Description of Courses

Analysis of Survey Data I

3 credit hours

Instructor: William Yeaton, Evaluation Consultant

Research in the social sciences has increasingly come to rely on statistical concepts in the development and evaluation of research designs, as well as in the presentation and analysis of data. The application of a wide variety of research designs, including both experimental and non-experimental designs, requires real understanding of fundamental statistical concepts. This course provides an introduction to the relationship between research design and statistical analysis. Its main objective is the conceptual understanding of statistical reasoning rather than the rote application of statistical formulae. The course begins with a broad overview of research designs frequently used by survey researchers. It then focuses upon estimation of sampling error, sampling design, and sampling distributions of sums, means, and percents for simple random samples. In the second half of the course, data analytic techniques most commonly used in the context of these research designs are presented (t-tests, correlation analysis, and regression analysis). Additional topics include: normal approximations, measurement error, hypothesis testing, probability samples, and calculating sample size for specified precision levels.

Prerequisite: Mathematics through college algebra.

2008 Syllabus (PDF)

Analysis of Survey Data II

3 credit hours

Instructor: Mick Couper, University of Michigan

Survey data have features that differ from data generated from other types of data collection methods. This course provides participants with an overview of the nature of those features and an introduction to methods that properly handle the unique features of survey data. The course begins with a brief overview of survey design and its implications for analysis, and then covers the logic and methods of analysis, measurement theory and evaluation, scaling and index construction, contingency table analysis, and linear and logistic regression methods for bivariate and multivariate models. Logistic regression is extended to incorporate multinomial and ordered logit types of models. Homework and examination problems emphasize conceptual issues in each topic. The focus is on choosing appropriate statistical tools for analysis and on interpretation of results. Application of methods taught in this course using computer software is taught in the companion course, Computer Analysis of Survey Data II.

Prerequisites: (1) Completion of at least one graduate course in statistics or an instructor approved equivalent, (2) working familiarity with statistics through product moment correlation and analysis of variance, and (3) basic familiarity with survey methods. A self-diagnostic examination is available through the Summer Institute office and must be used to assess whether students have adequate skills for this course. Contact the Summer Institute with questions about the diagnostic examination.

2008 Syllabus (PDF)

Analysis of Survey Data III

This course has been cancelled for the 2008 Summer Program

3 credit hours

Instructors: Michael Elliott, University of Michigan; Marc Musick, University of Texas

The analysis of data requires a broad understanding of how data are structured and how different statistical methods can and should be applied to data. Analysts have a wide variety of statistical methods to choose from, but they must understand the basic premise of the method and the fundamental assumptions which must be met for application to be valid. Analysis of Survey Data III is a continuation of Analysis of Survey Data II. The course concentrates on widely used methods for the analysis of survey data. The course covers the likelihood principle and the associated estimation and testing procedures that follow from it. Likelihood test procedures are applied to ordinal and multinomial logistic regression, probit and Tobit regression, Poisson and negative binomial regression, simple path models, factor analysis, and structural equation models. The course includes a treatment of weighting and variance estimation in complex sample designs and imputation of item missing values. Homework and examinations illustrate the use of such software as SAS, SPSS, Stata, LISREL, and IVEware. Students who need to acquire skills in the use of software for analysis must enroll in the companion course, Computer Analysis of Survey Data III.

Prerequisite: Completion of Analysis of Survey Data II or an instructor-approved equivalent.

2008 Syllabus (PDF)

Applied Questionnaire Design Workshop

Instructor: Nora Cate Schaeffer, University of Wisconsin-Madison

This workshop provides students with practice in applying principles of question design to practical problems, so that students leave the workshop with tools to use in diagnosing problems in survey questions and writing their own survey questions. Each day’s session combines lecture with group discussion and analysis. The lecture provides guidelines for writing and revising survey questions and then presents a set of troubled questions from a national survey for revision. Students, organized into small workgroups, use the guidelines to identify problems in the survey questions and propose solutions. Nightly assignments require that students revise the problematic questions and administer them to fellow students. Sessions consider both questions about events and behaviors and questions about subjective phenomena (such as attitudes, evaluations, and internal states).

