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Running head: NATIONAL DIFFERENCES IN PERCEPTIONS 1

National Differences in Perceptions About the Nature and Value of Mathematics Education: An

Exploratory Factor Analysis of Selected Variables From the Future Teacher Survey

Arlene J. Callwood, Tiffany Taofeng He, Samantha James, Richard Mardarello, Sondra

O'Connell, and Cindy Taylor

Long Island University

February 2018

Red Owl, 03/01/18,

Perfect title page!

NATIONAL DIFFERENCES IN PERCEPTIONS 2

Abstract

The purpose of this study was to examine the differences in views about the nature and value of

mathematics education as perceived by pre-service primary and secondary mathematics teachers

in the United States compared to pre-service primary and secondary mathematics teachers in

Bulgaria, Germany, South Korea, Mexico, and Taiwan. Data were collected from a survey of

2,628 pre-service mathematics teachers with usable data from 1,207 future teachers from the six

countries. Analysis showed three explicit factors: (a) mathematics as rigorous, precise and

procedural; (b) mathematics as flexible, creative, and discoverable; and (c) mathematics as useful

and practical. An exploratory factor analysis was conducted on selected variables and a series of

independent samples t-tests was performed to analyze the factor scores based on nationality.

Highly statistically significant differences based on nationality were found for F1 (one-sided

t(1,205) = 4.402, p < .001), where non-USA future teachers had lower mean scores than pre-service

teachers from the USA, and F3 (one-sided t(1,205) = -14.809, p < .001), where future teachers from

the U.S. had greater mean scores than those from other countries. Insufficient evidence exists to

conclude a statistically significant difference in views of mathematics as a flexible, creative and

discovery-focused activity between future teachers from the USA and those from other countries

(one-sided t(1,205) = 0.963, p = .168). Overall, teachers within the USA view mathematics as a

useful and practical activity compared to the teachers in other countries.

Keywords: exploratory factor analysis, mathematics, mathematics education, pre-service

mathematics teachers, teacher education

Red Owl, 03/01/18,

Excellent abstract


National Differences in Perceptions About the Nature and Value of Mathematics Education: An

Exploratory Factor Analysis of Selected Variables From the Future Teacher Survey

Teacher preparation, teacher beliefs, and values about mathematics are core components

in the development of qualified mathematics teachers. Schmidt, Bloemeke, and Tatto (2011)

argued that teaching competence is shaped by the cognitive abilities and professional beliefs of

teachers and it influences teachers’ instructional practices. The purpose of this study was to

examine the differences in views about the nature and value of mathematics education as

perceived by pre-service primary and secondary mathematics teachers in the USA compared to

pre-service primary and secondary mathematics teachers in Bulgaria, Germany, South Korea,

Mexico, and Taiwan.

The data used for this analysis were collected from the 2012 Future Teachers Survey,

which was a part of Schmidt’s (2013) Mathematics Teaching in the 21st Century project. The

research questions guiding this study include:

RQ1: What is the value of mathematics education as perceived by preservice primary and

secondary mathematics teachers in the USA compared to pre-service primary and

secondary mathematics teachers in Bulgaria, Germany, South Korea, Mexico, and

Taiwan?

RQ2: What is the nature of mathematics education as perceived by pre-service primary

and secondary mathematics teachers in the USA compared to pre-service primary and

secondary mathematics teachers in Bulgaria, Germany, South Korea, Mexico, and

Taiwan?

RQ3: Does teacher perception about the value and nature of mathematics vary by

culture?

Red Owl, 03/01/18,

Thoughtful and well-written introduction.Excellent!


An exploratory factor analysis of selected variables from the Future Teacher Survey was

conducted to examine these questions. Two-sample t-tests were performed on each factor to

determine whether statistically significant differences existed between the mean score of future

mathematics teachers in the USA and those in the other countries included in this study. The

following section describes the analytical process used in this study to examine the research

questions.

Methods

The data for this study were collected from the Future Teacher Survey (Schmidt, 2013)

and used to examine the value and nature of mathematics as perceived by pre-service primary

and secondary mathematics educators in six countries.

Participants

The participants in the study were pre-service middle school teachers studying

mathematics education in Bulgaria, Germany, South Korea, Mexico, Taiwan and the United

States; these future teachers were either at the beginning or near the end of their studies

(Schmidt, 2013). The sample initially consisted of 2,628 participants. Due to incomplete or

missing data, there were 1,207 usable cases. From the usable cases, 175 (14.5%) were future

teachers from the USA.

The participants ranged in age from 18 to 30 years, representing 2,379 (90.6%) of the

original sample. Participants ages 31 through 40 and above represented the remaining 239

(9.1%) of the sample. There were 1,540 female participants (58.6%) and 1,081 males (41.1%) in

the full survey sample. Upon completion of their teacher education program, participants’

licensure would qualify them to teach students in primary through lower secondary grade levels.

Data Collection and Instrumentation

Red Owl, 03/01/18,

I would alphabetize this list.

Red Owl, 03/01/18,

Superb Methods section!


Schmidt's (2013) larger project included three separate surveys: an Institution Survey, a

Faculty Survey, and a Future Teacher Survey. The Future Teacher Survey was the focus of this

analysis. Data were collected from pre-service teachers in their first and last year of school (two

separate groups) and a sampling from each country was included in the analysis. Survey

questions focused on the preservice teachers’ backgrounds, course curricula, and

accomplishments. The data measured participants’ mathematics and pedagogical beliefs as it

related to the value and nature of mathematical education and student achievement.

Measures

For this study, 25 variables were extracted from the larger survey data set. The first

variable, COUNTRY, is categorical in nature and included six levels: Bulgaria, Germany, Korea,

Mexico, Taiwan and the United States. This variable was collapsed into a new binary variable,

usa, showing the nationality of the participants where 1 = USA and 0 = other (non-USA

countries). Additionally, 24 Likert-type items addressing the nature and value of mathematics

were extracted. They are measured on a 6-point scale, which we presumed to represent an

underlying continuum and which we analyzed as continuous measures.

Three factor score variables were produced by exploratory factor analysis and added to

the data set. These variables, measured with M = 0 and SD = 1, were used as dependent

variables in a series of t-tests described below. Further details about the factor variables are

provided in the Results section below.

Data Analysis

This study employed Stata/IC version 15.1 for all the statistical analyses and to produce

the related graphs. The sortl.ado program (Enzmann, 2009), corr2.do (Red Owl, 2018a), and

factabexcel.do (Red Owl, 2018b) were also employed in developing the table of sorted factor


loadings with variable labels. Microsoft Excel version 2016 was used for formatting tabular

information but was not used for any calculations or analysis.

An initial unrotated exploratory factor analysis was conducted to determine the number

of factors to extract for rotation and further analysis. The determination of the number of factors

to extract was based on three a priori criteria and rules: (a) extract a sufficient number of factors

to cumulatively explain at least 75% of the data; (b) following Kaiser's rule, extract factors with

eigenvalues ≥1, but modify the rule to extract factors with eigenvalues ≥ .90 as we assume they

will increase to 1 or greater after rotation; and (c) based on a visual inspection of the scree plot,

extract the number of factors at or above the elbow point.

The factors extracted based on these decision rules were subjected to Varimax orthogonal

rotation with Kaiser normalization, and factor scores were saved to the working data set for

further analysis. These factor scores were measured on a scale based on the normal distribution

and were centered on M = 0 with intervals reflecting standard deviation units. To interpret and

label these factors, we set an a priori interpretive cut-off criterion for the factor loadings as λ ≥|

+/-.30|.

An independent two-sample t-test was conducted on each of the rotated factors to

determine whether a statistically significant difference existed in the way future teachers in the

USA view mathematics compared to future teachers in the other countries included in this study.

These results were evaluated using an a priori acceptance criterion for statistical significance of α

= .05.

Results

Exploratory factor analysis revealed a three-factor solution, which is displayed in Table

1. Overall, 98.7% of the variance within the data set was explained utilizing the three rotated


factors. This exceeds the a priori criterion of 75% and satisfies Kaiser's rule insuring that after

the rotation each factor’s eigenvalue is ≥1. Our results reveal Factor 1 with an eigenvalue =

4.196, Factor 2 with an eigenvalue = 2.696, and Factor 3 with an eigenvalue = 0.950. The scree

plot shown in Figure 1 confirms that there are three factors located at or above the elbow point.

[Insert Table 1 about here.]

[Insert Figure 1 about here.]

Only two of the 24 items did not load at or above the interpretive cut-off criterion on any

of the three factors; however, they both loaded only slightly below the cut-off criterion. The

uniqueness of the three-factor solution for the 24 manifest variables reflects a minimum U

= .477, a median U = .688, and a maximum U = .878. The details of each factor are described in

turn below.

