Course website: github.com/jbryer/IS606Spring2016
This course covers basic techniques in probability and statistics that are important in the field of data analytics. Discrete probability models, sampling from infinite and finite populations, statistical distributions, basic Bayesian statistics, and non-parametric statistical techniques for categorical data are covered in this course. Each of these statistical concepts will be applied in a variety of real-world scenarios through the use of case studies and customized data sets.
##### Doctoral seminar at the University at Albany in the Fall 2014 semester.
Course website: github.com/jbryer/EPSY887DataScience
Data Science is the intersection of statistics, computer science, and research. This seminar will introduce the key concepts of data science with an emphasis on data science in education. We will cover the important statistical and programming concepts necessary for conducting reproducible research on large datasets. The open source program R will be used throughout the course. No programming experience is required but at least two semesters of graduate statistics is highly recommended.
##### Graduate course at University at Albany in the Fall 2013, Summer 2014, and Fall 2014 semesters.
Course Website: epsy530.bryer.org
Descriptive statistics including measures of central tendency and variability, correlation and regression. Introduction to statistical inference, including sampling distributions, significance tests, confidence intervals, and power of tests of significance.
##### Pre-Conference workshop at the 2013 useR! conference.
The use of propensity score methods (Rosenbaum & Rubin, 1983) for estimating causal effects in observational studies or certain kinds of quasi-experiments has been increasing in the social sciences (Thoemmes & Kim, 2011) and in medical research (Austin, 2008) in the last decade. Propensity score analysis (PSA) attempts to adjust selection bias that occurs due to the lack of randomization. Analysis is typically conducted in two phases where in phase I, the probability of placement in the treatment is estimated to identify matched pairs or clusters so that in phase II, comparisons on the dependent variable can be made between matched pairs or within clusters. R (R Core Team, 2012) is ideal for conducting PSA given its wide availability of the most current statistical methods vis-à-vis add-on packages as well as its superior graphics capabilities.
The proposed workshop will provide participants with a theoretical overview of propensity score methods as well as illustrations and discussion of PSA applications. Methods used in phase I of PSA (i.e. models or methods for estimating propensity scores) include logistic regression, classification trees, and matching. Discussions on appropriate comparisons and estimations of effect size and confidence intervals in phase II will also be covered. The use of graphics for diagnosing covariate balance as well as summarizing overall results will be emphasized. Lastly, the extension of PSA methods for multilevel data will also be presented.
##### Pre-Conference workshop at the 2011, 2012, 2013, and 2015 NEAIR conferences.
This workshop will provide an overview as well as hands-on exercises for using R and LaTeX to perform data analysis and report generation. Participants will learn to perform basic statistical analyses in R and to generate reports with LaTeX in spreadsheet, presentation, and document formats.
##### Doctoral seminar at the University at Albany in the Spring 2013 semester.
This seminar will provide an introduction to statistical programming for data analysis with an emphasis on the analysis of large datasets. With the increased availability of large national and international datasets (e.g. PISA, TIMMS, NAEP, ECLS) there is a great opportunity and potential for researchers to focus on important substantive research questions that are difficult to address by other means. However, the analysis of large datasets requires special analytical procedures not found in commercial statistics software. Utilizing the open source statistical software R, students will be introduced to tools and procedures for analyzing large datasets with an emphasis on conducting transparent and reproducible research.
##### Undergraduate course at the University at Albany in the Fall 2008 and Spring 2009 semesters
This course covers theory and research in social, emotional, physical, and intellectual development and its application to instruction with an emphasis on late childhood through middle adolescence. Prerequisite: Junior or senior class standing.