2008 Syllabus (PDF)

Building and Testing Structural Equation Models

3 credit hours

Instructor: Amiram Vinokur, University of Michigan

Since the early 80's, Structural Equation Modeling (SEM) analyses, first using LISREL and later using EQS, and AMOS user friendly software packages, have gained prominence as they replace the older traditional analytic methods of factor analysis and path analysis. SEM merges confirmatory factor analysis with path analysis and provides means for constructing, testing, and comparing comprehensive structural path models as well as comparing the goodness of fit of models and their adequacy across multiple groups (samples). This course will cover the conceptual and technical issues of Structural Equation Modeling (SEM). Following the presentation of major conceptual issues, five basic structural models will be described in detail. The models vary from simple to more complex ones. They also cover a wide range of situations including longitudinal and mediational analyses, comparisons between groups, and analyses that include data from different sources such as from parents, teachers, and children. The description and discussion of the models will provide students with the knowledge and skills to apply SEM techniques using EQS software for analyzing, evaluating, and reporting results produced by this analytic method. This knowledge is easily transferable to the use of LISREL or AMOS software. Course work will include five structured assignments that will be completed at the lab and a final paper based on a structural model that the students will construct and test with their own data. The paper will provide a report of the model, analyses, results and conclusions.

Prerequisite: One or more courses in statistics that included in-depth treatment of linear regression analysis, basic knowledge of the concepts of item analysis and internal reliability, and some familiarity with factor analysis. At least some hands-on experience with data analysis using SPSS, SAS, or similar software for data analysis is also required.

2008 Syllabus (PDF)

Combining Qualitative and Quantitative Methods: Introduction and Overview

1.5 credit hours

Instructor: Wiliam Axinn, University of Michigan

In this course, participants will become familiar with multiple methods of data collection and how to combine them within a single research project. We will focus on collecting data using unstructured or in-depth interviews, focus groups, participant observation, archival research, survey interviews, and hybrid methods. We will discuss the strengths and weaknesses of each approach, and we will focus on how each different method can contribute to the research question in unique ways. This course is designed for those with a specific research question in mind, but who are new to collecting data (or new to multi-method approaches to collecting data). Throughout the course, participants will be asked to design and present multi-method data collection approaches for a research question of their choice. By the end of this module, participants will have an overview of a multi-method data collection project that will enable them to design, understand, and evaluate multi-method approaches within a single project.

Prerequisite: An introductory course in survey research methods or equivalent experience.

2008 Syllabus (PDF)

Computer Analysis of Survey Data II

1 credit hour

Instructor: Patricia Berglund, University of Michigan

Students enrolled in Computer Analysis of Survey Data II must also be enrolled in Analysis of Survey Data II.

This course is an optional computer laboratory designed to accompany Analysis of Survey Data II. It will emphasize the use of SAS to obtain results related to topics discussed in Analysis of Survey Data II. Particular attention will be paid to manipulating software in order to complete assignments from Analysis of Survey Data II. Secondarily, some attention to interpretation of results will also be included. The course will cover file preparation and manipulation, exploring data structure preparatory to index construction, index construction and evaluation, data exploration using descriptive and graphic techniques, bivariate and multivariate regression analyses, logistic regression analysis, and contingency table analysis. SAS will be used through the University of Michigan computing environment.

Prerequisite: (1) Completion of at least one graduate course in statistics, or an instructor approved equivalent level of experience in statistical methods, and (2) basic familiarity with survey methods. Enrollment in Analysis of Survey Data II is required.

2007 Syllabus (PDF)

Computer Analysis of Survey Data III

This course has been cancelled for the 2008 Summer Program

1 credit hour

Instructor: Joe Sakshaug, University of Michigan

Students enrolled in Computer Analysis of Survey Data III must also be enrolled in Analysis of Survey Data III.

This course is a computer laboratory designed to accompany Analysis of Survey Data III. It emphasizes the use of computer statistical packages to obtain results related to topics discussed in Analysis of Survey Data III. Particular attention will be paid to manipulating software and interpretation of results. The course provides practical experience in all methods discussed in the companion course using SAS, LISREL, and IVEware in the University of Michigan computing environment. The SAS statistical software system will be used, but students do not need to be familiar with SAS in order to take the course. The SAS Assist system is used to introduce students to SAS, and eases the task of using the system. SAS is one of several languages that can be used to obtain results discussed in the companion course.

Prerequisite: Enrollment in Analysis of Survey Data III.

