Edward W. Wiley, PhD

Teaching

Courses frequently taught

EDUC 5716: Basic Statistical Methods
This course covers the basic principles of statistical analysis.  Statistical tools can be used for two general purposes:  (1) summary and description of data, and (2) testing of inferences to address relational questions.  The course should provide the skills necessary to use statistical tools for one’s own descriptive and inferential purposes.

As social science researchers, we not only address our own research questions; we also must read and evaluate the research of others.  Thus, a second course theme is the critical scrutiny and evaluation of the research of others.  To this end, we devote a significant portion of our time to consideration and critique of examples of journal articles and other research products.

Course content is structured in a progressive manner.  Data analysis – which is where many statistics courses begin – is meaningless if your data is junk (i.e. your study is not “valid”).  Thoughtful design of research studies and measurement of variables is essential to “valid” research.  Therefore, before we do anything truly quantitative, we discuss how to best design research studies and how to ensure good measurement.  With these basics down we progress to how to summarize data.  We discuss a variety of tools to make sense of large groups of data – both via visual/graphical means and via summary statistics.  This leads to the final topic – using statistical methods to answer inferential questions.   Topics under this last umbrella include testing of group differences and relationships between variables, techniques for analyzing data in categorical form, and summarizing and presenting statistical results.  Examples typical of contemporary social science research ground the introduction and application of new ideas and methods.

This course aims to provide the experience and confidence to begin to carry out the various steps of social science research – from research design to data collection, data analysis, and ultimately to communicating your findings.   As such, an independent research project (as well as an integrated data collection activity) requires students to follow a single research question through these various steps.  The research project is typically due the last day of class.

EDUC 8240: Intermediate Quantitative Methods Ii (Intermediate Quantitative Methods)

Quote describing findings from a national study of 2003 charter school performance:
In mathematics, fourth-grade charter school students as a whole did not perform as well as their public school counterparts [in 2003].  In reading, there was no measurable difference in performance between charter school students in the fourth grade and their public school counterparts as a whole. -NCES

Quote describing findings from a national study of 2003 charter school performance:
Charter schools are succeeding in their mission to provide an educational alternative more likely to lead to student proficiency, according to a study released today by Harvard economist Caroline Hoxby. Across the nation, charter school students are more likely to be proficient in math and reading than students in the nearest comparable regular public school. –Heritage Foundation
Do charter schools work?  Whom should we believe?

Quantitative methods are often employed to address problems in education, psychology, and the social sciences.  As demonstrated above, however, results from quantitative studies often confuse and obfuscate rather than provide clarity.  The field of statistics provides a variety of powerful analytic tools – as with any power tools, however, expertise and caution are necessary for responsible use.

A general class of statistical methods – known collectively as the “General Linear Model” (“GLM”) – provides the basis for analyzing data from randomized experiments, quasi-experiments, surveys, and correlational/observational studies.  Two special cases of the GLM – multiple regression and ANalysis Of VAriance (or “ANOVA”) – are common to social science research and receive the bulk of our attention.  We especially focus on how to incorporate regression and ANOVA into the unified GLM framework.  In doing so, this course covers the GLM in detail – from positing alternative hypotheses to specifying and comparing models based on these hypotheses, to assessing the fit of various models, and finally to interpretation within the substantive context of interest.

The object of this course is to provide the context and experience necessary to build quantitative reasoning skills.  Students leaving this course should be able to carry out quantitative methods responsibly and read others’ quantitative research with informed skepticism. 

EDUC 7396: Multivariate Statistics
This course covers the basic principles and applications of several multivariate statistical techniques.   Multivariate methods generally fall into one of two families: dependence models (e.g., multiple and multivariate regression, multivariate analysis of variance, analysis of covariance, path analysis, canonical analysis, discriminant analysis, and logistic regression) and interdependence models (e.g., principal components, factor analysis, cluster analysis, multidimensional scaling, and loglinear models).

The course starts with dependence models:  we review the general linear model and show how most inferential techniques (simple and multivariate regression, analysis of variance, etc.) actually represent special cases of this more general model.  From there we move onto interdependence models and discuss how these techniques help identify interrelatedness among a large number of variables.  We cover the idea of “latent” factors in the context of factor analysis.

After establishing the basics of both dependence and interdependence models, we move to an even more general class of models: structural equation models.  Characterized by both dependence and interdependence elements, structural equation models are increasingly central to research in many fields.

Finally, we introduce another statistical tool that is increasingly employed in social science research – the mixed-effects model.  Examples of mixed-effects models include “multilevel” or “hierarchical linear” models (“HLM”) and individual growth curve models

Matrix algebra is the mathematical language of multivariate techniques.  We introduce the fundamentals of matrix algebra toward the beginning of the course in the context of understanding the general linear model formulation.  From then on each new model is introduced using matrix algebra as well as visual geometrical descriptions.