Regression Analysis

SummaryView full course description

This workshop will provide participants with an overview of commonly used methods in simple linear regression and multiple linear regressions. There will be both lecture and hands-on computer work, using SPSS. Topics will include: the basic regression model, model assumptions, interpretation of coefficients, significance testing, interactions between variables and the use and interpretation of dummy variables. Model checking methods, including residual plots, collinearity diagnostics, and influence plots will also be covered. Several methods for model selection, including all possible regressions and stepwise selection will be included.

Introduction to Survey Design: Data Collection, Questionnaire Design and Response Processes-Lecture

SummaryView full course description

This lecture-format workshop will present an overview of available modes and methods of survey data collection as well as an introduction to the survey response process and implications for questionnaire design.  Participants will gain an appreciation of the tradeoffs inherent in survey design decisions and how design can affect data quality and survey errors. Topics will include:

  • Survey errors, in particular measurement, coverage, and nonresponse error.
  • What to consider when selecting a data collection method for a particular research question.
  • Measurement (response) error and how to reduce it through question wording/format and questionnaire structure.
  • The role of the interviewer and interviewer effects.

Applied Survival Analysis

SummaryView full course description

This workshop, held over two days, covers basic concepts of and common analytical approaches for time-to-event data, known variously as survival analysis (in biological and medical sciences), event history analysis (in social sciences), or reliability analysis (in engineering).

Statistical Analysis with R

SummaryView full course description

R is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphing capability is another reason R is gaining wide popularity.


  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive and Exploratory Statistics
  • Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

Python for Data Analysis

SummaryView full course description

This workshop, held over two half-days, will provide participants with an overview of pandas; a Python library for data analysis. The workshop will focus on pandas; a Python library for data analysis. There will be both lecture and hands-on computer work using Python (via Spyder IDE and IPython notebook) on real datasets (crash data will be used). Topics will include how to load in different data formats (csv, excel, access database, etc.), clean, manipulate, process, and crunch data, graph results (using matplotlib and bokeh) and write results to an output file. Computers will be provided but participants are encourage to bring their own laptops and datasets to work with using pandas. Call (734) 764-7828 to register

Issues in Analysis of Complex Sample Survey Data

SummaryView full course description

This workshop will provide participants with an introductory, hands-on overview of issues frequently encountered when conducting secondary computer analyses of survey data collected from samples with complex, multi-stage designs (e.g., PSID, NHANES, NCS), including design-based weight determination, software choice, and proper analysis methods.  The workshop is not intended for participants looking to design a survey, but rather for participants who have a desire to analyze complex sample survey data.  Topics to be covered include:


  • Recognizing a sample with a complex design
  • Calculation of sample weights based on sample designs / non-response / post-stratification
  • Calculation of new weights for subgroups / longitudinal analyses
  • Weighted vs. unweighted analyses
  • Calculation of correct confidence intervals for population quantities
  • Hypothesis Testing based on sample estimates
  • Design Effects
  • Software packages capable of complex sample survey data analysis
  • Common analysis methods (linear modeling, descriptive statistics), interpretation of results
  • Hands-on examples using procedures in SAS and Stata to analyze real survey data (web links to example code in other software packages, such as SPSS and R, will be provided as well)