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# Introduction to Quantitative Methods & Data Analysis - SOC00028M

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• Department: Sociology
• Module co-ordinator: Dr. Sarah Stopforth
• Credit value: 20 credits
• Credit level: M
• Academic year of delivery: 2024-25
• See module specification for other years: 2023-24

## Module summary

The module aims to introduce you to a range of common quantitative data analysis skills and techniques and to help understand the key concepts of quantitative analysis.

## Module will run

Occurrence Teaching period
A Semester 1 2024-25

## Module aims

The module aims to introduce you to a range of common quantitative data analysis skills and techniques, and to help you understand the key concepts of quantitative analysis. At the end of the module, you should be able to carry out your own analyses of large scale statistical datasets using statistical data analysis software and be able to critically interpret the use of such techniques in the work of others. You will be taught how to manipulate, explore and analyse large datasets, including secondary data, and understand the rationale for using a range of quantitative data techniques. You will gain practical insights into how to formulate a research problem and question and related hypotheses and to use an appropriate quantitative technique to test these hypotheses, providing an adequate justification for the choice of the technique. A critical attitude with regard to the various quantitative methods is encouraged. To help you in this approach, many examples of the way statistical techniques have been used in social sciences will be given and discussed.

## Module learning outcomes

1. Articulate a critical understanding of quantitative methods

2. Demonstrate how specific research questions can be answered by quantitative methods of analysis and what kind of research designs quantitative analysis requires

3. Demonstrate a broad range of descriptive and inferential statistical procedures both exploratory and confirmatory (e.g. contingency table, chi-square test, ANOVA, linear and logistic regression)

4. Manage, manipulate, explore and analyse large quantitative datasets, including secondary data

5. Conduct bivariate and multivariate analysis and use statistical data analysis software

6. Articulate the concept of hypothesis testing and statistical significance and notions of probability and sampling as well as notions of generalisability, causality, validity and reliability

## Assessment

Task Length % of module mark
Essay/coursework
Portfolio
N/A 100

None

### Reassessment

Task Length % of module mark
Essay/coursework
Portfolio
N/A 100

## Module feedback

For formative work, students will receive feedback on how to improve their skills in areas that contribute towards their summative assessment. The formative assessment provides practice for the summative tasks, in line with MLOs particularly 1-2.

For summative work, students will receive an overall mark and grading according to clearly defined criteria for assessing their knowledge, skills and abilities in line with MLOs 1-6. They will also receive written feedback showing areas in which they have done well and areas in which they need to improve that will contribute to their progress.