David MacKinnon

David MacKinnon
Arizona State University | ASU · Department of Psychology

Ph.D.

About

335
Publications
222,952
Reads
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55,036
Citations
Additional affiliations
August 1990 - present
Arizona State University
Position
  • Foundation Professor
January 1986 - July 1990
University of Southern California
Position
  • Research Assistant

Publications

Publications (335)
Preprint
Full-text available
Religion makes unique claims (such as in the existence of supernatural agents) not found in other belief systems, but is religion itself psychologically special? Furthermore, religion is related to many domains of psychological interest, like morality, health and well-being, self-control, meaning, and death anxiety. Does religion act on these domai...
Preprint
Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral researc...
Article
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Introduction: Self-regulation has been implicated in health risk behaviors and is a target of many health behavior interventions. Despite most prior research focusing on self-regulation as an individual-level trait, we hypothesize that self-regulation is a time-varying mechanism of health and risk behavior that may be influenced by momentary conte...
Article
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In response to the importance of individual-level effects, the purpose of this paper is to describe the new randomization permutation (RP) test for a mediation mechanism for a single subject. We extend seminal work on permutation tests for individual-level data by proposing a test for mediation for one person. The method requires random assignment...
Article
Objective Naltrexone is an effective treatment for heavy drinking among young adults, and laboratory-based studies have shown that naltrexone dampens the subjective response to alcohol and craving. However, few studies have tested naltrexone’s dynamic, within-person effects on subjective response and craving among young adults in natural drinking...
Preprint
BACKGROUND Self-regulation refers to a person’s ability to manage their cognitive, emotional, and behavioral processes to achieve long-term goals. Most prior research has examined self-regulation at the individual-level, but individual-level assessments does not allow examining dynamic patterns of intra-individual variability in self-regulation and...
Article
Background: Self-regulation refers to a person’s ability to manage their cognitive, emotional, and behavioral processes to achieve long-term goals. Most prior research has examined self-regulation at the individual level; however, individual-level assessments do not allow the examination of dynamic patterns of intraindividual variability in self-re...
Article
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Objective: To describe the bias assessment practice in recently published systematic reviews of mediation studies and to evaluate the quality of different bias assessment tools for mediation analysis proposed in the literature. Method: We conducted an overview of systematic reviews by searching MEDLINE (OvidSP), PsycINFO (OvidSP), Cochrane Datab...
Article
Full-text available
Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An...
Article
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Background Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The...
Article
Importance Mediation analyses of randomized trials and observational studies can generate evidence about the mechanisms by which interventions and exposures may influence health outcomes. Publications of mediation analyses are increasing, but the quality of their reporting is suboptimal. Objective To develop international, consensus-based guidance...
Article
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Mediation analysis helps explain how and why two variables are related, providing information for investigating causal processes useful for theoretical and applied research (MacKinnon 2008). Inference from mediation analysis typically applies to the population, but researchers and clinicians are often interested in making inference to individual cl...
Article
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Knowledge of causal processes through mediation analysis can help improve the effectiveness and reduce costs of public health programs, like HIV prevention and treatment interventions. Advancements in mediation using the potential outcomes framework provide a method for estimating the causal effect of interventions on outcomes via a mediating varia...
Article
Objective : To determine whether improvements in protective stepping experienced after repeated support surface translations generalize to a different balance challenge in people with multiple sclerosis (PwMS) Background : MS affects almost 1 million people in the United States and impairs balance and mobility. Perturbation practice can improve as...
Article
Science is an inherently cumulative process, and knowledge on a specific topic is organized through synthesis of findings from related studies. Meta-analysis has been the most common statistical method for synthesizing findings from multiple studies in prevention science and other fields. In recent years, Bayesian statistics have been put forth as...
Article
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Third-variable effects, such as mediation and confounding, are core concepts in prevention science, providing the theoretical basis for investigating how risk factors affect behavior and how interventions change behavior. Another third variable, the collider, is not commonly considered but is also important for prevention science. This paper descri...
Article
Background: Mediated and moderated processes that lead to intervention efficacy may underlie results of trials ruled as non- efficacious. The overall purpose of this study was to examine such processes to explain the findings of one of the largest, rigorously conducted behavioral intervention randomized controlled trials, EXPLORE. Methods: 4,295...
Article
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Technological advances have increased the prevalence of intensive longitudinal data as well as statistical techniques appropriate for these data, such as dynamic structural equation modeling (DSEM). Intensive longitudinal designs often investigate constructs related to affect or mood and do so with multiple item scales. However, applications of int...
