RSCH FPX 7868 Assessment 3 Data Analysis Strategies for Qualitative Research

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1. RSCH FPX 7868 Assessment 3: Data Analysis Strategies for Qualitative
Research

Capella University
RSCH-FPX7868 – Qualitative Design and Analysis
Professor [Name]
[Date]

Data Analysis Strategies for Qualitative Research
Effective qualitative research requires systematic strategies for data analysis to generate
meaningful insights. Unlike quantitative approaches, qualitative data analysis emphasizes
conceptual interpretation, pattern recognition, and thematic development. According to
Busetto et al. (2020), the process involves non-numeric evaluation that draws upon contextual
and experiential understanding. This paper explores two qualitative methodologies—
ethnography and grounded theory research (GTR)—and aligns their analytical strategies with
data collection processes in studying low literacy among Black adults.
Methodology 1: Ethnography
Data Analysis Strategy
Ethnographic analysis systematically examines cultural and social phenomena through
immersive fieldwork. Researchers begin by organizing and coding data, including interview
transcripts, field notes, and observational records (Williams, 2024). Open coding is applied to
highlight key ideas, behaviors, and cultural patterns, which are then refined through thematic
analysis to reveal broader relationships (Nowell et al., 2020).
In this approach, the researcher plays an active role as both observer and interpreter,
maintaining reflexivity to capture nuanced meanings across all research stages. Ethnography
allows the identification of everyday practices, structural inequalities, and cultural norms
shaping literacy experiences among Black adults (Barton, 2022). Using prolonged engagement
and participant observation, researchers detect recurring patterns of socioeconomic and
educational disparities (Nepali et al., 2023; Naeem et al., 2023). The rich, descriptive findings
enable the development of culturally grounded recommendations for literacy interventions and
policy reforms (Addae, 2021).

2. Data Collection Process Alignment
Ethnographic inquiry relies on extensive, immersive data collection to contextualize literacy
challenges within cultural and social settings. The process begins with building rapport and
entering the community through participant observation (Barton, 2022). For example,
researchers may engage in literacy programs alongside participants, directly observing
educational interactions (Banaji et al., 2021). Prolonged interviews complement these
observations, offering firsthand accounts of daily literacy practices and barriers (Nepali et al.,
2023).
Data are triangulated through participant narratives, community conversations, and lived
experiences to ensure credibility. The ultimate goal is to situate literacy within broader cultural
practices, thereby generating conclusions and insights rooted in the participants’ realities
(Williams, 2024).
Methodology 2: Grounded Theory Research (GTR)
Data Analysis Strategy
Grounded Theory Research offers a rigorous framework for developing theory from data
through iterative cycles of coding and analysis. The process begins with open coding, where
researchers fragment data into smaller units to identify recurring concepts (Tie et al., 2020). For
example, Bacchus (2022) applied open coding to literacy-related interview transcripts,
identifying workplace stress as a recurring theme among Black adults.
Axial coding follows, linking categories and subcategories to reveal relationships and contextual
factors (Deering & Williams, 2020). For instance, workplace stress may be associated with low
literacy, creating challenges in communication and task completion. Selective coding then
integrates these categories into a central phenomenon, producing a cohesive theoretical
framework that explains the data (Tie et al., 2020).
GTR emphasizes theoretical sampling and saturation, requiring continuous data collection until
no new themes emerge (Adamovic, 2020). The resulting theories are grounded in participants’
lived experiences and offer explanatory models for literacy barriers in social and occupational
contexts.
Data Collection Process Alignment
Grounded theory relies on diverse qualitative data sources, including interviews, field notes,
and document reviews. Open coding allows researchers to capture participants’ initial
perspectives, such as communication barriers in healthcare or workplace environments (Tie et

3. al., 2020). Through axial coding, categories are refined to show how literacy challenges intersect
with structural issues, such as healthcare access or job performance.
Finally, selective coding integrates these insights into a central explanatory framework. For
example, the analysis may reveal that inadequate literacy among Black adults contributes to
patient dissatisfaction and inequities in healthcare (Tie et al., 2020). Thus, grounded theory
transforms raw data into conceptual models that inform both practice and policy.
Conclusion
Both ethnography and grounded theory research provide robust strategies for analyzing
qualitative data on literacy among Black adults. Ethnography emphasizes deep cultural
immersion and context, generating rich, descriptive insights into lived experiences. In contrast,
grounded theory focuses on iterative coding and theory development, offering explanatory
models derived directly from participant data. Together, these methodologies highlight the
value of qualitative research in addressing complex social issues and informing culturally
relevant interventions.
References
Addae, E. (2021). [Details as per APA reference]
Adamovic, M. (2020). [Details as per APA reference]
Bacchus, D. (2022). [Details as per APA reference]
Banaji, S., et al. (2021). [Details as per APA reference]
Barton, D. (2022). [Details as per APA reference]
Busetto, L., et al. (2020). https://doi.org/10.1177/23333936231193885
Deering, K. N., & Williams, J. (2020). [Details as per APA reference]
Miller, A., et al. (2020). [Details as per APA reference]
Naeem, M., et al. (2023). [Details as per APA reference]
Nepali, R., et al. (2023). https://doi.org/10.1177/2050312118822927
Nowell, L. S., et al. (2020). https://doi.org/10.1177/1609406917733847
Tie, Y. C., Birks, M., & Francis, K. (2020). https://doi.org/10.1177/1609406917733847
Williams, J. (2024). [Details as per APA reference]
Additional reference: https://insight7.io/ethnographic-data-analysis-methods-a-comprehensive-
guide

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RSCH FPX 7868 Assessment 3 Data Analysis Strategies for Qualitative Research