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2010 Summer Statistics Institute Courses
----------------------------------------
Attention: open in a new window. PDFPrintE-mail

Click on a course title to see its description, course outline, &
prerequisites.
Download the full 2010 SSI brochure (includes more detailed info,
e.g., instructor bios)
Cancellation Policy

Category
Morning (9:00 AM - 12:00 PM)

Afternoon
(1:30 PM - 4:30 PM)
Introductory Topics

(no statistical experience necessary)
Introduction to Statistics

Questionnaire Design & Survey Analysis
The Fundamentals of Statistics

Introduction to Software
Introduction to R

Introduction to SPSS
Introduction to SAS

Introduction to Stata
Intermediate & Advanced Topics

Common Mistakes in Using Statistics - Spotting Them and Avoiding Them
Event History Analysis

Hierarchical Linear Modeling
Power Analysis for Proposal Writing

Regression Analysis
Bayesian Statistics for Social Sciences

Data Analysis and Mining
Hierarchical Linear Modeling

Spatial Analysis and GIS
Structural Equation Modeling

Time Series Analysis
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Course Descriptions (9:00 AM- 12:00 PM):
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Introduction to Statistics
Time: 9:00 AM - 12:00 PM
Instructors: Dr. Natalie Stroud (Assistant Professor, Communication
Studies) & Dr. Dan Robinson (Associate Professor, Educational
Psychology)
Course Description: This course will emphasize (a) an introduction to
the computation of basic descriptive and inferential statistics using
Excel and (b) an interpretation of key statistical concepts.
Prerequisites: Familiarity with Microsoft Excel would be helpful.
Notes: Students should bring a personal laptop equipped with Microsoft
Excel and the Data Analysis Toolpak. This add-in comes with all
versions of Microsoft Excel except for Excel 2008 for Macintosh. Excel
2008 for Macintosh has many of the same functionalities, but presented
differently so be advised that it may be difficult to follow along in
class if you have this version of Excel. Click HERE for more
information. A limited number of laptops are available for loan.
Please email This e-mail address is being protected from spambots. You
need JavaScript enabled to view it for more information.
Course Outline

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Introduction to R

Time: 9:00 AM - 12:00 PM
Instructor: Dr. Brandon Vaughn (Assistant Professor, Educational
Psychology)
Course Description: This four-day course focuses on a broad spectrum
of topics for the free statistical program R. We will learn the
essentials of installing R (Windows or Mac), importing/exporting data,
data manipulation, graphical visualization and R programming
(including the use of functions, libraries, and simulation). We will
explore the statistical data analysis tools (including hypothesis
testing and correlation/regression) with data sets from areas such as
education, psychology, and business. Participants are encouraged to
bring their own data for an interactive session with the instructor.
Various online resources for R will be discussed.
Prerequisites: A basic course in Statistics. Knowledge of correlation
and regression is preferred.
Notes: Students should bring a personal laptop. Installation of R will
be covered on the first day of the course. A limited number of laptops
are available for loan. Please email This e-mail address is being
protected from spambots. You need JavaScript enabled to view it for
more information.
Course Outline
---------------------------------------------------------------------

Introduction to SPSS
Time: 9:00 AM - 12:00 PM
Instructor: Dr. Michael Mahometa (Statistical Consultant, Division of
Statistics and Scientific Computation)
Course Description: This course will teach students how to perform
descriptive and inferential statistics on data in SPSS. Students will
also learn how to perform basic data mainipulations within SPSS.
Prerequisites: Students should have completed an introductory
statistics course within the last two years that included t-tests,
ANOVA, and ideally regression and correlation. Students should know
what the following terms mean: mean, standard deviation, p-value, and
frequency. If students do not meet the prerequisite, they should read
up on the above topics so as to be familiar with them.
Notes: Introduction to SPSS will be held in a computer classroom where
students will have access to SPSS.
Course Outline

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Common Mistakes in Using Statistics - Spotting Them and Avoiding Them