2008 Syllabus (PDF)

Data Collection Methods

3 credit hours

Instructor: Fred Conrad, University of Michigan

This course reviews alternative data collection methods used in surveys, focusing on interviewer-administered methods. It concentrates on the impact these techniques have on the quality of survey data, including measurement error properties, nonresponse, and coverage errors. The course reviews the literature on data collection methods, focusing on comparisons of major modes (face-to-face, telephone, and mail) and alternative methods of data collection (diaries, administrative records, direct observation, etc.) The implications of mode decisions for data quality and the data collection process are discussed. Special attention is paid to the statistical and social science literatures on interviewer effects and nonresponse. Current advances in computer-assisted survey information collection (including CATI, CAPI, TDE, and VRE) will be reviewed. This is not a how-to-do-it course on survey data collection, but rather focuses on the error properties of key aspects of the data collection process.

Prerequisite: An introductory course in survey research methods or equivalent experience.

2008 Syllabus (PDF)

Design and Analysis of Complex Sample Survey Data

3 credit hours

Instructor: Steven Heeringa, University of Michigan; Patricia Berglund, University of Michigan; Brady West, University of Michigan

This is an advanced course on the analysis of survey data from complex sample designs covering methods for incorporating weighting, stratification, and clustering in estimation and inference for a wide variety of statistical analytical techniques. Alternative variance estimation procedures for statistics such as means, proportions, and percentiles as well as the coefficients of linear, logistic, log-linear and event history regression models will be discussed, as will methods for handling missing data arising from both unit and item nonresponse. Monday and Wednesday lectures and discussions will cover each of the major topics in-depth. Friday computer laboratory sessions will include exercises and actual survey data to illustrate the methods covered in the class. Students will learn the use of computer software for imputation of item missing data and variance estimation in the analysis of complex sample survey data.

Prerequisite:Two graduate-level courses in statistical methods, familiarity with basic sample design concepts, and data analytic techniques such as linear and logistic regression.

2008 Syllabus (PDF)

Envisioning the Survey Interview of the Future

1 credit hour

Instructor: Fred Conrad, University of Michigan

This course will explore the impact of new and emerging communication technologies on the survey interview and explore methods and criteria that can be used to evaluate these technologies. The course will combine material and ideas from Human-Computer Interaction, Cognitive and Social Psychology, and Survey Methodology. Students will integrate these ideas in a project that evaluates the likely impact of a new or emerging interviewing technology on the survey data it is used to collect. The first three days will provide substantive background for student projects; the discussion will be split into conceptual material and an introduction to a new technology. Days four and five will be devoted to presentation of student projects.

2008 Syllabus (PDF)

Event History Analysis

This course has been cancelled for the 2008 Summer Program

2 credit hours

Instructor: Jay Teachman, Western Washington University

This course is designed as a survey of statistical methodologies useful for the examination of events as they occur over time. As dynamic models and theories have gained prominence in the social sciences and longitudinal/retrospective data sets have proliferated, researchers have become increasingly concerned about describing and modeling the number, timing and sequencing of important events. The events of interest can be life-course events that occur to individuals (e.g., marriages, divorces, births, job changes) or to any other unit of analysis (organizational death processes, wars, formation of nation states).

Procedures useful for both description and causal analysis of event history data will be covered, including the following: life tables; discrete-time procedures; partial likelihood procedures; parametric procedures and accelerated failure time models; proportionality and time dependence in covariates; multiple origins and destinations (competing risks); and repeatable events. If time permits, other topics that may be covered include unobserved heterogeneity, simulation models and left censoring.

2008 Syllabus (PDF)

Examining the Health and Retirement Study (HRS) Workshop

not for credit

Instructors: Mary Beth Ofstedal, University of Michigan; Helen Levy, University of Michigan

The HRS is a large-scale longitudinal study of the labor force participation and health transitions that individuals undergo toward the end of their work lives and in the years that follow. The survey collects information about income, work, assets, pension plans, health insurance, disability, physical health and functioning, cognitive functioning, and health care expenditures. The HRS Summer Workshop is intended to give participants an introduction to the HRS that will enable them to use the data for research. The format of the Summer Workshop is morning lectures on topics including basic survey content, sample design, weighting, and restricted data files and a hands-on data workshop each afternoon in which participants learn to work with the data under the guidance of HRS staff. In 2008, the workshop will feature a topical focus on physical measures and biomarkers that are collected in the HRS.

