Step 6 Process and Record Data Immediately As soon as data is collected it is critical that you immediately process the information and record detailed notes. These notes could include:
Positioning Methods in Qualitative Research Processes 1 The field of qualitative research encompasses a wide range of aims and methods which are only weakly related to each other. This makes choosing the best method for a specific project a difficult and risky decision.
The aim of this article is to support the choice of methods for qualitative data analysis for one type of research aims—the search for causal mechanisms. To this end, we compare the suitability of two of the most widespread methods of qualitative data analysis, namely coding and qualitative content analysis.
With Miles and huberman 1994 comparison, we would like to contribute to a qualitative research methodology that systematically links types of research problems to methods. First, there is a great variety of types of research goals of qualitative research.
Its empirical objects include phenomena from all levels of aggregation and include individual constructions of meaning as well as societal processes, and its ambitions range from describing empirical phenomena to providing theoretical explanations.
Second, protagonists of methods are often reluctant to link their methods to specific types of goals, which would include characterizing not only the input but also the output of the methods.
The enormous variation between the approaches, their partial overlap, and the breadth of legitimate research goals in qualitative research make it impossible to construct a framework in which all methods can be located. Methods are usually described without any reference to other methods.
This is not surprising because qualitative research places heavy emphasis on interpretation. Interpretation is an ill-structured activity for which no algorithm can be provided.
At the same time, the widespread reluctance to define intermediary steps and their outputs makes it often difficult to assess the contribution of a specific method along the way from texts to answers to research questions and the quality of that contribution.
First, the comparison will lead to a better understanding of the logic underlying the first steps of qualitative data analysis for causal explanations. Second, we present an alternative to coding that achieves a stronger reduction and structuration of the data during these first steps, and therefore might be more appropriate for some qualitative research processes.
Third, our comparison provides at least some ideas about the range of applicability of methods, thereby contributing to the badly needed methodological background that tells us what to use which qualitative method for.
This is an enormous task that has not yet enjoyed much attention. We will address it only briefly to the extent to which clarification is necessary for the purposes of this article.
Thus, we will establish theoretical explanations that use the identification of causal mechanisms as one type of research goals of qualitative research Section 2discuss the steps that need to be taken in order to reach this goal Section 3position both coding Section 4 and qualitative content analysis Section 5 in this sequence of steps.
Our comparison demonstrates that while coding is often focused on the earliest stage of data analysis, both coding and qualitative data analysis can be used equivalently because they both lead to a data base that is used in the subsequent search for patterns in the data Section 6.
Theoretical Explanation as an Aim of Qualitative Research The methodological discussion on data analysis is characterized by a strange division of labor. One strand in this discussion is concerned with the question how causal arguments can be made with qualitative data. This strand largely ignores the question how data should be created and processed in order to best support such analyses.
The current discussion on "causality" and "comparative case studies" just assumes that the data are there, i. No requirements concerning data collection or data analysis are formulated in the various suggestions for producing theories with case studies.
When research goals of qualitative sociological research are mentioned, they remain highly abstract and sufficiently vague to suggest that methods for the analysis of qualitative data are only weakly associated with different types of research goals. Theory building does not occur in this list and generally appears to play a minor role.
The only methodological context in which it is systematically treated is that of the grounded theory approach that builds new theory from empirical data e. Doing this systematically requires a whole research program.
For the purposes of this article, we take one of the goals of social science research—causal explanation—as a desired outcome of qualitative data analysis, and work our way backwards from this outcome towards the first steps.
This way we can chart one way from start to finish without having to take into account all other possible outcomes and ways towards them.
For our purposes, it is sufficient to assume that at least some strategies of qualitative research will be covered by the frame suggested here.
We thus can leave the classification of research goals and the systematic comparison between types of goals and sequences of steps from data to answers to research questions to future work.
Some of these approaches use the co-occurrence of causes and effects, e. The major difference between QCA and statistical approaches based on covariation is that the former is applied to cases that are described by dichotomous or fuzzy values of variables, and uses Boolean logic rather than statistical reasoning.
QCA has interesting problems of its own, which we cannot discuss here see e. These approaches attempt to go beyond cause-effect relationships by providing "full" explanations that describe how the effects are produced. A "mechanismic" explanation does not leave any black boxes between causes and effects.Miles and Huberman () constructed an array of visual displays (i.e., n = 19) for one case at a time (i.e., within-case displays).
According to these authors, within-case displays.
Grounded theory was developed by Glaser and Strauss who believed that theory could emerge through qualitative data analysis. In grounded theory, the researcher uses multiple stages of collecting, refining, and (Miles & Huberman, ).
Data reduction is a form of analysis that can be used to combine pieces of information into categories. Miles and huberman thematic analysis essay, essays nancy jaax pictures essay about a personal quality talent why i took a gap year essay argumentative essays helpme chapelle sixtine judgement dernier descriptive essay ptsd persuasive essay my village essay in sanskrit language importance of transport and communication essays kawania essay.
). Data tend to be analyzed through an inductive, ongoing and evolving process of identifying themes within a particular context (Miles & Huberman, ). Chapter 4, “Analyzing Qualitative Data,” provides a detailed description of qualitative analysis based on the Miles and Huberman () three-step process (1.
data reduction, 2. data display, and 3. conclusion drawing and verification). Review: Michael Huberman & Matthew B. Miles (Eds.) (). The Qualitative Researcher's Companion "Qualitative Data Analysis," first published in and updated in The new companion volume offers a set of readings that includes some of the outstanding contributing names in social science research.
There are chapters from EISENHARDT.