Can you respond to this person's comment and include a question?

Why is generalizing the results of an evaluation frequently problematic? While it is expected to focus on the results of a particular project or initiative< it can be not easy to truly understand the scope of its success without considering it in the larger context of the organization or industry in which it operates.

One potential solution to this problem is to establish a standardized set of metrics that can be used to evaluate results across a variety of projects or initiatives. Focusing on a consistent set of criteria makes it easier to compare and contrast results in a meaningful way. Of course, developing these metrics can be a time-consuming process, but the benefits of doing so can be significant.

Another approach is to consider the broader implications of a project or initiative beyond its immediate results. For example, in the non-profit sector, it's essential to consider the long-term impact a particular program might have on the community it serves. By framing the evaluation in this way, it becomes easier to understand how a successful outcome might lead to broader systemic change over time.

Overall, while evaluating the results of a particular project or initiative is essential, it's equally important to consider these results in a broader context. By developing standardized metrics or taking a long-term view of a project's impact, it becomes easier to understand the success or failure of a given initiative. Throughout this reading, I've learned that there is no one-size-fits-all approach to results evaluation and that each situation requires a unique approach.

How might a program evaluator enhance the likelihood of generalizability of results? Give public administration examples to bolster your discussion. Quantitative data collection, Qualitative data, and statistical generalizability are all ways a program evaluator can enhance the likelihood of generalizability results.

Quantitative data collection is the ability to generalize findings from a statistical sample to a larger population (or other program sites of future clients). It refers to the validity of the statistical conclusion (Newcomer, Hatry, Wholey, 2015).

Quantitative data collection methods offer a rigorous and systematic approach to program evaluation that can enhance the likelihood of generalizability of results by ensuring representative sampling, employing random techniques, using standardized measurement tools, and applying statistical analysis techniques to draw valid conclusions about the population of interests in the public administration context.

For Qualitative data, transferring findings from one site to another (or the future) may present different, or additional challenges (Newcomer, Hatry, Wholey, 2015). Diverse data, in-depth exploration, contextual analysis, and comparative analysis are just a few ways a program evaluator can enhance the likelihood of generalizability of results using qualitative data in public administration.

Statistical generalizability requires testing the statistical significance of findings from probability samples and greatly depends on the sample size used in an evaluation (Newcomer, Hatry, Wholey, 2015). Statistical generalizability plays a crucial role in program evaluation within public administration by allowing evaluators to make valid inferences about the larger population based on the data collected from the sample. By employing appropriate statistical techniques and methods, evaluators can enhance the likelihood that their findings are applicable and relevant to the broader context in which the program operates.

References

Newcomer, K. E., Hatry, H. P., & Wholey, J.S. (2015). Handbook of Practical Program Evaluation (4^th ed.). Jossey-Bass