding skills and not be able to read with fluency (Geva &

Yaghoub-Zadeh, 2006). It is at this time that reading development and reading fluency more

closely align with language comprehension for the first group but not for the other two groups.

Thus, typically developing ELL readers whose English language skills are relatively better

developed can read English texts with more fluency, while their ELL peers with less developed

English language skills and/or continued difficulties with decoding will struggle. The current

study extends the literature by examining word- and text-level reading fluency and their

relationships with decoding, language, and reading comprehension in ELLs with and without

decoding and language difficulties. These relationships are also explored longitudinally for a

developmental perspective.

Inferencing and its Role in Comprehension

Inferencing is the ability to use the information provided in text, as well as one’s

background knowledge, to integrate ideas, and extrapolate meaning that is not explicitly

provided. Good readers know that there may be considerable information and meaning implicit

in the texts they read, and that a sufficient and sometime conscious application of inferencing

strategies may be essential for comprehension (Cain & Oakhill, 1999; Pressley & Gaskins,

2006). Pressley and Gaskins (2006) discuss the various dynamic ways that effective readers infer


30

or “read between the lines” to make meaning when reading. They note that readers may need to:

(1) infer who or what pronouns are referring to; (2) infer the meanings of words that are

unfamiliar (i.e., lexical inferencing), using the context provided in text and knowledge about

other words and types of words (e.g., cognate knowledge, morphological awareness); (3) infer

connotations—ideas or feelings invoked beyond literal meaning—implied by certain words and

phrases; (4) identify the purpose of the text; (5) infer the intentions of the author; (6) recognize

bias; (7) make interpretations of the text based on inferred purpose, intentions, and bias; (8)

determine central ideas as they emerge in the text; (9) use patterns and structure of text to guide

how information and meaning is organized; (10) make decisions about whether the additional

meaning fits with the ideas in the text and with their prior knowledge; and if not (11) a

reinterprete or reread as be required (i.e., comprehension monitoring). Readers may also need to:

(12) infer meaning about the logical relationships (sometimes signaled by connectives) between

ideas in text (Geva, 2007; Geva & Ryan, 1985); (13) make connections between the text and

individual experience to create personal meaning (e.g., a lesson learner from a fable); and/or (14)

understand more subtle literary techniques—personification, irony, metaphors—used by a writer

(Geva & Fraser, 2016). Because of the complex, wide-ranging, and at times metacognitive nature

of inferencing, inferencing is often considered a higher-level processing skill (Cain, Oakhill, &

Bryant, 2004; Hannon & Daneman, 2001; Klauda & Guthrie, 2008; Oakhill & Cain, 2012;

Pressley, 2000).

Much of the research investigating the role of inferencing in reading comprehension in

school-aged children has been conducted with English monolinguals (e.g., Bowyer-Crane &

Snowling, 2005; Cain & Oakhill 1999; 2006; Cain, Oakhill, & Bryant, 2004; Oakhill 1982;

1984; Oakhill & Cain, 2012; Oakhill, Cain, & Bryant, 2003). Several conclusions characterize

the results of this research. First, children’s reading comprehension is related to their ability to


31

generate inferences, and a compromised ability in making effective and sufficient inferences

impacts reading comprehension. Second, skilled readers construct text representations that are

both “integrated and coherent” (Cain & Oakhill, 1999, p. 489); inferencing is a required

component of this process. Third, younger and less-skilled readers are likely to draw fewer

inferences and this lack of strategy is likely to have a negative impact on their reading ability.

That said, some less skilled comprehenders are able to make inferences when their errors are

pointed out to them and they are allowed to go back to the text (Cain & Oakhill, 1999). This

suggests that in part, knowing when to make an inference is as much a part of effective

inferencing strategy as the inference itself. Finally, due to the inherent cognitive demands

involved in keeping implicit and explicit information accessible for processing while inferencing,

good working memory is a necessary but not sufficient skill for being able to infer (Cain &

Oakhill, 1999; Cain et al., 2004).

An early study by Cain and Oakhill (1999) illustrates these central conclusions and has

provided the foundational work for much of the further work by Oakhill, Cain, and colleagues

(e.g., Cain & Oakhill, 2006; Cain, Oakhill, Barnes, & Bryant, 2001; Cain, Oakhill, & Bryant,

2004; Cain, Oakhill, & Lemmon, 2004; Oakhill & Cain, 2012; Oakhill, Cain, & Bryant, 2003)

that has built up our understanding about the role of inferencing in reading comprehension. In

their landmark study Cain and Oakhill (1999) assigned 80 children aged 6 to 8 years old to either

a less-skilled comprehenders group (determined via comprehension scores below their

chronological age), a skilled comprehenders group (determined via comprehension scores that

were at or above that predicted by their reading accuracy age), or a comprehension-age matched

control group. Two types of inference generation were investigated: (1) text-connecting

inferences which required the integration of information explicitly provided by the text to

establish cohesion between ideas, and (2) gap-filling inferences which required the incorporation


32

of prior knowledge with information in the text to fill in missing details (Oakhill 1982, 1984).

Results indicated that: (a) good inferencing ability was not the consequence of good reading

comprehension ability but more likely the cause of it, and (b) a failure to make inferences was

not related to a lack of prior knowledge or poor working memory, but rather to lack of knowing

when to apply and then applying, sufficient inferencing strategies. The authors speculated that

one reason that less-skilled comprehenders had difficulties in applying inferencing strategies

when compared to skilled comprehenders was that they might have been approaching the task of

reading with different purposes in mind. Less-skilled comprehenders might be more focused on

word reading accuracy and applying fluent decoding strategies (thus having fewer cognitive

resources available for higher-level processing), while skilled comprehenders might be more

focused on comprehension monitoring and striving for coherence (Cain & Oakhill, 1999).

It is also important to acknowledge the role of inferencing in reading comprehension

from a developmental perspective. In the past, the thinking was that younger children had not yet

developed the metacognitive ability to apply inferencing strategies successfully. Further, young

children’s reading comprehension is strongly predicted by lower-level reading and language

skills (e.g., word reading accuracy and semantic skills). However, the relationship between

inferencing and reading comprehension has been established in children as young as 6 and 7

years old (e.g., Cain & Oakhill, 1999; Oakhill, Cain, & Bryant, 2003; Oakhill & Cain, 2012)

suggesting that even early readers can capitalize on inferencing strategies when reading.

Research with older children has shown that inferencing strategy emerges as a distinct predictor

of reading comprehension after controlling for working memory, word reading accuracy, and

components of language skill, such as for example vocabulary and verbal ability (Bowyer-Crane

& Snowling, 2005; Cain, Oakhill, & Bryan, 2004; Oakhill & Cain, 2012; Oakhill, Cain, &

Bryant, 2003). Cain, Oakhill, and Bryant (2004) reported that at each of three time points (ages


33

8, 9, and 11), working memory and component skills of comprehension (inference making,

comprehension monitoring, story structure knowledge) predicted unique variance in reading

comprehension after word reading ability and language controls.

In a subsequent, four-year longitudinal study, Oakhill and Cain (2012) found that three

comprehension components (inference, comprehension monitoring, and knowledge and use of

story structure) were unique predictors of reading comprehension in 10 and 11 year olds, even

after the autoregressive effect of earlier reading comprehension was controlled. In their study

with children aged 7 to 9 years old, Oakhill, Cain, and Bryant (2003) found that significant

variance in reading comprehension skill (but not word reading ability) was accounted for by

measures of text integration (inferencing), metacognitive monitoring, and working memory.

Bowyer-Crane and Snowling (2005) in their study with 9 year olds classified as poor

comprehenders or typical readers, found that what distinguished the typical readers from poor

comprehenders was their ability to generate more effortful, elaborative, types of inferences; no

significant differences were found in the ability to make cohesive inferences, or to draw

inferences that use the linguistic cues present in the text (e.g., knowing to whom a pronoun is

referring). To note, research with monolingual learners suggests that cohesive inferences may be

one of the less effortful types of inferences generated during reading (Bowyer-Crane &

Snowling, 2005).

Clearly, inference generation is a fundamental component of reading that may become

increasingly important for school-aged children as they progress through the elementary grades,

and as the texts they encounter become more demanding. For typically developing readers,

accompanying this progression is an increased automaticity of lower-level word reading skills,

allowing for attention to be focused on higher-level processing like inferencing and


34

comprehension monitoring. For readers with delayed development due to difficulties with

decoding, language learning, or both, inferencing skill can be compromised as attention is

focused on word recognition and meaning (Prior, Goldina, Shany, Geva, & Katzir, 2014).

Less is known about the relationship between inferencing strategy and reading

comprehension for younger L2 children. The research that does exist appears to address two

central issues: (1) the role of lexical inferencing in vocabulary development and relatedly, its

impact on reading, and (2) the role of the integrative and cohesive qualities of inferencing in

reading comprehension. Lexical inferencing has been found to have significant relationships with

both vocabulary knowledge and reading comprehension (Nassaji, 2004; Prior et al., 2014; Zhang

& Koda, 2012). To illustrate, Li and Kirby (2014) looked at inferencing strategy in their study

involving ELLs in grade 8 with differing levels of reading comprehension skill. Based on their

reading comprehension and word reading skills participants were classified as: unexpected poor

comprehenders (i.e., learners with poor reading comprehension despite good word reading

skills), expected average comprehenders, and unexpected good comprehenders (i.e., learners

with superior reading comprehension but only average word reading). The inferencing measure

they used involved items that assessed coherence and elaborative inferences, as well as items that

assessed the application of various reading strategies, such as predicting, inferencing, and

summarizing (Li & Kirby, 2014). In line with findings in the L1 research literature, results

indicated that good reading comprehension is associated with effective inferencing strategy;

unexpected good comprehenders who were defined by their superior reading comprehension but

average word reading skills, performed better than expected average and unexpected poor

comprehenders on inferencing. Li and Kirby (2014) speculated that higher-level processes such

as inferencing, may be responsible at least in part, in explaining different levels of reading

comprehension among ELLs.


35

Geva and Massey-Garrison (2013) examined language and reading difficulties in a

sample of ELLs of various home language backgrounds, and L1 (English) children who attended

the same schools. Children in the ELL and L1 groups were classified separately as typical

readers, poor decoders, or poor comprehenders (with no decoding problems) in relation to the

sample (and not monolingual norms). Results indicated that just like their their L1 counterparts,

ELLs classified as poor comprehenders had difficulties in making inferences while listening to

stories. They also had difficulties with other higher-level aspects of language related to semantic

relations and the production of syntactically complex sentences (Geva & Massey-Garrison,

2013). In sum, the same pattern of difficulty was noted among poor comprehenders regardless of

their L1 or ELL status.

Inferencing is a process requiring that relevant information, either from text or world

knowledge, be identified and integrated in a way such that the reader can create a unified and

cohesive mental model or text representation of what they are reading. Research findings from

the L1 and L2 research literature suggest that inferencing strategy is a critical component of

reading comprehension, without which reading performance and the ability to make meaning

through text is seriously compromised. Poor reading comprehension may be at least in part,

failure in the ability to make sufficient inferences. The current research adds to this growing

body of literature by examining profiles of inferencing strategy (text-connecting and gap-filling)

for different subtypes of ELLs, including those at-risk for poor reading comprehension as a result

of difficulties in decoding, language comprehension, or both.


36

Chapter 2: The Current Research

The primary goal of the current research was to investigate the language and reading

profiles of ELL readers classified as typically developing or at-risk for poor reading

comprehension, based on their relative strengths and weaknesses in decoding and language

comprehension. Bishop and Snowling’s (2004) two-dimensional model was used in the

conceptualization and classification of typically developing and at-risk ELL readers. This model,

based on the SVR (Gough & Tunmer, 1986) is presented in Figure 1 and conceptualizes the

relationship between phonological and nonphonological skills (in this research defined as

decoding and language comprehension respectively). Three types of at-risk ELL readers were

examined in comparison to typically developing ELLs: ELL poor decoders who exhibited

difficulties with word reading, ELL poor language comprehenders who exhibited compromised

language skills, and ELL readers who exhibited deficits in both decoding and language

comprehension, defined as multi-deficit at-riskers. The goal of Study 1 was to examine

concurrent linguistic and reading profiles of these ELL reading groups. The goal of Study 2 was

to identify longitudinal predictors of at-risk ELL reading group membership. Studies 1 and 2 are

discussed in more detail in Chapters 5 and 6, respectively.


37

Figure 1. The two-dimensional model for conceptualizing the relationship between language

comprehension and decoding in typical developing and at-risk reader groups (Bishop &

Snowling, 2004).

Typical developers

Multi-deficit at-riskers

Poor decoders

Poor language comprehenders

+

+ −

Language Comprehension

(Nonphonological)

Decoding

(Phonological)

−


38

As can be seen in Figure 1, the model characterizes the relationship between children

with difficulties in decoding, children with difficulties in language, children with difficulties in

both decoding and language comprehension, and children who are typically developing on both

continua. The model is useful in that it allows the characterization of reading difficulties to be

conceptualized in a two-dimensional space rather than on a single continuum, or two

independent continua (Bishop & Snowling, 2004; Branum-Martin, Fletcher, & Stuebing, 2013).

Children with varying degrees of decoding and/or language deficits can be identified with a

simultaneous consideration of both domains. This is an important consideration in the context of

reading development, as deficits in decoding and/or language, although presenting as a unitary

global problem with reading comprehension, may be related to different underlying causes and

therefore require different intervention approaches (as discussed in Chapter 1). The model is also

advantageous in that it allows for comparison of at-risk status with typically developing learners

within the ELL sample. Historically, the identification of ELLs with reading difficulties has been

conducted through the use of monolingual norms as a point of reference (Geva & Herbert, 2012).

Given that ELLs are developing their English oral language proficiency, this approach has led to

concerns about the over-identification of ELLs as having difficulties in reading when in fact,

their language and reading development is typically developing with respect to their ELL status.

Concerns about the under-identification of ELLs with reading problems has also been of issue

due to a tendency to attribute challenged reading skills to poor language proficiency as a result of

developing L2 status (Limbos & Geva, 2001).

Methodological Considerations

There are benefits and drawbacks in using a quadrant method and cut-off scores in the

identification of at-risk ELL readers and the classification of those readers into different

subtypes. On the one hand, it is not easy to tease apart difficulties in L2 reading that reflect the


39

typical course of development, from difficulties that reflect real problems with language and

reading. L2 learners generally have a lower command of vocabulary and lesser reading

comprehension skills in the L2 than their monolingual counterparts (Droop & Verhoeven, 2003;

Farnia & Geva, 2011; Hutchinson, Whitely, Smith, & Connors, 2003; Lesaux, Rupp, & Siegel,

2007; Proctor, Carlo, August, & Snow, 2005; Verhoeven, 2000). That said, some L2 children

have pervasive language or reading difficulties that cannot be attributed simply to their language

learning status, and that may reflect, if not a diagnosable language disorder or specific learning

disorder at least a need for focussed attention to help them to be successful readers (DSM-5;

Geva & Herbert, 2012; Geva & Massey-Garrison, 2013; Paradis, Genesee, & Crago, 2011;

Sparks, Patton, Ganschow, & Humbach, 2009). A quadrant method with decoding and language

as intersecting continua allows for the joint consideration of these two foundational skills in

reading, while at the same time considering their distinct relationships with reading

comprehension.

A quadrant model approach also allows for ELL children with deficits to be contrasted

with typically developing ELL readers rather than English monolingual readers (Geva &

Massey-Garrison, 2013; Ndlovu, 2010). This is of particular importance given the growing

evidence that typically developing ELL readers (who develop their word reading and language

skills in tandem) do not profile in the same way as typically developing native speakers of

English, whose reading skills are built on a foundation of language proficiency (Farnia & Geva,

2011; 2013; Jean & Geva, 2009). As mentioned earlier, reports of issues related to the over-

identification of L2 learners as having a reading disability when they do not, have been

highlighted in the literature (Cummins, 1984; Harry & Anderson, 1994; Larry P. v. Riles, 1972,

1974, 1979, 1984, 1986; Paton, 1998). Cases of under-identification in L2 learners who have a

reading disability but are not identified are also prevalent in the literature, particularly in models


40

of identification where ELLs are compared to monolingual English learners (Limbos & Geva,

2001; Solari, Petscher, & Pfolsom, 2012; U.S. Department of Education, 2003; Zehler,

Fleischman, Hopstock, Pendzick, & Stephenson, 2003).

It is important to note that because we cannot directly observe the brain and what it can

(and cannot) do, researchers and clinicians must measure performance on various cognitive,

language, and reading tasks to understand where and how children with reading problems

struggle. Children of course do not naturally fall into groups on these tasks; these tasks are

continuous in nature and children’s performance falls along a continuum. This research takes the

position that by considering at-risk children through a subtype classification system that is

informed by their primary difficulties, research can be conducted that helps to reveal the profiles

of various types of poor readers and the sources of their difficulties. That said, there is no

standardized method for subgrouping children based on their varying levels of reading skill or

component reading skills, and some statisticians question the appropriateness of group-making

via the use of cut-off scores, particularly in the study of learning disabilities where variables are

likely to represent a “correlated continuum of severity” (Branum-Martin, Fletcher, & Stuebing,

2013, p. 1). Branum-Martin, Fletcher, and Stuebing (2013) emphasize the “conceptual and

statistical dangers in using cut scores without a specific method designed to account for the

correlation among the measures” (p. 4). Nonetheless, some reading researchers have opted for

cut-off scores (raw or standardized) and covariates in their statistical analyses as a method of

group classification (e.g., Geva & Herbert, 2012; Geva & Massey-Garrison, 2013), while others

have used regression as a precursor to using cut points such that factors that are known to impact

reading development (e.g., age, nonverbal cognitive ability, decoding) are accounted for prior to

subgroup-making (e.g., D’Angelo & Chen, 2016; Li & Kirby, 2014; Tong, Deacon, Kirby, Cain,

& Parrila, 2011). The regression approach has been particularly useful in studies involving poor


41

comprehenders, where there are a number of confounding factors (e.g., Li & Kirby, 2014). For

example, poor comprehenders are defined by their poor reading comprehension in the presence

of accurate and fluent word reading ability. Using a regression approach allows for skill in word

reading or word-level reading fluency (both highly correlated with reading comprehension) to be

appropriately co-varied prior to subgroup classification.

