Applied Social Research in Education using SPSS

Instructor : Kyriakos Drivas

Introduction:

This course will provide the general tools needed to carry out empirical studies. Students will be taught how to design and complete a study from start to finish. It will teach students how to search for and collect data from publicly available sources, and how to design a questionnaire based on international standards with a solid theoretical and econometric foundation.

With respect to the latter, students will be introduced—through practical applications—to the basic tools required for performing econometric analysis. By the end of the course, students will have a better understanding of how to conduct research using empirical data, how to frame the research question under investigation, and how to present the results clearly and concisely.

Objectives:

The short-term goal of the course is to help students in writing a thesis based on empirical data.

The long-term and main objective of any academic process is to cultivate critical thinking. Within the context of this Master's program, graduates will eventually be called upon to understand and evaluate research findings that are used to answer key education policy questions. This course will provide students with the tools to assess how each study was conducted and to identify any unanswered questions.

Perhaps the most important goal of the course is that many graduates will eventually be expected to produce empirical results on critical issues themselves. Thus, the course aims to teach students how to carry out a study and how to effectively communicate their findings and conclusions.

Lecture Outline:

• Lecture 0: How to write a thesis. How to find a research question. How to conduct a literature review. How to design a questionnaire (finding standardized international questionnaires, Google Forms, etc.).
• Lecture 1: Data entry in SPSS. Variable coding. Creating and computing new variables in SPSS. Importing data from Excel.
• Lecture 2: Descriptive statistics (e.g., correlation tables). Chart presentation.
• Lecture 3: Hypothesis testing (t-tests).
• Lecture 4: Simple linear regression with one independent variable. Ordinary Least Squares method. Statistical significance and interpretation of coefficients.
• Lecture 5: Multiple linear regression. Hypothesis testing (F-tests).
• Lecture 6: Problems in linear regression and how to address them – Part 1.
• Lecture 7: Problems in linear regression and how to address them – Part 2.
• Lecture 8: How a metro (railway network) is connected to the Nobel Prize in Economics. Probit and Logit models using SPSS.
• Lecture 9: Review session.

Students are encouraged to share during the course the research question they plan to explore in their Master's thesis.

Assessment:

Assessment will be based on multiple-choice questions. These questions aim to evaluate the students' ability to understand and interpret the statistical results produced in research and to analyze them.

Indicative Bibliography:

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics, 4th Edition, Sage Publications Ltd, London.
• Anastasiasdou, D. S. (2012). Statistics and Research Methodology in the Social Sciences, Kritiki Publications.
• Gnardellis, Ch. (2019). Applied Statistics, Papazisi Publications.
• Gnardellis, Ch. (2013). Data Analysis Using IBM SPSS Statistics 21, Papazisi Publications.