Experimental and Quasi-Experimental Research Designs

3 credit hours

Instructor: William Yeaton, Evaluation Consultant

Studies of the highest quality require the strongest possible research design. This course provides various ways in which inference can be strengthened in those research contexts for which random assignment is not possible due to real-world constraints. Focus will be placed upon practical design and measurement strategies rather than on statistical analysis. Especially important among the methods presented are design procedures aimed to enhance one's ability to make causal inference and to generalize results. Methodological tactics for eliminating threats to validity will be emphasized using examples from a variety of disciplines including public health, education, political science, sociology, psychology, social work, and business. Studies combining both experiments and surveys will be included. Principles that establish causal inference will be illustrated in a wide range of observational designs (e.g., non-equivalent control group, pretest-posttest, time-series, cohort, case-control, regression-discontinuity, patched-up, reversal, multiple baseline, and case study) most commonly found in those disciplines. Recent developments in meta-analysis will be discussed in the context of inferences that cannot be made within single studies. The course should prove particularly useful for graduate students and researchers who are actively planning research, since feedback from the instructor and other students will allow them to improve their proposed studies.

Prerequisite: Knowledge of introductory statistics.

2008 Syllabus (PDF)

Introduction to Focus Groups as Qualitative Research

1.5 credit hours

Instructor: David Morgan, Portland State University

This course will cover the design and execution of research projects using focus groups. The course will emphasize four basic topics: 1) how to design projects using focus groups, including issues involved in the selection and recruitment of participants; 2) how to write interview guides; 3) how to moderate focus groups; and 4) how to analyze the data from focus groups. For each of these four topics, the variety of options that are available will be presented, followed by a discussion on how to evaluate these options for your particular research purpose.

Prerequisite: An introductory course in research methods or equivalent experience.

Introduction to Survey Research Techniques

6 credit hours

Instructors: Lynette Hoelter, University of Michigan; Felicia LeClere, University of Michigan

This eight-week course will acquaint students with the theory and practice of survey research broadly defined as research that relies upon face-to-face interviews, telephone interviews, or self-administered questionnaires as a primary means of data collection. The course involves lectures, readings, and discussions covering the basics of the major stages of a survey, including hypothesis and problem formulation, study design, sampling, questionnaire and interview design and evaluation, techniques of interviewing, code development and coding of data, data cleaning and management, data analysis, and report writing. Students will gain practical experience in these areas through the development and implementation of a survey. Participants are encouraged to bring materials related to their own research interests.

Prerequisite: Some familiarity with survey research methods is helpful, but not required.

2008 Syllabus (PDF)

Introduction to Survey Sampling

1 credit hour video course

Instructor: Roger Tourangeau, University of Maryland

This is a foundation course in sample survey methods and principles. The instructor will present, in a non-technical manner, basic sampling techniques such as simple random sampling, systematic sampling, stratification, cluster sampling, and probability proportional to size selection. The instructor provides opportunities to implement sampling techniques in a series of idealized exercises that accompany each topic. Group work is an integral part of the course. Participants collaborate on the solution of the course exercises. Participants should not expect to obtain sufficient background in this course to master survey sampling, but they can expect to become familiar with basic techniques adequate to converse with sampling statisticians more easily about sample design. All participants must bring a calculator to class in order to complete in class exercises that will be presented each day.

2008 Syllabus (PDF)

Latent Class Analysis of Survey Error

This course has been cancelled for the 2008 Summer Program

1 credit hour

Instructor: Paul Biemer, RTI International and the University of North Carolina at Chapel Hill

This course presents a statistical framework for modeling and estimating classification error in surveys. It begins by examining some of the early models for survey measurement error (Census Bureau models; Kish model; etc.) and demonstrating their similarities, strengths and weaknesses. Then these models are cast in a general latent class modeling (LCM) framework where the true values of a variable are assumed to be unobserved (latent) and the survey response constitutes a single indicator of this latent variable. The model parameters include the target population proportions for a categorical variable to be estimated in the survey and the misclassification probabilities (for e.g., false positive and false negative, for dichotomous response variables) for measuring the variable. Survey item reliability and construct validity as well as estimator bias are defined and interpreted within this general framework. Methods for estimating the model parameters and issues of model identifiability will be discussed.

