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.

Advisory Prerequisite: PSYCH,Introductory psychology and statistics and permission of instructor.

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 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.

Advisory Prerequisite: PSYCH,Two courses in statistics.

This course introduces important design principles used in complex sample surveys, and then examines the analysis of data from complex sample designs. It covers the development and handling of selection and other compensatory weights; methods for handling missing data; the effect of stratification and clustering on estimation and inference; alternative variance estimation procedures; methods for incorporating weights, stratification, clustering, and imputed values in estimation and inference procedures for complex sample survey data; and generalized design effects and variance functions.

Advisory Prerequisite: SURVMETH 612 (prior completion or enrollment)

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.).

Advisory Prerequisite: Graduate standing or permission of instructor

This course is about the development of the survey instrument, the questionnaire. Topics include wording of questions (strategies for factual and non-factual questions), cognitive aspects, order of response alternatives, open versus closed questions, handling sensitive topics, combining individual questions into a meaningful questionnaire, issues related to questions of order and context, and aspects of a questionnaire other than questions. Questionnaire design is shown as a function of the mode of data collection such as face-to-face interviewing, telephone interviewing, mail surveys, diary surveys, and computer-assisted interviewing.

Advisory Prerequisite: SOC,Graduate standing. An introductory course in survey research methods or equivalent experience.

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 I is required.

Advisory Prerequisite: SURVMETH 681 (prior completion or current enrollment)

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.

Advisory Prerequisite: SURVMETH 682 (prior completion or current enrollment)

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.

This course covers the design and execution of research projects using focus groups, emphasizing 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 varieties of options that are available are presented, followed by a discussion on how to evaluate these options for your particular research purpose.

Advisory Prerequisite: An introductory course in survey research/equivalent experience

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.

Advisory Prerequisite: An introductory course in survey research/equivalent experience

This course builds upon the topics taught in the qualitative methods courses, such as Introduction to Focus Groups (SURVMETH 652). 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 learn methods for organizing, interpreting, and drawing and verifying conclusions from qualitative data. The approach throughout is 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 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 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. Student's who have qualitative research projects of their own, such as dissertations, may bring a sample of their data on diskette.

Advisory Prerequisite: An introductory course in qualitative research methods

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.

Advisory Prerequisite: Mathematics through college algebra

This 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, SURVMETH 641.

Advisory Prerequisite: SOC,PSYCH 613/SOC 510 or PSYCH 684/SOC 614 or equivalent and statistics.

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 that must be met for application to be valid. Analysis of Survey Data III is a continuation of Analysis of Survey Data II (although prepared students may take this course without having completed Analysis of Survey Data II). The course concentrates on widely used methods for the analysis of survey data. Before considering those analytic methods, the course examines the likelihood principle and the associated estimation and testing procedures that follow from it. The principle and methods of maximum likelihood estimation and likelihood test procedures are then applied to methods developed in Analysis of Survey Data II, and extended to additional methods such as Tobit regression, Poisson and negative binomial regression, mixed effects analysis of variance models, simple path models, factor analysis, and structural equation models. Several multivariate analysis methods such as multivariate regression and analysis of variance are also examined. The course concludes with an examination of weighting, imputation, and variance estimation in complex sample designs. Application of methods in lecture is taught in the companion Computer Analysis of Survey Data III, SURVMETH 642.

Advisory Prerequisite: SURVMETH 681/permission of instructor

This course covers the conceptual and technical issues of Structural Equation Modeling (SEM). Following the presentation of major conceptual issues, five basic structural models are described in detail. The models vary from simple to more complex one. 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 provides 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 required students to construct and test a structural model using their own data, or data from available data sets, and produce a paper that reports their analysis and conclusions.

Directed research on a topic of the student's choice. An individual instructor must agree to direct such research, and the requirements are specified when approval is granted.

Advisory Prerequisite: Graduate standing and permission of instructor

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.

Advisory Prerequisite: Graduate standing and permission of instructor

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.

Advisory Prerequisite: Graduate standing and permission of instructor

This course focuses on the design of questionnaires used in survey research and the practical issues that arise in their development, application, and interpretation. The major emphasis is on the selection of appropriate measurement techniques for assessing attitudes, opinions, behaviors, events, factual material, subjective experiences and self-assessments using survey questions. Topics include: unstructured vs. structured interviews, open-ended vs. fixed-response forms, the effects of question wording, response formats, and question sequence on survey responses, strategies for obtaining sensitive or personal information, and techniques for identifying and revising problematic questions. The course will provide practical recommendations for how to develop survey questionnaires. The course involves lectures, discussions, and exercises. The homework assignments are intended to offer practical experience by critiquing existing questionnaires and by developing new questions. Participants are strongly encouraged to email question or questionnaire examples from their own work to the instructor a week before the beginning of the course.

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

Advisory Prerequisite: Graduate standing and permission of instructor

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 causalinference 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 theinstructor and other students will allow them to improve their proposed studies. Prerequisite: Knowledge of introductory statistics.

Advisory Prerequisite: Graduate standing and permission of instructor

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.

