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2018 Summer Quantitative Methods Series at Portland State University



This 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, 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, 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.


Introduction to R for Statistical Analysis , June 17, 2018

David Caughlin, PhD, Portland State University

This course will provide an introduction to data analysis using R. Hands-on tutorials will teach participants how to import (i.e., read), manipulate (e.g., merge, subset, filter), and export (i.e., write) data, run basic descriptive statistics (e.g., mean, standard deviation) and inferential statistics (e.g., correlation, regression), and generate simple data visualizations (e.g., bar chart, scatterplot). Each participant will have access to a desktop computer with R already installed but should feel welcome to bring a personal laptop. This course is designed for individuals who already have a basic understanding of concepts related to data manipulation, measurement, inferential statistics, and some statistical software experience; no prior experience with R or other programming languages is required or expected.

David Caughlin, PhD, is an Instructor of Management in the School of Business at Portland State University. He is an industrial/organizational psychologist whose research areas include supervisor support and occupational safety and health. Recently, he began investigating how managers and HR professionals comprehend and make decisions based on data visualizations and dashboards. David teaches courses on topics related to human resource management, human resource information systems, and human resource analytics.


Data Visualization with R, June 18, 2018
David Gerbing, PhD, Portland State University

This course provides the basics to generate graphics with the R system of data analysis. Two visualization systems are explored. One system is the instructor's lessR system, which requires minimal investment to obtain a wide range of presentation ready visualizations accompanied by the corresponding statistical analyses, what most analysts want most of the time. The other system is ggplot2, which has become the standard R visualization system, essentially an extensible tool-kit that can produce both standard and customized visualizations. The seminar will apply both systems to generate visualizations for univariate distributions of categorical variables and for continuous variables, for the joint distribution of two variables for combinations of categorical and continuous variables such as scatterplots, and for distributions of continuous variables over time. The ggplot2 system will also be used to generate some geographic maps. All code presented in the seminar will be available to participants in the form or R markdown files for straightforward reproduction of all the presented visualizations.

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.

Advanced R Programming for Statistical Analysis and Data Management, June 19, 2018
Joel S. Steele, PhD, Portland State University

This workshop introduces attendees to advanced programming concepts in R. Attendees will learn common programming techniques to increase their efficiency when using R for statistical analysis. Topics will include how to write functions for reusing code and automating tasks, advanced data manipulation using base-level functions and R packages such as dplyr, for advanced data manipulation, and sqldf, for incorporating Structured Query Language (SQL) into data analysis. Attendees will also be introduced to automated report generation using markdown and knitr. Although desktop computers with necessary software will be provided, attendees are encouraged to bring their own laptop computers capable of running R statistical software and RStudio. A basic understanding of concepts related to data manipulation, descriptive statistics, and inferential tests  and some prior experience using R for statistical analysis are assumed.

Joel Steele is Associate Professor of Psychology at Portland State University. He earned his doctorate in quantitative psychology from University of California Davis in 2011. His work focuses on longitudinal data analysis and spans multiple substantive disciplines. He regularly teaches courses on univariate and multivariate statistics, longitudinal data analysis using structural equation modeling and mixed effects modeling, and introduction to statistical computing with R. Joel also regularly provides students with individual directed learning opportunities covering a host of topics including: Bayesian analysis, computational skills for data science, and dyadic data analysis.


Introduction to Structural Equation Modeling using R, June 20-21, 2018
Alex Beaujean, PhD, Baylor University

This course will provide an introduction to latent variable models. At the end of the course, participants should be able to complete (a) a path analysis, (b) confirmatory factor analyses with a single group and multiple groups, and (c) a power analysis using Monte Carlo methods. Presentations will be conceptual and rely on path models. All analyses will be illustrated using the lavaan package in R. Suggested background includes familiarity with multiple regression and some basic concepts in psychological assessment, such as reliability.   A copy of Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (Loehlin & Beaujean, 2016) is complimentary with the course registration.

Alex Beaujean, Ph.D., is Associate Professor of Psychology and Neuroscience at Baylor University. His research area is in individual differences, with a particular focus on quantitative methods and psychological assessment. At Baylor, he teaches a variety of courses, including latent variable models, applied regression, psychometric theory, meta-analysis, and psychological assessment. He has published many quantitatively-focused articles and chapters, including two books: Latent Variable Modeling using R: A Step-by-Step Guide (2014) and the fifth edition of Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (2016, co-authored with John Loehlin).


Longitudinal Structural Equation Modeling, June 22-23, 2018

Jason T. Newsom, PhD, Portland State University 


This course is an introduction to the range of options available for analyzing longitudinal data with structural equation modeling.  Topics will include longitudinal invariance, definitions of change and stability, cross-lagged panel models, linear and non-linear latent growth curve models, latent difference score models, survival analysis models, time series models, and missing data and attrition.  The course will include examples of structural models with continuous and categorical variables.  The essential statistical concepts and applications will be covered, illustrating models using Mplus and lavaan, the R package.  Participants are expected to have completed a graduate-level course in SEM, to have taken the preceding structural equation modeling workshop, or to have commensurate experience.  A copy of Dr. Newsom's book, Longitudinal Structural Equation Modeling:  A Comprehensive Introduction (Routledge, 2015), is included in the registration fee.


Jason T. NewsomPh.D, Professor of Psychology at Portland State University, is a social psychologist with 25 years of experience teaching statistics, research methods, and advanced applied statistics topics such as structural equation modeling and hierarchical linear models. He is author of Longitudinal Structural Equation Modeling:  A Comprehensive Introduction (Routledge, 2015), the editor (with Richard N. Jones and Scott M. Hofer) of Longitudinal Data Analysis:  A Practical Guide for Researchers in Aging, Health, and Social Science (Routledge, 2012), and has over 60 publications in journals ranging from Structural Equation Modeling to Health Psychology to Social Science and Medicine.  His interests include structural equation modeling, longitudinal analysis, measurement, and social relationships and health behaviors among older adults.

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."