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RiPL (Research in Prevention Lab) focuses on prevention research to influence public health and choosing healthy behaviors. Additionally, the lab conducts quantitative research focused on methodology, exploring innovative methods to analyze data gathered for research within all disciplines. Current and past projects include work on mediation analysis, identifying risky behaviors for health problems due to impaired self-regulation (ONTOLOGY), reducing risk of obesity among adolescents (ORBIT), healthy behaviors in firefighters and police officers (IGNITE, PHLAME, and SHIELD), steroid use prevention (ATLAS), drug testing of student athletes (SATURN), and alcohol warning labels (ABLE).
David MacKinnon, Ph.D. is the faculty director of RiPL and a Foundation Professor in the Department of Psychology at Arizona State University. He received the B.A. from Harvard University and the Ph.D. in Measurement and Psychometrics from UCLA. His current research interests are in statistical methods, particularly as applied in health promotion and disease prevention research. He also conducts research on the role of social influence and cognitive factors in health behavior.
Current Team Members
Matthew Valente, M.A. received his B.S. in psychology with a minor in statistics from the University of North Florida and his M.A. in quantitative psychology from Arizona State University. He is currently a Ph.D. student in the quantitative psychology program at Arizona State University working with David MacKinnon. His research interests include longitudinal mediation models, potential outcomes framework for causal inference in mediation models, and potential outcomes framework for causal inference in longitudinal models. His research interests also include prevention science methodology.
Oscar Gonzalez, M.A. is a National Science Foundation graduate research fellow at Arizona State. He received his B.A. in psychology and a minor in European Studies from the University of Notre Dame and his M.A. in quantitative psychology from Arizona State University. He is currently pursuing a Ph.D. in quantitative psychology at Arizona State working with David P. MacKinnon. His research interests include psychometric and measurement issues in statistical mediation, item selection for short scale development, and data mining/big data techniques for the social sciences. Oscar also has interned at Educational Testing Service (ETS) working in the NAEP (the Nation's Report Card) agenda for assessment innovations in early science education.
Gina Mazza, M.A. is currently pursuing a Ph.D. in Psychology, Quantitative Research Methods at Arizona State University under the direction of Drs. Kevin Grimm and Stephen West. While at Arizona State University, she received an M.A. in Psychology, Quantitative Research Methods under the direction of Dr. Craig Enders and a B.S. in Psychology with a minor in Mathematics. Her research focuses on the development and advancement of methods for addressing two pervasive issues in medical, pharmacological, and psychological research—missing data and treatment nonadherence.
Heather Smyth , Masters Student, Quantitative Psychology, Department of Psychology. Heather received her B.S. in Psychology with a concentration in Psychological Science from ASU. Her research interests include person-oriented mediation models, the adaptation of dynamical models to mediation, and prevention research with a focus on academic achievement.
Jessica Canning is an undergraduate research assistant in the Research in Prevention Lab (RiPL) at Arizona State University. She is studying for her B.S. in Psychology (Psychological Sciences) and is interested in pursuing her Ph.D. in Clinical Psychology. Jessica is interested in researching how variability of state-level characteristics, such as impulsivity and self-regulation, contribute to the development of alcohol and substance use disorders. She has recently accepted an offer to continue this research and study for her Ph.D. at the University of Washington in Seattle, WA.
Amanda Baraldi, M.A. received her B.S. in mathematics from UMASS Amherst and her M.A. in clinical psychology from Columbia University, and her Ph.D. in quantitative psychology at Arizona State University. She formerly worked for the Nathan Kline Institute. Her current research interests include missing data analyses, methods for assessing mediation, longitudinal growth modeling, and health and prevention research. Amanda is now an Assistant Professor at Oklahoma State University.
Hanjoe Kim, M.A. received his M.A. from SungKyunKwan University, Seoul, South Korea and he is currently a Ph.D. student in the Quantitative Psychology program at Arizona State University. His current research interests are in statistical methods such as survival analysis, mediation analysis, multilevel analysis, and measurement invariance. He is also interested in applying these methods to prevention science. In the fall of 2017, Hanjoe will begin an Assistant Professor position in the psychology department at the University of Houston.
Milica Miočević, M. A. received her Ph.D. in Quantitative Psychology at Arizona State University. Her research interests include Bayesian mediation, Bayesian SEM, statistical methods for improving power in small samples, data synthesis, and causal inference in mediation models. Milica is now an Assistant Professor at the University of Utrecht.
Holly O’Rourke, M.A. received her B.S. and M.A. and Ph.D. in quantitative psychology from Arizona State University under the advisement of Dave MacKinnon. Her current research interests are in power and significance testing methods for mediation models, causal inference, effect sizes for mediation, and health and prevention research with specific attention to methods in addiction and alcohol treatment research. Holly is now an Assistant Professor in the Family and Human Development Department at ASU.
