2016/17 Taught Postgraduate Module Catalogue

EDUC5063M Introduction to quantitative data analysis

15 creditsClass Size: 25

Module manager: Dr Matt Homer
Email: m.s.homer@leeds.ac.uk

Taught: 1 Nov to 31 Dec, Semester 2 (Jan to Jun) View Timetable

Year running 2016/17

This module is mutually exclusive with

EDUC5031M

Making Sense of Numeric and Non-numeric Data

Module replaces

EDUC5031M

This module is approved as an Elective

Module summary

This module provides an introduction to quantitative data analysis through the application to real datasets of appropriate software tools (e.g. SPSS). The educational research process is viewed holistically, and the quality of the research is shown to be dependent on a chain of activities, (such as formulating research questions, data collection, data analysis, and reporting of findings) of which the statistical element is one component.

Objectives

Following on from the theoretical considerations in the prerequisite modules, the objective of this module is to introduce the key elements of effective statistical exploration, analysis and reporting of findings in quantitative research.

Learning outcomes
On completion of this module, students will be able to apply appropriate tools such as SPSS to real world datasets in order to:
- Carry out initial exploratory analysis of quantitative datasets using statistical and graphical approaches.
- Translate research questions into statistical methods appropriate to the data available.
- Accurately report the findings of such analyses, including an understanding of the necessary limitations that quantitative research brings, and how this affects what can properly be inferred.

Syllabus

Topics to be covered:
- Levels of measurement
- Descriptive statistics - averages and spread
- Graphical methods
- Hypothesis testing - t-tests, ANOVA, chi-square test of association
- Introductory statistical modelling - correlation, regression and general linear models.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	6	1.00	6.00
Practical	6	1.00	6.00
Independent online learning hours			138.00
Private study hours			0.00
Total Contact hours			12.00
Total hours (100hr per 10 credits)			150.00

Private study

In order to prepare for the workshops, students will have to read and comment on academic and non-academic articles, explore datasets, write-up findings, read additional material about specific statistical techniques and about how to apply them using SPSS.

Opportunities for Formative Feedback

In every session, students will be set tasks – ranging from the simple production of a graph, to more sophisticated data modelling exercises. This work will be formatively assessed by the lecturer monitoring students as they work through these tasks. Additional tasks will be assigned for work outside the class, and these will be followed up in the subsequent sessions.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Data analysis and reporting task	100.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 21/11/2016

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