Syllabus

Doctor of Education in Educational Leadership (Ed.D.)

EDU 803: Interpreting Empirical Data – Summer A 2016

Credits - 3

Description

This course introduces basic concepts of quantitative research design, methodology, and interpretation of results. Students will be asked to engage in data collection, preparation and analysis. This set of conceptual and methodological skills is applied to initial research design, review of research finding reports and presentations as well as examination of empirical quantitative data.

Materials

Learning Objectives and Outcomes

Required Course Text

Creswell, J. W. (2014). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Boston, MA: Pearson.

NOTE: The 5^th edition of this text is available and may be selected if students individually wish to do so. The concepts and ideas in both texts are very similar, but page numbers may vary. It will be the student’s responsibility to determine corresponding assigned readings, since instructors will only be using the required 4^th edition text.

Required Course Readings

Aargaard, M. (2012). How to determine the statistical significance of an A/B test. ContentVerve.com Retrieved from https://www.youtube.com/watch?v=AuQXipyv520

Algebra1ism. (2012, Sept 11). Standard deviation and z-scores. Retrieved from https://www.youtube.com/watch?v=dMpnHbLsA9I

Baker, F. (2001). The basics of item response theory. Retrieved from http://info.worldbank.org/etools/docs/library/117765/Item%20Response%20 Theory%20-%20F%20Baker.pdf

Cassuto, L. (2002, Sept 16). Big trouble in the world of “big physics.” Salon Retrieved from http://www.salon.com/2002/09/16/physics/

DeCoster, J. (1998). Overview of factor analysis. Retrieved from http://www.stat-help.com/notes.html

Feynman, R. (1974, June). Cargo cult science. Engineering and Science, 10-13.

Fisher, W.P. (2006). Survey design recommendations. Retrieved from http://www.rasch.org/rmt/rmt203f.htm

Goldin, R. (2015, Aug 19). Causation vs. correlation. Sense about Statistics. Retrieved from http://www.stats.org/causation-vs-correlation/

Gorman, E. H. (2005). Gender stereotypes, same-gender preferences, and organizational variation in the hiring of women: Evidence from law firms. American Sociological Review, 70(4), 702-728.

Jensenius, F. R. (2014). The Fieldwork of Quantitative Data Collection. PS: Political Science & Politics, 47(02), 402-404.

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26.

Ladd, H. F. (2012). Education and poverty: Confronting the evidence. Journal of Policy Analysis and Management, 31(2), 203-227.

Lazer, D., Kennedy, R., King, G. & Vespignani, A. (2014, Mar 14) The parable of Google flu: Traps in big data analysis. Science, 343, 1203-1205.

MuKherjee, S. (2002, Jan 21) Fighting chance. New Republic. Retrieved from http://www.newrepublic.com/article/fighting-chance-0

Núñez, J. C., Cerezo, R., Bernardo, A., Rosário, P., Valle, A., Fernández, E., & Suárez, N. (2011). Implementation of training programs in self-regulated learning strategies in Moodle format: Results of a experience in higher education. Psicothema, 23(2), 274-281.

Pole, K., (2007). Mixed method designs: A review of strategies for blending quantitative and qualitative methodologies. Mid-Western Educational Researcher, 20(4), 35-38.

Smith, J. K., & Heshusius, L. (1986). Closing down the conversation: The end of the quantitative-qualitative debate among educational inquirers. Educational researcher, 15(1), 4-12.

Smith & Rabin, C. (2015). Group work, publicly posted instructor commentary, and self- assessment: A motivating combination in the online environment. Society for the Information Technology and Teacher Education International Conference (SITE), March 2 – 6, Las Vegas, NV.

Spyridakis, J.H., Wenger, M.J., & Andrew, S. (1991). Technical communicator’s guide to understanding statistics and research design. Journal of Technical Writing and Communication, 21(3), 207-219.

Taylor, R. (1990). Interpretation of the correlation coefficient: A basic review. Journal of Diagnostic Medical Sonography (JDMS) January 6.1: 35-39.

Thompson, B., Diamond, K., McWilliam, R., Snyder, P., Snyder, S.W. (2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children, 71(2), 181-194.

Trochim, W.K. (2006). Correlation. Research Methods Knowledge Base. Retrieved from http://www.socialresearchmethods.net/kb/statcorr.php

Recommended Course Texts

Salkind, N. J. (2013). Statistics for people who (think they) hate statistics (4th ed.). Thousand Oaks, CA: Sage Publications.

(This text is a good introductory source that is useful for completing many of the assignments throughout the course and for those who may conduct quantitative studies. It is currently in its 4th edition; however, earlier editions are substantially similar to the latest edition, are much cheaper (e.g. search Amazon.com, etc.), and should suffice for the purposes of this course.)

Agresti, A. & Finlay, B. (2013). Statistical methods for social sciences: International edition. Essex, England: Pearson Education Limited

Khan Academy. Probability and Statistics. https://www.khanacademy.org/math/probability

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-Assessments

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 Board Posts & Comments

Online discussions provide participants with opportunities to make meaning of new theory, key concepts, and applications of theory to practice. Participants contribute to the intellectual development of the cohort by offering insights, synthesizing understandings, and responding to the postings of others. The co-construction of knowledge is especially transparent in the online environment, unlike face to face classrooms where only a few individuals may offer ideas in a public forum. While somewhat less formal than a paper, the quality of your postings should reflect the standards of a submitted paper.

Assignments: Reading Synthesis, Statistical and Quantitative Research Definitions Quiz, and Preparing Data for Correlation Analysis

The three assignments required for this course (Reading Synthesis, Statistical and Quantitative Research Definitions Quiz, 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.

Quantitative Research Analysis & Evaluation Essay

Read one summary report and three full reports of empirical studies. In this essay, you should analyze and evaluate the methods, findings, conclusions and presentation of data of all four reports. Discuss elements that are common to the reports collectively, as well as, some characteristics that may be specific to individual reports. In your evaluation, identify a strength of one or multiple reports. In addition, identify one weakness in a quantitative methodology of one of the three original studies and propose an alternative that would strengthen this area.

Grading Policy

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

Grade Breakdown

Assignments	Points
Discussion Board Posts & Comments (5 points each)	30
Assignments (10 points each)	30
Self-Assessments (5 points each)	10
Quantitative Research Analysis & Critique Essay	30
Total	100

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; Purdue OWL is an excellent, user-friendly resource).