Computer Science (Human Computer Interaction) (3 Years) [BSc]

Essentials of survey design and analysis

Unit code: SOST20022
Credit Rating: 20
Unit level: Level 2
Teaching period(s): Semester 2
Offered by Social Statistics
Available as a free choice unit?: Y




NB - students are NOT allowed to take this course if they are taking SOST20012 The Survey Method in Social Research

The unit aims to:

(i) Introduce the practical issues involved in the planning and management of surveys and basic analysis of survey data.
(ii) Introduce the planning, organization and implementation of a sample survey, including the design of questionnaires.
(iii) Sampling and sample size calculations using Excel.
(iv) Provide an understanding of basic statistical methods and models for the analysis of quantitative social science data and their application in a range of disciplines.
(v) Provide practical applications of statistical methods and the interpretation of results using the computer software SPSS.
(v) Introduce a range of international and UK social science data sources.


This course is divided into two parts: the first part will provide practical issues involved in the planning of surveys, including representation and measurement, total survey error, sampling frames, methods of data collection, questionnaire design, probability versus non-probability samples, simple random sampling, stratified and cluster sampling, sample size calculations, standard errors and survey processing. The tutorials in part one will be based around the planning, design and implementation of a survey using Excel. The second part of the course will provide practical topics involved in the analysis of survey data taking into account the design of the survey, management and handling of survey data from a range of social science datasets, exploratory analysis, comparing differences between means and proportions and chi-square tests for testing associations. Such skills are in demand in social research across the public and private sector. This in part will include developing the student's critical data skills. The tutorials in the second part will involve hands-on training and practice analyses of survey data using SPSS.

Lecture Schedule (10 x 2hr lectures)

1. Introduction: what is a survey, representation and measurement, types of surveys, total survey error, method of data collection, sampling frame.
2. Questionnaire design.
3. Probability versus non-probability sampling, simple random sampling, standard error, confidence intervals, stratified and cluster sampling.
4. Sample size calculation, survey processing (dealing with non-response).
5. Examples of social science datasets, data manipulation, missing data and non-response adjustments and computer workshop introducing SPSS.
6. Introduction to statistical analysis: exploratory analysis and descriptive statistics (histograms, means, proportions, medians, standard deviations) and computer workshop.
7. Comparing means and proportions (t-tests) and computer workshop.
8. Analysis of contingency tables (chi-square tests) and computer workshop.
9. Correlations and computer workshop.
10. Reporting statistical analysis, writing skills and course overview.

Tutorials (10 x 1hr tutorials)

The tutorials will be linked to the lectures and based around embedding practical skills learning, using tasks and group work. The tutorials for the first part of the course will revolve around the planning and design of a survey, questionnaire design, sample frame considerations, sample size calculations and drawing a sample (Excel). The tutorials for the second part of the course will revolve around basic statistical analysis of survey data, histograms, descriptive statistics and statistical testing.

Teaching and learning methods

The course will involve: lectures, group work and computing lab classes. Extensive use will be made of relevant on-line resources where students can learn about social science data.

Blackboard resources will be used to enable students to access teaching data and data sources.

The lecture component will provide a theoretical and methodological framework for learning about the design of surveys and analysis of survey data. Group work in the tutorials will give students hands on experience on the design and implementation of a survey. Practical sessions in the computing lab will give students hands on experience in the basic exploratory analysis of survey data, data manipulation, interpretation and reporting results. Such skills are highly transferable.

The emphasis on the use of real data to answer real questions is designed to engage students and enable students to consider using such approaches as part of their own dissertation research.

Learning outcomes

Student should/will be able to

Knowledge and Understanding: An understanding of the main requirements and problems of planning and implementing a sample survey and designing questionnaires; basic principles for preparing survey data for statistical analysis; carry out and interpret statistical analysis such as exploratory analysis and testing for differences and associations.

Intellectual skills: Skills and an understanding of good practices in planning and implementing a survey. Skills in problem solving, data analysis and evaluation methods through lectures, practicals, group work and independent reading.

Practical skills: Skills in using social science datasets and practical experience of sampling and data analysis including using software (Excel, SPSS).

Transferable skills and personal qualities: Data handling, interpretation and reporting of statistical analysis. Social statistics and data analysis skills are highly in demand in the labour market. The group work will also aid the student in the development of their communication and team working skills.

Assessment methods

  • Written exam - 50%
  • Written assignment (inc essay) - 50%

Recommended reading

Part I:
Czaja, R. and Blair, J. (2005) Designing Surveys: A Guide to Decisions and Procedures, 2nd ed. Pine Forge Press, London
De Vaus, D. (2002) Surveys in Social Research, 5th ed. Routledge, London.
Groves, R.M. et al. (2004) Survey Methodology. J. Wiley, Hoboken.
Oppenheim, A.N. (1992) Questionnaire Design, Interviewing and Attitude Measurement. Pinter Publishers, London.
Part II:
Clarke, G.M. and Cooke, D. (2004) A Basic Course in Statistics, 5th ed. Hodder Arnold H&S.
Diamond, I and Jefferies, S. (2005) Beginning Statistics: An Introduction for Social Scientists. Sage, London.
Field, A. P. (2011) Discovering Statistics Using SPSS, 3rd ed. Sage, London.
Jaisingh, L. (2006) Statistics for the Utterly Confused, 2nd ed. McGraw-Hill.
Mason, J. and Dale, A. (2011) Understanding Social Research. Sage.

On-line Resources
UK Data Archive
Survey Network
Research Methods Centre

Feedback methods


All Social Statistics courses include both formative feedback – which lets you know how you’re getting on and what you could do to improve – and summative feedback – which gives you a mark for your assessed work.

Study hours

  • Lectures - 20 hours
  • Practical classes & workshops - 10 hours
  • Independent study hours - 170 hours

Teaching staff

Patricio Troncoso Ruiz - Unit coordinator

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