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Courses in SRC-PSM Graduate Program
Survey Methodology (SURVMETH)
SURVMETH 600. Fundamentals of Survey Methodology
Graduate level status, or upper-level undergraduate with permission of instructor, required. (3). May not be repeated for credit. (non-LSA credit).

The course is intended as an introduction to the field, taught at a graduate level. It introduces a set of principles of survey design that are the basis of standard practices in the field. The course examines research literatures that use both observational and experimental methods to test key hypotheses about the nature of human behavior that affect the quality of survey data. It also presents statistical concepts and techniques in sample design, execution, and estimation, and models of behavior describing errors in responding to survey questions. The course uses techniques to sample design, execution, and estimation, and models of behavior describing errors in responding to survey questions. The course uses total survey error as a framework to discuss coverage properties of sampling frames, alternative sample designs, and their impact on standard errors of survey statistics, alternative modes of data collection, field administration operations, the role of the survey interviewer, impacts of non-response on survey static's, the effect of questions structure, wording and context on respondent behavior, models of measurement error, post-survey processing, and estimation in surveys.

SURVMETH 601 / PSYCH 688 / SOC 688. Introduction to Survey Research Techniques
Introductory psychology and statistics and permission of instructor. (6). May not be repeated for credit.

This course acquaints students with the theory and practice of survey research, which is broadly defined as research that relies upon face-to-face 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.

SURVMETH 612 / PSYCH 687 / SOC 612. Methods of Survey Sampling
Two courses in statistics; and graduate standing. (3). May not be repeated for credit.

This 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.

SURVMETH 613. Analysis of Complex Sample Survey Data
SURVMETH 612 or SOC 612 or PSYCH 697. (3). May not be repeated for credit. (non-LSA credit).

This introductory course on the analysis of data from complex sample designs covers the development and handling of selection and other compensatory weights; methods of 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.

SURVMETH 614. Design and Analysis of Complex Sample Survey Data
SURVMETH 612 (prior completion or enrollment). (3). May not be repeated for credit. (non-LSA credit).

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.

SURVMETH 616 / PSYCH 683 / SOC 621. Workshop in Sampling Techniques
SOC 612/PSYCH 687/SURVMETH 612 and/or concurrent enrollment in SOC 613/PSYCH 618/SURVMETH 615 and permission of instructor. Graduate standing. (3 - 6). May not be repeated for credit.

SURVMETH 617 / BIOSTAT 617 / SOC 717 / STATS 580. Methods and Theory of Sample Design
Three or more courses in statistics and preferably a course in methods of survey sampling. (3). (BS). May not be repeated for credit.

This course is concerned with the theory underlying the methods of survey sampling widely used in practice. It covers the basic techniques of simple random sampling, stratification, systematic sampling, cluster and multi-stage sampling, and probability proportional to size sampling. It also examines methods of variance estimation for complex sample designs, including the Taylor series expansion method, balanced repeated replications, and jackknife methods.

SURVMETH 618. Inference From Complex Samples
BIOSTAT 602/STAT 511, SURVMETH 612 and 617. (3). May not be repeated for credit. (non-LSA credit).

This course covers the theoretical and empirical properties of various variance estimation strategies (e.g., Taylor series approximation, replicated methods, and bootstrap methods for complex sample designs) and how to incorporate those methods into inference for complex sample survey data. Variance estimation procedures are applied to descriptive estimators and to analysis techniques such as regression, analysis of variance, and analysis of categorical data. Generalized variances and design effects are presented. Methods of model-based inference for complex sample survey are also examined, and the results contrasted to the design-based type of inference used as the standard in the course. The course will use real survey data to illustrate the methods discussed in class. Students will learn the use of computer software that takes account of the sample design in estimation. Students will carry out a research and analysis project, using techniques and skills learned during the course. A paper describing the student's research will be submitted at the end of the course, and each student will give a short presentation of his/her findings.

