UNDERSTANDING ANALYSIS OF COVARIANCE (ANCOVA) In general, research is conducted for the purpose of explaining the effects of the independent variable on the dependent variable, and the purpose of research design is to provide a structure for the research. In the research design, the researcher identifies and controls independent variables that can help to explain the observed variation in the dependent variable, which in turn reduces error variance (unexplained variation). Since the research design is structured before the research begins, this method of control is called experimental control. Research design– the science (and art) of planning procedures for conducting studies so as to get the most valid findings. Called “design” for short. When designing a research study, one draws up a set of instructions for gathering evidence and for interpreting it. Experiments, quasi-experiments, double-blind procedures, and correlated groups design are examples of types of research design (Vogt, 1999). Control for– to subtract statistically the effects of a variable (a control variable) to see what a relationship would be without it (Vogt, 1999). Hold constant– to “subtract” the effects of a variable from a complex relationship so as to study what the relationship would be if the variable were in fact a constant. Holding a variable constant essentially means assigning it an average value (Vogt, 1999). In addition to controlling and explaining variation through research design, it is also possible to use statistical controlto explain variation in the dependent variable. Statistical control, used when experimental control is difficult, if not impossible, can be achieved by measuring one or more variables in addition to the independent variables of primary interest and by controlling the variation attributed to these variables through statistical analysis rather than through research design. The analysis procedure employed in this statistical control is analysis of covariance (ANCOVA). Statistical control– using statistical techniques to isolate or “subtract” variance in the dependent variable attributable to variables that are not the subject of the study (Vogt, 1999). Analysis of Covariance (ANCOVA)– an extension of ANOVA that provides a way of statistically controlling the (linear) effect of variables one does not want to examine in a study. These extraneous variables are called covariates, or control variables. (Covariates should be measured on an interval or ratio scale.) ANCOVA allows you to remove covariates from the list of possible explanations of variance in the dependent variable. ANCOVA does this by using statistical techniques (such as regression to partial out the effects of covariates) rather than direct experimental methods to control extraneous variables. ANCOVA is used in experimental studies when researchers want to remove the effects of some antecedent variable. For example, pretest scores are used as covariates in pretest-posttest experimental designs. ANCOVA is also used in non-experimental research, such as surveys or nonrandom samples, or in quasi-experiments when subjects cannot be assigned randomly to control and experimental groups.