Abstract
Ignoring a nested factor can influence the validity of statistical decisions about treatment effectiveness. Previous discussions have centered on consequences of ignoring nested factors versus treating them as random factors on Type I errors and measures of effect size (B. E. Wampold & R. C. Serlin, 2000). The authors (a) discuss circumstances under which the treatment of nested provider effects as fixed as opposed to random is appropriate; (b) present 2 formulas for the correct estimation of effect sizes when nested factors are fixed; (c) present the results of Monte Carlo simulations of the consequences of treating providers as fixed versus random on effect size estimates, Type I error rates, and power; and (d) discuss implications of mistaken considerations of provider effects for the study of differential treatment effects in psychotherapy research.
| Original language | English |
|---|---|
| Pages (from-to) | 497-517 |
| Number of pages | 21 |
| Journal | Psychological Methods |
| Volume | 8 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 1 2003 |
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ASJC Scopus subject areas
- Psychology(all)
Cite this
Power and Measures of Effect Size in Analysis of Variance with Fixed Versus Random Nested Factors. / Siemer, Matthias; Joormann, Jutta.
In: Psychological Methods, Vol. 8, No. 4, 01.12.2003, p. 497-517.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Power and Measures of Effect Size in Analysis of Variance with Fixed Versus Random Nested Factors
AU - Siemer, Matthias
AU - Joormann, Jutta
PY - 2003/12/1
Y1 - 2003/12/1
N2 - Ignoring a nested factor can influence the validity of statistical decisions about treatment effectiveness. Previous discussions have centered on consequences of ignoring nested factors versus treating them as random factors on Type I errors and measures of effect size (B. E. Wampold & R. C. Serlin, 2000). The authors (a) discuss circumstances under which the treatment of nested provider effects as fixed as opposed to random is appropriate; (b) present 2 formulas for the correct estimation of effect sizes when nested factors are fixed; (c) present the results of Monte Carlo simulations of the consequences of treating providers as fixed versus random on effect size estimates, Type I error rates, and power; and (d) discuss implications of mistaken considerations of provider effects for the study of differential treatment effects in psychotherapy research.
AB - Ignoring a nested factor can influence the validity of statistical decisions about treatment effectiveness. Previous discussions have centered on consequences of ignoring nested factors versus treating them as random factors on Type I errors and measures of effect size (B. E. Wampold & R. C. Serlin, 2000). The authors (a) discuss circumstances under which the treatment of nested provider effects as fixed as opposed to random is appropriate; (b) present 2 formulas for the correct estimation of effect sizes when nested factors are fixed; (c) present the results of Monte Carlo simulations of the consequences of treating providers as fixed versus random on effect size estimates, Type I error rates, and power; and (d) discuss implications of mistaken considerations of provider effects for the study of differential treatment effects in psychotherapy research.
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UR - http://www.scopus.com/inward/citedby.url?scp=0346199347&partnerID=8YFLogxK
U2 - 10.1037/1082-989X.8.4.497
DO - 10.1037/1082-989X.8.4.497
M3 - Article
VL - 8
SP - 497
EP - 517
JO - Psychological Methods
JF - Psychological Methods
SN - 1082-989X
IS - 4
ER -