Research Methods Guide: Data Analysis

Tools for Analyzing Survey Data

  • Excel
  • R (open source)
  • SAS 
  • SPSS 
  • Stata 
  • DataCracker (free up to 100 responses per survey)
  • SurveyMonkey (free up to 100 responses per survey)

Tools for Analyzing Interview Data

  • AQUAD (open source)
  • R (open source)
  • NVivo 
  • TAMS 
  • Dedoose
  • ATLAS.ti

Data Analysis and Presentation Techniques that Apply to both Survey and Interview Research

  • Create a documentation of the data and the process of data collection.
  • Analyze the data rather than just describing it - use it to tell a story that focuses on answering the research question.
  • Use charts or tables to help the reader understand the data and then highlight the most interesting findings.
  • Don’t get bogged down in the detail - tell the reader about the main themes as they relate to the research question, rather than reporting everything that survey respondents or interviewees said.
  • State that ‘most people said …’ or ‘few people felt …’ rather than giving the number of people who said a particular thing.
  • Use brief quotes where these illustrate a particular point really well.
  • Respect confidentiality - you could attribute a quote to 'a faculty member', ‘a student’, or 'a customer' rather than ‘Dr. Nicholls.'

Survey Data Analysis

  • If you used an online survey, the software will automatically collate the data – you will just need to download the data, for example as a spreadsheet.
  • If you used a paper questionnaire, you will need to manually transfer the responses from the questionnaires into a spreadsheet.  Put each question number as a column heading, and use one row for each person’s answers.  Then assign each possible answer a number or ‘code’.
  • When all the data is present and correct, calculate how many people selected each response.
  • Once you have calculated how many people selected each response, you can set up tables and/or graph to display the data.  This could take the form of a table or chart.
  • In addition to descriptive statistics that characterize findings from your survey, you can use statistical and analytical reporting techniques if needed.

Interview Data Analysis

  • Data Reduction and Organization: Try not to feel overwhelmed by quantity of information that has been collected from interviews- a one-hour interview can generate 20 to 25 pages of single-spaced text.   Once you start organizing your fieldwork notes around themes, you can easily identify which part of your data to be used for further analysis.
  • Conceptualization: Try to categorize the data into concepts -  you can begin this process asking questions such as:
    • What were the main issues or themes that struck you in this contact / interviewee?"
    • Was there anything else that struck you as salient, interesting, illuminating or important in this contact / interviewee? 
    • What information did you get (or failed to get) on each of the target questions you had for this contact / interviewee?
  • Connection of the data: You can connect data around themes and concepts - then you can show how one concept may influence another.
  • Examination of Relationships: Examining relationships is the centerpiece of the analytic process, because it allows you to move from simple description of the people and settings to explanations of why things happened as they did with those people in that setting.