Course Description
Applied data analysis and use of secondary datasets in higher education and institutional research. Assumes previous knowledge of relevant statistical principles. Emphasis on measurement, design, and analysis as interrelated components of rigorous empirical inquiry. Covers descriptive and exploratory data analysis and data management issues relevant to the examination of research problems in higher education.
Athena Title
QUAN MTH HED I
Prerequisite
ERSH 4300/6300
Corequisite
EDHI 8910L
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
In this course students will learn to: 1. Identify the elements defining disciplined and rigorous empirical inquiry in higher education. 2. Develop the skills necessary to propose researchable problems linked to more general theories and to investigate those problems empirically using appropriate statistical techniques. 3. Locate, evaluate, and manage secondary datasets frequently used in higher education research. 4. Use a computer to analyze secondary quantitative data relating to higher education issues and problems. 5. To develop a mastery of descriptive and exploratory statistical techniques used to assess large scale datasets.
Topical Outline
1. Scientific explanation and knowledge, research methodologies, proposing research problems, conceptualizing theory at different levels of abstraction. 2. Measurement of scientific phenomena. Disciplinary differences in methodlogical approaches--connecting methodological terminology, language, concepts, and foci. How do we turn abstractions into variables? Experimental, quasi-experimental, and non-experimental research. Introduction to computer use for data analysis. 3. Survey of major secondary data sources frequently used in higher education research. 4. Research questions 1: Science & Technology. Accessing National Science Foundation datasets. 5. Research questions 2: Finance in Higher Education. Accessing National Center for Education Statistics datasets. 6. Research questions 3: Variations in Socioeconomic Context. Accessing Census Bureau and Bureau of Labor Statistics datasets. 7. Research questions 4: Institutional Research Issues: Accessing and analyzing campus based datasets. 8. Aggregating, merging, subsetting large scale datasets. Data verification using descriptive statistics (frequencies, means, standard deviations, variance, skewness/kurtosis). Exploratory data analysis. 9. Working with descriptive statistics and tests of significance. 10. Describing variance in joint distributions 11. Model building and Analysis of Variance.
Syllabus
Public CV