Courses

This course introduces theory and assumptions behind parametric statistics and provides hands-on experience in conducting basic quantitative research (t-test, correlation, regression, analysis of variance). Students will be able to 1) recognize and define basic descriptive and inferential statistical terms and concepts; 2) arrive at accurate answers to selected statistical problems and procedures; 3) demonstrate competence in using SPSS for data manipulations and analysis; and 4) recognize when and when not to use certain statistical procedures. Required core course for the MEd program. Waived for demonstrated equivalent coursework. Offered: Fall, Spring, and Summer semesters. 4 credit hours.

View EPSY 503 Sample Syllabus

The course introduces students to the process of planning, designing and conducting educational research. Upon presenting an overview of common quantitative, qualitative and mixed-method research methods, the course focuses on taking students through the process of writing a complete research proposal to address a particular research topic. It is suggested that students use this course to explore various methodologies that they might incorporate into their research interests and use the course project to design a pilot study. Required core course for the MEd program. Waived for demonstrated equivalent coursework. Offered: Fall semesters. 4 credit hours.

View EPSY 509 Sample Syllabus

This course introduces key concepts and methods of ‘qualitative research’, an umbrella term that refers to a family of research traditions, methodologies, and methods. The course will provide a conceptual overview of the characteristics, uses, and ethical considerations associated with qualitative methods of inquiry. Students will also gain practical, hands-on experience carrying out qualitative research by developing instruments, gathering and analyzing data, and identifying and communicating findings. Offered: Fall semesters in 2021, 2023, 2025. 4 credit hours.

View EPSY 540 Sample Syllabus

This course provides detailed coverage of the principles of analysis of variance and the analysis of data collected from research employing experimental designs. Offered: Fall semesters. 4 credit hours.

View EPSY 543 Sample Syllabus

This course familiarizes students with classical test theory, including test reliability and validity. It also introduces item analysis useful in test construction, factor analysis, as well as the major extensions and alternatives to classical test theory: generalizability theory and item response theory. Four computer programs will be used in the class: Excel (to assist hand calculation for conceptual understanding), SPSS (for item analysis, reliability, factor analyses), GENOVA (for G theory), and Bilog (for IRT). Offered: Spring semesters. 4 credit hours.

View EPSY 546 Sample Syllabus

This course introduces students to multiple correlation and regression techniques as tools for the analysis and interpretation of educational and behavioral science data. Offered: Fall semesters. 4 credit hours.

View EPSY 547 Sample Syllabus

This course will prepare students with the skills necessary to develop rating scales designed to measure latent constructs and questionnaires designed to gather factual information with the primary emphasis on rating scales. Topics covered include Messick’s unified validity theory, assessing the reliability and validity for person and item responses, evaluating the functioning of a rating scale, assessing dimensionality and analyzing and reporting results using methods based in latent trait theory, specifically Rasch measurement. Students will analyze and summarize the results of their own rating scale analysis. Examples will be drawn primarily from the fields of education, psychology and physical rehabilitation. Offered: Summer 2023 and subsequent Spring and Summer semesters. 4 credit hours.

View EPSY 550 Sample Syllabus

This course deals with Item Response Theory (IRT) measurement models that are useful for analyzing test data. Compared to Classical Test Theory, IRT provides better information about examinees and may improve the efficiency of test development and subsequent testing when it is applied properly. IRT models may be used in a variety of applications (e.g., achievement tests, attitude surveys, and personality inventories). Much of this course will focus on unidimensional IRT models for dichotomous data (scored 0 or 1) because this content provides the necessary basis for understanding more advanced IRT models. Treatment will also be given to topics such as polytomous IRT models, test development, computerized adaptive testing, item bias, and test equating. Although significant time will be dedicated to discussing IRT concepts, this is intended to be an “applied” course. Several classes will be dedicated to examining examples and learning how to use IRT software with real data sets. It is expected that, by the end of the term, students will be able to apply their newfound knowledge and skills. Offered: Fall semesters. 4 credit hours.

View EPSY 551 Sample Syllabus

In this course students will craft different types of assessment instruments to measure a variety of learning outcomes. They will learn about the characteristics and strengths/limitations of various types of assessment methods, and how to select assessment methods that are most appropriate for particular purposes. Students will develop specifications for assessments and create technically sound paper-and-pencil tests that incorporate different types of item formats (e.g., multiple-choice, true-false, matching, short-answer, completion, essay, interpretive exercises). They will construct performance (or product) assessments, as well as tools to evaluate performances or products (i.e., checklists, rating scales, and rubrics). Later in the course, we will look at the selection and use of standardized tests. Students will learn how these tests are constructed, and they will practice interpreting statistics included in score reports. We will discuss universal test design principles, as well as assessment modifications and accommodations that persons with disabilities and non-native language learners can use to participate meaningfully in assessment activities. Finally, students will learn how to develop defensible grading procedures for combining scores from different assessments to arrive at a grade. Throughout this course students will read and discuss key pieces of assessment-related research, focusing on validity and reliability issues that different types of assessments raise. Offered: Spring semesters. 4 credit hours.

