Because quantitative methodology requires measurement, the concepts being investigated need to be defined in a way that can be measured. Organizational change, reading comprehension, emergency response, or depression are concepts, but they cannot be measured as such. Frequency of organizational change, reading comprehension scores, emergency response time, or types of depression can be measured. They are variables (concepts that can vary).
Quantitative research involves many kinds of variables. There are four main types discussed in further detail below:
Independent variables (IV) are those that are suspected of being the cause in a causal relationship. If you are asking a cause and effect question, your IV will be the variable (or variables) that you suspect causes the effect.
There are two main sorts of IV—active independent variables and attribute independent variables:
Independent variables are frequently called different things depending on the nature of the research question. In predictive questions, where a variable is thought to predict another but it is not yet appropriate to ask whether it causes the other, the IV is usually called a predictor or criterion variable rather than an independent variable.
Dependent variables are those that are influenced by the independent variables. If you ask, Does A cause [or predict or influence or affect, and so on] B? Then B is the dependent variable (DV).
In questions where full clausation is not assumed, such as a predictive question or a question about differences between groups but no manipulation of an IV, the dependent variables are usually called outcome variables, and the independent variables are usually called the predictor or criterion variables.
In some studies, some characteristic of the participants must be measured for some reason, but that characteristic is not the IV or the DV. In this case, these are called sample variables. For example, suppose you are investigating whether amount of sleep affects level of concentration in depressed people. In order to obtain a sample of depressed people, a standard test of depression will be given. So the presence or absence of depression will be a sample variable. That score is not used as an IV or a DV, but simply to get the appropriate people into the sample.
When there is no measure of a characteristic of the participants, the characteristic is called a sample characteristic. When the characteristic must be measured, it is called a sample variable.
Extraneous variables are not of interest to the study, but may influence the dependent variable. For this reason, most quantitative studies attempt to control extraneous variables. The literature should inform you what extraneous variables to account for. For example, in the study of third graders' reading scores, variables such as noise levels in the testing room, the size or lighting or temperature of the room, and whether the children had had a good breakfast might be extraneous variables.
There is a special class of extraneous variables called confounding variables. These are variables that can cause the effect we are looking for if they are not controlled for, resulting in a false finding that the IV is effective when it is not. In a study of changes in skill levels in a group of caseworkers after a training program, if the follow-up measure is taken relatively late after the training, the simple effect of practicing the skills might explain improved scores, and the training might be mistakenly thought to be successful when it was not.
There are many details about variables not covered in this handout. Please consult any text on research methods for a more comprehensive review.
Doc. reference: phd_t2_coun_u04s2_h02_quantvar.html