Quantitative methods for Social Research
The following workshop will be held by Professor Horber on Week 1 (17 - 21 August, 2020).
Workshop objectives and content
This workshop introduces students to quantitative research methods that are commonly used for the collection and analysis of social science data. Participants will learn how to distinguish between research paradigms and methods, formulate research questions, and analyse data using statistical tools.
The focus of this workshop is on tools dealing with many variables (multivariate analysis): building models with several variables and reducing complexity (data reduction). The focus is methodological, conceptual and practical, oriented towards the application of these tools to typical analysis problems in social research. Statistical tools are only useful and meaningful when they serve a research project, based on a well defined theoretical framework (research question, research design, hypotheses) and good quality data.
At the end of the workshop, active participants should be able to:
- Design their own research project, based on a sound theoretical framework,
- Understand and critically assess publications in their scientific field using statistical techniques
- Apply the various statistical tools to their own research projects, within a well designed and defined, theoretically grounded, as well as realistic (i.e. applicable) framework.
The aim of the workshop is to provide:
- Stress the importance of embedding the use of statistical tools in a complete research process, from the initial research question, to data collection and analytics, as well as reporting the results.
- Knowledge and practical skills with data and statistical software, as well as awareness of both the potential and the shortcomings and limitations (assumptions, pitfalls) of commonly used statistical tools.
- Sound foundations of knowledge and skills with statistical tools applied to the Social Sciences for participants who had an introductory course in statistics and need to go beyond basics.
The various tools will be presented and discussed using numerous examples. Participants will then apply these tools in the context of their projects (hands-on learning). (Software used: IBM SPSS Statistics)
Part 1: Empirical Research design
- Defining a theoretical framework: Research question, research design, hypotheses
- Data collection, different types of data and data collection (surveys, observational data, administrative data, experimental data)
- Assessing validity and reliability
- Publishing research findings
- Statistics and social research; the role of computer software
Part 2: Basic statistics
- Describe and diagnose categorical and continuous variables
- Study bivariate relationships:
- Crosstabulations and association
- Tables of means and simple analysis of variance
- Scatterplots and bivariate regression
- Statistical inference and statistical tests
Part 3: Tools
- Building models using regression techniques
- Multiple linear regression; regression assumptions and diagnosis; analysis of residuals
- Linear Regression with categorical variables
- Logistic regression
- Other regression types (overview)
- Data Reduction techniques
- Unidimensional scaling: Likkert, Guttman, ...
- Multidimensional scaling: Principal component analytics, factor analysis
To optimize your learning experience it is advised to attend the free 2-day Statistics/SPSS preliminary workshop as well.
Before attending the workshop, you should read some introductory text to social research and basic statistical concepts. Some References and Advice here
Kim,Jae-On & Mueller, Charles W.(2015) Introduction to Factor Analysis: What It Is and How to Do It. Sage.
Lewis-Beck, Colin & Lewis-Beck, Michael S. (2015). Applied Regression: An Introduction. Sage
McIver, John, and Edward Carmines(1981). Unidimensional Scaling. Sage.
Schroeder,Larry D., Sjoquist, David L., Stephan, Paula E. (2017). Understanding Regression Analysis: An Introductory Guide. Sage.
Tabachnick, Barbara G. & Linda S. Fidell (2006). Using Multivariate Statistics. 6th ed. Pearson.