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2019 Summer Quantitative Methods Series at Portland State University



The Summer Quantitative Method Series is comprised of one-day or 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.


Past instructors include Chris Aberson, Alex Beaujean, Alan Agresti, Todd Bodner, David Caughlin, Sy-Miin Chow, Clyde Dent, Craig Enders, David Gerbing, Ron Heck, Nathalie Huguet, Jong Sung Kim, Rex Kline, Stephanie Lanza, Dave MacKinnon, Jack McArdle, Jason Newsom, Karen Nylund-Gibson, Tenko Raykov, Jeffrey A. Smith, Joel Steele, Mo Wang, Hyeyoung Woo.


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.


Measurement Development and Evaluation, June 16-17, 2019

Deborah Bandalos, PhD, James Madison University


This workshop is designed for those interested in developing attitude, personality, opinion, or other noncognitive scales (i.e., excluding aptitude testing) for use in research studies. We will begin by reviewing current recommendations for best practices in noncognitive scale construction. In particular, we will review the research on such topics as the impact of vaguely worded and of negatively worded items on scale reliability and validity, the optimal length of a survey, how many scale points to include, whether scale points should be labeled or unlabeled, whether to include a neutral option, and how item order effects may impact responses. We then focus on the use of exploratory and confirmatory factor analysis (EFA and CFA) in the scale development process. In this part of the workshop we will focus on basic model set-up and interpretation of model parameters, and how to use the information from these analyses to inform scale development. Multiple analysis illustrations with real data sets will be presented in Mplus software. All registrants will receive a copy of Dr. Bandalos's recent book, Measurement Theory and Applications for the Social Sciences (2018). Familiarity with statistical hypothesis testing and basic analyses (t-tests, correlation, regression) is assumed, but no prior experience with Mplus is required.


Deborah Bandalos, Ph.D., is a Professor and Director of the Assessment and Measurement PhD program within the Department of Graduate Psychology at James Madison University. Her research areas include scale development, exploratory factor analysis, and structural equation modeling. She has taught courses in measurement theory, exploratory factor analysis, latent variable models, and missing data methods. Dr. Bandalos is the author of Measurement Theory and Applications for the Social Sciences (Guilford, 2018) and has published a variety of articles and book chapters on statistical methods and their applications.


Canceled Introduction to Linear Models and Beyond with Stata, June 18-19, 2019

Alan Acock, PhD, Oregon State University


This course is designed for investigators and analysts in the health, behavioral, and social sciences who are interested in using Stata to analyze their applied research projects. A general introduction to Stata analysis software will review its menu system and its remarkably simple and consistent command language. The software overview will be followed by an introduction to the most widely implemented analyses in applied research using extensive examples and without assuming extensive statistical background knowledge:  logistic regression models, multiple regression, and modern missing data handling approaches. The logistic regression introduction makes use of a number of tools in Stata that are valuable for explaining the analysis results. The coverage of multiple regression analysis will involve graphic interpretations of multiple regression models, including interactions. Missing data analysis will focus on two methods of multiple imputation (maximum likelihood and chained equations) along with a simple way to use full information maximum likelihood using a single command. Stata code and dataset will be provided and registration will include a copy of Dr. Acock's book, A Gentle Introduction to Stata, 6th ed. (2018). No prior experience with Stata is assumed, but basic knowledge and experience with research design, some statistical analyses, and hypothesis testing are expected.


Alan Acock, PhD, is a University Distinguished professor emeritus from Oregon State University. In addition to over 150 refereed publications he has two books on Stata (A Gentle Introduction to Stata, 6th ed., 2018 and Structural Equation Modeling, Revised ed., 2013) published by Stata Press.

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."