Time: 9:00 AM - 12:00 PM
Instructor: Dr. Martha Smith (Professor Emeritus, Mathematics)
Course Description: We often hear results of studies that appear to
contradict studies that were widely publicized just a couple of years
ago. Medical researcher, John P. Ioannidis has claimed that, in fact,
most claimed research findings are false. Most of his arguments
involve the misunderstanding and misuse of statistics. Sometimes
misunderstandings are passed down from teacher to student or from
colleague to colleague. In some cases, policies based on these
misunderstandings have become institutionalized. This workshop will
discuss some of these misunderstandings and misuses, and offer
suggestions for what teachers, readers, researchers, reviewers, and
editors can do to deal with this fact of life and to help improve the
situation.
Prerequisites: Familiarity with the basics of statistical inference
(random variable, sampling distribution, hypothesis testing,
confidence interval, simple linear regression, transformations of
random variables, especially the logarithm). Some acquaintance with
Analysis of Variance and Multiple Regression would be helpful but not
necessary.
Course Outline
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Event History Analysis
Time: 9:00 AM - 12:00 PM
Instructor: Dr. Dan Powers (Associate Professor, Sociology)
Course Description: The purpose of this course is to introduce and
apply methods and models for event history analysis. Event history
analysis deals with methods for events occurring in time. This topic
is also known as Survival Analysis, and includes the study of methods
and models for the analysis of transition rates. This is an applied
course that will draw on data from sociology, demography and health
fields. The course will provide in-depth treatment of the most
widely-used methods for event-history analysis. This course should be
useful for graduate students in the social, behavioral, biological,
and health sciences as well as applied researchers.
Prerequisites: Students should have had a course in basic statistics
that covers probability distributions, random variables, statistical
testing (t-test, F-test, Chi-Square test, etc.), in addition to a
course in linear regression. Calculus is not required or assumed, but
the results of differential and integral calculus operations will be
used and analytic notation of these operations will be presented (and
explained).
Course Outline

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Hierarchical Linear Modeling (AM)

Time: 9:00 AM - 12:00 PM
Instructor: Dr. Keenan Pituch (Associate Professor, Educational
Psychology)
Course Description: This workshop will help you begin to learn how to
analyze multilevel data sets and interpret results of multilevel
modeling analyses. Contextual analysis and growth curve modeling, the
most common multilevel modeling applications, will be featured in the
workshop, with coverage of two-and three-level designs. Using data
sets provided in the workshop, students will learn how to use the HLM
software program to obtain analysis results. Further, the workshop
will emphasize proper interpretation of analysis results and
illustrate procedures that can be used to specify multilevel models.
Coverage of multilevel models for binary outcomes will also be
included.
Prerequisites: Students should be comfortable with the use and
interpretation of multiple regression. In particular, students should
be familiar with the use of dummy-coding for categorical independent
variables and the use of polynomial regression to model nonlinear
relationships. Prior exposure to logistic regression is helpful, but
not necessary.
Notes: Hierarchical Linear Modeling will be held in a computer
classroom where students will have access to SPSS and HLM software.
Course Outline
---------------------------------------------------------------------

Power Analysis for Proposal Writing
Time: 9:00 AM - 12:00 PM
Instructor: Dr. C. Nathan Marti (Manager of Consulting Services and
Lecturer, Division of Statistics and Scientific Computation)
Course Description: Power analysis is a critical component of research
planning that conveys the feasibility of achieving research goals with
finite amounts of time and resources. The course will begin with
strategies for research synthesis and effect size conversions that
will form the basis of estimating power. We will use GPower to cover
comparisons of means, comparisons of proportions, correlation,
analysis of variance (ANOVA), repeated measures ANOVA, and regression
models. Next, the course will cover simulation-based power analysis
methods, using examples that may include nested data, auto-correlated
data, and missing data.
Prerequisites: Familiarity with regression models.
Notes: Power Analysis for Proposal Writing will be held in a computer
classroom where students will have access to the following software:
R, Mplus, and Sample Power.
Course Outline

