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Step 8: Data Analysis

Page history last edited by Caroline Storer 7 years, 9 months ago

HEALER Research Toolkit



Step 8: Analyse the Data and Interpret Findings 


Your method of data analysis will depend upon the type of data you have collected. For this reason you should have already considered what your options are for data analysis even before you have begun to collect your data. If your data can be easily reduced to categories and counts then you will be interested in using quantitative data analysis. For example multiple choice, tick box and yes/no options in a questionnaire can all be analysed quantitatively. If, however, your data require analysis for themes and sub-themes then qualitative data analysis will be more useful. For example the transcript of a semi-structured interview or a focus group allows respondents to expand on answers with themes of their own concern.  Typically there will be elements of both types of analysis, the so-called "mixed methods" approach. For example, a questionnaire that requires selection of multiple choice options (which can subsequently be easily quantified) may conclude with a free-text option e.g. "Any Other Comments". These may be analysed qualitatively. 


You may find it helpful to distinguish between how you are going to compile your data and how subsequently you are going to analyse it. For example transcripts may be initially compiled in Microsoft Word but analysed using a specialist qualitative data analysis package. Quantitative questionnaires may be compiled using Microsoft Excel and coded upon entry so that "Yes" becomes "1"; "No" becomes "2" and "No Response" becomes "0" (and similarly for multiple choice responses). Some researchers have found it helpful, particularly when analysing a mixed methods questionnaire, to input all data into a single entry form using Microsoft Access or Microsoft Excel and then to export the quantitative responses to one specialist package and the qualitative data to another specialist package. Obviously transferability between packages becomes a key consideration when planning your analysis.   


Quantitative Data Analysis


Quantitative data analysis refers to the numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect. For this reason quantitative researchers tend to use deductive analysis of data, meaning that a framework is used to explore the data and subsequently either to accept or to reject a hypothesis (Patton, 1990).  


Quantitative research techniques generate a mass of numbers that need to be summarised, described and analysed. 


Characteristics of the data may be described and explored by drawing graphs and charts, doing cross tabulations and calculating means and standard deviations. 


Further analysis will build on these initial findings, seeking patterns and relationships in the data by comparing means, exploring correlations, performing multiple regressions, or analyses of variance. 


Advanced modelling techniques may eventually be used to build sophisticated explanations of how the data addresses the original question. 


Although methods used can vary greatly, the following steps are common in quantitative data analysis: 


  • Identifying a data entry and analysis manager (e.g. SPSS) 
  • Reviewing data (e.g. surveys, questionnaires etc) for completeness 
  • Coding data 
  • Conducting data entry 
  • Analysing data (e.g. sample descriptives, other statistical tests)

Qualitative Data Analysis 


Qualitative data analysis has been defined as "working with data, organizing it, breaking it into manageable units, synthesizing it, searching for patterns, discovering what is important and what is to be learned, and deciding what you will tell others" (Bogdan and Biklen 1982, p. 145). For this reason qualitative researchers tend to use inductive analysis of data, meaning that the critical themes emerge out of the data (Patton, 1990). 


Qualitative data analysis describes and summarises the mass of words generated by interviews or observational data.  It allows researchers to seek relationships between various themes that have been identified or relate behaviour or ideas to biographical characteristics of respondents.  Implications for policy or practice may be derived from the data, or interpretation sought of puzzling findings from previous studies.  Ultimately theory could be developed and tested using advanced analytical techniques. 


Although methods of analysis can vary greatly (e.g. grounded theory, discourse analysis) the following steps are typical for qualitative data analysis:


  • Familiarisation with the data through repeated reading, listening etc. 
  • Transcription of interview etc. material 
  • Organisation and indexing of data for easy retrieval and identification (e.g. by hand or computer programs such as NVIVO - formerly Nud*ist)
  •  Anonymising of sensitive data 
  • Coding (may be called indexing) 
  • Identification of themes. 
  • Development of provisional categories 
  • Exploration of relationships between categories 
  • Refinement of themes and categories 
  • Development of theory and incorporation of pre-existing knowledge 


For more information see 'Qualitative Research' from East Midlands RDS.


Interpreting Data


The last step of data analysis consists of interpreting the findings to see whether they support your initial study hypotheses, theory or research questions.  Data interpretation methods vary greatly depending on the theoretical focus (i.e., qualitative or quantitative research) and methods (e.g. multiple regression, grounded theory).


You should seek further advice for this step from:


  • Your supervisor/Other experts within your organization
  • Computer Package Manuals (e.g., SPSS, Nvivo) and methodology books
  • Statistics in Research from East Midlands RDS
  • The material in Step 3 on Design the Study and Develop Your Methods, particularly the section on statistics and sampling issues


Suggested Reading


  • Bogdan, R. C., & Biklen, S. K. (1982). Qualitative research for education: An introduction to theory and methods. Boston: Allyn and Bacon, Inc.
  • Electronic resources for research methods. InformationR.net, n.d. accessed 15/5/2013
  • Eldredge J (2004). Inventory of research methods for librarianship and informatics. Journal of the Medical Library Association 92(1) 83-90.
  • Library Trends, Summer 2006: theme issue on LIS research. Gorman GE, Clayton P (2005). Qualitative research for the information professional: a practical handbook, 2nd ed. London : Facet.
  • ibec. The Outcomes Toolkit 2.0. STEP THREE: Analyzing Data. 
  • Lawal, I (2009). Library and information science research in the 21st century: a guide for practicing librarians and students. Cambridge: Woodhead.
  • Liebscher, P (1998) Quantity with quality? Teaching quantitative and qualitative methods in an LIS master’s program. Library Trends 46(4) 668-680.
  • Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Newbury Park, CA: Sage Publications, Inc.
  • Pickard AJ (2007). Research methods in information. London: Facet.
  • Powell, RR., and Connaway LS. 2004. Chapter 9: Analysis of Data In: Basic research methods for librarians. 4th ed. Westport, CT: Libraries Unlimited. 
  • Vaughan, L (2001). Statistical methods for the information professional: a practical, painless approach to · understanding, using, and interpreting statistics. Medford, NJ: Information Today.
  • Books on data analysis and interpretation from the reading list from the University of Leeds' School of Medicine's Health Research course MEDR 5110M Module 5: Handling Data in Research.

Comments (2)

Catherine Ebenezer said

at 10:04 am on Nov 9, 2010

Link for last bullet point is http://lib5.leeds.ac.uk/rlists/broker/index.php?mod=MEDR5110M&sess=200809&arc=host_vlebb

Could a link be added to the Eldredge article? It is from the JMLA and should be stable.

Caroline Storer said

at 9:38 am on Jul 17, 2014

reviewed 17/07/2014

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