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2017 Summer Quantitative Methods Series at Portland State University

Courses

 

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.

 

Introduction to Dynamical Systems Modeling , June 19-20, 2017

Sy-Miin Chow, PhD, Pennsylvania State University

The course will provide an overview of the concepts and theory behind dynamical systems. Practical methods for exploring linear and nonlinear relations in multivariate longitudinal data, as well as methods for fitting dynamical systems models to panel and intensive longitudinal data (e.g., diary/experience sampling/ecological momentary assessments) will be presented. Topics to be covered include state-space/difference equation models, differential equation models, dynamic structural equation models, and mixture as well as random effects extensions of these models. A variety of examples of dynamical systems from developmental research, family studies, psychology, and intervention science will be presented to provide some perspectives on when dynamical systems techniques might be useful. Examples use R and Mplus structural equation modeling software. Students will be guided through using software programs to simulate and develop hands-on familiarity with different types of dynamical systems, followed by model explorations, estimation, interpretations, and diagnostics.  Participants are expected to have completed a graduate course on regression techniques and are comfortable working with multivariate data. Experience with mixed effects modeling and R is helpful but not required.

Sy-Miin Chow is Associate Professor in the Department of Human Development and Family Studies at the Pennsylvania State University. She is a winner of the Alexander von Humboldt Fellowship, the Cattell Award from the Society of Multivariate Experimental Psychology (SMEP), and an Early Career Award from the Psychometric Society. The focus of her work has been on developing and testing longitudinal and dynamical systems models and collaborating with scholars and practitioners in emotion, aging, child development, family dynamics and prevention research to bring new methodological developments to these areas. She has authored and edited numerous articles and books on dynamic modeling that target a broad, interdisciplinary audience. She has lectured and taught classes to students with diverse backgrounds from several academic disciplines, including Psychology, Human Development and Family Studies, Education, Business, Kinesiology, Public Health, and Health Policy and Administration.

 

An Introduction to Latent Class Analysis, June 21-22, 2017
Karen Nylund-Gibson, Ph.D., University of California, Santa Barbara

This course will provide and introduction to latent class analysis (LCA) and its application in Mplus.  Topics will include a brief overview of mixture models, including latent class, latent profile and latent transition analysis, with the primary focus on the specification and interpretation of latent class analysis. The course will also cover the inclusion of auxiliary variables, covariates and distal outcomes, into LCA models using modern methods including the BCH and 3-step methods. Modeling extensions of the basic LCA model will be provided, with examples and Mplus syntax.  The workshop will take an applied approach–that is, specifying, testing, and interpreting LCA model output in Mplus. Ideally, participants should have an understanding of multiple regression and some exposure to and familiarity with exploratory factor analysis, SEM techniques, and categorical data analysis.

Karen Nylund-Gibson, Ph.D, is an Associate Professor of Quantitative Research Methods in the Department of Education at the University of California, Santa Barbara. Her research area is on latent variable models, specifically cross-sectional and longitudinal mixture models, class enumeration, and application of these models in Social Science areas.  She has published many articles and book chapters on developments, best practices, and applications of latent class analysis, latent transition analysis, and growth mixture modeling.  Her research helps to bridge the gap between advanced statistical literature and application of these models to substantive issues.

Item Response Theory and Modeling, June 23-24, 2017
Tenko Raykov, Ph.D., Michigan State University

Item response theory (IRT) and item response modeling (IRM) are used to investigate the item quality of tests and other measures, for measurement development and evaluation purposes, as well as for investigating measurement bias and group differences. This short course will discuss the measurement theory, statistical underpinnings, and practical application of the comprehensive IRT measurement approach.  Topics will include: testing unidimensionality of a measuring instrument, popular unidimensional IRT models, parameter and ability estimation in item response models, item information and test information functions, test characteristic curves, instrument construction, development and revision using IRT/IRM, polytomous IRT models, an introduction to multidimensional IRT/IRM, as well as numerous empirical illustrations of behavioral and social measurement using IRT/IRM and latent variable modeling, and extensions of IRT/IRM.  The workshop will use Mplus and Stata software. No familiarity with either of these packages is required or expected. Free Stata licenses for the duration of the workshop will be provided, and Mplus examples will be possible to also do with the free demo version of Mplus.  Participants are welcome to bring laptops, but desktop laptops with the software programs are provided. Suggested background includes familiarity with regression and multiple regression and basic measurement concepts, such as reliability and validity, but no extensive familiarity with behavioral or social measurement is required. No prior exposure to IRT is needed.

Tenko Raykov, Ph.D., is Professor of Measurement and Quantitative Methods at Michigan State University (home page: www.msu.edu/~raykov).  His research contributes to several methodological areas with many practical applications.  These include latent variable modeling, structural equation modeling, measurement and scale construction and development, multilevel modeling, longitudinal data modeling, analysis of incomplete data sets, latent class analysis, survival, event-history and duration analysis, as well as item response theory and modeling. Dr. Raykov has published over 150 peer-reviewed articles in widely circulated and influential quantitative behavioral and social science journals.  He has co-authored several textbooks with Dr. G. A. Marcoulides: A First Course in Structural Equation Modeling (2006), An Introduction to Applied Multivariate Analysis (2008), Introduction to Psychometric Theory (2011), Basic Statistics: An Introduction with R (2012), and Item Response Theory and Modeling (2017). At Michigan State University, he has been teaching courses in structural equation and latent variable modeling, psychometric theory (behavioral and social science measurement), univariate and multivariate statistics, item response theory, multilevel modeling, as well as units on missing data analysis, survival analysis, latent class analysis, and statistical software (statistics with Stata and with R).

Past Instructors

Chris Aberson, Alex Beaujean, Alan Agresti, Todd Bodner, Clyde Dent, Craig Enders, David Gerbing, Ron Heck, Nathalie Huguet,  Jong Sung Kim, Rex Kline,  Stephanie Lanza, Dave MacKinnon, Jack McArdle, Jason Newsom, Jeffrey A. Smith, Mo Wang, Hyeyoung Woo.

 

Reviews from Past SQMS Participants

 

"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."