caa a22 4500 606153292 CHVBK 20210128100553.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s11121-015-0561-z doi (NATIONALLICENCE)springer-10.1007/s11121-015-0561-z Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples [Elektronische Daten] [David Henry, Allison Dymnicki, Nathaniel Mohatt, James Allen, James Kelly] Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a "real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities. Society for Prevention Research, 2015 Mixed methods nationallicence Cluster analysis nationallicence Simulation nationallicence Community leadership nationallicence Henry David University of Illinois at Chicago, Chicago, IL, USA aut Dymnicki Allison American Institutes for Research, Washington, D.C., USA aut Mohatt Nathaniel University of Colorado, Nederland, CO, USA aut Allen James University of Minnesota, Minneapolis, MN, USA aut Kelly James University of Illinois at Chicago, Chicago, IL, USA aut Prevention Science Springer US; http://www.springer-ny.com 16/7(2015-10-01), 1007-1016 1389-4986 16:7<1007 2015 16 11121 https://doi.org/10.1007/s11121-015-0561-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11121-015-0561-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Henry David University of Illinois at Chicago, Chicago, IL, USA aut NATIONALLICENCE 700 1- Dymnicki Allison American Institutes for Research, Washington, D.C., USA aut NATIONALLICENCE 700 1- Mohatt Nathaniel University of Colorado, Nederland, CO, USA aut NATIONALLICENCE 700 1- Allen James University of Minnesota, Minneapolis, MN, USA aut NATIONALLICENCE 700 1- Kelly James University of Illinois at Chicago, Chicago, IL, USA aut NATIONALLICENCE 773 0- Prevention Science Springer US; http://www.springer-ny.com 16/7(2015-10-01), 1007-1016 1389-4986 16:7<1007 2015 16 11121