Factor 1: Mathematics as a Rigorous, Precise, Procedural Activity

Eight variables loaded at or above the interpretive cut-off for the first factor. Factor 1 has

an eigenvalue = 3.084 and explains 38.816% of the variance. There is a cross-loading on Factor

1 of .449 with Factor 3 of .335 under the variable of TBMSTMTT (“Mathematics means

learning, remembering, and applying.”). The minimum value of the loadings that satisfy the

interpretive cut-off for this factor = .499, with median loading = .550, and maximum = .721. The

eight items loading at or above the cut-off on Factor 1 have acceptable degrees of explanation

with minimum U = .477, median U = .641, and maximum U = .878. The mean of Factor 1 for

other countries = 0.047 (SD = 0.028), while the mean for the USA = -0.277 (SD = 0.061).

Factor 2: Mathematics as a Flexible, Creative, Discovery-Focused Activity

Nine variables loaded on Factor 2 at or above the interpretive cut-off. This factor has an

eigenvalue = 2.856 and explains 35.941% of the variance. There is a cross-loading on Factor 2

Red Owl, 03/01/18,

Because factor scores can exceed |+/-1.0|, you must use a leading zero for factor scores.Never show a mean without an SD because means are “meaningless” unless we know the variation around them.


of .392 with Factor 3 of .334 for the variable TBMSTMTK (“Mathematics entails a fundamental

benefit for society.”). Another cross-loading exists on Factor 2 of .488 and Factor 3 of .416 for

the variable TBMSTMTP (“Many aspects of mathematics have practical relevance.”). The

minimum factor loading of the variables that load on Factor 2 = .392, with a median = .492, and

maximum = .625. The nine items loading at or above the cut-off on Factor 2 had acceptable

degrees of explanation with the minimum U = .552, median U = .640, and maximum U = .735.

The mean of Factor 2 for future teachers from other countries = 0.010 (SD = 0.027)05, while the

mean for preservice teachers from the USA = -0.059 (SD = 0.066)2.

Factor 3: Mathematics as a Useful and Practical Activity

Nine variables loaded at or above the interpretive cut-off for the third factor. This factor

has an eigenvalue = 1.902 and explains 23.935% of the variance. There is a cross-loading on

Factor 2 of .461 with Factor 3 of .485 for the variable TBMSTMTQ (“Mathematics helps solve

everyday problems and tasks.”). The minimum value of the factor loadings that satisfy the

interpretive cut-off for variables that explain this factor = .301, with median = .465, and

maximum = .530. The nine items loading at or above the cut-off on Factor 3 had acceptable

degrees of explanation with the minimum U = .552, the median U = .705, and the maximum U

= .802. The mean of Factor 3 for preservice teachers from other countries = -0.132 (SD = 0.024),

while the mean for future teachers from the USA = 0.780 (SD = 0.046).

Comparison of Factor Scores of USA vs. Other Nations

An independent samples t-test found a difference between the two groups concerning the

perceptions described by Factor 1, with pre-service teachers from other countries (M = 0.047, SD

= 0.028) viewing mathematics as a more rigorous, procedural, precise activity compared to pre-


service teachers from the USA (M = -0.277, SD = 0.061). This difference is highly statistically

significant (one-sided t(1,205) = 4.402, p < .001) and is depicted visually in Figure 2.


Regarding the perception reflected in Factor 2, there is insufficient evidence (one-sided

t(1,205) = 0.963, p = .168) to conclude with statistical confidence that there is a difference between

pre-service teachers based on their nationality regarding viewing mathematics as a flexible,

creative, discovery-focused activity. The similarity in these views is shown in Figure 3.


There is a highly statistically significant difference (one-sided t(1,205) = 14.810, p < .001)

regarding the view of mathematics as a useful and practical activity as reflected in Factor 3. Pre-

service teachers from the USA (M = 0.780, SD = 0.046) viewed mathematics as a useful and

practical activity compared to pre-service teachers from other countries (M = -0.132, SD =

0.024), who did not. These competing views are displayed in Figure 4.


Discussion

Guided by the three research questions which examined the value and nature of

mathematics as perceived by future teachers in the USA compared to teachers in other countries,

distinct differences were found between the two groups on two of the three factors. T-tests

conducted for each factor helped us determine whether there were statistically significant

differences between the value and nature of mathematics as perceived by preservice teachers

from the USA compared to preservice teachers from the other five nationalities.

As shown in Figure 2, our results indicate that future teachers outside the USA hold the

view of mathematics as a rigorous, precise, procedural activity compared to teachers in the USA.

Red Owl, 03/01/18,

Excellent Discussion section except for one important missing word that changed the meaning of a statement.

Red Owl, 03/01/18,

You must always put a space before and after mathematical operators like =, <, and >. There are several places here where you did not do that.


These findings suggest that mathematics instruction in countries outside the USA may be taught

within a procedural rather than conceptual framework. In these countries, an emphasis on

memorization of formulas and step-by-step techniques to solve problems may dominate

instructional practices. Whereas, in the USA, instructional practices may include an emphasis on

thinking about or discovering the underlying mathematical concepts from which formulas are

derived. Also, this result can suggest that instructional practices in other countries, compared to

the USA, may emphasize the use of one method or strategy of solving a problem as opposed to

exploring multiple strategies that can lead to the same accurate result.

Teachers modeling step-by-step solution solving, or a one solution method, would

substantiate their beliefs of mathematics as a rigorous, precise, procedural activity or exact

science with a definite structure that should be transferred to students.

Our results, as shown in Figure 3, indicate that there is insufficient evidence to conclude

with statistical confidence that a difference exists among future teachers based on their

nationality regarding mathematics as a flexible, creative, and discovery-based activity. This

suggests that future teachers from these six nations hold similar views of mathematics as a

flexible, creative, and discovery-based activity. This may suggest that instructional practices

within mathematics classrooms in the six countries may employ the use of hands-on projects and

group activities to help students develop mathematical fluency and apply those skills to real-

world situations.

However, while future teachers may view mathematics as flexible, creative and

discovery-based, the dominant view of mathematics held by these teachers, and the culture of

their schools or school systems, may dictate how mathematics is primarily taught. For example,

compared to future teachers in the USA, future teachers outside the USA view mathematics as a

Red Owl, 03/01/18,

Without this, your statement was wrong.


rigorous, precise, procedural activity. As such, teachers may emphasize more procedurally-

based activities than ones that show math as creative, flexible or discovery-based.

The results, displayed in Figure 4, suggest that future teachers in the USA hold the view

of mathematics as a useful and practical activity compared to other countries. This highly

statistically significant difference may indicate a cultural view in the USA where mathematic

skills or expertise are viewed as a necessary component for employment at different levels within

an organization or for everyday use in solving problems. Within the USA, mathematics

expertise may also be an extremely important prerequisite for highly skilled, high-earning jobs in

the sciences, technology, and engineering fields which would allow the USA to remain globally

competitive. Viewing mathematics as useful and practical may reflect a cultural belief.

Within the USA, math can be a foundational discipline not only for economic factors but

for the development of critical thinking skills for students. Instructional practices may include

helping students solve application-based questions as well as procedural-type questions.

According to the data, while similarities exist, the value and nature of mathematics varies

by culture and or nationality. The implications of these findings are based on teacher

instructional practices and student achievement.

Conclusion and Implications

There are direct links between teacher preparation, teacher instructional practices, and

student achievement. Overall, this study suggests that there are statistically significant

differences in the way that pre-service teachers in the USA view mathematics education

compared to pre-service mathematics teachers in other countries. The two groups differ in their

views of mathematics as a rigorous, precise and procedural activity and mathematics as a useful

and practical activity. However, we did not find any statistically significant difference in their

Red Owl, 03/01/18,

Insightful, thoughtful, and well-written final section!


views on math as a flexible, creative, and discovery-based activity. These findings can impact

curriculum choices, instructional practices, and student achievement within the six countries.

Teachers who view mathematics as a rigorous, precise and procedural activity may

design instructional practices and activities around rote memorization and the development of

computation or procedural fluency. Students who can memorize procedures and recall multiple

steps and formulas when tested will be considered successful mathematics students. Students in

this group may enjoy mathematics or at least not view it as a struggle or burden. However,

students who lack computational and procedural fluency may consistently earn below average

scores on mathematics assessments and develop an aversion to the subject. Students in this

group may hate mathematics and view it is a lifelong struggle and burden they may never be able

to overcome. Consequently, a student’s relationship with mathematics has the potential to

strongly influence the profession he or she chooses. Students who may dream of becoming

engineers but lack understanding of math concepts, procedures, and formulas may decide to

change their professional aspirations to pursue a career that does not focus on mathematical

concepts.

In addition, curriculum choices within the view of mathematics as a rigorous, procedural

activity may tend to feature drill and skill activities over reflective, application-based activities.