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Regression Analysis

Time: 9:00 AM - 12:00 PM
Instructor: Dr. Christopher Jablonowski (Assistant Professor,
Petroleum and Geosystems Engineering)
Course Description: The objective of this course is to provide an
introduction to regression analysis, including motivation, basic
theory, applications, and common mis-specification issues.
Prerequisites: Familiarity with the basics of the statistical
inference (e.g., random variables, probability distributions, sample
statistics, hypothesis testing, confidence intervals).
Notes: Students should bring a personal laptop equipped with Microsoft
Excel and the Data Analysis Toolpak. This add-in comes with all
versions of Microsoft Excel except for Excel 2008 for Macintosh. Excel
2008 for Macintosh has many of the same functionalities, but presented
differently so be advised that it may be difficult to follow along in
class if you have this version of Excel. Click HERE for more
information. A limited number of laptops are available for loan.
Please email This e-mail address is being protected from spambots. You
need JavaScript enabled to view it for more information.
Course Outline
---------------------------------------------------------------------

Course Descriptions (1:30 PM - 4:30 PM):
----------------------------------------
---------------------------------------------------------------------

Questionnaire Design and Survey Analysis
Time: 1:30 PM - 4:30 PM
Instructor: Dr. Marc Musick (Associate Professor and Associate Dean in
the College of Liberal Arts, Sociology)
Course Description: The goal of this course is to introduce students
to the construction and analysis of social surveys. In the first part
of the course, students will be taught the tools needed to 1) create
effective and reliable questions, 2) craft questionnaires that could
be used in multiple settings (e.g., telephone, written, web-based), 3)
test questionnaires to ensure their effectiveness, and 4) design
implementation strategies that will increase the likelihood of good
response rates. The second part of the course will focus on
collecting, cleaning and analyzing survey data.
Prerequisites: An introductory social research class would be helpful,
but is not necessary.
Course Outline

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The Fundamentals of Statistics

Time: 1:30 PM - 4:30 PM
Instructor: Dr. James Bryant (Lecturer, Biological Sciences)
Course Description: An appreciation of the importance of statistics to
academic pursuits has become apparent to most students, instructors
and researchers. It is readily appreciated that the majority of
subjects require the description and understanding of issues that are
not innately black and white. Statistics is a vital philosophical way
for us to more readily comprehend and understand the world around us
and our daily lives and not just our academic work.
This course is designed for an audience ranging from graduate students
requiring help with analysis and design of their research, to the
complete novice wanting to understand the fundamental concepts of
statistics. The course is designed to emphasize a simple systematic
approach to problem solving and will highlight the fundamental central
philosophies of statistics. Students completing the course will learn
how to:
ask appropriate questions

design surveys and controlled experiments
acquire information and supplies

efficiently summarize information for a wide variety of audiences
understand distributions, such as the sample distribution and the
 normal distribution

manipulate and recode data for analysis (80% of analysis)
draw statistically sound conclusions

assess the strength of conclusions
understand common and advanced statistical techniques

how to conduct multiple rounds of hypothesis testing
report research findings in a scientifically sound fashion

Prerequisites: NoneNotes: Day 4 will be spent working with SPSS.
Students should bring a personal laptop on the last day of the
workshop and a temporary license for SPSS will be provided. A limited
number of laptops are available for loan. Please email This e-mail
address is being protected from spambots. You need JavaScript enabled
to view it for more information.Course Outline
---------------------------------------------------------------------

Introduction to SAS
Time: 1:30 PM - 4:30 PM
Instructor: Dr. Matt Hersh (Specialist, Division of Statistics and
Scientific Computation)
Course Description: Purpose: To provide instruction in the use of SAS
for data handling and conducting statistical analyses. Content: Day 1
will cover opening of datasets and data manipulation. Days 2 and 3
will cover basic statistical analyses, including categorical analyses,
two-sample tests, ANOVA, correlation and regression, and repeated
measures analyses. Appropriate graphs will be taught along with the
analyses. The basic statistics behind each type of analysis will be
reviewed. Day 4 will cover special topics such as programming in SAS
and working with sample data.
Prerequisites: An Introductory Statistics class that covered that
following concepts: mean, standard deviation, normal distribution,
t-tests, chi-square, regression, ANOVA.
Notes: Introduction to SAS will be held in a computer classroom where
students will have access to SAS software.
Course Outline:

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Introduction to Stata (sponsored by StataCorp)

