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Jeffrey A Gliner

Publications and source records attributed to Jeffrey A Gliner.

9 recordsLinked to original sources
PubMed2002

General design classifications.

This column described the general design classifications of between-groups, within-subjects, and mixed designs. Remember that in between-groups designs, each participant is in only one group or condition. In within-subjects or repeated-measures designs, on the other hand, each participant receives all the conditions or levels of the independent variable. In mixed designs, there is at least one between-groups independent variable and at least one within-subjects independent variable. In classifying the design, do not consider the dependent variable(s). The classifications and descriptions presented in this column are for difference questions, using the randomized experimental, quasi-experimental, and comparative approaches to research. Appropriate classification and description of the design are crucial for choosing the appropriate inferential statistic, which is the topic of the next column and several to follow.

Adolescent↗
PubMed2002

Selection of inferential statistics: an overview.

This column serves as an introduction to selection of appropriate statistical methods. In the next five columns we will discuss conceptually, and in more depth, these statistical methods. We will use clinical examples and discuss why the author(s) selected a particular statistical method and how the results of the statistical method were interpreted.

Humans↗
PubMed2002

Single-factor repeated-measures designs: analysis and interpretation.

In this column we discussed the selection and interpretation of appropriate statistical tests for single-factor within-subjects/ repeated-measures designs and provided an example from the literature. The parametric tests that we discussed were the t test for paired or correlated samples and the single-factor repeated-measures ANOVA. We also mentioned four nonparametric tests to be used in single-factor within-subjects/repeated-measures designs, but they are relatively rare in the literature. The Compton et al. (2001) article did not provide effect size measures, but they could be computed from the means and standard deviations. Remember that a statistically significant t or ANOVA (even ifp < .001) does not mean that there was a large effect, especially if the sample was large. In the Compton example, the sample was quite small (N = 14), and the findings do reflect a large effect size.

Adolescent↗