Step 8: Data Analysis

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: 



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:



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:



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