Data Analysis in Social Science

Module titleData Analysis in Social Science
Module codePOL1041
Academic year2019/0
Credits15
Module staff

Dr Patrick English (Convenor)

Duration: Term123
Duration: Weeks

11

Number students taking module (anticipated)

50

Description - summary of the module content

Module description

The purpose of the module is to introduce you to data analytical tools commonly used in social science research. It is designed for students with no previous experience of quantitative methods or statistics. It will provide you with a basic knowledge of the foundations of descriptive statistics and inference, focusing especially on methods for data presentation, description, and visualization.  You will also become familiar with statistical software packages (Excel and R) commonly used in academic and workplace settings. Laboratory (Lab) sessions will be used to re-enforce the material covered in the lectures and to help apply the core concepts seen in class to relevant practical problems. Throughout the module, you will link the techniques and methods learned in class to substantively relevant political science questions – e.g., are there differences in corruption levels and its determinants across countries?  What are the key determinants of income inequality in the UK?

POL1008/SOC1004 is a pre-requisite and no co-requisites are required.  

Module aims - intentions of the module

This module aims to provide you with an introductory knowledge of data analytical tools, including techniques for both descriptive and basic inferential statistics. It aims to teach you how to read and interpret quantitative information, to construct datasets from individual and aggregate level data, to summarize and present the important quantitative information in an effective and rigorous way, to look for and identify relevant trends and patterns in your data, and to conduct basic hypothesis tests. By the end of the module, you should be able to understand basic quantitative methods, to critically interpret quantitative information, and to conduct basic statistical analyses.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

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

  • 1. understand and apply a variety of statistical methods used in quantitative political science research;
  • 2. evaluate and contrast alternative quantitative methods based on an understanding of their advantages, drawbacks, and compatibility with the available data and the substantive questions of interest;
  • 3. demonstrate acquired skills: confidence and competence in a computer package for statistical analysis (e.g. Excel, SPSS, Stata);

ILO: Discipline-specific skills

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

  • 4. read, understand, interpret and evaluate basic statistical analyses in the professional literature;
  • 5. use statistical evidence to empirically evaluate the (relative) validity of social science theories and hypotheses;
  • 6. construct arguments based on (quantitative) empirical evidence for both written and oral presentation;
  • 7. examine relationships between theoretical concepts in social science with real world data;

ILO: Personal and key skills

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

  • 8. study independently;
  • 9. communicate effectively in speech and writing;
  • 10. use statistical software packages to summarize, analyze, and present statistical information; and
  • 11. demonstrate the ability to work independently, within a limited time frame, and without access to external sources, to complete a specified task.

Syllabus plan

Syllabus plan

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

  • Introduction to data analysis in the social sciences
  • Creating data: conceptualization, operationalization, and measurement
  • Describing data I: tables and figures
  • Describing data II: descriptive “statistics”
  • Correlation and dependence
  • Randomness and probability
  • Sampling and “sampling distributions”
  • Statistical inference: confidence intervals
  • Hypothesis testing: introduction
  • Testing the difference between two means
  • Using quantitative methods in politics, sociology and criminology: illustration and examples

Learning and teaching

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
26.5123.50

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activities16.511 x 1.5 hour sessions
Scheduled Learning and Teaching Activities1010 x 1 hour computer lab sessions
Guided independent study50Writing up problem sets and completing lab assignments
Guided independent study45.5Reading and preparing for lectures and tutorials, and completing online quizzes
Guided independent study28Web-based activities to familiarise students with statistical software.

Assessment

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Lab assignments8 statistical software-related activities to be complete during lab sessions1-10Written

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
Problem set402500 words1-11Written
Data analysis essay503500 words1-10Written
Online quizzes103-5 multiple choice questions each week X 101-11Written

Re-assessment

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Problem set (2500 words)A data analysis exercise that reinforces lecture material, from data collection to hypothesis testing (2500 words)1-10August/September re-assessment period
Data analysis (3500 words)A data analysis essay that demonstrates that ability to analyse and effectively communicate empirical information (3500 words)1-10August/September re-assessment period
Online quizzesA quiz on key data analysis techniques that reinforce the material presented in lecture, tutorials, and the reading1-11August/ September re-assessment period

Resources

Indicative learning resources - Basic reading

Imai, Kosuke. 2016. A First Course in Quantitative Social Science. Princeton, NJ: Princeton University Press.

Argesti, Alan and Finlay, Barbara. 1997. Statistical Methods for the Social Sciences, 3rd edition. Upper

Saddle River, NJ: Prentice Hall.

Dalgaard, Peter. 2002. Introductory Statistics with R. New York: Springer.

Dilnot, Andrew and Michael Blastland. 2007. The Tiger That Isn't: Seeing Through a World of Numbers. London: Profile Books Ltd.

Module has an active ELE page

Key words search

Quantitative Data, Regression, Statistics; Econometrics; Political Science

Credit value15
Module ECTS

7.5

Module pre-requisites

POL1008/SOC1004

Module co-requisites

None

NQF level (module)

4

Available as distance learning?

No

Origin date

01/02/2013

Last revision date

12/07/2016