How do researchers make sense of large datasets using specialised software? How do they identify patterns? When do they know that a pattern has a 'real-world' meaning rather than just reflecting a random event? In this module you will learn how to make a rational and logical assessment of numerical data within the context of research design and hypothesis testing. You will understand how to explore and prepare data for analyses. You will learn about the role of probability and master the use of specialised software that automatically and seamlessly analyses data using parametric and non-parametric statistics (T-Tests, ANOVA, Regression, Correlation, Chi Square, Discriminant Function Analysis). Upon completion you
will be proficient with the principle statistical approaches that are used for quantitative research across disciplines, and the best ways of presenting these results.
Lecture 8 hours, Computer practical 20 hours, workshop 4 hours
Short Writing Piece: Critical Appraisal of Literature (800 words) worth 20%
Report: Statistical Report (3000 word) worth 80% This assessment is pass/compulsory
Reassessment: 100% Written Assessment (3,000 words)
On successfully completing the module students will be able to:
1. Compose valid research questions based on the key principles of research design.
2. Construct a research report with testable hypotheses.
3. Critically discuss the differences between quantitative and qualitative data.
4. Align inferential statistical tests with different types of variables and different research designs.
5. Critically analyse quantitative data with inferential statistical tests using specialised software.
6. Critically appraise and present scientific results in ways that are suitable for a range of professional report formats.
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