With the impact winning athletic teams have on a university it is not surprising that pressure to produce winning teams is enormous. Coaches are expected to recruit the most athletically talented players to provide the university with winning seasons (Letawsky, Palmer & Schneider, 2005). In order for institutions to bring in quality athletes who are able to excel academically and athletically, it is important for the administrators, coaches, and recruiters to understand what characterizes the college selection decision making process for student-athletes. Therefore, an important step in this regard would be to develop instrumentation to measure this process. Hence, the purpose of this study was to conduct pilot research to develop instrumentation in which the underlying structure of student-athletes' college selection processes could be better understood. The study took place at the University of Nevada, Las Vegas (UNLV) and in cooperation with the UNLV Department of Athletics. Based on the literature and structured interviews with UNLV athletic coaches, administrators, and student-athletes, it was determined that the instrument should attempt to measure the following 6 components: (a) relationship with coaching staff, (b) success of program, (c) personal achievement, (d) academics, (e) teammates, (f) and UNLV/Las Vegas. Items were reviewed by a panel of experts, then revised and placed within one of the six components. The result was a 45-item instrument using a 4 point Likert scale ranging from "completely disagree" to "disagree" to "agree" to "completely agree". The field test of the instrument included 290 current UNLV student-athletes. Cronbach's alpha was calculated for each of the six original components and each was found to be above the recommended .7. Principal Components Analysis (PCA) was used to identify the components that comprise the instrument. PCA is often used in the early stages of research to gather information about the interrelationships among a set of variables (Pallant, 2005). Results of the PCA revealed 5 components with eigenvalues > 1.0 that explained 68.45% of the variance. Further inspection of the data demonstrated difficulty in identifying unique relationships between items based on their loadings. Additionally, since the first two components comprised the majority of the cumulative variance, two components were selected for further analysis. A second PCA conducted resulted in a 2 component solution, with 15 items explaining 43.6% of the variance. These items conceptually fit with one another, identifying the two major components: (1) Relationship with Coach and (2) Family Perceptions of UNLV/Las Vegas. Keyword(s): research, sport management, sport topics