Your objectives when collecting data should be:
- To obtain original data first-hand through a number of methods where you communicate directly with one or more people who are the source of the required information (primary data)
- To collect and organise in original ways data obtained by others in sources such as financial reports and articles produced by and about the businesses in question (secondary data)
Methods of obtaining primary data include:
- written exchanges electronically and in letter form between you and respondents
You should also collect secondary data produced within and about the organizations being studied. This data will have been collected and used for purposes other than those of your project, so the challenge is for you to sift, select and sort the data in ways that match your research aims. Sources of secondary data typically comprise:
- reports and accounts of the subject organizations
- publications put out by them, including publicity material
- reports about them written by business analysts and other academics
- general internet data (with suitable caution in selecting reputable sources)
You can obtain statistical data from a wide variety of public and private sources, although your choices will be easier to validate and explain if they are from large international organizations such as the World Bank or the Organisation for Economic Cooperation and Development (OECD).
In terms of data analysis, you can analyse primary and secondary data by drawing on established analytical tools. Different analytical tools may be used for different research methodologies. For example, as qualitative research mainly involves textual analysis of orally communicated data, analytical tools may be drawn from discursive methods, including narrative analysis, to study speech in the recorded text. Sophisticated data analysis software such as Nvivo-9 may be used for particular qualitative data analysis functions like coding and thematic analysis.
Statistical analysis in both qualitative and quantitative research may draw on a range of elementary and advanced tools to test relationships between variables. Selection of suitable statistical tools depends on the complexity of the relationships between variables that are being tested, although in your project elementary statistical tools will usually suffice for data analysis.
Larger datasets, for example of financial data, are often analysed with software programs like SPSS (Statistical Package for Social Sciences) that can apply both elementary (F and t-tests, ANOVA) and advanced (multivariate regression analysis, forecasting) statistical tools.
Drawing on a range of statistical techniques can increase the sophistication of findings in your project, where selected techniques should directly test specified hypotheses.