SGIA49915: Quantitative Methods and Analysis

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• To provide an opportunity for students with a range of disciplinary backgrounds and experiences, to assimilate and develop their knowledge, critical understanding and skills in quantitative data analysis.
• To enable students to understand how to use statistical techniques for exploration and description of data sets.
• To enable students to make appropriate statistical inferences about associations between social phenomena.

• Indicative content as follows:
• Foundational statistical methods for quantitative inquiry of data
• Performing quantitative data analysis using statistical software
• The nature of quantitative data and levels of measurement
• Exploring and describing data with statistics and graphs.
• Populations, sampling and sampling distributions.
• Point estimates and confidence intervals.
• Significance tests.
• Association, cross-tabulation and Chi-Square tests.
• Association and differences in means: t-tests and ANOVA.
• Correlation and simple linear regression.
• Dealing with alternative explanations the role of research design, statistical control and multiple linear regression.
• Discussion of formative assignments, review, questions and answers.

Subject-specific Knowledge:

• At the end of this module students will be able to:
• Understand the fundamentals of applied statistical analysis for social data.
• Understand the concept of statistical inference and carry out basic inferential procedures.
• Understand the concept of statistical association and how to use statistical methods to test for such association.

Subject-specific Skills:

• Use statistical software to explore quantitative social data.
• Calculate basic descriptive statistics for a set of data and construct appropriate graphical representations.
• Obtain and interpret confidence intervals and significance tests for single variables.
• Construct and interpret contingency tables.
• Carry out and interpret tests for differences in means across two or more groups.
• Estimate and interpret simple and multiple linear regression models.
• Distinguish correlation and cause.
• Use statistical software to execute the procedures covered in the module.

Key Skills:

• By the end of this module, students will be able to demonstrate:
• An ability to undertake and interpret statistical analyses of social data;
• An ability to critically explain and evaluate quantitative evidence;
• Specialist IT and research skills
• Basic written communication skills;
• Basic learning and study skills;
• An ability to plan and manage time effectively.

• The lectures on the module will outline the key statistical methods and concepts, how they can be used in social science, and their appropriate interpretation
• The computer classes will involve students undertaking practical data analysis exercises using statistical software, which will enable students to develop skills in data analysis and in interpreting and evaluating the results of this analysis
• The summative data analysis exercise for the module requires students to demonstrate their knowledge and understanding of applied statistics for social science by carrying out an analysis of pre-specified secondary quantitative data and writing up the results in the form of a report.
• The formative assessment provides students with an opportunity to perform, write up and obtain feedback on a series of analyses of a pre-specified secondary data set.

This will be a short formative assignment in which students must carry out a series of analyses on pre-specified dataand interpret the results of the analyses. Although not all procedures will be covered in this work, and different data will be used, the formative assignment follows the similar format as the summative assignment. It is therefore aimed to assist students to become familiar with requirements and expectations of summative work. To be submitted in session eight. Students will receive individual feedback on the formative.

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