Syllabus

Doctor of Education (Ed.D.)

EDU 803: Interpreting Empirical Data – Summer A 2021

Credits - 3

Description

Students are introduced to basic concepts of quantitative research design, methodology, and interpretation of results. Students analyze data sets to become adept at interpreting a wide range of statistical results. Quantitative analysis and interpretation are applied to typical local based samples and large databases. This set of conceptual and methodological skills is applied to proposed research designs. 

Materials

Required Materials

Creswell, J. W. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson. (ISBN: 978-0134519364, E-text ISBN: 978-0134546568)

  • Optional Pearson Resource – MyLab & Mastering (Instructor Course ID: generic30007) – Registration Instructions
  • If using an earlier edition of this text, it will be the student’s responsibility to determine corresponding assigned readings; instructors will only be using the required 6th edition text.

OpenStax College. (2018). Introductory statistics. OpenStax CNX.

Other required reading will be provided within the weekly course modules.

Recommended Course Texts

Lane, D. (2003). Introduction to statistics. Open Textbook Library.

American Psychological Association. (2019). Publication manual of the American Psychological Association (7th ed.). (ISBN: 9781433832154, E-text ISBN: 9781433832185)

Learning Objectives and Outcomes

Course Objectives:

This course presents the broad scope and methods of quantitative research. Students will be introduced to and supported to use generally accepted methods for quantitative data organization and management. Students will employ specific analytical approaches in interpreting data. Students will be introduced to several quantitative methodologies and required to articulate the ways that researchers organize and execute quantitative inquiry to inform organizational inquiry and transformation.

Note: It will be assumed that students have prior knowledge of some basic statistics terminology and applications to fully understand the concepts introduced in this course. Advanced computational mathematics and statistics will not be required to complete the course assignments, but a foundational understanding of applied statistics concepts may be necessary. Students may gain this knowledge with recommended supplemental readings embedded within the course modules.

Student Learning Outcomes:

  1. Articulate beliefs about quantitative research relative to a personal research epistemology.
  2. Recognize and apply quantitative data collection and analysis techniques to support empirical systematic inquiry.
  3. Interpret data reports that include preliminary statistical modeling techniques for selected datasets.
  4. Communicate and report technical information about data and analyses to support research and evaluation activities.
  5. Analyze and interpret a wide range of statistical results.
  6. Communicate findings of a quantitative study to appropriate audiences.
  7. Describe how a leader can use quantitative research methodologies to strategically support and develop organizations.
  8. Use peer review and critique to improve and refine research and leadership skills.

Assignments

Self-Assessment

Self-assessment should address all of the readings and tasks you completed individually and in your small group for the assigned time period. Refer to your mastery of objectives, clarity/accuracy, and participation in the assigned tasks during the time assigned, providing a recommended grade (low pass, pass, high pass) and a justification for your recommendation.

Class and Small Group Discussion Posts & Comments

Online discussions are the most important part of the course and expectations for engagement throughout the entire week increase from this point forward in the sequence. Discussions provide participants with opportunities to explore new ideas, concepts, and applications of theory to practice. A thorough initial post will likely be in the range of 500-750 words.

The minimum number of substantive, meaningful responses you should make before the stated deadline is two.

Participants contribute to the intellectual development of the cohort by offering insights, synthesizing understandings, and responding to the postings of others through responses to peers. While less formal than a paper, the quality of all postings should reflect the standards of a submitted paper. Review the rubric for discussions.

If your goal is to earn a High Pass in the course, you will need to significantly exceed participation and engagement requirements from week to week.

Assignments: Causation and Correlation, Statistical and Quantitative Research Definitions Quiz, Type I and Type II Errors, and Preparing Data for Correlation Analysis

The four assignments required for this course (Causation and Correlation, Statistical and Quantitative Research Definitions Quiz, Type I and Type II Errors, Preparing Data for Correlation Analysis) vary somewhat in the nature of the task. However, each rubric addresses the general expectations of the work. Specific comments/feedback relevant to each assignment may be added by the instructor on assessed work. See the assignments on Brightspace for specific instructions.

Quantitative Research Analysis & Evaluation Essay

The goal of this final assignment (due in week 7) is to give you an opportunity to demonstrate your new skills at reading, comprehending, and critiquing research with quantitative components. There are four (4) total components to be analyzed for this essay. Read one summary report and three full reports of empirical studies. In this essay, you will analyze and evaluate the methods, findings, conclusions, and presentation of data of all four reports. High-quality essays will have clear viewpoints on each of the four papers, supported by analytical claims, evidence from the articles, and other course materials or relevant external sources. Be sure to develop your own ideas and criticisms around each of the three empirical studies; this is not a summary of materials. Your paper should be approximately 2,000 words (not including title page or reference page) and follow APA guidelines throughout. See the full assignment prompt on Brightspace.