Article
The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generat...
Article
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An important recent development in mediation analysis is the use of causal mediation analysis. Causal mediation analysis decomposes the total exposure effect into causal direct and indirect effects in the presence of exposure-mediator interaction. However, in practice, traditional mediation analysis is still most widely used. The aim of this paper...
Article
Method: Two hundred Hispanic emerging adults from Arizona (n = 99) and Florida (n = 101) completed a cross-sectional survey, and data were analyzed using hierarchical multiple regression and moderation analyses. Results: Higher social media discrimination was associated with higher symptoms of depression and generalized anxiety. Moderation analy...
Article
Mediation analysis is a methodology used to understand how and why an independent variable (X) transmits its effect to an outcome (Y) through a mediator (M). New causal mediation methods based on the potential outcomes framework and counterfactual framework are a seminal advancement for mediation analysis, because they focus on the causal basis of...
Article
Full-text available
In many disciplines, mediating processes are usually investigated with randomized experiments and linear regression to determine if the treatment affects the outcome through a mediator. However, randomizing the treatment will not yield accurate causal direct and indirect estimates unless certain assumptions are satisfied since the mediator status i...
Article
Latent class mediation modeling is designed to estimate the mediation effect when both the mediator and the outcome are latent class variables. We suggest using an adjusted one-step approach in which the latent class models for the mediator and the outcome are estimated first to decide on the number of classes, then the latent class models and the...
Article
In psychology, the causal process between 2 variables can be studied with statistical mediation analysis. To make a causal interpretation about the relation between variables, researchers who use the statistical mediation model make many assumptions about the variables in the model, among which are measurement assumptions about the mediator. For ex...
Article
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Self-regulation is studied across various disciplines, including personality, social, cognitive, health, developmental, and clinical psychology; psychiatry; neuroscience; medicine; pharmacology; and economics. Widespread interest in self-regulation has led to confusion regarding both the constructs within the nomological network of self-regulation...
Article
Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin Stat...
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In manifest variable models, Bayesian methods for mediation analysis can have better statistical properties than commonly used frequentist methods. However, with latent variables, Bayesian mediation analysis with diffuse priors can yield worse statistical properties than frequentist methods, and no study to date has evaluated the impact of informat...
Article
In psychology, there have been vast creative efforts in proposing new constructs and developing measures to assess them. Less effort has been spent in investigating construct overlap to prevent bifurcated literatures, wasted research efforts, and jingle-jangle fallacies. For example, researchers could gather validity evidence to evaluate if two mea...
Article
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Mediation analysis is a methodology used to understand how and why behavioral phenomena occur. New mediation methods based on the potential outcomes framework are a seminal advancement for mediation analysis because they focus on the causal basis of mediation. Despite the importance of the potential outcomes framework in other fields, the methods a...
Article
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Objective Nearly all studies treat the Five Facet Mindfulness Questionnaire as five independent scales (one measuring each of the five facets), yet almost no methodological work has examined the psychometric structure of the facets independently. We address this issue using factor analytic methods. Methods Exploratory and confirmatory factor model...
Article
The Brief Self-Control Scale (BSCS) is a widely used measure of self-control, a construct associated with beneficial psychological outcomes. Several studies have investigated the psychometric properties of the BSCS but have failed to reach consensus. This has resulted in an unstable and ambiguous understanding of the scale and its psychometric prop...
Article
Full-text available
Objectives The Five Facet Mindfulness Questionnaire (FFMQ) is a self-report measure of mindfulness with forms of several different lengths, including the FFMQ-39, FFMQ-24, and FFMQ-15. We use item response theory analysis to directly compare the functioning of these three forms. Methods Data were drawn from a non-clinical Amazon Mechanical Turk st...
Article
Background Low-intensity psychosocial interventions have been effective in targeting perinatal depression, but relevant mechanisms of change remain unknown. Aims To examine three theoretically informed mediators of the Thinking Healthy Programme Peer-delivered (THPP), an evidence-based psychosocial intervention for perinatal depression, on symptom...
Article
Grit, the passion and perseverance for long-term goals, has received attention from personality psychologists because it predicts success and academic achievement. Grit has also been criticized as simply another measure of self-control or conscientiousness. A precise psychometric representation of grit is needed to understand how the construct is u...
Chapter
Mediation analysis is a statistical method used to quantify the causal sequence by which an antecedent variable causes a mediating variable that causes a dependent variable. Although mediation analysis is useful for observational studies, it is perhaps most compelling for answering questions of cause and effect in randomized treatment and preventio...
Article
