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2011 Summer Quantitative Methods Series at Portland State
University
Courses
This
Summer Quantitative Method Series is comprised of one- and 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 as 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. For courses with computer lab time, 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.
An Introduction
to Modern Missing Data Handling Techniques, June 13-14 Craig Enders, Ph.D., 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. Ender's recent book on missing data analysis. Craig Enders, Ph.D., is an Associate 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 recently published with Guilford Press. Applied Power
Analysis, June 15 Christopher L. Aberson, Ph.D., Humboldt State University This one-day workshop addresses theoretical and practical
power analysis considerations for research using a wide range of designs. The
primary focus of the workshop will be “how-to” examples for
conducting analyses using the widely available, standard software package for
SPSS and the freeware program G*Power. Topics include power for designs
employing t-tests, chi-square, ANOVA (between, within, and mixed between and
within designs), multiple regression, and logistic regression. An
understanding of the basic concepts of statistical power and effect size,
consistent with the coverage in most introductory statistics textbooks, will
be helpful. Attendees will receive a packet demonstrating use of SPSS syntax
and other materials for analyses and a copy of the Dr. Aberson's
recent book on power analysis. Chris Aberson, Ph.D., Professor of Psychology at Humboldt
State University earned his doctorate at the Claremont Graduate University in
1999. His topical research interests include prejudice, racism, and attitudes
toward affirmative action. His text, Applied Power Analysis
for the Behavioral Sciences (Routledge
Psychology Press) was published in 2010. Hierarchical Linear Models and Their
Applications, June 16-17
Jason T.
Newsom, Ph.D., Portland State University
This
course is intended to introduce participants to multilevel regression
techniques, also known as hierarchical linear models or random coefficient
models. Material is presented with an applied researcher's perspective in
mind, covering fundamental concepts, basic mathematical and statistical
underpinnings, and illustrations using computer software (HLM, SPSS, &
SAS examples). Topics include nested data and growth curve applications,
missing data, centering, statistical assumptions, and sample size issues.
Participants are assumed to have a prior understanding of multiple regression
analysis, but no prior knowledge of multilevel models is necessary.
Jason T. Newsom, Ph.D,
Associate 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 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, expected July 2011) and has over 40 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
among older adults.
Introduction to R/lessR, June 18 Using R and the lessR Enhancements to Facilitate Ease of Use for a Free, Powerful, Cross-Platform, Open-Source System for Statistical Analysis David Gerbing,
Ph.D., Portland State University The practice of statistics and data analysis is undergoing a dramatic transformation as the statistical programming environment of R is experiencing 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 proprietary statistical packages, but R is available as a free download to anyone with a Windows, Macintosh or Linux computer. Further, R is extensible so that anyone is able to contribute additional statistical functions which the user is free to separately download. The result is a vibrant, cutting-edge collection of statistical and graphical functions which traditionally have had one major liability: the learning curve to entry can be steep. To address this issue, the lessR contributed functions developed by the author are designed to enhance accessibility within the traditional R environment with continued access to all traditional functions. This workshop introduces the R environment as well as the lessR enhancements and provides multiple examples of their use. 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 published one textbook, 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. One paper on structural equation modeling now lists over 7300 citations by scholar.google.com. He has been continuously teaching statistics since 1978 to many students in different contexts from large undergraduate sections to small Ph.D. seminars. 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." |
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