Core quantitative data analysis 1 and 2 (P02137)

? Credit Points : 20 ? SCQF Level : 11 ? Acronym : SPS-P-P02137

The course will cover descriptive and exploratory data analysis principles of inference, measures of association and elementary multivariate analysis. Course content will include: the structure of social science data - cases, variables, values, data sets and missing data; levels of measurement, univariate data analysis including frequency distributions and the graphical representation of data; measures of central tendency, dispersion and variability; normal distribution, standard scores and regrouping variables; distributions and confidence intervals and population variance; hypothesis testing and significance tests; tabular data and measures of association between categorical variables, correlation and regression; the use and interpretation of multivariate data and data management and analysis using statistical software (SPSS).

Entry Requirements

none

Subject Areas

Home subject area

Sociology, (School of Social and Political Studies, Schedule J)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 1 (Blocks 1-2)

Summary of Intended Learning Outcomes

By the end of the course students will:

- Be able to understand and apply a range of quantitative methods and tools
- Understand the logic of statistical description and inference
- Know how to interpret basic statistics
- Have a thorough grounding in descriptive and exploratory data analysis techniques
- Provide a full account of descriptive statistics for 1 and 2 variables
- Understand statistical modelling and be capable of using SPSS for Windows to perform advanced statistical analysis
- Be able to understand and apply multiple linear regression analysis
- Be able to fit and interpret models for categorical dependent variables
- Have experience of working with large data sets
- Understand how to access information about data sources
- Have experience of utilising web-based resources for learning
- Be able to efficiently access IT resources
- Have an understanding of the capabilities of computer software for statistical analysis