Lincoln, Yvonna, and Egon Guba, "Postpositivism and the Naturalist Paradigm," pp. 14-46 in Yvonna Lincoln and Egon Guba, Naturalistic Inquiry . Beverly Hills, CA: Sage, 1985.*

Principles and Practice of Structural Equation Modeling (2nd ed.)

The authors conducted a content analysis on new scale development articles appearing in the Journal of Counseling Psychology during 10 years (1995 to 2004). The authors analyze and discuss characteristics of the exploratory and confirmatory factor analysis procedures in these scale development studies with respect to sample characteristics, factorability, extraction methods, rotation methods, item deletion or retention, factor retention, and model fit indexes. The authors uncovered a variety of specific practices that were at variance with the current literature on factor analysis or structural equation modeling. They make recommendations for best practices in scale development research in counseling psychology using exploratory and confirmatory factor analysis.

Scale Development Research: A Content Analysis and Recommendations for Best Practices

This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.

Applications of Structural Equation Modeling in Psychological Research

A review of 13 years of research into antecedents of university students' grade point average (GPA) scores generated the following: a comprehensive, conceptual map of known correlates of tertiary GPA; assessment of the magnitude of average, weighted correlations with GPA; and tests of multivariate models of GPA correlates within and across research domains. A systematic search of PsycINFO and Web of Knowledge databases between 1997 and 2010 identified 7,167 English-language articles yielding 241 data sets, which reported on 50 conceptually distinct correlates of GPA, including 3 demographic factors and 5 traditional measures of cognitive capacity or prior academic performance. In addition, 42 non-intellective constructs were identified from 5 conceptually overlapping but distinct research domains: (a) personality traits, (b) motivational factors, (c) self-regulatory learning strategies, (d) students' approaches to learning, and (e) psychosocial contextual influences. We retrieved 1,105 independent correlations and analyzed data using hypothesis-driven, random-effects meta-analyses. Significant average, weighted correlations were found for 41 of 50 measures. Univariate analyses revealed that demographic and psychosocial contextual factors generated, at best, small correlations with GPA. Medium-sized correlations were observed for high school GPA, SAT, ACT, and A level scores. Three non-intellective constructs also showed medium-sized correlations with GPA: academic self-efficacy, grade goal, and effort regulation. A large correlation was observed for performance self-efficacy, which was the strongest correlate (of 50 measures) followed by high school GPA, ACT, and grade goal. Implications for future research, student assessment, and intervention design are discussed.

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Psychological correlates of university students' academic performance: a systematic review and meta-analysis

To complement recent articles in this journal on structural equation modeling (SEM) practice and principles by Martens and by Quintana and Maxwell, respectively, the authors offer a consumer’s guide to SEM. Using an example derived from theory and research on vocational psychology, the authors outline six steps in SEM: model specification, identification, data preparation and screening, estimation, evaluation of fit, and modification. In addition, the authors summarize the debates surrounding some aspects of SEM (e.g., acceptable sample size, fit indices), with recommendations for application. They also discuss the need for considering and testing alternative models and present an example, with details on determining whether alternative models result in a significant improvement in fit to the observed data.

A Brief Guide to Structural Equation Modeling

A growing body of literature supports the relationship between students’ self-efficacy beliefs for academic tasks and milestones and their academic performance. Not surprisingly, some researchers have investigated the role that academic self-efficacy beliefs play in predicting college success. Two incremental validity studies were conducted to determine the extent to which academic self-efficacy beliefs could account for variance in college outcomes beyond that accounted for by standardized test scores. Results suggest that academic self-efficacy beliefs predict college outcomes but that this relationship is dependent on when efficacy beliefs are measured, the types of efficacy beliefs measured, and the nature of the criteria used.

Academic Self-Efficacy as a Predictor of College Outcomes: Two Incremental Validity Studies:

Publisher Summary
This chapter provides an overview of the uses of cluster methods and describes the procedures involved in conducting cluster analyses. Cluster analysis is a term used to describe a family of statistical procedures specifically designed to discover classifications within complex data sets. The objective of cluster analysis is to group objects into clusters such that objects within one cluster share more in common with one another than they do with the objects of other clusters. Thus, the purpose of the analysis is to arrange objects into relatively homogeneous groups based on multivariate observations. Furthermore, the chapter discusses the uses for cluster analysis. Clustering methods are useful whenever the researcher is interested in grouping together objects based on multivariate similarity. Cluster analysis can be employed as a data exploration tool as well as a hypothesis testing and confirmation tool. The most frequent use of cluster analysis is in the development of a typology or classification system. However, cluster analysis is not a single standardized procedure, and there are pitfalls associated with its improper use, therefore, care is required in its application.

11 – Cluster Analysis

This study tested path models of academic interest and performance that were derived from social-cognitive theory. Two samples of high school students took part in the study: 151 geometry students and 145 advanced algebra students. Measures of objective math ability, perceived sources of efficacy information, outcome expectations, course-specific self-efficacy, interest in mathematics and science activities, and math course grades were obtained. Results were generally consistent with causal models in which (a) several types of direct and vicarious experience give rise to math self-efficacy percepts, (b) self-efficacy and outcome expectations predict subject matter interest, and (c) self-efficacy partially mediates the effect of ability on course grades. Implications for the further extension of social-cognitive theory to career and educational research and practice are discussed.

Paul A. Gore

Papers

A Brief Guide to Structural Equation Modeling

Academic Self-Efficacy as a Predictor of College Outcomes: Two Incremental Validity Studies:

11 – Cluster Analysis

Role of Social-Cognitive Expectations in High School Students' Mathematics-Related Interest and Performance.

Latent structure of the sources of mathematics self-efficacy.