Qualitative Analysis of Data
What is Qualitative Analysis of Data?
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 colours, then you would explain the colour differences rather than the heat’s numerical value.
Qualitative research and qualitative data analysis are often used for policy and program evaluation research since it 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.
First, cases for qualitative data analysis can be selected purposefully, according to whether or not 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 transcendental 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.
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. This is because establishing content validity – do measures measure what a researcher thinks they measure? – is seen as one of the strengths of qualitative data analysis. While quantitative methods are seen as providing more representative, reliable and precise measures through focused hypotheses, measurement tools and applied mathematics. By contrast, qualitative data analysis is usually difficult to graph or display in mathematical terms.