


2018 Summer Quantitative Methods Series at Portland State University Courses
This Summer Quantitative Method
Series is comprised of oneday or 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.
Past instructors include Chris Aberson, Alex Beaujean, Alan Agresti, Todd Bodner, SyMiin 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 NylundGibson, 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
coursespecific questions.
Introduction to R for
Statistical Analysis , June 17, 2018 David
Caughlin, PhD, Portland State University
Data Visualization
with R, June 18, 2018
Introduction to
Structural Equation Modeling using R, June 2021, 2018 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.
Longitudinal
Structural Equation Modeling, June 2223, 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, crosslagged panel models, linear and nonlinear 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 graduatelevel 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. Newsom, Ph.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.
"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." 

