In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.

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Structural equation modeling in practice: a review and recommended two-step approach

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.

Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.

The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

/pdf/the-prisma-statement-for-reporting-systematic-reviews-and-3j53e5oe33.pdf

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Part One: The Context Of Educational Research Part Two: Planning Educational Research Part Three: Styles Of Educational Research Part Four: Strategies And Instruments For Data Collection And Researching Part Five: Data Analysis

Research Methods in Education

During the past 30 years, meta-analysis has been an indispensable tool for revealing the hidden meaning of our research literatures. The four articles in this special section on meta-analysis illus...

Meta-Analysis: A Constantly Evolving Research Integration Tool

Systematic reviews and meta-analyses are essential to summarise evidence relating to efficacy and safety of healthcare interventions accurately and reliably. The clarity and transparency of these reports, however, are not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (quality of reporting of meta-analysis) statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realising these issues, an international group that included experienced authors and methodologists developed PRISMA (preferred reporting items for systematic reviews and meta-analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this explanation and elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA statement, this document, and the associated website (www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

/pdf/the-prisma-statement-for-reporting-systematic-reviews-and-48ibbzju0k.pdf

The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration

For many years, policymakers expressed increasing frustration with social science research. On every issue there were studies arguing for diametrically opposed conclusions. Methods of meta-analysis that correct for the effects of sampling error have shown that almost all such conflicting results were caused by sampling error. Furthermore, the effects of sampling error are greatly exaggerated by using significance test methodology. In many areas, meta-analysis has now provided dependable answers to the original research questions. Meta-analysis is now increasingly being used by policymakers, by textbook writers, and by theorists to provide the basic facts needed to draw both practical and explanatory conclusions. Sophisticated meta-analysis procedures are now used to correct for the effects of other study imperfections, such as measurement error, range restriction, and artificial dichotomization. In domains where the data on artifacts are available, the effect sizes in necessarily imperfect studies have been found to be considerably understated. Path analysis can be applied to the findings from meta-analysis to yield improved causal analyses that result in both explanation of results and improved generalization of results to new settings.

CUMULATIVE RESEARCH KNOWLEDGE AND SOCIAL POLICY FORMULATION: The Critical Role of Meta-Analysis

Validity generalization results for two job groups in the petroleum industry.

The thesis of this paper is that many proposed moderators in personnel psychology are probably illusory, having been created solely by belief in the law of small numbers. Evidence is presented that race as a moderator of test validity is one such illusory moderator. In addition, a model for validity generalization is described which, in addition to eliminating the need for criterion-related validity studies under certain circumstances, strongly calls into question the idea that situations moderate test validity, i.e., the traditional doctrine of situational specificity of test validities. Calculations are presented which show that adequate statistical power in moderator research requires much larger sample sizes than have typically been employed. This requirement is illustrated empirically using validity data for the Army Classification Battery for 35 jobs and 21,000 individuals. These analyses show that (1) even when a moderator is generally assumed to be large, large samples are required to gauge its effect reliably and (2) large sample research may show that moderators that appear plausible and important a priori are nonexistent or trivial in magnitude. The practice of pooling across numerous small sample studies to obtain statistical power equivalent to that of large sample studies is recommended. In light of the evidence that many proposed moderators may not exist, the authors hypothesize that the true structure of underlying relationships in personnel psychology is considerably simpler than personnel psychologists have generally imagined it to be.

Moderator Research and the Law of Small Numbers.

The study tested six axioms concerning the effect of language intensity on receiver attitudes. The axioms specify source and receiver variables which might moderate the relationship between intensity and attitude change. The results were consistent with an information processing model based on message discrepancy, expectancy and elaboration likelihood theories. Intensity affected attitudes through three routes. First, intensity increased attitude change via message clarity, with clarity acting as a mediator variable. Second, intensity inter acted with discrepancy and perceived source likeability to produce attitude change. Third, the effect of intensity on attitudes was moderated by source expectations. Results showed that intensity enhances persuasiveness for a high credibility source, inhibits persuasiveness for a low credibility source, and has no effect for a moderate credibility source. Receiver anxiety moderated the effect of source gender expectations on the intensity-attitude relationship. A revis...

John E. Hunter

Papers

CUMULATIVE RESEARCH KNOWLEDGE AND SOCIAL POLICY FORMULATION: The Critical Role of Meta-Analysis

Validity generalization results for two job groups in the petroleum industry.

Moderator Research and the Law of Small Numbers.

An Empirical Test of an Axiomatic Model of the Relationship Between Language Intensity and Persuasion

Racial and ethnic bias in psychological tests: Divergent implications of two definitions of test bias.