EDAD 8194 A04: Dissertation Data Analysis

EDAD 8194 - Seminar in Educational Research – Dissertation: Data Collection Analysis

Fall 2026 Syllabus, Section A04, CRN 42867,

Credit hours: 2

Course Meeting Times

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Instructor

Edwin Christmann

Email: echristmann@ysu.edu

Public Instructor Information

Instructor Title: Doctor
Instructor Professional Qualifications:
PhD, Education, Old Dominion University;
M.Ed, Educational Administration, Pennsylvania State University,
AdvDip, Research in the Sciences, University of Cambridge,
BSEd, Education, Pennsylvania Western University

Instructor Office Location: Beeghly Hall 
Instructor Office Phone: 330-941-3251

Course Description

8194. Seminar in Educational Research – Dissertation: Data Collection & Analysis. The purpose of this course is to build candidate knowledge and skills in developing Chapter 4 of their dissertation. Students will collect the data for their dissertation and complete the analysis of that data and write up their findings in Chapter 4 of their dissertation, broken down into manageable milestones, following the guidelines set forth in the YSU Educational Leadership Dissertation Guidelines and template. Prereq.: EDAD 8191, EDAD 8192, and EDAD 8193. 2 s.h.

Course Readings

Group Title Author ISBN
Research design : qualitative, quantitative, and mixed methods approaches Creswell, J. W., & Creswell, J. D.
Surviving your dissertation: A comprehensive guide to content and process (4th ed.) Rudestam, K. E. & Newton, R. E. (2014)
Publication manual of the American Psychological Association (7th ed.). The American Psychological Association.
Intermediate Statistics: Using SPSS. SAGE Publications Knapp, H (2018)
Qualitative Research: A guide to design and implementation. 4th Edition. Jossey Bass: San Francisco. Merriam, S.B. & E.J. Tisdell (2016)

Rudestam, 209 – 227

Read Creswell & Creswell pp. 1-79

Merriam 191-236

Read Creswell & Creswell pp. 79-155

Merriam 267-292

Read Creswell & Creswell pp. 156-260

The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.  

Course Learning Outcomes/Objectives/Goals

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How to Succeed in This Course

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Attendance Expectations

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Late Work Submission Policy

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Additional Course Expectations

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Artificial Intelligence Policy Statement

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Assignments/Assessments

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Grading and Grading Scale

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University Policies

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Schedule of Topics and Assignments

Week of Proposed Topic Due/To Prepare for Class
9/14 Introduction Subsection Sept 18
10/5 Finding #1-#2 etc, Discussion & Summary Oct 9
11/2 Data Analysis Confirmation Nov 6
11/30 Final Draft of Chapter 4 Due
Data Interpretation
Dec 4
Dec 4

The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning.