Tuesday, April 18, 20231

Outline of today’s presentationOutline of today’s presentation

1. The concept and definition of variable

2. Variables in research

3. Constructs versus variables

4. Operationalization

5. Types and functions of variables

6. Measurement Scales

VariablesVariables It is very important

in research to see variables, define them, and control or measure them.

The concept of variableThe concept of variable

The concept of variable is basic but very important in research. You will not be able to do very much in research unless you know how to deal with variables.

The concept of variableThe concept of variable

A variable is a measured characteristic that can assume different values or level.

A measure that has only one value is called a constant.

A variable can be defined as an attribute of a person, a piece of text, or an object which “varies” from person to person, text to text, object to object, or from time to time.

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Variables in the classroomVariables in the classroomAn EFL Student’s language skill may vary from

week to week.The ability to speak a variety of languages. Some

people are monolingual, others are bilingual, and others multilingual.

IQ Scores, reading speed, accuracy, fluency, proficiency.

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Some examplesSome examplesAge can be considered a variable

because age can take different values for different people or for the same person at different times.

Similarly, country can be considered a variable because a person's country can be assigned a value.

Some examplesSome examplesGrade level can be considered a

variable because Grade level can take different values for different people or for the same person at different times.

Height, gender, e.t.c.

Variables in researchVariables in researchVariables are things that we measure,

control, or manipulate in research. Variables can be very broad or very

narrow. For example, the discourse, semantic, syntactic, phonological elements of language are attributes of language.

They are also something attributed to people in varying degree of proficiency.

Variables in researchVariables in researchA variable such as phonological

system is broad, indeed, when assigned to Students. The variable rising tone is less so.

RememberRemember

The broader the variable, the more difficult it may be to define, locate and measure accurately.

The more specific a variable is, the easier it will be to locate and measure.

OperationalizationOperationalization

Variables such as intelligence, motivation, and academic achievement are concepts, constructs, or traits that cannot be observed directly.

They should be stated in precise definitions that can be observed and measured. This process is called operationalization.

OperationalizationOperationalization

Intelligence

Trait or construct

Scores on the Wechsler Adult

Intelligence Scale

Operational definition of intelligence

operationalization

OperationalizationOperationalization

Proficiency

Trait or construct

Scores on the TOEFL test

Operational definition of proficiency

Operational definition of a variableOperational definition of a variable

With students’ intelligence scores or TOEFL scores, we now have observable and quantifiable definitions of what the researcher means by the constructs of “intelligence” and “proficiency”.

This is an operational definition of the variable.

Important point!Important point!

Operational definitions must be based upon a theory that is generally recognized as valid.

For example, to operationalize the construct of “proficiency” we should construct a test based on an accepted theory or model of language proficiency.

Different types and functions of variablesDifferent types and functions of variables

In addition to knowing how constructs are operationalized as variables, it is important to understand how such variables are classified and manipulated by researchers in their quest to empirical knowledge.

To that end, we describe five different functions of variables.

Functions of variablesFunctions of variables To assess the relationship between

variables in research, we must be able to identify each variable. Variables can be classified as:

1.Independent2.Dependent3.Moderator4.Control5.Intervening

Independent vs. Dependent VariablesIndependent vs. Dependent Variables

An important distinction having to do with the term 'variable' is the distinction between an independent and dependent variable.

This distinction is particularly relevant when you are investigating cause-effect relationships (experiment). However, the concept is also used in other research designs.

Independent vs. dependent V.Independent vs. dependent V.

In fact the independent variable is what you (or nature) manipulates -- a treatment or program or cause. The dependent variable is what is affected by the independent variable -- your effects or outcomes.

Independent VariablesIndependent Variables The independent variable is the major

variable which you hope to investigate. It is the variable which is selected, manipulated, and measured (its effect) by the researcher. Examples:

The effect of your instruction on reading scores of your students.

The effect of social class on language use.

Dependent variableDependent variable

The dependent variable is the variable which you observe and measure to determine the effect of the independent variable.

In the previous examples, the reading scores of your students and the use of language would be the dependent variable.

Two pointsTwo points1. A variable that functions as a

dependent variable in one study may be an independent variable in another study.

2. Depending on the design of the study, we may have more than one independent and even more than one dependent variable in the study.

Moderator variableModerator variableA moderator variable is a special type

of independent variable which you may select for study in order to investigate whether it modifies the relationship between the dependent and independent variables.