SURVMETH 619. Topics in Survey Sampling
SURVMETH 612 or SOC 612 or PSYCH 697; and prior or concurrent enrollment in SURVMETH 617 (or SOC 717 or STATS 580 or BIOSTAT 617). (3). May not be repeated for credit. (non-LSA credit).

This course is an advanced course in selected topics in survey sampling. Topics to be covered include: estimation and imputation approaches; small area estimation; and sampling methods for rare populations. A selection of additional topics, chosen by the instructor, will also be covered. Examples of such additional topics are: sample designs for time and space; panel and rotating panel survey design; maximizing overlap between samples; controlled selection and lattice sampling; sampling with probabilities proportionate to size without replacement; multiple frame sampling; adaptive cluster sampling; capture-recapture sampling; sampling for telephone surveys; sampling for establishment surveys; and measurement error models. Both applied and theoretical aspects of the topics will be examined.

SURVMETH 623. Data Collection Methods
Current registration in a Program in Survey Methodology degree program OR previous completion of Fundamentals of Survey Methodology (SURVMETH 600). (3). May not be repeated for credit. (non-LSA credit).

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, non-response, 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 (dairies, administrative records, direct observation, etc).

SURVMETH 630 / PSYCH 711 / SOC 711. Questionnaire Design
An introductory course in survey research methods or equivalent experience. (3). May not be repeated for credit.

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 question 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.

SURVMETH 632. Cognition, Communication, and Survey Measurement
Background in Psychology is helpful, but not required. (3). May not be repeated for credit. (non-LSA credit).

Survey data are only as meaningful as the answers that respondents provide. Hence, the processes that underlie respondents' answers are of crucial importance. This course draws on current theorizing in cognitive and social psychology pertaining to issues like language comprehension, information storage and retrieval, autobiographical memory, social judgment, and the communicative dynamics of survey interviewing to understand how respondents deal with the questions asked and arrive at an answer.

SURVMETH 641. Computer Analysis of Survey Data II
SURVMETH 681 (prior completion or current enrollment). (1). May not be repeated for credit. (non-LSA credit).

This course is a computer laboratory accompanying Analysis of Survey Data II. Students taking this course will benefit from the development of skills in the use of computer statistical packages that can obtain results for methods discussed in Analysis of Survey Data II. Particular attention will be paid to manipulating software and interpretation of results. The course will cover file preparation and manipulation, exploring data structure preparatory to index construction, index construction and evaluation, bivariate and multivariate regression analyses, logistic regression analysis, and contingency table analysis. 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.

SURVMETH 651. Semi-Structured Interviewing
(1 - 3). May not be repeated for credit. (non-LSA credit).

This course focuses on semi-structured, or in-depth, interviewing methodologies. The course will cover the goals, assumptions, process, and uses of interviewing. We will 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).

SURVMETH 652. Introduction to Focus Groups
An introductory course in survey research/equivalent experience. (1 - 3). May not be repeated for credit. (non-LSA credit).

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.

SURVMETH 653. Combining Qualitative and Quantitative Data
An introductory course in survey research/equivalent experience. (1 - 3). May not be repeated for credit. (non-LSA credit).

In this course, participants become familiar with multiple methods of data collection and how they can be combined within a single research project. The methods of focus are unstructured or in-depth interviews, focus groups, participant observation, archival research, and survey interviews. Emphasis is placed on the strengths and weaknesses of each approach, and we examine 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 are asked to design and present multi-method approaches to a research question of their choice. By the end of this module, participants have an overview of multi-method research that will enable them to design, understand, and evaluate multi-method approaches within a single project.

SURVMETH 654. Qualitative Data Analysis with Computers
An introductory course in qualitative research methods. (1 - 3). May not be repeated for credit. (non-LSA credit).

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.

SURVMETH 660. Survey Management
Degree-seeking student in Program in Survey Methodology or permission of instructor. (3). May not be repeated for credit. (non-LSA credit).

