Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the…

A multiple testing method for examining factorial invariance for latent constructs evaluated by multiple indicators in distinct populations is outlined. The procedure is based on the false discovery rate concept and multiple individual restriction tests and resolves general limitations of a popular factorial invariance testing approach. The…

Following an exploratory factor analysis, factor scores may be computed and used in subsequent analyses. Factor scores are composite variables which provide information about an individual's placement on the factor(s). This article discusses popular methods to create factor scores under two different classes: refined and non-refined. Strengths and…

Studies that examine the impacts of education interventions on key student, teacher, and school outcomes typically collect data on large samples and on many outcomes. In analyzing these data, researchers typically conduct multiple hypothesis tests to address key impact evaluation questions. Tests are conducted to assess intervention effects for…

Discusses two criticisms raised by L. Hayduk and D. Glaser of the most commonly used point estimate of the Root Mean Square Error (RMSEA) and points out misconceptions in their discussion. Although there are apparent flaws in their arguments, the RMSEA is open to question for several other reasons. (SLD)

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) have contributed to test development and validation in counseling psychology, but additional applications have not been fully realized. The author presents an overview of the goals, terminology, and procedures of factor analysis; reviews best practices for extracting,…

Structural equation modeling (SEM) refers to a family of statistical techniques that explores the relationships among a set of variables. Structural equation modeling provides an extremely versatile method to model very specific hypotheses involving systems of variables, both measured and unmeasured. Researchers can use SEM to study patterns of…

Provides an exercise for introducing research methods to undergraduates. The students view a graph revealing that left-handed people are underrepresented in older age groups. Small group discussions attempt to explain this phenomenon. A follow-up class discussion focuses on the different approaches and methods available for interpreting the data.…