Branum-Martin, Fletcher, and Stuebing (2013) offer several solutions for dealing with the

statistical circularity inherent in using ANOVA and regression procedures where a majority of

the measures are continuous in nature, and strongly correlated. They suggest for example,

longitudinal models with randomized control treatment groups, and cluster analyses. At the same

time, it is important to acknowledge that these approaches require large samples and may not be

appropriate where participants are selected to represent the extreme ends of a continuum

(Branum-Martin, Fletcher, & Stuebing, 2013). This is the case with the current research. Cluster

analysis may be true to the theoretical notion that variables such as vocabulary skills and reading

comprehension are dimensional and related. However, a large sample would be needed in order

to conduct such an analysis. Since this longitudinal study involved a smaller sample of 127

ELLs, using a cut point method for group classification was not only suitable, but the only viable

means for conducting such an investigation (See Chapter 4 regarding data preparation for more

details).


42

Chapter 3: General Method

The current research is comprised of two studies. A general method section with the

following methodological details of both studies is presented here: recruitment and data

collection, educational context of the participants and research, participant data, and descriptions

of measures used in analyses. Details about the preparation of data for analyses, including the

classification of ELL participants to reader group, is presented in Chapter 4; results for studies 1

and 2 are reported separately in Chapters 5 and 6, respectively.

Recruitment and Data Collection

Participants were recruited through an information letter and a consent form that were

sent home by classroom teachers to all students with appropriate ELL backgrounds. The letters

and forms were distributed in English as well as the child’s home language. Only students whose

parents agreed to participate were included in the study. Parents were also informed in the

information letter that they could withdraw their child from the study whenever they wanted, if

they so choose.

The ELL status of the participants was determined by the school board, and confirmed by

their teachers. The children’s home language was also corroborated by parental report. Initial

recruitment criteria required that participants spoke either Portuguese, Chinese, or Spanish as

their first language, and had lived in an English-speaking country for at least 4 months prior to

participation in the study. All the participants in the study had begun learning English formally

upon school entry in kindergarten.

Trained graduate students and research assistants tested the participants in the spring of

each year that data was collected. Testing was carried out over a 2-week period in two or three

testing sessions that lasted approximately one hour; the total testing time per participant was

approximately three hours. To reduce cognitive load and maintain participant engagement, tasks


43

were organized in a way that balanced task difficulty and type of response required (i.e., written

or oral). Individual testing sessions took place in a quiet room provided by the school of each

participant. If the participant expressed discomfort or fatigue, testing was stopped and continued

at a later time.

Educational Context

In Ontario, ELL students are those who have recently immigrated to Canada from non-

English-speaking countries or who were born in Canada but may have limited English language

proficiency for other reasons (e.g., some Canadian-born children are raised in “home language”

communities). ELL children in Ontario are entitled to receive ELL support for up to 2 years.

ELL support may be organized in one of three ways: (1) ELLs may be withdrawn from their

homeroom classroom for specialized language instruction, in this case, the withdrawal support is

provided by teachers with specialized ELL training and consists of 30 to 40 minutes of explicit

English language instruction per day; (2) ELL support is provided by the ELL teacher in the

child’s own classroom where all instruction takes place in English and many of the children in

the class are English native-speakers; or (3) English support is given through an adapted

curriculum provided by the homeroom teacher and teachers make appropriate program and

curriculum accommodations tailored to support the individual ELLs in their classroom.

In this research, English was the language of instruction in all participating schools.

Depending on their English language proficiency and school’s assessment of their needs, some

of the participants in this study may have been receiving ELL support at their school at the time

of participation in this research. Some of the participants may have also been receiving special

education programming since learning disability or language impairment status was not

exclusionary for participation in this research. Since data regarding ELL support or special


44

education programming was not accessible, the numbers of participants in either or both

categories is not known.

Participants

Data for this study are part of a larger longitudinal study where 2 cohorts of children (N =

298) coming from three home language backgrounds (i.e., Chinese, Spanish, and Portuguese)

were recruited from 22 elementary schools in two large and nearby cities in Southern Ontario,

Canada. Children were followed from kindergarten to grade 4 and tested on a range of cognitive,

linguistic, reading, and higher-order processing measures. The first cohort began the study in

kindergarten (n = 262). Because of attrition between kindergarten and grade 2 (n = 67) and to

increase the sample size, a second cohort was recruited in grade 2 (n = 36). This research focuses

on the combined cohorts and includes those children who attended the participating schools from

grade 2 to grade 4 (n = 231). Only participants with complete data at both time points (grade 2

and 4) were used in analyses (n = 155). Attrition (n = 76) from grade 2 to grade 4 occurred

primarily because children moved away from the school board where data collection took place.

In addition, 28 more children were removed from the data set because they missed the

administration of one or more of the testing batteries due to being absent on testing days, and

were unable to make it up. Because data was missing at the level of the test battery (i.e., the child

missed an entire battery of tests) and not at the level of the individual measures (e.g., the child

completed all the test batteries but one test was missed due to tester error), imputation was not a

reasonable option for dealing with the missing data for these 28 children. There were no other

missing data. The final sample size was n = 127.

First, it was necessary to examine whether the attrition was at random. Analyses

indicated that the attrited students did not differ statistically from the remaining participants on

home language background, gender, age, school site, or any of the cognitive (nonverbal


45

cognitive ability, phonological awareness, naming speed, working memory), linguistic (language

comprehension, vocabulary, oral expression), or reading measures of interest (decoding, word

reading accuracy and fluency, connected text reading fluency, reading comprehension,

inferencing). The final sample consisted of 51 males and 76 females. There were 37 children

who spoke Portuguese as their first language, 54 children who spoke Chinese as their first

language, and 36 who spoke Spanish as their first language. A Chi-square analysis was

conducted to determine whether gender differed based on home language background; the test

was not significant, χ2(3) = 3.13, p = .21; there was no relationship between gender and home

language. The mean age of the participants was 93 months (SD = 4.3 months) in grade 2, and

116 months (SD = 4.6 months) in grade 4. Analysis of variance (ANOVA) was conducted to

determine whether age was related to home language background; the test was not significant.

Age was not related to home language in grade 2 or grade 4: F(2, 123) = 2.73, p > 05; F(2, 123)

= 2.97, p = .06, respectively (see Tables 2 and 3).

Measures

Thirteen standardized measures and one experimental measure, all administered in

English, were used in this research. Four areas supporting reading development were assessed:

cognitive ability (non-verbal cognitive ability, naming speed, phonological awareness, and

working memory), oral language proficiency (oral expression and vocabulary), reading skills

(word reading, word- and text-level fluency, and reading comprehension), and higher-order

processing (inferencing strategy). The classification of participants by reading subtype was based

on the decoding and language comprehension measures administered in grade 4; these two

measures were not included in subsequent analyses in studies 1 and 2. Table 1 presents a

summary of the variables used in analysis by time point of measurement. To note, test

developers’ internal consistency coefficients, as well as sample-specific coefficients have been


46

reported for measures where item level data was available. For those measures where item level

data was not available, only the internal consistency coefficients reported by the test developers

are presented.


47

Table 1.

Summary of the variables used in analyses

1 Nonverbal cognitive ability was measured in grade 3.

Construct Variable Grade 2 Grade 4

Grouping Variables Decoding

Language comprehension

Cognitive ability Nonverbal cognitive ability1

Phonological awareness

Naming speed

Working memory

Oral language proficiency Receptive vocabulary

Oral expression

Higher-order processing Inferencing strategy Reading skills Word reading accuracy:

Word reading in isolation

Word reading in context

Reading fluency:

Word-level reading fluency

Text-level reading fluency

Reading comprehension


48

Measures used for group classification. Two measures representing the decoding and

language comprehension components of the SVR (Gough & Tunmer, 1986), were used to

classify participants into reader groups. These variables were only used for group classification.

For details about the relationships among the decoding and language comprehension measures,

and the other variables used in analyses, see Table 5 (for grade 4) and Table 9 (for grade 2).

Decoding. The Word Attack subtest of the Woodcock Reading Mastery Test – Revised

(WRMT-R; Woodcock, 1987) was used to test participant’s untimed ability to read pseudowords

in isolation in grade 4. This task was chosen because it is the “cleanest” measure of decoding; it

does not require knowledge of irregular English words that do not follow the phonological

“rules” of English (e.g., yacht). For this task, participants were asked to read aloud a list of

pseudowords that increased in length and difficulty. Task administration was discontinued when

6 items in a row for a given page were read incorrectly. The total number of items on this task is

45. The reported internal consistency for this task as measured using the split-half method

corrected by the Spearman-Brown formula is .97 for children in grade 4 (Woodcock, 1987). The

sample-specific Cronbach’s alpha for the grade 4 ELLs in this research is .90.

Language comprehension. The Recalling Sentences subtest of the Clinical Evaluation of

Language Fundamentals (CELF-3; Semel, Wiig, & Secord, 1995) was used as a measure of oral

language proficiency in grade 4. This task was chosen because this subtest is used in clinical

settings to diagnose language impairment in children having significant and pervasive oral

language difficulties. The Recalling Sentences subtest was used to assess the participant’s ability

to listen to spoken sentences of increasing length and complexity, and to repeat the sentences

without changing word meaning and content, word structure, or sentence structure. In this task,

participants repeated sentences of increasing difficulty that were orally presented by the task

administrator. There are 32 items in this task. Each item is worth up to 3 points depending on the


49

number of errors made in each repetition. The total score on this task is 96. The reported internal

consistency on this task as measured by Cronbach’s alpha is .91 for children aged 9 years old

(Semel, Wiig, & Secord, 1995). The sample-specific Cronbach’s alpha for the grade 4 ELLs in

this research is .90. No inter-rater reliability related to scoring was available for this task.

Cognitive ability

Nonverbal cognitive ability. The Matrix Analogies Test – Expanded Form (MAT –

Expanded Form; Naglieri, 1985) was used to measure nonverbal cognitive ability in grade 3.

This test is suitable for use with ELL populations as it is considered to be comparatively culture-

free and does not require verbal responses, thus cognitive ability is not confounded with

language proficiency. There are four subtests in this measure all related to reasoning and

problem solving about visual patterns: Pattern Completion, Reasoning by Analogy, Serial

Reasoning, and Spatial Visualization. For each item, participants were presented with an

incomplete pattern and asked to point to one piece from 6 option pieces that would complete the

pattern. Items were scored either 1 for correct, or 0 for incorrect. The total score on this task is

64. The reported internal consistency for this task as measured using Cronbach’s alpha is .94 for

children aged 8 (Naglieri, 1985).

Phonological awareness. Phonological awareness (PA) in grade 2 was assessed using the

Comprehensive Test of Phonological Processing (CTOPP; Wagner, Torgesen & Rashotte, 1999).

PA was measured using the Segmenting Nonwords and Segmenting Words elision tasks. In these

tasks, children were asked to delete individual sounds from nonwords or words and produce

orally the remaining part (e.g., say dog, now say dog without /d/). There were six practice items

and 20 test items in each task, which include initial, middle and last phoneme elision. The test

was discontinued when the participant responded incorrectly on three consecutive items. Items

were scored either 1 for correct, or 0 for incorrect. The combined total score for the two tasks is


50

40. Internal consistency for this task is measured using Cronbach’s alpha. The reported

coefficient for the combined Segmenting Nonwords and Segmenting Words elisions tasks is .91

for children aged 7 (Wagner et al., 1999).

Naming speed. The Digits and Letters – Rapid Automatized Naming (RAN) tasks of the

CTOPP (Wagner, Torgesen, & Rashotte, 1999) were used to measure naming speed in grade 2.

In this task, participants read a list of digits or letters, presented in rows, as quickly as possible.

Each task involved two trials. The time taken to read each trial was combined for a total score in

seconds. Internal reliability for this task is measured using alternate-form reliability (ideal for

speeded tasks). The reported coefficients for the digits and letters tasks are .84 and .70,

respectively, for children at aged 7 (Wagner et al., 1999). Times from the digits and letters task

were combined for a total RAN time used in analysis.

Working memory. The Backward Digit Span subtest of the Wechsler Intelligence Scale

for Children – Third Edition (WISC III; Wechsler, 1991) was used to measure participant’s

working memory in grade 4 (i.e., the ability to retain and manipulate information in memory).

For this task, children listened to orally presented sets of digits (i.e., numbers) and were then

asked to repeat out loud the set of digits in the reverse order. As the test continued, the series of

digits increased in set size. There are 8 items in the task with 2 trials per item. The task was

discontinued when both trials of an item were recalled incorrectly. Items were scored either 1 for

correct, or 0 for incorrect. The total score on this task is 16. Internal consistency for this task is

measured using the split-half method corrected by the Spearman-Brown formula; the coefficient

is .77 for children aged 9 (Wechsler, 1991).

Oral language proficiency

Vocabulary knowledge. Receptive vocabulary was measured in grades 2 and 4 using the

Peabody Picture Vocabulary Test Third Edition – Form B (PPVT III; Dunn & Dunn, 1997). For


51

the PPVT III, children listened to a word spoken by the task administrator, and then pointed to

the picture which they thought corresponded to that word. There were four pictures from which

to choose. The words became increasingly less common and more complex as the task

continued. Testing was discontinued when the participant failed to identify 8 word-to-picture

associations in a set. There are 228 items in this task. Items were scored either 1 for correct, or 0

for incorrect. Cronbach’s alpha is used as a measure of internal consistency for this task; the

reported coefficient is .95 for children aged 7, and .96 for children aged 9 (Dunn & Dunn, 1997).

Oral expression. The Oral Expression subtest of the Test of Language Competence –

Expanded Edition (TLC; Wiig & Secord, 1989) was used to measure oral language proficiency

in grade 2. On this task, which is administered orally, children were given two words (e.g., late,

dinner) and a context (e.g., In the yard). The children were then instructed to create a sentence

reflective of the context, using the two words (e.g., I was late for dinner because I was playing in

the back yard). Responses were given a holistic score and a word count score, each worth 3

points. The total possible score for each item was 6. For the holistic score, responses were scored

as follows: 0 if the response was nonsensical and not appropriate to the scenario, 1 point if the

response was appropriate to the scenario but grammatically incorrect, or 3 points if the response

was both grammatically correct and appropriate to the scenario (a score of 2 points is not an

option in this standardized test). For the word count score, responses were scored as follows: 0 if

neither of the two provided words were used in the response, 1 point if one word was used, and 3

points if both words were used. There are 16 items in this task. The total score on this task is 96.

Reported reliabilities for subtests of the TLC are .86 to .92 (Wiig & Secord, 1989). No inter-rater

reliability related to scoring was available for this task.

Reading skills


52

Word reading. Two measures of word reading were administered to participants: (1)

word identification which requires the correct reading of words in isolation, and (2) word reading

accuracy which requires the correct reading of words in context (i.e., within a text).

Word reading in isolation. The Word Identification subtest of the Woodcock Reading

Mastery Test – Revised (WRMT-R; Woodcock, 1987) was used to test participants’ untimed

ability to read real words in isolation in grades 2 and 4. For this task, participants were asked to

read aloud a list of real words that increased in length and difficulty. Task administration was

discontinued when 6 items in a row for a given page are read incorrectly. The total number of

items on this task is 106. Internal consistency for this task is measured using the split-half

method corrected by the Spearman-Brown formula. The reported coefficient for this task for

children in grades 2 and 4 is the same at .97 (Woodcock, 1987).

Word reading in context. The Neale Analysis of Reading Ability (Neale, 1997) was used

to measure word reading in context. Accuracy scores were recorded and calculated during the

reading comprehension administration of the task (described below). For each of the six passages

in this task, a maximum accuracy score has been determined by the task developers. The first

five passages have a maximum score of 16; the sixth and final passage has a maximum score of

20. As the participant read out loud each passage, the number of decoding errors made by the

participant was noted and deducted from the maximum score for the passage. The score for each

passage was totaled for a total possible score on the task of 100. Testing was discontinued when

the participant made more errors during reading of the passage than the maximum accuracy

score. For example, if the participant made 18 errors during the reading of the third passage

(which has a maximum accuracy score of 16), the test was stopped and the accuracy score for

that passage was recorded as zero; no further passages were administered. The internal reliability

coefficient for word reading in context for children in grade 4 is .88 (Neale, 1997).


53

Reading fluency. Fluency—the ability to read orally with speed and accuracy—was

measured at the word-level (using lists of words) in grades 2 and 4, and at the text-level (using

passages of text) in grade 4.

Word-level reading fluency. Word-level reading fluency was measured in grades 2 and 4

using the Word and Nonword subtests of the Test of Word Reading Efficiency (TOWRE;

Wagner, Torgesen, & Rashotte, 1999). In this test, participants read aloud lists of words or

nonwords as quickly and correctly as possible. There are 104 words in the Word subtest and 63

words in the Nonword subtest. The score on each subtest is based on the number of words read

in 45 seconds. Because the number of variables used in analyses was a concern and because

fluency measures using both words and nonwords tend to be highly reliable and highly correlated

(Lervag, Braten, & Hulme, 2009), the word and nonword subtests were combined for a total

word-level reading fluency score. The reported internal consistency for the subtest combined and

calculated using alternate-form reliability, is .98 for children aged 7 and also aged 9 (Wagner et

al., 1999).

