Introductory description

Surveys are an important tool in social science research. Their results are used by researchers and decision-makers in both the public and private sectors. This module develops students' skills in the analysis and reporting of survey data. This includes all the key steps in preparing survey data for analysis, carrying out the analysis and effectively presenting the survey results. The module will also show how Python, a popular programming language in data science, can be used to analyse survey data.

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: Getting to Know Your Software and Data I
This session provides an introduction to working with Python and data cleaning such as dealing with missing values and recoding variables.

Week 2: Getting to Know Your Software and Data II
This session continues with an introduction to working with Python and data cleaning such as dealing with missing values and recoding variables.

Week 3: Describing Your Data
This session covers descriptive statistics and how they are best reported in survey research.

Week 4: Uncovering and Testing Bivariate Associations in Survey Data
This session discusses different approaches to detecting and testing relationships between two variables.

Week 5: Estimating Bivariate and Multiple Linear Regression Models I
This session covers the foundations of bivariate and multiple linear regression models and how to report model results.

Week 6: Reading Week

Week 7: Estimating Bivariate and Multiple Linear Regression Models II
This session continues with bivariate and multiple linear regression models and how to report model results.

Week 8: Estimating Bivariate and Multiple Non-Linear Regression Models I
This session covers the foundations of different types of logit models.

Week 9: Estimating Bivariate and Multiple Non-Linear Regression Models II
This session continues with different types of logit models.

Week 10: Visualising and Presenting Survey Findings Effectively
This session provides a review of the basic principles of presenting survey results.

Learning outcomes

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

• To familiarise students with foundations of survey data analysis,
• To raise students’ awareness of the potential and pitfalls of survey data analysis,
• To equip students with the skills to understand and undertake survey data analysis.

Indicative reading list

Aneshensel, C.S. 2002. Theory-based data analysis for the social sciences. Thousand Oaks: Pine Forge Press.

Best, H. and Wolf, C. (Eds.). 2014. The SAGE Handbook of Regression Analysis and Causal Inference. London: SAGE.

Brooker, P. 2020. Programming with Python for Social Scientists. London: SAGE.

Ellison, S.F. 2022. Data Analysis for Social Scientists: A Foundational Crash Course. Independent Publisher.

Long, J.S., and Freese, J. 2014. Regression Models for Categorical Dependent Variables Using Stata. 3rd Edition. College Station, Texas: Stata Press.

Lynch, S.M. 2013. Using Statistics in Social Research. A Concise Approach. New York: Springer.

Menard, S. 2002. Applied Logistic Regression Analysis (3rd ed.). Thousand Oaks, CA: SAGE.

Paczkowski, W.R. 2022. Modern Survey Analysis: Using Python for Deeper Insights. Cham: Springer.

Rahlf, T. 2017. Data Visualisation with R: 100 Examples. Cham: Springer.

Tufte, E. 2001 [1983]. The Visual Display of Quantitative Information. Second Edition. Cheshire: Graphics Press.

Research element

Development of own data analysis.

Interdisciplinary

Survey data analysis crosses several disciplinary boundaries and benefits from the insights of different social science disciplines.

Subject specific skills

Systematic understanding, coherent and detailed knowledge of key concepts and approaches to survey data analysis

Ability to describe and comment on the advantages and pitfalls of different approaches to survey data analysis

Ability to conduct survey data analysis

Transferable skills

Developing and conducting own data analysis,

Exercise of initiative and personal responsibility,

Decision-making in complex and unpredictable contexts when analysing data.