Prerequisite: This course is taught at an intermediate level, emphasizing both the theory and practice of the analysis of survey error. The course does not require rigorous training in mathematics; however, proficiency in basic mathematics is essential. Knowledge of calculus is useful but not required for the course. A first course in survey sampling methods and a basic understanding of sampling concepts such as stratification, clustering and weighting is required. Students should also have familiarity with basic statistical concepts, such as point estimates, sampling variance, confidence intervals, p-values and the maximum likelihood method of estimation. Familiarity with logistic regression models is useful but not required.

2007 Syllabus (PDF)

Measurement Errors in Surveys

1.5 credit hours

Instructor: Duane Alwin

Measurement issues are critical in scientific research because analysis and interpretation of patterns and processes depend ultimately on the ability to develop high quality measures that accurately assess the phenomenon of interest. In the case of survey research, a great deal of attention has been paid to measurement issues, and almost everyone agrees that because of the nature of cognitive and communication processes inherent in gathering information via interviews and questionnaires, the potential for measurement error is nontrivial.

This course focuses on three general topics relevant to evaluating the presence and extent of measurement errors in surveys: (1) valuable conceptual tools and statistical theories of response error that permit the specification of models relating observed responses to the latent processes that produce them; (2) statistical designs for gather information, given models of the response process, permitting the estimation of components of error for populations of interest; and (3) results and their interpretation from a wide range of studies, enabling researchers in the fields of survey methods, public opinion research and related fields to evaluate elements of measurement error in survey data in a way that will reduce measurement error and improve the quality of inferences.

Readings will be drawn from several assigned journal articles and two recent books:

Alwin, D.F. (2007). Margins of error: A study of reliability in survey measurement. New York: John Wiley & Sons.

Saris, W.E. & Gallhofer, I. N. (2007). Design, evaluation and analysis of questionnaires for survey research. Hoboken, NJ: John Wiley & Sons.

Methods of Survey Sampling

3 credit hours Video Course

Instructor: Jim Lepkowski, University of Michigan

A fundamental feature of many sample surveys is a probability sample of subjects. Probability sampling requires rigorous application of mathematical principles to the selection process. Methods of Survey Sampling is a moderately advanced course in applied statistics, with an emphasis on the practical problems of sample design, which provides students with an understanding of principles and practice in skills required to select subjects and analyze sample data. Topics covered include stratified, clustered, systematic, and multi-stage sample designs, unequal probabilities and probabilities proportional to size, area and telephone sampling, ratio means, sampling errors, frame problems, cost factors, and practical designs and procedures. Emphasis is on practical considerations rather than on theoretical derivations, although understanding of principles requires review of statistical results for sample surveys. The course includes an exercise that integrates the different techniques into a comprehensive sample design.

Prerequisite: Two graduate-level courses in statistical methods.

2008 Syllabus (PDF)

Multi-Level Analysis of Survey Data

3 credit hours Video Course

Instructors: Valerie Lee, University of Michigan; Robert Croninger, University of Maryland

Although many surveys gather data on multiple units of analysis, most statistical procedures cannot make full use of these data and their nested structures: for example, individuals nested within groups, measures nested within individuals, and other nesting levels that may be of analytic interest. In this course, students are introduced to an increasingly common statistical technique used to address both the methodological and conceptual challenges posed by nested data structures -- hierarchical linear modeling (HLM). The course demonstrates multiple uses of the HLM software, including growth-curve modeling, but the major focus is on the basic logic of multi-level models and the investigation of organizational effects on individual-level outcomes. Although we use data drawn from a nationally representative sample of U.S. elementary schools, students, and teachers for instructional exercises, students should feel free to use their own data provided the data have a multi-level structure and are suitable for course goals (developing and interpreting a two-level model with a random intercept and a random slope). The multi-level analysis skills taught in this course are equally applicable in many social science fields: sociology, public health, psychology, demography, political science, and in the general field of organizational theory. Typically the course enrolls students from all these fields. Students will learn to conceptualize, conduct, interpret, and write up their own multi-level analyses, as well as to understand relevant statistical and practical issues.

Prerequisite: At least one graduate-level course in statistics or quantitative methods, and experience with multivariate regression models, including both analysis of data and interpretation of results. School of Education students must have successfully completed ED-795. If you can not meet this criterion, you must speak directly to the instructor prior to being given permission to enroll.

