Exploratory factor analysis (EFA) methods are used extensively in the field of assessment and evaluation. Due to EFA's widespread use, common methods and practices have come under close scrutiny. A substantial body of literature has been compiled highlighting problems with many of the methods and practices used in EFA, and, in response, many…

Multiple-group confirmatory factor analysis (MG-CFA) is among the most productive extensions of structural equation modeling. Many researchers conducting cross-cultural or longitudinal studies are interested in testing for measurement and structural invariance. The aim of the present paper is to provide a tutorial in MG-CFA using the freely…

Propensity score matching is a statistical technique in which a treatment case is matched with one or more control cases based on each case's propensity score. This matching can help strengthen causal arguments in quasi-experimental and observational studies by reducing selection bias. In this article we concentrate on how to conduct…

"R" (R Development Core Team, 2011) is a very powerful tool to analyze data, that is gaining in popularity due to its costs (its free) and flexibility (its open-source). This article gives a general introduction to using "R" (i.e., loading the program, using functions, importing data). Then, using data from Canivez, Konold, Collins, and Wilson…

Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as under- or over-extraction may lead to erroneous conclusions. Although recent advancements have been…

This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…

Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen's book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the…

While linear programming is a common tool in business and industry, there have not been many applications in educational assessment and only a handful of individuals have been actively involved in conducting psychometric research in this area. Perhaps this is due, at least in part, to the complexity of existing software packages. This article…

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…

Introduces the problem of hierarchical, or nested, data structures and discusses how this problem is dealt with effectively. Provides examples of the pitfalls of not doing appropriate analyses. New software is making hierarchical linear modeling more user-friendly and accessible. (SLD)

Provides an overview of some practical considerations in evaluating high-end record-keeping software products. Emphasis is on the evaluation process and the identification of value-based evaluative criteria. (SLD)