Syllabus Fall 2016-17

DIGM 710: Digital Media Research Methods I

Fall Term 2016-2017

Meeting Time: Mondays 18:00 – 20:50, URBN Center 202

Instructor: Prof. Stefan Rank [stefan.rank (AT) drexel.edu]

Office Hours: by appointment

Course Description

This course focuses on quantitative research methodologies and statistical analysis tools and methods relevant for digital media research. The course also introduces students to basic concepts of statistical analysis and data science as well as to epistemological and ethical positions regarding the use of statistics in science.

Special topics of interest are data analysis for typical questionnaires as well as critique of significance testing and the usefulness of Bayesian approaches.

The main purpose of this course is to enable you to plan and perform relevant data collection and analysis for your thesis work, as well as the necessary background knowledge to understand and evaluate the data analysis you will find in literature. A significant part of the class will focus on gaining a general understanding of quantitative research methods and approaches and their relevance to research research related to the DIGM program.

Learning Objectives

• • You will explore approaches to quantitative data collection and analysis and relate them to your own thesis work.

  • You will analyze the different types of quantitative research in the DIGM field.

• You will gain knowledge of useful tools to perform quantitative research in the DIGM field.

Format

Regular classroom sessions will consist of:

• • Discussion of reading assignments OR

  • Discussion of practical analysis exercises.

Texts

• [Creswell 2014]: John W. Creswell: Research Design. Qualitative, Quantitative, and Mixed Methods Approaches. 4th Edition, Sage Publications.

• [Bertram]: Dane Bertram: Likert Scales... are the meaning of life

• [Petrillo et al 2011]: Petrillo, Spritzer, Freitas, Pimenta: Interactive analysis of Likert scale data using a multichart visualization tool, IHC+CLIHC '11 Proceedings of the 10th Brazilian Symposium on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction, pp. 358-365.

• [De Winter and Dodou 2010] Joost C. F. de Winter and Dimitra Dodou: Five-Point Likert Items: t test versus Mann-Whitney-Wilcoxon. In: Practical Assessment, Research & Evaluation 15(11).

Software Resources

Evaluation and Data science tools:

• Jupyter https://jupyter.org/

• SciPy, matplotlib, seaborn and pandas https://www.scipy.org/ (integrated in Jupyter)

• R https://www.r-project.org/ (usable from Jupyter)

• IBM SPSS https://www.ibm.com/analytics/us/en/technology/spss/

Tutorials, other Resources, and Further Reading

• http://people.duke.edu/~ccc14/sta-663/DataProcessingSolutions.html

• https://hamelg.blogspot.jp/search/label/Python%20for%20data%20analysis

• Watson speech analysis: https://github.com/swbiggs4/WatsonPundit

Statistical Functions for common tests (paired/unpaired t-test, chisquare, Mann-Whitney Wilcoxon aka Mann-Whitney U, Wilcoxon rank-sum, Kruskal-Wallis H-test, ANOVA, McNemar, Cochran Q):

• https://docs.scipy.org/doc/scipy/reference/stats.html#statistical-functions

• https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html

• http://statsmodels.sourceforge.net/devel/index.html

Common Game-related Questionnaires:

• • Game Experience Questionnaire: [IJsselsteijn et al. 2013] IJsselsteijn, W. A., de Kort, Y. A. W., & Poels, K. (2013). The Game Experience Questionnaire. Eindhoven: Technische Universiteit Eindhoven.

  • Game Engagement Questionnaire: [Brockmeyr et al. 2009] Jeanne H. Brockmyer, Christine M. Fox, Kathleen A. Curtiss, Evan McBroom, Kimberly M. Burkhart, Jacquelyn N. Pidruzny: The development of the Game Engagement Questionnaire: A measure of engagement in video game-playing. Journal of Experimental Social Psychology 45:624–634

  • [Norman 2013]: Kent L. Norman: GEQ (Game Engagement/Experience Questionnaire): A Review of Two Papers. In: Interacting with Computers, http://dx.doi.org/10.1093/iwc/iwt009

  • Chapter 3 (Conceptualization and Measurement of User Experiences) of [Roth 2015] Christian Roth: Experiencing Interactive Storytelling, PhD Thesis, VU University of Amsterdam.

  • http://dare.ubvu.vu.nl/bitstream/handle/1871/53840/complete_dissertation.pdf?sequence=1

Beautiful Data:

• • Seaborn plot types: http://seaborn.pydata.org/examples/index.html#example-gallery

  • Why you should use the Viridis colormap in future scipy-based visualization: https://bids.github.io/colormap/

  • Plotly online and offline: from 2D to scientific to maps to 3D. Caveat: for profit. Plot types:

  • 3d plots: MayaVi, http://docs.enthought.com/mayavi/mayavi/auto/examples.html http://www.scipy-lectures.org/advanced/3d_plotting/

  • Geographical/GIS data: folium https://github.com/python-visualization/folium and GeoPandas https://github.com/geopandas/geopandas

Bayesian Approaches in HCI and Criticism of Traditional Significance Testing:

• • Special Interest Group on Transparent Statistics in HCI: http://mjskay.com/chistats/

  • Book by Richard McElreath: Statistical Rethinking http://xcelab.net/rm/statistical-rethinking/ (chapters 1-3 in particular)

  • [Greenland et al 2016] Greenland, S., Senn, S.J., Rothman, K.J. et al. (2016): Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology 31(4): 337-350. http://dx.doi.org/10.1007/s10654-016-0149-3

Requirements and Grading Policy

Active attendance of the classroom sessions is required and will count towards your grade. This includes participation in discussions and providing feedback to classmates.

You are expected to attend all classes. Class participation is an important part of your grade. Missing 3 classes may result in automatic failure. If a student must miss class, it is the student's responsibility to contact the instructors the day prior to the missed class. Students will also be responsible for getting missed notes from the other students.

Grading System

• 40 points - Class participation

• 30 points - Analysis Exercises

• 30 points - Paper summaries and reviews

A+: 100-97, A: 94-96, A-: 90-93, B+: 87-89, B: 84-86, B-: 80-83,

C+: 77-79, C: 74-76, C-: 70-73, D+: 67-69, D: 60-66, F: 0-59

Schedule (may be updated during the term)

Drexel University Code of Conduct

Academic Integrity, Plagiarism, and Cheating Policy

http://drexel.edu/provost/policies/academic_dishonesty.asp

Drexel University Student Handbook

http://drexel.edu/studentaffairs/community_standards/studentHandbook/

Students with Disability Statement

http://drexel.edu/ods/student_reg.html

Course Drop Policy

http://drexel.edu/provost/policies/course_drop.asp

Course Change Policy

The instructor reserves the right to change the course during the term at his or her discretion. These changes will be communicated to students via the syllabus, website announcement, or email.