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Research Guide: Data collection techniques

This Guide provides post graduate students with the tips and tools necessary to successfully complete their research.

Google Data Search

Statistical Software Access - contacts and 'how-to'

Various statistical software exists to analyse both quantitative and qualitive data. Examples include R, Stata, SAS, SPSS, EViews, NVivo, Qualtrics and so on.  Here we will provide you with ways to install some of the top analysis tools on your personal computers.

Software on Research Commons computers

R , IBM SPSS and Atlas.ti  are installed on all Hatfield and Groenkloof Research Commons computers. The Veterinary Research Commons has SPSS & ArcGIS installed.

Download Free software on personal computer.

  1. Download R and R studio
  2. Download Tableau Public
  3. Download Python
  4. SAS Statistical Software Download

Procedure for requesting software to install on personal computer.

UP registered students can request software (including To have IBM SPSS, Atlas.ti and Stataon the Service Management Automation portlet on the UP portal by typing the software name on the search tab, then Request Service then follow the prompts.


Past presentations

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Data collection techniques

Under the main three basic groups of research methods (quantitative, qualitative and mixed), there are different tools that can be used to collect data. Interviews can be done either face-to-face or over the phone. Surveys/questionnaires can be paper or web based. Observations and experiments can be conducted to collect either quantitative, qualitative or a mixture of the two methods. Records can also be used to study previous information by other researchers.  


  • Organise collected data as soon as it is available
  • Begin with the end in mind - know what message you want to get across and then collect data that is relevant to the message
  • Collect more data
  • Create more data
  • Regularly run experiments or collect data
  • Challenge your assumptions
  • Set reasonable expectations
  • Take note of interesting or significant data
  • Quantity is good but quality is even better

Recommended Quantitative Data Collection books

Recommended Qualitative Data Collection books