2008 Syllabus (PDF)

Psychology of Survey Response

1 credit video course

Instructor: Roger Tourangeau, University of Maryland

This course examines survey questions from a psychological perspective. It describes the major psychological components of the response process, including comprehension of the questions, retrieval of information from memory, combining and supplementing information from memory through judgment and inference, and the reporting of an answer. It discusses several models of how respondents answer questions in surveys, reviews the relevant psychological and survey literatures, and traces out the implications of these theories and findings for survey practice, especially for the design of questionnaires.

2008 Syllabus (PDF)

Qualitative Data Analysis: With and Without the Use of Computers

1.5 credit hours

Instructor: Eben Weitzman, University of Massachusetts-Boston

This course builds upon the topics taught in the qualitative methods courses, An Introduction to Focus Groups as Qualitative Research, Combining Qualitative and Quantitative Methods: Introduction and Overview, and Qualitative Methods: Semistructured Interviewing. Once qualitative data have been collected, the researcher is faced with the (often daunting) task of making sense of it all. In this two-week course, participants will learn methods for organizing, interpreting, and drawing and verifying conclusions from qualitative data. Our approach throughout will be active, participatory, and engaged with real data. As there is a wide variety of software available to assist the researcher in managing and analyzing qualitative data, we will become familiar with some of the options and, more importantly, learn how to make intelligent, individualized selections of software that best meet the needs of a particular researcher faced with a particular project. We will apply what we learn to the analysis of real data, as we use selected software to enter, summarize, and code data collected in the previous qualitative methods courses, ending in a research report. Students who have qualitative research projects of their own, such as dissertations, may bring a sample of their data on diskette. There will be an opportunity for students in this situation to choose software for their own projects, and take some early steps in analysis. During the second week of the course, there will be a mandatory lab session held 1:00-5:00 p.m. every weekday for all participants to become familiar with relevant software.

Prerequisite: An introductory course in qualitative research methods (e.g. the previous courses in this sequence), or permission of instructor.

2007 Syllabus (PDF)

Questionnaire Design

3 credit hours Video Course

Instructors: Pamela Campanelli, UK Survey Methods Consultant

This course focuses on the design of questions and questionnaires used in survey research. The course will explore the theoretical and experimental literature related to question and questionnaire design as well as focusing on practical issues in the design, critique, and interpretation of survey questions that are often not taught in formal courses. There will be exercises both in and outside of class to reinforce both theory and practice, including the construction and testing of a class questionnaire.

Discussion will focus on the measurement of factual, non-factual and quasi-factual phenomena. Topics include cognitive guidelines for question construction to ensure respondent understanding, techniques for measuring the occurrence of past behaviors and events, the effects of question wording, response formats, and question sequence on responses, an introduction to the psychometric perspectives in multi-item scale design, combining individual questions into a meaningful questionnaire, special guidelines for self-completion surveys (including web surveys) versus interview surveys, strategies for obtaining sensitive or personal information, issues in translating questionnaires, and an introduction to techniques for testing survey questions.

Prerequisite: An introductory course in survey research methods or equivalent experience.

2008 Syllabus (PDF)

Question Testing Methods

1 credit hour

Instructor: Pamela Campanelli, UK Survey Methods Consultant

This course aims to introduce the broad range of techniques currently available to test and improve survey questionnaires. It will have two strands: the first focusing on the theoretical and experimental literature related to question testing; the second, a "hands-on" approach, focusing on how to implement each method. Question testing methods covered include standard field pretesting, expert review, cognitive forms appraisal, interviewer rating form, respondent debriefing and vignettes, classical behavior coding and sequence-based approaches, cognitive interviewing and the "3 Step Test Interview", focus groups, and split ballot experiments. Discussion will also focus on the strengths and weaknesses of each individual method as well as proposals for multi-method question evaluation strategies.

Prerequisite: A course in questionnaire design or equivalent experience.

2008 Syllabus (PDF)

Qualitative Methods: Overview and Semi-Structured Interviewing

1.5 credit hours

Instructor: Nancy Riley, Bowdoin College

This course will focus on semi-structured, or in-depth, interviewing. It will examine the goals, assumptions, process, and uses of interviewing and compare these methods to other related qualitative and quantitative methods in order to review strategies for choosing the appropriate mix of methods in light of research goals. The course will cover interviewing techniques, including how to decide who to interview and how to conduct successful interviews; students will conduct interviews, and discuss the process and outcome of those interviews. We will examine the strengths and weaknesses of this methodology, particularly through discussion of some of the critiques of these methods (from feminist researchers and others).