Grading Policy

Your grade in this course will be determined by the following criteria:

Grade Breakdown

AssignmentsPoints
Discussion Board Posts & Comments (5 points each)30
Causation and Correlation Assignment10
Statistical and Quantitative Research Definitions Quiz10
Type I and Type II Errors5
Preparing Data for Correlation Analysis10
Week's 3 and 4 Self-Assessment5
Quantitative Research Analysis & Critique Essay30
Total100

Grading

The criteria for all courses in the Ed. D. program are described in the modules and/or rubrics. Assignments will include guidelines with rubrics, descriptions of expectations, or examples, and include point values. Coursework will be assessed and graded using individual evaluation protocols that are provided for the three major assignments. Final “grades” will reflect the following schema:

  • High Pass (HP): Work that exceeds all or most of the criteria of the respective assignment. To receive a high pass the work must demonstrate exceptional command and display of all or most required elements;
  • Pass (P): Work that meets all requirements and expectations as specified in assignments, and is fully satisfactory in every respect;
  • Low Pass (LP): Work is deemed unsatisfactory.

Note** The instructor will determine if an assignment may be revised and resubmitted for rescoring. Candidates may proceed to subsequent courses in the curriculum with one LP grade, and although there is no failing grade, a second LP course grade results in termination from the doctoral program.

All assignments are to be completed in a timely manner with appropriate accuracy, detail, thought and reflection fitting of doctoral-level degree candidates. All assignments (done in writing or with other media applications) are graded on the basis of faculty assessment of your ability to accurately apply concepts from readings, organization, and mechanics. Please note that you must save all submitted documents in Microsoft Word/Excel/PowerPoint in order for them to transmit successfully. All work must be properly identified and include author(s)’ name(s). Submit all written work in APA style (Refer to the APA Publication Manual for guidance and Help with Citations on UNE Library Services web page under Research Help). 

Schedule

With the exception of Week 1, which opens on a Wednesday, each week opens on Monday at 12:01 AM Eastern Time. Each week closes on Sunday at 11:59 pm ET. Specific due dates can be found within Brightspace.

Week 1: 4/28 – 5/2
Week 2: 5/3 – 5/9
Week 3: 5/10 – 5/16
Week 4: 5/17 – 5/23
Week 5: 5/24 – 5/30
Week 6: 5/31 – 6/6
Week 7: 6/7 – 6/13
Week 8: 6/14 – 6/20

Notes:

  1. All assignments will be due by 11:59 p.m. ET on the day they are due unless otherwise noted in the syllabus or by the instructor.
  2. Zoom is available using your UNE credentials for small group interaction. See the Zoom Resources link in your course. See course announcements for details from your instructor for tutoring sessions and office hours.

Week

Topic

Activities & Assignments

Assignments Due

1

4/28 – 5/2

Quantitative Methods

Introduction Class Discussion Board (DB)

Readings: Creswell ch.1, 2 & 4, and assigned articles

Intro DB Initial Post (Ungraded) – by 11:59 PM ET on Friday

Intro DB Comments (Ungraded) – by 11:59 PM ET on Sunday

 

2

5/3 – 5/9

Quantitative Data Collection

Small Group DB & Comments – Data Collection

Readings: Creswell ch.5, video, online resources and datasets, and other required reading

Sm Group DB Initial Post – by 11:59 PM ET on Wednesday

Sm Group DB Comments – by 11:59 PM ET on Sunday

 

3

5/10 – 5/16

Quantitative Research Designs

Small Group Collaborative Project – Definitions & Quiz – Begin working on the project

Class DB Post & Comments – Quantitative Study Design

Causation & Correlation Assignment

Readings: Creswell ch.10-12, video, web resources, and assigned articles

Class DB Initial Post – by 11:59 PM ET on Wednesday

Class DB Comments – by 11:59 PM ET on Sunday

Causation and Correlation Assignment – by 11:59 pm ET Sunday

Small Group Project due next week on Wednesday

4

5/17 – 5/23

Statistics & Quantitative Data Analysis

Small Group Collaborative Project – Definitions

Small Group Quiz

Self Assessment 

Readings: Creswell ch.6, videos, and assigned articles

Statistical and Quantitative Research Quiz copy and answer sheet (individual submission) – by 11:59 PM ET on Wednesday

Self Assessment – by 11:59 PM ET on Sunday

5

5/24 – 5/30

Quantitative Data Preparation

Type I and Type II Errors Individual Assignment

Class DB Post & Comments – Survey Question

Readings: Web-based and other assigned readings

Class DB Initial Post – by 11:59 PM ET on Wednesday

Class DB Comments – by 11:59 PM ET on Sunday

Type I/II Errors Assignment – by 11:59 PM ET on Sunday

6

5/31 – 6/6

Interpretation of Results & Reporting Findings

Class DB Post & Comments – Presenting Data

Data Preparation Individual Assignment

Readings: Nellie Mae report & supporting studies; SPSS output; DataGraph scatterplot; S.Moon data map; data viz galleries

(Optional) Small Group: Post DRAFT Quantitative Research Analysis & Evaluation Essay

Class DB Initial Post – by 11:59 PM ET on Wednesday

Class DB Comments – by 11:59 PM ET on Sunday

Data Preparation Assignment – by 11:59 PM ET on Sunday

Small DB (optional) Draft Essay – by 11:59 PM ET on Sunday

7

6/7 – 6/13

 

