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Age 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.
Variables in research
Variables are things that we measure, control, or manipulate in research.
The measurement may be different from everyday notions of measurement such as weight and temperature.
Measurement can involve merely categorization (e.g. sex, country, etc.)
Most variables that differ over time also vary among individuals, but the reverse is not true. That is, the variables that differ among individuals may not necessarily differ over time.
An example for the former is “proficiency” and for the latter is “sex.”
Can you give some more examples for the two variables?
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.
Operationalization Intelligence Trait or construct Scores on the Wechsler Adult Intelligence Scale Operational definition of intelligence operationalization
Operationalization Proficiency Trait or construct Scores on the TOEFL test Operational definition of proficiency
Operational 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.
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 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 variables
To assess the relationship between variables in research, we must be able to identify each variable. Variables can be classified as:
Independent 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.
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.
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.
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.
A variable that functions as a dependent variable in one study may be an independent variable in another study.
Depending on the design of the study, we may have more than one independent and even more than one dependent variable in the study.
A 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, sex in the study of the effect of instruction on students’ reading scores
Independent 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.
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 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 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.
The relationship among variables Independent Variable(s) Dependent Variable(s) Intervening Variable(s) Moderator Variable(s) Control Variable(s) The Study
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.
To measure different variables, we have four measurement scales:
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).
True 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 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
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.
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.
V ery similar to interval 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.