The Extent to Which Group Policy Evaluation vs. Individual Evaluation Differ

Problems That Arise When Groups are Categorized as Individuals
The difficulty of determining the extent to which group policy evaluation vs. individual evaluations differ is that context is the deciding factor, and contextual factors are always shifting. Therefore, a case by case analysis is required rather than a one size fits all universal evaluation. As Briggs and Helms (2015) explain, there are actors and roles and the inputs and outputs of each impact one another so that the relationship between parts, between groups and individuals is really a dynamic one.
One of the important points that Reich (2010) makes is that groups are not individuals and should not be counted as such, because it alters the character of the population when they are. He cites the decision in Citizens United vs. the Federal Election Commission, which led to the creation of the super political action committee (PAC). The super PAC now is permitted to spend without limits on election campaigns so long as the candidate is not involved. The group has been given the same rights as the individual and the effect is that the group becomes more powerful than the individual, as it has more influence.
This is problematic because one is evaluating policy based on group vs. individual differences and yet policymakers and lobbyists are being influenced by groups and individuals in different ways. As Briggs and Helms (2015) point out, both groups and individuals will have their policy entrepreneurs, who are “people willing to invest their resources in return for future policies they favor” (p. 22). That exchange, however, is the issue that Reich (2010) touches on: the more well-financed group will have an advantage of the less well-financed individual.
Another issue is that the small group may not represent the larger population’s interests at all, while a larger group might yet it will have less impact on policy. Policy evaluation itself can be undermined by the inputs of powerful groups, and so as Schaufele et al. (2010) note, it is far from certain whether the group or the individual is going to be more representative of the whole.
Schaufele et al. (2010) conclude that “eliciting preferences from groups leads to lower mean coefficient estimates than from surveying individuals. Further, when preference heterogeneity is assumed, density estimates of the directly calculated parameters reveal that inequality aversion parameters based on mean values appear to be poor representations of the distribution social preferences” (p. 8). In other words, the balance is tilted when context is considered and evaluations focus on specific groups that have specific characters.
Group evaluations end up tilting the scales in one way that overlooks individual needs and differences. The group is more likely to represent a portion of what a collection of individuals want—in terms of a series of preferences, whereas the individual evaluation will give a more complex and perhaps complicated view of issues, which, when added together over the whole of society, provides a better picture of what there is to see.
Group evaluations likewise will be conducted in a more quantitative manner whereas individual evaluations can allow for greater exploration of individual issues. That data can be compiled qualitatively, which can allow for the formulation of new hypotheses. Examples, expert opinions and statistics are helpful in reasoning, whether it is inductive, deductive or causal. Examples can be anecdotal or case studies in which the specific problem being worked out by the group is touched upon in other cases. The examples show what happened when certain variables were at play and can help the group to contextualize their own problem. Expert opinions are conclusions that are arrived at by researchers or professionals or academics in the field who have conducted the research, done the studies, looked at the data, and interpreted them from the standpoint of theory and published those observations in peer reviewed journals or voiced their opinions in other formats. Because experts are trusted based on their authority in the field, their voices and opinions can be used to move a group to accept the conclusion of the expert. Statistics are used in quantitative research and are typically seen as objective and conclusive so long as the research is valid and reliable. Statistics can help the group to discuss a problem without deviating into subjective experiences. Qualitative research focuses on exploring a problem and investigating it using in-depth methods such as interviews, focus groups, case study analysis or immersion into the field to experience firsthand what the subjects experience. The data collected is usually analyzed in a subjective manner. Quantitative research is more concerned with testing a hypothesis and identifying variables to see what their relationship is. It collects data that is more objectively analyzed via statistical analysis, such as t-tests or chi-square tests, and this shapes group vs. individual evaluations as well. While it is true that both qualitative and quantitative studies can have hypotheses, the reality is that quantitative studies tend to have testable hypotheses while qualitative studies tend to formulate an hypothesis during the iterative process of obtaining and analyzing data. In other words, a quantitative study will pose hypotheses at the outset and then set about testing them to see whether they can be accepted or rejected. A qualitative study on the other hand will pose central questions at the outset and then arrive at a possible hypothesis to explain the data collected at the end of the evaluation. From this perspective, the two evaluations—group vs. individual—serve very different purposes
References
Briggs, S., & Helms, L. B. (2015). The practice of American public policymaking. New York: Routlege Taylor & Francis Group.
Reich, R. (Harvard Book Store, Producer). (2010). Robert B. Reich: The next economy and Americla's future. Frontline: Inside the Meltdown Series. [Video] Retrieved from [https://forum-network.org/lectures/robert-b-reich-the-next-economy-and-america-s-future/]
Schaufele, B., McMillian, M. L., Boxall, P. C., Adamowicz, W. L., & Rivera, C. S. (2010). Measuring social preferences in groups versus as individuals: Income inequality aversion using the leaky bucket method. Journal of Applied Economics and Policy, 29(1), 1-1