Essential Questions

1. What does data management in a qualitative, descriptive study entail?
2. How does one apply a general set of steps to analyze descriptive data?
3. What are the two main types of data analysis in qualitative, descriptive studies? 

Data Management in Qualitative, Descriptive Research  

All data are documented through maintaining an audit trail of decisions that are preserved in memos throughout the course of the study (Willis, Sullivan-Bolyai, Khafl, & Cohen, 2016). For example, in-depth interviews are conducted, adding or eliminating questions as experiences are shared. At the same time, the researcher begins to look for commonalities and differences in participants’ experiences within and across the data (Sullivan-Bolyai, Knafl, Deatrick, & Grey, 2003). If interviews are the primary data, analysis begins with the first interview. A detailed summary of the first interview is developed, and this development continues for each subsequent interview. Observational data from field notes are included in the summaries for data analysis.

 Willis et al. (2016) offered an example of how raw data were prepared for analysis:

1. Interviews were conducted: Fathers were interviewed about their experiences with using an insulin pump to manage their child’s type 1 diabetes.
2. Descriptive summary was developed: The researcher compiled a descriptive summary of   each interview.
3. Interviews were transcribed verbatim soon after the conclusion of each interview.
4. The researcher verified the transcription with the audiotape.
5. The written transcripts are uploaded as a rich text file into a software program for data management.
6. Field notes, observations, and procedural and personal reflections were
7. incorporated into each uploaded comprehensive data document.

Member or peer checking is often part of this process, whereby the researcher sends the transcripts to participants to review for accuracy and possible corrections. Analysis: initial summary of each interview; read then and code chunks of data; identify similar ideas, experiences, issues (Willis et al., 2016); member checking after analysis, meaning is described “at the level of the obvious” (Willis et al, 2016, p. 1197). Findings are developed into practical recommendations. 

General Data Analysis in Qualitative, Descriptive Research

The goal of qualitative, descriptive data analysis is to make sense of a phenomenon and understand it from the another’s perspective (Magilvy & Thomas, 2009). The authors identified a general overarching approach to data analysis in qualitative, descriptive studies as indicated in Figure 1.

Figure 1. General data analysis process adapted from Magilvy & Thomas (2009). 

Colorafi and Evans (2016) provided a concrete, general example of steps used to analyze data their qualitative, descriptive studies:

1. Prior to collecting data, researchers developed a code book (a priori) containing codes identified from the theoretical framework, the literature review and prior studies on the topic, along with data from field tests.
2. Transcribed documents were developed with wide right margins, so the researcher could read and make comments and apply the a priori codes.
3. Transcripts were coded and the researchers extracted chunks of text (sentences or paragraphs that represented a single idea or one meaning unit.
4. The researcher revisited the theoretical framework and grouped similar codes into categories at a more abstract level, identifying patterns and relationships in the data.
5. The researcher reviewed patterns of categories, revised, and redefined in the coding manual. Examples were identified to clarify the meaning of each category.
6. Analytic memos were developed, describing the researcher’s thought process about the data.
7. Data displays were created (code book, tables, graphs, figures) to help develop a visual understanding of the results.
8. Data report was written.

Two Prevalent Types of Data Analysis in Descriptive, Qualitative Studies

Two main approaches are used for data analysis in descriptive, qualitative research: content analysis, and thematic analysis. However, there are similarities between the two approaches. Viasmoradi, Turunen, and Bondas, (2013) and Neudorf (2019) highlighted the following as attributes the two approaches have in common:

1. Both approaches are appropriate when the researcher wants to employ a relatively low level of interpretation (Viasmoradi, et. al., 2013).     
2. Both approaches consist of analytically examining narrative material from life stories by breaking the text into relatively simple parts (Viasmoradi, et. al., 2013).     
3. Both involve codes and coding (Neudorf, 2019)
4. Both methods also may attempt to tap both manifest (i.e., directly observable variables) and latent (i.e., unobservable constructs) content (Joffe & Yardley, 2004 as cited in Neudorf, 2019).
5. Both methods should be applied only after research questions or hypotheses have been forwarded, to appropriately guide the analysis (Neudorf, 2019).
6. Both methods are taxing and time consuming (Neudorf, 2019).

When doing content analysis, the researcher analyzes data qualitatively, but also quantifies the data by counting frequencies of words during the coding process and interpreting the quantitative counts of those codes (Viasmoradi, et. al., 2013). Conversely, thematic analysis provides a detailed, contextual version of data (Braun & Clarke, 2013). The key attributes of each type of analysis are presented in Module 6. While these two approaches can be very complex, the following outlines are intended to provide a definition, characteristics of a step-by-step approach to data analysis.

References

Clarke, V. and Braun, V. (2013) Teaching thematic analysis: Over-coming challenges and developing strategies for effective learning. The Psychologist, 26 (2). pp. 120-123. ISSN 0952-8229

Neuendorf, K. A. (2019). Content analysis and thematic analysis. In P. Brough (Ed.), Research methods for applied psychologists: Design, analysis and reporting (pp. 211-223). New York: Routledge

Sullivan-Bolyai, S.; Knafl, K, Tamborlane, W. & Grey, M. (2004). Parents' reflections on managing their children's diabetes with insulin pumps; Journal of Nursing Scholarship, 36 (4), pg. 316-23.

Willis, D. G., Sullivan-Bolyai, S., Khafl, K. and Cohen, M. (2016). Distinguishing features and similarities between descriptive phenomenological and qualitative description research. Western Journal of Nursing Research. 38(9), 1185–1204