Overcoming the Flaws of Needs Assessments

September 5, 2017, Department, by Ananda Mitra, Ph.D.

2017 September Member to Member Overcoming the Flaws of Needs Assessment 410

Using survey questionnaires with multiple-choice questions has been the traditional form of doing needs assessments in the recreation industry. The process involves collecting attitudes, behaviors, needs and demographics information from the citizens served by public recreation agencies. The impetus of “positivist” social science has been to rely on this form of data collection, and related statistical quantitative analysis, to delve into the intricacies of what a community truly thinks about leisure and recreation opportunities and the associated needs. This is the best method available to decision makers for planning the future of recreation in the communities they serve.

Entire master plans are designed and executed based on the numerical data that serves to encapsulate the narrative of a community. Yet, the decisions made using such data often result in investments that might not prove to be as successful as expected. This is often because the only voice the community had in building this narrative comes from pre-defined questions. And, those questions allowed for responses measured on a five- or seven-point scale with terms such as “strongly agree” in response to statements that the planners and architects crafted to the best of their ability, without discovering the nuances of the narrative that defines the character of a community.

Surveys and Public Meetings
This method, although widely used by many industries, lacks the ability to capture the details of the narrative of the community. Consequently, decisions made based on this survey data are often flawed. Consider, for example, the presidential polling data in the United States that has, on several occasions, completely missed the opinion of the nation. Sometimes, the numerical data is accompanied by data gathered in public meetings. The data gathered in this forum usually is statistically biased and only garners unreliable data. These public meetings attract the vocal minority — the “squeaky wheels” of the community if you will — who then become the representation of the silent majority who are not at these meetings.

Thus, there are two issues with the data from surveys and public meetings. Either the authentic narrative of a community is inaccessible because the “voice” of the community is reduced to numeric responses from “randomly selected” representatives of the community who are expected to respond with the numbers on multiple-choice questions, or the generalized voice of the community is hijacked by the people who attend the public meetings. To add to the confusion, unscrupulous researchers can effectively mask response rates — percentage of the randomly selected sample who completed a questionnaire — and hypnotize recreation professionals with arbitrary promised numbers, such as “500 completed responses,” with non- response error skyrocketing to 90 percent and higher. Not only is the data incomplete because emotions are reduced to five-point scales, but also the source of the data is unreliable because only the self-selected chose to respond.

However, with the increasing popular access to digital technologies, there are other options that can supplement the traditional “social science” numerical data and help to enrich the narratives about communities.

Digital Networks
The advent of digital networks, such as Facebook and Twitter, allow community members to present their points of view, in their own language. The community is no longer limited by prepared questionnaires that offer researcher-produced statements that respondents could only agree or disagree with, or by the vocal special interest groups that mob the public meetings.

This digital space is an opportunity that the public recreation industry should embrace. There, the decision makers in public recreation can capture the narratives told by the people they serve. Just as the hospitality industry has turned to platforms, such as TripAdvisor, the public recreation industry needs to actively solicit the voices of the people it serves. This could be as simple as a feedback button on the agency website, where citizens can state an opinion, leave a comment, praise or a complaint, or talk about what they want for their community or how they want to spend their leisure hours. These statements are no longer censored by pre-defined questions and rigid responses on a survey, or limited to a set time for a attending public meeting. In the realm of traditional survey research, these were called “open-ended” questions, and those responses would fall into the “other” or “comment” part of a questionnaire. Quite often, these responses were given cursory attention and relegated to coders who would type out the response in the hope that some decision maker would browse through the typed pages at some time.

The Narrative Paradigm
The situation has changed significantly with the availability of tools for analysis of textual data where computer software can be used to create meaningful analysis of open-ended responses. The process is based on the narrative paradigm, suggested by Professor Walter Fisher in the 1980s, which claims that it is possible to understand the opinions of a community by seeing the story that emerges from the comments offered by its members. Given the fact that there is virtually no limit to the amount of data that can be collected and analyzed, very large amounts of information can be collected and then analyzed to create a visual representation of the story that emerges from the comments. These visuals, also called “narrative maps,” can then become the source for the creation of the specific narrative about the behavior and opinions of those who voice themselves.

The narrative map is based on responses to an open-ended question about the feelings of 350 respondents.

As the map shows, people are often conflicted about their opinions, and it is unrealistic to consider that they have either clear-cut positive or negative opinions. The line between the ‘negative opinion’ and ‘positive opinion’ suggests this duality of opinion. However, the thick connection between ‘parks’ and ‘positive opinion’ also suggests that people are relatively happy with the parks, just as people are happy with the ‘location’ of the parks. These maps are read by looking at the size of each node (e.g., the larger blue circle for ‘positive opinion’ suggests that many respondents used positive supportive language in their comments), and the thickness of the lines shows the strength of the relationship between nodes. For instance, these respondents felt that the location of the parks tends to allow for family use of the parks. I urge the reader to consider this map carefully and elicit other information encapsulated in it.

It is possible to create maps periodically to see how the story changes as decision makers implement changes in the community, and, thus, retain a constant sense of the pulse of the community by following exactly what people are saying about recreation and leisure in a community.

This approach releases the decision-making process from the constraints of charts, graphs and percentages and harnesses the voice of the people. It is this voice that makes up the notion of “big data,” which has become a mantra in contemporary discussion about the way in which information is transforming decision making. The volume of “big data” related to community opinions and behaviors is only expected to increase, as more people can be queried about their needs and interests.

This wealth of data needs to be harnessed and analyzed in a meaningful way. The cost of doing this is minimal, since data collection using the internet is relatively inexpensive, compared to mailing out printed questionnaires or interviewing people on the phone. The computational needs are not exorbitant (the computer program used to create the map above is under $2,500). All that’s needed is the will of the decision makers and elected officials to invest in listening to the communities they serve.

Eventually, a more complete and robust narrative can be obtained by the combination of numerical data and the narrative data extracted from narrative maps. Both have their specific limitations, but in combination they can produce a story that not only shows the percentage of people who would express a need for a new facility, but also offers greater depth in relation to the specific needs. These methods complement each other with the properly conducted numeric part offering generalizable data and the narrative maps, allowing many community members to weigh in with their feelings in their own language.

The limitation of sample size in the case of the numeric data is offset by the “richness” of the big data analysis, whereas the linguistic biases of the voices of the people is offset by the statistical rigor of the numeric data. It would be particularly useful for recreation professionals to embrace this “mixed method” approach to more effectively arrive at decisions about how to spend the limited resources available.

View the narrative map that was included in the print edition of Parks & Recreation magazine.


Ananda Mitra, Ph.D., is Professor of Communication at Wake Forest University.