2013 Summer Quantitative Methods Series at Portland State University
This Summer Quantitative Method Series is comprised of two-day courses on data analysis taught by nationally recognized methodological experts. Course descriptions and more information about instructors can be found below. Our goal is to provide additional statistical and methodological training for research professionals from either the private or public sector. Although course credit is not available, graduate students are welcome and offered a discounted fee. Participants may enroll in courses separately or in combination. Each course takes an applied perspective with special attention given to when to use and how to implement each technique. Statistical, mathematical, and conceptual foundations will be included with the objective of providing a solid introduction to each area. All courses will provide extensive software illustrations, and, unless otherwise specified, will provide computer lab time where participants have one-on-one assistance available when running computer examples. Some graduate-level coursework in statistics (social science departments or otherwise) and some experience with one or more statistical software packages are usually assumed. Individual courses may require additional prerequisite knowledge if indicated, however.
All classes will be held at the Portland State University Campus located in beautiful downtown Portland, OR between 9 am and 5 pm with an hour break for lunch. Rooms are equipped with desktop computers for each participant, although participants are welcome to bring their own laptops with appropriate software. Register>>
Contact Jason Newsom, Series Director, with general questions. Contact individual instructors (email links below) with course-specific questions.
David P. MacKinnon, Ph.D, Arizona State University
The goal of the workshop is to describe statistical, methodological, and conceptual aspects of mediation analysis. The two-day workshop consists of four parts. Part 1, covers definitions, history, and applications for the mediation model. The purpose of this section is to provide an overview of the research questions the mediation model can answer. Examples from mediation analysis in many substantive areas are described including prevention and treatment research. In Part II, the conceptual model described in Part I is quantified in the estimation of mediation in single and multiple mediator models. Estimation of mediation effects including assumptions of the methods, different statistical tests, effect size, controlled and natural indirect effects, and construction of confidence limits for the mediated effect are covered. The methods described in this section serve as the foundation for more advanced methods in Part III consisting of advanced mediation models including mediation in path analysis, longitudinal mediation models, and mediation in the context of moderation. In Part IV, general issues in the investigation of mediation including methods to adjust for confounders, additional approaches to identifying mediating variables, and future directions are described. The workshop will generally follow Dr. MacKinnon’s 2008 book Introduction to Statistical Mediation Analysis, and all students will receive a copy as part of the enrollment fee. Illustrations will be given for SAS, SPSS, or Mplus software.
David P. MacKinnon, Ph.D., is a Foundation Professor in the Department of Psychology at Arizona State University. He received the Ph.D. in measurement and psychometrics from UCLA in 1986. He was an Assistant Professor of Research at the University of Southern California’s Institute for Prevention Research from 1986 to 1990. He has been at Arizona State University since 1990 and is affiliated with the Prevention Intervention Research Center and the Research in Prevention Laboratory. Dr. MacKinnon teaches graduate analysis of variance, mediation analysis, and statistical methods in prevention research. He has given numerous workshops in the United States and Europe. In 2011 he received the Nan Tobler Award from the Society for Prevention Research for his book on statistical mediation analysis. He has served on federal review committees and is on the editorial board of the journals Prevention Science (Consulting Editor) and Psychological Methods. Dr. MacKinnon has been principal investigator on several National Institute on Health grants and is a Fellow of the Association for Psychological Science and American Psychological Association Measurement and Statistics Division. His primary interest is in the area of statistical methods to assess how prevention and treatment programs achieve their effects.
Stephanie T. Lanza, Ph.D., The Pennsylvania State University
The goal of this two-day course is to help you gain the theoretical background and applied skills necessary for addressing research questions from a variety of disciplines using latent class analysis. Latent class analysis involves identification of latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent transition analysis investigates changes in latent class membership over time. Topics include an introduction to latent class analysis (LCA), model interpretation, model selection, model identification, multiple-groups LCA, measurement invariance across groups, LCA with covariates and distal outcomes, and an introduction to latent transition analysis (LTA). Workshop time will be spent in lecture, software demonstrations, computer exercises, and discussion. All exercises will be demonstrated in SAS using PROC LCA and PROC LTA (downloadable add-on procedures). Other software options for conducting LCA will be discussed and demonstrated for participants. Time will be reserved for participants to discuss how the concepts learned in class can be applied in their research. All enrollees will receive a copy of Dr. Lanza's book, Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. An understanding of regression analysis is expected and familiarity with logistic regression is helpful but not necessary.
Stephanie T. Lanza, Ph.D., is Research Associate Professor of Health and Human Development and Scientific Director of The Methodology Center at The Pennsylvania State University. She has a background in research methods, human development, and substance use and comorbid behaviors with first-authored publications appearing in methodological journals such as Structural Equation Modeling and Psychological Methods and applied journals including Development and Psychopathology and Prevention Science. She is the author (with Linda Collins) of Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences (Wiley, 2010) and leads the ongoing development of PROC LCA & PROC LTA, a suite of SAS procedures for fitting latent class and latent transition models. Her research interests include advances in finite mixture modeling, including extensions to predict distal outcomes and a framework for conducting causal inference in LCA, time-varying effect models for intensive longitudinal data, and the application of these methods in health and behavioral research.
Chris Aberson, PhD, Humboldt State University
This course addresses theoretical and practical power analysis considerations across a wide range of research designs. The workshop focuses on “how-to” examples for conducting analyses using computer software (e.g., SPSS, G*Power). Topics include power for designs employing approaches such as t-tests, Chi-square, ANOVA (between, within, and designs with both between and within subjects factors), multiple regression, logistic regression, and multilevel models. Special topics include writing effective power analysis statements for grant proposals and design issues that deflate power. A basic understanding of power and effect size, consistent with the coverage in most introductory statistics textbooks, will be helpful. Attendees will receive a packet demonstrating use of SPSS syntax and other materials for analyses and a copy of Dr. Aberson's book, Applied Power Analysis for the Behavioral Sciences (Psychology Press).
Chris Aberson, PhD, is currently Professor of Psychology at Humboldt State University. He earned his Ph.D. at the Claremont Graduate University in 1999. His topical research interests include prejudice and racism. He is presently Associate Editor of the Journal of Applied Social Psychology. His text, Applied Power Analysis for the Behavioral Sciences was published in 2010.
"The instructor is extremely knowledgeable and personable. I would highly recommend any class with this instructor."
"Really clear and well put together."
"Very comprehensive overview of the topic. Very useful."
"Very informative. I appreciated that the theoretical constructs were connected to real data sets and output in SAS, SPSS, and HLM."
"Excellent baseline introduction."
"Well organized & nice handout booklet."
"I especially appreciated the concrete examples that were used to illustrate each concept. It was very helpful to hear the real-world, layman's terminology used to describe the results of models, as well."
"The reading list and online resources are great for follow-up."