Module SGIA40220: CORE QUANTITATIVE DATA ANALYSIS FOR SOCIAL RESEARCH

Department: Government and International Affairs

SGIA40220: CORE QUANTITATIVE DATA ANALYSIS FOR SOCIAL RESEARCH

Prerequisites

• None.

Corequisites

• None.

Excluded Combination of Modules

• None.

Aims

• This module is a key element in the research training provided for all research students in Edinburgh University and in their ESRC-recognised research training Masters programmes. It gives students and understanding of core statistical concepts and methods for social research. In line with ESRC guidelines, the module enables students to achieve proficiency in key methods for collecting, presenting and interpreting quantitative data.

Content

• Part one of the module will explore epistemological paradigms, descriptive and exploratory data analysis and data management and analysis using statistical software (SPSS). Course content will include: the structure of social science data - cases, variables, values, data sets and missing data; levels of measurement, frequency distribution and the graphical representation of data; measures of central tendency, dispersion and variability; normal distribution, standard scores and regrouping variables.
• Part two of the module will explore principles of inference, measures of association and elementary multivariate analysis. Course content will include: 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).

Learning Outcomes

• Students will:
• be able to understand the significance of links between theory and method and understand the epistemological implications of particular methodological approaches
• be able to understand and apply a range of quantitative methods and tools
• know how to interpret basic statistics
• understand statistical modeling and be capable of using SPSS for Windows to perform advanced statistical analysis
• have thorough grounding in descriptive statistics for 1 and 2 variables
• 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

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• The module has been developed as a web-based package (with face-to-face teaching support throughout), which will enable students to take the course at a time and pace most appropriate to their overall training needs during the first semester (Oct-February). Tutorials will be held on a weekly basis to offer responsive help as needed to understand the statistical concepts and their application using computer software.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

There is no written formative assessment in this module. Students receive feedback through their interaction with staff in weekly tutorials.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University