Ingrid Carlson Wurpts, M.A. received her Quantitative Psychology Ph.D. at Arizona State University. She received her M.A. in Quantitative Psychology from ASU and her B.A. in Mathematics and Psychology from Northwestern College. She is interested in latent class analysis, person-oriented and idiographic mediation models, as well as the psychology of eating and eating prevention methodology. Ingrid is now a Data Scientist for Dignity Health.
Olivera-Aguilar, M., Kisbu-Sakarya, Y., *Gonzalez, O., Rikoon, S., & MacKinnon, D. P. (in press). Bias, Type I Error Rates and Statistical Power of a Latent Mediation Model in the Presence of Violations of Invariance. Effects of measurement invariance in the mediator. Educational and Psychological Measurement.
*Valente, M.J., & MacKinnon, D. P. (in press). Comparing models of change to estimate the mediated effect in the pretest-posttest control group design. Structural Equation Modeling: A Multidisciplinary Journal.
*Miočević, M., *O’Rourke, H. P., MacKinnon, D. P., & Brown, C. H. (in press). Statistical properties of five effect size measures for mediation models. Resubmitted to Behavior Research Methods.
*Miočević, M., MacKinnon, D. P., Levy, R. (in press). Power in Bayesian mediation analysis for small sample research. Structural Equation Modeling.
*Jewell, S. L., Letham-Hamlett, K., *Ibraham, M. H., Luecken, L.J., & MacKinnon, D. P. (in press). Family support and family negativity as mediators of the relation between acculturation and postpartum weight in low-income Mexican-origin women. Annals of Behavioral Medicine.
Goldsmith, K., MacKinnon, D. P., Chalder, T., White, P. D., Sharpe, M., & Pickles, A. (in press). Tutorial: The practical application of longitudinal mediation models. Psychological Methods.
*Miočević, M., *Gonzalez, O., *Valente, M.J., & MacKinnon, D. P. (in press). A tutorial in Bayesian potential outcomes mediation analysis. Submitted to Structural Equation Modeling.
Tofighi D. & MacKinnon, D. P. (2016). Monte Carlo confidence intervals for complex functions of indirect effects. Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 194-205.
*Valente, M.J., *Gonzalez, O., *Miocevic, M. & MacKinnon, D. P. (2016). A note on testing mediated effects in structural equation modeling: Reconciling past and current research on the performance of the test of joint significance. Educational and Psychological Measurement 76(6), 889-911.
*Pirlott, A., & MacKinnon, D. P. (2016). Design approaches to experimental mediation. Journal of Experimental Social Psychology, 66, 29-38.
Huang, S., MacKinnon, D. P., Perrino, T., Gallo, C., Cruden, G., & Brown, H. C. (2016). A statistical method for synthesizing mediation analyses using product of coefficient approach across multiple trials. Statistical Methods and Applications, 25(4), 565-579.
Fritz, M. S., Kenny, D. A., MacKinnon, D. P. (2016). The Combined Effects of Measurement Error and Omitting Confounders in the Single-Mediator Model. Multivariate Behavioral Research, 51(5), 681-697.
Gelfand, L., *Baraldi, A., DeRubuis, R., & MacKinnon, D. P. (2016). Considerations for mediation analysis with survival outcomes. Frontiers in Psychology, 7, 423.
Ames, S. L.,*Wurpts, I., Pike, J. R., MacKinnon, D. P., Reynolds, K. D., Stacy, A. W. (2016). Self-Regulation interventions to reduce consumption of sugar-sweetened beverages in adolescents appetite, 105, 652-662.
Kuehl, K. S., Elliot, D. L., MacKinnon, D. P., *O’Rourke, H. P., DeFrancesco, C., *Miocevic, M. *Valente, M., Sleigh, A., Garg, B., *McGinnis, W., & *Kuehl, H. (2016). The SHIELD (Safety & Health Improvement: Enhancing Law Enforcement Departments) Study: Mixed methods longitudinal findings. Journal of Occupational and Environmental Medicine, 58(5), 492-498.
Elliot, D. L., Goldberg, L., MacKinnon, D. P., Ranby, K. W., Kuehl, K. S., & Moe, E. L. (2016) Empiric validation of a process for behavior change. Translational Behavior Medicine, 6(3), 449-456.
*Gonzalez, O., & MacKinnon, D. P. (2016). A bifactor approach to model multifaceted constructs in statistical mediation analysis. Educational and Psychological Measurement. DOI: 10.1177/0013164416673689
Fritz, M. S., *Cox, M. G., & MacKinnon, D. P. (2015). Increasing Statistical Power in Mediation Models without Increasing Sample Size. Evaluation and the Health Professions, 38(3), 343-366.