Curriculum resources may feature or encourage one method of helping students to gain

computational fluency thereby ignoring the multiple ways in which students may learn or

process information. Teachers may also teach based on their preferred way of learning

mathematics which caters to some but not as many students as possible. Teacher education

programs in mathematics should develop teachers’ fluency in multiple strategies designed to


provide students with a conceptual framework or understanding of math concepts which can lead

to improved computational fluency.

Teachers who view mathematics as a flexible, creative, and discovery-based activity may

implement group projects or application-based projects in their classrooms; curriculum choices

will incorporate these types of activities. Project and application-based activities will allow

students to understand how concrete mathematical ideas merge with abstract mathematics and

can be used to solve real world problems. Teacher education programs should design

mathematics curriculum and activities to promote math as flexible, creative and discovery-based

rather than simply procedural and precise. This will allow students to connect mathematical

ideas using multiple intelligences based on their learning styles and be able to see mathematics

as a useful concept in our world.

Mathematics may also be viewed as a discipline that values different ways of

understanding or getting to the same solution. The instructional practices of teachers who view

mathematics as a useful and practical activity may involve these teachers finding ways to

connect mathematical concepts and ideas with students' real lives. For example, a real-life

connection to a mathematics topic may occur at the opening of the lesson to gain student interest

in the topic. Curriculum materials and teacher education programs should promote mathematics

as useful and practical. This has the potential to aid in helping students see mathematics not

simply as formulas mixed with numbers and variables but as a symbolic language.

Mathematics is a core subject in both primary and secondary education within the United

States, Bulgaria, Germany, South Korea, Mexico, and Taiwan. Teacher preparation programs,

curriculum, and instructional practices should promote student-friendly mathematical strategies.

Red Owl, 03/01/18,

Again, I would alphabetize this list.


Strategies which are useful, practical, flexible, creative, and discovery-based can aid in

procedural fluency and provide a more positive mathematics experience for students.


References

Enzmann, D. (2009). sortl.ado [Computer software]. Available from

http://ideas.repec.org/c/boc/bocdode/s457098.html

Red Owl, R. H. (2018a). corr2.do [Computer software]. Available from

http://datalibrary.us/corr2.do

Red Owl, R. H. (2018b). factabexcel.do [Computer software]. Available from

http://datalibrary.us/factabexcel.do

Schmidt, W., Bloemeke, S., & Tatto, M. (2011). Teacher education: A study of middle school

mathematics teacher preparation in six countries. Available from

https://www.amazon.com/dp/0807751626/ref=rdr_ext_tmb

Schmidt, W. (2013). Mathematics Teaching in the 21st Century (ICPSR 34430). Retrieved from

Inter-university Consortium for Political and Social Research website:

https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34430/version/1

https://ideas.repec.org/c/boc/bocode/s457098.html


Table 1

Factor Loadings (After Varimax Rotation With Kaiser Normalization)

Variable Factor1 Factor2 Factor3 U Variable LabelTBMSTMTR .721 .043 .023 .477 Math is characterized by rigor of definition and formal argumentation.TBMSTMTL .631 .192 .133 .548 Fundamental to mathematics is its logical rigor and preciseness.TBMSTMTH .608 .144 -.103 .598 Definitional rigor is essential for mathematics with exact/precise language.TBMSTMTE .554 .017 .063 .689 Math involves remembering/applying definitions, formulas, facts and procedures.TBMSTMTI .546 -.190 .093 .657 To solve mathematical tasks you must know the correct procedure or be lost.TBMSTMTS .522 -.050 .169 .696 Mathematics requires practice correctly applying routines and strategies.TBMSTMTD .505 .241 -.005 .686 Hallmarks of mathematics are clarity, precision and unambiguousness.TBMSTMTT .499 -.117 .335 .625 Mathematics means learning, remembering, and applying.TBMSTMTA .286 -.204 .160 .851 Math is a collection of prescribed rules and procedures.TBMSTMTB .265 .224 .039 .878 Mathematical thought is characterized by abstraction and logic.TBMSTMTM .062 .625 .082 .598 Mathematical problems can be solved correctly in many ways.TBMSTMTG -.101 .623 .092 .594 In mathematics many things can be discovered and tried out by oneself.TBMSTMTJ .014 .540 .132 .691 If one engages in mathematical tasks, one can discover new things.TBMSTMTO -.135 .525 .259 .640 Every person can discover or rediscover mathematics.TBMSTMTF .105 .492 .113 .735 Mathematics means creativity and new ideas.TBMSTMTP -.041 .487 .416 .588 Many aspects of mathematics have practical relevance.TBMSTMTC .186 .481 .090 .726 Usually there is more than one way to solve mathematical tasks and problems.TBMSTMTK .112 .392 .334 .723 Mathematics entails a fundamental benefit for society.TBMSTMTN -.006 .256 .530 .653 Mathematics is useful for every profession.TBMSTMTQ .025 .461 .485 .552 Mathematics help solve everyday problems and tasks.TBMROLEC -.029 .195 .484 .727 Mathematics is needed for many jobs and careers.TBMROLED .251 .026 .481 .705 To succeed in school, it is important to have learned math.TBMROLEB .235 .107 .465 .717 To be a well-educated person, it is important to study math.


TBMROLEA .224 .240 .301 .802 Mathematics helps you learn to think better.Eigenvalue 3.084 2.856 1.902 % Variance 38.816 35.941 23.935 Note, . Total % variance explained = 98.692

Red Owl, 03/01/18,

Note is always followed by a period and never by a comma.I corrected this in the draft, but you did not fix it.I always deduct an extra penalty for mistakes I have corrected on a draft which are not fixed in the final.It may be hard to see small errors like this, but the line in the margin shows that there was a correction. Watch for those lines in the margins to make sure you’re not missing corrections.


Figure 1. Scree plot of Eigenvalues of the 24 Likert-type variables.

Red Owl, 03/01/18,

Perfect!


Figure 2. Histogram of Factor 1 showing the comparison of USA and other countries.

Red Owl, 03/01/18,

Perfect!


Figure 3. Histogram of Factor 2 showing the comparison of USA and other countries.

Red Owl, 03/01/18,

Perfect!


Appendix A

Stata Log of Commands and Output

--------------------------------------------------------------------------------------- name: <unnamed> log: /Users/taofenghe/Documents/DAP1-log.txt log type: textopened on: 6 Feb 2018, 20:02:46 . ***************************************** . ** DAP 1 - Exploratory Factor Analysis ** . ***************************************** . . . * STEP 3a . * Load Future Teacher Survey (2012) data set from the web. . use "http://datalibrary.us/DAP1.dta", clear(ICPSR 34430 Mathematics Teaching in the 21st Century Future Teacher Survey 2012) . * STEP 3b . * Display codebook. . codebook, compact Variable Obs Unique Mean Min Max Label---------------------------------------------------------------------------------------COUNTRY 2628 6 . . . CountryTDGENDER 2621 2 1.412438 1 2 Gender (0 = Male 1 = Female)TBMROLEA 1318 6 4.08346 0 5 Mathematics helps you learn to think bet...TBMROLEB 1316 6 3.069149 0 5 To be a well-educated person, it is impo...TBMROLEC 1317 6 3.990888 0 5 Mathematics is needed for many jobs and ...TBMROLED 1314 6 3.395738 0 5 To succeed in school, it is important to...TBMSTMTA 2543 6 3.375934 1 6 Math is a collection of prescribed rules...

