2017
DOI: 10.3758/s13428-017-0979-2
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A single-level random-effects cross-lagged panel model for longitudinal mediation analysis

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Cited by 31 publications
(29 citation statements)
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“…Cross-lagged panel models with longitudinal data provide a way to test the reciprocal relationship between life satisfaction and perceived social justice change over time. It allows for inferences of causality by comparing the relative effect of the two variables on each other across time (Wu et al, 2018), and reduces the probable parameter bias that cross-sectional data arises through the inclusion of the effect of the dependent variable at the previous time (Selig and Preacher, 2009).…”
Section: Within-person Effect and Between-person Effectmentioning
confidence: 99%
“…Cross-lagged panel models with longitudinal data provide a way to test the reciprocal relationship between life satisfaction and perceived social justice change over time. It allows for inferences of causality by comparing the relative effect of the two variables on each other across time (Wu et al, 2018), and reduces the probable parameter bias that cross-sectional data arises through the inclusion of the effect of the dependent variable at the previous time (Selig and Preacher, 2009).…”
Section: Within-person Effect and Between-person Effectmentioning
confidence: 99%
“…Antecedents of the work–caregiving interface have previously been reported to have small-to-medium effect sizes (e.g., Boumans & Dorant, 2014 ; Fujihara et al, 2019 ). Therefore, a sample size of approximately 395 participants for cross-sectional analysis and at least 200 participants for longitudinal analysis ( Wu et al, 2018 ) was considered to provide adequate power.…”
Section: Methodsmentioning
confidence: 99%
“…The longitudinal mediational effect of enrichment was examined using significant indicators from the cross-sectional SEM. A random-effects cross-lagged panel model (RE-CLPM; Wu et al, 2018 ) was used to examine four parameters: Wave 1 independent variable on Wave 2 mediational variable (“a1”); Wave 2 mediational variable on Wave 3 dependent variable (“b2”); direct effect of Wave 1 independent variable on Wave 3 dependent variable when the mediational variable effect was partialled out (“c”); and the product of the “a1” and “b2” path (“a1b2”), which represented the indirect effect of Wave 1 independent variable on Wave 3 dependent variable. Significance of “a1b2” indicated a longitudinal mediational effect.…”
Section: Methodsmentioning
confidence: 99%
“…Power was considered for these analyses based on a previous simulation of CLPMs by Wu and colleagues ( 29 ) demonstrating that a sample of 500 was sufficient to study mediation across three cross‐lagged waves of data, a power of 0.80, type I error robust to 0.025 to 0.075, and a ratio of exposure mediator to mediator‐outcome effects of 0.30, yielding Akaike's information criterion of over 0.80. Based on a sample size above 1200, it is anticipated that our analyses were adequately powered.…”
Section: Participants and Methodsmentioning
confidence: 99%