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.

/pdf/structural-equation-modeling-in-practice-a-review-and-1ue2omj492.pdf

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

T he measurement of stability and growth in earnings per share, or EPS, has been an important concern of researchers and practitioners of financial analysis for years The generally accepted measures of EPS stability and growth are derived from the loglinear regression model Logarithms cannot be used, however, if the EPS series has a negative or zero value In this paper, we introduce measures of EPS stability and growth derived from the simple OLS linear regression model for raw-score data The presence of negative or zero EPS values leaves these measures unaffected Our new measures will be compared to the log-linear regression measures using 10 years of annual EPS data, from the period 1970-1980, collected for each of the Dow Jones 30 industrial companies' Our choice of 10 years of data is consistent with the usual time frame employed in this type of stability and growth analysis The EPS data have been adjusted for stock dividends and splits

Measuring stability and growth in annual EPS

Three models of a three-nation arms race are presented: an all-against-all model, a model in which two nations are allied against the third, and a model in which one of the nations has a secret alliance with the other two. In the all-against-all situation, the model predicts an infinitely explosive arms race as each country fears an alliance of the other two. In the two-sided alliance, the model predicts that there will be an mitial increase until the weaker side approaches parity, after which there will be a mutual deescalation. If one nation signs secret treaties with each of the others, then it immediately deescalates. The other two nations then enter a two-sided arms race in which there will be an initial increase until the weaker nation reaches parity followed by mutual deescalation. These models suggest that two-way arms races would not infinitely escalate if it were not for the fear of third party alliances.

Mathematical Models of a Three-Nation Arms Race

Dustbathing as a regulatory mechanism

An Unequal Speaking Linear Discrepancy Model: Implications for Polarity Shift

Many purposes can be served by using multiple dependent variables in a study, and the application of path analysis to multiple measures can increase both conceptual and statistical power.

John E. Hunter

Papers

Measuring stability and growth in annual EPS

Mathematical Models of a Three-Nation Arms Race

Dustbathing as a regulatory mechanism

An Unequal Speaking Linear Discrepancy Model: Implications for Polarity Shift

Multiple Dependent Variables in Program Evaluation.