Introductory description

When learning about quantitative data analysis we spend most of our time figuring out how to perform and interpret statistical models. However, in our daily practice we will spend the majority of our time preparing our data for analysis. This course will introduce the skills and techniques required to plan, organise, document and execute research in a manner which will encourage efficiency, accuracy and replication.

The lectures of this module will focus on introducing students to principles of good practice in the process of conducting quantitative research. The module will introduce: key issues in the evaluation of large scale data resources (e.g. sampling structure, missing data, and data quality); the operationalisation of key measures in quantitative social research (e.g. Social Class, Education and Ethnicity); and how suitable variables can be selected for statistical analyses. Students will be introduced to practical techniques for preparing variables and datasets for analysis in Stata. Centrally, this module will emphasise the importance of documentation of the research process.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Week 1: Induction (No Teaching)
Week 2: Introduction to the Workflow and Reproducibility
Week 3: Handling Complex Data Structures
Week 4: Analysing Complex Samples
Week 5: Producing Automated Tables and Effective Graphs
Week 6: Handling Missing Data
Week 7: Enabling Variables Part 2
Week 8: A little more on reproducibility
Week 9: Further topics: Version Control, Notebooks and Dynamic Documents

Learning outcomes

By the end of the module, students should be able to:

• Understand the pitfalls that are often involved when analysing real-world, complex, data (e.g. missing data, attrition, complex samples);
• Appreciate of how theoretical concepts can be operationalised in quantitative research;
• Appreciate the importance of planning, organisation and documentation to the research process;
• Understand of the role of sensitivity analysis and replication in quantitative research;
• Have practical skills in the preparation of variables and datasets using Statistical software (e.g. Stata).

Indicative reading list

Rose, David. (2000). Researching Social and Economic Change: The Uses of Household Panel Studies. London: Routledge. Chapters 1, 4 and 5.

Longhi, S., and Nandi, A. (2015). A Practical Guide to Using Panel Data. London: Sage.

Harford, T. (2014). Big data: A big mistake? Significance 11, 14-19.

Bollier, D. (2010). The Promise and Peril of Big Data. Washington: The Aspen Institute.

Price, M., and Ball, P. (2014). Big Data, Selection Bias, and the Statistical Patterns of Mortality in Conflict. SAIS Review of International Affairs 34(1), 9-20.

Strand, Steve. (2014). "Ethnicity, gender, social class and achievement gaps at age 16: Intersectionality and ‘Getting it’ for the white working class." Research Papers in Education 29 (2), 131-171.

Platt, L. (2011). Understanding Inequalities: Stratification and Difference. London: Polity. Introduction.

Blumer, H. (1956). "Sociological Analysis and the Variable." American Sociological Review: 683-690.

Bulmer, M., J. Gibbs and L. Hyman (2010). Social measurement through social surveys: an applied approach. M. Bulmer, J. Gibbs and L. Hyman. Farnham, Ashgate.

Treiman, D.J. (2009). Quantitative data analysis: Doing social research to test ideas: John Wiley & Sons.

Long, J.S. (2009). The Workflow of Data Analysis Using Stata. College Station: Stata Press.

Freese, J. (2007). "Replication Standards for Quantitative Social Science Why Not Sociology?" Sociological Methods & Research 36(2), 153-172.

Subject specific skills

• the ability to enable complex real world data for analysis
• proficiency in the statistical software STATA
• an understanding of the importance of transparency, reproducibility and the workflow in social science research

Transferable skills

• practical experience of the technical and theoretical dimensions of data preparation prior to analysis
  -the ability to identify, access and work with complex real world data in the form of existing secondary datasets