Statistical Methods for Social Research using SPSS

Description:

Statistical Methods for Social Research using SPSS

For those undertaking research in the social sciences, an ability to handle data is an essential skill. Many researchers in the social sciences use SPSS to perform data analysis, but often formal training in use of the software and how to interpret output is severely lacking.  This course concentrates on transforming participants into competent and confident users of SPSS to enable them to conduct independent data analysis for their own research needs. 

Statistical Methods for Social Research using SPSS takes a more applied approach to conventional statistics by focusing on encouraging participants to "get their hands dirty with data". Instead of being purely theory-oriented, emphasis will be more on the practical application of a variety of statistical techniques to supplied datasets. 

Working with datasets, the course will cover widely-used statistical methods including descriptive statistics, data visualisation, statistical inference, categorical data, correlation and regression, analysis of variance and multivariate analysis (such as factor analysis).

Who is this course aimed at?
This applications-oriented course is designed for researchers who lack the confidence to perform data analysis independently due to:

• a lack of understanding of various statistical methods
• not knowing which techniques are appropriate for different types of data
• inexperience with using statistical software packages (specifically SPSS here)
• not knowing how to interpret output from software packages and what conclusions can be drawn.

Course benefits
After successful completion of the course, participants should be able to:

• perform independent data analysis in the social sciences
• determine which statistical method is appropriate in a given situation and be able to discuss the merits and limitations of a particular method
• use SPSS to analyse datasets and be able to interpret output
• draw appropriate conclusions following empirical analysis

Prerequisites
A foundation course in statistics at undergraduate level is recommended.

Course outline
The course will consist of daily lectures supported by computer-based practical classes which will allow course participants to practise implementing the lecture material hands-on in SPSS. SPSS is a popular choice of statistical software and is ideally suited for empirical research in the social sciences.

Topics covered in the course will be wide-ranging, such that participants will be exposed to a variety of statistical methods reflecting the different sorts of data which a researcher may be required to analyse. Assumptions, merits and limitations of methods will be discussed.

The course will begin with an overview of the SPSS environment, followed by data visualisation and descriptive statistics. Other topics to be covered include interval estimation and hypothesis testing (for one and two samples), categorical data, correlation and regression, analysis of variance and several multivariate analysis techniques, such as factor analysis.

This course is offered as part of the LSE Methods Summer Programme, a summer school of intensive short courses in social science research methods for students, researchers and professionals. A number of social events will be held throughout the programme. Participants will be provided with a transcript and certificate upon completion of the course.

Cost:

Students: £1,435
Academic/charity staff: £1,930
Professionals: £2,425

Website and registration:

http://www.lse.ac.uk/study/summerSchools/Methods/Quantitative/ME108.aspx

Region:

Greater London

Keywords:

Quality in Quantitative Research, Quantitative Data Handling and Data Analysis, Correlation, Variance estimation, Regression Methods, ANOVA, Linear regression, Logistic regression, Categorical data analysis, Factor analysis, SPSS, Data Visualisation

Related publications and presentations:

Quality in Quantitative Research
Quantitative Data Handling and Data Analysis
Correlation
Variance estimation
Regression Methods
ANOVA
Linear regression
Logistic regression
Categorical data analysis
Factor analysis
SPSS
Data Visualisation

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