Resumen
Helping people adopt behaviors to improve social, economic, and environmental conditions is central to Extension’s mission. This new publication describes cluster analysis, a quantitative technique that can be used to identify audience subgroups so that tailored education and communications can be designed, and conveys its value in supporting behavior change to help readers understand how this technique is applied and encourage others to consider using it. Written by Laura A. Warner; 5 pp.
https://edis.ifas.ufl.edu/wc399
Citas
Ali, A. D., Warner, L. A., & Kumar Chaudhary, A. (2018). Using perceived landscape benefits to subgroup Extension clients to promote urban landscape water conservation. EDIS, 2018(4). https://doi.org/10.32473/edis-wc291-2018
Andreasen, A. R. (2006). Social marketing in the 21st century. Thousand Oaks, California: Sage Publications.
Burns, R. B., & Burns, R. A. (2008). Cluster analysis. In R. B. Burns & R. A. Burns (Eds.), Business research methods and statistics using SPSS (pp. 552–567). London: Sage.
Gibson, K. E., Fortner, A. R., Lamm, A. J., & Warner, L. A. (2021). Managing demand-side water conservation in the United States: An audience segmentation approach. Water, 13(21), 2992. https://doi.org/10.3390/w13212992
Gibson, K. E., Lamm, A. J., & Lamm, K. W. (2020). Identifying audience needs to effectively communicate about the cost of implementing sustainable farming practices. Journal of Applied Communications, 104(3). https://doi.org/10.4148/1051-0834.2334
IBM Corporation. (2021a). Hierarchical cluster analysis. https://www.ibm.com/docs/en/spss-statistics/28.0.0?topic=features-hierarchical-cluster-analysis
IBM Corporation. (2021b). K-means cluster analysis. https://www.ibm.com/docs/en/spss-statistics/28.0.0?topic=features-k-means-cluster-analysis
IBM Corporation. (2021c). Two step cluster analysis. https://www.ibm.com/docs/en/spss-statistics/23.0.0?topic=option-twostep-cluster-analysis
Khachatryan, H., Rihn, A., Warwick, C. R., & Dukes, M. (2019). Who is interested in purchasing smart irrigation systems? EDIS, 2019(5), 7. https://doi.org/10.32473/edis-fe1069-2019
King, R. S. (2015). Cluster analysis and data mining. Dulles, VA: Mercury Learning and Information.
Kumar Chaudhary, A., & Warner, L. A. (2018). Understanding good irrigation and fertilization behaviors among households using landscape design features. EDIS, 2018(1), 4. https://doi.org/10.32473/edis-wc292-2018
Monaghan, P., Warner, L., Telg, R., & Irani, T. (2014). Improving Extension program development using audience segmentation. EDIS, 2014(6). http://edis.ifas.ufl.edu/wc188
Shaw, B. R. (2009). Using temporally oriented social science models and audience segmentation to influence environmental behaviors. In L. Kahlor & P. Stout (Eds.), Communicating Science (pp. 109–130). https://doi.org/10.4324/9780203867631
Warner, L., Galindo-Gonzalez, S., & Gutter, M. S. (2014). Building impactful Extension programs by understanding how people change. EDIS, 2014(6). http://edis.ifas.ufl.edu/wc189
Warner, L. A., Israel, G. D., & Diaz, J. M. (2019). Identifying and meeting the needs of Extension’s target audiences. EDIS, 2019(3). https://doi.org/10.32473/edis-wc336-2019
Warner, L. A., Kumar Chaudhary, A., Rumble, J. N., Lamm, A. J., & Momol, E. (2017). Using audience segmentation to tailor residential irrigation water conservation programs. Journal of Agricultural Education, 58(1), 313–333. https://doi.org/10.5032/jae.2017.01313
Warner, L. A., Lamm, A. J., Rumble, J. N., Martin, E., & Cantrell, R. (2016). Classifying residents who use landscape irrigation: Implications for encouraging water conservation behavior. Environmental Management, 58(2), 238–253. https://doi.org/10.1007/s00267-016-0706-2
Yim, O., & Ramdeen, K. T. (2015). Hierarchical cluster analysis: Comparison of three linkage measures and application to psychological data. The Quantitative Methods for Psychology, 11(1), 8–21. https://doi.org/10.20982/tqmp.11.1.p008