A quantitative investigation into "attainment gaps" within the SEC undergraduate student cohorts at Kingston University

Ruvinga, Stenford (2018) A quantitative investigation into "attainment gaps" within the SEC undergraduate student cohorts at Kingston University. (MSc(R) thesis), Kingston University, .

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Abstract

This thesis examines data on the retention, progression and attainment of actual Kingston University undergraduate students. The thesis attempts to identify the extent to which the "attainment gaps" between Black, Asian and Minority Ethnic (BAME) and white students and between mature and "regular" students, identified as existing in higher education across the United Kingdom also occur at Kingston University. This thesis analysed data within the Faculty of Science, Engineering and Computing Undergraduate student cohorts at Kingston University for patterns and trends in attainment and how they can be explained by differences in ethnicities, age and UCAS points. After an initial investigation of the data using descriptive statistics, statistical modelling techniques are applied to the students' data. Firstly, multiple linear regression models are fitted to each department or subject area within the Faculty to analyse the factors affecting students' mean marks at all levels (1st year, 2nd year and final year undergraduate). Secondly, multiple logistic regression models will be fitted to each department within the Faculty to calculate the odds ratios with respect to each of the categorical variables. Finally, two types of 2- level multilevel data analysis, namely a hierarchical model and a growth model are employed. In the hierarchical model, the data is analysed with individual students being level one and the different faculty departments being level two and in the growth model with time in years at level one and the students at level two. All things being equal, this should be able to account for the variation between students and subjects.

Item Type: Thesis (MSc(R))
Physical Location: This item is held in stock at Kingston University library.
Research Area: Education
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Depositing User: Jennifer May
Date Deposited: 17 Sep 2018 15:11
Last Modified: 06 Nov 2018 12:53
URI: http://eprints.kingston.ac.uk/id/eprint/41966

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