RSCH FPX 7868 Assessment 3 Sample FREE DOWNLOAD
RSCH FPX 7868 Assessment 3 Data Analysis Strategies for Qualitative Research
Student Name
Capella University
RSCH-FPX7868 – Qualitative Design and Analysis
Professor Name
Date
Data Analysis Strategies for Qualitative Research
Practical data analysis approaches are required to identify research outcomes. According to Busetto et al. (2020), qualitative research data analysis requires interpreting conceptual and non-numerical data. The research explores low literacy comprehension among Black adults using ethnographic and grounded theory data collection approaches within qualitative frameworks. The research results will be used to develop educational interventions and policy changes to enhance literacy achievements.
Methodology 1: Ethnographic
Data Analysis Strategy
| Data Analysis Strategy | Source |
|---|---|
| Systematic examination of cultural phenomena requires researchers to complete ethnographic data analysis by conducting fieldwork within the studied community. | — |
| Data organization is the initial step because researchers convert interview recordings, field notes, and additional materials into formatted data for coding and analysis purposes. | (Williams, 2024) |
| Researchers use open coding to detect important notions and situations embedded in the data, which they confirm through reliability analysis. | (Williams, 2024) |
| An interpretation process occurs next as researchers use thematic analysis to reveal connections between assigned codes before generating meanings that matter for the identified themes. | (Nowell et al., 2020) |
| The researcher must maintain awareness about the effect of the proposed study throughout the research process. | — |
| Ethnographers researching black adults’ low literacy perceptions interact with relevant communities to observe daily interactions and conduct open-ended interviews. | (Barton, 2022) |
| Researchers could use open coding methods to detect repeated patterns consisting of socioeconomic difficulties and educational inequality distributions. | (Nepali et al., 2023) |
| The analysis discovered that historical components and common systemic hurdles exist, deepening the comprehension of the literacy gap. | (Naeem et al., 2023) |
| The researcher can deliver contextual insights into how black adults handle low literacy by studying patterns within the cultural and social environment. | (Addae, 2021) |
| The methodology provides a complete perspective on data through participant feedback, generating essential recommendations for practice and policy implementation. | — |
Data Collection Process Alignment
Ethnographic research relies on deep data collection methods to understand cultural and social practices within specific communities. The process begins with the primary research method, participant observation, during which researchers become actively engaged with the daily activities of the studied group (Barton, 2022). Black adult literacy research requires the direct involvement of researchers in community literacy programs since they observe educational interactions occurring between members (Banaji et al., 2021). Research using ethnographic methods enables professionals to acquire truthful findings about community literacy practices and cultural values.
After utilizing the participant observation method in ethnography, ethnographers conduct extensive interview sessions to collect individual narratives and knowledge (Nepali et al., 2023). Interview data showcases the life experiences of black adults to create literacy knowledge for analysis purposes (Miller et al., 2020). Finally, academic researchers use research documents about historical community communication to study contemporary literacy practices in communities (Williams, 2024). Ethnographic research methods help investigators maintain distinct cultural elements and social contexts to develop findings that value community insights.
Methodology 2: Grounded Theory Research
Data Analysis Strategy
| Step / Process | Data Analysis Strategy |
|---|---|
| Data Collection Purpose | The data collection process in Grounded Theory Research (GTR) produces new theoretical guidance through research data, bypassing the hypothesis confirmation requirement. |
| GTR Execution | The execution of GTR uses data collection phases followed by open coding, axial coding, and selective coding to produce theory development (Tie et al., 2020). |
| Initial Data Collection | Qualitative data gathered from interviews and observational methods alongside the examination of documents serves as the research’s opening data collection approach. |
| Open Coding | After data collection ends, researchers start open coding to segment data into distinct units and apply thematic codes that emerge directly from the gathered information (Deering & Williams 2020). |
| Low Literacy Coding Example | Bacchus (2022) implemented low literacy codes on interview transcript sections to detect repeated themes within the study of work-related stress among Black adults. |
| Transition to Axial Coding | The next step is open coding before transitioning to axial coding to establish connections between newly generated codes, which develop data pattern categories (Tie et al., 2020). |
| Stress Factor Analysis | The stress-related work study merges work pressure indicators within the workforce while investigating insufficient work support resources to generate stress factor categories (Bacchus, 2022). |
| Selective Coding | Selective coding unites the developed categories to create an organized explanation of the research’s central phenomenon (Tie et al., 2020). |
| Theoretical Sampling | Researchers obtain supplementary data to enrich categories through theoretical sampling until the research reaches theoretical saturation (Adamovic, 2020). |
| Theory Development | Through continuous iteration, researchers establish a theory based on data representing the studied subject matter. |
Data Collection Process Alignment
Using a qualitative research approach, the grounded theory produces theoretical models directly derived from observational data. During GRT data analysis, researchers carry out an iterative sequence of key stages. Primary qualitative data collection begins with researcher methods such as interviews, observations, or document analysis (Tie et al., 2020). Each section of the empirical data goes through open coding followed by segmentation, representing distinct concepts and ideas obtained from the data. The open coding method would discover communication challenges in an example study about patient healthcare experiences.