Review & Epistemology

Class DB Post & Comments – Methodology & Epistemology

Readings: Pole; Johnson & Onwuegbuzie; Feynman; Smith & Heshusius

Quantitative Research Analysis & Evaluation Essay

Class DB Initial Post – by 11:59 PM ET on Wednesday

Class DB Comments – by 11:59 PM ET on Sunday

Essay – by 11:59 PM ET on Sunday

8

6/14 – 6/20

Course Reflections & Evaluation

Class DB Post – Bias

Course Reflection & Self Assessment (ungraded)

Class DB Initial Post – by 11:59 PM ET on Wednesday

Class DB Comments – by 11:59 PM ET on Friday

Course Reflection & Self Assessment – by 11:59 PM ET on Friday

Student Resources

Online Student Support

Your Student Support Specialist is a resource for you. Please don't hesitate to contact them for assistance, including, but not limited to course planning, current problems or issues in a course, technology concerns, or personal emergencies.

Questions? Visit the Student Support Education page

UNE Libraries:

UNE Student Academic Success Center

The Student Academic Success Center (SASC) offers a range of services to support your academic achievement, including tutoring, writing support, test prep and studying strategies, learning style consultations, and many online resources. To make an appointment for tutoring, writing support, or a learning specialist consultation, go to une.tutortrac.com. To access our online resources, including links, guides, and video tutorials, please visit:

Accommodations

Any student who would like to request, or ask any questions regarding, academic adjustments or accommodations must contact the Student Access Center at (207) 221-4438 or pcstudentaccess@une.edu. Student Access Center staff will evaluate the student's documentation and determine eligibility of accommodation(s) through the Student Access Center registration procedure.

Policies

Turnitin Originality Check and Plagiarism Detection Tool

The College of Professional Studies uses Turnitin to help deter plagiarism and to foster the proper attribution of sources. Turnitin provides comparative reports for submitted assignments that reflect similarities in other written works. This can include, but is not limited to, previously submitted assignments, internet articles, research journals, and academic databases.

Make sure to cite your sources appropriately as well as use your own words in synthesizing information from published literature. Webinars and workshops, included early in your coursework, will help guide best practices in APA citation and academic writing.

You can learn more about Turnitin in the Turnitin Student quick start guide.

Technology Requirements

Please review the technical requirements for UNE Online Graduate Programs

Course Evaluation Policy

Course surveys are one of the most important tools the University of New England uses for evaluating the quality of your education, and for providing meaningful feedback to instructors on their teaching. In order to assure that the feedback is both comprehensive and precise, we need to receive it from each student for each course. Evaluation access is distributed via UNE email at the beginning of the last week of the course.

Information Technology Services (ITS)

ITS Contact: Toll Free Help Desk 24 hours/7 days per week at 1-877-518-4673

Attendance Policy

Online students are required to submit a graded assignment/discussion prior to Sunday evening at 11:59 pm ET of the first week of the term. If a student does not submit a posting to the graded assignment/discussion prior to Sunday evening at 11:59 pm ET, the student will be automatically dropped from the course for non-participation. Review the full attendance policy.

Late Policy

Assignments: Late assignments will be accepted up to 3 days late; however, there is a 10% grade reduction (from the total points) for the late submission. After three days the assignment will not be accepted.

Discussion posts: If the initial post is submitted late, but still within the discussion board week, there will be a 10% grade reduction from the total discussion grade (e.g., a 3 point discussion will be reduced by 0.3 points). Any posts submitted after the end of the Discussion Board week will not be graded.

Please make every effort ahead of time to contact your instructor and your student support specialist if you are not able to meet an assignment deadline. Arrangements for extenuating circumstances may be considered by faculty.

Student Handbook Online - Policies and Procedures

The policies contained within this document apply to all students in the College of Graduate and Professional Studies. It is each student's responsibility to know the contents of this handbook.

UNE Online Student Handbook

UNE Course Withdrawal

Please contact your student support specialist if you are considering dropping or withdrawing from a course. The last day to drop for 100% tuition refund is the 2nd day of the course. Financial Aid charges may still apply. Students using Financial Aid should contact the Financial Aid Office prior to withdrawing from a course.

Academic Integrity

The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable. For information about plagiarism and academic misconduct, please visit UNE Plagiarism Policies.

Academic dishonesty includes, but is not limited to the following:

  1. Cheating, copying, or the offering or receiving of unauthorized assistance or information.
  2. Fabrication or falsification of data, results, or sources for papers or reports.
  3. Action which destroys or alters the work of another student.
  4. Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
  5. Plagiarism, the appropriation of records, research, materials, ideas, or the language of other persons or writers and the submission of them as one's own.

Charges of academic dishonesty will be reviewed by the Program Director. Penalties for students found responsible for violations may depend upon the seriousness and circumstances of the violation, the degree of premeditation involved, and/or the student’s previous record of violations.  Appeal of a decision may be made to the Dean whose decision will be final.  Student appeals will take place through the grievance process outlined in the student handbook.