Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability.

Lubinski, David; Humphreys, Lloyd G.

doi:10.1037/0033-2909.107.3.385

Cited by 331 publications

(313 citation statements)

References 39 publications

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“…These misleading circumstances involve quadratic or multiplicative effects of solely genetic or solely environmental influences, but do not contain GxM. The issue of spurious moderator effects arising in the presence of untested quadratic effects has been discussed for structural equation models in general (MacCallum and Mar, 1995;Lubinski and Humphreys, 1990).…”

Section: Discussionmentioning

confidence: 99%

Specification, Testing, and Interpretation of Gene-by-Measured-Environment Interaction Models in the Presence of Gene–Environment Correlation

et al. 2008

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Purcell (2002) proposed a bivariate biometric model for testing and quantifying the interaction between latent genetic influences and measured environments in the presence of gene-environment correlation. Purcell's model extends the Cholesky model to include gene-environment interaction. We examine a number of closely-related alternative models that do not involve gene-environment interaction but which may fit the data as well Purcell's model. Because failure to consider these alternatives could lead to spurious detection of gene-environment interaction, we propose alternative models for testing gene-environment interaction in the presence of gene-environment correlation, including one based on the correlated factors model. In addition, we note mathematical errors in the calculation of effect size via variance components in Purcell's model. We propose a statistical method for deriving and interpreting variance decompositions that are true to the fitted model.

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Section: Discussionmentioning

confidence: 99%

Specification, Testing, and Interpretation of Gene-by-Measured-Environment Interaction Models in the Presence of Gene–Environment Correlation

et al. 2008

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“…Namely, a significant contrast could be produced by an additive effect of either NA or SI alone; two additive first order effects without any trace of an interaction. We add that a spurious interaction effect due to either an overlap in unmeasured nonlinear terms [33,34], or, as we have noted, the benefits of combining two imperfectly reliable measures of negative affect rather than relying on one imperfectly reliable measure.…”

Section: Rescuing Type D Personality As An Interaction Term In a Regrmentioning

confidence: 99%

Are we witnessing the decline effect in the Type D personality literature? What can be learned?

Coyne¹,

Voogd²

2012

Journal of Psychosomatic Research

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a b s t r a c t a r t i c l e i n f oAfter an unbroken series of positive, but underpowered studies seemed to demonstrate Type D personality predicting mortality in cardiovascular disease patients, initial claims now appear at least exaggerated and probably false. Larger studies with consistently null findings are accumulating. Conceptual, methodological, and statistical issues can be raised concerning the construction of Type D personality as a categorical variable, whether Type D is sufficiently distinct from other negative affect variables, and if it could be plausibly assumed to predict mortality independent of depressive symptoms and known biomedical factors, including disease severity. The existing literature concerning negative affect and health suggests a low likelihood of discovering a new negative affect variable that independently predicts mortality better than its many rivals. The apparent decline effect in the Type D literature is discussed in terms of the need to reduce the persistence of false positive findings in the psychosomatic medicine literature, even while preserving a context allowing risk-taking and discovery. Recommendations include greater transparency concerning research design and analytic strategy; insistence on replication with larger samples before accepting "discoveries" from small samples; reduced confirmatory bias; and availability of all relevant data. Such changes would take time to implement, face practical difficulties, and run counter to established practices. An interim solution is for readers to maintain a sense of pre-discovery probabilities, to be sensitized to the pervasiveness of the decline effect, and to be skeptical of claims based on findings reaching significance in small-scale studies that have not been independently replicated.© 2012 Elsevier Inc. All rights reserved. IntroductionMany "discoveries" turn out to be exaggerated or simply false across diverse research areas and disciplines. John Ioannidis [1] provoked concern with his demonstration that of the 49 most cited clinical research studies in major journals, replications of 34 had been attempted, and 41% of key findings had been refuted or shown to be substantially diminished. In a subsequent paper [2] he offered evidence that "most claimed research findings are false" and he and others [3-6] have since identified some mechanisms by which discoveries appear in the literature and then undergo a decline or refutation. Claims of breakthrough discoveries frequently arise in small positive studies that would seem to be too underpowered to detect an effect, and the inadequate sample size makes findings all the more attention grabbing.Yet, portrayals in the literature of such "discoveries" ignore the larger context of a strong confirmatory bias in published research papers, with unknown numbers of negative findings not being published and findings being declared discoveries simply because of having achieved an arbitrary level of significance. Moreover, statistically significant findings in small studies are nece...

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“…The main and interactive effects of positive affect and perceived racism on depressive symptoms were assessed using hierarchical multiple regression procedures outlined by Cohen, West, and Aiken (2003) and Lubinski and Humphreys (1990). Scales were standardized before forming cross-product terms and before running the regression to reduce possible multicollinearity (Dunlap & Kemery, 1987;Jaccard, Wan, & Turrisi, 1990).…”

Section: Main and Interactive Effectsmentioning

confidence: 99%

“…Scales were standardized before forming cross-product terms and before running the regression to reduce possible multicollinearity (Dunlap & Kemery, 1987;Jaccard, Wan, & Turrisi, 1990). Lubinski and Humphreys (1990) outlined procedures for the detection of spurious moderator effects. They argued that moderator effects and qua- dratic trends are likely to share a large proportion of the variance.…”

Section: Main and Interactive Effectsmentioning

confidence: 99%

Positive Affect and Adjustment to Perceived Racism

Ong¹,

Edwards²

2008

Journal of Social and Clinical Psychology

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Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability.

Cited by 331 publications

References 39 publications

Specification, Testing, and Interpretation of Gene-by-Measured-Environment Interaction Models in the Presence of Gene–Environment Correlation

Specification, Testing, and Interpretation of Gene-by-Measured-Environment Interaction Models in the Presence of Gene–Environment Correlation

Are we witnessing the decline effect in the Type D personality literature? What can be learned?

Positive Affect and Adjustment to Perceived Racism

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