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Research Process: Data analysis

This guide gives a full overview of all the aspects of the research process and where to get assistance.

Data analysis

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

The following are common data analysis tools or programmes:

  • ATLAS.ti - Workbench for the qualitative analysis of large bodies of textual, graphical, audio and video data.
  • LISREL - An acronym for linear structural relations. A statistical software package used in structural equation modeling.
  • MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming.
  • Nvivo - A software for gaining richer insights from qualitative and mixed-methods data
  • R (statistics) -  R is a language and environment for statistical computing and graphics.
  • SPSS - Statistical Product and Service Solutions. Among the most widely used programs for statistical analysis in social science.
  • STATISTICA - A statistics and analytics software package developed by StatSoft. STATISTICA provides data analysis, data management, statistics, data mining, and data visualization procedures.
  • Other data analysis software

Also consult Web Pages that Perform Statistical Calculations (StatPages.org)

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.

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/

  • 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.

A short video by Prof Jeff Leeks on the landscape of data analysis.