Supplemental Material for The Problem of Effect Size Heterogeneity in Meta-Analytic Structural Equation Modeling

Abstract. Meta-analytic structural equation modeling (MASEM) is a relatively new method in which effect sizes of different independent studies between multiple variables are typically first pooled into a matrix and next analyzed using structural equation modeling. While its popularity is increasing, there are issues still to be resolved, such as how to deal with primary studies in which variables have been artificially dichotomized. To be able to advise researchers who apply MASEM and need to deal with this issue, we performed two simulation studies using random-effects two stage structural equation modeling. We simulated data according to a full and partial mediation model and systematically varied the size of one (standardized) path coefficient (β MX = .16, β MX = .23, β MX = .33), the percentage of dichotomization (25%, 75%, 100%), and the cut-off point of dichotomization (.5, .1). We analyzed the simulated datasets in two different ways, namely, by using (1) the point-biserial and (2) the biserial correlation as effect size between the artificially dichotomized predictor and continuous variables. The results of these simulation studies indicate that the biserial correlation is the most appropriate effect size to use, as it provides unbiased estimates of the path coefficients in the population.

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Incorporating Effect Size Variation into Meta-Analytic Structural Equation Modeling

Academy of Management Proceedings ◽

10.5465/ambpp.2015.269 ◽

2015 ◽

Vol 2015 (1) ◽

pp. 17695

Author(s):

Jia Yu ◽

Patrick E. Downes ◽

Kameron Carter ◽

Ernest H O'Boyle

Keyword(s):

Structural Equation Modeling ◽

Effect Size ◽

Structural Equation ◽

Size Variation ◽

Equation Modeling

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Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

Journal of Individual Differences ◽

10.1027/1614-0001/a000135 ◽

2014 ◽

Vol 35 (4) ◽

pp. 201-211 ◽

Cited By ~ 3

Author(s):

André Beauducel ◽

Anja Leue

Keyword(s):

Structural Equation Modeling ◽

Negative Correlation ◽

Structural Equation ◽

Error Score ◽

Classical Test Theory ◽

Test Theory ◽

Equation Modeling ◽

Eysenck Personality Questionnaire ◽

Personality Questionnaire ◽

True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

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The Measurement Invariance of the Short Dark Triad

Journal of Individual Differences ◽

10.1027/1614-0001/a000322 ◽

2020 ◽

Vol 41 (4) ◽

pp. 207-218

Author(s):

Mihaela Grigoraș ◽

Andreea Butucescu ◽

Amalia Miulescu ◽

Cristian Opariuc-Dan ◽

Dragoș Iliescu

Keyword(s):

Structural Equation Modeling ◽

Item Response ◽

Structural Equation ◽

Dark Triad ◽

Theory And Practice ◽

Equation Modeling ◽

Scale Level ◽

High Stake ◽

Differential Functioning ◽

Item Functioning

Abstract. Given the fact that most of the dark personality measures are developed based on data collected in low-stake settings, the present study addresses the appropriateness of their use in high-stake contexts. Specifically, we examined item- and scale-level differential functioning of the Short Dark Triad (SD3; Paulhus & Jones, 2011 ) measure across testing contexts. The Short Dark Triad was administered to applicant ( N = 457) and non-applicant ( N = 592) samples. Item- and scale-level invariances were tested using an Item Response Theory (IRT)-based approach and a Structural Equation Modeling (SEM) approach, respectively. Results show that more than half of the SD3 items were flagged for Differential Item Functioning (DIF), and Exploratory Structural Equation Modeling (ESEM) results supported configural, but not metric invariance. Implications for theory and practice are discussed.

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Childhood Intelligence Predicts Adult Trait Openness

Journal of Individual Differences ◽

10.1027/1614-0001/a000194 ◽

2016 ◽

Vol 37 (2) ◽

pp. 105-111 ◽

Cited By ~ 6

Author(s):

Adrian Furnham ◽

Helen Cheng

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Social Background ◽

Study Data ◽

Equation Modeling ◽

Direct Effects ◽

Data Set ◽

National Child Development Study ◽

Nationally Representative ◽

The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

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