Concurrent validity refers to the extent to which items comprising a scale behave as if they are measuring the same variable. Correlation analysis was used in the study reported in the critiqued article to measure concurrent validity.

Variance is critiqued in two contexts. These contexts are error and method.

Error variance, also referred to as sampling error, reflects the influence of chance in the selection of sample members. In theory, error variance refers only to random fluctuations. A small standard error of the estimate would indicate that error variance was adequately accounted for in the research reported in the critiqued article; however, such information was not included in the article.

Method variance refers to the variation in measures due to influences, known or unknown, that cause scores to tend toward one direction rather than another. No method variance will be present in a sample that accurately reflects the population bing measured. While the critiqued article identifies samples involved in the reported research, no information is provided on the sample selection process; therefore, the effectiveness of the

addressing of method variance cannot be assessed.

A major difference between the analysis of variance procedure and regression analysis is that, in analysis of variance, the emphasis is on analysis of the variations in the independent variable, as opposed to the joint interaction of the variations in dependent and independent variables. One result of this difference in emphasis is that, in regression analysis, both the independent and the dependent variables must be measured on an interval scale, whereas, in analysis of variance procedures, only the dependent variable is required to be measured on an interval scale. Simple regression analysis involves the analysis of the relationship between one dependent variable and one explanatory, or independent, variable. Multiple regression, on the other hand, is a general statistical technique through which one can analyze the relationship between a dependent or criterion variable and a set of independent or predictor variables. Multiple regression may be viewed either as a descriptive tool by which the linear dependence of one variable on others is summarized and decomposed, or as an inferential tool by which the relationships in the population are evaluated from the examination of sample data. Regression analysis is used as a descriptive tool in three types of situations: to develop a self-weighting estimating equation by which to predict values for a dependent variable; to control for confounding variables so as to better evaluate the contribution of other variables; and to test explicit causal theories. In the research reported in the critiqued article, multiple regression analysis was used to de