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| Module Availability |
Year |
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| Assessment Pattern |
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Unit(s) of Assessment
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Weighting Towards Module Mark (%)
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One practical computing exercise
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50%
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One practical statistical exercise
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50%
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| Module Overview |
The first 10 sessions consist of a short lecture followed by a practical to give students a thorough grasp of how to use SPSS, the most popular and one of the most powerful computer packages for analysing quantitative data, on a personal computer.
The following sessions take a very hands-on approach. Under supervision and in a step-by-step manner, you will investigate the chosen topic, with a data set sourced from the Data Archive at the
University
of
Essex
and using SPSS. We shall also learn how to interpret and present results of quantitative analyses.
The first part of the course is mainly concerned with giving students a thorough grasp of SPSS. The second part aims to explain regression and interval level data in non-technical terms using SPSS.
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| Prerequisites/Co-requisites |
| Module SOC1007 - Quantitative Methods 1. |
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| Module Aims |
During the Autumn semester each session consists of a short lecture followed by a practical to give students a thorough grasp of how to use SPSS, the most popular and one of the most powerful computer packages for analysing quantitative data, on a personal computer.
In the Spring semester the course takes a very hands-on approach. Under supervision and in a step-by-step manner, you will investigate the chosen topic, with a data set sourced from the Data Archive at the
University of
Essex and using SPSS. We shall also learn how to interpret and present results of quantitative analyses.
The Autumn semester course is mainly concerned with giving students a thorough grasp of SPSS. The Spring semester course aims to explain regression and interval level data in non-technical terms using SPSS.
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| Learning Outcomes |
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Having completed this module students should:
· Be able to create simple data sets for statistical analysis using the personal computer.
· Be able to carry out simple statistical analyses on their own data set or on other secondary data sources.
· Be able to carry out simple data management tasks prior to statistical analysis.
· Be able to understand the logic behind, and the appropriate time to use regression analysis as a tool for social research.
· Be able to carry out a regression analysis using SPSS.
· Be able to understand and describe the content of statistical tables derived from published statistical sources.
· Be able to search for appropriate data for secondary quantitative analysis using online data archives.
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| Module Content |
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| Methods of Teaching/Learning |
Lectures and classes |
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| Selected Texts/Journals |
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Agresti A and B Finlay (1997) Statistical Methods for the Social Sciences 3rd edn, Prentice-Hall
Allison PD (1999) Multiple Regression: A Primer, Pine Forge Press
Fielding J and N. Gilbert (2000) Understanding Social Statistics, Sage
Freedman D, R Pisani, R Purves and A Adikhari (1997) 3rd edn, Norton
Norusis M (1998) SPSS Base 8.0 Users Guide, SPSS Inc
Norusis M (1999) The SPSS 9.0 Guide to Data Analysis, Prentice Hall
Wallgren A (1997) Graphing Statistics, Sage
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| Last Updated |
September 2010 |
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