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2016 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.

 

Multilevel Structural Equation Modeling, June 13-14, 2016
Ron Heck, Ph.D., University of Hawaii at Manoa 

This course provides an introduction to the range of options available for analyzing multilevel data with structural equation modeling (SEM) techniques.  Topics will include a brief overview of multilevel regression models (with univariate and multivariate observed outcomes) but will focus primarily on defining and testing multilevel models with latent and observed variables (i.e., two- and three-level confirmatory factor models, structural models, and mixture models). The course will address multilevel SEM for continuous and categorical measured variables. The focus of the course is more applied in nature–that is, specifying, testing, and interpreting output from basic types of multilevel models using Mplus (i.e., the demo version). Ideally, participants should have an understanding of multiple regression and some exposure/familiarity with exploratory factor analysis and SEM techniques. Attendees will receive a copy of Dr. Heck's book  An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus, Third Edition (Routledge).

Ron Heck, Ph.D, is professor of educational administration and policy at the University of Hawaii at Manoa. He has over 20 years of experience teaching organizational theory and research methods including SEM and multilevel modeling. He is author (with Scott Thomas) of An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus (2015),  (with Scott Thomas and Lynn Tabata) Multilevel and Longitudinal Modeling with IBM SPSS, Second Edition (2014), and (with Scott Thomas and Lynn Tabata) Multilevel Modeling of Categorical Outcomes Using IBM SPSS (2012). His research interests focus on organizational and policy-related applications of multilevel modeling (e.g., school and teacher effects on student learning).

 

Categorical Data Analysis, June 15-16, 2016

Alan Agresti, PhD, University of Florida

This short course surveys the most common methods for analyzing categorical data. The first day focuses on contingency table analysis, logistic regression for binary data, logistic model-building including dealing with infinite estimates, and loglinear models. The second day introduces logistic models for ordinal responses (emphasizing cumulative logit models), multinomial logit models for nominal responses, probit models, and the analysis of clustered, correlated data using generalized estimating equations (GEE) and random effects. The presentation emphasizes interpretation rather than technical details. Examples use R and SAS, with some information also given about Stata and SPSS for implementing the methods. Attendees will receive a copy of Dr. Agresti's Categorical Data Analysis, 3rd edition (Wiley).

 

Alan Agresti is Distinguished Professor Emeritus, Department of Statistics, University of Florida. He has written seven books, including Categorical Data Analysis, 3rd edition (2013), which has received nearly 20,000 citations, Foundations of Linear and Generalized Linear Models (2015), Statistical Methods for the Social Sciences, 5th edition (2017), and Statistics: The Art and Science of Learning from Data, 4th edition (2016). He has received many honors and has lectured on categorical data methods in more than 30 countries.

 

Review of Basic Data Analysis Procedures and the Tools to Accomplish Them, June 17-18, 2016

David Gerbing, PhD, Portland State University

This course is designed for researchers or other professionals who need an introduction or refresher on how to analyze data sets.  Data analysis is becoming an increasingly important skill given the wealth of data now available and the modern computing resources to process those data.  This course reviews the basic data analysis concepts from the perspective of the motivation for the analysis and its interpretation. The general topic is the how and why of data analysis. It is not a course in statistics and statistical theory, but in the application of statistical concepts as they pertain to answering real world questions. The course begins with bar charts, histograms and scatter plots that help to describe and visualize data. Next is the extension to inferential statistics with the comparison of means with t-tests and ANOVA and the concepts of confidence intervals and hypothesis tests, always illustrated in the context of analyzing data. The course provides a central role to the primary data analytic tool of regression analysis, and its variant logistic regression. Basic time series concepts are also reviewed. All examples are provided using the author's extension to R data analysis software called lessR, both freeware programs. The lessR extensions greatly simplify the use of the popular R system, so that in class we can concentrate on meaning and interpretation, yet everyone will be able to run the examples and similar analyses on their own outside of class.  A copy of Dr. Gerbing's book R Data Analysis without Programming (Routledge) is included for each participant.

 

David Gerbing, Ph.D., Professor of Quantitative Methods, School of Business Administration, Portland State University, received his B.A in psychology from what is now Western Washington University in 1974 and his Ph.D. in quantitative psychology from Michigan State University in 1979. From 1979 until 1987 he was Assistant Professor and then Associate Professor of Psychology and Statistics at Baylor University. He has authored R Data Analysis without Programming (2014), and many articles on statistical techniques and their application in a variety of journals that span several academic disciplines including Psychology, Sociology, Business and Education.

 

 

Past Instructors

Chris Aberson, Alex Beaujean, 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."