Advisory Prerequisite: Graduate standing and permission of instructor

This one-week course will span a range of topics dealing with the quality of data collected through the survey process. The course begins with discussion of total survey error, as measured by the mean squared error, and its relationship to survey costs and general quality dimensions such as timeliness, coherence, and accessibility. Then the major sources of error in surveys are discussed in some detail, including (a) the origins of each error source (i.e., its root causes), (b) the most successful methods known for reducing the errors emanating from these error sources, and (c) methods that are most often used in practice for evaluating the effects of the sources on total survey error. The course will introduce participants to concepts and ideas for understanding the nature of survey error, techniques for improving survey quality, and, where possible, their cost implications, and methods for evaluating data quality in ongoing survey programs. The course is not designed to provide an in-depth study of any topic but rather as an introduction to the field of survey quality.

Prerequisite: Some prior research experience is helpful, but not required.

Advisory Prerequisite: Graduate standing and permission of instructor

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. The course does not focus on the technical aspects of web survey implementation (hardware, software, programming, etc.) as these are covered in the companion course, Web Survey Implementation, taught by Scott Crawford.

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

Advisory Prerequisite: Graduate standing and permission of instructor

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.

Advisory Prerequisite: Graduate standing and permission of instructor

Multi-level analysis techniques are becoming standard models for data that have a natural hierarchical structure, such as individuals nested within groups. However, they are also increasingly used in less obvious applications, such as analysis of longitudinal and panel data, growth curve modeling, and meta-analysis. The model has been extended to cover dichotomous data and proportions, ordinal data, multivariate outcomes, and data structures that include crossed as well as nested factors. The course includes a brief introduction on multilevel modeling. The main focus will be the use of multilevel analysis in a variety of applications, including three-level and longitudinal data, with both normal and non-normal data. It also will review some new estimation techniques that are available in the latest versions of popular software packages as HLM and MlwiN. The course is not meant as software training, but where relevant it does include a discussion of the capacities of current multi-level software. Prerequisite: One graduate-level course in statistical methods (through analysis of variance and multiple regression models, including both analysis of data and interpretation of results).

Advisory Prerequisite: Graduate standing and permission of instructor

The purpose of this course is to provide participants with an efficient and general tool-bag to fight both unit-and item nonresponse. The theme of the course is Integrated Management of Missing Data, that is, planning for nonresponse in the design phase of a survey. Reduction of unit and item nonresponse requires a careful survey design and planned fieldwork procedures. In addition it also requires the collection of auxiliary data to inform decisions about nonresponse follow up, and the use of such information to enhance adequate postsurvey adjustment for nonresponse. This implies careful consideration of the likely impact of nonresponse on key statistical estimates and a thorough theoretical understanding of unit and item nonresponse. The course starts with a basic introduction in nonresponse, discussing different types of nonresponse and its consequences. This is followed by a thorough discussion of methods intended to reduce unit-nonresponse. At the end of the course the emphasis is on understanding item nonresponse, and we will finish with procedures to diagnose nonresponse mechanisms in data already collected. Prerequisite: Basic coursework in social science research methods and statistics. Advanced coursework in survey methods would be helpful, but is not essential.

Advisory Prerequisite: Graduate standing and permission of instructor

Comparative research — -to be understood as research across populations — differs from mono-cultural research in important ways. We aim to sharpen participant's awareness of the differences between assumed mono-cultural research practice and methodology for comparative surveys. The course sets out to introduce you to the landmarks, landmines and lifesavers of cross-national and cross-cultural survey research. We focus on key areas of research including the survey lifecycle, study design and design implementation, questionnaire design, questionnaire adaptation and translation, sampling issues, data collection, including structures, modes and non-response, interviewer selection and training, quality assurance, data processing and harmonization and data publishing and access. Examples and exercises will be drawn from the International Social Survey (ISSP) the European Social Survey (ESS) surveys from the Barometer family, and the WHO World Mental Health 2000 survey.

Prerequisite: The inherent complexity of the material means that participants are expected to be familiar with the basic principles of survey research.

Advisory Prerequisite: Graduate standing and permission of instructor

The course will focus on the practical implementation of web surveys. We will trace the process that begins with a questionnaire and follow it through to creating a survey, selecting sample, administering the survey, and processing the results. Criteria for selection of Web survey software will be presented, including a review of the major types of software, available capabilities, and cost implications. Examples of survey design software will be shown. Students will learn how to effectively manage Web-based data collections with regards to survey process, survey quality, and survey design. While this course will not focus on the methodological design decisions involved in creating web surveys, current standards for Web survey design in use by survey research professionals will be presented. Specific issues relating to implementing Web surveys as part of a multi-mode data collections, confidentiality, and human subjects issues will be discussed. While a Web survey systems may be used in a hands-on setting during this course, and basic instruction will be provided on its use, due to time constraints, students should not expect to master any Web survey software systems as part of this course.

Prerequisite: Basic coursework in social science research methods, including survey research. Students should have a basic understanding of computers and have experience in using email and web browser software.

Advisory Prerequisite: Graduate standing and permission of instructor