1. Independent variables / Predictor variables
It is a variable that a researcher manipulates in order to determine its effect or influence on another variable. They predict the amount of variation that occurs in another variables.
Types of independent variables
i. Experimental variables: They are variables which the researcher has manipulative control over them. Are commonly used in biological and physical sciences e.g. influence of amount of fertilizer on the yield of wheat, influence of alcohol on reaction time.
ii. Measurement types of independent variables: Are variables, which have already occurred. They have fixed manipulative and uninfluenceable properties. Most of the variables are either environmental or personalogical e.g. age, gender, marital status, race, colour, geographical location, nationality, soil type, altitude etc. (e.g. influence of nationality on choice of food).
2. Dependent variables / criterion variables
It is the variable that is measured, predicted or monitored and is expected to be affected by manipulation of an independent variable. They attempt to indicate the total influence arising from the effects of the independent variable. It varies as a function of the independent variable e.g. influence of hours studied on performance in a statistical test, influence of distance from the supply center on cost of building materials.
3. Extraneous variables
They are those variables that affect the outcome of a research study either because the researcher is not aware of their existence or if the researcher is aware, she or he has no control over them.
Extraneous variables are often classified into three types:
1. Subject variables, which are the characteristics of the individuals being studied that might affect their actions. These variables include age, gender, health status, mood, background, etc.
2. Experimental variables are characteristics of the persons conducting the experiment which might influence how a person behaves. Gender, the presence of racial discrimination, language, or other factors may qualify as such variables.
3. Situational variables are features of the environment in which the study or research was conducted, which have a bearing on the outcome of the experiment in a negative way. Included are the air temperature, level of activity, lighting, and the time of day.
4. Control variables / concomitant / covariate or blocking variables
They are extraneous variables that are built into the study. Extraneous variables are variables, which influence the results of a study when they are not controlled.
Reasons for introducing control variables:
• It increases the validity of the data.
• It leads to more convincing generalizations.
Since absolute control of extraneous variables is not possible in any study, results are interpreted on the basis of degrees of confidence rather than certainty.
Once the major extraneous variables are identified, the researcher can control them by:-
i. Building the extraneous variable into the study: i.e. including it as an independent variable. E.g. in determining the effect of alcohol on reaction time, sex may influence reaction time. Therefore, sex can be introduced as an independent variable. Using regression, one can measure the effect of alcohol on reaction time, controlling sex.
ii. Include them in the study but only at one level e.g. time is the dependent variable, alcohol level - the independent and sex the extraneous variable. Sex can be controlled by sampling only females or males of a given age. The disadvantage of this method is that generalizations are limited to a smaller population.
iii. By removing the effects of the extraneous variables by statistical procedures i.e. by siphoning its effects on the dependent variable. This can be done by:
? Analysis of co-variance
? Partial correlation.
5. Intervening variables
They are a special case of extraneous variables. The difference between the intervening and extraneous variables is in the assumed relationship among the variables. An intervening variable is a hypothetical internal state that is used to explain relationships between observed variables, such as independent and dependent variables, in empirical research. With an extraneous variable, there is no causal link between the independent and dependent variable, but they are independently associated with a third variable – the extraneous variable. An intervening variable is recognized as being caused by the independent variable and as being a determinant of the dependent variable.
Independent intervening dependent
The total effect of an independent variable on a dependent variable can be subdivided into direct and indirect effects.
• Indirect effects are those effects of an intervening variable.
• Direct effects are not transmitted through another variable.
The choice of the right intervening variables helps one not only to determine accurately the total effects of an independent variable on the dependent variable but also partition the total effects into direct and indirect.
Examples of intervening variables include: motivation, intelligence, intention, and expectation.
6. Antecedent variables
They do not interfere with the established relationship between an independent and dependent variable but clarifies the influence that precedes such a relationship.
Antecedent independent dependent
Conditions that must hold for a variable to be classified as a antecedent variable:-
• The variables including the antecedent variable must be related in some logical sequence.
• When the antecedent variable is controlled for, the relationship between the independent and the dependent variables should not disappear. Rather it should be enhanced.
• When the independent variable is controlled for or its influence removed, there should not be any relationship between the antecedent variable and the dependent variable.
e.g. political stability – attracts investors – increased job opportunities – high standards of living – reduction of poverty.
7. Suppressor variables
It is an extraneous variable which when not controlled for, removes a relationship between the two variables. When a suppressor variable is introduced in the study as a control variable, a true relationship emerges.
8. Distorter variables
It is a variable that converts what was thought of as a positive relationship into a negative relationship and vice-versa. Its effects lead a researcher into drawing erroneous conclusions from the data. When the distorter variable is controlled, a true relationship is obtained. Consideration of distorter variables in a study reduces the chances of making a type I (rejecting a true null hypothesis) or type two error (accepting a false null hypothesis).
9. Exogenous and endogenous variables
They are commonly used in testing hypothesized causal models. Path analysis ( a procedure that tests causal links among several variables) is often used in testing the validity of causal relationships in a theory or model.
C and D are called endogenous variables. Each endogenous variable is caused or explained by the variable that precedes it. E.g. D is caused by A, B and C.
A and B are called exogenous variables. They lack hypothesized causes in the model.
Titany answered the question on
October 21, 2021 at 12:54