Example, gender in the study of the effect of instruction on students’ reading scores

Independent vs. moderator variableIndependent vs. moderator variable

The essential difference between independent and moderator variables lies in how the researcher views each in the study.

For independent variables, the concern is with their direct relationship to the dependent variable, whereas for moderator variables, the concern is with their effect on that relationship.

Suppose you were investigating the effect of conversation practice on the speaking fluency of foreign students. Conversation practice, then , would be the independent variable that you are interested in investigating. Fluency, operationally defined, is the dependent variable. However, you may have a hunch (feeling) that conversation practice works better for your Spanish students than for your Chinese students. Or you may have a hunch that it works better for men than for women or vice versa. Thus, language and/or gender could be moderator variable.

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Control variablesControl variables

It is virtually impossible to include all the potential variables in each study. As a result, the researcher must attempt to control, or neutralize, all other extraneous variables that are likely to have an effect on the relationship between the independent, dependent, and moderator variables.

Control variablesControl variablesControl variables, then, are those that

the researcher has chosen to keep constant, neutralize, or otherwise eliminate so that they will not have an effect on the study.

Example, the effect of outside practice on reading in the previous example.

Intervening variablesIntervening variables

Intervening variables are constructs (other than the construct under study) that may explain the relationship between independent and dependent variables but are not directly observable themselves.

We are somehow aware of their effects, but we are not able to account for them.

Intervening variablesIntervening variables

Usually the effect of the independent variable on the dependent variable is shown in terms of scores, counts, time measurement, etc.

That is, the dependent variable is measured is some way to determine the effect of the independent variable.

Intervening variablesIntervening variables

However, there is a process underlying the behavior we are measuring which is usually neither observable nor measurable.

Intervening variablesIntervening variablesFor example, in the study of oral fluency,

oral fluency is measured. We have not, however, said anything about the process underlying the acquisition of fluency. A number of variables have not been measured which may or may not be part of that process – learning , intelligence, frustration. These have not been measured or manipulated. These are called intervening variables.

The relationship among variablesThe relationship among variables

Independent

Variable(s)

Dependent

Variable(s)

Intervening

Variable(s)

Moderator

Variable(s)

Control

Variable(s)

The Study

Two pointsTwo points

When designing a study, the researcher determines which variables fall into each category.

In real situations, all five types of variables may not be included in all studies.

MeasurementMeasurementMeasurement is defined as assigning

numbers to observations according to certain rules.

Lyle F. Bachman (1990:19) explains that measurement is the process of quantifying the characteristics of an object of interest according to explicit rules and procedures.

Measurement ScalesMeasurement Scales

To measure different variables, we have four measurement scales:

1. Nominal Scale2. Ordinal Scale3. Interval Scale4. Ratio Scale

Measurement ScalesMeasurement Scales

For all four scales we use numbers, but the numbers in each scale have different properties and should be manipulated differently.

It is the duty of the researcher to determine the scale of the numbers used to quantify the observations in order to select the appropriate statistical test that should be applied to analyzed the data.

Nominal ScaleNominal Scale Nominal scale classifies persons or

objects into two or more categories. Members of a category have a common set of characteristics, and each member may only belong to one category. Other names: categorical, discontinuous, dichotomous (only two categories).

Nominal ScaleNominal Scale In nominal scales, numbers are used

to label, classify, or categorize data. For example, in coding data from a

survey to facilitate computer analysis, boys may be coded as “1” and girls as “2”. In this instance, it clearly does not make sense to add or divide the numbers.

True vs. artificial categoriesTrue vs. artificial categories

True categories are those to which the member naturally falls, such as gender (male vs. female).

Artificial categories are those to which the researcher places the members, such as learning style (field independent versus field dependent).

Ordinal ScaleOrdinal Scale Ordinal variables allow us to rank order the

items we measure in terms of which has less and which has more of the quality represented by the variable, but still they do not allow us to say "how much more.“

Example: Ranking students This scale has the concept of less than or more

than. The three medals winners in the long jump at

the Olympic Games. The gold medalist performed better than the silver medalist. The silver medalist performed better than the bronze medalist.

Ordinal ScaleOrdinal Scale

Ordinal scales both classify subjects and rank them in terms of how they possess the characteristic of interest. Members are placed in terms of highest to lowest, or most to least. Students may be ranked by height, weight, or IQ scores. Ordinal scales do not, however, state how much difference there is between the ranks.

