Methodological approach: quantitative secondary data analysis data analysis

This project makes use of quantitative, secondary analysis of pre-existing data to explore questions related to the extent and nature of learner mobility in contemporary urban South Africa. Secondary analysis of pre-existing data is a well-accepted research method with a long history of use in educational research as well as the study of mobility (McMillan and Schumacher 2005;

Smith 2006; Fleisch and Schindler 2008). As will be described later, taking a quantitative approach to secondary analysis is particularly well suited to answering the questions posed in this thesis.

Secondary data analysis is an approach to research that is based on the analysis, or in some cases the reanalysis, of pre-existing data (Bryman 2004;

McMillan and Schumacher 2005). Typically, this data, which may be quantitative or qualitative, and may consist of primary or secondary sources, was originally collected for a particular purpose other than the research project under consideration. Secondary data analysis allows this data to be reused, to answer a different set of research questions. A major strength of this methodology is therefore the ability to make use of pre-existing data, eliminating the need for time-consuming and expensive data collection, and allowing for more time and effort to be dedicated to analysis. Eliminating the need to collect data also allows time and resources for the analysis of a greater volume of data, possibly covering a longer period of time, and greatly improving the breadth and reliability of work. Additionally, it enables research projects to make use of data from multiple sources, increasing the depth of findings, or to explore a particular historical era, generating period-specific conclusions. The volume of data available for analysis is likely to be far greater, and potentially of higher quality, than the data that could be collected

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during the limited time, and with the limited resources, available for most doctoral research. Quality of data is also less of a concern, as most large, pre-existing datasets have already gone through multiple levels of quality checking.

Quantitative analysis typically refers to the use of statistical approaches to deriving meaning from numerical data. Strengths of quantitative research include its potential for extracting meaningful and non-obvious information from large pools of data, and the efficacy with which it can be used on large sample sizes. Weaknesses include an inherent assumption that the principles of the scientific method apply to human phenomena, the inability to incorporate qualitative contextual information, and a deceptive sense of accuracy generated by the availability of numerical results (Bryman 2004). Its strength in aggregation, which makes it so valuable in providing an overall measure of a phenomenon, does, however, often also result in a substantial loss of individual detail.

Taking a quantitative approach to secondary data analysis provides a research method well-suited to the major empirical questions the project answers, particularly in the context of an extremely limited and almost entirely qualitative pre-existing empirical literature. Measuring the scope of learner mobility in contemporary urban South Africa – the first major empirical task undertaken in this thesis – is essentially a quantitative question, and requires a quantitative approach. While we already have some information about children travelling to particular schools, and about learner mobility within particular, fairly constrained, communities (Sekete, Shilubane et al. 2001; Fiske and Ladd 2004; Msila 2005; Msila 2009), we don‘t currently have a broader understanding of the scale of this mobility. Qualitative approaches have proved informative in exploring some reasons for learner mobility, as well as documenting the behaviours of individuals, but they cannot give us an overview of overall levels of learner mobility in a major urban area. For this, a

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quantitative approach to the analysis of data drawn from a fairly large sample is required.

Answering questions about the scale of mobility also requires the use of data collected at a population level. To date, the large majority of research on learner mobility has explored the question either by focusing on particular schools, or by making use of a non-representative sample, typically drawn from a fairly geographically constrained area (Sekete, Shilubane et al. 2001;

Fiske and Ladd 2004; Msila 2005; Msila 2009; Hunter 2010). While these approaches provide valuable data, particularly with regards to the causes and implications of the phenomenon, learner mobility appears to be highly clustered around particular schools, and amongst particular groups of people.

This means that any sample that is not drawn to be relatively representative of a fairly sizeable and varied population is unlikely to provide an accurate measure of the overall scope of learner mobility. Unfortunately, collecting data on a relatively representative sample of a substantial population, such as that found in major urban hubs, is an extremely complex and time-consuming process, particularly if data is wanted for more than one point in time. Drawing on a dataset that has already been collected offers a way to gain access to a volume of reasonably representative, high-quality data that could not be otherwise be obtained in the context of a PhD project.

A second major empirical question posed by this thesis relates to the patterns and correlates of learner mobility in contemporary urban South Africa, particularly with respect to socio-economic status. In answering this question, using pre-existing data is particularly valuable, as it allows for access to a wider range of variables, often over a wider interval of time, than would be feasible to collect for a single thesis. In particular, using secondary data provides access to data from a range of different time points. It also allows the researcher to tap into data from a range of different sources, and combine these to enable the exploration of dimensions of the phenomenon that might

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otherwise not be possible. Identifying patterns in learner mobility also requires access to data for a large and reasonably representative sample of individuals.

This provides further support for the use of quantitative secondary data analysis for this project.