This course describes modern practices in the administration of large-scale surveys. It reviews alternative management structures for large field organizations supervisory and training regimens, handling of turnover, and multiple surveys with the same staff. Practical issues in budgeting of surveys are reviewed with examples from actual surveys. Scheduling of sequential activities in the design, data collection, and processing of data is described.

SURVMETH 670. Design Seminar I
SURVMETH 612, 623. (3). May not be repeated for credit. (non-LSA credit).

This is a wide-ranging graduate seminar in which several Program faculty members join with the students in attempting to solve design issues presented to the seminar by clients from the private, government, or academic sectors of research. Readings are selected from literatures not treated in other classes and practical consulting problems are addressed.

SURVMETH 672. Survey Practicum: Data Collection
SURVMETH 600 or graduate standing in the Program in Survey Methodology. (4; 2 - 4 in the half-term). May not be repeated for credit. (non-LSA credit).

This is the first course in the two semester sequence that constitutes the practicum in survey research. Class time will be devoted to instruction and practice in questionnaire development, pretesting, blocklisting, sampling, coding, and interviewer training. The skills taught during class periods are preparation for out-of-class fieldwork that culminates in the conduct of a survey interview.

SURVMETH 673. Survey Practicum: Data Analysis
SURVMETH 672. (2). May not be repeated for credit. (non-LSA credit).

This course is the second in the series of courses comprising the survey research practicum. The course focuses on lectures and readings on most of the following issues; data cleaning and file preparation; classification systems and recodes; descriptive statistics and hypothesis testing; sums of squares and the analysis of variance; data reduction through factor and/or cluster analysis and the development of indices; cross-classification of categorical data and the measurement of association; multivariate linear regression tools; dummy-variable regression and multiple classification analysis; the logic of causal analysis and multiple dependent variables; multiple indicators, measurement errors and statistical analysis; report writing, graphics and the presentation of data.

SURVMETH 680. Analysis of Survey Data I
Mathematics through college algebra. (3). May not be repeated for credit. (non-LSA credit).

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.

SURVMETH 681 / PSYCH 616 / SOC 616. Analysis of Survey Data II
SOC,PSYCH 613/SOC 510 or PSYCH 684/SOC 614 or equivalent and statistics. (3). May not be repeated for credit.

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.

SURVMETH 685. Statistical Methods I
TWO COURSE SEQUENCE IN PROBABILITY & STATS OR EQ. (3). May not be repeated for credit. (non-LSA credit).

This is the first course in a two term sequence in applied statistical methods covering topics including regression, analysis of variance, categorical data, and survival analysis. The purpose of this class is to learn basic statistical methods. The emphasis will be to understand and apply the methods.

SURVMETH 686. Statistical Methods II
SURVMETH 685 OR P.I. (3). May not be repeated for credit. (non-LSA credit).

Builds on the introduction to linear models and data analysis provided in Statistical Methods I. Topics include analysis of longitudinal data and time series, categorical data analysis and contingency tables, logistic regression, log-linear models for counts, statistical methods in epidemiology, and introductory life testing.

SURVMETH 688. Building and Testing Structural Equation Models
(3). May not be repeated for credit. (non-LSA credit).

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.

SURVMETH 697. Special Courses
Some background in survey methodology is desirable. (3; 2 in the half-term). May be repeated for a maximum of 6 credits. (non-LSA credit).

This course will address specific research problems in survey methodology currently under study by faculty members.

SURVMETH 699. Directed Research
Graduate standing and permission of instructor. (1 - 3). (INDEPENDENT). May be repeated for a maximum of 3 credits. May be elected more than once in the same term. This course has a grading basis of "S" or "U". (non-LSA credit).

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.

SURVMETH 720. Total Survey Error I
SURVMETH 612, 623. (2). May not be repeated for credit. (non-LSA credit).

This is the first course in two course sequence that reviews the total error structure of sample survey data, reviewing current research findings on the magnitudes of different error sources, design features that affect their magnitudes, and interrelationships among the errors. Coverage, non-response, sampling, measurement errors, interviewer effects, questionnaire effects, and mode of data collection effects are reviewed. Statistical and social science approaches to the error sources are compared.