http://datalibrary.us/DAP1.dta


TBMSTMTB 2549 6 4.704982 1 6 Mathematical thought is characterized by...TBMSTMTC 2544 6 5.523192 1 6 Usually there is more than one way to so...TBMSTMTD 2549 6 4.572381 1 6 Hallmarks of mathematics are clarity, pr...TBMSTMTE 2544 6 4.531447 1 6 Math involves remembering/applying defin...TBMSTMTF 2539 6 4.594722 1 6 Mathematics means creativity and new ideas.TBMSTMTG 2537 6 4.763106 1 6 In mathematics many things can be discov...TBMSTMTH 2545 6 4.601179 1 6 Definitional rigor is essential for math...TBMSTMTI 2540 6 3.414961 1 6 To solve mathematical tasks you must kno...TBMSTMTJ 2534 6 4.923836 1 6 If one engages in mathematical tasks, on...TBMSTMTK 2530 6 4.877866 1 6 Mathematics entails a fundamental benefi...TBMSTMTL 2526 6 4.416469 1 6 Fundamental to mathematics is its logica...TBMSTMTM 2536 6 5.358044 1 6 Mathematical problems can be solved corr...TBMSTMTN 2531 6 4.758989 1 6 Mathematics is useful for every profession.TBMSTMTO 2529 6 4.665085 1 6 Every person can discover or rediscover ...TBMSTMTP 2531 6 4.786646 1 6 Many aspects of mathematics have practic...TBMSTMTQ 2524 6 4.807052 1 6 Mathematics help solve everyday problems...TBMSTMTR 2526 6 4.059778 1 6 Math is characterized by rigor of defini...TBMSTMTS 2528 6 4.528481 1 6 Mathematics requires practice correctly ...TBMSTMTT 2533 6 3.84169 1 6 Mathematics means learning, remembering,...TBWOLRNA 1311 6 4.742944 1 6 The best way to do well in mathematics i...TBWOLRNB 1304 6 3.721626 1 6 Students need to be taught exact procedu...TBWOLRNC 1304 6 5.320552 1 6 Understanding a math problem doesn't mat...TBWOLRND 1296 6 3.682099 1 6 To be successful in mathematics, a stude...TBWOLRNE 1293 6 2.213457 1 6 To be good in mathematics you must be ab...TBWOLRNF 1299 6 3.372594 1 6 Students learn mathematics best by atten...TBWOLRNG 1302 6 2.048387 1 6 More emphasis should be put on getting r...TBWOLRNH 1299 6 5.504234 1 6 Beyond right answers in math, it's impor...TBWOLRNI 1290 6 5.016279 1 6 We should let students having difficulty...TBWOLRNJ 1294 6 5.272025 1 6 We should allow students to figure out t...TBWOLRNK 1287 6 2.393162 1 6 Nonstandard procedures should be avoided...TBWOLRNL 1284 6 5.338006 1 6 Hands-on math experiences aren't worth t...TBWOLRNM 1287 6 4.68143 1 6 Time spent on why a problem's mathematic...TBWOLRNN 1289 6 4.128782 1 6 One can learn a lot by watching an exper...TBWOLRNO 1280 6 4.638281 1 6 Students can figure out a way to solve m...TBWOLRNP 1281 6 4.675254 1 6 We must encourage students to find their...TBWOLRNQ 1284 6 5.327882 1 6 It's helpful for students to discuss dif...TBWOLRNR 1288 6 4.485248 1 6 For students to get better at mathematic...TBWKGRP1 1273 6 2.874313 0 5 There is too much time pressure from the...TBWKGRP2 1273 6 2.183818 0 5 There's too much planning and preparatio...TBWKGRP3 1262 6 1.583201 0 5 Student work groups are seldom used by o...TBWKGRP4 1271 6 1.842644 0 5 Students are not familiar with the stude...TBWKGRP5 1262 6 2.495246 0 5 Student work groups are too chaotic and ...TBWKGRP6 1266 6 2.578989 0 5 There are too many students in classes f...TBWKGRP7 1266 6 1.262243 0 5 Student work groups are only appropriate...TBWKGRP8 1266 6 2.112954 0 5 There are not enough appropriate materia...


TBWKGRP9 1267 6 3.006314 0 5 Group work makes it too easy for student...TBIAEMPA 1263 6 3.806017 0 5 Explaining mathematical ideas to the who...TBIAEMPB 1266 6 3.421011 0 5 Having students work in small groups is ...TBIAEMPC 1258 6 3.557234 0 5 Having students work on mathematical pro...TBIAEMPD 1256 6 3.583599 0 5 Reviewing homework intensely is very app...TBIAEMPE 1255 6 3.477291 0 5 Working out math problems at the board a...TBIAEMPF 1248 6 3.892628 0 5 Having students work out math problems a...TBIAEMPG 1255 6 2.890837 0 5 Having students make oral reports in mat...TBIAEMPH 1253 6 3.327215 0 5 Having students do special projects on m...TBIAEMPI 1251 6 3.752998 0 5 Organizing student discussions of math p...TBIAEMPJ 1254 6 3.367624 0 5 Measuring student achievement with writt...TBIAEMPK 1236 6 3.509709 0 5 Diagnosing student learning with formati...TBIAEMPL 1244 6 3.756431 0 5 Having students write about their thinki...TBTROLE1 1249 6 3.972778 0 5 I believe that teachers can often learn ...TBTROLE2 1249 6 3.39952 0 5 If a student asks a question in mathemat...TBTROLE3 1247 6 2.436247 0 5 A teacher must serve as the judge of wha...TBTROLE4 1242 6 3.958132 0 5 The role of the teacher is to impart kno...TBTROLE5 1247 6 3.828388 0 5 A teacher creates learning opportunities...TBSNAPA 1198 6 5.224541 1 6 The main job of schooling supporting the...TBSNAPB 1180 6 3.827119 1 6 The main job of schooling is fairer sort...TBSNAPC 1191 6 4.940386 1 6 The main job of schooling is to develop ...TBSNAPD 1183 6 3.422654 1 6 The main job of schooling is to sort chi...TBSNAPE 1193 6 5.217938 1 6 The main job of schooling is to transmit...TBSNAPF 1180 6 4.34322 1 6 The main job of schooling is to sort stu...TBSNAPG 1185 6 5.361181 1 6 The main job of schooling is to develop ...TBSNAPH 1189 6 4.238015 1 6 The main job of schooling is to protect ...TBSNAPI 1185 6 4.324895 1 6 The main job of schooling is to transmit...TBDIVSEA 1182 6 4.263113 1 6 We should avoid grouping students by abi...TBDIVSEB 1183 6 3.34235 1 6 Teachers with slow learners should focus...TBDIVSEC 1179 6 4.768448 1 6 We should give problems that force bette...TBDIVSED 1177 6 3.081563 1 6 It's impractical for us to tailor instru...TBDIVSEE 1167 6 3.850043 1 6 Regular students benefit by being in cla...TBDIVSEF 1169 6 4.223268 1 6 Students with special needs benefit from...TBDIVSEG 1166 6 3.531732 1 6 Students with special needs learn academ...TBGDTEAA 1177 6 4.187766 0 5 It's very important for me to develop my...TBGDTEAB 1175 6 2.754043 0 5 It's very important that I copy techniqu...TBGDTEAC 1172 6 3.321672 0 5 It's very important that I read research...TBGDTEAD 1172 6 3.357509 0 5 It's very important that I obtain the go...TBGDTEAE 1171 6 3.04953 0 5 It's very important that I learn the sch...TBGDTEAF 1174 6 3.918228 0 5 It's very important that I observe other...TBGDTEAG 1170 6 3.117949 0 5 It's very important that I take more cou...TBDISTRA 1136 6 4.529049 1 6 Even if I'm disrupted, I can maintain my...TBDISTRB 1132 6 4.619258 1 6 I know that I can motivate my students t...TBDISTRC 1132 6 4.587456 1 6 When I try really hard, I am able to rea...TBDISTRD 1130 6 4.436283 1 6 I can maintain a positive relationship w...


TBDISTRE 1126 6 4.430728 1 6 I can carry out innovative projects even...TBDISTRF 1129 6 5.249779 1 6 I am convinced that I will increasingly ...TBDISTRG 1126 6 4.412966 1 6 I can successfully teach relevant conten...TBDISTRH 1127 6 5.021295 1 6 If I try hard enough, I can positively i...TBDISTRI 1123 6 4.666073 1 6 I know I can develop creative ways to te...TBDISTRJ 1126 6 4.671403 1 6 I'm confident I can be responsive to my ...TBTPROF1 1111 6 5.061206 1 6 Being a lower secondary teacher of mathe...TBTPROF2 1109 6 2.116321 1 6 This profession offers very few satisfac...TBTPROF3 1107 6 2.30533 1 6 I often think that I would like to choos...TBTPROF4 1110 6 5.236937 1 6 The teaching profession is very importan...--------------------------------------------------------------------------------------- . . * STEP 4 . * Generate a new variable usa to compare USA respondents . * to respondents from other countries. . gen usa = 0 if !missing(COUNTRY) . replace usa = 1 if COUNTRY == "USA"(384 real changes made) . label var usa "Respondent Origin (1=USA 0=Other)" . label def usaval 0 "Other" 1 "USA" . label val usa usaval . * STEP 5 . * Conduct initial, unrotated EFA. . factor TBMROLEA - TBMSTMTT(obs=1,207) Factor analysis/correlation Number of obs = 1,207 Method: principal factors Retained factors = 11 Rotation: (unrotated) Number of params = 209