Prerequisite: An introductory course in survey research methods or equivalent experience.

2008 Syllabus (PDF)

Scaling Methods

This course has been cancelled for the 2008 Summer Program

1.5 credit hours

Instructor: William Jacoby, Michigan State University and ICPSR

This course will focus on several strategies for producing geometric representations of structure in data. These methods tend to be used for three main reasons: (1) Data reduction. For example, a researcher may want to combine individual responses across a set of survey questions which ask about a common topic. In this case, the objective may be to obtain more fine-grained resolution of respondents= attitudes on the topic than can be obtained from any single survey item. (2) Evaluating dimensionality. For example, a researcher may want to determine the evaluative criteria that respondents bring to bear on a given stimulus object. The objective may be to recover the Amental maps that underlie individual beliefs and attitudes. (3) Measurement. Scaling methods are often used to assign numerical scores to aspects of empirical objects (which may be qualitative in nature). Here, the objective often is to obtain reliable interval-level variables that can be employed in subsequent statistical analyses. Specific scaling methods to be covered in the course include principal components analysis, factor analysis, multidimensional scaling, and correspondence analysis. In-class examples will rely primarily (but not exclusively) on Stata software. However, the scaling techniques covered in this course also are available in all of the other major statistical packages (e.g., SPSS and SAS).

Prerequisite: No prior exposure to, or experience with, scaling methods is necessary; however, course participants should be familiar (and comfortable) with multiple regression analysis.

Statistical Analysis of Incomplete Data

1 credit hour

Instructor: Trivellore E. Raghunathan, University of Michigan

Missing data is a pervasive problem faced by many analysts. This course will discuss several approaches and methods for analyzing data with missing values. The course will be offered at an advanced statistical level and include: a discussion of ignorable and nonignorable missing data mechanisms; unit nonresponse adjustments through weighting and poststratification; multiple imputation for item nonresponse; and maximum likelihood with incomplete data. Methods for nonignorable missing data mechanism covered in this course include selection models, pattern-mixture models and informative censoring models. Several software options for analyzing data with missing values will also be discussed.

Prerequisite: Advanced practical and technical knowledge of standard statistical distributions and models for complete data, e.g., normal linear model, loglinear model for contingency tables, logistic regression models, and basic understanding of the method of maximum likelihood.

Web Survey Design

1 credit hour

Instructor: Mick Couper, University of Michigan

The course focuses on the design of web survey instruments and procedures, based on theories of human-computer interaction, interface design, and research on self-administered questionnaires and computer-assisted interviewing. The course begins with a brief review of web or Internet surveys in the general context of sources of survey error (sampling, coverage, nonresponse, measurement error) and costs. The course then discusses different approaches to web survey design and effective use of HTML tools for developing web surveys. The course draws on empirical results from experiments on alternative design approaches as well as practical experience in the design and implementation of web surveys.

Prerequisite: Basic coursework in social science research methods, including survey research. A working knowledge of survey research methods will be assumed.

2008 Syllabus (PDF)

Workshop in Survey Sampling Techniques

6 credit hours

Instructors: Steve Heeringa and Jim Lepkowski, University of Michigan

The Workshop in Sampling Techniques is a component of the Sampling Program for Survey Statisticians. The workshop can only be taken in conjunction with the sampling methods courses, Methods of Survey Sampling and Analysis of Complex Sample Survey Data. The workshop allows students the opportunity to implement methods studied in the companion methods courses such as segmenting and listing in area sampling; selection of a national sample of the U.S.; stratification; controlled selection; telephone sampling; national samples for developing countries; and sampling with microcomputers.

The workshop is a required class for the Sampling Program for Survey Statisticians (SPSS). The SPSS is an eight-week program. It consists of three courses: a methods course (SurvMeth 612), a course on the analysis of complex sample survey data (SurvMeth 614), and a hands-on daily workshop (SurvMeth 616). Students enrolled in these three courses are considered Fellows in the Program. The methods and the analysis courses may be taken without being a Fellow. However, the workshop cannot be taken alone. Fellows receive a certificate upon successful completion of the program.

2008 Syllabus (PDF)

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