Quantitative Data Analysis

Summary of module for applicants:

This module aims to provide an understanding of how quantitative data are analysed in social science research, to develop the necessary practical skills through project work using key software including Excel, and open source software packages R and SQLite, and confidence in handling large quantitative datasets.

Main topics of study:

• Quantitative research processes; relationship with qualitative research; mixed mode approaches.
• Sources of data and official statistics; handling large data sets.
• Data quality (metadata), cleaning and outlier detection; data integration issues.
• Building a database; database query and exporting tables to other software.
• Exploration of univariate, bivariate and multivariate relationships.
• Creating data visualisations: tables, graphs and maps.
• Probability: normal, binomial, Poisson distributions; Bayesian probability.
• Formulating and testing hypotheses: parametric (incl. ANOVA) and non-parametric techniques.
• Deriving statistical models: factor analysis, clustering, regression, decision trees; multi-level models.
• Presentation and evaluation of quantitative analyses.

Learning Outcomes for the module

• Digital Proficiency - Code = (DP)
• Industry Connections - Code = (IC)
• Emotional Intelligence Development - Code = (EID)
• Social Intelligence Development - Code = (SID)
• Physical Intelligence Development - Code = (PID)
• Cultural Intelligence Development - Code = (CID)
• Community Connections - Code = (CC)
• UEL Give-Back - Code = (UGB)

At the end of this module, students will be able to:

Knowledge

1 Demonstrate a high level of understanding of the benefits and limitations of quantitative methods for promoting understanding and knowledge production in the social sciences and their relationship to other methodological approaches

2 Demonstrate a high level of understanding of the dual role of exploratory and confirmatory approaches to data analysis

3 Demonstrate a high level of understanding of the assumptions underlying parametric and non-parametric approaches to statistical testing  

Thinking skills

4  Develop a strategy for data analysis (DP, PID)

5  Interpret in the context of domain and method, the results of quantitative analyses (EID)

6  Evaluate in the context of domain and method, published analytical results (IC)

Subject-based practical skills

7  Be proficient in the use of open source R and SQL-based database (DP)

8 Access data sources, build a database, conduct queries and export tables to other software (DP)

9  Develop quantitative graphics for inclusion in papers and thesis (DP)

Skills for life and work (general skills)

10 Approach quantitative research methods and data handling with confidence (EID, PID)

11 Present quantitative analyses to technical and non-technical audiences (SID, CID)