CARPINTERÍA EXTERIOR

Credits 20

Timetable Spring term

Tutors Various (Module leader – Cylcia Bolibaugh) Module Number EDU00064M

Core/Option Option Aims

To develop students’ understanding of and ability to perform data analysis, building upon the basic understanding of data collection and analysis provided in introductory modules in the Autumn term. This is an advanced module, particularly useful for students who are considering applying for a PhD, and will involve advanced

statistical methods. This module is available to MA students and can be attended by first year PhD students.

Objectives

To provide students with knowledge that will enable them to:

 Understand the debate surrounding quantitative, qualitative and mixed methods approaches to data collection and analysis, including ethical and political

concerns

 Exhibit knowledge and understanding of a range of methods for analysing quantitative and qualitative data in education and related social sciences  Understand the ethical issues involved with data management

 Critically consume research results reports

 Understand issues relating to criteria for assessing the validity of data and the interpretation of claims about the results of research.

Assessment

The module is assessed by means of a single written assignment comprising a portfolio of tasks based on the weekly content.

Reading

The recommended basic texts for this course are:

Field, A. (2009) Discovering statistics using SPSS. London: Sage.

Bazeley, P., & Jackson, K. (2013). Qualitative data analysis with NVivo (2nd ed.). London: Sage.

Greene, J. & D'Oiveira, M. (1999). Learning to use statistical tests in psychology. Buckingham: Open University Press.

Miles, M. B., & Huberman, M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). London: Sage.

In preparation for each class you may be required to read one or more chapters from one of these books or from other sources. You should supplement this reading with more advanced materials.

Course outline

Week 2 Methodological Approaches

Week 3 Qualitative Data Analysis using NVivo (Part 1) Week 4 Qualitative Data Analysis using NVivo (Part 2)

Department of Education Modules handbook

September 2015 Page 78

Week 5 Classroom Observation and Data Analysis Week 6 Regression

Week 7 Analysis of Variance Week 8 Non-parametric Tests

Week 9 Principal Component and Factor Analysis Week 10 Dealing with Missing Data

Course details Week 2

Methodological approaches in educational research

Students will be introduced to the different philosophies of social science which underpin research in education. We will consider so-called quantitative, qualitative and mixed methods approaches and the underlying assumptions associated with each, in particular their different views about the purpose and possibilities of empirical research for understanding education.

Week 3

Qualitative Analysis with NVivo (Part 1)

The session will start with an introduction to computer-assisted qualitative data analysis (CAQDA), clarifying what it can and cannot do, dispelling some of the myths surrounding it and helping approach data analysis with more realistic expectations. The session will introduce NVivo – a widely used CAQDA software package -

exploring the user interface, key NVivo/ CAQDA terminology and main functions with the help of a sample project.

Week 4

Qualitative Analysis with NVivo (Part 2)

Building on the previous week, this session will cover importing data sources into NVivo, coding, auto-coding and visualisation. We will work with a variety of data formats, e.g., text-based documents, photos and audio/video recordings.

Week 5

Classroom Observation and Data Analysis

This session will focus on observation research and data analysis relevant for this type of research. The strength and weaknesses of using observations will be first considered and then different types of observation, such as structured, semi- structured, unstructured, and when these types are appropriate, will be discussed. Methods of analysing data from different types of observations will be then evaluated. Students will also have an opportunity to look at examples of different types of

observation and examine their application in different learning and teaching contexts. Week 6

Non-parametric Tests

In the first part of this session non-parametric tests, including ranks tests (e.g.

Wilcoxon rank-sum test, Wilcoxon signed rank test, and Spearman rank correlation ) and the Chi-square test, will be introduced. The assumptions of these tests will be considered and compared with those of parametric tests. In addition, the relative power of non-parametric and parametric tests will be discussed. The second part of

Department of Education Modules handbook

September 2015 Page 79

the session will focus on using SPSS to run non-parametric tests. It will comprise a series of practical activities in which students will re-analyse data from published research using the tests introduced in the session.

Week 7 Regression

This session will introduce simple regression, multiple regression and logistic

regression. We will run regressions using SPSS, from data entry to the interpretation of the output.

Week 8

Analysis of Variance

This session will introduce Analysis of Variance (ANOVA). We will look at one-way and factorial ANOVA, including within- and between-group designs, post hoc tests and planned contrasts, and effect sizes. We will run tests using SPSS, from data entry to the interpretation of the output.

Week 9

Principal Component and Factor Analysis

This session will look at the use of factor analysis as a means of identifying key dimensions which might be involved in exploring multi-faceted constructs. Week 10

Missing Data

Real research, particularly involving primary data collection, usually involves some sort of missing data problem. We may be missing whole cases or particular items within cases. In this session we will consider the effect which missing data might have on our ability to make inferences and draw conclusions and consider what we might do to mitigate missing data problems.

Department of Education Modules handbook

September 2015 Page 80