View EPSY 561 Sample Syllabus

This course provides an introduction to large-scale testing, presenting an overview of the various tasks that employees perform in testing organizations, city-wide testing bureaus, professional licensing and certification boards, statewide educational testing programs, testing units that are part of state merit systems, etc. The course should be useful for students considering working for such organizations in a variety of capacities (e.g., item writers, statisticians, psychometricians, researchers, testing program managers), employees currently working in these organizations who would like to increase their understanding of the field, and students who want to gain an understanding of the challenges of creating and administering large-scale tests. The course is organized around the key processes common to all large-scale testing programs: design, administration, scoring, reporting, and validating. The course is not designed to develop the technical skills to carry out specific tasks such as writing items, equating tests, setting cut scores, etc. Rather, the focus is on gaining a conceptual understanding of what is involved in performing these kinds of tasks, and why each task is important. After completing this course, students should have an appreciation for what is involved in producing large-scale tests, as well as an awareness of some of the pressing issues that testing organizations face. Offered: Fall semesters in 2022, 2024, 2026. 4 credit hours.

View EPSY 562 Sample Syllabus

Evaluation is a practice and a profession focused on examining the quality or success of educational and social interventions, with the aim of informing decisions about intervention design, implementation, adaption, and resource allocation. This course introduces the concepts and methods used to conduct evaluations of programs, projects, curricula, policies, and other educational interventions. Students will gain foundational knowledge of how to carry out evaluations with attention to context, stakeholder engagement, values, and issues of equity and justice. Students will apply this knowledge to design an evaluation plan for a real-world intervention and also gain skills to critique evaluation studies as an informed, critical evaluation stakeholder. Offered: Spring semesters. 4 credit hours.

View EPSY 564 Sample Syllabus 

Conducting high-quality evaluation studies requires evaluators to possess the knowledge and skills to carry out evaluation activities, as well as understanding of the theoretical and conceptual basis for evaluation practice. This course addresses the relationship between theory and practice by reflecting on students’ (formal or informal) experiences with evaluation of educational and social interventions, critically analyzing major categories of approaches to evaluation, examining evaluation studies illustrative of each category, and identifying implications for evaluation practice. Students will also explore scholarship that takes up key issues in the field such as the role of evaluation in advancing social justice, what constitutes credible evidence, and the use of evaluation by stakeholders and communities. Students will develop foundational knowledge of evaluation theory and explore the application of that theory to guide and strengthen evaluation practice. Offered: Fall semesters 2022, 2024, 2026. 4 credit hours.

View EPSY 565 Sample Syllabus

This course is an introduction to multivariate statistical methods including data screening, canonical correlation, MANOVA/MANCOVA, DFA, profile analysis, logistic regression, component/factor analysis, confirmatory factor analysis, and structural equation modeling. The course will examine the assumptions underlying each method, teach students to run analyses for each method, assist students with interpreting the relevant sections of computer output, and discuss how results may be written for possible publication. Offered: Spring semesters. 4 credit hours.

View EPSY 583 Sample Syllabus

Hierarchical Linear Modeling (HLM) is an advanced statistical method widely used in social sciences including education, sociology, and organizational research. It is capable of dealing with situations where units of observations are nested under clusters (i.e., students nested under classrooms, children nested under families) and the assumption of independence of observations is violated. This course is designed to help students develop a conceptual understanding of HLM and the skills to conduct HLM analyses; interpret results; and understand and critique studies using HLM. This course will start with a review of regression and cover modeling setting, testing and evaluating assumptions, estimation of model parameters, and hypothesis testing. Students will learn HLM through typical model examples, including two and three-level models, growth models, hierarchical generalized linear models, and hierarchical models for latent variables. Lab sessions will follow each lecture. Lab sessions are designed with the goal to help students apply the concepts learned during the lecture and develop analytical and communication skills. Offered: Spring semesters in 2022, 2024, 2026. 4 credit hours.

View EPSY 584 Sample Syllabus

Extremely rapid pace of change in statistics and methodology in education requires that graduate students (and newly minted PhDs in academic and applied settings) be well versed in current data analytic techniques and able to keep abreast of emergent techniques by being aware of contemporary methodological literature. This course will illustrate the uses of structural equation models (SEM, also known as linear structural relations models) for cross-sectional, longitudinal, and experimental data analysis. The course is organized to take participants through each of the cumulative steps in the analysis: deciding which type of model is appropriate, setting up the data file and coding variables, interpreting and displaying empirical findings, and presenting results in both verbal and written form. Topics that will be covered are: Introduction to matrix notation (some basics) of structural equation models; model specification, identification, model fit, and SEM assumptions;  path diagrams and path models; confirmatory factor analysis; structural equation models; some special models such as latent growth models, multigroup models, measurement invariance; and Mplus as a useful tool for latent variable modeling. Offered: Spring semesters in 2023, 2025, 2027. 4 credit hours.

View EPSY 587 Sample Syllabus