Developments in Mediation Analysis

MacKinnon, David P.; Kisbu-Sakarya, Yasemin; Gottschall, Amanda C.

doi:10.1093/oxfordhb/9780199934898.013.0016

Cited by 68 publications

(52 citation statements)

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“…First, there were four to five times more missing data for the social capital mediator than there was for the three cognitive mediators. Even though FIML is one of the best procedures for handling missing data and bias-corrected bootstrapping is currently considered the optimal approach for managing non-normality in both the indirect effect and outcome measure (MacKinnon et al 2013;Pituch and Stapleton 2008;Rucker et al 2011), it is always possible that with over a third of the social capital scores missing some sort of bias was introduced into the results. Second, the social capital measure used in the current study focused primarily on attachment to the community.…”

Section: Discussionmentioning

confidence: 99%

“…The strengths of this study include: (1) the use of adjudicated delinquents as participants, which raised the rate of offending to a level where the relationships of interest could be studied; (2) the presence of a prospective design in which the predictor, mediator, and outcome variables were measured at different waves and in their proper temporal sequence and where precursor measures for both the mediator and dependent variables were included in the analysis; (3) the inclusion of control variables and pathways from several major criminological theories, two of which have been used to explain crime continuity (i.e., low self-control, cumulative disadvantage); (4) the availability of three different cognitive mediators (OEC, SE, GCT) to test the additive postulate of the psychological inertia theorem; (5) use of bias-corrected bootstrapped confidence intervals to evaluate the results given research showing that this approach does a better job of accounting for nonnormality in indirect effects as well as in the dependent variables than the standard z-test approach (Hayes 2013;MacKinnon et al 2013;Pituch and Stapleton 2008). These strengths notwithstanding, the current study also suffered from several noteworthy limitations.…”

Section: Discussionmentioning

confidence: 99%

“…The continuous dependent and mediator variables permitted the use of a maximum likelihood (ML) estimator and an ML estimator is required for computing bias-corrected bootstrapped estimates and 95 % confidence intervals. Bootstrapping is currently considered the optimal procedure for calculating indirect (mediation) effects (Hayes 2009;Rucker et al 2011) and research indicates that it does a better job of accounting for non-normality in both the indirect effect and dependent variable than the standard z-test approach (MacKinnon et al 2013;Pituch and Stapleton 2008). The path analysis was performed with 5000 bootstrapped replications in MPlus 5.2 (Muthén andMuthén 1998-2007).…”

Section: Statistical Analysesmentioning

confidence: 99%

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Crime Continuity and Psychological Inertia: Testing the Cognitive Mediation and Additive Postulates with Male Adjudicated Delinquents

Walters

2015

J Quant Criminol

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Objectives Crime continuity is one of the best documented and least understood aspects of criminal behavior. Psychological inertia, the notion that cognitive variables mediate the relationship between earlier and later expressions of the same behavior, was tested as a possible explanation for crime continuity.Methods The cognitive mediation and additive postulates of the psychological inertia theorem were tested in a path analysis using self-report data from 1170 male delinquent members of the Pathways to Desistance study (Mulvey in Paper presented at the American Society of Criminology Annual Meeting, Chicago, IL, 2012). Wave 1 delinquency served as the independent variable, Wave 3 delinquency served as the dependent variable, Wave 2 outcome expectancies for crime, self-efficacy, general criminal thinking, and social capital served as the mediator variables, and 12 different baseline measures from criminological theory served as control variables in this study.Results General criminal thinking and low self-efficacy successfully mediated the relationship between past and future offending after age, race, early behavioral problems, peer criminality, family criminality, parental knowledge and monitoring, parental hostility, routine activities, measured intelligence, and precursors for each of the mediators were controlled. Social capital (cumulative disadvantage), by comparison, failed to mediate crime continuity in this study.Conclusions Effective cognitive mediation of the relationship between Wave 1 offending and Wave 3 offending and evidence that the effect may be additive offer preliminary support for the cognitive mediation and additive postulates of the psychological inertia theorem. Practical implications of these results include the need to routinely assess cognitive factors in criminal populations and target these factors for intervention.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

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Crime Continuity and Psychological Inertia: Testing the Cognitive Mediation and Additive Postulates with Male Adjudicated Delinquents

Walters

2015

J Quant Criminol

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“…Testing for mediation by the intervention’s key mechanisms (MacKinnon, Kisbu-Sakarya, & Gottschall, 2013) is one criterion for establishing a causal relation between improved regulatory processes and enhanced resilience. Several of the programs listed in Table 1 conducted such analyses, particularly when the intervention focused on parents’ use of limit setting or monitoring.…”

Section: Family-based Interventions To Optimize Regulation and Resilimentioning

confidence: 99%

Resilience as Regulation of Developmental and Family Processes

2015

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Resilience can be defined as establishing equilibrium subsequent to disturbances to a system caused by significant adversity. When families experience adversity or transitions, multiple regulatory processes may be involved in establishing equilibrium, including adaptability, regulation of negative affect, and effective problem-solving skills. The authors’ resilience-as-regulation perspective integrates insights about the regulation of individual development with processes that regulate family systems. This middle-range theory of family resilience focuses on regulatory processes across levels that are involved in adaptation: whole-family systems such as routines and sense of coherence; coregulation of dyads involving emotion regulation, structuring, and reciprocal influences between social partners; and individual self-regulation. Insights about resilience-as-regulation are then applied to family-strengthening interventions that are designed to promote adaptation to adversity. Unresolved issues are discussed in relation to resilience-as-regulation in families, in particular how risk exposure is assessed, interrelations among family regulatory mechanisms, and how families scaffold the development of children’s resilience.

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“…Even conditioning on observed confounders for the relation between M and Y , there can still be unobserved confounders. This assumption is strong and is usually ignored in studies even though a causal interpretation of a mediated effect is not possible without its existence (MacKinnon, Kisbu-Sakarya, and Gottschall in press; MacKinnon 2008, chapter 13). Assumption (iv) is made by the methods described in this article and methods to investigate this assumption are only starting to be developed (Coffman and Zhong 2012).…”

Section: Potential Outcomes Frameworkmentioning

confidence: 99%

Sensitivity Plots for Confounder Bias in the Single Mediator Model

et al. 2013

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Background Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable. Objectives This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods. Design Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. Subjects The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely. Conclusions We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.

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Developments in Mediation Analysis

Cited by 68 publications

References 0 publications

Crime Continuity and Psychological Inertia: Testing the Cognitive Mediation and Additive Postulates with Male Adjudicated Delinquents

Crime Continuity and Psychological Inertia: Testing the Cognitive Mediation and Additive Postulates with Male Adjudicated Delinquents

Resilience as Regulation of Developmental and Family Processes

Sensitivity Plots for Confounder Bias in the Single Mediator Model

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