Time: 1:30 PM - 4:30 PM
Instructor: Dr. Greg Hixon (Lecturer, Psychology)
Course Description: The purpose of the course is to provide
instruction in the use of Stata for data handling and conducting
statistical analyses. Day 1 will cover opening of data sets and data
manipulation. Days 2 and 3 will cover basic statistical analyses,
including categorical analyses, two-sample tests, ANOVA, correlation
and regression, and repeated measures analyses. Appropriate graphs
will be taught along with the analyses. The basic theory behind each
type of analysis will be reviewed. Day 4 will cover special topics
such as mediation analysis, diagnostics for ANOVA and regression, and
basic programming in Stata.
Prerequisites: An introductory statistics course that covered
p-values, confidence intervals, t-tests, ANOVA, correlation, and
regression.
Notes: Students should bring a personal laptop. Temporary Stata
licenses will be provided.A limited number of laptops are available
for loan. Please email This e-mail address is being protected from
spambots. You need JavaScript enabled to view it for more information.
Course Outline
---------------------------------------------------------------------

Bayesian Statistics for Social Sciences
Time: 1:30 PM - 4:30 PM
Instructor: Dr. Stephen Jessee (Assistant Professor, Government)
Course Description: This course will provide a basic introduction to
the Bayesian framework in statistics including the differences between
Bayesian approaches and the more standard frequentist approach. It
will cover basic single parameter models, regression, Bayesian
estimation (including MCMC simulation techniques) and hierarchical
models. The computing aspect of the course will be minimal although if
time allows, we will briefly cover software packages used for the
estimation of more complicated models.
Prerequisites: Students will be expected to have a basic knowledge of
introductory statistics such as the concept of a probability
distribution, estimation and hypothesis testing. Students should also
have some familiarity with maximum likelihood estimation, most
importantly the idea of a likelihood function.
Course Outline

---------------------------------------------------------------------
Data Analysis and Mining

Time:1:30 PM - 4:30 PM
Instructor: Dr. Weijia Xu (Research Associate and Lecturer, Texas
Advanced Computing Center)
Course Description: This course introduces students to the basic
concepts in data mining, common methods for data preprocessing, and
techniques for different data mining tasks. Three types of data mining
tasks will be discussed: association analysis, cluster analysis, and
classification. The purpose of the this course is to teach students
broad and applicable knowledge about data mining. The broad topics
covered in class help students grasp the basics of the field. At the
same time, students will be able to choose and apply appropriate
mining methods to suit their own data sets.
Prerequisites: Students are expected to be familiar with fundamental
concepts in statistics, such as statistical summary, conditional
probability, probability distribution functions, estimation (e.g.,
confidence level, maximum likelihood) and hypothesis testing (e.g.,
p-values, power).
Notes: Students are encouraged to bring a laptop, but this is not
required. A limited number of laptops are available for loan. Please
email This e-mail address is being protected from spambots. You need
JavaScript enabled to view it for more information.
Course Outline
---------------------------------------------------------------------

Hierarchical Linear Modeling (PM)
Time: 1:30 PM - 4:30 PM
Instructor: Dr. Tasha Beretvas (Associate Professor and Chair of the
Quantitative Methods Program, Educational Psychology)
Course Description: The purpose of this workshop is to introduce the
basic principles of multilevel/hierarchical linear modeling. The
workshop is designed to help attendees understand the following: the
need for the appropriate modeling of dependencies (e.g., clustering of
students within classrooms with schools, etc.), how to formulate and
interpret two-and three-level multilevel models and the relevant
parameters, and how to use HLM software to estimate the models'
parameters. In addition to conventional hierarchical linear models
(e.g., students within classroom = two-level models and students
within classrooms within schools = three-level models) that will be
discussed and explained, the workshop will demonstrate the use of
multilevel modeling for growth curve modeling. Using data sets
provided by the instructor, the students will be provided with
step-by-step instructions to help them learn how to use HLM software
to estimate the models. The workshop's focus will be on how to specify
multilevel models and how to interpret the resulting parameter
estimates. Given the short time-frame of the workshop, only models for
interval-scaled outcomes will be covered.
Prerequisites: Students should have a thorough understanding of
correlation and of multiple regression. Exposure to multivariate
techniques (e.g., MANOVA) and use of SPSS is helpful, but not
required.
Notes: Hierarchical Linear Modeling will be held in a computer
classroom where students will have access to SPSS and HLM software.
Course Outline

