Case 2: Global Shared Services, McDonald's Corporation

Case 2: Global Shared Services, McDonald’s Corporation As the world’s L

Identify the actual assignment question/prompt and clean it: remove any rubric, grading criteria, point allocations, meta-instructions to the student or writer, due dates, and any lines that are just telling someone how to complete or submit the assignment. Also remove obviously repetitive or duplicated lines or sentences so that the cleaned instructions are concise and non-redundant. Only keep the core assignment question and any truly essential context.

Discuss issues pertaining to quantitative, qualitative, and mixed methods data collection. How does this affect your instrument and data collection method you chose in the mock program evaluation? Use the readings in Module 3 and other relevant resources to write a 3- to 5-page paper on validity and reliability issues in program evaluation.

Paper For Above instruction

Program evaluation is a critical component in assessing the effectiveness, efficiency, and overall impact of programs aimed at improving educational or social outcomes. When conducting evaluations, researchers confront numerous issues related to the validity and reliability of their data collection methods. These issues vary depending on whether the data collection approach is quantitative, qualitative, or a combination of both (Creswell & Plano Clark, 2017). Understanding these issues and their implications is essential for selecting appropriate instruments and ensuring meaningful, trustworthy results.

Quantitative data collection methods rely on numerical data to measure variables and allow for statistical analysis. Common tools include surveys, standardized tests, and structured observation checklists. One of the primary validity issues in quantitative research pertains to construct validity, or whether the instrument accurately measures the theoretical construct it intends to measure (Carmines & Zeller, 1979). For example, in a program evaluation assessing student engagement, a survey instrument must be validated to ensure it genuinely captures engagement rather than unrelated attitudes or behaviors. Reliability issues also pose challenges; instruments must produce consistent results under similar conditions (Nunnally & Bernstein, 1994). An unreliable survey could yield inconsistent data, leading to invalid conclusions about program impacts.

Qualitative data collection, on the other hand, involves non-numerical data such as interviews, focus groups, and observations. Validity concerns such as credibility and transferability are central. Credibility refers to the authenticity and believability of the findings, which can be compromised by researcher bias or poor data collection procedures (Lincoln & Guba, 1985). Transferability concerns whether the findings are applicable to other contexts; poor sampling or superficial data collection can hinder this. Reliability in qualitative research is often addressed through techniques like triangulation—using multiple data sources or methods to confirm findings—and maintaining detailed audit trails to ensure consistency in data interpretation (Patton, 2002).

Mixed methods research combines quantitative and qualitative approaches to leverage the strengths of both, but it introduces complexities related to integration and consistency. Validity issues in mixed methods include convergent validity, or whether the different data types corroborate each other (Creswell & Plano Clark, 2017). For instance, if quantitative data suggest program success but qualitative feedback indicates dissatisfaction, this discrepancy may signal problems with either measurement approach or the instruments themselves. Reliability concerns extend to ensuring both data types are collected systematically and that the mixed methods design is properly implemented to avoid bias or misinterpretation (Tashakkori & Teddlie, 2010).

The choice of data collection instruments and methods directly impacts the validity and reliability of the evaluation. For example, using poorly validated surveys can threaten construct validity, leading to inaccurate assessments of program impact. In qualitative studies, inadequate interview protocols or insufficient sample sizes threaten credibility and transferability. Mixed methods evaluations require careful planning to ensure data integration is meaningful and that both qualitative and quantitative data are valid and reliable within their respective paradigms (Johnson & Onwuegbuzie, 2004).

In the context of the mock program evaluation, understanding these issues guides the selection of appropriate instruments. For instance, if the evaluation aims to measure participant satisfaction quantitatively, a well-validated Likert-scale survey can provide reliable data. To capture nuanced experiences, qualitative interviews conducted systematically and with clear protocols can enhance validity. Combining these approaches enhances the overall trustworthiness of the findings, provided that the instruments are appropriately validated, pilot-tested, and administered consistently.

In conclusion, validity and reliability are foundational to the integrity of program evaluation. Each data collection approach—quantitative, qualitative, or mixed—poses specific challenges that must be addressed through careful instrument design, rigorous data collection protocols, and transparent analysis practices. Recognizing these issues ensures that evaluation findings are accurate, credible, and useful for informing program improvements and decision-making.

References

• Carmines, E. G., & Zeller, R. A. (1979). Reliability and Validity Assessment. Sage Publications.
• Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.
• Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26.
• Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
• Patton, M. Q. (2002). Qualitative Research & Evaluation Methods (3rd ed.). Sage Publications.
• Tashakkori, A., & Teddlie, C. (2010). Mixed Methods in Social & Behavioral Research. Sage Publications.