Interpretation and Evaluation of Research 

This course will improve a students' understanding of the empirical research literature in education and psychology. It concentrates on developing the conceptual foundations of empirical research and the practical analytic skills needed by a competent reader and user of research articles. Topics address purpose statements, hypotheses, sampling techniques, sample sizes and power, instrument development, internal and external validity, and typical quantitative research designs. Exercises emphasize the critical evaluation of published research. 

Critical Perspectives on Research Methodology 

This course explores foundational concepts that influence the methodologies and methods employed by social science researchers. Core topics explored include:

• Enlightenment’s influence on scientific inquiry
• Influence of empiricism, positivism, and universalism on social science research
• Objectivity and subjectivity
• Verification, falsification and paradigm shifts
• Principles of measurement

Also explored are the influences White racial framing has and continues to have on the research questions posed, the methods embraced, interpretations made, and discourse produced by social science researchers; and the role Critical Theory, Quantitative Critical Race Theory, and Intersectionality Theory have begun to play in reorienting social science research to challenge various forms of oppression, with a specific focus on racialized oppression.  Upon completion of the course, participants will be positioned to critically examine quantitative methods and measurement techniques commonly employed for educational research that are introduced in future coursework. 

Instrument Design and Development

This course is designed to familiarize students with the strategies, techniques, tactics, and issues in the development and administration of survey instruments. It will emphasize theoretical, measurement and practical considerations in the development of attitudinal instruments. The development and analysis of data resulting from several types of measurement scales will be covered.

Evaluation Practice & Methods

This course introduces the process of conducting evaluations from beginning to end. Evaluation is a form of applied social science research focused on systematically assessing the value--merit, worth, or significance--of interventions. Taking an interdisciplinary approach to evaluation, this course draws on scholarly articles and examples from multiple fields including education, public health, social services, and international development and addressing evaluation at various scales including program, organizational, and systems-level evaluations. By the end of the course, students will gain knowledge of how to carry out evaluations; apply this knowledge to develop evaluation plans for real-world interventions; and gain skills to critique existing evaluations supporting their development as informed, critical consumers of evaluations.

Design of Qualitative Research 

Introduces the foundations and techniques of carrying out qualitative research. Topics include philosophical underpinnings, planning for a qualitative research project, negotiating entry, ethics of conducting research, data collection and analysis, and writing/presenting qualitative research. Requires a research project involving participant observation and/or interviewing.

Mixed Methods Research

This course introduces the theory and practice of mixing inquiry methodologies in research and evaluation across the social sciences. Mixing primarily means the integration of qualitative and quantitative approaches, methods, and data in a single study. More broadly, mixing involves intentionally combining multiple ways of knowing to generate a richer understanding of social phenomena. This course covers the historical roots of mixed methods, contemporary interests, and points of contention. Readings bridge theoretical engagement and practical applications across education, psychology, public health, management, and sociology. The primary purposes are to understand and practice the foundations for developing a high-quality mixed methods proposal and learn from quality mixed methods studies. Assignments allow students to practice challenging aspects of mixed methods, including philosophical paradigms and theoretical frameworks, visually diagramming a study design and data integration. Throughout the semester, students develop original proposals that may be useful in their dissertations or ongoing research agendas. They present their work for peer review and instructor feedback in draft and finalized versions.

Introductory Statistics 

An introduction to descriptive statistics. Topics include methods of data summarization and presentation; measures of central tendency and variability, correlation and linear regression; the normal distribution; probability; and an introduction to hypothesis testing. Provides computer instruction on PC and Mac platforms and in the SPSS statistical package.

Intermediate Statistics 

Topics and computer exercises address tests of means, partial and part correlations, multiple regression, analysis of variance with planned and post hoc comparisons, analysis of covariance, repeated measures analysis, elements of experimental design, and power analysis.

Multivariate Statistical Analysis 

This course provides lectures, examples, and lab analyses that address multinomial and ordinal logistic regression models, multiple group discriminant analysis, cluster analysis, multivariate analysis of variance, principal component analysis, factor analysis, and structural equation modeling. We cover various issues related to research design, model building, and the interpretation of the output from SPSS, R, Lisrel, and SAS software programs.

General Linear Models

Addresses the construction, interpretation, and application of linear statistical models. Specifically, lectures and computer exercises cover ordinary least squares regression models; matrix algebra operations; parameter estimation techniques; missing data options; power transformations; exploratory versus confirmatory model building; linear-model diagnostics, sources of multicollinearity; diagnostic residual analysis techniques; variance partitioning procedures; dummy, effect, and orthogonal coding procedures; and an introduction to structural equation modeling.