Introductory description

The module will be used as part of the PhD upgrade process from MPhil to PhD (along with an upgrade paper) a pass at Masters Level (50) will ensure that the student has understood the research requirements of the PhD. There will be three module assessments - students will be required to choose two (this being one of the three).

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Introduction to quantitative methods, considering the nature and phenomenological background
of quantitative methods, introducing the different type of quantitative research such as
experimental and survey research and study methods of data collection and sampling.
Numeric and graphical methods of describing univariate data. Issues regarding validity and
reliability will be explored, as well as the notion of probability and statistical significance (Type 1
and Type 2 error). Presentation of levels of measurements and descriptive statistics (central
tendency and measure of dispersion), focusing on single variables.
Consideration of further techniques for representing and analyzing bivariate data (i.e two
variables), including a number of analyses that involve two variable such as cross-tabulation,
bivariate correlation, one-way ANOVA and t-test. These analyses will enable us to compare
between-group means, including post-hoc comparisons and also focus on the use of effect size indices.
Consider more basic techniques for representing and analyzing bivariate data i.e., t-tests and one-way ANOVA.
Consider techniques such as multiple regression, General Linear Model (GLM) of which part is n-way Analysis of Variance (ANOVA) for representing and analyzing multivariate data as well as their underlying statistical and theoretical assumptions.
Consider further techniques for representing and analyzing multi-variate data, in particular
Multilevel Modeling and Structural Equation Modeling (including factor analysis and path
analysis). Focus on the theoretical assumptions that underlie these statistical techniques.

Learning outcomes

By the end of the module, students should be able to:

• This module will address conceptual issues and advanced methods of quantitative data collection and analysis, grounded in practical examples and the study of researchliterature. Methodological issues and statistical techniques will be introduced through discussion of research papers and the statistical package SPSS.· To introduce students to the paradigms that underlie quantitative educational research;
• To encourage students to identify the methodological grounds on which quantitative educational research is based;
• To develop students' competence in using the statistical package SPSS to do quantitative analysis;
• To develop students awareness of the potential of the internet as a research tool.
• To support students in assessing the efficacy of such research from a variety of perspectives and practices linked to thesis planning.

Indicative reading list

Aliaga, M. & Gunderson, B., (1999), Interactive Statistics. NJ: Prentice Hall
Brace, N., Kemp, R., & Snelgar, R., (2003), SPSS for Psychologists: A Guide to Data Analysis using SPSS for Windows (Second Edition). Basingstoke, Palgrave Macmillan.
Bryman, A., & Cramer, D., (1994), Quantitative Analysis for Social Scientists, London, Routledge Coolican, H., (1990), Research Methods and Statistics in Psychology, London: Hodder & Stoughton Lindley, D. V., Scott, W. F. (1984), New Cambridge Elementary Statistical Tables, Cambridge, C.U.P.
Muijs, D., (2004), Doing Quantitative Research in Education with SPSS, London, Sage.

View reading list on Talis Aspire

Subject specific skills

1. Gain familiarity with different quantitative research methods in relation to various empirical investigations within education.
2. Develop code and implement web-based surveys.

Transferable skills

No transferable skills defined for this module.