Electronic Supplementary Materials belonging to the paper entitled ‘Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling’
Code for: Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling
Author(s) / Creator(s)
de Jonge, Hannelies
Jak, Suzanne
Kan, Kees-Jan
Abstract / Description
Electronic Supplementary Material 1: R scripts for simulation study 1 (full mediation) and Electronic Supplementary Material 2: R scripts for simulation study 2 (partial mediation).
Code for: de Jonge, H., Jak, S., & Kan, K.-J. (2020). Dealing With Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling. Zeitschrift Für Psychologie, 228(1), 25–35. https://doi.org/10.1027/2151-2604/a000395.
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 (βMX = .16, βMX = .23, βMX = .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.
Keyword(s)
meta-analytic structural equation modeling artificially dichotomized variables point-biserial correlation biserial correlationPersistent Identifier
Date of first publication
2019-10
Publisher
PsychArchives
Is referenced by
Citation
De Jonge, H., Jak, S., & Kan, K.-J. (2019, October). Electronic Supplementary Materials belonging to the paper entitled ‘Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling.’ PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.2618
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ESM1_Rscripts_SIM1.RR script - 75.24KBMD5: 953710ab4c700322d3b81c8d89d4d5a2Description: R scripts for simulation study 1 (full mediation)
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ESM2_Rscripts_SIM2.RR script - 81.21KBMD5: 2eb74bef315709826921949168d45f56Description: R scripts for simulation study 2 (partial mediation)
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There are no other versions of this object.
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Author(s) / Creator(s)de Jonge, Hannelies
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Author(s) / Creator(s)Jak, Suzanne
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Author(s) / Creator(s)Kan, Kees-Jan
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PsychArchives acquisition timestamp2019-10-11T11:53:05Z
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Made available on2019-10-11T11:53:05Z
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Date of first publication2019-10
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Abstract / DescriptionElectronic Supplementary Material 1: R scripts for simulation study 1 (full mediation) and Electronic Supplementary Material 2: R scripts for simulation study 2 (partial mediation).en
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Abstract / DescriptionCode for: de Jonge, H., Jak, S., & Kan, K.-J. (2020). Dealing With Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling. Zeitschrift Für Psychologie, 228(1), 25–35. https://doi.org/10.1027/2151-2604/a000395.en
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Abstract / DescriptionMeta-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 (βMX = .16, βMX = .23, βMX = .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.en
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Publication statusacceptedVersionen
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Review statuspeerRevieweden
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SponsorshipSuzanne Jak was supported by the Netherlands Organization for Scientific Research (NWO) (NWO-VENI-451-16-001)en
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CitationDe Jonge, H., Jak, S., & Kan, K.-J. (2019, October). Electronic Supplementary Materials belonging to the paper entitled ‘Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling.’ PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.2618en
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/2238
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.2618
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Language of contenteng
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PublisherPsychArchivesen
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Is referenced byhttps://doi.org/10.1027/2151-2604/a000395
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Is related tohttps://doi.org/10.23668/psycharchives.2617
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Is related tohttps://doi.org/10.1027/2151-2604/a000395
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Keyword(s)meta-analytic structural equation modelingen
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Keyword(s)artificially dichotomized variablesen
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Keyword(s)point-biserial correlationen
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Keyword(s)biserial correlationen
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Dewey Decimal Classification number(s)150
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TitleElectronic Supplementary Materials belonging to the paper entitled ‘Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling’en
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Alternative titleCode for: Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modelingen
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DRO typecodeen