Text-level reading fluency. The Neale Analysis of Reading Ability (Neale, 1997) was

used to measure text-level reading fluency in grade 4. For this task, fluency is a function of

words read (within a passage of text) per minute. The time taken to read up to six passages was

recorded during the reading comprehension administration of the task (described below). A

fluency score was calculated by dividing the total combined number of words read correctly by

the participant (i.e., there are 26 words in passage 1, 52 words in passage 2, 73 words in passage

3, 96 words in passage 4, 117 words in passage 5, and 141 words in passage 6), by the total time

it took the participant to read the passages; this total was then multiplied by 60 seconds. For

example, if the participant read 3 passages in 143 seconds, and made 26 word reading errors

before the test was discontinued (the test was discontinued when the participant made 16 word


54

reading errors in a passage), their fluency score would be: [151 (26 words for passage 1 + 52

words for passage 2 + 73 words for passage 4) – 26 (errors)] / 143 seconds * 60 seconds; or a

fluency score of 43.10.

Reading comprehension. Reading comprehension was measured in grades 2 and 4. The

passage comprehension subtest of the Woodcock Language Proficiency Battery – Revised

(Woodcock, 1991) was used to assess reading comprehension in grade 2. This test involved the

child reading passages silently. Each passage consisted of one to three sentences. The child was

then asked to provide a missing word represented by black lines in the text. The passages became

increasingly difficult. Testing was discontinued when the child missed or provided the incorrect

word for six consecutive sentences. The reported split-half reliability coefficient for this task .88

for children aged 7 (Woodcock, 1991).

The Neale Analysis of Reading Ability (Neale, 1997) was used to measure reading

comprehension in grade 4. This task involves reading aloud a series of short passages, and

answering open-ended comprehension questions. There are 6 passages of increasing length and

complexity with 8 questions per passage, except for the first passage that has 4 questions. Two

practice passages with questions are administered prior to beginning the task. Passages are

administered in an order of increasing difficulty and testing is discontinued when the participant

makes 16 or more word reading errors for passages 1 to 5, or 20 word reading errors on passage

6. The total score on this task is 44. Internal reliability for this task is measured using Cronbach’s

alpha. The reported internal consistency coefficient for reading comprehension is .95 for children

in grade 4 (Neale, 1997). The sample-specific coefficient for the grade 4 ELLs in this study is

.97.

Higher-order processing


55

Inferencing strategy. Inferencing skill was measured in grade 4 using Cain and Oakhill’s

(1999) task, in which participants read short passages silently and then orally answered open-

ended questions. Each question required the participant to generate an inference using the

information provided in the text. Items included literal inferences, text-connecting inferences

(i.e., requiring the integration of information presented in clauses or sentences in the text; Cain &

Oakhill, 1999) and gap-filling inferences (i.e., requiring the incorporation of participants’ prior

knowledge with information in the text to fill in missing details; Cain & Oakhill, 1999). There

was one passage with 6 questions for practice. There were 3 passages in total for the task, and 6

questions per passage. Questions were not answered from memory; the participants kept the text

in sight while answering the questions. Passages ranged in length from approximately 130 to 150

words. Each correct response received 1 point. The total score on this task is 18. The sample-

specific Cronbach’s alpha for this task is .80 for the grade 4 participants.


56

Chapter 4: Data Preparation

Two preliminary steps were taken in preparation of the data for analyses: (1) the

amalgamation of participants from different home language backgrounds, and (2) the assignment

of participants to one of four reading subtype groups.

Amalgamation of Participants From Different Home Language Backgrounds

Participants for this research were recruited from three home language backgrounds:

Portuguese, Chinese, and Spanish. The addition of home language as a control variable in

analyses led to concerns about statistical power and low cell counts, with some cells having only

1 participant. Hence, a series of analyses (e.g., ANOVA, Box’s M) were conducted to examine

whether there was statistical support for amalgamating participants from the different home

language groups into one sample for analysis as a way of increasing power. Tables 2 and 3

present descriptive statistics and post hoc comparisons related to three home language

backgrounds for the variables under study in grades 2 and 4, respectively.

To evaluate differences between the three home language groups in grade 2, multiple

analyses of variance (ANOVA) using the 7 variables of interest in grade 2 (i.e., naming speed,

phonological awareness, receptive vocabulary, oral expression, word-identification, word-level

reading fluency, and reading comprehension) were conducted. A Bonferroni-corrected -value

of .007 to indicate significance (i.e., = .05 divided by 7 variables) was employed. Table 2

presents descriptive statistics and post hoc comparisons for the three home language groups on

the variables under study in grade 2. The three home language groups did not perform

significantly differently on any of the variables of interest except for phonological awareness,

F(2, 124) = 17.63, p <.001, η2 = .22 (indicating a medium effect size; Cohen, 1992). The

Portuguese- (M = 21.73, SD = 7.82) and Spanish-speaking (M = 21.11, SD = 7.23) groups


57

outperformed the Chinese-speaking group (M = 13.09, SD = 8.21) on phonological awareness

(PA). In the literature, it has been hypothesized that L2 learners with typologically similar L1s

(e.g., English and Spanish are both alphabetic orthographies) might have an advantage in

phonological awareness (and relatedly decoding) when compared to L2 learners with an L1 that

is highly typologically different to their L2, for example Chinese and English. That said, findings

from studies comparing phonological awareness and decoding are not consistent and have found

variations in the significance and extent of group differences (Melby-Lervag & Lervag, 2014). In

a recent meta-analysis reviewing 82 studies, Melby-Lervag and Lervag (2014) concluded that

there were no reliable differences in PA or decoding between children with a logographic L1

(e.g., Chinese) and alphabetic L2 (e.g., English), and children with an L1 and L2 that were both

alphabetic (e.g., English and Spanish). Findings from this study appear to support a typological

differences hypothesis (Wade-Woolley, 1999; Wade-Woolley & Geva, 2000); the Chinese-

speaking group performed less well to the Portuguese- and Spanish-speaking groups on

phonological awareness. Nonetheless, there were no significant differences across the home

language groups on word reading (accuracy or fluency). It may be that the Chinese-speaking

children rely more heavily on an orthographic processing route rather than a phonological

processing route when word reading (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Wade-

Woolley, 1999; Wang, Koda, & Perfetti, 2003), and are using visual spelling patterns as an

alternate path to word identification. Despite lower PA skills, the word reading skills of the

Chinese-speaking children were on par with the Spanish- and Portuguese-speaking children

(Melby-Lervag & Lervag, 2014; Wade-Woolley, 1999). Further investigation involving an

orthographic processing task designed to assess children’s knowledge of visual patterns versus

word retrieval via phonological processing and letter-sound knowledge would be needed to

confirm this hypothesis.


58

Table 2.

The effect of home language background on the cognitive, linguistic, and reading skills in grade 2: Descriptive statistics and post hoc

comparisons

Portuguese-speaking

(P; n = 37)

Chinese-speaking

(C; n = 36)

Spanish-speaking

(S; n = 54)

Variable M SD M SD M SD F

Statistic

Effect

Size (η2) †

Post-Hoc

Comparisons

Age (in months) 94.47 4.24 92.85 4.06 92.53 4.44 2.72 .05 ns

Naming speed (in seconds) 47.25 12.03 43.81 13.42 45.92 8.44 .98 .02 ns

Phonological awareness (/40) 21.73 7.82 13.09 8.21 21.11 7.23 17.62*** .22 P = S > C

Receptive vocabulary (/228) 107.57 14.83 104.28 19.06 96.31 18.73 3.89 .06 ns

Oral expression (/96) 68.70 15.78 69.83 14.24 67.53 13.02 .28 .01 ns

Word reading in isolation (/106) 51.03 13.08 56.30 13.37 51.42 12.52 2.36 .04 ns

Word-level fluency (words/45 seconds) 70.08 26.00 81.30 27.47 71.58 25.12 2.47 .04 ns

Reading comprehension (/43) 16.38 4.62 17.61 4.39 15.69 3.91 2.27 .04 ns

Notes. N = 127; * p < .007 (Bonferroni corrected); SD = standard deviation, η2 = eta squared, ns = not statistically significant;

† .10, .24, and .40 indicate small, medium, and large effect sizes, respectively (Cohen, 1992)


59

An additional comparison was conducted to check whether either the Portuguese- or

Spanish-speaking groups were benefiting from an advantage on L2 English vocabulary in grade

2 due to shared cognates (e.g., ambalancia, triangulo, dentista) between Portuguese and English,

or Spanish and English vocabulary. Cognates are words that share common roots and spellings,

sounds, and meanings across languages. Some research findings have indicated that knowledge

about cognates can facilitate L2 vocabulary acquisition and as a consequence, reading

comprehension. For example, Spanish ELLs may be able to use their knowledge of Spanish-

English cognates to aid their English reading comprehension when encountering new and

unfamiliar words in English (Proctor and Mo, 2009; Ramirez, Chen, Geva, & Kiefer, 2010). The

same benefit could be hypothesized with Portuguese ELLs given the similarities between

Portuguese and Spanish. Sixty cognate items were removed from the English vocabulary task

and between-language comparisons were conducted again using the remaining 108 items and

using the Bonferroni-corrected -value of .007 to indicate significance. The results were similar

to the group comparisons on the full English vocabulary measure, F(2, 123) = 4.70, p = .01, η2 =

.08, with no significant differences across home language groups on the English vocabulary

measure in grade 2. This suggests that children in the Portuguese- and Spanish-speaking groups

were not at an advantage due to their potential cognate knowledge, nor were the Chinese-

speaking group at a disadvantage, given that the removal of the cognates did not change the

mean differences in general vocabulary across the three language groups.

To evaluate differences between the three home language groups in grade 4, multiple

ANOVAs were conducted using the 11 variables of interest in grade 4 (i.e., the decoding and

language comprehension grouping variables, nonverbal cognitive ability, working memory,

receptive vocabulary, five reading measures, and inferencing strategy). A Bonferroni-corrected


60

-value of .0044 to indicate significance (i.e., = .05 divided by 11 variables) was employed.

Table 3 presents descriptive statistics and post hoc comparisons for the three home language

groups based on the variables under study in grade 4. The mean performance of the three home

language groups were not significantly different from each other on either of the grouping

variables (i.e., decoding and language comprehension), or any of the cognitive, oral language,

reading, or higher-order variables of interest except for nonverbal cognitive ability, F(2, 124) =

12.33, p <.001, η2 = .17 (indicating a small to medium effect size; Cohen, 1992). The Chinese-

speaking group (M = 38.11, SD = 9.56) outperformed the Spanish- (M = 28.89, SD = 8.04) and

Portuguese-speaking (M = 31.95, SD = 9.05) groups on nonverbal cognitive ability. This

difference in nonverbal cognitive ability has been previously documented in the literature and is

attributed to an increased spatial ability exhibited by Chinese-speakers perhaps as a consequence

of their exposure to Chinese orthography and home-practices related to learning (e.g., Ramirez,

Chen, Geva, & Luo, 2011). Nonverbal cognitive ability was used as a cognitive control in

subsequent analyses to account for the potential variability on this task across participants.

Like in the grade 2 analyses, an additional comparison was conducted to check whether

either the Portuguese- or Spanish-speaking groups were benefiting from an advantage on L2

English vocabulary in grade 4 due to shared cognates between Portuguese and English, or

Spanish and English vocabulary. Sixty cognate items were removed from the English vocabulary

task and between-language comparisons were conducted again using the remaining 108 items.

The ANOVA was significant, F(2, 123) = 8.68, p = .000, η2 = .12; post hoc analyses revealed

that the Portuguese- (M = 87.41, SD = 6.25) and Chinese-speaking groups (M = 86.62, SD =

8.63) were performing significantly better than the Spanish-speaking group (M = 79.97, SD =

10.24) once the cognate items were removed. This finding does not support a cognate-advantage


61

hypothesis. It was concluded that none of the groups were at an advantage (or disadvantage) due

to cognate knowledge.


62

Table 3.

The effect of home language background on age, the grouping measures, and the cognitive, linguistic, higher-order, and reading skills

in grade 4: Descriptive statistics and post hoc comparisons

Portuguese-speaking

(P; n = 37)

Chinese-speaking

(C; n = 36)

Spanish-speaking

(S; n = 54)

Variable M SD M SD M SD F

Statistic

Effect

Size (η2) †

Post-Hoc

Comparisons

Age (in months) 118.32 4.28 116.11 4.60 116.28 4.60 2.96 .05 ns

Decoding (/45) 28.19 7.79 30.09 8.48 28.72 8.04 .67 .01 ns

Language comprehension (/96) 60.35 13.19 55.85 12.40 53.67 14.79 2.42 .04 ns

Nonverbal cognitive ability (/64) 31.95 9.05 38.11 9.56 28.89 8.04 12.33* .17 C > P = S

Working memory (/16) 4.65 1.69 4.67 1.41 4.33 1.79 .53 .009 ns

Word reading in isolation (/106) 65.81 11.26 72.17 11.80 69.31 13.06 3.08 .05 ns

Word reading in context (/100) 53.70 19.31 60.22 20.91 56.03 19.15 1.26 .02 ns

Word-level fluency (words/45 seconds) 99.24 23.56 108.46 21.53 100.47 21.63 2.37 .04 ns

Text-level fluency (words/minute) 78.38 23.26 88.23 23.17 74.02 23.26 4.02 .06 ns

Receptive vocabulary (/228) 131.57 12.30 131.00 16.01 119.06 17.72 7.91 .11 ns

Inferencing strategy (/18) 11.73 3.72 12.46 3.02 11.19 3.91 1.47 .02 ns

Reading comprehension (/44) 22.14 7.87 25.44 8.06 21.17 7.83 3.67 .06 ns

Notes. N = 127; *p < .004 (Bonferroni corrected); SD = standard deviation, η2 = eta squared, ns = not statistically significant;



63

Box’s M tests, used to determine whether two or more covariance matrices are equal,

were conducted using the grade 2 variables and grade 4 variables separately, to confirm that any

observed differences across the home language groups were typical variations occurring as a

result of immigration, demographic, and cultural differences etc., and that the groups were not

qualitatively different from each other on the cognitive, reading, and language skills that

mattered for this research. The test in grade 2 was not significant: Box’s M = 60.24, p = .351;

there were no significant differences in the covariance matrices in grade 2 among the

Portuguese-, Spanish-, and Chinese-speaking groups. The test in grade 4 was significant, Box’s

M = 247.54, p < .001. The Box’s M test is highly sensitive to number of variables used in

analysis, and to unequal sample sizes across groups; Tabachnick and Fidell (2007) report that

under these conditions the test may not be robust. Given that, (a) 11 variables were used in the

grade 4 analysis, (b) participants in the home language groups were not evenly distributed

(Spanish-speaking = 56, Portuguese-speaking = 37, and Chinese-speaking = 36), and (c) there

were no significant differences in the grade 2 analysis, the significant result of this test must be

taken with caution as it may not reflect meaningful differences between groups (Tabachnick &

Fidell, 2007). Based on the results of these preliminary analyses and despite the observed

differences in grade 2 phonological awareness, grade 3 nonverbal cognitive ability, and the

correlation matrices in grade 4, it was decided that the amalgamation of participants from the

three home language backgrounds into one sample to increase statistical power was theoretically

and statistically warranted. All analyses and results reported from this point onward are based on

the combined sample with participants from the three home language backgrounds amalgamated.

Classification of ELL Participants To Reading Groups


64

Participants were classified into one of four groups for analyses using measures of

decoding and language comprehension§: typically developing, poor decoders, poor language

comprehenders, and multi-deficit at-riskers. Each participant could only be a member of one

group and as noted earlier, the variables used in classification were not used again in analyses. In

line with prior research in the area (e.g., Geva & Herbert, 2012; Geva & Massey-Garrison,

2013), cut-off values at the 30th and 40th percentiles on decoding (using the Word Attack subtest

of the Woodcock Reading Mastery Test – Revised; Woodcock, 1987) and language

comprehension (using the Recalling Sentences subtest of the Clinical Evaluation of Language

Fundamentals; Semel, Wiig, & Secord, 1995) were used to identify children experiencing

difficulties on those skills in relation to the full sample of ELLs. Although this 30th percentile

cut-off is more liberal than what is used by clinicians (e.g., the 25th percentile is commonly used

to diagnose reading disability) and what is typically found in the reading disability literature

(Catts, Fey, Zhang, & Tomblin, 2001), it was felt that this more inclusive approach would

capture most if not all children who were significantly at-risk or already experiencing reading

problems. Moreover, one should be mindful of the dimensional nature of the skills involved in

poor reading (refer back to Figure 1 and the discussion about dimensionality in Chapter 2). The

ELL participants in this sample had been attending school in Canada and receiving English

instruction since kindergarten (i.e., four years), thus deficits in decoding or language by grade 4

§ A preliminary hierarchical regression analysis was performed to confirm the contributions of

decoding and language comprehension to reading comprehension. Age, nonverbal cognitive

ability, working memory (entered as the first step); and decoding and language comprehension

(entered as the second step); were regressed on reading comprehension. The model was

significant, F(2, 121) = 40.26, p < .001, R2 = .56, accounting for 56% of the variance in reading

comprehension. An examination of beta coefficients indicated decoding (β = .49, t = 7.16, p <

.001) and language comprehension (β = .22, t = 3.46, p = .001) both made unique contributions

to reading comprehension over and above age, nonverbal ability, and working memory, thus

supporting the use of these measures in the identification of different subtypes of ELL readers at-

risk for poor reading comprehension.


65

would represent a real risk to their reading comprehension and future reading success.