-------------------------------------------------------------------------- Factor | Eigenvalue Difference Proportion Cumulative -------------+------------------------------------------------------------ Factor1 | 4.19611 1.50028 0.5281 0.5281 Factor2 | 2.69583 1.74600 0.3393 0.8674 Factor3 | 0.94982 0.33747 0.1195 0.9869 Factor4 | 0.61236 0.17187 0.0771 1.0640 Factor5 | 0.44049 0.05984 0.0554 1.1194 Factor6 | 0.38064 0.11853 0.0479 1.1673 Factor7 | 0.26211 0.11720 0.0330 1.2003 Factor8 | 0.14491 0.05988 0.0182 1.2186 Factor9 | 0.08503 0.04469 0.0107 1.2293 Factor10 | 0.04034 0.01273 0.0051 1.2343 Factor11 | 0.02761 0.03258 0.0035 1.2378 Factor12 | -0.00497 0.03889 -0.0006 1.2372 Factor13 | -0.04386 0.01207 -0.0055 1.2317 Factor14 | -0.05593 0.00660 -0.0070 1.2246 Factor15 | -0.06253 0.03969 -0.0079 1.2167 Factor16 | -0.10222 0.02814 -0.0129 1.2039 Factor17 | -0.13036 0.02953 -0.0164 1.1875 Factor18 | -0.15990 0.01698 -0.0201 1.1674 Factor19 | -0.17687 0.01414 -0.0223 1.1451 Factor20 | -0.19101 0.01770 -0.0240 1.1211 Factor21 | -0.20871 0.01560 -0.0263 1.0948 Factor22 | -0.22431 0.02538 -0.0282 1.0666 Factor23 | -0.24969 0.02945 -0.0314 1.0351 Factor24 | -0.27915 . -0.0351 1.0000 -------------------------------------------------------------------------- LR test: independent vs. saturated: chi2(276) = 7575.89 Prob>chi2 = 0.0000 Factor loadings (pattern matrix) and unique variances -------------------------------------------------------------------------- Variable | Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 -------------+------------------------------------------------------------ TBMROLEA | 0.4376 -0.0023 0.0827 -0.2808 0.0649 -0.0671 TBMROLEB | 0.4447 0.0573 0.2857 -0.3444 0.0664 -0.0336 TBMROLEC | 0.3651 -0.2090 0.3093 -0.2024 -0.1512 0.0200 TBMROLED | 0.4103 0.1141 0.3370 -0.2164 0.1649 -0.0380 TBMSTMTA | 0.1121 0.3220 0.1797 0.0713 0.1104 0.1149 TBMSTMTB | 0.3109 0.0803 -0.1359 -0.1209 0.0114 0.0500 TBMSTMTC | 0.4598 -0.1372 -0.2091 -0.0428 0.1101 0.3418 TBMSTMTD | 0.4322 0.2714 -0.2306 -0.0795 -0.0780 -0.0696 TBMSTMTE | 0.3511 0.4281 -0.0681 0.0343 0.0525 0.0815 TBMSTMTF | 0.4335 -0.2124 -0.1795 -0.0603 0.2571 -0.1460


. * STEP 6a . * Create a scree plot of eigenvalues from unrotated factors. . * This scree plot will be Figure 1 in your DAP1 narrative. . * Note: The following code must be on a single line. . scree, yline(1, lcolor(red)) ylabel(0(.5)4, labsize(vsmall) angle(hor) format(%3.1f))> ytitle(Eigenvalue) xlabel(0(1)25, alt labsize(vsmall) format(%2.0f) grid) xtitle(Fac> tor Number) title("") scheme(s1color) name(DAP1screeplot, replace) . . * STEP 6b . * Export the scree plot as a high resolution graph file to the working directory. . graph export DAP1-screeplot.tif, width(800) replacecould not find Graph windowr(693); . . * STEP 7 . * Conduct initial, unrotated EFA. . factor TBMROLEA - TBMSTMTT, factors(3)(obs=1,207) Factor analysis/correlation Number of obs = 1,207 Method: principal factors Retained factors = 3 Rotation: (unrotated) Number of params = 69 -------------------------------------------------------------------------- Factor | Eigenvalue Difference Proportion Cumulative -------------+------------------------------------------------------------ Factor1 | 4.19611 1.50028 0.5281 0.5281 Factor2 | 2.69583 1.74600 0.3393 0.8674 Factor3 | 0.94982 0.33747 0.1195 0.9869 Factor4 | 0.61236 0.17187 0.0771 1.0640 Factor5 | 0.44049 0.05984 0.0554 1.1194


TBMSTMTO | 0.3993 -0.4477 -0.0257 | 0.6395 TBMSTMTP | 0.5109 -0.3743 0.1051 | 0.5878 TBMSTMTQ | 0.5668 -0.3164 0.1634 | 0.5519 TBMSTMTR | 0.4397 0.5542 -0.1496 | 0.4772 TBMSTMTS | 0.3469 0.4241 0.0607 | 0.6961 TBMSTMTT | 0.3789 0.4181 0.2378 | 0.6251 ----------------------------------------------------------- . * STEP 8 . * Rotate the 3 factors using varimax orthogonal rotation with Kaiser normalization. . rotate, orthogonal varimax normalize Factor analysis/correlation Number of obs = 1,207 Method: principal factors Retained factors = 3 Rotation: orthogonal varimax (Kaiser on) Number of params = 69 -------------------------------------------------------------------------- Factor | Variance Difference Proportion Cumulative -------------+------------------------------------------------------------ Factor1 | 3.08419 0.22839 0.3882 0.3882 Factor2 | 2.85579 0.95401 0.3594 0.7476 Factor3 | 1.90178 . 0.2393 0.9869 -------------------------------------------------------------------------- LR test: independent vs. saturated: chi2(276) = 7575.89 Prob>chi2 = 0.0000 Rotated factor loadings (pattern matrix) and unique variances ----------------------------------------------------------- Variable | Factor1 Factor2 Factor3 | Uniqueness -------------+------------------------------+-------------- TBMROLEA | 0.2237 0.2400 0.3011 | 0.8017 TBMROLEB | 0.2346 0.1065 0.4650 | 0.7174 TBMROLEC | -0.0293 0.1949 0.4835 | 0.7274 TBMROLED | 0.2510 0.0257 0.4809 | 0.7051 TBMSTMTA | 0.2856 -0.2035 0.1598 | 0.8515 TBMSTMTB | 0.2646 0.2237 0.0389 | 0.8785 TBMSTMTC | 0.1864 0.4808 0.0902 | 0.7260 TBMSTMTD | 0.5054 0.2413 -0.0051 | 0.6863 TBMSTMTE | 0.5540 0.0167 0.0634 | 0.6888 TBMSTMTF | 0.1050 0.4915 0.1125 | 0.7347 TBMSTMTG | -0.1014 0.6226 0.0918 | 0.5937


TBMROLED | 0.02882 -0.06596 0.20636 TBMSTMTA | 0.05871 -0.07476 0.07082 TBMSTMTB | 0.05816 0.05106 -0.02524 TBMSTMTC | 0.04503 0.14335 -0.05229 TBMSTMTD | 0.12343 0.07710 -0.07613 TBMSTMTE | 0.13547 -0.00423 -0.01232 TBMSTMTF | 0.02137 0.13927 -0.03454 TBMSTMTG | -0.03363 0.21735 -0.06083 TBMSTMTH | 0.18422 0.07224 -0.13056 TBMSTMTI | 0.13620 -0.08216 0.03625 TBMSTMTJ | -0.00154 0.15161 -0.02726 TBMSTMTK | 0.00358 0.06560 0.08390 TBMSTMTL | 0.18320 0.05588 -0.02053 TBMSTMTM | 0.01722 0.22460 -0.07871 TBMSTMTN | -0.04406 -0.00814 0.21490 TBMSTMTO | -0.06163 0.13392 0.04982 TBMSTMTP | -0.05307 0.10916 0.13753 TBMSTMTQ | -0.03769 0.08446 0.18663 TBMSTMTR | 0.24421 0.01295 -0.06849 TBMSTMTS | 0.12019 -0.04221 0.05235 TBMSTMTT | 0.12629 -0.10977 0.15775 -------------------------------------------- . * STEP 10a . * Install/update the sortl.ado program to sort the rotated factor loadings. . * Note: The final character in the program name is a lower-case "l" rather . * than the number 1. . * You will need to cite this program in your References list. . ssc install sortl, replacechecking sortl consistency and verifying not already installed...installing into /Users/taofenghe/Library/Application Support/Stata/ado/plus/...installation complete. . . * STEP 10b


