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2015 Summer Quantitative Methods Series at Portland State University



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.


Applied Missing Data Analysis, June 13-14, 2015

Craig Enders, Arizona State University


There have been substantial methodological advances in the area of missing data analyses during the last 25 years. Methodologists currently regard maximum likelihood estimation (ML) and multiple imputation (MI) as two state of the art missing data handling procedures. The purpose of this course is to familiarize participants with ML and MI and to demonstrate the use of these techniques using popular software packages (SPSS, SAS, Mplus). The goal of this course is to provide participants with the skills necessary to understand and appropriately implement ML and MI in their own research. To this end, the course will provide a mixture of theoretical information and computer applications. The course content will be accessible to researchers with a foundation in multiple regression.  Attendees will receive a copy of Dr. Enders' recent book Applied Missing Data Analysis (2010, Guilford).


Craig Enders, Ph.D., is a Professor in the Quantitative Psychology concentration in the Department of Psychology at Arizona State University. The majority of his research focuses on analytic issues related to missing data analyses. His book, Applied Missing Data Analysis, was published with Guilford Press in 2010.



Introduction to Social Network Analysis, June 15, 2015

Jeffrey A. Smith, University of Nebraska-Lincoln


Social network analysis has experienced a dramatic increase in popularity over the last 15 years. Network techniques are now utilized by a wide range of academic disciplines, as well as by some of the most successful companies in the world (amazon, twitter, google…). Network analysis is a unique approach as the focus is on the relationships connecting actors, rather than on the actors themselves. This class offers an introduction to social network analysis. Students will learn how to analyze, visualize and interpret network data using R, a free statistical language and platform. By the end of the class students should be able to: understand the unique features of network data; read network data into R; compute simple network measures; graph a network; and understand the potential and limitations of a network approach. This is an introductory course and there are no mathematical or programming prerequisites.


Jeffrey A. Smith, Ph.D., is an Assistant Professor in sociology at the University of Nebraska-Lincoln. His research interests broadly include networks, quantitative methodology, and stratification. He has done methodological work on network sampling and missing data and is completing a book (with Dan Mcfarland and James Moody) on network analysis with R. His substantive research area focuses on social distance, status, and homophily.


Latent Variable Modeling using R, June 16-17, 2015
A. Alexander Beaujean, Baylor University

The use of the open-source R statistical programming language has grown considerably over the past decade and some estimate that it is the most widely used analytics software for scholarly articles. Recently, some R packages have been developed for latent variable models (LVMs) that can analyze the same types of models as more traditional LVM software. The purpose of this course is to familiarize participants with the R programming language and work through a variety of LVM examples. At the end of the course, participants should be able to estimate basic LVMs with their own data using R. The course will focus largely on computer applications. Presentation of LVMs will be conceptual, using path models rather than matrices. Some familiarity with multiple regression is assumed; while previous experience with LVMs will be helpful, it is not required. Attendees will receive a copy of Dr. Beaujean's recent book, Latent Variable Modeling Using R: A Step-By-Step Guide (2014, Routledge). on LVM analysis in R.

A. Alexander Beaujean, Ph.D., is an Associate Professor in the Department of Educational Psychology at Baylor University. He received a BA from Cedarville University and PhDs from the University of Missouri. The majority of his research focuses on individual differences, including their structure and measurement. His book, Latent Variable Modeling using R: A Step-by-Step Guide, was recently published with Routledge Press.


Longitudinal Structural Equation Modeling, June 18-19, 2015

Jason T. Newsom, Ph.D., 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, repeated-measures approaches to comparing mean and proportions with SEM,  definitions of change and stability, cross-lagged panel models, linear and non-linear latent growth curve models, latent difference models, survival analysis models, time series models, and missing data and attrition.  The course will address structural modeling for continuous, binary, and ordinal measured variables.  The essential statistical concepts and applications will be covered, illustrating models using Mplus and lavaan, the R package.  Participants are expected to have had a graduate-level course in SEM, the preceding latent variable workshop, or commensurate experience. All enrollees will receive a copy of Dr. Newsom's book, Longitudinal Structural Equation Modeling:  A Comprehensive Introduction (Routledge, 2015), included in the registration fee.


Jason T. Newsom, Ph.D, Professor at the Institute on Aging and the School of Community Health, is a social psychologist with 20 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 50 publications in journals ranging from Structural Equation Modeling to Health Psychology to Social Science and Medicine.  His interests include 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."