1. Relevance. Do not blindly follow the data you have collected; make sure your original research objectives inform which data does and does not make it into your analysis. All data presented should be relevant and appropriate to your aims. Irrelevant data will indicate a lack of focus and incoherence of thought.

2. Analysis. Use methods appropriate both to the type of data collected and the aims of your research. Explain and justify these methods with the same rigour with which your collection methods were justified. Identify significant patterns and trends in the data and display these findings meaningfully.

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3. Quantitative work. The quantitative data typical of scientific and technical research, as well as sociological and other disciplines, should be treated with the appropriate statistical measures. With quantitative data, statistical analysis will represent the largest part of your data analysis.

4. Qualitative work. Non-numerical data should not be seen as ‘soft’. Apply the same level of analytical acuity to qualitative information as one would quantitative data. Show evidence of close scrutiny and measurements of reliability, validity and significance.

5. Thoroughness. The data never just ‘speaks for itself’. Thoroughly analyse all data which you intend to use to support or refute academic positions, demonstrating in all areas a complete engagement and critical perspective, especially with regard to potential biases and sources of error.

6. Presentational devices. It can be difficult to represent large volumes of data in intelligible ways. In order to address this problem, consider all possible means of presenting what you have collected. Charts, graphs, diagrams, quotes and formulae all provide unique advantages in certain situations.

7. Appendix. You may find this chapter becoming cluttered, yet feel yourself unwilling to cut down too heavily the data which you have spend so long collecting. If data is relevant but hard to organise within the text, remove it to an appendix. Data sheets, sample questionnaires and transcripts of interviews and focus groups should be placed in the appendix.

8. Discussion. In discussing your data, you will need to demonstrate a capacity to identify trends, patterns and themes within the data. Consider various theoretical interpretations and balance the pros and cons of these different perspectives. Discuss anomalies as well consistencies, assessing the significance and impact of each.

9. Findings. What are the essential points that emerge after the analysis of your data? These findings should be clearly stated, their assertions supported with tightly argued reasoning and empirical backing.

10. Relation with literature. Towards the end of your data analysis, it is advisable to begin comparing your data with that published by other academics, considering points of identity and difference. Are your findings consistent with expectations, or do they make up a controversial or marginal position? Discuss reasons as well as implications.

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