. * Sort the rotated factor loadings with sortl. . sortl Rotated factor loadings (pattern matrix) and unique variances sorted ---------------------------------------------------------- Variable | Factor1 Factor2 Factor3 | Uniqueness -------------+------------------------------+------------- TBMSTMTR | 0.7214 0.0435 0.0230 | 0.4772 TBMSTMTL | 0.6306 0.1918 0.1326 | 0.5480 TBMSTMTH | 0.6085 0.1444 -0.1031 | 0.5983 TBMSTMTE | 0.5540 0.0167 0.0634 | 0.6888 TBMSTMTI | 0.5460 -0.1896 0.0927 | 0.6574 TBMSTMTS | 0.5223 -0.0499 0.1692 | 0.6961 TBMSTMTD | 0.5054 0.2413 -0.0051 | 0.6863 TBMSTMTT | 0.4992 -0.1167 0.3348 | 0.6251 TBMSTMTA | 0.2856 -0.2035 0.1598 | 0.8515 TBMSTMTB | 0.2646 0.2237 0.0389 | 0.8785 TBMSTMTM | 0.0625 0.6253 0.0819 | 0.5984 TBMSTMTG | -0.1014 0.6226 0.0918 | 0.5937 TBMSTMTJ | 0.0143 0.5396 0.1319 | 0.6912 TBMSTMTO | -0.1351 0.5247 0.2588 | 0.6395 TBMSTMTF | 0.1050 0.4915 0.1125 | 0.7347 TBMSTMTP | -0.0406 0.4875 0.4158 | 0.5878 TBMSTMTC | 0.1864 0.4808 0.0902 | 0.7260 TBMSTMTK | 0.1116 0.3917 0.3335 | 0.7229 TBMSTMTN | -0.0060 0.2561 0.5300 | 0.6535 TBMSTMTQ | 0.0253 0.4606 0.4851 | 0.5519 TBMROLEC | -0.0293 0.1949 0.4835 | 0.7274 TBMROLED | 0.2510 0.0257 0.4809 | 0.7051 TBMROLEB | 0.2346 0.1065 0.4650 | 0.7174 TBMROLEA | 0.2237 0.2400 0.3011 | 0.8017 ---------------------------------------------------------- . . . * STEP 11a . * Load Red Owl's factabexcel.do program to use in adding variable labels . * to the factor loadings table and exporting the sorted and labeled table


. * to an Excel file. . * You will need to cite this program in your References list. . run "http://datalibrary.us/factabexcel.do". * STEP 11b . * Run the factabexcel.do program to add variable labels, eigenvalues, and proportion . * of variance explained to the rotated factor loadings table, add labels, and export . * the Excel file Table1.xlsx to the working directory. After running this step, you . * will find the Excel file dap1Table1.xlsx in the current working directory on your . * computer. . putexcel set DAP1-Table1.xlsx, sheet(FactorLoadings) replaceNote: file will be replaced when the first putexcel command is issued . putexcel close . factabexcel DAP1-Table1.xlsx FactorLoadings(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)

http://datalibrary.us/factabexcel.do


(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(1 real change made)(2,604 observations deleted) +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTR | 0.721 | 0.043 | 0.023 | .4772312 | |---------------------------------------------------------------------------------| | Label | | Math is characterized by rigor of definition and formal argumentation. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTL | 0.631 | 0.192 | 0.133 | .5479945 | |---------------------------------------------------------------------------------| | Label | | Fundamental to mathematics is its logical rigor and preciseness. |


+---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTH | 0.608 | 0.144 | -0.103 | .5982719 | |---------------------------------------------------------------------------------| | Label | | Definitional rigor is essential for mathematics with exact/precise language. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTE | 0.554 | 0.017 | 0.063 | .688837 | |---------------------------------------------------------------------------------| | Label | | Math involves remembering/applying definitions, formulas, facts and procedures. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTI | 0.546 | -0.190 | 0.093 | .657378 | |---------------------------------------------------------------------------------| | Label | | To solve mathematical tasks you must know the correct procedure or be lost. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTS | 0.522 | -0.050 | 0.169 | .696108 | |---------------------------------------------------------------------------------| | Label | | Mathematics requires practice correctly applying routines and strategies. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTD | 0.505 | 0.241 | -0.005 | .6863058 | |---------------------------------------------------------------------------------| | Label | | Hallmarks of mathematics are clarity, precision and unambiguousness. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+


| Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTT | 0.499 | -0.117 | 0.335 | .6251018 | |---------------------------------------------------------------------------------| | Label | | Mathematics means learning, remembering, and applying. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTA | 0.286 | -0.204 | 0.160 | .8514827 | |---------------------------------------------------------------------------------| | Label | | Math is a collection of prescribed rules and procedures. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTB | 0.265 | 0.224 | 0.039 | .8784515 | |---------------------------------------------------------------------------------| | Label | | Mathematical thought is characterized by abstraction and logic. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTM | 0.062 | 0.625 | 0.082 | .5983751 | |---------------------------------------------------------------------------------| | Label | | Mathematical problems can be solved correctly in many ways. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTG | -0.101 | 0.623 | 0.092 | .5936618 | |---------------------------------------------------------------------------------| | Label | | In mathematics many things can be discovered and tried out by oneself. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTJ | 0.014 | 0.540 | 0.132 | .6911991 | |---------------------------------------------------------------------------------| | Label |


| If one engages in mathematical tasks, one can discover new things. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTO | -0.135 | 0.525 | 0.259 | .6395023 | |---------------------------------------------------------------------------------| | Label | | Every person can discover or rediscover mathematics. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTF | 0.105 | 0.492 | 0.113 | .7346869 | |---------------------------------------------------------------------------------| | Label | | Mathematics means creativity and new ideas. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTP | -0.041 | 0.487 | 0.416 | .5878402 | |---------------------------------------------------------------------------------| | Label | | Many aspects of mathematics have practical relevance. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTC | 0.186 | 0.481 | 0.090 | .7260118 | |---------------------------------------------------------------------------------| | Label | | Usually there is more than one way to solve mathematical tasks and problems. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTK | 0.112 | 0.392 | 0.334 | .7228834 | |---------------------------------------------------------------------------------| | Label | | Mathematics entails a fundamental benefit for society. | +---------------------------------------------------------------------------------+


+---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTN | -0.006 | 0.256 | 0.530 | .6534831 | |---------------------------------------------------------------------------------| | Label | | Mathematics is useful for every profession. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMSTMTQ | 0.025 | 0.461 | 0.485 | .5519224 | |---------------------------------------------------------------------------------| | Label | | Mathematics help solve everyday problems and tasks. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMROLEC | -0.029 | 0.195 | 0.484 | .7273567 | |---------------------------------------------------------------------------------| | Label | | Mathematics is needed for many jobs and careers. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMROLED | 0.251 | 0.026 | 0.481 | .7050768 | |---------------------------------------------------------------------------------| | Label | | To succeed in school, it is important to have learned math. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMROLEB | 0.235 | 0.107 | 0.465 | .717373 | |---------------------------------------------------------------------------------| | Label | | To be a well-educated person, it is important to study math. | +---------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------+ | Variable | Factor1 | Factor2 | Factor3 | U | | TBMROLEA | 0.224 | 0.240 | 0.301 | .8017031 | |---------------------------------------------------------------------------------|


| Label | | Mathematics helps you learn to think better. | +---------------------------------------------------------------------------------+ | Factor1 Factor2 Factor3 -------------+------------------------------ Eigenvalue | 3.084 2.856 1.902 % Variance | 38.816 35.941 23.935 Total % Variance Explained = 98.692 file DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx saved


file DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx savedfile DAP1-Table1.xlsx saved . * STEP 13 . * The following code calculates various descriptive statistics for the . * factors with positive factor loadings which satisfy the interpretive . * cut-off criterion (i.e., loading > .30) and for the U statistic. . * You should include these statistics in the results section of your DAP1 text. . * You could manually calculate these statistics by reviewing the results in . * the Excel file in DAP1-Table1.xlsx, but I am providing the following code to . * simplify the task for you. I recognize that this Stata code is complex and . * I would not expect you to be able to develop this code yourself at this point. . cap restore . preserve . import excel DAP1-Table1.xlsx, first sheet(FactorLoadings) cellrange(B4:E28) clear . forval i = 1/`=_N' { 2. if abs(Factor1[ì']) < .30 | Factor1[ì'] < 0 replace Factor1 = . in ì' 3. if abs(Factor2[ì']) < .30 | Factor2[ì'] < 0 replace Factor2 = . in ì' 4. if abs(Factor3[ì']) < .30 | Factor3[ì'] < 0 replace Factor3 = . in ì' 5. }(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)


(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing)(1 real change made, 1 to missing) . format Factor1-Factor3 %5.3f . tabstat Factor1 Factor2 Factor3 U, statistics(min median max n) format(%5.3f)


stats | Factor1 Factor2 Factor3 U---------+---------------------------------------- min | 0.499 0.392 0.301 0.477 p50 | 0.550 0.492 0.465 0.688 max | 0.721 0.625 0.530 0.878 N | 8.000 9.000 9.000 24.000-------------------------------------------------- . restore . . * STEP 14 . * Calculate descriptive statistics on the factor scores, which were . * predicted in STEP 9 above. . tabstat F1-F3, stats(mean sd min p25 median p75 max n) format(%5.2f) stats | F1 F2 F3---------+------------------------------ mean | -0.00 -0.00 0.00 sd | 0.91 0.88 0.82 min | -3.31 -4.67 -3.18 p25 | -0.59 -0.49 -0.52 p50 | 0.08 0.10 0.05 p75 | 0.66 0.64 0.57 max | 2.27 1.96 2.22 N | 1207.00 1207.00 1207.00---------------------------------------- . * STEP 15 . * Conduct independent samples t-tests on each factor to determine . * whether there are statistically significant differences in the . * mean factor scores between US respondents and those from the . * other countries included in the study.


