Hypothesis formulation and testing; estimation and confidence limits; one and two-sample tests; and statistical decision theory. Study inferences arising from distribution functions: t, F, chi-square, binomial, normal.
An overview and basic understanding of qualitative analysis software including preparation of data files, managing text and images, creating codes, memos, queries models and reports.
The course is designed to familiarize students with the use of statistical packages on both the mainframe and microcomputer platforms. Students will learn to organize, input, and analyze data.
Course will acquaint students with the data management, data transformation and statistical analysis procedures available in SPSS for Windows.
Concurrent Prerequisite
SRM 602 with a minimum grade of C
The R programming language is an important and current research tool for statisticians. Students will receive an introduction to data manipulation, graphical techniques, model building and some programming using R.
This course provides an introduction to the Structured Query Language (SQL). Students will learn to write retrieval queries and manage data in a relational database.
The goal of this course is to familiarize students with the use of the Mplus software (Muthen and Muthen, 1998-2017). Students will become acquainted with the basics of Mplus. The course will focus on using Mplus for latent variable modeling.
This course is intended to present an introduction to the concepts and issues surrounding statistical consulting. Students will learn and practice the process of consulting and communicating with clients.
Study of probability, random variables, distributions, moments, expected values and standard probability laws, probability bounds and point estimation.
Continuation of
SRM 551. Sampling distributions, estimation techniques, maximum likelihood, tests of hypothesis, confidence intervals, regression and chi-square tests.
Specialized topics or contemporary issues. Topics vary.
Principles of research, design and analysis. Read and critique published research. Required of all first year graduate students except in those departments with substitutes. Taught every semester.
Brief review of descriptive statistics. Covers probability, inference and sampling, correlation, hypothesis testing one-way ANOVA and an introduction to computer statistics packages.
Continuation of
SRM 602. Review of one-way ANOVA. Covers multiple comparisons, factorial designs, nested and mixed models, repeated measures, analysis of covariance and use of computer statistics packages.
Matrix approach to continuous and categorical variables, polynomial Selected non-linear models; formulation of ANOVA and ANCOVA designs and collinearity; regression methods; backward elimination, forward selection, stepwise regression.
Study non-parametric tests; the rationale underlying the tests; examples of application of the tests in behavioral research; and comparison of the tests with their parametric equivalents.
Topics include factorial designs, crossed/nested designs, repeated measurements, blocking, analysis of covariance, pre- and post-multiple comparisons, trend analysis, power and use of computer software.
Learn methods of survey sampling, including such topics as simple and stratified random sampling, ratio estimation, cluster sampling, systematic sampling, questionnaire design, problems of non-response and non-sampling errors.
Additional multiple regression topics. Introduction to MANOVA designs, discriminant analysis, factor analysis, cluster analysis, and path analysis.
This course is intended to provide students with the tools to perform analyses that go beyond traditional methods such as multiple regression, multiple ANOVA, and classical repeated-measures ANOVA, including data situations in which these modeling techniques have failed assumptions. General topics include diagnostics and remedies for failed traditional linear modeling assumptions, missing data techniques, power and sample size calculations and simulations, and other modern data methods.
Advanced topics in matrix algebra with applications to statistics. Development of the theory of linear models as a structure for handling problems in regression, analysis of variance, and experimental design.
This course is intended to provide students with the tools to perform appropriate and informative longitudinal data analyses. The level of the topics and discussions are intended to appeal to both established researchers with experience working with longitudinal data as well as new researchers with no background in longitudinal data.
Introduces advanced programming tools using the SAS System. Designed to better qualify students for jobs in statistical data analysis.
Individualized investigation under the direct supervision of a faculty member. (Minimum of 37.5 clock hours required per credit hour.)
Special Notes
Maximum concurrent enrollment is two times.
Acquaint students with the major applications of and issues related to multiple regression analysis. Gain the skills necessary for conducting and interpreting studies involving multiple regression analysis.
Acquaint students with the major applications and issues related to multilevel modeling. Gain the skills necessary for conducting and interpreting studies involving multilevel modeling.
Covers the uses of surveys, the process involved in designing and implementing a survey study, and general issues related to survey research.
Applications of and issues related to covariance structure modeling. Students will gain skills needed for designing, conducting, and interpreting studies involving confirmatory factor analysis and latent variable path modeling.
Principles of Categorical Data Analysis. Emphasis on log-linear and logic modeling techniques, which parallel many features of the general linear model in the continuous case. Taught alternate years.