Researcher applications start with axial coding when investigating the previously obtained themes by determining the relationships and patterns throughout the analysis. Through axial coding, researchers learn how separate groupings connect (Tie et al., 2020). Through analysis of health care data, researchers using axial coding discovered that poor reading abilities among Black adults commonly result in unsatisfied patients. Selective coding represents the last stage, which involves identifying the core category and synthesizing the data to develop a substantive theory through a coherent (Tie et al., 2020). In the example, the core category could be factors influencing patient satisfaction, with the theory explaining how various factors like communication and support contribute to overall satisfaction.
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References For RSCH FPX 7868 Assessment 3
Adamovic, M. (2020). Analyzing discrimination in recruitment: A guide and best practices for resume studies. International Journal of Selection and Assessment, 28(4), 3–7. https://doi.org/10.1111/ijsa.12298
Addae, D. (2021). Adults who learn: Evaluating the social impact of an adult literacy project in rural South Africa. Social Sciences & Humanities Open, 3(1), 5–7. https://doi.org/10.1016/j.ssaho.2021.100115
Bacchus, D. (2022). Coping with work-related stress: a study of the use of coping resources among professional Black adults. Journal of Ethnic & Cultural Diversity in Social Work, 17(1), 60–81. https://doi.org/10.1080/15313200801906443
Banaji, M. R., Fiske, S. T., & Massey, D. S. (2021). Systemic racism: Individuals and interactions, Institutions and society. Cognitive Research: Principles and Implications, 6(1), 3–7. https://doi.org/10.1186/s41235-021-00349-3
Barton, D. (2022). Ethnographic approaches to literacy research. The Encyclopedia of Applied Linguistics, 12(4), 3–7. https://doi.org/10.1002/9781405198431.wbeal0398
Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological Research and Practice, 2(1), 1–10. https://doi.org/10.1186/s42466-020-00059-z
Deering, K., & Williams, J. (2020). Approaches to reviewing the literature in grounded theory: A framework. Nurse Researcher, 28(4), 9–15. https://doi.org/10.7748/nr.2020.e1752
Miller, B., McCardle, P., & Hernandez, R. (2020). Advances and remaining challenges in adult literacy research. Journal of Learning Disabilities, 43(2), 101–107. https://doi.org/10.1177/0022219409359341
Naeem, M., Ozuem, W., Howell, K. E., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22(1), 1–18. https://doi.org/10.1177/16094069231205789
Nepali, S., Einboden, R., & Rudge, T. (2023). The social relations of ethnographic fieldwork: access, ethics, and research governance. Global Qualitative Nursing Research, 10(3). https://doi.org/10.1177/23333936231193885
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2020). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. SagePub. https://doi.org/10.1177/1609406917733847
Tie, Y. C., Birks, M., & Francis, K. (2020). Grounded theory research: a design framework for novice researchers. Open Medicine, 7(1), 1–8. https://doi.org/10.1177/2050312118822927
Williams, B. (2024, July 23). Ethnographic data analysis methods: A comprehensive guide – Insight7 – Interview analysis & market research. Insight7 – Interview Analysis & Market Research. https://insight7.io/ethnographic-data-analysis-methods-a-comprehensive-guide/
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