Exploring the Content of Introductory Quantitative Methods Courses for Education Doctoral Students

Quantitative methods training is an integral part of an Education Doctoral Degree (EdD). Students tend to struggle with the content of these courses and consequently their quantitative proficiency has suffered. Currently, there is no consensus on the learning outcomes of introductory quantitative methods classes, leaving instructors to individually decide the content and teaching methods of their classes. The aim of this study was to capture the content of introductory quantitative methods course and determine their alignment with current best practices in statistics education. Q1 What curricular and pedagogical techniques are used in introductory quantitative methods course for EdD students? Q2 How do the teaching practices of introductory quantitative methods courses for EdD students align with the Guidelines for Assessment and Instruction in Statistics Education (GAISE)? Instructors of quantitative methods courses from EdD programs were surveyed and asked to submit a class syllabus. Descriptive statistics and graphical summaries were used to analyze the survey results (n=23). Syllabi were coded in two separate processes – open coding and structural coding (n = 19). In general, the quantitative methods courses tended to be a mixture of quantitative literacy and statistical analysis. The placement of a class on the continuum dictated the types of learning outcomes and assessments; quantitative literacy classes have more article critiques and design components, while statistical analysis courses have more research papers and programming assignments. Overall, the courses utilize the best practices for teaching introductory statistics; there is strong alignment between the quantitative survey results and the qualitative analysis. Despite meeting best practices guidelines, more information is needed on whether these classes meet the needs for EdD students.