*O’Rourke, H. P., & MacKinnon, D.P. (2015). When the test of mediation has more power that the test of the total effect. Behavior Research Methods, 47, 424-442.
MacKinnon, D. P., & *Pirlott, A., (2015). Statistical approaches to enhancing the causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19, 30-43.
Lee, M. R., Chassin, L., & MacKinnon, D. P. (2015). Role transitions and young adult maturing out of heavy drinking: Evidence for larger effects of marriage among more severe premarriage problem drinkers. Alcoholism: Clinical and Experimental Research, 39(6). 1064-1074.
Koning, I. M., Maric, M., MacKinnon, D. P., Vollebergh, W. A. M., & Wilma, A. M. (2015). Effects of a parent-student alcohol prevention program on intermediate factors and adolescents’ drinking behavior: a sequential mediation model. Journal of Consulting and Clinical Psychology, 83(4), 719-727.
Luecken, L. J., MacKinnon, D.P., *Jewell, S., Gonzalez, N. (2015). Effects of prenatal factors and temperament on infant cortisol regulation in low-income Mexican American families. Developmental Psychobiology, 57, 138-144.
MacKinnon, D. P., & Pirlott, A. G. (2014). Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review. Advance online publication. PMCID: PMC Journal - In Process doi: 10.1177/1088868314542878
MacKinnon, D. P., & Valente, M. J. (2014). Mediation from multilevel to structural equation modeling. Annals of Nutrition and Metabolism, 65, 196-202. PMCID: PMC Journal - In Process doi: 10.1159/000362505
MacKinnon, D. P., Wurpts, I. C., & Valente, M. J. (2014). Imagery and memory theory as known effect validation for mediation analysis. Manuscript submitted for publication.
Mahrer, N. E., Winslow, E., Wolchik, S. A., Tein, J.-Y., & Sandler, I. N. (2014). Effects of a preventive parenting intervention for divorced families on the intergenerational transmission of parenting attitudes in young adult offspring. Child Development, 85(5), 2091–2105. PMCID: PMC Journal - In Process doi: 10.1111/cdev.12258
Mayer, A., Thoemmes, F., Rose, N., Steyer, R., & West, S. G. (2014). Theory and analysis of total, direct, and indirect causal effects. Multivariate Behavioral Research, 49(5), 425-442. PMCID: PMC Journal - In Process doi:10.1080/00273171.2014.931797
Miočević, M., O’Rourke, H. P., MacKinnon, D. P., & Brown, H. C. (2014). The bias and efficiency of five effect size measures for mediation models. Manuscript submitted for publication.
O’Rourke, H. P., & MacKinnon, D. P. (2014). When the test of mediation is more powerful than the test of the total effect. Behavior Research Methods. Advance online publication. PMCID: PMC Journal - In Process doi: 10.3758/s13428-014-0481-z
Olivera-Aguilar, M., Kisbu-Sakarya, Y., & MacKinnon, D. P. (2014). Effects of measurement invariance in the mediator. Manuscript submitted for publication.
Maric, M., Heyne, D. A., MacKinnon, D. P., van Widenfelt, B. M., & Westenberg, P. M. (2013). Cognitive mediation of cognitive-behavioural therapy outcomes for anxiety-based school refusal. Behavioural and Cognitive Psychotherapy, 41(5), 549-564. PMCID: PMC3772992 doi: 10.1017/S1352465812000756
MacKinnon, D. P., Coxe, S., & Baraldi, A. N. (2012). Guidelines for the investigation of mediating variables in business research. Journal of Business and Psychology, 27(1), 1-14. PMCID: PMC4165346 doi: 10.1007/s10869-011-9248-z
Introduction to Statistical Mediation Analysis, David Mackinnon (Amazon link)
David MacKinnon, Oscar Gonzalez, Gina Mazza, Holly O'Rourke, and Matt Valente will present a one-day preconference workshop on Tuesday, May 30th before the Society for Prevention Research Conference in Washington, DC. The workshop will cover modern methods for statistical mediation.
Oscar Gonzalez has been awarded the National Science Foundation Graduate Research Fellowship. The importance of this fellowship can be summarized below:
"The purpose of the NSF Graduate Research Fellowship Program (GRFP) is to help ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science and engineering. The GRFP provides three years of full stipend support for the graduate education of individuals who have demonstrated their potential for significant achievements in science and engineering." (Synopsis of the program -- nsf.gov)
Matt Valente has been awarded a National Research Service Award for work applying the potential outcomes model to longitudinal data.