SURVMETH 721. Total Survey Error II
SURVMETH 720. (2). May not be repeated for credit. (non-LSA credit).

This is the second course in two course sequence that reviews the total error structure of sample survey data, reviewing current research findings on the magnitudes of different error sources, design features that affect their magnitudes, and interrelationships among the errors. Coverage, non-response, sampling, measurement errors, interviewer effects, questionnaire effects, and mode of data collection effects are reviewed. Statistical and social science approaches to the error sources are compared.

SURVMETH 790 / EDUC 890. Multi-level Analysis of Survey Data
At least one graduate-level course in statistics or quantitative methods, and experience with multivariate regresion models, including both analysis of data and interpretation of results. (3). May not be repeated for credit.

In this course, students are introduced to an increasingly common statistical technique, hierarchical linear modeling (HLM). Multi-level methods and the HLM software can be used to analyze nested data and multi-level research questions. Although the course demonstrates multiple uses of the HLM software, including growth-curve modeling, the major focus is on the investigation of organizational effects on individual-level outcomes.

SURVMETH 890. Doctoral Seminar I
Candidacy and permission of instructor. (3). May be repeated for a maximum of 3 credits. May be elected more than once in the same term. This course has a grading basis of "S" or "U". (non-LSA credit).

This is the first course in a two-term introduction to the integration of social science and statistical science approaches to the design, collection, and analysis of surveys. The seminar will focus on six to eight areas of statistical and methodological literature that have benefited from alternative approaches. Students demonstrate mastery of those literatures through critical review papers, ideas, for extensions of the literature, and empirical projects related to research reviewed.

SURVMETH 891. Doctoral Seminar II
SURVMETH 890/permission of instructor. (3). May be repeated for a maximum of 3 credits. May be elected more than once in the same term. This course has a grading basis of "S" or "U". (non-LSA credit).

This is the second course in a two-term seminar designed to develop skills in the identification of research problems, specification of hypothesis / theorems to extend current understanding of the field, and planning for original research. A common set of readings in four to six advanced research activities of the faculty are studied, with the faculty engaged in research discussing areas of potential innovation.

SURVMETH 895. Special Seminars
Graduate Standing and permission of instructor. (1 - 3). May be repeated for credit. May be elected more than once in the same term. (non-LSA credit).

This course will address specific research problems currently under study by faculty members.

SURVMETH 899. Directed Research
Candidacy and permission of instructor. (1 - 3). (INDEPENDENT). May be repeated for a maximum of 3 credits. May be elected more than once in the same term. (non-LSA credit).

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.

SURVMETH 988. Advanced Seminars in Survey Methodology
Graduate standing and permission of instructor. (1 - 5). May be repeated for a maximum of 20 credits. May be elected more than once in the same term. (non-LSA credit).

SURVMETH 990. Dissertation Pre-Candidacy
Graduate standing and permission of instructor. (1 - 8). (INDEPENDENT). May be repeated for credit. This course has a grading basis of "S" or "U". (non-LSA credit).

Election for dissertation work by doctoral student not yet admitted as a Candidate.

SURVMETH 995. Dissertation Candidacy
Graduate school authorization for admission as a doctoral Candidate. (Prerequisites enforced at registration.) (8; 4 in the half-term). (INDEPENDENT). May be repeated for credit. This course has a grading basis of "S" or "U". (non-LSA credit).

Graduate School authorization for admission as a doctoral Candidate. N.B. The defense of the dissertation (the final oral examination) must be held under a full term. Candidacy enrollment period. Students who have advanced to candidacy for the Ph.D. are required to register for Sociology 995 in any term when they are consulting with member of their dissertation committee or using the Library or other facilities of the University. If the student is to be engaged in a period of study away from the University, the student should file a Certification for Detached Study in advance. Students doing dissertation work prior to achieving candidacy should register for Sociology 990 for that portion of their schedule spent on dissertation work.

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