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Conditional Process Analysis in Two-Instance Repeated-Measures Designs

Montoya, Amanda Kay
http://rave.ohiolink.edu/etdc/view?acc_num=osu1530904232127584

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Psychology.
Conditional process models are commonly used in many areas of psychology research as well as research in other academic fields (e.g., marketing, communication, and education). Conditional process models combine mediation analysis and moderation analysis. Mediation analysis, sometimes called process analysis, investigates if an independent variable influences an outcome variable through a specific intermediary variable, sometimes called a mediator. Moderation analysis investigates if the relationship between two variables depends on another. Conditional process models are very popular because they allow us to better understand how the processes we are interested in might vary depending on characteristics of different individuals, situations, and other moderating variables. Methodological developments in conditional process analysis have primarily focused on the analysis of data collected using between-subjects experimental designs or cross-sectional designs. However, another very common design is the two-instance repeated-measures design. A two-instance repeated-measures design is one where each subject is measured twice; once in each of two instances. In the analysis discussed in this dissertation, the factor that differentiates the two repeated measurements is the independent variable of interest. Research on how to statistically test mediation, moderation, and conditional process models in these designs has been minimal. Judd, Kenny, and McClelland (2001) introduced a piecewise method for testing for mediation, reminiscent of the Baron and Kenny causal steps approach for between-participant designs. Montoya and Hayes (2017) took this piecewise approach and translated it to a path-analytic approach, allowing for a quantification of the indirect effect, more sophisticated methods of inference, and the extension to multiple mediator models. Moderation analysis in these designs has been described by Judd, McClelland, and Smith (1996), Judd et al. (2001), and Montoya (in press). However, the generalization to conditional process analysis, or moderated mediation, remains unknown. Describing this approach is the purpose of this dissertation. In this dissertation I propose a path analytic approach to assessing moderated mediation models for two-instance repeated-measures designs. In Chapter 1, I review the development of conditional process analysis in between-subjects designs. In Chapter 2, I review existing methods for mediation and moderation in two-instance repeated-measures designs. In Chapter 3, I provide a general framework for estimating conditional process models for two-instance repeated-measures designs with one moderator and one mediator. I describe how simplifications of this general model correspond to more commonly used conditional process models, such as first-stage conditional process models and second-stage conditional process models. In Chapter 4, I provide examples of the models described in Chapter 3 using data sets from a variety of areas of psychology. These examples show how to implement conditional process analysis and how to interpret the results of such an analysis. In Chapter 5, I discuss alternative methods for evaluating moderated mediation in two-instance repeated measures designs. I describe how the first-stage conditional process model in Chapter 3 is related to previous methods for testing mediation using a 2(within) X 2(between) design as described by MacKinnon (2008) and Valente and MacKinnon (2017). Two particularly popular alternative methodological approaches include multilevel models and structural equation modeling. I connect the regression based methods proposed in Chapter 3 to those described for 1-1-1 moderated mediation models. There is very little existing literature on assessing moderated mediation in repeated measures designs with structural equation models. Thus, I connect the proposed methods in this dissertation to existing approaches to mediation in repeated-measures designs with structural equation models: correlated residuals, latent-difference score models, latent-growth curve models, and cross-lag panel models. The final chapter provides a discussion of the general framework for approaching conditional process analysis in two-instance repeated-measures designs, including future directions as well as limitations of the design and analytic framework presented.
Andrew Hayes (Advisor)
Jolynn Pek (Committee Member)
Paul De Boeck (Committee Member)
163 p.
Applied Mathematics; Behavioral Sciences; Biostatistics; Experimental Psychology; Psychology; Quantitative Psychology; Statistics
experimental design; mediation analysis; conditional process analysis; moderated mediation; repeated-measures; regression; data analysis; within-subjects design; mediation; moderation

Recommended Citations

Citations

  • Montoya, A. K. (2018). Conditional Process Analysis in Two-Instance Repeated-Measures Designs [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1530904232127584

    APA Style (7th edition)

  • Montoya, Amanda. Conditional Process Analysis in Two-Instance Repeated-Measures Designs. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1530904232127584.

    MLA Style (8th edition)

  • Montoya, Amanda. "Conditional Process Analysis in Two-Instance Repeated-Measures Designs." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1530904232127584

    Chicago Manual of Style (17th edition)

osu1530904232127584
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© 2018, some rights reserved.Creative Commons License Conditional Process Analysis in Two-Instance Repeated-Measures Designs by Amanda Kay Montoya is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.