The Impact of Air Shows, Fly-overs, Open Houses, and Guest Days
on Public Opinion
Jon Connor, Patricia Huizinga, Peter Kerr
|In addition to a complete
cost breakdown of each event, data must be collected on the public's attitude
change due to having visited each event. The hypothesis is that people's
attitude toward the military will rise due to having attended the event.
To collect this data, people entering events should be polled using the
seven-point Likert scale, with individual surveys handed out on clipboards.
The first 10 questions listed in Appendix A should be given upon entrance,
and the subject should then be given a ticket worth $5 if they return after
the event to be polled again. Questions 7 through 11 should be polled upon
the subject's exiting the event and then the $5 award would be given. The
tickets used to claim the award will be used as the subject's identification
number so that the pre-test scores can be compared to an individual's post-test
scores. The necessary sample size at each event will be calculated using
the estimated crowd attendance in a power-analysis.
The polling booth and personnel should make it very clear that they are not in any way associated with the military, in order to discourage potential bias. None of the researchers should wear any uniform or military-affiliated attire. Polling should be located in a position so as to be convenient for attendees to stop by while entering and exiting the event. Typically, the location will be between the parking lot and the main audience area.
In order to obtain comparative data, questions 1,3,4,7,8,9 and 10 should also be polled at a local mall at the same time the military-sponsored event is taking place.
The independent variable is the degree of familiarity a person has with the military's equipment, mission and people. It is assumed that military events will inherently increase familiarization, and this variable will quantifiably be identified as having been affected by including the following question in the survey; "I am familiar with the military's equipment, mission and people." The dependent variable is the public's opinion of the military, measured on a seven-point Likert scale. Giving the exact questions before and after the event ensures a solid pre-test/post-test of public attitude change incurred at the event.
Other details about each event should also be noted, in order to assist in identifying the cause of outliers. The event's major attractions should be annotated (one of the service's premier flying demonstration teams, etc.), the approximate media coverage garnered (local public affairs estimate derived by tallying the media channels' audience sizes that carried the event's advertisement), and weather data. Furthermore, data can also be collected at a local mall using the same survey, in order to compare air show attendees with the general public.
Attitude change data can be compiled by event, to show which type of event most impacts positive attitude shifts. A two-tailed t-test could then be run on the data to see if attendance had an impact on people's view of the military. Furthermore, total costs of events can be divided by the audience size to get a cost per person for this attitude shift. Other extrapolations can be conducted to determine which service is most cost effective in hosting these types of events, possibly with an eye toward benchmarking their cost savings initiatives.
Many subsidiary calculations can also be gained from this data, which may prove useful in understanding military-sponsored events. Demographic data can be gathered, and conclusions can be made as to what type of person is most likely to attend these various events. Question 11 can be used to see if there is recruiting value to these types of events. Information can also be ascertained from question 6 (see appendix A) as to what types of activities may compete with event attendance. Such data may be used to better market military events.
Other interesting tests may include seeing if any of the answers are correlated. Depending on the data type, a Pierson product moment correlation, or a factorial ANOVA could assist in finding correlations. Data may answer questions such as: Do men or women feel most familiar with the military? Do people dedicated to drive long distances typically have strong military ties? Do men or women have differences in opinion as to whether or not funding the event is a good use of tax dollars?
The entrance surveys results can also be compared with the poll at a local mall, to better understand differences in the two populations. This will ensure there is a comparison group for findings such as; "People who go to military events already have a favorable opinion about the military."
Finally, the independent variable is pre-tested and post-tested to ensure it has been manipulated by airshow attendance. This manipulation differential is vital to maintain the researches' theoretical underpinnings.