. ttest F1, by(usa) Two-sample t test with equal variances------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]---------+-------------------------------------------------------------------- Other | 1,032 .0469218 .0284358 .9134942 -.0088769 .1027205 USA | 175 -.2767047 .0612087 .8097153 -.3975118 -.1558976---------+--------------------------------------------------------------------combined | 1,207 -7.96e-10 .0260802 .9060752 -.0511676 .0511676---------+-------------------------------------------------------------------- diff | .3236265 .0735148 .1793953 .4678577------------------------------------------------------------------------------ diff = mean(Other) - mean(USA) t = 4.4022Ho: diff = 0 degrees of freedom = 1205 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000 . ttest F2, by(usa) Two-sample t test with equal variances------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]---------+-------------------------------------------------------------------- Other | 1,032 .01005 .0274535 .881938 -.0438211 .0639212 USA | 175 -.0592664 .0661088 .8745378 -.1897449 .071212---------+--------------------------------------------------------------------combined | 1,207 -2.81e-10 .025354 .8808465 -.0497429 .0497429---------+-------------------------------------------------------------------- diff | .0693165 .0720126 -.0719674 .2106004------------------------------------------------------------------------------ diff = mean(Other) - mean(USA) t = 0.9626Ho: diff = 0 degrees of freedom = 1205 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0Pr(T < t) = 0.8320 Pr(|T| > |t|) = 0.3360 Pr(T > t) = 0.1680 . ttest F3, by(usa) Two-sample t test with equal variances------------------------------------------------------------------------------


Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]---------+-------------------------------------------------------------------- Other | 1,032 -.1323338 .0241682 .7763977 -.1797582 -.0849093 USA | 175 .7803911 .0456362 .6037103 .6903193 .8704629---------+--------------------------------------------------------------------combined | 1,207 1.28e-09 .0235827 .8193081 -.0462677 .0462677---------+-------------------------------------------------------------------- diff | -.9127249 .0616331 -1.033645 -.7918048------------------------------------------------------------------------------ diff = mean(Other) - mean(USA) t = -14.8090Ho: diff = 0 degrees of freedom = 1205 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000 . * STEP 16a . * Create a histogram of F1 overlaid by country comparison represented by transparent > bars. . * This will be inserted as Figure 2 in the DAP1 narrative. . twoway (histogram F1 if usa==1, percent width(1) bfcolor(green%10) blcolor(dkgreen)) > (histogram F1 if usa==0, width(1) percent bfcolor(teal%10) blcolor(blue)) , ylabels(> 0(5)45, angle(horizontal)) xlabels(-3(.5)3, format(%3.1f)) xtitle(Factor 1 Score) leg> end(order(1 "USA" 2 "Other") pos(10) ring(0) col(1)) scheme(s1color) name(DAP1histF1X> usa, replace) . graph save DAP1histF1Xusa "/Users/taofenghe/Documents/DAP1histF1Xusa.gph"(file /Users/taofenghe/Documents/DAP1histF1Xusa.gph saved) . * STEP 16b . * Export graph file to working directory. . graph export DAP1-histF1Xusa.tif, width(1000) replace(file DAP1-histF1Xusa.tif written in TIFF format) . * STEP 17a . * Create a histogram of F2 overlaid by country comparison represented by transparent > bars.


. * This will be inserted as Figure 3 in the DAP1 narrative. . twoway (histogram F2 if usa==1, percent width(1) bfcolor(green%10) blcolor(dkgreen)) > (histogram F2 if usa==0, width(1) percent bfcolor(teal%10) blcolor(blue)) , ylabels(> 0(5)45, angle(horizontal)) xlabels(-5(.5)3, format(%3.1f)) xtitle(Factor 2 Score) leg> end(order(1 "USA" 2 "Other") pos(10) ring(0) col(1)) scheme(s1color) name(DAP1histF2X> usa, replace) . * STEP 17b . * Export graph file to working directory. . graph export DAP1-histF2Xusa.tif, width(1000) replace(file DAP1-histF2Xusa.tif written in TIFF format) . . * STEP 18a . * Create a histogram of F3 overlaid by country comparison represented by transparent > bars. . * This will be inserted as Figure 4 in the DAP1 narrative. . twoway (histogram F3 if usa==1, percent width(1) bfcolor(green%10) blcolor(dkgreen)) > (histogram F3 if usa==0, width(1) percent bfcolor(teal%10) blcolor(blue)) , ylabels(> 0(5)45, angle(horizontal)) xlabels(-3(.5)3, format(%3.1f)) xtitle(Factor 3 Score) leg> end(order(1 "USA" 2 "Other") pos(10) ring(0) col(1)) scheme(s1color) name(DAP1histF3X> usa, replace) . * STEP 18b . * Export graph file to working directory. . graph export DAP1-histF3Xusa.tif, width(1000) replace(file DAP1-histF3Xusa.tif written in TIFF format) . * STEP 19 . * Close the log file, saving it to the working directory on your local computer.


. * You will copy/paste this log file into the DAP1 project document per the instructio> ns. . cap log close


Factor Analysis Articles Used as Models

Arlene J. Callwood

Cornish, I. M. (2000). Factor structure of the Everyday Memory Questionnaire. The British

Journal of Psychology, 91, 427-438. Retrieved from the EBSCOhost database.

(Accession No. 3484644)

Schugar, H. R. & Dreher, M. J. (2017). U.S. fourth graders’ informational text comprehension:

Indicators from NAEP. International Electronic Journal of Elementary Education, 9,

523-552. Retrieved from https://www.iejee.com/index.php/IEJEE

Tiffany Taofeng He




Kuterbach, J. M. (2007). Factor structure of the new Imaginary Audience Scale in a sample of

female college students. College Student Journal, 41, 813-822. Retrieved from

EBSCOhost database. (Accession No. 28351177)

Samantha James

Begun, A. L., Early, T. J., & Hodge, A. (2016). Mental health and substance abuse service

engagement by men and women during community reentry following incarceration.

Administration & Policy in Mental Health & Mental Health Services Research, 43, 207-

218. doi:10.1007/s10488-015-0632-2

Garland, B., Wodahl, E., & Cota, L. (2016). Measuring public support for prisoner reentry

options. International Journal of Offender Therapy and Comparative Criminology, 60,

1406-1424. doi:10.1177/0306624X15578438


Richard Mardarello




Lago, R. M., & DiPerna, J. C. (2010). Number sense in kindergarten: A factor-analytic study of

the construct. The School Psychology Review, 39, 164-180. Retrieved from the


Sondra O'Connell







Cindy Y. Taylor








Researchers’ Contributions and Statements of Academic Integrity

Arlene J. Callwood

Arlene J. Callwood read and reflected on Cornish (2000) and Schugar and Dreher (2017) as

models for factor analysis. On February 6 and 7 she read the model articles and the project

outline (3 hours). February 8 and 9 she re-listened to online videos on explanatory factor analysis

and transcribed the video the simplified example exercise and ran the codes (4 hours). On

February 10 she worked alone interpreting the codes and formatting the tables in excel and word

(4 hours). On February 11 she reread the instructions, went through each step of the code to

understand fully what was happening, worked on naming the factors and then met with her group

and Red Owl (5 hours). On February 12 she met with Samantha and Tiffany in the library to

review the factors and t-tests (2 hours). On February 13 she worked with group members online

to write the introduction and participant sections (2 hours). On February 14 she worked online

with group members to write the methods section (3 hours). On February 15 she reviewed and

edited what the group had written so far (1 hour). On February 17 she worked online with group

members to work on different sections of the project (4 hours). On February 18 she worked

alone writing and editing various sections of the project and met with her group to review and

update changes (7 hours). On February 19 she worked alone rereading and editing then met with

the group online (4 hours).

Total time spent on DAP1: 39 hours

Statement of Academic Integrity of this Project

I, Arlene J. Callwood, did not consult on any aspect of this project with anyone other than my

official team members and Red Owl, and I did not review any previous examples of this project

from previous sessions of the course. I have not shared a copy of any part of this project in draft

Red Owl, 03/01/18,

Thanks for the intellectual energy you devoted to this project, but how is it that the time did not change from the draft to the final. Did you not do any work on finalizing the project?