Two methods from the potential outcomes framework – inverse propensity weighting (IPW) and sequential G-estimation – were evaluated and compared to linear regression for estimating the mediated effect in a two-wave design with a randomized intervention and continuous mediator and outcome. Baseline measures of the mediator and outcome can be conside...
Presentation
For binary outcomes and in the presence of an XM interaction in the single mediator model, direct effects can be estimated using traditional logistic regression or methods from the potential outcomes framework. Links between conditional direct effects using traditional logistic regression and potential outcomes-based direct effects are explained us...
Article
Full-text available
Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues that are particularly damaging for the study of multifaceted constructs like self-regulation. Here...
Article
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The development and evaluation of mindfulness-based interventions for a variety of psychological and medical disorders have grown exponentially over the past 20 years. Yet, calls for increasing the rigor of mindfulness research and recognition of the difficulties of conducting research on the topic of mindfulness have also increased. One of the maj...
Article
Full-text available
The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using...
Chapter
This chapter describes the theoretical and practical importance of mediation analysis in substance-use prevention research. The most important reason for including mediators in a research study is to examine the mechanisms by which prevention programs influence substance-use outcomes. Understanding the mechanisms by which prevention programs achiev...
Article
A risk factor or intervention (an independent variable) may influence a substance abuse outcome (the dependent variable) indirectly, by affecting an intervening variable (a mediator) that in turn affects that outcome. Mediation analysis is a statistical method commonly used to examine the interrelations among independent, mediating, and dependent v...
Preprint
The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using...
Article
Full-text available
Latent growth curve mediation models are increasingly used to assess mechanisms of behavior change. For latent growth mediation model, like any another mediation model, even with random treatment assignment, a critical but untestable assumption for valid and unbiased estimates of the indirect effects is that there should be no omitted variable that...
Preprint
Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues we address by examining individual differences across an unprecedented range of behavioral tasks,...
Conference Paper
Full-text available
Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the effect of an independent variable to a dependent variable. Because it is used in many fields, there have been rapid developments in statistical mediation. The most cutting-edge statistical mediation analysis focuses on the causal i...
Article
Full-text available
This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation—(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect—are described...
Article
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Objective: Mediation models are used in prevention and intervention research to assess the mechanisms by which interventions influence outcomes. However, researchers may not investigate mediators in the absence of intervention effects on the primary outcome variable. There is emerging evidence that in some situations, tests of mediated effects can...
Article
Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the effect of an independent variable to a dependent variable. Because it is used in many fields, there have been rapid developments in statistical mediation. The most cutting-edge statistical mediation analysis focuses on the causal i...
Article
The study of mediation of treatment effects, or how treatments work, is important to understanding and improving psychological and behavioral treatments, but applications often focus on mediators and outcomes measured at a single time point. Such cross-sectional analyses do not respect the implied temporal ordering that mediation suggests. Clinical...
Article
Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psycholo...
Article
Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to ou...
Conference Paper
Full-text available
Randomized interventions involving a treatment and control group are used to study intervention effects on hypothesized mediators and subsequent effects of hypothesized mediators on drug-use outcomes over two or more measurement waves. When three waves of data are collected on mediator and outcome variables, autoregressive mediation models can be u...
Conference Paper
Full-text available
Time metric is an important consideration for all longitudinal models because it influences the interpretation of estimates, parameter estimate accuracy, and model convergence in longitudinal models (O’Rourke, Grimm, & MacKinnon, in preparation). Currently, the literature on latent change score (LCS) models does not discuss the importance of time m...
Article
Background Obesity presents a significant health concern among low-income, ethnic minority women of childbearing age. PurposeThe study investigated the influence of maternal acculturation, family negativity, and family support on postpartum weight loss among low-income Mexican-origin women. Methods Low-income Mexican-origin women (N=322; 14% born i...
Article
Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last two decades. One of the...
Article
Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence int...
Conference Paper
Full-text available
Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the relation of an independent variable to a dependent variable. Because it is useful in many fields, there have been rapid developments in statistical mediation methods. The most cutting-edge statistical mediation analysis focuses on...
Article
Full-text available
This project examined the performance of classical and Bayesian estimators of four effect size measures for the indirect effect in a single-mediator model and a two-mediator model. Compared to the proportion and ratio mediation effect sizes, standardized mediation effect-size measures were relatively unbiased and efficient in the single-mediator mo...