Furthermore, other researchers have found below the 30th and above the 40th percentile cut-offs

to be helpful in identifying those children in need of “further assessment and attention in the

classroom” (Geva & Massey-Garrison, 2013, p. 391), and those who would represent a skilled

group of learners with little future risk for difficulty in reading (Geva & Massey-Garrison, 2013).

It is important to remember that the decoding and language comprehension variables used

in classification are continuous variables, and that children’s performance on these skills fall

along a continuum. For example, a child performing at the 30th percentile would likely be

indistinguishable from a child performing at the 31st percentile. Therefore, for statistical purposes

it was important to ensure that each group was distinct. Thus, cut-off scores at the 30th and 40th

percentiles on the decoding and language comprehensions measures were used in the

classification of participants to groups. Children falling at or below the 30th percentile on either

the decoding or language comprehension tasks were deemed at-risk, while children at or above

the 40th percentile were considered typically developing. The cut-off values on the decoding

measure (out of 45) were 25.40 for the 30th percentile, and 28.00 for the 40th percentile. The cut-

off values on the language comprehension measure (out of 96) were 48.00 at the 30th percentile

and 53.00 at the 40th percentile. The four reader groups were then classified as follows: children

who were at or above the 40th percentile in both decoding and language comprehension were

classified as typical developers; children who were at or below the 30th percentile on decoding

and at or above the 40th percentile on language comprehension were classified as poor decoders;

conversely, children who were at or below the 30th percentile on language comprehension and at

or above the 40th percentile on decoding were classified as poor language comprehenders; and

children who were at or below the 30th percentile on both decoding and language comprehension

were classified as multi-deficit at-riskers. Children falling between the 30th and 40th percentile on


66

either decoding or language comprehension were designated to a “buffer zone”, and in order to

form discrete groups independent of one another for analyses, participants in the buffer zone

were removed from further analyses (n = 18). The buffer group consisted of 4 Portuguese-, 4

Spanish-, and 10 Chinese-speaking participants. There were 6 males and 12 females in the buffer

group. Chi-square analyses indicated that there was no significant differences between ELL

reader group (including the buffer group) and gender, χ2(4) = 3.91, p = .42.

Figure 2 presents a scatterplot of home language by participants’ decoding and language

comprehension raw scores including those in the buffer zone. Figure 3 displays a bar graph with

the number of participants in each reader group by home language group. A visual inspection of

the bar graph revealed that children across the three home language backgrounds were not

distributed evenly across the four ELL reading groups. In the poor decoder group there were 9

(out of 17) Portuguese-speaking, 1 (out of 17) Spanish-speaking, and 7 (out of 17) Chinese-

speaking participants; in the poor language comprehender group there were 1 (out of 15)

Portuguese-speaking, 8 (out of 15) Spanish-speaking, and 6 (out of 15) Chinese-speaking

participants; in the multi-deficit at-risker group there were 5 (out of 20) Portuguese-speaking, 9

(out of 20) Spanish-speaking, and 6 (out of 20) Chinese-speaking participants; and in the typical

developers group there were 18 (out of 57) Portuguese-speaking, 14 (out of 57) Spanish-

speaking, and 25 (out of 57) Chinese-speaking participants.

A chi-square analysis was conducted to confirm the observation that participants from the

three home language backgrounds were not distributed evenly across the four ELL reading

groups. The chi-square was significant, χ2(6) = 14.81, p = .02; there was at least one significant

difference between at least two of the ELL reading groups. Post hoc analyses using adjusted

residuals (an adjusted residual is a z-score representing the difference between the observed

value and expected value for a cell), and a Bonferroni-corrected -value of .0042 (i.e., = .05


67

divided by 12 comparisons) were conducted to locate the difference(s) between groups. That

said, after implementing the Bonferroni-correction (used to reduce Type I error when multiple

comparisons are conducted; Tabachnick & Fidell, 2007), no significant differences between ELL

reader group and home language background were found. This finding further supports the

earlier decision to amalgamate participants from the different home language backgrounds.


68

Figure 2. Scatterplot showing home language background of the reader groups by their decoding

and language comprehension raw scores.


69

Figure 3. Bar graph displaying the number of participants in each reader group by home

language group.


70

The final total sample size for analyses was N = 109. Sample sizes for each reader group

in grade 4 were as follows: poor decoders (n = 17; 15.6% of sample), poor language

comprehenders (n = 15; 13.8% of sample), multi-deficit at-riskers (n = 20; 18.3% of sample),

and typical developers (n = 57; 52.3% sample). At first glance, these numbers might seem high

when compared to reported percentages of children with dyslexia and/or language impairment;

the prevalence of dyslexia in the population is often reported as approximately 10% (Snowling &

Hulme, 2012), while the percentage of children with language impairment is estimated to be

about 7% (Tomblin et al., 1997). As mentioned earlier, the goal of the present research was to

cast a wide net and to include all children who might be at-risk for reading comprehension

problems, including those who meet criteria for formal diagnoses, as well as those who do not

but who still fall at the problematic end of the continuum and struggle with reading. Thus, higher

percentages in comparison to those reported in studies involving children with formal diagnoses

would be expected.

Profiles of the reading groups using decoding and language comprehension z-scores are

presented in Figure 4. Z-scores were calculated in SPSS and are standardizations of the raw

scores (on decoding and language comprehension) with a mean of 0 and a standard deviation of

1. Figure 4 displays visually the differences among the four groups defined on the basis of their

relative performance on decoding and language comprehension. A visual inspection of the bars

shows that typical developers were good at both decoding and language comprehension when

compared to the other three groups, the poor language comprehenders had poor language

comprehension but decoding that was on par with that of the typical developers, the poor

decoders had poor decoding but language comprehension that was similar to that of the typical

developers, and the multi-deficit at-riskers had both poor decoding and language comprehension

when compared to the other three groups.


71

Figure 4. Decoding and language comprehension profiles for the reader groups: multi-deficit at-

riskers, poor decoders, poor language comprehenders, and typical developers.


72

Visual observations of decoding and language comprehension skill levels in Figure 4

were confirmed by a significant MANOVA test using decoding and language comprehension as

dependent variables and ELL reader group as the fixed factor, Wilks’ Λ = .09, F(6, 208) = 79.64,

p < .001. There was a significant group effect for at least one group on at least one of the

dependent variables. Pairwise post hoc comparisons were conducted to determine across which

groups and on which variables significant differences occurred. Table 4 presents descriptive

statistics and post hoc comparisons for the ELL reader groups on age, and the two measures used

to define the reading groups: decoding and language comprehension. These post hoc results

support the classification model: typical developers (M = 35.12, SD = 84.23) and poor language

comprehenders (M = 32.53, SD = 3.70) performed significantly better than the poor decoders (M

= 18.76, SD = 6.49) and multi-deficit at-riskers (M = 18.50, SD = 4.45) on decoding; typical

developers (M = 65.12, SD = 7.41) and poor decoders (M = 63.47, SD = 9.18) performed

significantly better than poor language comprehenders (M = 39.93, SD = 8.67) and multi-deficit

at-riskers (M = 40.80, SD = 5.49) on language comprehension; no other significant differences

were observed.

Also reported in Table 4 are English monolingual testing norm scores for the ELL

reading groups on each grouping measure. Test norms for the decoding measure are standardized

using a mean of 100 and an SD of 15 (Word Attack subtest, Woodcock Reading Mastery Test –

Revised; Woodcock, 1987); test norms for the language comprehension measure are

standardized using a mean of 10 and a SD of 3 (Recalling Sentences subtest, Clinical Evaluation

of Language Fundamentals; Semel, Wiig, & Secord, 1995). On decoding, poor decoders (SS =

86) and multi-deficit at-riskers (SS = 86) performed at almost 1 standard deviation below the

mean when compared to monolingual norms, while poor language comprehenders (SS = 105)

and typical developers (SS = 109) performed above the mean when compared to monolingual


73

norms. On language comprehension, the poor language comprehenders (SS = 5) and multi-deficit

at-riskers (SS = 5) performed one standard deviation below the mean when compared to

monolingual norms, while poor decoders (SS = 10) and typical developers (SS = 10) performed

at the mean when compared to monolingual norms. These observations of test norm standardized

scores further supported the validity of the classification model.


74

Table 4.

The effect of ELL reader group on age, decoding, and language comprehension: Descriptive statistics and post hoc comparisons

1

Poor

Decoders

(n = 17)

2

Poor Language

Comprehenders

(n = 15)

3

Multi-deficit

At-riskers

(n = 20)

4

Typical

Developers

(n = 57 )

Measure M SD SS M SD SS M SD SS M SD SS F

Statistic

Effect

Size (η2)†

Post Hoc

Comparisons

Age (in months) 117.00 5.10 — 116.60 4.97 — 116.10 5.08 — 116.56 3.97 — 0.12 .004 ns

Decoding (/45)1 18.76 6.49 86 32.53 3.70 105 18.50 4.45 86 35.12 4.23 109 91.98*** .72 4 & 2 > 1 & 3

Decoding (z-score) -1.28 .80 — .42 .46 — -1.19 .55 — .74 .52 — 91.98*** .72 4 & 2 > 1 & 3

Language comprehension (/96)2 63.47 9.18 10 39.93 8.67 5 40.80 5.49 5 65.12 7.41 10 81.53*** .70 4 & 1 > 2 & 3

Language comprehension (z-score) .52 .68 — -1.23 .64 — -1.17 .41 — .64 .55 — 81.53*** .70 4 & 1 > 2 & 3

Notes. N = 109; SD = standard deviation; SS = standardized score based on monolingual norms; η2 = eta squared; *** p <.001, ** p <

.01,* p <.05; ns = not statistically significant

1 The standardized score for the decoding measure has a mean of 100 and a standard deviation of 15.

2 The standardized score for the language comprehension measure has a mean of 10 and a standard deviation of 3.



75

Summary of Data Preparation Techniques

This chapter has provided details of, and support for, the amalgamation of participants

from three home language groups into one sample, and the identification and classification of

ELL participants into one of four reading groups for analyses. Support for the amalgamation of

participants from differing L1s was gained through the following statistical procedures: (1)

ANOVA, (2) cognate-item analyses, and (3) Box’s M tests. Results of the ANOVAs using the

grade 2 and 4 variables, indicated one group difference in each grade across the Chinese,

Portuguese, and Spanish groups. Specifically, in grade 2 the groups were different in their mean

scores on phonological awareness; the Spanish and Portuguese groups performed better on the

measure than the Chinese group. It was concluded that this difference could be explained by

typological differences across orthographies (Gottardo, Pasquarella, Chen, & Ramirez, 2015;

Wade-Woolley, 1999; Wade-Woolley & Geva, 2000). This difference did not translate into

differences across home languages groups on word reading or decoding however. In grade 4, the

home language groups were different in their mean scores on nonverbal cognitive ability with the

Chinese group performing better on the measure than the Spanish and Portuguese groups. This

difference has been documented in other research in the literature (e.g., Ramirez, Chen, Geva, &

Luo, 2011). As a result of this finding, it was decided that nonverbal cognitive ability would be

used as a control variable for all further analyses.

Given the possibility that groups could be performing differentially in their vocabulary

knowledge due to a cognate knowledge advantage or disadvantage, the home language groups

were evaluated on their cognate knowledge in grades 2 and 4 using a general vocabulary

knowledge measure, the Peabody Picture Vocabulary Test (PPVT III; Dunn & Dunn, 1997). No

differences were initially observed in grade 2 or 4 across the home language groups on this

vocabulary measure. When cognate items were removed, again no meaningful differences across


76

the groups were found. It was concluded that the Portuguese and Spanish participants were not at

an advantage due to their potential cognate knowledge, nor were the Chinese participants at a

disadvantage due to lack of cognate knowledge, in their general vocabulary.

Finally, Box’s M tests were conducted to evaluate differences in the correlation matrices

on the variables under study across the three home language groups. The test was nonsignificant

when using the grade 2 variables. In grade 4 however, the test was significant. Given the small

and unequal group sizes, it was concluded that results of this test should be interpreted with

caution, as it has been reported that the test is not as robust under those group conditions

(Tabachnick & Fidell, 2007). All in all, based on a pattern of statistical results suggesting that

there were few significant or meaningful differences across the Chinese, Spanish, and Portuguese

groups, the three home language groups were amalgamated into one larger sample to increase

power in subsequent analyses.

The second data preparation technique presented in this chapter was the identification and

classification of the ELL participants into one of four reading groups for analyses, using the

relationship between their individual scores on decoding and language comprehension (as per the

SVR). Using below the 30th percentile and above the 40th percentile on these measures as cut-off

points, participants were identified as having poor decoding, poor language comprehension, or

poor at both, and classified as such (Geva & Herbert, 2012; Geva & Massey-Garrison, 2013). To

ensure that the four reading groups were distinct from each other for further statistical analyses,

participants with scores falling between the 30th and 40th percentiles on either measure were

removed from analyses. Follow-up tests were conducted to evaluate the success of this

classification method in producing the intended theoretical reading groups (see Table 4). An

evaluation of the reading groups’ mean scores on decoding and language comprehension, as well

as a comparison of the groups’ standardized scores to monolingual norms confirmed that: (1) the


77

poor decoders group were impaired in their reading when compared to the typically developing

group, (2) the poor language comprehenders were impaired in their language comprehension

when compared to the typically developing group, and (3) that the multi-deficit at-riskers were

impaired in their decoding and language comprehension when compared to the typically

developing group.


78

Chapter 5: Study 1 – Linguistic and Reading Profiles of At-risk ELL Readers

The previous chapter focused on distinguishing participants in the sample who were

typically developing in their decoding and language comprehension skills, from those who were

at-risk for poor reading comprehension due to compromised skills in those areas. This chapter

focuses on examining the concurrent linguistic and reading skill profiles of the at-risk reading

groups in grade 4. Readers at-risk for poor comprehension can display different sources of

reading problems (Geva & Herbert, 2012; Geva & Massey-Garrison, 2013; Li & Kirby, 2014;

Tong, Deacon, Kirby, Cain, & Parrila, 2011). Thus, the goal of Study 1 was to examine the grade

4 skill profiles—word reading accuracy, fluency at the word- and text-levels (i.e., efficiency in

reading lists of words and efficiency in reading words within connected text, respectively),

vocabulary, inferencing strategy, and reading comprehension—of ELLs grouped into typically

developing or at-risk subtypes based on their decoding and language comprehension skills in

grade 4. Profiles of ELLs in grade 4 rather than at an earlier time point were chosen for two

reasons. First, a lot more is known about what sources of reading difficulties look like for

children in the primary grades (Vellutino, Fletcher, Snowling, & Scanlon, 2004). Early reading

problems tend to manifest as poor word reading accuracy and fluency, and children with

language problems (and no decoding issues) are difficult to identify because early reading does

not rely heavily on complex language skills. Less is known about ELL children with later

emerging reading problems as a consequence of difficulties with language. Second, with the shift

from “learning to read”, to “reading to learn” that occurs in the middle elementary grades (Chall,

1996), the application and implications of poor reading change. As children progress through

elementary school, the texts they read become more difficult, the demands of reading more

challenging, and the repercussions of not being able to read well, more severe. By grade 4

children with language problems should be identifiable, though one of the issues is whether it is


79

possible to identify ELLs with language difficulties at an earlier time point (and this is the focus

of the Study 2).

Research Question and Hypotheses for Study 1

For Study 1, one broad research question was posed: Do distinct profiles describe the

performance of typical developers, poor decoders, poor language comprehenders, and multi-

deficit at-riskers on word reading (accuracy and fluency), vocabulary, text-level reading fluency,

inferencing strategy, and reading comprehension over the role of nonverbal cognitive ability and

working memory? Five hypotheses about the profiles of the ELL at-risk reading subtypes were

made in relation to these five areas of interest:

1. It was hypothesized that ELL readers classified as at-risk due to compromised

decoding skills (i.e., poor decoders and multi-deficit at-riskers) would have

difficulties with all aspects of word reading when compared to typical developers,

due to their inability to decode words accurately and efficiently. Poor language

comprehenders (with intact decoding skills) would perform similarly to typical

developers, and show no specific difficulties with the word reading tasks.

Compromised decoding skills include: word reading using lists of words, word

reading using passages of text, and word-level reading fluency, also using lists of

words.

2. It was hypothesized that the multi-deficit at-riskers and poor language comprehenders

would have difficulty with vocabulary when compared to the typically developing

children because of their compromised language development. Poor decoders would

perform similarly to the typical developers given that their language comprehension

skills (for which they have no impairment) would likely encompass a good level of

vocabulary development.


80

3. It was hypothesized that a different profile of reading fluency would be observed

across the reading subtypes for text-level reading fluency in grade 4 (when compared

with word-level reading fluency – see hypothesis 1). Because text-level reading

fluency encompasses automatic word-level reading processes but also involves

language and meaning-making processes (Ehri, 2002; Geva & Farnia, 2012; Kim &

Wagner, 2015), it was predicted that all three poor reading subtypes would have

difficulties with text-level fluency. Due to their already inefficient word recognition

skills, the poor decoders and multi-deficit at-riskers would have difficulty with text-

level reading fluency; theoretically it would be impossible to read efficiently at the

text-level if automaticity had not already been achieved at the word-level. The poor

language comprehenders would also have difficulty with this skill due to their

compromised ability to make the meaning required to “bridge” word reading with

reading comprehension through language.

