Types of Variables in Research & Statistics for Thesis, Dissertation, Articles and Grants

There are different types of variables in research and statistics. Generally, variables are concepts and constructs that are studied in research and statistics, and are measured for the purpose of data collection, analysis and interpretation in order to draw inferences about a study. Hence, in research - projects, theses, dissertations, manuscripts/articles, and grants, variables are unavoidable. Although variables are concepts and constructs, no research study without variables.
the different types of variables include dependent variables, independent variables, moderator variables, mediator or intervening variables, extraneous variables, confounding variables, control variables, categorical variables, dummy variables, binary variables, dichotomous variables, and polychotomous variables. In broad forms, variables may be said to be qualitative and quantitative. Qualitative variables include nominal variables and ordinal variables; while quantitative variables include discrete variables and continuous variables. Examples of variables are gender, sex, marital status, job performance, academic engagement, commitment, performance, stress, electricity, marital status, academic achievement,
Research titles, questions and hypotheses usually have variables in them. Hence, variables are measured and tested with statistical tools such as student t-test, analysis of variance (ANOVA), analysis of covariance (ANCOVA), correlations, regressions, path analysis, structural equation modeling (SEM). In SEM, there are latent and observed variables as well as endogenous and exogenous variables.
Variables are the center of different types of research designs - experimental, survey, correlational, case study, historical, causal comparative or expos factor. In measurement instruments such as questionnaires, rating scales, checklist, anecdotal records, interview, variables form the items and are therefore measured. The different types of variables are applicable in different types of research designs; however, some types may be more applied to a particular design. For instance, confounding and extraneous variables are more applied to experimental designs.
Additionally, a good understanding of the types variables helps to design testable conceptual models or frameworks as well as structural models.

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