Problems with the use of "moderator" variables.

Zedeck, Sheldon

doi:10.1037/h0031543

Cited by 534 publications

(290 citation statements)

References 53 publications

(67 reference statements)

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“…The potential for high probability of Type II error (and thus lower power) in testing for the existence of interaction effects is often recognized (Zedeck 1971;Morris et al 1986;Chronbach 1987;Champoux and Peters 1987;Cortina 1993;McClelland and Judd 1993). Such potential low power, however, is often attributed to multicollinearity, and some authors have offered solutions to this "problem" (Morris et al 1986;Cortina 1993).…”

Section: Remark 4 Multicollinearity Does Not Results In Low Power In mentioning

confidence: 99%

Key Results of Interaction Models with Centering

Afshartous

Preston

2011

Journal of Statistics Education

121

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We consider the effect on estimation of simultaneous variable centering and interaction effects in linear regression. We technically define, review, and amplify many of the statistical issues for interaction models with centering in order to create a useful and compact reference for teachers, students, and applied researchers. In addition, we investigate a sequence of models that have an interaction effect and/or variable centering and derive expressions for the change in the regression coefficients between models from both an intuitive and mathematical perspective. We demonstrate how these topics may be employed to motivate discussion of other important areas, e.g., misspecification bias, multicollinearity, design of experiments, and regression surfaces. This paper presents a number of results also given elsewhere but in a form that gives a unified view of the topic. The examples cited are from the area of medical statistics.1

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Section: Remark 4 Multicollinearity Does Not Results In Low Power In mentioning

confidence: 99%

Key Results of Interaction Models with Centering

Afshartous

Preston

2011

Journal of Statistics Education

121

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“…This problem has been recognized before (e.g., Humphreys & Fleishman, 1974;Zedeck, 1971) but is rarely considered in practice. When the possibility of a nonlinear relationship between x and y cannot be legitimately dismissed, EGA should not be used.…”

Section: Ega and Model Misspecificationmentioning

confidence: 99%

Use of the Extreme Groups Approach: A Critical Reexamination and New Recommendations.

Preacher¹,

Rucker²,

MacCallum³

et al. 2005

Psychological Methods

602

491

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Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized costs associated with EGA that must be considered. The authors illustrate the effects EGA can have on power, standardized effect size, reliability, model specification, and the interpretability of results. Finally, the authors discuss alternative procedures, as well as possible legitimate uses of EGA. The authors urge researchers, editors, reviewers, and consumers to carefully assess the extent to which EGA is an appropriate tool in their own research and in that of others.

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“…The most noteworthy effect was that they were larger than at the incumbent level (e.g., average r = .39 vs..19 in Table 4, and .46 vs..25 in Table 5). The (Champoux & Peters, 1980;Peters & Champoux, 1979;Stone & Hollenbeck, 1984;Zedeck, 1971). In this method, the job design scale and preferences/tolerances measure were added first to the equation to predict the outcome composite, then the incremental contribution of the interaction term was tested.…”

Section: Research Questionmentioning

confidence: 99%

Interdisciplinary approaches to job design: A constructive replication with extensions.

Campion¹

1988

Journal of Applied Psychology

203

259

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This study replicated Campion and Thayefs (1985) research, which drew from many disciplines (e.g., psychology, engineering, human factors, physiology) to demonstrate four approaches to job design and their corresponding outcomes: motivational approach with satisfaction outcomes, mechanistic approach with efficiency outcomes, biological approach with comfort outcomes, and perceptual/motor approach with reliability outcomes. This study extended the research in five ways. First, it used an expanded sample of 92 jobs and 1,024 respondents from a different industry. Second, a selfreport measure was developed and evaluated, because many jobs cannot be analyzed observationally. Third, method bias was addressed by not finding evidence of priming effects, by demonstrating strong relationships even when within-subject bias was avoided, and by relating job design to independent opinion survey data. Fourth, reliability of aggregate responses was demonstrated, and relationships at the job level of analysis were larger than at the individual level. Fifth, neither individual differences in terms of preferences/tolerances for types of work nor demographics moderated job design-outcome relationships. It was concluded that different approaches to job design influence different outcomes, each approach has costs as well as benefits, trade-offs may be needed, and both theory and practice must be interdisciplinary in perspective.

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Problems with the use of "moderator" variables.

Cited by 534 publications

References 53 publications

Key Results of Interaction Models with Centering

Key Results of Interaction Models with Centering

Use of the Extreme Groups Approach: A Critical Reexamination and New Recommendations.

Interdisciplinary approaches to job design: A constructive replication with extensions.

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