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This is the "Analyse data" page of the "The research process" guide.
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This Libguide provides a systematic guide to the different phases and activities of a master's or doctoral research project and introduces the researcher and research student to relevant Library sources, tools and services offered along the way.
Last Updated: Jun 6, 2017 URL: http://libguides.sun.ac.za/researchprocess Print Guide RSS Updates

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Data analysis: what is it?

Data analysis refers to the inspection of results to determine any relationships between concepts, constructs or variables; to identify patterns or trends; or to establish themes in the data. 

Regardless of whether the data is qualitative or quantitative, analysis may: 

  • describe and summarise the data
  • identify relationships between variables
  • compare variables
  • identify the difference between variables
  • forecast outcomes

Source:
Baxter, L., Hughes, C. & Tight, M. 2010. Ch.8: Preparing to analyse your data. How to research. Berkshire: Mcgraw-Hill. 211-226

 

Qualitative analysis

Common features in qualitative analysis include:

  • Looks further than precise numerical evidence
  • Looks for categories such as events, descriptions, comments, behaviour
  • An inductive process - developing theories from the data you have gathered
  • Coding of categories and sub-categories identified
  • Compares codes, looking for consistencies, differences, patterns etc
  • Looks for new and emerging categories.

Analysis can be done manually or with the assistance of Computer Aided Qualitative Data Analysis (CAQDAS) tools. Examples of these are NVivo and Atlas-ti.

Sources:
Manchester Metropolitan University & Learn Higher. 2008. Analyse This!!! Learning to analyse data. [Electronic]. Available at:  http://learnhigher.ac.uk/analysethis/main/qualitative7.html. Accessed: 26 July 2013

Pell Institute for the Study of Opportunity in Higher Education 2013. Research Toolkit [Electronic]. Available at: http://toolkit.pellinstitute.org/evaluation-guide/analyze/. Accessed: 26 July 2013

 

Quantitative analysis

Common features of quantitative analysis include:

  • Focuses on gathering numerical data and generalising it across groups of people
  • Data are in the form of numbers and statistics
  • There are several approaches to quantitative research which include experimental, descriptive, correlational and causal comparison. Inferential statistics are frequently used to generalise what is found about the study sample to the population as a whole
  • Sampling bias is important in determining how generalisable the results are. The type of statistical analysis you do, will depend on the sample type you have
  • Software for Statistical Analysis include: Excel, SPSS, SAS, R – Freeware, SPlus

Sources:
Sibanda, N. 2009. Quantitative Research. Victoria University [Electronic]. Available: http://www.victoria.ac.nz/postgradlife/downloads/quantitative%20seminar18Aug09.pdf [Accessed: 26 July 2013]

Hesketh, E.A. & Laidlaw, J.M. Quantitative Research [Electronic]. Available at: http://www.nes.scot.nhs.uk/nes_resources/ti/QuantativeResearch.pdf [Accessed: 26 July 2013]

Manchester Metropolitan University & Learn Higher. 2008. Analyse This!!! Learning to analyse data. [Electronic]. Available at:  http://learnhigher.ac.uk/analysethis/main/qualitative7.html

 

Methods of analysis

The type of analysis you do is generally dependent on whether your data is quantitative or qualitative. Methods of analysis may also differ by scientific discipline. The optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought.

Image source: http://9mathsmrsrae.edublogs.org/

 

Interpretation

In order to effectively interpret your observations you need to relate it to existing theoretical frameworks, take rival explanations into account and explain how the data supports your interpretation. Interpretations are articulated in the form of hypotheses or theories.

Source:
Mouton, J. 2001. How to succeed in your master's & doctoral studies: a South African guide and resource book. Pretoria: Van Schaik.

 

Visualisation

Visualisation is the process of applying advanced computing techniques to data in order to provide insight into the underlying structures, relationships, and processes. As such, visualisation finds applications across a wide range of disciplines especially those with datasets that are large or contain complicated relationships. Bernstein (2013) offers tips and tools at: http://memeburn.com/2013/07/tips-tricks-and-tools-for-data-visualisation-15-of-the-best-tools-for-the-job/. [Accessed: 26 July 2013]

Source:
iVec.n.d. [Electronic]. Available at:  http://www.ivec.org/services/visualisation/. Accessed: 26 July, 2013

 

Common errors in data analysis

  • Using inappropriate statistical techniques in quantitative analysis
  • Drawing inferences from data that are not supported by the data
  • Biased interpretation of the data through selectivity

Source:
Mouton, J. 2001. How to succeed in your master's & doctoral studies: a South African guide and resource book. Pretoria: Van Schaik.

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