Instructions For Milestone 2 Data Analysis Draft

Instructionsmilestone 2 Data Analysis Draftwork With Your Dissertatio

Milestone 2: Data Analysis Draft Work with your dissertation chair to determine any specific instructions or guidance that he or she may have for you. Now that you have collected the data for your study, it is time to demonstrate doctoral ability to analyze the data. For a quantitative study, you will complete the descriptive and inferential statistical tests. For a qualitative study, you will likely complete some type of coding (although note that the emergent nature of qualitative research means that data analysis happens throughout the process). Using the Dissertation Template for BUS8115 and BUS8120, draft the initial data examination and statistical analysis section of chapter 4.

Depending on your study, you may or may not use the provided terminology (in the template) for your headings. Check with your dissertation chair for advice. Note: You will use the same template document for both BUS8115 and BUS8120. Your objective for this task is to do your analysis and start reporting your findings succinctly. You should expect several iterations of feedback with your dissertation chair.

Your dissertation chair will assess this task as complete when he or she concludes that you have conducted all the necessary analysis and have drafted clear and accurate findings. The analysis does not have to be in completely finished form (next task), but it needs to be well developed, with only minor revisions required. Important note: Dissertation writing is highly recursive. As you write material in one section, you may need to make some adjustments or additions in other sections in order to build a cohesive document. Your analysis and reporting of data might necessitate some updates to chapters 1 and 3 of your dissertation.

Submission Details: Submit a draft of your analysis. Use APA style in preparing your paper and citing references. Post the paper to the Milestone 2 Submissions Area. Notify your dissertation chair (e.g., via e-mail) when you have submitted the paper. Note: A successful dissertation requires self-directed behaviors.

To successfully pass each dissertation course, you must successfully complete (pass) each milestone presented in the course materials. Additionally, you must complete the milestones in the order they are presented in the course. The tasks in some milestones may take you more than a week to complete. Finish each milestone before you move on to the next milestone. In your planning, also allow time for feedback from your dissertation chair/committee and revisions as part of completing each task.

Paper For Above instruction

Introduction

The completion of a doctoral dissertation represents a significant academic achievement that demands rigorous data analysis to validate research findings. Milestone 2 focuses on drafting the initial data analysis section, which involves systematically examining and interpreting collected data in accordance with the research design—whether quantitative or qualitative. This paper details the process of conducting and reporting data analysis for a dissertation, emphasizing the importance of thoroughness, collaborative feedback, and alignment with institutional guidelines, particularly using the provided dissertation template.

Initial Data Examination and Preparation

The initial phase involves preparing the data for analysis, which includes checking for completeness, accuracy, and consistency. In quantitative studies, this step often entails cleaning the dataset by handling missing values, assessing normality, and identifying outliers through descriptive statistics. In qualitative research, data preparation may involve transcribing interviews, organizing coded data, or categorizing responses to facilitate later analysis.

Quantitative Data Analysis: Descriptive and Inferential Statistics

For quantitative studies, descriptive statistics form the foundation—providing summaries such as means, medians, modes, standard deviations, and frequency distributions. These statistics offer insights into data patterns and inform the suitability of subsequent inferential tests.

Inferential statistical tests are then employed to test hypotheses or research questions. These may include t-tests, ANOVAs, correlation analyses, regression models, or non-parametric alternatives, depending on data types and research design. For example, a study examining relationships between variables might utilize Pearson correlation coefficients, while differences between groups could be explored through t-tests or ANOVA. The choice of tests must align with the research questions, data level, and assumptions.

Qualitative Data Analysis: Coding and Pattern Identification

In qualitative research, data analysis primarily involves coding—assigning meaningful labels to segments of textual or visual data. Open coding, axial coding, and selective coding are common methods that facilitate the emergence of themes and categories. Coding helps identify patterns, relationships, and phenomena relevant to the research questions.

Throughout the coding process, constant comparison and memoing enhance the rigor and depth of analysis. As emergent themes develop, evidence is gathered to support interpretations, leading to rich, descriptive findings.

Reporting Data Findings

Effective reporting integrates statistical outputs with narrative explanation, providing a clear interpretation of what the data reveal relative to the research questions. Tables and figures are commonly utilized to present descriptive statistics, test results, and sample characteristics succinctly.

It is essential to contextualize findings within the broader literature, discussing implications, limitations, and unexpected results. Transparency in reporting statistical assumptions, effect sizes, and confidence intervals enhances credibility.

Alignment with Dissertation Structure and Feedback Loops

The analysis section should reflect the structure specified in the dissertation template, with headings and subheadings tailored to the study. Collaboration with the dissertation chair ensures adherence to expectations, and iterative feedback helps refine the analysis and improve clarity.

Addressing recursive writing, adjustments in Chapters 1 and 3 become necessary if analysis results impact research questions, hypotheses, or methodology descriptions. Maintaining coherence and consistency throughout the document is crucial.

Conclusion

The drafting of the data analysis section signifies progress toward completing a rigorous dissertation. This process requires attention to detail, methodological rigor, and ongoing dialogue with academic advisors. Proper documentation and interpretation of results lay the groundwork for meaningful conclusions, implications, and future research recommendations.

References

  • Gray, D. E. (2018). Doing Research in the Business World. Sage Publications.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Leech, N. L., & Onwuegbuzie, A. J. (2007). An Array of Data Analysis Techniques for Mixed Data Types. Multivariate Behavioral Research, 42(1), 115-143.
  • Patton, M. Q. (2015). Qualitative Research & Evaluation Methods. Sage Publications.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • McMillan, J. H., & Schumacher, S. (2014). Research in Education: Evidence-Based Inquiry. Pearson.
  • Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research. Sage Publications.
  • Yin, R. K. (2018). Case Study Research and Applications: Design and Methods. Sage Publications.
  • Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16, 1-13.
  • Braun, V., & Clarke, V. (2006). Using Thematic Analysis in Psychology. Qualitative Research in Psychology, 3(2), 77-101.

Related Assignments