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Rethinking Survey Research: The Implication of Cluster Analysis Findings on Museum Practice at the Sports Legends Museum at Camden Yards Amanda Krantz Research Associate Randi Korn & Associates, Inc. krantz@randikorn.com CARE Poster Session – May 1, 2009. introduction.

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  1. Rethinking Survey Research:The Implication of Cluster Analysis Findings on Museum Practice at the Sports Legends Museum at Camden YardsAmanda KrantzResearch AssociateRandi Korn & Associates, Inc.krantz@randikorn.comCARE Poster Session – May 1, 2009

  2. introduction To help disseminate the information presented in the poster session Rethinking Survey Research: The Implication of Cluster Analysis Findings on Museum Practice at the Sports Legends Museum at Camden Yards, the poster has been dissected into slides, and supplemental information has been added. See the next slide for the complete poster.

  3. Rethinking Survey Research: The Implications of Cluster Analysis Findings on Museum Practice at The Sports Legends Museum at Camden Yards 1 Background 3 4 Results implications • Following the study, RK&A spoke with Museum staff about their experiences with the study and implications of the analysis, which were as follows: • A museum’s audience is not homogeneous, as most museum practitioners realize. Cluster analysis helps staff see and characterize cross-sections of visitors, helping them to (re)consider their audience. • Example: It was revealing that the largest cluster were Middle-Road Fans and that there are nuances between sports enthusiasts (e.g., Active Enthusiasts versus TV Enthusiasts) • Through identifying clusters, staff can consider to which segment of their audience to best allocate resources,meaning that staff may consider marketing or programming to the largest cluster, with the intent of also drawing some visitors from other clusters. • Example: Focus on the Middle-Road Fans, but plan to draw some Enthusiasts and a few Indifferent Companions. • Additionally, staff can identify nuances that are most similar and dissimilar among clusters,also using it to inform marketing, programming, and even exhibition design. • Example: Ratings of the statement, “I watch sports to see the athletes I like,” were most similar, while ratings of the statement, “Sports has great meaning in my life,” were most dissimilar. The Sports Legends Museum at Camden Yards approached Randi Korn & Associates, Inc. (RK&A) to conduct audience research. This project was one of several the Museum engaged in to better understand its visitors. Data were collected between May and July 2008 through standardized questionnaires and in-depth interviews. RK&A identified four clusters: Active Enthusiasts, TV Enthusiasts, Middle-Road Fans, and Indifferent Companions Active enthusiasts This cluster consists of participatory, engaged, emotional sports fans. They prefer to attend or participate in sporting events rather than watch them on TV. Tv enthusiasts These visitors are also avid sports fans; they regularly attend sporting events but prefer to follow their favorite teams and athletes on TV. Middle-road fans This cluster consists of visitors who pay attention to sports but are not emotional, die-hard fans, and who do not regularly participate in sports. Indifferent companions These visitors do not participate in sports and would not describe themselves as sports fans. 2 Methods • The focus of this presentation is cluster analysis, a statistical procedure that groups like individuals into clusters. In the study for the Sports Legends Museum at Camden Yards, RK&A used ratings of statements about sports to form the clusters. On a scale from 1, “Does not describe me,” to 7, “Describes me well,” 302 visitors rated the following statements: • I prefer to watch sports by going to games. • When my team is losing, I usually feel bad. • I prefer to watch sports on TV. • I watch sports to cheer the entire team’s effort. • I watch sports to see the athletes I like. • Sports has great meaning in my life. • I like learning about the history of my favorite teams. • I regularly attend sporting events. • I regularly participate in sports. • I go out of my way to learn the latest sports news. Amanda Krantz Research Associate Randi Korn & Associates, Inc.

  4. background The Sports Legends Museum at Camden Yards approached Randi Korn & Associates, Inc. (RK&A) to conduct audience research. This project was one of several the Museum engaged in to better understand its visitors. Data were collected between May and July 2008 through standardized questionnaires and in-depth interviews.

  5. methods As part of the study, RK&A conducted interviews with and administered surveys to visitors. The focus of this presentation, however, is cluster analysis, a statistical procedure that groups like individuals into clusters. The instrument and analysis is described in the next two slides.

  6. instrument In the study, RK&A collected data for the cluster analysis through a survey administered on surveymonkey.com at the Sports Legends Museum at Camden Yards. Data for the cluster analysis came solely from visitors’ ratings of ten statements about sports (see the next slide for the statements). A total of 302 visitors rated the statements on a scale from 1, “Does not describe me,” to 7, “Describes me well.”

  7. Rating statements Rating statements I prefer to watch sports by going to games. When my team is losing, I usually feel bad. I prefer to watch sports on TV. I watch sports to cheer the entire team’s effort. I watch sports to see the athletes I like. Sports has great meaning in my life. I like learning about the history of my favorite teams. I regularly attend sporting events. I regularly participate in sports. I go out of my way to learn the latest sports news.

  8. analysis While there are several ways to conduct a cluster analysis, RK&A conducted a k-mean cluster analysis, in which data are grouped into clusters by the nearest centroid, also called the “core” of a cluster, which are determined by means. The objective of cluster analysis is to create distinct groups of visitors. Thus, the greater the similarity of visitors within the cluster and the greater the difference between clusters, the better the clusters.

  9. results RK&A identified four clusters: Active Enthusiasts, TV Enthusiasts, Middle-Road Fans, and Indifferent Companions (see the next slides for descriptions).

  10. Active enthusiasts Cluster consists of participatory, engaged, emotional sports fans. They prefer to attend or participate in sporting events rather than watch them on TV.

  11. Tv enthusiasts Cluster also consists of avid sports fans. They regularly attend sporting events, but they prefer to follow their favorite teams and athletes on TV.

  12. Middle-road fans This cluster is the largest visitor cluster. Middle-Road Fans pay attention to sports, but they are not emotional, die-hard fans, and they do not regularly participate in sports.

  13. Indifferent companions This cluster is the smallest visitor cluster. These visitors do not participate in sports and would not describe themselves as sports fans.

  14. distinction of clusters As mentioned previously, the objective of cluster analysis is to create distinct clusters of visitors. To see how distinct and nuanced the clusters are, refer to the next slide, which shows the mean ratings of each statement organized by cluster.

  15. implications Following the study, RK&A spoke with Museum staff about their experiences with the study and implications of the analysis. These findings were as follows:

  16. First implication A museum’s audience is not homogeneous, as most museum practitioners realize. Cluster analysis helps staff see and characterize cross-sections of visitors, helping them to (re)consider their audience. Example: It was revealing that the largest cluster were Middle Road Fans and that there are nuances between sports enthusiasts (e.g., Active Enthusiasts versus TV Enthusiasts)

  17. Second implication Through identifying clusters, staff can consider to which segment of their audience to best allocate resources,meaning that staff may consider marketing or programming to the largest cluster, with the intent of also drawing some visitors from other clusters. Example: Focus on the Middle-Road Fans, but plan to draw some Enthusiasts and a few Indifferent Companions.

  18. third implication Staff can identify nuances that are most similar and dissimilar among clusters,also using it to inform marketing, programming, and even exhibition design. Example: Ratings of the statement, “I watch sports to see the athletes I like,” were most similar, while ratings of the statement, “Sports has great meaning in my life,” were most dissimilar.

  19. For more information For the full report by Randi Korn & Associates, Inc. for the Sports Legends Museum at Camden Yards, go to: http://informalscience.org/evaluation/show/194

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