4. It was hypothesized that all three at-risk reader subtypes would struggle with

inferencing when compared with typically developing ELLs and after controlling for

working memory, but for different reasons. Poor decoders and multi-deficit at-riskers

would struggle due to a lack of automaticity in their word reading (Cain & Oakhill,

1999; Prior, Goldina, Shany, Geva, & Katzir, 2014). Because of their poor decoding,

their cognitive resources would be directed toward lower-level reading skills

(decoding, word recognition, and fluency) leaving them little attention for the higher-

level text processing required by inferencing. Given that the multi-deficit at-riskers

also were defined by their difficulties with language (another necessary component of

effective inferencing), it was hypothesized that problems with inferencing might be

even more pronounced when compared to the poor decoders. Conversely, although


81

poor language comprehenders would in theory have fluent word reading skills

(accuracy and speed), they would not be able to capitalize on the understanding

gained from applying inferencing strategies because of their impaired language, as

successful inferencing is also heavily reliant on oral language proficiency (Geva &

Massey-Garrison, 2013; Li & Kirby, 2014; Prior, Goldina, Shany, Geva, & Katzir,

2014).

5. Based on the SVR (Gough & Tunmer, 1986), it was hypothesized that due to their

varying profiles of deficits in decoding and/or language, all three at-risk reading

subtypes would have significant difficulty with their reading comprehension when

compared to typically developing ELL readers.

Two analyses were conducted to examine the relationships between ELL reading group

in grade 4, and word reading accuracy and fluency, vocabulary, text-level reading fluency,

inferencing strategy, and reading comprehension. First, a preliminary correlation analysis was

conducted to examine the relationships among the variables under study. Second, a MANCOVA

was conducted. This analysis was selected because MANCOVA allows for the exploration of

how the means of a set of dependent variables vary across levels of a factor (Meyers, Gamst, &

Guarino, 2013). It also allows for the inclusion of a covariate or variable known to have a

potential impact on the dependent variables under study. ELL reader group was the factor

variable and included the four ELL reader groups as classified in grade 4: typical developers,

poor decoders, poor language comprehenders, and multi-deficit at-riskers. Word reading in

isolation, word reading in context, word-level reading fluency, text-level reading fluency,

vocabulary, inferencing strategy, and reading comprehension were the dependent variables of

interest; nonverbal cognitive ability and working memory were used as covariates due to their

potential impact on the relationships of interest.


82

Relationships Among the Grade 4 Variables

Table 5 displays the correlation matrix for grade 4 concurrent variables used in Study 1.

In support of the SVR, decoding and language comprehension were positively and significantly

correlated with each other (r = .32, p < .001), with reading comprehension (r = .66, p < .001; r =

.45, p < .001, respectively), and with all other reading skill measures. This observation

corroborates the importance of these two skills for reading and provides additional support for

their use in the making of at-risk reader subgroups. Also noteworthy was that the two cognitive

controls, nonverbal cognitive ability and working memory, were significantly correlated with all

the measures under study, save the relationship between working memory and text-level reading

fluency (r = .01, p > .05). This finding supports the use of nonverbal cognitive ability and

working memory as covariates in the analysis.

With regard to the two skill areas of special focus, word- and text-level reading fluency

were significantly correlated with all the measures under study but text-level reading fluency did

not correlate with working memory (as mentioned previously). Word- and text level fluency

were highly correlated with each other (r = .60, p < .001). Word-level reading fluency was also

most highly correlated with reading comprehension (r = .73, p < .001), and the other measures of

word reading: decoding (r = .78, p < .001); word reading in isolation (r = .81, p < .001), and

word reading in context (r = .72, p < .001), and moderately correlated with vocabulary (r = .56, p

< .001). Conversely, text-level fluency was highly related to vocabulary (r = .56, p < .001) and

modestly related to reading comprehension (r = .39, p < .001) and language comprehension (r =

.29, p < .001). Inferencing was significantly correlated with all the variables in the analyses,

showing the strongest relationships with reading comprehension (r = .61, p < .001) and word

reading skills – decoding, word reading accuracy, word-level fluency (r = .50 to .57, p < .05).


83

Table 5.

Correlation matrix showing the relationships among the grade 4 variables in Study 1

1 2 3 4 5 6 7 8 9 10 11

1 Decoding† 1 .32*** .32*** .33*** .82*** .71*** .78*** .30*** .45*** .50*** .66***

2 Language comprehension† 1 .25** .19* .39*** .35*** .38*** .29*** .42*** .45*** .45***

3 Nonverbal cognitive ability 1 .43*** .38*** .40*** .29*** .19* .45*** .33*** .47***

4 Working memory 1 .33*** .41*** .22* .01 .19* .32*** .37***

5 Word reading in isolation 1 .81*** .81*** .41*** .55*** .57*** .77***

6 Word reading in context 1 .72*** .33*** .54*** .54*** .82***

7 Word-level fluency 1 .60*** .54*** .56*** .73***

8 Text-level fluency 1 .56*** .36*** .39***

9 Vocabulary 1 .45*** .61***

10 Inferencing strategy 1 .58***

11 Reading comprehension 1

Notes. N = 127; † variable used for grouping of ELL readers; *** p <.001, ** p < .01,* p <.05


84

Do children with compromised decoding and/or language comprehension also have

difficulties with their current word reading, fluency, vocabulary, inferencing strategy, and

reading comprehension?

A MANCOVA was conducted to examine differences among the ELL reader groups on

the reading skill measures (word reading in isolation, word reading in context, word- and text-

level reading fluency, vocabulary, inferencing strategy, and reading comprehension), after

controlling for nonverbal cognitive ability and working memory. The MANCOVA was

significant, Wilks’ Λ = .42, F(21, 279) = 4.71, p < .001, with partial η2 = .25, indicating a

medium effect (Meyers, Gamst, & Guarino, 2013); there was at least one group difference for at

least one dependent variable after controlling for working memory and nonverbal cognitive

ability. Working memory was not significant, Wilks’ Λ = .91, F(7, 97) = 1.42, p = .20, η2 = .09;

while nonverbal cognitive ability was significant, Wilks’ Λ = .81, F(7, 97) = 3.26, p < .05, η2 =

.19.

Bonferroni-corrected ANOVAs were conducted for each dependent variable to determine

on which variable(s) the reader groups were different from each other, and to account for the

percent of variance unique to each predictor. Statistically significant differences were observed

on all seven dependent variables: word reading in isolation, F(3,103) = 20.45, p <.001, η2 = .37;

word reading in context, F(3,103) = 15.40, p <.001, η2 = .31; word-level reading fluency,

F(3,103) = 26.58, p <.001, η2 = .44; text-level reading fluency, F(3,103) = 3.06, p <.05, η2 = .08;

vocabulary, F(3,103) = 6.08, p <.001, η2 = .15; inferencing strategy, F(3,103) = 5.94, p <.001, η2

= .15; and reading comprehension, F(3,103) = 18.75, p <.001, η2 = .35. In support of the SVR,

ANOVA results indicated that word reading in isolation and reading comprehension showed

strong differences across ELL reader groups accounting for 37% and 35% of the variance (as

indicated by partial η2), after controlling for nonverbal cognitive ability and working memory.


85

However in support of an expanded SVR, the variable that most clearly distinguished the groups

was reading fluency, accounting for 44% of the variance. Word reading in context followed

closely behind fluency, word reading in isolation, and reading comprehension in explaining

differences across the ELL reading groups, accounting for 31% of the variance. Vocabulary and

inferencing strategy accounted for 15% of the variance. Finally, text-level reading fluency was

also significant, accounting for 8% of the variance between ELL reading groups. Table 6

displays descriptive statistics and post hoc results for the ELL reader groups on the grade 4

dependent variables of interest.


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Table 6.

The effect of ELL reader group on word reading, fluency, vocabulary, inferencing, and reading comprehension variables of interest in

grade 4: Descriptive statistics and post hoc comparisons

1

Poor

Decoders

(n = 17)

2

Poor Language

Comprehenders

(n = 15)

3

Multi-deficit

At-riskers

(n = 20)

4

Typical

Developers

(n = 57 )

Measure M SD M SD M SD M SD F

Statistic

Effect

Size (η2)†

Post Hoc

Comparisons††

Word reading in isolation (/106) 58.82 13.51 71.47 9.61 57.15 6.29 69.75 9.18 20.45*** .37 4 > 1 & 3

2 > 1 & 3

Word reading in context (/100) 42.53 15.74 60.93 17.60 36.80 6.20 67.46 17.20 15.40*** .31 4 > 1 & 3

2 > 1 & 3

Word-level fluency (words/45 seconds) 80.29 20.28 103.20 19.98 82.45 12.36 118.23 16.85 26.58*** .44 4 > 1, 2, & 3

2 > 1 & 3

Text-level fluency (words/minute) 70.08 22.09 73.22 22.28 77.89 24.53 89.59 26.21 3.06** .08 ns

Vocabulary 124.24 11.43 123.73 17.73 115.65 14.95 135.35 14.41 6.08*** .15 4 > 3

Inferencing strategy (/18) 10.29 3.43 11.40 3.92 8.95 3.62 13.39 2.91 5.94*** .15 4 > 1 & 3

Reading comprehension (/44) 18.65 6.52 22.73 8.96 13.90 3.29 27.96 6.09 18.75*** .35 4 > 1, 2, & 3

2 > 3

Notes. N = 109; SD = standard deviation, η2 = eta squared; *** p <.001, ** p < .01,* p <.05; ns = not statistically significant;

† .10, .24, and .40 indicate small, medium, and large effect sizes, respectively (Cohen, 1992);

†† significant Bonferroni-corrected pairwise comparisons


87

An examination of post hoc pairwise comparisons revealed several interesting findings.

Figures 5, 6, and 7 visually display the differences across the groups; Table 6 provides

significant pairwise comparisons in the post hoc labeled column. As can be seen in Figure 5,

poor decoders and multi-deficit at-riskers had significant difficulties in their word reading skills

in grade 4—word reading in isolation using lists of words, and in context using passages of

text—when compared with poor language comprehenders and typical developers groups. As can

be seen in Figure 6, all three at-risk reader groups read words with significantly less fluency than

typical developers; no significant differences among the groups were observed with regard to

text-level reading fluency. Figure 7 displays the differences on vocabulary, inferencing strategy,

and reading comprehension; all three at-risk groups exhibited some constellation of problems

with these skills when compared with typical developers: (1) the multi-deficit at-riskers had

significant difficulties with vocabulary when compared to the typical developers – no other

significant difficulties in vocabulary were observed among the other two ELL reader groups; (2)

along with poor decoders, the multi-deficit at-riskers also had significant difficulties with

inferencing strategy; and (3) all three at-risk groups performed more poorly than the typical

developers on reading comprehension.


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Figure 5. Word reading profiles (in isolation and in context) for the typically developing and at-

risk reader groups under study


89

Figure 6. Word- and text-level fluency profiles for the typically developing and at-risk reader

groups under study


90

Figure 7. Vocabulary, inferencing, and reading comprehension profiles for the typically

developing and at-risk reader groups under study


91

Types of Inferencing Strategies Used by the ELL Reader Groups

An additional set of analyses were conducted to examine the types of inferencing

strategies used by the ELL readers (Canet-Juric, Andrés, Urquijo, & Burin, 2011), and to test the

hypothesis that the at-risk groups would have difficulty with inferencing in different ways. There

were three types of inferencing questions: (1) literal inference items (n = 6) which required the

reader to make a direct connection between the question, and information explicitly provided

within the text, (2) text-connecting inference items (n = 6) which required the reader to integrate

information provided in two sentences or clauses to answer the question, and (3) gap-filling

inference items (n = 6), which required the reader to use their prior or background knowledge to

answer the question. Table 7 presents descriptive statistics (mean and standard deviation) and

item analyses (internal consistency reliabilities, item-test correlations, and difficulty indexes) for

each type of inference, for the whole sample. A visual inspection of the means for each inference

type revealed that gap-filling inferences were the most difficult, followed by text-connecting

inferences. Literal inferences were the least difficult. This observation was confirmed by an

evaluation of difficulty index scores. A difficulty index is a measure of the proportion of

participants who responded to items successfully. This difficulty index can range from 0 (0 items

out of 6 correct) to 1 (6 items of out 6 correct). For this task, a score of 5 or 6 (out of 6) was

considered “successful”. As can be seen in Table 7, 75% of the sample received a score of 5 or 6

(out of 6) on items involving literal inferences, 62% of the group received a score of 5 or 6 (out

of 6) on items involving text-connecting inferences, and only 10% of the group received a score

of 5 or 6 (out of 6) on items involving gap-filling inferences. Text-connecting inferences, those

requiring connections to background knowledge, were clearly the most difficult type of inference

to make for the sample as a whole.


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Table 8 presents descriptive statistics and post hoc comparisons for type of inferencing

strategy by ELL reading group. An inspection of the means suggests that the pattern of difficulty

for the three types of inferences was similar across the reading groups, with all four groups

finding gap-filling inferences to be the most difficult (i.e., averaging the lowest score out of 6 for

all four groups), and literal inferences the least difficult (i.e., averaging the highest score out of 6

for all four groups). ANOVAs were conducted to determine differences among reader groups by

type of strategy the reader. Statistically significant differences were observed on all three

inference types: literal inferences, F(3,104) = 10.92, p <.001, η2 = .24, indicating a medium

effect (Cohen, 1992); text-connecting inferences, F(3,104) = 10.35, p <.001, η2 = .23, indicating

a medium effect (Cohen, 1992); and gap-filling inferences, F(3,104) = 10.21, p <.001, η2 = .23,

indicating a medium effect (Cohen, 1992). An examination of post hoc comparisons revealed

that: (1) multi-deficit at-riskers had difficulty with all three type of inferences when compared to

typical developers, (2) poor language comprehenders performed similarly to typical developers

on all inferencing strategies, and (3) poor decoders had difficulties with text-connecting and gap-

filling inferences when compared to typical developers, but not with literal inferences.


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Table 7.

Descriptive statistics and item analyses for different inferencing strategy types

Inference Type M SD Cronbach’s α Item-test Correlation Difficulty Index

Literal (/6) 5.09 1.26 .64 .77 .75

Text-connecting (/6) 4.51 1.51 .65 .78 .62

Gap-filling (/6) 3.60 1.41 .47 .73 .10

Notes. N = 127; SD = standard deviation


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Table 8.

The effect of ELL reader group on inference strategy type: Descriptive statistics and post hoc comparisons

1

Poor

Decoders

(n = 17)

2

Poor Language

Comprehenders

(n = 15)

3

Multi-deficit

At-riskers

(n = 20)

4

Typical

Developers

(n = 57 )

Inference Type M SD M SD M SD M SD F Statistic Effect Size

(η2) †

Post Hoc

Comparisons††

Literal (/6) 4.94 1.44 5.07 1.22 3.74 1.49 5.49 0.91 10.92*** .24 1, 2, & 4 > 3

Text-connecting (/6) 3.52 1.70 4.69 1.40 3.53 1.90 5.18 1.05 10.35*** .23 4 > 1 & 3

Gap-filling (/6) 2.00 1.00 2.60 1.55 1.42 1.17 3.18 1.33 10.21*** .23 4 > 1 & 3

Notes. N = 109; SD = standard deviation, η2 = eta squared; *** p <.001, ** p < .01,* p <.05;




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Summary of Study 1

The goal of Study 1 was to examine the concurrent linguistic and reading skill profiles of

at-risk ELL reading subtypes. One broad research question was posed: Do distinct profiles

describe the performance of typical developers, poor decoders, poor language comprehenders,

and multi-deficit at-riskers on word reading (accuracy and fluency), vocabulary, text-level

reading fluency, inferencing strategy, and reading comprehension over the role of nonverbal

cognitive ability and working memory? To answer this question, two analyses were undertaken.

First, correlational analyses were conducted to explore relationships among the grade 4 variables.

Varying degrees of significant relationships were observed among all of the grade 4 variables

under study except working memory with text-level reading fluency.

Second, a MANCOVA was conducted to investigate differences between the typically

developing and at-risk reader groups on the linguistic, cognitive, and reading measures of

interest. For poor decoders, it was hypothesized that they would have difficulty with all word

reading-related measures under study, that is, word reading accuracy using lists of words and

passages of text, and also word-level reading fluency. It was expected that they would also have

difficulty with text-level fluency, inferencing, and reading comprehension as a result of their

inability to read accurately and fluently at the word-level. For poor language comprehenders, it

was hypothesized that they would have difficulty with vocabulary knowledge, an important

supporting component of language. It was also expected that they would have difficulties with

text-level fluency, inferencing, and reading comprehension due to the reliance of these skills on

good oral language proficiency. Finally, it was hypothesized that multi-deficit at-riskers,

classified as such due to their compounded difficulties with decoding and language, would have

difficulties with all the aforementioned reading and linguistic measures of interest.


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As expected by the SVR, word reading and reading comprehension showed strong

differences across the reading groups. Typical developers followed by poor language

comprehenders performed significantly better than the poor decoders and multi-deficit at-riskers

on word reading. All three at-risk groups exhibited significant difficulties with reading

comprehension with the multi-deficit group experiencing the most difficulty. The skill that most

clearly differentiated the groups however, was word-level reading fluency. The typically

developing group performed the best on this measure, and the poor decoders and multi-deficit at-

riskers performed the worst. Significant differences were also observed on the other variables

(word reading in context, vocabulary, text-level fluency, and inferencing), but to a lesser extent

than those observed on list-word reading accuracy, reading comprehension, and word-level

reading fluency.

It was hypothesized that all three at-risk groups would have difficulty with inferencing

dependent on their core deficits: poor decoders due to their lack of automaticity in their word

reading (automaticity is required to free up cognitive resources required for inferencing), poor

language comprehenders due to their problems with language, and multi-deficit at-riskers due to

compound problems in automaticity and language. Thus, to further explore possible reasons for

difficulty in this task, the types of inferencing strategies used by the ELL reading groups was

also examined in Study 1. On the whole, gap-filling inferences were the most difficult for the

ELL participants, followed by text-connecting inferences; literal inferences were the easiest for

the group. This pattern of difficulty was also observed when the reading groups were examined

separately. Further investigation revealed that as expected, the multi-deficit group had significant

difficulties with all three types of inferencing strategy. Contrary to expectations, the poor

language comprehenders did not perform more poorly than the typical developers on any of the

three types of inferencing despite their diminished language skills. This was likely due to a


97

power issue, both in sample size and also number of items examined. Finally, the poor decoders

had no difficulty with literal inferences, but gap-filling and text-connecting inferences were

challenging for this group when compared with the typically developing group.