Red Owl, 03/01/18,

How is it that none you increased the times spent on this project between the draft and the final version? This is an important part of the assignment, and you obviously did not provide accurate data on the time you spent. I know at least some of you spent at least some time on finalizing the project, but that is not shown in the times you have reported.Please don’t do this again on future assignments in my courses. I pay attention to this information, which is important to me.


or final form with anyone other than my official team members or Red Owl, and I promise not to

publish this project in any form (including posting on the internet) without Red Owl’s written

permission. If I include this project in my digital portfolio, I will password-protect the web site

or the project document or both.

Tiffany Taofeng He

Tiffany read and reflected on Cornish (2000) and Kuterbach (2007) as models of factor analysis.

Time Log: She ran Stata code and worked on the insert/format/color Table 1 from the output

Excel sheet on February 6 and 7 (5 hours); she met with the team to preview the project on

February 11 and met with Red Owl for a very helpful overview preview session (5 hours online);

she met with Arlene and Samantha in the LIU library for 2 hours to further review the tasks; she

met with the team members online as well as worked individually, with a different focus:

February 13 (4 hours) on Methods, February 14 (3 hours) on Results, February 15 (3 hours) on

importing the Table and Figures and Stata output to the Appendix, February 16 (4 hours) on

Discussion/Interpretation, February 17 (5 hours) on Citation of Software in APA format (5

hours), February 18 (2 hours) on Conclusion and Implication wording. February 19 (3 hours) for

go over the whole draft.



I, Tiffany Taofeng He, did not consult on any aspect of this project with anyone other than my





Red Owl, 03/01/18,





Samantha James

Samantha James read and reflected on Begun, Early, and Hodge (2016) and Garland, Wodahl,

and Cota (2016) as models of factor analysis. She worked alone on February 11 (4 hours) and

read the model articles, cried on the floor in the fetal position, reread DAP1 instructions, read the

Professor’s APA tips, watched “How to Format Hanging Indents for Citations with MS Word,”

reviewed rules in the APA manual, set up headings and formatted the document in MS Word, ran

the Stata code, tried to understand it, cried some more, reviewed the factors for themes, and

attempted to name them. She also worked online with some group members on February 11 (3

hours) and then with the same group members and the Professor (2 hours). On February 12 she

met with Arlene and Tiffany in the library (2 hours) to review the factors and t-tests. On

February 13 she worked with several group members online (2 hours) to write the introductions

and participants sections. On February 14 she worked with several group members online (3

hours) to write the methods section. On February 15 she worked with several group members

online (5 hours) to write the methods and results sections; on February 16 she worked with

several group members online (4 hours) to write the discussion and conclusion sections; on

February 17 (5 hours) she worked online with several group members to work on various

sections of the project; on February 18 she worked online with several group members (6.5

hours) to rewrite various sections of the paper and then review and edit it; on February 19 she

worked alone (1.5 hours) reading through the paper and editing it and then met with the group

online (3 hours) for the penultimate review to catch any errors and reword certain sentences for

clarity. She spent an additional time (2 hours) reviewing the format and citations to make sure


they adhered to the APA guidelines. She assisted with the formatting of Table 1 in consultation

with her team members. She ran all the factor analysis code herself even though other members

of the team did the same and she participated in every aspect of this project.

Total Time spent on DAP1: 43 hours


I, Samantha James, did not consult on any aspect of this project with anyone other than my







Richard Mardarello

Richard Mardarello read and reflected on Cornish (2000) and Lago and DiPerna (2010) as

models of factor analysis. On February 8th he spent 3 hours reading his reflection readings. On

February 9th he spent 2 hours running Stata for the project. On February 10th he then practiced

Stata for 1.5 hours, he then read notes from the class for 1 hour on the project. On February 11th

he met with the group online and worked for 5 hours on various sections of the project. On

February 12 he watched videos for 2 hours then reflected on the class notes for 1.5 hours to

better understand various parts of the project. February13 he spent 3 hours with his group online

working on various topics on the project, also after the online conference he then spent 1 hour

editing the draft. On February 14th he spent 3 hours with his group working on results and

discussion section. On February 15th he spent 2 hours with his group online and worked on the

Red Owl, 03/01/18,



discussion and methods section and spent another 1.5 hours of his time editing. February16 he

spent 4 hours with his group online and worked on the discussion section, the tables and figures

he also spent 1 hour on a phone conference with Sondra then another .5 hours editing. On

February 17 he met with the group online and worked for 4 hours on methods, discussion and

results then spell checking. On February 18 he spent 2 hours with the group online working on

the conclusion and implication section. On February 19 he worked with his group online for 2

hours editing the whole project.

Total Time spent on DAPI: 40 hours

Statement of Academic Integrity of this project

I, Richard Mardarello, did not consult on any aspect of this project with anyone other than my




publish this project in any form (including posting on the internet) without Red Owl' s written



Sondra O'Connell

Sondra O'Connell read and reflected on Cornish (2000) and Lago and DiPerna (2010) as models

of factor analysis. February 9 (2 hours reading and reflecting). She met with the team on

February 11 (2.5 hours online), February 12 (2.5 individual, phone conference with Richard 1

hour), February 13 (phone conference with Cindy 1 hour, 4 hours online), February 14 (1 hour

individual), February 15 (4 hours on line with group), February 16 (phone conference with

Richard 1 hour, editing individually 1.5 hours), February 17 (4 hours individual), February 18 (5

Red Owl, 03/01/18,



hours online with group, 3 hour individual) and February 19 (to approve final for submission by

team, 3 hour online). Sondra aided in the format of Table 1 in consultation with her team

members. Sondra ran all the factor analysis codes even though other members of the team did

the same, February 10 and 11 (5 hours). Sondra participated in every aspect of the project. She

also assisted in reviewing the draft and final versions with Red Owl’s APA Tips and the APA

Manual to make sure the documents were fully compliant with APA guidelines.

Total time spent on DAP1: 40 hours, 30 minutes


I, Sondra O'Connell, did not consult on any aspect of this project with anyone other than my







Cindy Y. Taylor

Cindy Y. Taylor read and reflected on Cornish (2000) and Lago and DiPerna (2010) as models

of factor analysis. February 5 (1 hour printing DAP instructions and formatting time log).

February 8 (2 hours reading and reflecting). February 9 (2 hours further reading and reflecting).

She met with the team on February 12 (1 hour) at library to discuss notes from February 11

online meeting and schedule future meetings; 3 hours reviewing APA guidelines, APA

Manuscripts review, comparing written notes with Red Owl's in class exemplar, February 13 (2

hours editing, 1-hour phone conference with Sondra, 4 hours online group conference, typing,

Red Owl, 03/01/18,



reviewing Red Owl's recording), February 14 (2 hours individual editing), February 15 (4 hours

online with group), February 18 (1 hour individual editing and notations, 2 hours online with

group) and February 19 (2 hours) individual editing and preparation for group session, 3 hours

online with group to review for final submission, 2 hours for document review and revisions, and

email correspondence. Cindy ran all the factor analysis codes required of all group members to

confirm the final data included in the DAP1 assignment. On February 10 and 11 (4.5 hours) she

aided in the format of Table 1 in consultation with her team members. Cindy participated in

every aspect of the project. She also assisted in reviewing the draft and final versions utilizing

DAP1 instructions, class notes and recordings, Red Owl’s APA Tips and the APA Manual, 6th

edition to ensure the documents were consistent with the assignment guidelines.



I, Cindy Y. Taylor, did not consult on any aspect of this project with anyone other than my







See Red Owl’s general comments and grade on the next page.

Red Owl, 03/01/18,



Arlene, Tiffiany, Samantha, Richard, Sondra, and Cindy,

Please see my detailed comments, suggestions, questions, and editorial quibbles in the

body of the text above using Microsoft Word’s commenting and tracking features. You will

need to switch back and forth between “Original,” “Simple Markup,” and “All Markup” views in

the Review tab. Also, please be sure to expand any comment boxes that show arrowed icons, as

they may need to be expanded to see the full text of my comments.

This is a superb final project and reflects a very good command of the statistical concepts

and techniques of t-tests and exploratory factor analysis. It also reflects a very good command of

APA style (other than the repeated error in the note to Table 1 and failures to place spaces on

both sides of mathematical operators like =, <, and >).

I am disappointed that you did not correct some of the errors I marked in the draft and

that none of you updated the time spent on the project between the draft and final. It is not clear

to me whether all of you were actually involved in finalizing this project. (For example, if all six

of you proofread the final document, how did all of you miss the correction to the note to Table 1

that I had marked on the draft?) If you did not all participate in finalizing the project after the

draft, that would be unacceptable, as I intend for all of you to learn from my suggestions and

corrections on the draft. Please be sure all of you are involved in finalizing future projects and

that you update the time you spent on the project to reflect the time you spent after you received

my comments on the draft.

Congratulations! I have assigned a grade of 97.5 (A) for DAP1, which is a very high

grade for a project in my courses. Now onto DAP2!

Red Owl