---------------------------------------------------------------------
Spatial Analysis and GIS

Time: 1:30 PM - 4:30 PM
Instructor: Dr. Jennifer A. Miller (Assistant Professor, Geography and
the Environment)
Course Description: This course focuses on spatial analysis and the
technology that facilitates it, geographic information systems (GIS).
The course addresses 'spatial problem solving' by focusing on both the
theoretical/conceptual and practical aspects of GIS modeling and
spatial analysis and the importance of treating spatial data
differently. This course will provide a general overview of the steps
involved in analyzing spatial data in a GIS context and specifically
methods used for point pattern analysis, spatial auto correlation
measurement, spatial interpolation, and spatial modeling (including a
brief introduction to geographically weighted regression).
Prerequisites: (Ideally, but not required) a GIS class and a
statistics class covering basic regression. Familiarity with R would
be very useful.
Notes: Spatial Analysis with GIS will be held in a computer classroom
where students will have access to ESRI's ArcGIS.
Course Outline
---------------------------------------------------------------------

Structural Equation Modeling
Time: 1:30 PM - 4:30 PM
Instructor: Dr. Tiffany Whittaker (Assistant Professor, Educational
Psychology)
Course Description: This course will build upon participants' previous
knowledge of multiple linear regression, expanding it to allow for
correlated and causally related latent variables. This course assumes
no prior experience with Structural Equation Modeling, and is intended
as both a theoretical and practical introduction. Topics covered in
the course will include path analysis with measured variables,
confirmatory factor analysis, structural equation models with latent
variables, and a preview of more advanced models. The software package
Mplus will be used for exploring and providing support for structural
models. Participants will conduct hands-on practice exercises using
Mplus software throughout the course.
Prerequisites: Knowledge of correlation, regression and multiple
regression.
Notes: Students should bring a personal laptop that runs Windows. The
demo version of Mplus will be used in class and are freely available
on the web. A limited number of laptops are available for loan. Please
email This e-mail address is being protected from spambots. You need
JavaScript enabled to view it for more information.
Course Outline

---------------------------------------------------------------------
Time Series Analysis

Time: 1:30 PM - 4:30 PM
Instructor: Dr. Thomas Sager (Professor, Information Risk and
Operations Management)
Course Description: This course will teach you how to model time
series data. The goal of modeling is to account for why a phenomenon
varies over time and to predict its future. The course focus is on
empirical modeling, rather than theoretical properties. Participants
will learn how to propose models, estimate them with data, diagnose
whether they fit, and interpret their meanings. Models covered include
regression, random walks, autoregression, moving averages, and related
structures. Computer demonstrations with both real and simulated data
will be used extensively.
Prerequisites: Students should be very comfortable with the use and
interpretation of multiple regression, logarithms and exponentials,
and Excel. Some familiarity with SAS would be desirable, but a short
SAS tutorial will be included. Calculus is not required.
Notes: Time Series Analysis will be held in a computer classroom where
students will have access to SAS software.
Course Outline
---------------------------------------------------------------------

-- For all classes requiring students to bring a laptop with Microsoft
Excel, Excel's Analysis ToolPak will be used (and the ToolPak VBA will
be used in Time Series Analysis). These add-ins come with all versions
of Microsoft Excel except for Excel 2008 for Macintosh. Excel 2008 for
Macintosh has many of the same functionalities, but presented
differently so be advised that it may be difficult to follow along in
class if you have this version of Excel.
Click on your version of Microsoft Excel for more information on
loading the ToolPak:

Excel 2000 or 2002 for Windows, Excel 2003 for Windows, Excel 2007 for
Windows, Excel 2004 for Mac
Click HERE for information on data analysis tools in Excel 2008 for
Mac.

mean, standard deviation, correlation and
regression.     
Early Career Grant

Lecture Series
Summer Statistics Institute

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Natural Sciences, The University of Texas at Austin
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