Description: Basic techniques used in quantitative political science research. The general linear model. Basic probability theory, ordinary least squares regression, and how to solve problems often encountered when conducting quantitative analyses in political science.
Description: Introduction to the basic principles of causality and inductive logic in contemporary social and behavioral science. One, two, and multi-way layouts in analysis of variance, fixed effects models, and linear regression in several variables; the Gauss-Markov-Theorem; multiple regression analysis; and basic principles of experimental and quasi-experimental designs.
This course is a prerequisite for: SRAM 917
Description: Multi-national research projects and the methodological challenges. Key aspects of cross-national, cross-cultural survey research, study design and organization; survey error and bias; question design; harmonization; adaptation and translation; survey process quality monitoring and control; and process and output documentation.
Description: Effects of various data collection methods on survey errors. The strengths, weaknesses, and challenges of data collection modes and mixed-mode methods. Processes underlying data collection and practical challenges that arise with each mode; coverage error; nonresponse error; interviewer effects and training; timing; and mode effects.
Description: Design of probability samples, sampling populations of humans and unique challenges posed by such populations, restricted by cost and available sampling frames. Simple random sampling, stratification, cluster sampling, systematic sampling, multistage sampling, and probability proportional to size sampling, area probability sampling, and telephone samples.
Prerequisites: GRBA 813 or equivalent
Description: Review, evaluation, and design of advanced marketing research investigations. State-of-the-art methodological issues relevant to marketing to provide an understanding of multivariate data analysis pertinent to the marketing literature. Analysis of linkage, structure, and causality/change for marketing phenomena.
Description: Basic principles of practice including ethical requirements and procedures, Institutional Review Board (IRB) and Collaborative Institutional Training Objective (CITI), personal conduct, plagiarism. Introduction to relevant databases, data archives, key surveys. Practice in critical discussion, report and abstract writing, creating and presenting conference papers. Provides first year MS students with a grounding in key principles and components of professional practice needed for a career in survey research and related fields.
Description: Experience applying concepts and methods of survey research in preparation for a professional career.
Description: Application of theory and research gained during internship.
Description: Topic varies.
Prerequisites: Admission to masters degree program and permission of major adviser
Description: Advanced topics related to sampling error in surveys. Complex sample designs used to measure populations of humans, effect of nonresponse on sampling error and data analysis; methods available to "repair" the missing information; the implications of complex sample designs for analyses; and variance estimation.
Prerequisites: SRAM 816
Description: Key components of analytic models used in analysis of survey data. Analysis of variance (anova), linear regression (ols) and generalized linear model (glm) to include estimation of coefficients for a specified set of "structural equations" designated by a hypothesized causal structure (i.e., SEMs). Main statistical models for estimating nonlinear regression coefficients. Introduction to principles of maximum likelihood estimation (mle) and alternative estimation approaches. Focus on development of the ability to conduct independent quantitative research.
Description: Design instruments for multi-population surveys and to produce versions in different languages. Major approaches and strategies used in cross-national and cross-cultural research to design, test, adapt, and translate instruments for multilingual use.
Description: Common language of survey errors across social science disciplines. Causes of survey coverage, nonresponse, measurement, and processing errors; techniques used to reduce the error in practice; and statistical models and designs that exist to measure the error. Implications of cost and trade-offs between error sources.
Description: Logic of causal inference in research design. Obstacles to causal inference, faulty measurement, un-representativeness, spuriousness, specification errors, and confounds, Experimental and quasi-experimental designs, with inferential pitfalls peculiar to each design. Statistical procedures to illustrate the logic behind various data analytic approaches and the different problems that can limit conclusions derived from these tools.
Description: Various correlational-based statistical procedures presented, including linear and nonlinear regression, multiple regression, statistical control, analysis of interactions, the general linear model, factor analysis, and discriminant analysis.
Description: Cognitive and communicative processes affect on dynamics of survey interviewing and relationships to principles of survey design. Effects of question wording on comprehension; question order and context on attitude; communicative and retrieval processes on validity of retrospective behavioral reports; and impact of response alternatives on answers.
Description: Design of questionnaires for survey research and the theoretical and practical issues arising from them. Selection of appropriate measurement techniques for assessing opinions, past behaviors and events, and factual material.
Prerequisites: EDPS 870
Description: Presentation of various measurement theories and concepts, including classical true-score theory, reliability and validity, test construction, item response theory, test equating, test bias, and criterion-referenced tests.
This course is a prerequisite for: EDPS 980
Description: Introduction to the techniques of path analysis, confirmatory factor analysis, and structural equation modeling with emphasis on the set-up and interpretation of different models using the LISREL program. Model testing and evaluation, goodness-of-fit indices, violations of assumptions, specification searches, and power analyses..
Description: Techniques of multivariate analyses, including multivariate analysis of variance and covariance, multivariate multiple regression, multigroup discriminant analysis, canonical analysis, repeated measures (Multivariate model), and time series. Mathematical models presented and analyzed. Instruction complemented by appropriate statistical software packages.
Prerequisites: Admission to doctoral degree program and permission of supervisory committee chair