Guidelines for the Investigation of Mediating Variables in Business Research

MacKinnon, David P.; Coxe, Stefany; Baraldi, Amanda N.

doi:10.1007/s10869-011-9248-z

Cited by 267 publications

(219 citation statements)

References 84 publications

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“…To explore the psychological mechanisms underpinning the intervention, we intended to conduct mediation analyses between intervention participation, job resources, workrelated needs, and work engagement, using the PROCESS macro (Hayes, 2013), Model 6, following the guidelines outlined by MacKinnon, Coxe, and Baraldi (2012). This procedure allows indirect mediation relationships to be tested via 1000 bias-corrected bootstrapped 95% confidence intervals (CI) and significant results are indicated by CI which do not span zero.…”

Section: Discussionmentioning

confidence: 99%

Building and sustaining work engagement – a participatory action intervention to increase work engagement in nursing staff

Knight

Patterson

Dawson

et al. 2017

European Journal of Work and Organizational Psychology

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This study evaluated whether a participatory action research intervention with nursing staff on acute care older people National Health Service wards in the United Kingdom was effective for increasing work engagement. Mediation analyses between job resources (social support, influence in decisionmaking), job demands, work-related needs (autonomy, competence, relatedness), and work engagement explored the presumed psychological mechanisms underlying the intervention. A non-randomized, matched control group, pretest, post-test design involved three intervention and five control wards. A significant decrease in relatedness, and a borderline significant decrease in competence, was observed in the intervention group compared to the control group, with no effect on work engagement (N = 45). Work-related needs mediated between resources and work engagement, supporting the job demands-resources model and self-determination theory as an underlying explanatory theory. Intervention implementation was difficult, highlighting the need for participant and organizational readiness for change, and strong management support. This is the first known study to apply participatory techniques to increase work engagement in nursing staff and explore the underlying explanatory psychological mechanisms, offering a novel means of taking work engagement research forward. Crucially, it highlights the challenges involved in intervention research and the importance of including evaluations of intervention implementation alongside statistical evaluations to avoid erroneous conclusions. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Building and sustaining work engagement – a participatory action intervention to increase work engagement in nursing staff

Knight

Patterson

Dawson

et al. 2017

European Journal of Work and Organizational Psychology

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“…To assess and test the indirect effects, there are six main sets of techniques (Little, 2013;MacKinnon et al, 2012;MacKinnon et al, 2004).…”

Section: Methods Of Estimationmentioning

confidence: 99%

“…The focus of this paper is on the mediator variable, which addresses the mechanisms through which an effect occurs (MacKinnon, Coxe, & Baraldi, 2012). The mediation hypotheses posit how X affects Y through one or more potential intervening variables, or mediators (M).…”

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confidence: 99%

Strategies to Measure Direct and Indirect Effects in Multi-mediator Models

Turnes¹,

Ernst²

2015

CUBR

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The analysis of mediators, multi-mediators, confounders, and suppression variables often presents problems to the scientists that need to interpret them correctly. After clarifying main differences among these terms, this paper focuses on the techniques to conduct and estimate multi-mediation effects. Multi-mediator effects are very common in social science literature, however, many studies do not report their analysis, or even worse, do not explore the significance of the indirect effects in the outcome variable. In exploring the underlying mechanism of observed variables, mediation addresses a key important aspect: Mediation explains how the changes occur. The measurement of direct and indirect effects involves the combination of several techniques, especially under multiple mediators. The objective of this paper is to show different approaches that should be used to investigate indirect and direct effects in order to shed some light on how to conduct a mediation analysis, how to assess the model estimation, and how to interpret mediation effects. The main conclusion of this paper is that by applying traditional methodologies (causal steps, product of coefficients, and the indirect approach), the real mediation effect could be overestimated or underestimated. This paper explains new methods that overcome the difficulties of traditional approaches. Examples of Mplus syntax are provided to facilitate the use of these methods in this application. Keywords: mediator, moderator, cofounder, multi-mediators The Concept of Mediation and Approaches to Test Multi-mediation Effects In social sciences, researchers are interested in explaining the mechanisms that illustrate the relationship between an independent variable (X) and a dependent variable (Y) (Figure 1). This paper extents previous studies by considering the difficulties of analyzing multi-mediation effects that appear in complex situations in which there is a chain of effects that mediate the relationship between X and Y. The main contribution of this paper is to provide a methodology that includes how to conduct and assess the mediation analysis and interpret results.Essentially, mediation analysis is the set of techniques used in conducting and testing the mediation

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“…The way to prove the mediation effect is to analyze different moments in time to support the relations among the variables, using longitudinal models (Cole & Maxwell, 2003;Little, 2013;Maxwell & Cole, 2007;Selig & Preacher, 2009). Main limitations of cross-sectional models to analyze causal and mediation effects are overcome by longitudinal models (Cole & Maxwell, 2003;Cheong, MacKinnon, & Khoo, 2003;Gollob & Reichardt, 1987;Maxwell & Cole, 2007;MacKinnon, Coxe, & Baraldi, 2012;Selig & Preacher, 2009). In summary, cross-sectional models describe relations among variables that occur instantaneously and this consideration biases the parameter estimation (Maxwell & Cole, 2007).…”

Section: Mediation Effects In Longitudinal Modelsmentioning

confidence: 99%