Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques

Bauer, Daniel J.; Curran, Patrick J.

doi:10.1207/s15327906mbr4003_5

Cited by 1,200 publications

(960 citation statements)

References 31 publications

(31 reference statements)

Supporting

Mentioning

952

Contrasting

Unclassified

Order By: Relevance

“…Rather than choosing a limited number of arbitrary conditional values of W and investigating the significance of the simple slopes at those values, we can instead seek the values of W for which the simple slope of Y regressed on X is significant (Johnson & Neyman, 1936 (Bauer & Curran, 2005;Preacher, Curran, & Bauer, 2006;Rogosa, 1980Rogosa, , 1981. W moderates the relationship between X and Y for values of W where the confidence bands do not contain zero.…”

Section: Moderationmentioning

confidence: 99%

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions

Preacher

Rucker²,

Hayes

2007

Multivariate Behavioral Research

7,868

6,813

View full text Add to dashboard Cite

This article provides researchers with a guide to properly construe and conduct analyses of conditional indirect effects, commonly known as moderated mediation effects. We disentangle conflicting definitions of moderated mediation and describe approaches for estimating and testing a variety of hypotheses involving conditional indirect effects. We introduce standard errors for hypothesis testing and construction of confidence intervals in large samples but advocate that researchers use bootstrapping whenever possible. We also describe methods for probing significant conditional indirect effects by employing direct extensions of the simple slopes method and Johnson-Neyman technique for probing significant interactions. Finally, we provide an SPSS macro to facilitate the implementation of the recommended asymptotic and bootstrapping methods. We illustrate the application of these methods with an example drawn from the Michigan Study of Adolescent Life Transitions, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.

show abstract

Section: Moderationmentioning

confidence: 99%

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions

Preacher

Rucker²,

Hayes

2007

Multivariate Behavioral Research

7,868

6,813

View full text Add to dashboard Cite

show abstract

“…The problem of how to select values of the moderator without relying on arbitrary conventions or haphazard choices is solved through the use of the Johnson-Neyman (JN) technique (Johnson & Neyman, 1936;Bauer & Curran, 2005). The JN technique for probing an interaction is like the picka-point approach in reverse.…”

Section: The Johnson-neyman Techniquementioning

confidence: 99%

A Tutorial on Testing, Visualizing, and Probing an Interaction Involving a Multicategorical Variable in Linear Regression Analysis

Hayes

Montoya

2017

Communication Methods and Measures

317

198

View full text Add to dashboard Cite

“…We then describe the analytic model, followed by three examples of utilizing the model for the distinguishable case, with an example of the indistinguishable case in the Supplemental Materials. We assume the reader has a basic knowledge of multilevel modeling, dyadic data analysis, and moderation, and we recommend the following resources if this is not the case: Aiken & West, 1991;Bauer & Curran, 2005;Bolger & Laurenceau, 2013;Fitzmaurice, Laird, & Ware, 2011;Kenny et al, 2006.…”

Section: Stability and Influence Modelmentioning

confidence: 99%

Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies.

Thorson

West

Mendes

2018

Psychological Methods

View full text Add to dashboard Cite

Scholars across domains in psychology, physiology, and neuroscience have long been interested in the study of shared physiological experiences between people. Recent technological and analytic advances allow researchers to examine new questions about how shared physiological experiences come about. Yet, comprehensive guides that address the theoretical, methodological, and analytic components of studying these processes are lacking. The goal of this paper is to provide such a guide. We begin by addressing basic theoretical issues in the study of shared physiological states by presenting five guiding theoretical principles for making psychological inferences from physiological influence-the extent to which one dyad member's physiology predicts the other dyad member's physiology at a future time point. Second, keeping theoretical and conceptual concerns at the forefront, we outline considerations and recommendations for designing, implementing, and analyzing dyadic psychophysiological studies. In so doing, we discuss the different types of physiological measures one could use to address different theoretical questions. Third, we provide three illustrative examples in which we estimate physiological influence, using the stability and influence model. We conclude by providing detail about power analyses for the model and by comparing the strengths and limitations of this model to pre-existing models. Scholars have utilized physiological approaches to capture psychological experiences of individuals-including emotions, motivations, and attention-since the early 20 th Century (e.g., Cohen & Patterson, 1937;Darrow, 1929;Jacobson, 1930; Mittleman & Wolff, 1929). For example, early work by Carl Jung examined electrodermal activity as a measure of attention to different stimuli in healthy and clinical samples (Ricksher & Jung, 1908). Beginning in the 1950s, social scientists started to collect data from two or more people in interpersonal interactions to measure interdependence between their physiological states. Early work focused on how similarity between patients' and therapists' heart rates mapped onto behavioral processes such as rapport and antagonism (Coleman, Greenblatt, & Soloman, 1956;DiMascio, Boyd, & Greenblatt, 1957). Since that time, physiological influence has been used to study romantic couples, parent-child dyads, and newly-acquainted dyads and teams, and influence has been associated with relationship quality, individual differences like attachment, and the development of self-regulation and trust (Hill-Soderlund et al., 2008;Levenson & Gottman, 1983;Mitkidis, McGraw, Roepstorff, & Wallot, 2015;Suveg, Shaffer, & Davis, 2016; for reviews see Timmons, Margolin, & Saxbe, 2015;Palumbo et al., 2016).A primary strength of studying physiological influence in interpersonal encounters is that it allows scholars to test theoretical questions that are not testable using traditional measures of self-report or behavioral recordings alone. For example, physiological measures can provide continuous information ...

show abstract

Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques

Cited by 1,200 publications

References 31 publications

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions

A Tutorial on Testing, Visualizing, and Probing an Interaction Involving a Multicategorical Variable in Linear Regression Analysis

Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies.

Contact Info

Product

Resources

About