Interval ScaleInterval Scale

Interval scales have the same properties as ordinal scales, but they also have equal intervals between the point of the scale.

Not only rank order the items that are measured, but also to quantify and compare the sizes of differences between them.

For example: students performance on a spelling test A score of 16 will be higher than 14 and lower than 18 and the difference between them is 2 points (equal intervals).

Interval scales normally have an arbitrary minimum and maximum point. A score of zero in a spelling test does not represent an absence of spelling knowledge, nor does a score of 20 represent perfect spelling knowledge.

Table 1 Three Example Scales

Table 1 Three Example Scales

Students Test Scores (Interval)

Ranking (Ordinal)

Frequencies (Ordinal)

Remarks

Rosidi 97 1 1 “Top Group”Milano 85 2 1Liana 82 3 1Dean 71 4 1Heni 70 5.5 2 “Upper Middle

Group”Billy 70 5.5Komar 69 7 1Randi 68 8 1

Monika 67 10 3 “Lower Middle Group”Wendi 67 10

Herman 67 10Sena 66 12 1 “Lower Group”Jeni 62 13 1

Elizabeth 59 14 1Ardi 40 15 1

Linda 31 16 1 44

Ratio ScaleRatio Scale

Very similar to interval scale; Ratio scale has all the properties of interval variables, it has absolute zero point. Height, weight, speed, and distance are examples of ratio scales. Measurements made with ratio scales can be added, subtracted, multiplied, and divided. For example, we can say that a person who runs a mile in 5 minutes is twice as fast as a person who runs the mile in 10 minutes. Because ratio scales are often used in physical measurements (where absolute zero exists), they are not often employed in educational research and testing.

Table 2 Four Scales of Measurement

Table 2 Four Scales of Measurement

  Name Categories

Shows Ranking

Gives Distances

Ratio Make Sense

Nominal    

Ordinal      

Interval        

Ratio        

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Remark:Remark: The table shows that nominal scale name and

categorize only, while ordinal scales uses categories but also give the ranking, or ordering of points within categories.

Interval scales provide information about the categories and ordering but also give additional details about the distances, or intervals, between points in that ranking.

Finally, ratio scales give the intervals, between points in the ordering of certain categories, but with even more information, because the ratio scales have a zero, and points along the scale make sense as multiples or ratios of other points on the scale.

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Table 3 Properties of Measurement Scales from Agresti & Finlay , 1986:16)

Table 3 Properties of Measurement Scales from Agresti & Finlay , 1986:16)

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Measurement Scales

Properties Ways of comparing measures

Typical examples

Ratio continuou

s

Interval continuou

s

Ordinal continuou

s

Nominal discrete

Absolute zeroEqual intervalsOrderingDistinctiveness

Equal intervalsOrderingDistinctiveness

OrderingDistinctiveness

Distinctiveness

How many times larger ?How much larger?Which one is larger?Are they different?How much larger?Which one is larger?Are they different?Which one is larger?Are they different?

Are they different?

Age of length of residence, cost per student, number of hour spent in study

Test scores, attitude scales

Ranking, judgments or self-assessment, using ratings scales, grade or level in schoolNative language, occupation, classroom in school

References References Main Sources

Coolidge, F. L.2000. Statistics: A gentle introduction. London: Sage.Kranzler, G & Moursund, J .1999. Statistics for the terrified. (2nd ed.). Upper Saddle River, NJ: Prentice Hall.Butler Christopher.1985. Statistics in Linguistics. Oxford: Basil Blackwell.Hatch Evelyn & Hossein Farhady.1982. Research design and Statistics for Applied Linguistics. Massachusetts: Newbury House Publishers, Inc.Ravid Ruth.2011. Practical Statistics for Educators, fourth Ed. New York: Rowman & Littlefield Publisher, Inc.Quirk Thomas. 2012. Excel 2010 for Educational and Psychological Statistics: A Guide to Solving Practical Problem. New York: Springer.

Other relevant sources

Agresi A, & B. Finlay.1986. Statistical methods for the social sciences. San Francisco, CA: Dellen Publishing Company.Bachman, L.F. 2004. Statistical Analysis for Language Assessment. New York: Cambridge University Press.Field, A. (2005). Discovering statistics using SPSS (2nd ed.). London: Sage. Moore, D. S. (2000). The basic practice of statistics (2nd ed.). New York: W. H. Freeman and Company. 

 Tuesday, April 18, 2023