Effect Sizes for Growth-Modeling Analysis for Controlled Clinical Trials in the Same Metric as for Classical Analysis

The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the dif...

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Bibliographic Details
Published inPsychological methods Vol. 14; no. 1; pp. 43 - 53
Main Author Feingold, Alan
Format Journal Article
LanguageEnglish
Published United States American Psychological Association 01.03.2009
Subjects
Change Scores
Control Groups
Controlled Clinical Trials as Topic - statistics & numerical data
Effect Size
Evaluation Methods
Humans
Linear Models
Measurement
Measurement Techniques
Meta-Analysis as Topic
Models, Statistical
Psychology, Social - statistics & numerical data
Psychometrics
Raw Scores
Reproducibility of Results
Research Design
Research Design - statistics & numerical data
Treatment Outcome
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ISSN1082-989X
1939-1463
DOI10.1037/a0014699

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Summary:The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the intervention and control groups that captures the treatment effect is rarely reported. This article first reviews 2 classes of formulas for effect sizes associated with classical repeated-measures designs that use the standard deviation of either change scores or raw scores for the denominator. It then broadens the scope to subsume GMA and demonstrates that the independent groups, within-subjects, pretest-posttest control-group, and GMA designs all estimate the same effect size when the standard deviation of raw scores is uniformly used. Finally, the article shows that the correct effect size for treatment efficacy in GMA--the difference between the estimated means of the 2 groups at end of study (determined from the coefficient for the slope difference and length of study) divided by the baseline standard deviation--is not reported in clinical trials. (Contains 2 tables and 2 footnotes.)
ISSN:1082-989X
1939-1463
DOI:10.1037/a0014699