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Chapter 6: Study 2 – Longitudinal Predictors of At-risk ELL Readers

It is easier to prevent reading problems than it is to remediate them. Thus, the focus of

Study 2 was on identifying early (grade 2) cognitive, linguistic, and reading predictors of later at-

risk ELL reader status (grade 4). Early identification of children with reading disabilities is

important for at least two reasons. First, children who have a slow start in reading rarely catch up

(Torgesen, 1998). Their poor reading skills worsen over time, leaving them more and more

behind; a poor reader in grade 2 will likely continue to be a poor reader without appropriate

intervention. Waiting for a child who is ELL to gain proficiency in English before assessing for

reading problems—a common but fading practice—may have serious and extenuating

consequences. Second, and as noted earlier, although poor readers are similar to one another in

that they all show difficulties on complex outcome measures such as reading comprehension,

poor readers may display different profiles of poor reading depending on the source(s) of the

problem (Geva & Herbert, 2012; Geva & Massey-Garrison, 2013; Li & Kirby, 2014; Tong,

Deacon, Kirby, Cain, & Parrila, 2011). Further, because skilled reading is comprised of an

complex interaction between word reading, language proficiency, and higher-order

comprehension strategies, and that the nature of some of these skills change over time—for

example the construct of fluency (Geva & Farnia, 2012; Farnia & Geva, 2013)—a

developmental perspective through longitudinal research is critical to understanding early and

emerging comprehension problems.

Research Questions and Hypotheses for Study 2

The purpose of Study 2 was to identify what early skills assessed in grade 2 (naming

speed, phonological awareness, vocabulary, oral expression, word reading, fluency, and reading

comprehension) could predict profiles of ELL readers in grade 4, namely, typically developing

ELL readers (typical developers), and ELL readers at-risk for poor reading comprehension due to


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their poor decoding (poor decoders), poor language comprehension (poor language

comprehenders), or impaired skill in both areas (multi-deficit at-riskers). Two research questions

were posed: (1) Do ELL reader groups classified in grade 4, differ on their earlier phonological

processing (i.e., phonological awareness and naming speed), oral language proficiency (i.e., oral

expression and vocabulary), and reading skills (i.e., word reading, word-level reading fluency,

and reading comprehension) in grade 2 after controlling for nonverbal cognitive ability (working

memory was not measured in grade 2)?, and (2) What grade 2 measures of phonological

processing, oral language proficiency, and reading skills best predict later at-risk ELL reading

group membership in grade 4?

Two broad hypotheses were made in response to these research questions. First, it was

important to consider the relationship between word reading and reading comprehension from a

developmental perspective. In young readers, automatic word recognition skills are the limiting

factor in reading comprehension. Young children with slow and effortful word reading skills will

have difficulties with reading comprehension unless they have received effective remediation.

Given that in grade 4, the poor decoders and multi-deficit at-riskers were characterized by

difficulties in decoding words, it was hypothesized that these two groups would show early

difficulties in word reading accuracy and fluency, as well as in the underlying processes

supporting these skills, namely phonological awareness and naming speed.

For the second hypothesis, a brief recap of poor comprehenders (also known as late-

emerging poor comprehenders) also is relevant. The language problems of these children are not

noted in the primary grades and their difficulties do not yet have an impact on their reading

comprehension due to the focus on decoding and word recognition. However, by the later

elementary grades when language skill is more central to the comprehension of text, their poor

language skills become apparent and their reading comprehension is challenged (Catts,


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Compton, Tomblin, & Bridges, 2012; Etmanskie, Partanen, & Siegel, 2015; Geva & Farnia,

2015; Tong et al., 2011). It was hypothesized that the poor language comprehenders would show

an earlier profile of language difficulties (in compromised vocabulary and oral expression), even

though these problems might not be indicative (yet) of early reading comprehension problems.

Three analyses were conducted. First, a preliminary correlation analysis was conducted to

examine the relationships among the variables of interest. Second, a multivariate analysis of

covariance (MANCOVA) was conducted to explore the differences in the means on the predictor

variables of interest across the four ELL reader groups. This analysis was chosen for three

reasons: (1) MANCOVA allows for the inclusion of multiple dependent variables including a

covariate(s), (2) MANCOVA allows for evaluating the relationship between a set of dependent

variables and a factor (in this case, ELL reading group), and (3) MANCOVA allows for the

exploration of how the means of a set of dependent variables vary across levels of that factor

(Meyers, Gamst, & Guarino, 2013). In the MANCOVA analysis, ELL reader group was the

factor variable (typical developers, poor decoders, poor language comprehenders, and multi-

deficit at-riskers); naming speed, phonological awareness, receptive vocabulary, oral expression,

word reading in isolation, word-level reading fluency, and reading comprehension in grade 2

were the dependent variables. Nonverbal cognitive ability was added to the analysis as a

covariate to reduce potential error caused by cognitive ability on the relationship between ELL

reading group and the phonological processing, oral language proficiency and reading skill

variables of interest.

A multinomial logistic regression was conducted as a follow-up to the MANCOVA.

Multinomial logistic regression allows for the investigation of various linear combinations of the

independent variables that best predict group membership of more than two groups. The four

ELL reader groups—poor decoders, poor language comprehenders, multi-deficit at-riskers, and


101

typical developers—were the focus of the predicted group membership using the phonological

processing, oral language proficiency, and reading skills variables as predictor variables.

Multinomial logistic regression requires a reference category in analysis to act as a point of

comparison for the other groups. The typical developers group was used as this reference

category as participants in this group would be considered “normative” in an applied setting.

Eight grade 2 predictor variables were considered for use in the analysis: nonverbal cognitive

ability, naming speed, phonological awareness, receptive vocabulary, oral expression, word

reading, word-level reading fluency, and reading comprehension.

Relationships Among the Grade 2 Variables Used in Study 2

A correlational analysis was conducted to examine the relationships among the variables

under study in Study 2, which included grade 2 variables of interest as well as the grade 4

grouping variables. Results of this analysis are reported in Table 9. Several points from this

analysis are relevant to this discussion. Significant relationships were observed between

nonverbal cognitive ability, the grouping variables, and the dependent variables of interest except

phonological processing and naming speed, thus justifying the inclusion of nonverbal cognitive

ability as a covariate in analyses (due to its potential impact on the linguistic and reading

measures of interest). Decoding and language comprehension were significantly correlated with

each other (r = .32, p < .001) and with reading comprehension (r = .70, p < .001; r = .47, p <

.001, respectively), thus offering support for an SVR approach in the conceptualization and

classification of ELL readers. Decoding, language comprehension, and reading comprehension

were also significantly correlated with all cognitive, linguistic, word reading, and high-order

variables used in analyses. Reading comprehension showed the strongest relationship with word

reading in isolation (r = .83, p < .001), and with word-level reading fluency (r = .78, p < .001).


102

Table 9.

Study 2 correlation matrix showing relationships among the grouping variables in grade 4 and variables of interest in grade 2

1 2 3 4 5 6 7 8 9 10

1 Decoding1 1 .32*** .32*** -.48*** .32*** .44*** .26** .80*** .78*** .70***

2 Language comprehension1 1 .25** -.21** .29** .47*** .49*** .38*** .40*** .47***

3 Nonverbal cognitive ability 1 -.13 .00 .44*** .26** .38*** .36*** .45***

4 Naming speed 1 -.11 .24** -.30** -.51*** -.60*** -.47***

5 Phonological awareness 1 .16 .19* .28* .29** .24*

6 Receptive vocabulary 1 .38*** .53*** .51*** .54***

7 Oral expression 1 .38*** .37*** .49***

8 Word reading in isolation 1 .90*** .83***

9 Word-level fluency 1 .78***

10 Reading comprehension 1

Notes. N = 127; *** p <.001, ** p < .01,* p <.05;

1 variable used for grouping of ELL readers in grade 4


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Do ELL reader groups classified in grade 4, differ on their phonological processing, oral

language, and reading skills in grade 2?

A MANCOVA was conducted to examine differences among the ELL reader groups in

grade 4 on the cognitive, linguistic, and reading variables of interest as assessed in grade 2, after

controlling for nonverbal cognitive ability. The MANCOVA was significant, Wilks’ Λ = .40,

F(21, 282) = 5.10, p < .001, and η2 = .27 indicating a large effect (Meyers, Gamst, & Guarino,

2013); there was at least one significant group difference on at least one dependent variable in

the analysis after controlling for nonverbal cognitive ability. Bonferroni-corrected ANOVAs

were conducted for each dependent variable to determine on what dependent variable(s) the

reader groups were different; statistically significant differences were observed on all seven

dependent variables in grade 2: naming speed, F(3,104) = 6.33, p <.001, η2 = .15; phonological

awareness, F(3,104) = 2.70, p <.05, η2 = .07; receptive vocabulary, F(3,104) = 7.24, p <.001, η2

= .17; oral expression, F(3,104) = 9.45, p <.001, η2 = .21; word reading, F(3,104) = 30.74, p

<.001, η2 = .47; word-level reading fluency, F(3,104) = 25.97, p <.001, η2 = .43; and reading

comprehension, F(3,104) = 22.59, p <.001, η2 = .39. Table 10 displays descriptive statistics and

results for significant post hoc pairwise comparisons for the four reader groups on these

variables.


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Table 10.

The effect of ELL reader group on the cognitive, linguistic, and reading variables of interest in grade 2: Descriptive statistics and post

hoc comparisons

1

Poor

Decoders

(n = 17)

2

Poor Language

Comprehenders

(n = 15)

3

Multi-deficit

At-riskers

(n = 20)

4

Typical

Developers

(n = 57 )

Measure M SD M SD M SD M SD F

Statistic

Effect

Size (η2)†

Post Hoc

Comparisons††

Naming speed (in seconds) 52.24 14.74 43.47 7.07 53.00 14.86 41.42 10.17 6.33*** .15 4 > 1 & 3

Phonological awareness (/40) 16.00 9.41 18.47 9.42 14.65 7.57 19.74 8.41 2.70* .05 ns

Receptive vocabulary (/228) 97.88 16.64 95.67 14.37 91.60 16.82 112.39 16.57 7.24*** .17 4 > 2 & 3

Oral expression (/96) 69.06 12.53 65.60 14.49 55.40 15.48 74.32 10.68 9.45*** .21 4 > 3

1 > 3

Word reading in isolation (/106) 39.06 12.28 55.73 10.22 40.05 9.37 61.46 8.80 30.75*** .47 4 > 1 & 3

2 > 1 & 3

Word-level fluency (words/45

seconds) 47.82 20.73 77.67 21.73 49.90 16.73 91.98 21.45 26.00*** .43

4 > 1 & 3

2 > 1 & 3

Reading comprehension (/43) 13.12 3.60 16.07 3.77 12.35 3.59 19.37 3.09 22.59*** .39 4 > 1 & 3

2 > 3

Notes. N = 109; SD = standard deviation, η2 = eta squared; *** p <.001, ** p < .01,* p <.05; ns = not statistically significant;




105

Similar to Study 1 which focused on grade 4 performance, word reading in isolation,

word-level reading fluency, and reading comprehension in grade 2 showed the strongest

differences across reader groups defined on the basis of grade 4 (holding constant nonverbal

cognitive ability). These variables accounted uniquely for 47%, 43%, and 39% of respectively,

as indicated by partial eta-squared (η2). Post hoc pairwise comparisons on these measures

indicated that in grade 2, ELLs who were poor decoders and multi-deficit at-riskers were

experiencing significant difficulties with their word reading, word reading fluency, and reading

comprehension when compared with typical developers or with their peers who were poor

language comprehenders. Interestingly, poor language comprehenders were not yet showing

difficulties with their reading comprehension. That is, no significant difference was observed

between the poor language comprehenders and typical developers group in grade 2. Given that

by grade 4, these poor language comprehenders do demonstrate difficulties with their reading

comprehension, this finding offers support for a late-emerging poor comprehender profile, like

the one that has been noted with regard to L1 populations and recently alluded to among ELLs

(e.g. Geva & Farnia, 2015). Differences in oral expression, receptive vocabulary, and naming

speed in grade 2 were less robust across the ELL reader groups, accounting for 21%, 17%, and

15% of the variance (as indicated by partial η2), respectively. As with reading fluency, the grade

2 poor decoders and multi-deficit at-riskers were already showing difficulties in naming speed,

suggesting a more global issue with processing speed that impacts their reading performance

concurrently, and in the future. In grade 2, multi-deficit at-riskers showed the most pervasive

difficulties with language, performing significantly more poorly on their vocabulary and oral

expression when compared to typical developers. Otherwise, the only other early language

indicator of later at-risk status was observed in the poor language comprehenders; they

demonstrated significant difficulties in their early receptive vocabulary when compared to the


106

typical developers group. To note, although significant, reader group in grade 4 accounted for

only 7% of the variability in phonological awareness in grade 2, with no significant differences

across the ELL readers group in post hoc tests.

Several candidates emerged as preliminary indicators in grade 2 for later at-risk status

across the ELL reader groups. First, multi-deficit at-riskers exhibited a range of early difficulties

in naming speed, word reading in isolation, word-level reading fluency, vocabulary, oral

expression, and reading comprehension. Second, poor decoders also exhibited early difficulties

with naming speed, word reading in isolation, word-level reading fluency, and reading

comprehension but showed no difficulties with early vocabulary or oral expression skills.

Finally, poor language comprehenders had only one compromised skill indicative of their later

problems in reading (when compared to typical developers): vocabulary.

What grade 2 cognitive, language, and reading skills predict at-risk ELL reading group

status in grade 4?

Multinomial logistic regression was conducted to determine which grade 2 predictor

variables (i.e., nonverbal cognitive ability, phonological processing, oral language proficiency,

and reading skills) best characterized the ELL reader groups as classified in grade 4. Multinomial

logistic regression is highly sensitive to multicollinearity (Meyers, Gamst, & Guarino, 2013),

thus grade 2 word reading was removed from analysis in favour of keeping word-level reading

fluency due to the high correlation observed between these two variables (r = .90, p < .001) in

preliminary analyses (see Table 9). Word reading in isolation and word-level reading fluency—

both measured using lists of words that are not in context—are highly similar skills falling on the

same developmental trajectory. Word reading is the ability to read words accurately without

consideration for speed of reading, while word-level reading fluency is defined as the ability to

read words accurately and quickly. Since it is widely agreed in the literature that word accuracy


107

precedes word fluency (see previous discussion about this in Chapter 1), it was decided to use

the variable which was likely to show more variability for analyses: word-level reading fluency.

Results of the multinomial logistic regression indicated that a 7-predictor model that

included nonverbal cognitive ability, naming speed, phonological awareness, receptive

vocabulary, oral expression, word-level reading fluency, and reading comprehension in grade 2,

provided a statistically significant prediction of ELL reader subtype classification, -2 Log

Likelihood = 166.23, χ2 (21, N = 109) = 98.18, p < .001. The Nagelkerke pseudo R2 indicated

that the model accounted for approximately 65.1% of the total variance. The prediction success

for the cases used in development of the model was moderately high (Meyers, Gamst, &

Guarino, 2013); using these 7, grade 2 measures was useful in the subsequent classification of

participants into the ELL reader subtypes (in grade 4) with an overall prediction success rate of

71.6%. Table 11 presents regression coefficients, Wald test results, adjusted odds ratio [Exp (B)],

and 95% confidence intervals (CI) for odds ratios for each predictor. The typical developers

group was used as the reference category and was contrasted with poor decoders, poor language

comprehenders, and multi-deficit at-riskers.


108

Table 11. The prediction of at-risk reader group in grade 4 using grade 2 variables: Multinomial logistic regression summary

Model B (SE) Wald df Exp (B) 95% CI

Poor decoders:

Intercept 10.38 (4.37) 5.65 1

Nonverbal cognitive ability .02 (.05) .15 1 1.02 .92 – 1.13

Naming speed -.03 (.04) .47 1 .98 .91 – 1.05

Phonological awareness -.01 (.05) .05 1 .99 .89 – 1.10

Receptive vocabulary -.02 (.03) .35 1 .98 .93 – 1.04

Oral expression .01 (.04) .05 1 1.01 .93 – 1.09

Word-level reading fluency -.09 (.03) 8.25** 1 .91 .86 – .97

Reading comprehension -.23 (.16) 2.06 1 .80 .59 – 1.09

Poor language comprehenders:

Intercept 11.80 (4.21) 7.84 1

Nonverbal cognitive ability .04 (.04) 1.34 1 1.04 .97 – 1.12

Naming speed -.04 (.04) .88 1 .96 .89 – 1.04


Receptive vocabulary -.05 (.03) 4.55* 1 .95 .90 – 1.00

Oral expression -.04 (.03 2.32 1 .96 .91 – 1.01

Word-level reading fluency -.002 (.02) .005 1 1.00 .95 – 1.05


Multi-deficit at-riskers:

Intercept 15.35 (4.27) 12.93 1

Nonverbal cognitive ability -.04 (05) .55 1 .96 .87 – 1.06

Naming speed -.02(.03) .27 1 .98 .92 – 1.05


Receptive vocabulary -.01 (.03) .10 1 .99 .94 – 1.05

Oral expression -.07 (.03) 5.51* 1 .93 .88 – .99

Word-level reading fluency -.06 (.03) 3.92* 1 .94 .89 – 1.00


Notes. The dependent variable was reader group and Typical Developers was the reference category; N = 109; B = B coefficient, SE =

standard error, df = degrees of freedom, CI = confidence interval; Multinomial Nagelkerke R2 = .65; *** p <.001, ** p < .01,* p <.05


109

From the set of grade 2 measures, the multinomial logistic regression identified four

significant variables that uniquely predicted the probability of at-risk status in grade 4, when

each of the groups was compared to typical developers: (1) word-level reading fluency (Wald χ2

= 8.25; p < .001) was the best predictor of subsequent poor decoder status in grade 4, (2)

receptive vocabulary (Wald χ2 = 4.55; p < .001) was the best predictor of subsequent poor

language comprehender status in grade 4, and (3) word-level reading fluency (Wald χ2 = 3.92; p

< .001) and oral expression (Wald χ2 = 5.51; p < .001) were the best predictors of subsequent

multi-deficit at-risk status in grade 4. Although poor decoders differed significantly from typical

developers in grade 2 on naming speed, word reading, word-level reading fluency, and reading

comprehension, the best early predictor of later difficulty in decoding in grade 4 was word-level

reading fluency. As word-level reading fluency scores decreased in grade 2, participants were

less likely (Exp (B) = 0.91¶; CI = .86, .97) to be a typical developer and more likely to be a poor

decoder in grade 4 after controlling for the other cognitive, language, and reading variables under

study. Receptive vocabulary was the best predictor of later difficulty in language comprehension.

