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Measurement, Evaluation, Statistics, Assessment (MESA) online program, M.Ed.

course descriptions

Please note these are general descriptions of the courses. Variations in content will be expected depending on the instructor.

EPSY 546 Educational Measurement

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).

EPSY 561 Assessment for Measurement Professionals

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.

EPSY 504 Rating Scale and Questionnaire Design and Analysis

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.

EPSY 503 Essentials of Quantitative Inquiry in Education

This course introduces theory and assumptions behind parametric statistics. Also 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.

EPSY 505 Advanced Analysis of Variance and Multiple Regression

This first half of the course will focus on ANOVA techniques, including single factor designs (One-way ANOVA), mean comparison, assumption of ANOVA, effect size and power, fixed and random effect models, two-way factorial design (two-way ANOVA), repeated measures designs (Random-block ANOVA), mixed designs (Split-plot ANOVA), and Analysis of Covariate (ANCOVA). The second half of the course will focus on multiple regression and related statistical techniques, including bivariate association, regression with multiple continuous and/or categorical independent variables, diagnostic techniques, model selection strategies, and interactions among independent variables. Finally, the course will demonstrate how General Linear Modeling (GLM) reveals the underlying continuity between ANOVA and regression. SPSS will be used for most data analyses.

EPSY 583 Multivariate Analysis of Educational Data

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.

EPSY 560 Educational Program Evaluation

The overarching goal of the course is for students to gain an appreciation for the importance of program evaluation, its role in the field of education, and the crucial role that evaluators, clients and stakeholders play in that complex enterprise. Topics addressed in the course will include key evaluation concepts and terms, purposes and goals of evaluation, history of evaluation, alternative approaches to evaluation, quantitative and qualitative measures, process and outcome evaluation, contracting and planning evaluations, designing evaluation instruments, reporting evaluation results, and political and ethical issues in evaluation.

EPSY 509 Research Design in Education

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.

EPSY 553 Assessment for Teachers

In this course you will learn how to design assessments that are carefully aligned with educational objectives. You will learn how to devise technically sound, content valid paper-and-pencil tests that incorporate different types of item formats (e.g., multiple-choice, true-false, matching, short-answer, completion, essay, interpretive exercises) and you will also learn to craft performance (or product) assessments, as well as tools (i.e., checklists, rating scales, and rubrics) to evaluate students performances or products. We will take a critical look at the selection and use of standardized achievement tests in classrooms today, including commercial achievement tests (e.g., Stanford 10, Stanford Reading First, Iowa Test of Basic Skills) and statewide achievement assessments. We will discuss test preparation and which activities are appropriate and ethical. You will learn about assessment bias, why it is problematic, and some approaches that test developers (and teachers) can use to screen assessments for bias. We will tackle the issue of developing defensible grading procedures for combining scores from different assessments to arrive at a grade. You will learn about different approaches you can use for assigning grades and principles to follow. We will discuss what should (and should not) be included in a grade, how to handle grading for cooperative learning, grading students with disabilities, how to convert rubric scores to grades, and methods other than report cards that you can use to communicate assessment results. Finally, we will examine laws and legislation affecting assessment programs that teachers need to understand.

EPSY 512 Hierarchical Linear Models

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.