This one-day short course will provide participants with an introductory overview of issues frequently encountered when conducting secondary computer analyses of data collected from sample surveys 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:

• Recognizing a sample with a complex design, and sampling error calculation models
• Calculation of survey weights based on sample designs / non-response / post-stratification
• Calculation of new weights for subgroups / longitudinal analyses
• Weighted vs. unweighted analyses
• Variance estimation, and calculation of correct confidence intervals for population parameters
• 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
• Using weights in regression modeling, and model-based approaches
• Examples using software programs to analyze real survey data

The Instructor:
Brady T. West is a Research Associate Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research (ISR) on the University of Michigan-Ann Arbor campus. He also serves as a Statistical Consultant on the University of Michigan Consulting for Statistics, Computing, and Analytics Research (CSCAR) Team. He earned in his PhD from the Michigan Program in Survey Methodology in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures, now in its second edition (Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition, Chapman Hall/CRC Press, 2014), and he is a co-author of Applied Survey Data Analysis, also now it its second edition.