Introductory description

This module aims to develop practical quantitative research methods skills, and a critical appreciation of such methods. It covers the secondary analysis of data from large-scale surveys, the collection or generation of survey data, and the use of non-survey sources for quantitative analysis. It also includes considerations of the evaluation and interpretation of existing data sources/published data (including official statistics) and the development of research instruments. The conceptual and contextual factors determining the meaning and value of survey data are also discussed. The module includes a number of ‘hands-on’ computing sessions (in which statistical software# is used to manipulate and analyse data). The statistical techniques considered (and applied using statistical software) within the module range from basic descriptive and inferential statistics (such as cross-tabulations and chi-square) to multivariate techniques (such as logistic regression). The components of the module assessment relate to quantitative data sources and questionnaire design, sampling and the production of primary data, descriptive and inferential statistics, multivariate analysis, and the interpretation of published statistical analyses.

Module web page

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 TOPIC
la Novice's guide to quantitative analysis.
lb Survey design and sampling.
2a Operationalising concepts.
2b Novice's guide to using SPSS.
3a Research instruments/Questionnaire design.
3b SPSS: Data management
4a Data sources, secondary analysis and official statistics.
4b Statistics without surveys: Content analysis.
5a Statistical inference.
5b SPSS: Descriptive (and inferential) statistics.
[6 Reading Week]
7a Cross-tabular analysis.
7b SPSS: Cross-tabular analysis.
8a (Multiple) regression (and correlation).
8b Logistic regression.
9a The use of multivariate analysis in published research.
9b SPSS: (Multiple) regression and logistic regression).
10a Log-Linear models.
10b SPSS: Log-linear models.

Learning outcomes

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

• (1) A knowledge and understanding of a range of issues relevant to the practicalities, value and limitations of quantitative methods and data sources.
• (2)A knowledge and understanding of statistical inference.
• (3)The ability to apply a quantitative software package to secondary data, including the application of a relevant multivariate analysis technique, and the ability to appropriately present and interpret the results from such an analysis.

Indicative reading list

Buckingham, A. and Saunders, P. 2004. The Survey Methods Workbook. Cambridge: Polity.
Dale, A., Fieldhouse, E. and Holdsworth, C. 2000. Analyzing Census Microdata. London: Arnold.
Dale, A., Wathan, J. and Higgins, V. 2008. 'Secondary Analysis of Quantitative Data Sources'. In
Alasuutari, P., Bickman, L. and Brannen, J. (eds) The SAGE Handbook of Social Research Methods.
London: Sage.
De Vaus, D. 2001. Surveys in Social Research. (5th edition). London: Routledge.
Diamond, I. and Jefferies, J. 2000. Beginning Statistics; An Introduction for Social Scientists.
London: Sage.
Field, A. 2009. Discovering Statistics Using SPSS (3rd edition). London: Sage.
Fielding, J. and Gilbert, N. 2006 Understanding Social Statistics (2nd edition) London: Sage.
Gray C.D. and Kinnear, P.R. 2011. IBM SPSS 19 Made Simple. Hove: Psychology Press.
Marsh, C. and Elliott, J. 2009. Exploring Data: An Introduction to Data Analysis for Social Scientists
(2nd edition). Cambridge: Polity Press.
Menard, S. 2001. Applied Logistic Regression Analysis (2nd Edition). London: Sage.
Pole, C. and Lampard, R. 2002. Practical Social Investigation' Qualitative and Quantitative
Methods in Social Research. Harlow: Prentice Hall (Pearson Education).
Tarling, R. 2008. Statistical Modelling for Social Researchers: Principles and Practice. London:

Subject specific skills

(i) A knowledge and understanding of a range of issues relevant to the value and limitations of quantitative data sources (including secondary sources).
(ii) A more specific knowledge and understanding of the conceptual and contextual factors determining the meaning and value of data from survey questions/research instruments.
(iii) A knowledge and understanding of the issues involved in sampling.
(iv) The ability to apply quantitative methods to qualitative source materials.
(v) A knowledge and understanding of descriptive statistics and statistical inference.
(vi) A knowledge and understanding of the use of multivariate analysis.
(vii) The ability to apply a quantitative software package to secondary data.
(viii) A knowledge and understanding of the relationships between quantitative data and substantive theory.
(ix) The ability to apply a quantitative software package to secondary data and to thus:

• generate and present descriptive and inferential statistics and graphics,
• manage data,
• test hypotheses.
  (x) The ability to apply a quantitative software package to secondary data in order to model data using a multivariate technique, and the ability to present and interpret the results from such an analysis.

Transferable skills

the qualities and transferable skills necessary for employment requiring:

• the exercise of initiative and personal responsibility
• decision-making in complex and unpredictable situations
• the independent learning ability required for continuing professional development
• advanced abilities in statistics