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2014 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.

 

Longitudinal Structural Equation Modeling, June 14-15, 2014

J.J. McArdle, Ph.D, University of Southern California

 

The purpose of this two-day workshop will be to introduce longitudinal structural equation modeling (SEM) to a group of active researchers familiar with SEM. Topics of the first portion of the workshop will include the logic of Autoregressive Time Series Models (ARTS), Latent Curves Models (LCM), Latent Change Score Models (LCS), and Multiple Group LCS. A theoretical basis of each will be presented, illustrated with practical examples using R and the associated lavaan SEM computer program. The second portion of the workshop will present an exploratory data mining perspective, discussing the Multiple Group LCS from a data mining perspective and introducing the R function, SEMtrees, created partially by the author (with colleagues).  SEMtrees is an exploratory data mining tool that can be used to divide data into groups when there are many measured covariates (>10). SEMtrees will be contrasted with a latent mixture modeling approach. The workshop will partially follow content of McArdle's recent book with Gilbert A. Ritschard Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences (2013, Routledge), and all registrants will receive a copy as part of the enrollment fee.  Familiarity with basic SEM techniques is expected and some experience with R code will be helpful.

 

John J. (Jack) McArdle, Ph.D., is now Senior Professor of Psychology at the University of Southern California (USC) where he heads the Quantitative Methods training program. He received a BA from Franklin & Marshall College in 1973 and the PhD from Hofstra University (in Hempstead, NY) in 1977 and moved to the University of Denver as an NIH Postdoctoral Fellow to work with Dr. John L. Horn.  In 1984 he moved to the University of Virginia to start a quantitative methods program, and in 2005 he moved to USC (with John L. Horn) to do the same. Professor McArdle is the author of several books, including the upcoming Longitudinal Data Analysis Using Structural Equation Modeling (with J.R. Nesselroade, APA Books) and Contemporary Issues in Exploratory Data Mining (with G. Ritschard, Routledge Press).  He was recently awarded an NIH-MERIT grant from the National Institute on Aging for his work on “Longitudinal and Adaptive Testing of Adult Cognition.” (2005-2016), and named as a Fellow of the American Association for the Advancement of Sciences (AAAS, 2013). He has also been heavily involved with research on the Academic Skills of College Student Athletes with the National Collegiate Athletics Association (NCAA) since 1988.

 

Introduction to Structural Equation Modeling, June 16-17, 2014

Rex B. Kline, Concordia University, Montréal

 

This two-day seminar deals with the principles, assumptions, strengths, limitations, and applications of structural equation modeling (SEM). Basic SEM techniques, including path analysis, confirmatory factor analysis (CFA), and full “LISREL” (structural regression [SR]) models are covered. Some familiarity with basic statistical techniques, such as multiple regression and exploratory factor analysis, is assumed, but higher levels of quantitative knowledge are not required. The presentation of topics will be conceptually rather than mathematically oriented, and examples of the application of SEM to different kinds of actual research problems are considered. There will also be an opportunity for those with no experience using a computer for SEM to practice on-site with the student version of LISREL. The presentation of topics will be conceptually rather than mathematically oriented, and examples of the application of SEM to different kinds of actual research problems are considered. There is a strong emphasis on avoiding common mistakes in SEM. Computer tools are described, and participants will gain hands-on practice with LISREL, a widely used computer application. Even if participants eventually use a different SEM computer tool in their own work, principles learned from working with LISREL will generalize to related applications. Enrollment fee includes a copy of Dr. Kline's book, Principles and Practice of Structural Equation Modeling, Third Edition (2010, Guilford)

 

Rex B. Kline, PhD, is professor of Psychology at Concordia University in Montréal. Since earning a doctorate in clinical psychology, his areas of research and writing have included the psychometric evaluation of cognitive abilities, child clinical assessment, structural equation modeling, training of behavioral science researchers, and usability engineering in computer science. Dr. Kline has published six books, nine chapters, and more than 40 articles in research journals.

 

 

Using R with lessR and Other Enhancements for Straightforward,

Free, Powerful, Cross-Platform, Statistical Analysis

David Gerbing, Ph.D., Portland State University

 

This workshop introduces the R environment and lessR, an R extension that removes the need for programming for statistical analysis as well as other enhancements.  A variety of data analysis examples, including descriptive analyses and graphs, t-tests, ANOVA, and multiple regression, exploratory factor analysis, and confirmatory factor analysis, will be used to explain and illustrate the software. The application of statistical applications to data analysis is undergoing a dramatic transformation as the R programming environment for data analysis experiences ever greater levels of adoption in a wide variety of academic, commercial, non-profit and government settings. The graphical and numerical capabilities of R are on par with the leading commercial statistical packages, yet R is available as a free download to anyone with a Windows, Macintosh or Linux/Unix computer. R provides a vibrant, cutting-edge, responsive collection of statistical and graphical functions that by themselves have one major liability: the learning curve to entry is steep. To address this issue, the lessR contributed functions developed by the author are designed to enhance accessibility within the traditional R environment.  With lessR, accomplish basic data analysis with simple function calls instead of writing code. The goal is that the use of R becomes so straightforward that R becomes the chosen environment for data analysis.  A copy of Dr. Gerbing's recent book, R Data Analysis Without Programming (2014, Routledge), is included in the registration fee.

 

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, Routledge), 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.

 

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."