As receptive vocabulary scores decreased in grade 2, children in this group were less likely (Exp

(B) = 0.95; CI = .90, 1.00) to be classified as a typical developer and more likely to be classified

as a poor language comprehender in grade 4 after controlling for the other cognitive, language,

and reading variables under study. Finally, of the earlier difficulties experienced by the multi-

deficit at-riskers in almost all reading-related skills areas when compared to typical readers

(namely, naming speed, receptive vocabulary, oral expression, word-level reading fluency, and

reading comprehension), oral expression and word-level reading fluency were the best predictors

of their later decoding and language comprehension deficits in grade 4. As scores in oral

¶ An odds ratio (i.e., Exp(B)) of 1 means equally likely, so an Exp(B) of less than 1 indicates less

likely and an Exp(B) of greater than 1 indicates more likely.


110

expression and word-level reading fluency decreased, children in this group were less likely

(Exp (B) = 0.93, CI = .88, .99; and Exp (B) = 0.94, CI = .89, 1.00 respectively) to be classified as

a typical reader and more likely to be classified a multi-deficit at-risker after controlling for the

other variables under study.

Summary of Study 2

The overarching goal of Study 2 was to identify longitudinal predictors of later at-risk

ELL reader status. Two research questions were posed: (1) Do ELL reader groups as classified in

grade 4, differ phonological processing, oral language, and reading skills in grade 2?, and (2)

What grade 2 measures of phonological processing, oral language proficiency, and reading skills

best predict at-risk ELL reading group status in grade 4? Two general hypothesis were made

with regard to these questions: (1) that poor decoders and multi-deficit at-riskers would show

early difficulties in word reading accuracy and fluency, as well as in the underlying processes

supporting those skills, namely, naming speed and phonological awareness, and (2) that poor

language comprehenders would show an earlier profile of language difficulties through

diminished vocabulary knowledge and skill in oral expression. The extent to which these

difficulties would impact their early reading comprehension problems was not clear, but it was

expected that if they followed the “late-emerging poor comprehender” profile addressed in the

literature (e.g., Catts, Compton, Tomblin, & Bridges, 2012; Etmanskie, Partanen, & Siegel,

2015; Geva & Farnia, 2015; Tong et al., 2011), their reading comprehension difficulties might

not yet be evident by grade 2.

These research questions and hypotheses were addressed using a trifecta of analyses.

First, to explore relationships among the variables in Study 2, correlational analyses were

conducted using the grouping variables in grade 4 and variables of interest in grade 2. Second, a

MANCOVA was used to examine differences across the four ELL reading groups (as classified


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in grade 4) on their earlier phonological processing, oral language proficiency, and reading skills

in grade 2. Third, as follow-up to the MANCOVA, multinomial logistic regression analyses were

performed to identify which grade 2 skills best predicted grade 4 at-risk reading group status.

With regard to the correlation analyses, significant relationships were observed among all

the variables under study in Study 2 except among the variables of phonological awareness,

nonverbal cognitive ability, and naming speed, which showed nonsignificant correlations.

Otherwise, the grouping measures in grade 4 (decoding and language comprehension) showed

significant correlations with all measures oral language proficiency and reading skill. In

particular, the grouping measures demonstrated significant relationships with each other, and

reading comprehension in grade 2. In support of its use as a covariate, nonverbal cognitive

ability showed significant relationships with all linguistic and reading measures.

Significant results from the MANCOVA indicated that the ELL reading groups in grade

4 could be distinguished from each other using grade 2 predictor skills. Multiple ANOVAs

showed that the reader groups in grade 4 were already significantly different from each other on

all earlier phonological processing, oral language proficiency, and reading skills measured in

grade 2. As in Study 1, the grade 2 skills that most clearly distinguished the grade 4 ELL reading

groups were word reading accuracy and fluency, and reading comprehension. Poor decoders and

multi-deficit at-riskers demonstrated significant difficulties in these skills when compared with

the typical developers. That said, no significant differences were observed in poor language

comprehenders on these skills when comparing with the typically developing group, even on

reading comprehension, suggestive of a late-emerging poor comprehender profile. In fact, the

only grade 2 skill that differentiated poor language comprehenders from typical developers was

performance on the vocabulary knowledge measure. Otherwise, the multi-deficit at-risker group

also showed significant difficulties in their naming speed and both measures of oral language


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proficiency: oral expression and vocabulary. Poor decoders also had difficulty with naming

speed, but performed on par with the typical developers in their oral language proficiency.

Finally, the 7-predictor multinomial logistic regression model was significant, and

indicated that reading subtypes identified in grade 4 could be predicted using the grade 2

variables with 72% success (considered moderately high; Meyers, Gamst, & Guarino, 2013).

Results of the model revealed the following longitudinal predictors of later at-risk group status

when compared with the typical developers: (1) grade 2 word-level reading fluency uniquely

predicted poor decoder group membership in grade 4, (2) grade 2 vocabulary uniquely predicted

poor language comprehender group membership in grade 4, and (3) grade 2 word-level reading

fluency along with oral expression uniquely predicted multi-deficit at-risk group membership in

grade. In sum, different combinations of early reading and linguistic skill were observed across

the at-risk ELL reading subtypes in grade 4, and as a consequence, distinct skills were predictive

of their subsequent and specific at-risk status for poor reading comprehension.


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Chapter 7: General Discussion

Study 1 compared the concurrent linguistic and reading comprehension skill profiles of

the at-risk ELL reader subtypes in grade 4. Study 2 identified longitudinal predictors (in grade 2)

of later at-risk ELL reader status in grade 4. At-risk reading status was defined by difficulties in

decoding, or poor language comprehension, or both using typically developing ELLs as a point

of reference. The goal of Chapter 7 is to present a general discussion stemming from these

results. Two areas of special focus—fluency and inferencing—are also discussed in light of

findings. The chapter closes by presenting future directions for research, and implications for

theory and practice.

Profiles and Predictors of ELLs At-risk for Poor Reading Comprehension

In line with previous research involving ELLs (Geva & Herbert, 2012; Geva & Massey-

Garrison, 2013), distinct profiles were observed across the three at-risk reader groups at both

time points, in grade 4, and retrospectively in grade 2. Findings also indicated that at-risk status

for poor decoding and/or poor language could be predicted as early as grade 2. These skill

profiles are presented in Tables 12 and 13, respectively. Table 12 presents the grade 4 reading

and linguistic profiles for the ELL reader subtypes using the typically developing ELL reader

subtype as a point of comparison. Table 13 presents the grade 2 cognitive, linguistic, and reading

profiles of those same at-risk ELL readers two years earlier, also using the typically developing

ELL reader subtype as comparison.


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Table 12. Grade 4 linguistic and reading profiles of ELL readers at-risk for poor reading

comprehension

Skills Poor Decoders

Poor Language

Comprehenders

Multi-deficit

At-riskers

Decoding y y

Language comprehension y y

Word reading in isolation

Word reading in context

y

y

y

y

Word-level fluency y y y

Text-level fluency no significant differences

Vocabulary y

Inferencing strategy y y

Reading comprehension y y y

Note. y = significant difficulty when compared to the typically developing reader subtype


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Table 13. Grade 2 cognitive, linguistic, and reading profiles of ELL readers who by grade 4 are

at-risk for poor reading comprehension

Skills Poor Decoders

Poor Language

Comprehenders

Multi-deficit

At-riskers

Naming speed y y

Phonological awareness no significant differences

Receptive vocabulary y* y

Oral expression y*

Word reading in isolation y y

Word-level fluency y* y*

Reading comprehension y y

Note. y = significant difficulty observed when compared to the typically developing reader

subtype;* skills predictive of later at-risk status in grade 4


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Poor decoder profile. Similar to findings from other research with ELL poor decoders

(e.g., Chung & Ho, 2009; Chung, Lo, Ho, Xiao, & Chan, 2014; Geva & Herbert, 2012;

Verhoeven, Reitsma, & Siegel, 2011), the poor decoders in this research experienced, as

expected, difficulties with word reading accuracy and fluency. They also had problems with

inferencing and reading comprehension. Because of core deficits related to their poor decoding,

their focus during reading was likely on the application of phonological and orthographic

strategies needed to decode and read at the word level (Coltheart, Rastle, Perry, Langdon, &

Ziegler, 2001). As a result, they were unable to progress to automaticity in their word reading

(i.e., fluency). Even though they had reasonable language skills necessary to support inferencing

and reading comprehension, the poor decoders may not have had the cognitive resources

available for this higher-level processing because decoding was difficult for them (Cain &

Oakhill, 1999; Geva & Yaghoub-Zadeh, 2006; Prior, Goldina, Shany, Geva, & Katzir, 2014).

A similar set of skill difficulties was observed for this ELL reading group earlier in grade

2. Poor decoders in grade 2 showed difficulties with word reading accuracy and fluency, as well

naming speed, and ultimately, with reading comprehension. Of the variables under study, word-

level reading fluency in grade 2 was the best predictor of later poor decoder at-risk status in

grade 4. Interestingly, and contrary to the double-deficit hypothesis which states that poor

decoders can have core deficits in PA, naming speed, or both (Wolf and Bowers, 1999), the poor

decoders had difficulties with only their naming speed when compared with typically developing

ELLs. PA was not a significant and defining feature that distinguished the ELL reading groups.

That said, it is possible that difficulties in PA were captured in the word reading measures which

encompass a number of cognitive skills, such as PA and naming speed; PA and naming speed are

critical precursors of word reading, while naming speed contributes at both the word- and text-

levels (see previous discussion about this is Chapter 1). The naming speed deficits exhibited by


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the poor decoders were likely the cause of their poor decoding and relatedly, poor word reading

fluency and reading comprehension (Geva & Yaghoub-Zadeh, 2006; Katzir et al., 2008;

McBride-Chang et al., 2012).

Poor language comprehender profile. Children with poor language comprehension

exhibited a different and narrower set of difficulties related to their compromised language

development when compared to the other at-risk subtypes. Some prior research has observed a

varying scope of linguistic deficits in ELL poor comprehenders, in addition to their defining poor

reading comprehension despite good word reading skills. For example, both breadth and depth in

vocabulary in grade 8 distinguished the poor comprehenders from average and good

comprehenders in Li and Kirby’s 2014 study. The grade 5 poor comprehenders in Geva and

Massey-Garrison (2013) had difficulties with listening comprehension and inferencing in

addition to their defining poor reading comprehension but good word reading skills. A slightly

different profile of difficulties emerged however in the current research. In addition to poor

language comprehension in grade 4, children with this profile also had difficulties in word-level

reading fluency and reading comprehension. Interestingly, they did not have difficulty with

vocabulary when compared with the typically developing ELL group like observed in Li and

Kirby (2014), nor did they have difficulty with inferencing as observed in Geva and Massey-

Garrison (2013). These contradictory findings may be related to methodological factors such as

age differences of the children. Both the Li and Kirby (2015) and Geva and Massey-Garrison

(2013) explored skill profiles in older learners, grades 8 and 5, respectively, than the ones

investigated in this dissertation research. It might also be that the poor language comprehenders

in this study were focused on reading fluency and on the syntactic qualities of what they were

reading (Geva & Farnia, 2012), two areas where they were already experiencing significant

difficulties.


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In grade 2, the only difficulty demonstrated by ELLs with poor language comprehension,

was lack of breadth in their receptive vocabulary, and not poor oral language or poor reading

comprehension (see Table 13). Relatedly, vocabulary knowledge was identified as being the best

predictor of their later at-risk status in grade 4. In line with prior research related to late-

emerging poor comprehenders (Catts, Compton, Tomblin, & Bridges, 2012; Etmanskie,

Partanen, & Siegel, 2015; Tong et al., 2011), their later poor fluency and reading comprehension

(as identified in Study 1 and represented in Table 12) had not yet begun to express itself. This is

an important finding as it speaks to some of the issues surrounding late-emerging poor

comprehenders. Indeed, there does appear to be a group of late-emerging ELL poor

comprehenders whose reading comprehension is on par with typically developing ELL children

in the early grades but whose poor comprehension difficulties become evident at a later time. In

addition, it is possible to identify early on, ELLs who will present 2 years later with a profile of

language problems by potentially tracking their vocabulary development in relation to their ELL

peers.

Children with a combined poor decoding and poor language comprehension profile.

A pervasive set of difficulties were observed in grade 4 and earlier in grade 2 for the multi-

deficit at-riskers, defined by their difficulties in decoding and language comprehension. This at-

risk reader subtype experienced problems with all language and reading skills measured in grade

4, including word reading accuracy and fluency, vocabulary, inferencing, and reading

comprehension. The only skill in grade 4 for which difficulty was not observed was text-level

reading fluency. Text-level reading fluency was not related to at-risk status for any of the groups;

the groups did not perform significantly differently to each other on this task. It is likely that this

skill, which is highly reliant on oral language proficiency (and automatized word reading), was

not yet developed in these ELL learners who are also developing their language skill. The multi-


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deficit at-riskers also experienced problems with most of the cognitive, language, and reading

skills measured in grade 2 (with the exception of phonological awareness). This profile can be

characterized as experiencing a multi-faceted array of skill difficulties in language and early

reading skills that emerged early, and continued to have an impact on their reading

comprehension in grade 2 and 2 years later when they reached grade 4. In support of an

expanded SVR framework for poor reader identification, which includes fluency as a distinct

contributor, the strongest predictors of later at-risk status for this group of readers were oral

expression and word-level reading fluency in grade 2.

As noted earlier, one ongoing issue of discussion in the literature is the extent to which

dyslexia and language impairment comprise a similar set of underlying deficits in phonological

processing but with different expressions (Bishop & Snowling, 2004; Cain, 2013). Some

researchers maintain that these are two disorders on the same continuum with language

impairment being some more extreme form of dyslexia (Catts, 1991; Kamhi & Catts, 1986).

Findings from the current research do not support this argument. The poor decoders and multi-

deficit at-riskers (akin to children with dyslexia and language impairment, respectively) did not

differ from each other on any measures of word reading (decoding, real word reading, or

fluency) nor on the supporting processing skills that underlie word reading, such as naming

speed and phonological awareness. And even though the multi-deficit at-riskers experienced

difficulties with language at both time points when compared to the poor decoders (who did not

experience language difficulties), this difficulty with language did not translate into more

extreme difficulties in their reading comprehension (for which the two groups performed

similarly to each other). It is likely that the compounded deficits exhibited by the multi-deficit at-

riskers would eventually impact their reading comprehension as they progress through the

elementary grades, but it appears that in grade 4 at least, the two groups (namely, poor decoders


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and multi-deficit at-riskers) were experiencing a distinct set of deficits with the compounded

deficit experienced by the multi-deficit at-riskers not worsening matters.

Fluency as an Index of Poor Reading in ELLs

A key focus of the current research was the role of fluency in reading comprehension for

ELL readers at-risk for poor reading comprehension. Word reading fluency is important for

reading because fluent readers read with a level of automaticity and effortlessness that allows for

their attention and cognitive resources to be directed toward the higher-level skills needed for

comprehension (Perfetti, 1985; 2007; Perfetti & Hart, 2002). Fluency was an important predictor

of poor reading comprehension currently and longitudinally in the current research. In grade 4,

word-level fluency was a significant predictor of at-risk status for all three at-risk groups. Given

that accuracy precedes fluency, it was hypothesized that children with poor decoding skills (i.e.,

the poor decoders and multi-deficit at-riskers) would also have difficulty with the quick and

accurate reading of lists of words. This was the case for poor decoders and children with

combined poor decoding and language difficulties, currently and longitudinally. Their inability

to decode efficiently emerged early in grade 2 and continued to be evident in grade 4, leaving

them unable to achieve automaticity in word reading to a level of fluency that would facilitate

their reading comprehension (Perfetti, 2007).

Contrary to expectations, poor language comprehenders (who had no difficulties in their

word recognition skills), also had difficulty with word reading fluency in grade 4. This finding

suggests that either there is also some language component involved in moving from accuracy to

fluency beyond automatic word reading skills as implicated by the lexical quality hypothesis

(Perfetti, 2007), and/or that the poor language comprehenders in addition to their language

impairment, also had some subtle deficit in speed of processing and lexical retrieval, that

impacted their reading fluency and consequently, their reading comprehension. This latter


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hypothesis does align with some researchers who argue that dyslexia and language impairment

are not distinct disorders but rather, are linked by some common underlying deficit(s). In the

current research however, poor language comprehenders did not demonstrate earlier difficulties

with naming speed or word-reading fluency, so this conclusion is not supported by the present

research. It may be that the poor language comprehenders were experiencing a problem with

their allocation of resources akin to that experienced by the poor decoders in their fluency.

