Two-condition within-participant statistical mediation analysis: A path-analytic framework.

By A. Montoya, Andrew F. Hayes. Psychological methods, 2017

864 citations recorded by the source corpus.

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of 2 different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this article we recast Judd et al.'s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path-analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al.'s method requires, because it relies only on an inference about the product of paths-the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path-analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses. (PsycINFO Database Record

Subfields: Applied Psychology, Computer Networks and Communications, Social Psychology

View Paper: https://www.semanticscholar.org/paper/5f28a8a266f33a544ffec3e625623e906b21e1af

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Citations in the corpus

‘Show me the goods’: Assessing the effectiveness of transparent packaging vs. product imagery on product evaluation (2018, methodology): “Two-condition, 335 within-participant mediation analyses were conducted using the MEMORE SPSS 336 macro (see Montoya & Hayes, 2016).”
Employee adaptive performance and job satisfaction during organizational crisis: the role of self-leadership (2018, methodology): “Additionally, MEMORE (Montoya & Hayes, 2017) uses a percentile-based bootstrap estimation as a default, which is less prone to inflation effects when compared to bias-corrected bootstrap estimations.”
Tourists’ Destination Loyalty through Emotional Solidarity with Residents: An Integrative Moderated Mediation Model (2018, methodology): “Montoya and Hayes (2016, 21) claim that “bootstrapping is a computationally-intensive procedure that involves sampling of the rows of the data with replacement to build a new sample of size n from the original sample.””
Fifty shades of food: The influence of package color saturation on health and taste in consumer judgments (2020, methodology): “We conducted all analyses with SPSS and R (R Core Team, 2016) statistics software, specifically the SPSS macro mediation and moderation analysis for repeated measures designs (MEMORE; Montoya & Hayes, 2017) and the R package rmcorr (Bakdash & Marusich, 2017).”