# Data Analysis in Social Science III

Module title Data Analysis in Social Science III POL3094 2019/0 15 Dr Alexey Bessudnov (Convenor)
 Duration: Term Duration: Weeks 1 2 3 11
 Number students taking module (anticipated) 30

## Description - summary of the module content

### Module description

Basic knowledge of statistics and data analysis is often not enough for dealing with more complicated problems in the social sciences, as well as in market research, applied policy analysis, and data-driven journalism. This module introduces you to more advanced techniques for social data analysis using the statistical programming language R and in particular the tidyverse framework. These techniques are especially useful while working with large longitudinal data sets. While some statistical theory is covered in this module, the discussion of statistical concepts is generally non-mathematical and intuitive and is based on numerous examples from social sciences. The module assumes familiarity with basic descriptive statistics and linear regression analysis.

POL/SOC1041 and POL/SOC2077 are the pre-requisites for this module.

## Module aims - intentions of the module

The aim of this module is to introduce you to more advanced quantitative techniques for the analysis of social data. More specifically, you will learn how to clean, transform, reshape and visualise data in R, a statistical programming language, and tidyverse, a collection of tidyverse packages. You will also learn the fundamentals of programming in R, such as conditional statements, loops and functions.  After completing this module, you will be able to independently conduct data analysis in R. Employers in many industries value this skill.

## Intended Learning Outcomes (ILOs)

### ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. clean and prepare your data for statistical analysis in R;
• 2. conduct statistical analysis using selected methods at the advanced level in R;

### ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. apply statistical data analysis techniques to social science problems;
• 4. clearly explain the results of statistical analysis in substantive terms and relate them to substantive social science problems;

### ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. report the results of statistical analysis in writing in a way that would be understood by non-specialists; and
• 6. use general-purpose statistical software (such as R) for the analysis of social data at the advanced level

## Syllabus plan

### Syllabus plan

Whilst the module’s precise content will vary from year to year, it is envisaged that the syllabus will cover some of the following themes:

• Data types and structures in R
• Data import with readr and data.table
• Data manipulation with dplyr
• Data visualisation with ggplot2
• Iteration
• Functions
• Reproducible research and effective presentation of statistical results

## Learning and teaching

### Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
221280

### Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning & Teaching activities2211 x 2 hour lectures / computer lab sessions
Guided independent study78Reading and preparation for lectures and lab sessions
Guided independent study50Reading, preparation and writing of the statistical report

## Assessment

### Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Formative statistical exercises in class6 exercises (about 15 minutes each)1-6Peer and tutor verbal feedback

### Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

### Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
5 short data analysis assignments to be submitted on Github Classroom50Data analysis exercises, about 500 words each assignment1-6Written feedback provided on Github
Final statistical report502000 words1-6Written feedback

## Re-assessment

### Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Final statistical reportStatistical report (2000 words)1-6August / September reassessment period
5 short data analysis assignments5 short data analysis assignments1-6August / September reassessment period

## Resources

### Indicative learning resources - Basic reading

G.Grolemund, H.Wickham. R for Data Science. O’Reilly (2017).

P.Spector, Data Manipulation with R, Springer (2008).

A.Unwin, Graphical Data Analysis with R, CRC Press (2015).

N.Matloff, The Art of R Programming, No Starch Press (2011).

W.Chang, R Graphics Cookbook, O’Reilly (2013).

### Indicative learning resources - Web based and electronic resources

http://vle.exeter.ac.uk/

“Data Analysis in Social Science 3”: http://abessudnov.net/dataanalysis3/

Module has an active ELE page

### Key words search

Social science, quantitative, data analysis, statistics, R

Credit value 15 7.5 POL/SOC1041 and POL/SOC2077 POL/SOC2077 if not taken before 6 No 30/03/2016 26/08/2018