Because they were narrowly focusing their attention on processing the meaning of the text they

were reading, they were not directing enough attention to efficient reading of words. When

interpreted through Perfetti’s lexical quality hypothesis (2007), it may be that the quality of

lexical representations held by poor language comprehenders, which Perfetti (2007) argues

facilitates fluency, is the issue. A reader having a large lexicon (breadth) with many high-quality

representations (depth) can read text with less effort and make fast word-to-text integrations

(Perfetti, 2007; Perfetti & Hart; 2002; Segers & Verhoeven, 2016). The current study examined

only breadth of general vocabulary through the popularly used Peabody Picture Vocabulary Test.

Poor language comprehenders in grade 4 demonstrated a breadth of vocabulary similar to that of

typical developers, as well as comparable word reading accuracy, and despite this, they still

exhibited difficulties with their word reading fluency. More research, including a thorough

examination of breadth and depth of vocabulary and of various syntactic and morphological

skills might help to test these hypotheses. Nonetheless, considered jointly, these findings suggest

that dysfluent reading may be an important early indicator of ELLs at-risk for poor reading

comprehension whose ELL status may be masking the emergence of their poor language

comprehension.

Interestingly, text-level reading fluency did not emerge as predictive of ELL reader

profile. Geva and Farnia (2012) observed in their work with ELL readers in grades 2 and 5 that


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in grade 2, word- and text-levels of fluency were one construct but with development by grade 5

had bifurcated into two distinct constructs with the latter being more closely linked to language

proficiency. It is likely that the current sample had not yet attained a level of oral language

proficiency that could advance their text-level fluency and point to differences across groups.

Further research with older ELL children would be helpful in establishing at what age in ELLs,

word- and text-level reading fluency in ELLs begins to make independent contributions to


The Role of Inferencing in Reading Comprehension

The role of inferencing in reading comprehension for at-risk ELL readers was another

area of focus in this research. Results indicated that inferencing was significantly correlated with

all cognitive, linguistic, and reading measures under study, thus demonstrating its ubiquitous

presence in higher-order processing that is important for reading comprehension (Cain, Oakhill,

& Bryant, 2004; Hannon & Daneman, 2001; Kauda & Guthrie, 2008; Pressley, 2000). The

strongest correlations were observed between inferencing and decoding (the grouping variable),

word reading accuracy and fluency, and reading comprehension. It was initially hypothesized

that all three at-risk ELL reader groups would struggle with inferencing when compared with

typically developing ELLs. As expected, results showed that ELLs with poor decoding or poor

decoding paired with poor language, had difficulties with inferencing when compared with

typically developing ELL readers. These poor decoders and multi-deficit at-riskers were likely

using all of their cognitive resources on lower-level reading skills (also evidenced in their poor

word-level fluency), leaving few resources available for the higher-order processing required by

inferencing (Prior, Goldina, Shany, Geva, & Katzir, 2014). Relatedly, it was predicted that

inferencing difficulty would be more pronounced in the multi-deficit at-risk ELL group for

whom decoding and language were problematic; this was not the case. Their added difficulties


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with language did not appear to exacerbate their poor inferencing skills or poor reading

comprehension. Nonetheless, these findings support the hypothesis that reading fluency is a

necessary requirement for higher-order processing skills; automaticity in lower-level skills free

up cognitive resources that can then be used for higher-order skills.

With regard to poor language comprehenders, it was additionally hypothesized that they

would have difficulties with inferencing skill when compared with typically developing ELLs

due to their diminished language comprehension. Contrary to expectations, no differences in

inferencing strategy were observed between the poor language comprehenders and the typical

developers. The ELL children with poor language comprehension performed similarly to the

typically developing ELL children on this task and difficulties in their language did not seem to

impact inferencing skill. This lack of observable differences across subtypes related to language

(i.e., the poor decoders were not different from the multi-deficit at-riskers in their inferencing,

nor the poor language comprehenders to typical developers) as with text-level reading fluency,

might be due to the ELL sample on the whole not yet having developed a level of language

proficiency that would distinguish them on this skill. A comparison with monolingual norms on

the language comprehension grouping measure, however, does not support this explanation. Poor

language comprehenders and multi-deficit at-riskers performed significantly below monolingual

norms on language comprehension measures, whereas the poor decoders and typical developers

did not; they performed on par with monolingual norms (see the previous discussion in Chapter 4

about standardized scores). Language comprehension is a broad construct that involves a wide

range of linguistic skills including vocabulary, morphology, syntax, semantics, and pragmatics.

A more extensive examination of language proficiency would best address questions related to

the overall language proficiency of this ELL sample.


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Inferencing was measured using a task that focused on two types of inference-making:

(1) text-connecting inferences, where the reader was required to make connections and integrate

information presented within the text; and (2) gap-filling inferences, where the reader was

required to make connections with background knowledge (Cain & Oakhill, 1999). A third type

of “literal” inference was also included in this task. On the whole, the ELL sample had the most

difficulty answering questions that required making connections with their prior knowledge (i.e.,

gap-filling inferences); only 10% of the sample was able to successfully answer these types of

questions. Questions requiring text-connecting inferences were much easier for the ELL readers

with a 62% success rate by the sample as whole. Questions requiring literal inferencing were, as

expected, the easiest for the ELL readers as a group. These findings draw attention to the prior

experiences that children bring to reading and highlight the importance of the need for sufficient

and comprehensive background knowledge for reading that might be different and/or

underdeveloped for varying types of immigrant and Canadian-born ELLs depending on their

prior educational and life experiences. It also points to how the ability to draw inferences is

assessed and the consideration of cultural inclusivity. Some ELL children may have good

inferencing skills that are masked by a lack of background knowledge required for answering

some gap-filling inference questions.

Item-analyses were conducted to examine how inferencing strategy use differed across

the ELL reader profiles (Canet-Juric, Andrés, Urquijo, & Burin, 2011). When the at-risk ELL

reader groups were compared with typically developing ELLs, a more refined pattern of

difficulty was observed. Children with compounded difficulties in decoding and language had

significant difficulties with all three types of inferences. Their poor word reading skills combined

with impaired language comprehension left them unable to use inferencing strategies to increase

meaning when reading. Conversely, children with poor decoding were able to answer questions


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involving simpler literal inferences, but had significant difficulties with gap-filling and text-

connecting inference-making. Similar to their performance on the task as a whole, ELL children

with poor language comprehension did not perform significantly differently to typically

developing ELLs on any of the subtypes of inferencing. Given this finding and that the same

pattern of difficulty in inferencing type was also observed in these two groups (i.e., gap-filling

was most difficult type followed by text-connecting), it is possible that it was their status as

English language learners that influenced their performance on this task and not their reading

profile. This finding is especially interesting given that the poor language comprehenders had

difficulties with fluency and reading comprehension when compared to the typically developing

ELLs. It highlights the difficulty in teasing apart typically developing language comprehension

from language impairment for ELL children. As discussed earlier, it was likely that their focus

on word reading dominated their cognitive resources leaving them little attention for the higher-

order processing required for the more difficult inference strategies like text-connecting and gap-

filling. Further research would be needed with larger groups and a more difficult task to establish

what aspects of inferencing, if any, distinguish ELL children with poor language comprehension

from those who are typically developing.

Findings from this research about inferencing skill in ELL children with poor language

do not align neatly with findings from other research in the area of reading comprehension in

ELLs. Li and Kirby (2014) found that inferencing distinguished good comprehenders from

average and poor comprehenders; Geva and Massey-Garrision (2013) also found that poor

comprehenders had difficulties with inferencing while listening (using a task that required no

reading), and syntactic aspects of language (i.e., grammar). On the other hand and similar to the

findings of the current work, Bowyer-Crane and Snowling (2005) found that poor

comprehenders (who were English monolinguals) could make cohesive types of inferences


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required during the reading of text (i.e., text-connecting inferencing), but not the more effortful

type of inferences required to make elaborations about meaning (i.e., gap-filling inferences). It

could be that the language measure used to identify children experiencing language problems

was effective in capturing the more severe difficulties of the multi-deficit at-riskers, but less

effective in identifying children with solely poor language comprehension. A battery of measures

which reflect the various subcomponents of oral language (e.g., syntax, semantics, pragmatics)

would likely yield a purer poor language comprehension group. This language-battery approach

might also help to better understand the high comorbidity that is observed between children who

are poor decoders and children with specific language impairment who are perceived to “share a

continuity of risk for decoding deficits in reading that can be traced to phonological problems”

(Snowling & Hayiou-Thomas, 2006, p. 110), and children with a wider range of language

problems that impact their reading comprehension.

Limitations and Future Directions for Research

A major limitation in the current research was sample size. The small sample size

impacted the methodology used to address the research questions in two important ways. First,

children from the Chinese, Portuguese, and Spanish home language backgrounds were

amalgamated into one sample to increase the power in analyses. Although not the focus of the

current research, this meant that language-specific and language-general processes could not be

accounted for or explored directly, in analyses. It would be interesting to explore the role that

language typology plays in poor reader classification and reading comprehensions outcomes in

ELL readers from different home language backgrounds.

Second, the small sample size also restricted the types of analyses that could be

conducted to address the research questions under investigation. It was decided that the

classification of participants into reading groups using theory and practice driven cut-off scores


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was the best approach for the current data set. Several issues arise in the literature related to the

use of groups and cut-off scores from a statistical standpoint (Branum-Martin et al., 2013). The

first is the variability that is lost in converting continuous, dimensional variables into nominal,

categorical variables, and the questionable impact on results. The second issue is in imposing

cut-offs for group classification when there are statistical approaches that allow for data-driven

groups to emerge from the data (e.g., cluster analysis). The inherent overlap amongst groups and

its impact on correlational-based analysis is also of issue. Future research with larger data sets

would be helpful in validating whether the risk involved in group classification using cut-off

scores meaningfully impacts results. Comparisons of the results from different approaches for

participant classifications and/or the use of analyses where the continuous and dimensional

nature of the skills under study are observed (e.g., regression procedures) would be an insightful

avenue for further research with potentially important implications for both theory and practice.

Implications for Theory and Practice

A specialized view of reading for special populations. Findings from this research

extend support for the need of a more nuanced view of reading for specialized populations, in

particular ELLs who are experiencing problems with their reading (e.g., Geva & Farnia, 2012;

Kirby & Savage, 2008; Pasquarella, Gottardo, & Grant, 2012; Yaghoub-Zadeh, Farnia, & Geva,

2012). Although the SVR is useful in framing reading development and conceptualizing reading

difficulties in ELL children (Bishop & Snowling, 2004; Gough & Tunmer, 1986), at-risk ELLs

can experience a complex (and distinctive) set of core deficits that may not accurately be

accounted for through a simple view perspective. The simple view of reading may be too simple

for certain readers. Some researchers have proposed that cognitive processes (i.e., naming speed

or working memory) should be accounted for in an applied model of reading, either directly or

through an explicit deconstructed understanding of the decoding component of reading


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comprehension (e.g., Cain, Oakhill, and Bryant, 2004; Johnston & Kirby, 2006; Kirby & Savage,

2008). The current research corroborates this recommendation, supporting the accounting for

speed through the inclusion of fluency. Naming speed at sublexical level and efficiency at the

word-level (i.e., the fluent reading of lists of words) were important predictors of reading

comprehension and at-risk status.

A refinement of the language component of the SVR also appears necessary. ELL

children with poor language comprehension may be a subgroup of at-risk readers that is

particularly difficult to identify. Findings from this dissertation suggest that in the early years of

schooling, the sole early indicator of their future reading problems were subtle differences in oral

language when compared to typically developing ELLs, specifically vocabulary. Study 2 pointed

to no other indicators of poor language in grade 2. Word-level reading fluency, oral expression,

and reading comprehension were even on par with the typical developers. But in as little as two

years later in grade 4, these readers were experiencing significant problems with language,

reading comprehension, and word-level fluency though their vocabulary knowledge appeared to

have caught up, or at least leveled out with the typically developing ELLs. This paints a complex

and dynamic profile that might be difficult for clinicians to diagnose, and educators to effectively

manage in instructional settings. The current research included a narrow set of language

measures. It is likely that children with poor language development present with a range

language issues beyond the vocabulary and oral expression tasks used in the Studies 1 and 2. A

reading model which allows for a deconstructed language component could help elucidate the

specific subcomponents of language (e.g., subtypes of vocabulary, cohesive strategies like

inferencing) that poor language comprehenders may exhibit, which in turn could lead to

enhanced research in the area, and better instructional outcomes. Targeted research with this

focus is needed.


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Differentiated instruction for subtypes of at-risk ELL readers. In general, reading

research endorses the need for explicit instruction in vocabulary and text comprehension

strategies for all readers (Baker, Gersten, Dimino, & Griffiths, 2004; National Reading Panel,

2000). Good instruction in these areas is likely to attend to the needs of typically developing

ELL readers and some ELL children experiencing difficulties with their reading development.

More than likely, however, that will not be enough to help at-risk ELL readers like those

highlighted in the current research. Findings from the present studies support the need for

differentiated instruction for subtypes of ELL readers that is reflective of their specific and

potential deficits. For poor decoders, a focus on strategies for increasing automaticity is

recommended. Fluency develops over time, through substantial practice. Thus an intensity in

opportunities for poor decoders to practice “breaking the code” and to learn sight word

vocabulary with an initial focus on accuracy, and then on speed is warranted: slow is smooth and

smooth is fast. This same remediation strategy would also recommended for children falling into

a “multi-deficit at-risker” profile because of their poor decoding, but additional remediation

strategies which attend to their language difficulties would also be required. Targeted strategies

for developing specific types vocabulary are recommended (Baker et al., 2014). Strategies

should include specific instruction in breadth and depth of conceptual knowledge. A focus on

academic terminology and words that help in text cohesion, like connectives, would also be

useful; cohesive instructional strategies work hand-in-hand with inferencing instructional

strategies. Structured oral language development that includes the explicit integration of

language with literacy activities is also needed (Baker et al., 2014). The practice of inferencing

orally (without reading materials) using oral inferential questioning techniques (Geva &

Ramirez, 2015), followed by discussions and practice using texts is a good example of this

language to literacy instructional integration. The aforementioned language remediation


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strategies for multi-deficit at-risk children would also be useful for poor language

comprehenders. Study 2 showed that early difficulties with vocabulary (when compared with

typically developing ELLs) indicated later difficulties in language comprehension, and

consequently, reading comprehension. Early explicit vocabulary would be critical for this group

of learners.

In applied settings it may be difficult for educators to distinguish between ELLs who are

typically developing, and those who are struggling due to impairments in language. Thus, a

Response to Intervention (RTI) approach is a recommended for ELL reading instruction. This

tiered approach is a model for on-going assessment, monitoring, and preventative intervention

for students. Through this model, students are provided with high-quality systematic and

sequential intervention as soon as a need is evident, and without the need of a formal asessment.

In tier 1, “effective, evidence-based instruction is given to all students, with on-going assessment

and progress monitoring by teachers to note any students who are experiencing difficulty”

(Adelson, Geva, & Fraser, 2014, p. 9). Subsequent tiers offer more focused and tailored

intstruction and how students respond to intervention provided in each tier guides future

decisions about the need to provide more or less support, or to the use of different approaches

and teaching methods. This approach might be particularly useful for identifying and providing

ongoing support for poor language comprehenders given their lack of early “symptoms” of their

later reading problems.

Conclusion

This dissertation investigated the linguistic and reading profiles of ELL children

classified in grade 4 as typically developing or at-risk for poor reading comprehension based on

their concurrent decoding and language comprehension abilities. Profiles were examined in

grade 4 (Study 1) and retrospectively in grade 2 (Study 2), in order gain an understanding of


131

concurrent and earlier skill profiles of subtypes of ELL readers. Developing profiles is useful in

applied settings because it allows for educators and clinicians to compare and contrast at set of

evidence-based criteria with a child’s skill levels across a number of domains for effective

diagnosis and intervention. Predictors of reading subtype in grade 4 using earlier grade 2

predictors were also examined in Study 2. The identification of early predictors for recognizing

children with later emerging difficulties is critical in providing at-risk students with effective

remediation because once children fall behind in their reading development, it is difficult for

them to catch up (Torgeson, 1998).

Findings from Study 1 indicated that the at-risk reading groups—poor decoders, poor

language comprehenders, and multi-deficit at-riskers—presented with unique skill profiles when

compared to typically developing ELLs at both time points. One common thread across all three

at-risk ELL reader groups however, was their diminished word-level reading fluency. Findings

from Study 2 indicated distinct predictors of later reading subtype. Children with poor decoding

in grade 4 could be accurately predicted by their poor word reading fluency in grade 2, children

with poor language comprehension in grade 4 could be accurately predicted by their diminished

general vocabulary in grade 2 in comparison to their ELL peers, and children with a combined

set of difficulties in decoding and language comprehension could be accurately predicted by their

poor word-level reading fluency and poor oral expression in grade 2 in comparison to their ELL

peers.

In conclusion, it seems clear that word reading fluency needs to be an area of focused

instruction for ELL readers profiling as at-risk for reading comprehension problems.

Additionally, differentiated instruction specific to an ELL child’s cognitive, linguistic, and

reading profile is critical for reading success. Sample size is often a major limitation in at-risk

research due to the smaller number of children that experience language and/or reading problems


132

(i.e., 10-15% of the population). That said, findings from this research provide important

theoretical and methodological contributions to future research in the area.


133

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