Qualitative data analysis

In simplified terms, qualitative refers to a non-numerical data collection or explanation based on the attributes of the graph or source of data.
Ivana Radivojevic
Manager for International Projects, Training & Partnership Development
  1. What is Qualitative Data Analysis?
  2. Overview: Qualitative Data Analysis Methods
  3. Further information

What is Qualitative Data Analysis?

In simplified terms, qualitative refers to a non-numerical data collection or explanation based on the attributes of the graph or source of data. For example, if you are asked to explain in qualitative terms a thermal image displayed in multiple colors, then you would explain the colour differences rather than the heat’s numerical value.

If you got a large amount of data sets, e. g. of group discussions or observations of real life situations, the next step is to transcribe and analyze your data.

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It can be beneficial to preface a quantitative research study with a qualitative study to address issues that were not originally envisioned but are important to the study.

Qualitative research and qualitative data analysis are often used for policy and program evaluation research since they can answer certain important questions more efficiently and effectively than quantitative approaches.

This is particularly the case for understanding how and why certain outcomes were achieved (not just what was achieved) but also answering important questions about relevance, unintended effects and impact of programs, such as:

  • Were expectations reasonable?
  • Did processes operate as expected?
  • Were key players able to carry out their duties?
  • Were there any unintended effects of the program?

Qualitative approaches have the advantage of allowing for more diversity in responses, as well as the capacity to adapt to new developments or issues during the research process itself. While qualitative analysis of data can be expensive and time-consuming to conduct, many fields of research employ qualitative techniques that have been specifically developed to provide more succinct, cost-efficient and timely results.

Figure 1: ATLAS.ti network

First, cases for qualitative data analysis can be selected purposefully, according to whether they typify certain characteristics or contextual locations.

Secondly, the role or position of the researcher in qualitative analysis of data is given greater critical attention. This is because in qualitative data analysis the possibility of the researcher taking a ‘neutral’ or transcendent position is seen as more problematic in practical and/or philosophical terms.

Hence, qualitative researchers are often exhorted to reflect on their role in the research process and make this clear in the analysis.

Thirdly, while qualitative data analysis can take a wide variety of forms it tends to differ from quantitative research in the focus on language, signs and meaning as well as approaches to analysis that are holistic and contextual, rather than reductionist and isolationist.

Nevertheless, systematic and transparent approaches to the analysis of qualitative data are almost always regarded as essential for rigor. For example, many qualitative research methods require researchers to carefully code data and discern and document themes in a consistent and reliable way.

Figure 2: Code network in ATLAS.ti

Perhaps the most traditional division in the way qualitative and quantitative research have been used in the social sciences is for qualitative methods to be used for exploratory (i.e., hypothesis-generating) purposes or explaining puzzling quantitative results, while quantitative methods are used to test hypotheses. The reason for this is to establish content validity – are the measures measuring what the researcher believes they are measuring? – This is considered one of the strengths of qualitative data analysis. Quantitative methods are seen as providing more representative, reliable and precise measures through focused hypotheses, measurement tools and applied mathematics, while qualitative data is usually difficult to graph or display in mathematical terms.

Overview: Qualitative Data Analysis Methods

Content Analysis

Content Analysis is a structured qualitative method for evaluating data. It refers to the process of categorizing verbal or behavioral data to classify, summarize and tabulate.

Narrative Analysis

In the narrative method, the researcher reviews what was said by the interviewee and restates it in context.

Grounded Theory

Grounded theory is a research approach. It is not a single theory, but a methodology for finding one. The starting point is a set of data, the analysis of which suggests the nature and direction of further data to be collected.

Interpretive Phenomenological Analysis (IPA)

Interpretive Phenomenological Analysis is an approach to psychological qualitative research with an ideographic focus, meaning that it aims to offer insights into what a particular person in a particular context means by a particular phenomenon.

Discourse Analysis

Discourse analysis is a method for analyzing naturally occurring conversations and different types of written texts in a social context.

Thematic Analysis

Thematic Analysis is defined as a method for identifying, analyzing and documenting themes in a data set.

ATLAS.ti software offers solutions for comprehensive questions in qualitative data analysis.

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