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At least 1,117 records · Page 62Linked to original sources
PubMed2004

Dependent effect sizes in meta-analysis: incorporating the degree of interdependence.

In the present article, a commonly used meta-analytic procedure for handling dependent effect sizes from a single sample was examined, and 2 revised procedures that estimate and incorporate the degree of interdependence were proposed. The authors' simulation results reveal that the commonly used procedure that averages the effect sizes from a single sample (denoted as the samplewise procedure) underestimates the degree of heterogeneity. The proposed variations are less biased than the samplewise procedure in estimating the degree of heterogeneity in most of the situations that we examined. Future directions to further improve the procedures for handling dependent effect sizes from a single sample are discussed.

Behavioral Research↗
PubMed1998

The presentation of statistics.

This article outlines the statistical requirements that authors should fulfill when submitting manuscripts to Pediatric Allergy and Immunology. The requirements are based on the 'Uniform Requirements for Manuscripts Submitted to Biomedical Journals' and the CONSORT statement. Common statistical flaws that routinely arise in the medical literature are described. The use of +/-, confidence intervals and P values, correlation and regression, multiple testing and repeated measures are discussed.

Data Interpretation, Statistical↗
PubMed2004

Statistical presentation and analysis of ordinal data in nursing research.

OBJECTIVES: The aim of this study was to review the presentation and analysis of ordinal data in three international nursing journals in 2003. METHOD: In total, 166 full-length articles from the 2003 editions of Cancer Nursing, Scandinavian Journal of Caring Sciences and Nursing Research were reviewed for their use of ordinal data. RESULTS: This review showed that ordinal scales were used in about a third of the articles. However, only about half of the articles that used ordinal data had appropriate data presentation and only about half of the analyses of the ordinal data were performed properly. CONCLUSIONS: Ordinal data are rather common in nursing research, but a large share of the studies do not present/analyse the result properly. Incorrect presentation and analysis of the data may lead to bias and reduced ability to detect statistical differences or effects, resulting in misleading information. This highlights the importance of knowledge about data level, and underlying assumptions for the statistical tests must be considered to ensure correct presentation and analyses of data.

Bibliometrics↗
PubMed2005

Designing, testing, and interpreting interactions and moderator effects in family research.

This article is a primer on issues in designing, testing, and interpreting interaction or moderator effects in research on family psychology. The first section focuses on procedures for testing and interpreting simple effects and interactions, as well as common errors in testing moderators (e.g., testing differences among subgroup correlations, omitting components of products, and using median splits). The second section, devoted to difficulties in detecting interactions, covers such topics as statistical power, measurement error, distribution of variables, and mathematical constraints of ordinal interactions. The third section, devoted to design issues, focuses on recommendations such as including reliable measures, enhancing statistical power, and oversampling extreme scores. The topics covered should aid understanding of existing moderator research as well as improve future research on interaction effects.

Behavioral Research↗
PubMed2004

The independent statistician for data monitoring committees.

Clinical trials are an essential part of the clinical research process. Recently, independent Data Monitoring Committees (DMCs) have been widely implemented to provide scientific and ethical oversight of pivotal clinical trials having irreversible outcomes such as death, stroke, disease recurrence or a serious adverse event. To carry out their responsibility, the DMC reviews interim analyses of accumulating data. We address the motivation for having the preparation and presentation of these interim analyses be conducted by an independent statistician who is not a member of the DMC and who is not the trial's lead or steering committee statistician. These views are based on having served as members of many DMCs as well as having been the independent statistician for several trials.

Bias↗