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2009 Summer
Quantitative Methods Series at
Courses This
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. The goal of the
Series 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 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, in
some cases, 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 Contact Jason
Newsom, Series Director, with general questions. Contact
individual instructors with course-specific questions (emails links below). Longitudinal Analyses for the Social Sciences, June 15-16, 2009Jason
T. Newsom, Ph.D., Associate Professor, Institute on
Aging, This
course is an introduction to the range of data analysis options available for
analyzing longitudinal data.
Statistical analysis topics will include repeated-measures approaches
to ANOVA, analysis of categorical data, regression, logistic regression,
growth curve analysis, path analysis and structural equation modeling of
panel data, and time-series analysis.
In addition to these statistical topics, the course will begin with a
discussion of issues related to longitudinal research design, such as the
advantages of longitudinal data over cross-sectional data, types of
longitudinal designs, internal validity, measurement issues, and data
collection issues. Although this course cannot include an exhaustive
treatment of each statistical analysis, material will include the concepts,
statistical underpinnings, and applications pertaining to each analysis using
illustrations in SPSS, SAS, Amos, HLM, and Mplus software. Participants should have a
graduate-level exposure to ANOVA and regression. Jason T. Newsom, Ph.D, Associate Professor in the Introduction to Factor Analysis and Structural Equation Modeling,
June 17-18, 2009 Todd
Bodner, Ph.D., Associate Professor, Department of
Psychology, This
short course will illustrate the uses of factor analysis and structural
equation modeling in summarizing data, exploring underlying constructs behind
the observed measures (e.g., survey responses), and investigating causal
relationships among the underlying constructs. Using examples from behavioral
science, social science, and marketing research to illustrate, material is
organized according to the steps involved in research applications: (1)
deciding the appropriate statistical model for a research question; (2) data
file construction and variable coding; (3) evaluating the quality of the
estimated model; (4) interpreting computer output; and (5) presenting
results. SPSS and AMOS software
will be used to illustrate these steps.
Participants should be familiar with regression analysis as
prerequisite knowledge, but no previous familiarity with measurement or
structural equation modeling is assumed. Todd
Bodner, Ph.D.,
Associate Professor in Department of Psychology at Portland State Uniersity,
is a Introduction to Survival Analysis, June 19-20, 2009
Jong-Sung Kim, Ph.D., Associate Professor, Department of
Mathematics & Statistics, This course introduces survival analysis to
students or practitioners from a variety of backgrounds, including health
sciences, biology, computer science and engineering, and social sciences.
Survival analysis is required when a discrete event is observed over a finite
period of time (e.g., disease diagnosis, bankruptcy filing, divorce,
equipment failure), leading to "censoring" of the data that
requires a special approach. Analyses will be illustrated using SPSS, R (a
freeware program), and SAS .
Topics of this course include types of censored data, difference
between survival analysis and the classical methods for uncensored data,
Kaplan-Meier estimator, nonparametric and parametric models, accelerated
failure time models, Cox proportional hazards models, model selection, model
assumptions,, competing risks, analysis of left-truncated and right-censored
data, and time-dependent covariates. A prior understanding of multiple
regression analysis but no experience with S-plus, R, or SAS is assumed. Jong S. Kim, Ph.D, Associate Professor, Department of Mathematics & Statistics, is
an nationally recognized scholar with publications in top journals such as Journal
of the Royal Statistical Society, B, Computational Statistics and Data
Analysis, Biometrical Journal, Journal of Clinical Oncology,
Journal of Social Service Research, and Economic Systems. He is co-author (with Mara Tableman) of
the book, Tableman,
M., & Kim, J-S. (2004). Survival Analysis Using S: Analysis
of Time-to-Event Data. Boca Raton, FL: Chapman & Hall/CRC. Dr. Kim is the recipient of the John Eliot Allen Outstanding Teaching Award in Statistics and has
extensive experience teaching courses in survival analysis, experimental
design, and bootstrapping and resampling methods. His research interests
include survival analysis, computational and graphical statistics,
longitudinal studies, medical and epidemiological data analysis, econometrics
and finance, and experimental design and regression analysis. Secondary Data and Complex Survey Design, June 22-23, 2009 This
course introduces researchers, students, and professionals to issues related to accessing, managing,
and analyzing large survey
datasets, particularly those surveys collected using complex
sampling designs. Topics will include identifying,
locating, and accessing secondary
sources; managing large survey datasets; complex survey sampling methods; analyses involving
sampling weights and variance
adjustments.
Statistical concepts and computer applications will be included, with illustrations using
SUDAAN, SAS, and SPSS. Clyde W. Dent , Ph.D., Senior Research Scientist at the Oregon Public Health Division,
has been involved for over 25 years in research that examines the onset,
prevention, and cessation of health compromising behaviors in large-scale,
school-based, medical clinic, and community contexts. He has expertise in
survey methodology, general evaluation methodology, study design, and
statistical analysis. As part of his interest in quantitative techniques, he
regularly conducts secondary analysis research involving the linking of large
databases. He is particularly interested in health issues among adolescent
populations. Prior to his position at the Oregon Public Health Division, Dr.
Dent was Professor of Preventive Medicine at the Nathalie Huguet, Ph.D., Research Associate,
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