This course provides an introduction to Bayesian statistical methods for inference. Topics include prior, likelihood, posterior, and predictive distributions, Bayesian analysis of single parameter models and simple multi-parameter models using conjugate, non-informative and informative priors, hierarchical modeling, and simulation of posterior distributions and posterior summaries using statistical packages.
Covers, the principles of analyzing time series data; descriptive techniques, time series models; ARIMA, SARIMA, fitting time series models in time domain, forecasting, model selection and diagnostic checking.
Advanced topics in applied statistics, measurement theory and research. Specific topics will be determined by the instructor and by current student needs.
Introduction to the use of statistical methods for quality improvement. Provides a comprehensive coverage of material from basic principles to state-of-the-art concepts and applications to both product and non-product situations.
Required of all Master's and doctoral students. Students present the results of their own research and critique and discuss the presentations of other students and faculty. S/U graded.
Topics will include the historical background, 'paradigm wars', design, theory, advantages/disadvantages, writing and defending proposals, validity/reliability and data analysis of mixed methods or complimentary research.
Decision-making inquiry for addressing real-world problems in education, health, and social science settings. Students will gain skills needed to design, analyze, and communicate findings to support data-informed decision-making.
Theories and methods of program evaluation, models of evaluation and the social context of evaluation. Nature and types of evaluation, planning, proposal writing and measurements.
Emphasis on application of advanced skills in research and evaluation to the pre-proposal, proposal and post-proposal phases of the grant writing process. Students will develop an applied project with stakeholders.
Advanced methodological techniques for program evaluation. Topics include tailoring evaluations to the needs of clients and stakeholders, diagnostic procedures and needs assessments, program monitoring and judging the impact of programs.
This course introduces qualitative research. Students will explore the foundations, methods and processes of qualitative research and will learn to evaluate published research.
Students will explore research topics in visual and virtual inquiry including: history, contemporary relationship to critical research, ethical dilemmas, and current use in their own discipline. Visual and virtual products will be developed as demonstrations of students' increased methodological understandings.
Study of ethics in human research including history, theory, disciplines' codes, IRB, distinctive respondents. Students receive an IRB training certificate, learn to prepare IRB application, and develop an ethical stance.
Provides in-depth study of ethnography as related to educational research including issues of ethics, politics, diversity, and the researcher's role. Students will propose and conduct a mini-educational ethnography.
In depth examination of qualitative case study research. Characteristics of general case studies along with specific types of case studies will be covered. Students will propose and conduct a mini-case study.
In depth study of narrative research including life history, oral history, biography, and auto-ethnography. Group and individual narrative inquiries will be conducted. Interviewing, ethics and research benefiting participants will be emphasized.
An in-depth study of the role writing plays in quantitative research data collection, analysis and representation. Students will use data they collected in a variety of analysis and writing activities.
Experiential learning in an on-campus setting, such as the Research Consulting Lab. Students work a minimum of 3 hours per week for each hour of credit. S/U graded.
Experiential learning in an on-campus setting, such as the Research Consulting Lab, in conjunction with supervision by a faculty member. Students work a minimum of 3 hours per week for each hour of credit. S/U graded.
This course is intended to facilitate a capstone project at or near the completion of all other required courses for the MS Applied Statistics and Research Methods degrees. S/U graded.
Advanced research designs, concepts and methods. Required of all specialist and doctoral candidates.
Seminar is designed to acquaint advanced doctoral students with selected current issues in the field of research methodology. Topics will vary based on instructor and student interest.
Introduces multivariate data structures including geometrical properties and interpretations, the multivariate normal distribution, multivariate one- and two-sample tests on mean vectors and covariance matrices, MANOVA, and profile analysis.
Skills and strategies for effective consulting in research, statistics, and evaluation. Students will learn about good consulting practice and will gain hands-on experience in oral and written communication with clients.
(
SRM 502 and Concurrent Prerequisite
SRM 700 with a minimum grade of C)
The theoretical fundamentals of mathematical statistics and inference including: limiting distributions, statistics and sampling distributions, point estimation, sufficiency and completeness, interval estimation, and tests of hypotheses.
Work with faculty member on professional endeavors such as research, writing, course planning or public service. Requires 3 hours per week for each credit. S/U graded.
Required of all doctoral students. Doctoral students must earn 4 hours as partial fulfillment of requirements for the doctorate. Check with the Graduate School regarding appropriate procedures and formats. S/U graded.
Required of all doctoral candidates. Must earn 12 hours as partial fulfillment of requirements for the doctorate. Dissertation must be approved by and defended before the dissertation committee. S/U graded.