



2016 Summer Quantitative Methods Series at Portland
State University
Courses
This
Summer Quantitative Method Series is comprised of twoday 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 oneonone assistance
available when running computer examples. Some graduatelevel 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
coursespecific questions.
Multilevel
Structural Equation Modeling, June 1314, 2016 Categorical Data
Analysis, June 1516, 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 modelbuilding 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, 3^{rd} edition (Wiley). Alan Agresti is Distinguished Professor
Emeritus, Department of Statistics, University of
Florida. He has written seven books, including Categorical Data Analysis, 3^{rd} edition (2013), which
has received nearly 20,000 citations, Foundations
of Linear and Generalized Linear Models (2015), Statistical Methods for the Social Sciences, 5^{th} 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 1718, 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 ttests 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. 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 realworld, layman's terminology used to describe the results of models, as well." "The reading list and online resources are great for followup." 



