For The Unit 3 Complete Assignment Write A Narrative 799196 ✓ Solved

For the Unit 3 Complete assignment, write a narrative essay

For the Unit 3 Complete assignment, write a narrative essay (minimum of 1200 words) which addresses the following:

• Describe the five-step process for data gathering.

• Describe the five common data gathering methods.

• Describe the five themes associated with data gathering.

• Identify an opportunity within your organization which you believe you could improve using data analysis. Which methods of data gathering would you utilize?

A minimum of three scholarly sources are required, and all sources should be cited and referenced in APA format.

Paper For Above Instructions

Introduction

Effective decision-making in organizations depends on reliable data gathered through deliberate processes and sound methods. This essay outlines a five-step process for data gathering, describes five common data collection methods, identifies five overarching themes that shape data gathering efforts, and applies these concepts to a practical organizational opportunity. The discussion draws on established research methods literature to provide actionable guidance (Creswell & Creswell, 2018; Saunders, Lewis, & Thornhill, 2019).

Five-Step Process for Data Gathering

1. Define the Research Question and Objectives: Begin by articulating the problem or opportunity clearly. Specific, measurable objectives orient the remainder of the process and determine what data are relevant (Fowler, 2014).

2. Design the Data Collection Plan: Choose the appropriate study design (qualitative, quantitative, or mixed methods), sampling strategy, instruments, and procedures to ensure validity and reliability (Yin, 2018; Creswell & Creswell, 2018).

3. Develop and Test Instruments: Create surveys, interview protocols, observation checklists, or sensor configurations and pilot them to detect ambiguities, bias, or technical issues (Groves et al., 2009).

4. Collect Data Systematically: Implement the plan while maintaining quality control—train data collectors, monitor response rates, and document context and deviations (Patton, 2015).

5. Clean, Store, and Document Data: Perform data cleaning, code qualitative inputs, secure storage, and metadata documentation to support later analysis and reproducibility (Provost & Fawcett, 2013).

Five Common Data Gathering Methods

1. Surveys and Questionnaires: Structured instruments that collect standardized responses are efficient for measuring attitudes, behaviors, and demographics at scale (Fowler, 2014).

2. Interviews (Structured, Semi-Structured, Unstructured): Interviews enable deeper exploration of motivations and context; semi-structured formats balance comparability and flexibility (Patton, 2015).

3. Observations and Field Notes: Direct observation captures behavior in natural settings and provides contextual detail often missed in self-reports (Denzin & Lincoln, 2018).

4. Administrative and Transactional Records: Existing organizational records, logs, and databases are valuable for longitudinal and operational analyses with minimal respondent burden (Provost & Fawcett, 2013).

5. Case Studies and Document Analysis: Intensive examination of specific units or documents supports theory-building and explanation in context-rich situations (Yin, 2018).

Five Themes Associated with Data Gathering

1. Validity and Reliability: Ensuring instruments measure what they intend (validity) and produce consistent results (reliability) is foundational to trustable findings (Creswell & Creswell, 2018).

2. Ethics and Consent: Respecting privacy, obtaining informed consent, and protecting sensitive information are ethical imperatives and legal requirements (Denzin & Lincoln, 2018).

3. Contextualization: Data are meaningful only within their context; documenting environmental, temporal, and organizational factors is critical (Saunders et al., 2019).

4. Mixed-Methods Complementarity: Combining quantitative breadth with qualitative depth often yields more robust insights than either approach alone (Creswell & Creswell, 2018).

5. Quality Assurance and Documentation: Piloting, training, data audits, and metadata creation preserve integrity and support replication (Groves et al., 2009).

Organizational Opportunity: Improving Customer Support Response Quality

Context: In my organization, customer support handles product issues via phone, email, and chat. We face inconsistent response quality, long resolution times, and uneven customer satisfaction (CSAT) scores. Improving support quality could reduce churn and improve brand reputation.

Objective: Use data analysis to identify root causes of delays and quality gaps and design interventions that elevate CSAT and reduce average handle time (AHT).

Selected Methods of Data Gathering

I would use a mixed-methods strategy combining the following methods:

1. Transactional Records Analysis: Extract support ticket logs, timestamps, channel metadata, and resolution codes from CRM systems. Administrative data provide objective measures of volume, AHT, escalation rates, and repeat contacts (Provost & Fawcett, 2013).

2. Surveys: Deploy short post-interaction CSAT surveys and targeted employee pulse surveys to capture perceived service quality and possible process barriers (Fowler, 2014).

3. Structured and Semi-Structured Interviews: Conduct interviews with frontline agents and supervisors to understand workflow constraints, knowledge gaps, and training needs (Patton, 2015).

4. Call and Chat Observations (and Text Analytics): Sample recorded calls and chat transcripts for qualitative coding of interaction patterns and apply natural language processing to detect sentiment and recurring complaint themes (Groves et al., 2009; Provost & Fawcett, 2013).

5. Case Studies of Escalated Tickets: Select in-depth cases of poor outcomes to reconstruct sequences, decisions, and handoffs that contributed to failures (Yin, 2018).

Rationale and Implementation

Combining administrative data with surveys and qualitative methods aligns with the themes of validity, contextualization, and mixed-methods complementarity. Administrative logs quantify the problem, surveys capture customer perceptions, interviews and observations explain processes, and case studies reveal complex failure modes. Piloting surveys and coding frameworks, ensuring anonymization, and maintaining documentation will secure data quality and ethics (Creswell & Creswell, 2018; Denzin & Lincoln, 2018).

Expected Outcomes

Data analysis should reveal bottlenecks (e.g., knowledge base gaps, routing inefficiencies), allow segmentation of issues by product and channel, and point to targeted training or process changes. Iterative measurement post-intervention will track impact on CSAT and AHT, enabling continuous improvement (Saunders et al., 2019).

Conclusion

Rigorous data gathering requires a structured process, choice of appropriate methods, attention to cross-cutting themes, and alignment with organizational objectives. By applying a mixed-methods approach to customer support, the organization can move from anecdote to evidence-based interventions that improve both efficiency and customer experience (Creswell & Creswell, 2018; Provost & Fawcett, 2013).

References

  • Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE Publications.
  • Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students (8th ed.). Pearson Education.
  • Denzin, N. K., & Lincoln, Y. S. (Eds.). (2018). The SAGE Handbook of Qualitative Research (5th ed.). SAGE Publications.
  • Fowler, F. J. (2014). Survey Research Methods (5th ed.). SAGE Publications.
  • Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.
  • Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). SAGE Publications.
  • Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology. Wiley.
  • Flick, U. (2018). Doing Qualitative Research (4th ed.). SAGE Publications.
  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media.
  • Kothari, C. R. (2004). Research Methodology: Methods and Techniques (2nd ed.). New Age International Publishers.

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