Evaluation Results: Generalization Issues and Enhancement Strategies

This discussion post addresses the challenges associated with generalizing the results of evaluations, focusing on issues such as sample representativeness and the potential for biased outcomes. The author highlights that generalizing from a sample to a broader population can be problematic due to the use of volunteer samples and the potential for gender-based discrepancies. The post further explores methods to enhance the generalizability of results, including providing detailed descriptions of the intervention and context, and examining quality improvement changes. Examples from medical treatments and psychology are used to illustrate the application of result generalization. The author references key concepts like external validity and the importance of statistical conclusion validity, providing a comprehensive analysis of the topic.

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Why is generalizing the results of an evaluation frequently problematic?
Generalization is an important concept that is used by many researchers for the purposes
of describing the process of developing general knowledge which applies to all units of a
population (Sekaran & Bougie, 2016). In the context of this module, generalizing refers to the
making of conclusions from statistical results about a much broader population than what the
sample actually represents. Different forms of generalization used to conduct both quantitative
and qualitative research evaluations cause different drawbacks.
A common problem as a result of generalization is the sample that was used to obtain the
results. In most studies, researchers use volunteers or opportunity sampling methods to acquire
the acquire participants for the study. Such participants may not a proper representative to
produce results that can be generalized to the entire population. The preferred sampling method
is the one that uses as many participants as possible to make sure that the data collected is
relevant to make inferences. It is also wise to use participants from various segments of the
sampled population.
Generalization also causes problems when different genders are involved in the
evaluation. Normally, a study uses both the male and the female gender in the samples, it would,
therefore, be wrong to generalize results of one gender to the other (Martens, 2014).
Generalization is also problematic because it makes the researchers look for the general aspects
of a sample to enable them to draw general findings from the larger fraction of the population.
Generalizing requires the researcher to take the most prevalent findings in the sample. The most
prevalent results are not primarily what everyone that you are investigating would have got and
the researcher is not in a position to know this because there is no chance to reach the

respondents again (Sekaran & Bougie, 2016). For instance, concluding that because 90% of the
population under the study scored more than seventy mars in a test that everyone else will.
How might a program evaluator enhance the likelihood of generalizability of results? Give
public administration examples to bolster your discussion
One of the ways that the evaluators can help in making the information or results which
are generalized is by providing descriptions of the intervention and context. In order to help both
the researchers and the decision makers to build theory, the evaluator should provide detailed
information in both controlled and uncontrolled case studies. Another solution would be to
examine how the quality improvement changes are amended in their execution and to come up
with a program to help the researcher to study specific quality improvement in different or a
variety of settings.
Examples of the application of the result generalization are seen in the medical
treatments. Practitioners try to use generalization to determine whether outcomes found in
randomized sample trials should apply to their patients (Lawless, 2011). Generalization is also
used in the field of psychology to help in the prediction of how people may behave. But the
samples used as representative are always carefully gotten from a good number of different
groups for the generation to be true.

References
Joint Committee Standards: feasibility, accuracy, utility, and propriety standards
http://www.jcsee.org/program-evaluation-standards-statements
Lawless, J. F. (2011). Statistical models and methods for lifetime data (Vol. 362). John Wiley &
Sons.
Martens, D. M. (2014). Research and evaluation in education and psychology: Integrating
diversity with quantitative, qualitative, and mixed methods. Sage publications.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach.
John Wiley & Sons.