ATTENTION: I will not be available until February 24.
Programme: Postgraduate
Type: Elective course
Curriculum: All
Semester: B
In collaboration with N.
Tsigilis

Description

The course provides review and analysis of some of the most common techniques for statistical analysis employed in quantitative research in communication and social sciences with the support of relevant software packages.

Based on the analysis of specific examples and on exercises drawn from actual surveys, the course familiarizes students with the basics of quantitative data analysis as well as with the interpretation of the results and their presentation, particularly for the purposes of writing a scientific text.

Particular emphasis is placed on statistical tests used in most surveys designed and conducted to complete the graduate study program at the Department of Journalism & Mass Communications. The course provides also knowledge necessary to understand specific types of surveys often presented in the public discourse, like opinion polls and other kinds of quantitative research, enhancing the ability for their critical reading and interpretation.

 

Indicative syllabus

  1. Introduction to the basics of data analysis (weeks 1-2).
  2. Comparing means (weeks 3-4).
  3. One factor analysis of variance (weeks 5-6).
  4. Correlation, regression, multiple regression (weeks 7-8).
  5. Exploratory factor analysis (weeks 9-10).
  6. Comparing categorical data (week 11).
  7. Presentation and critical review of scientific articles focusing on the analysis and interpretation of the results or presentation and discussion of term papers.

 

Objectives

  • To provide students with the basic knowledge and skills necessary for statistical analysis of quantitative data.
  • To provide students with an understanding of when and how some of the most common statistical tests can be used.
  • To support and enhance understanding of the relation among theoretical documentation, research question and hypothesis formulation, research design and interpretation of results.
  • To support and enhance a critical understanding of presenting publicly the results of scientific research.

 

Readings / supportive material

  1. Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7): 1-9.
  2. Field, A. (2009). Discovering statistics using SPSS (and sex and drugs and rock 'n' roll). London, Thousand Oaks, New Delhi, Singapore: SAGE.
  3. Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for intermediate statistics: Use and interpretation (2nd Ed.). Mahwah, New Jersey, London: Lawrence Erlbaum Associates.
  4. Morgan, G. A., Leech, N. L., Gloeckner, G. W., & Barrett, K. C. (2004). SPSS for introductory statistics. Use and interpretation (2nd Ed.). Mahwah, New Jersey, London: Lawrence Erlbaum Associates.
  5. Pallant, J. (2001). SPSS survival manual: A step-by-step guide to data analysis using SPSS for Windows (Version 10). Crows Nest: Allen & Unwin.
  6. Video tutorial on statistical methods using SPSS
  7. Video tutorial on SPSS (London School of Economics).

 

Course procedures / evaluation

The course includes lectures and exercises it the Department computer labs. It also includes example analysis by students, involvement in data collection and analysis, as well as written term paper. Performance is evaluated according to:

  • Participation in class (20%)
  • In-class presentation (30%)
  • Term paper (1.500-2.000 words) (50%)

Information about the next exam session and the final essay due date can be found in the announcements page (provided that the exam dates have been announced).