506 The Professional Counselor | Volume 10, Issue 4 Ramsey et al., 2002) is accurate, then this might impact doctoral students’ career aspirations as well as exposure to and engagement in research-related experiences. According to HLT, CES doctoral students’ career aspirations can influence how they engage in certain planned experiences and if they choose to engage in certain unplanned experiences (Krumboltz, 2009). For example, a student focused on a career at an institution with less emphasis on research (e.g., master’s university) may put forth minimal effort in research courses and opt out of any unplanned experiences related to scholarly activity, such as accepting an invitation to join a research team. Also, it is possible that CES doctoral students at R1, R2, and D/PU institutions might have varying exposure to opportunities to engage in unplanned experiences related to research and scholarship if faculty at those institutions are spending less time in the role of researcher. For instance, Goodrich et al. (2011) found that in a survey of 16 CACREP-accredited counseling programs, only six programs had established research teams and only four programs required students to submit scholarly work to a professional journal before they could graduate. Purpose This study was designed to explore the current trends in publication rates of faculty in CES programs over a 10-year time period. Using a Bayesian analysis, we examined the following questions: • Research Question 1: What are the differences among CES programs’ faculty publication rates based on all Carnegie classifications? o Research Question 1.a: Are there differences among master’s-level programs based on Carnegie classifications in terms of faculty publication rates? • Research Question 2: Does observable data support prior literature findings regarding publication trends among CES programs at institutions with different levels of Carnegie classification? Bayesian analysis is appropriate when “one can incorporate (un)certainty about a parameter and update his knowledge through the prior distribution” of probabilities (Depaoli & van de Schoot, 2017, p. 4). The inferences made by Newhart et al. (2020) were used as prior information to inform the collected observational data for this study. Newhart et al. used self-reported survey data to run a Poisson regression with the same variables proposed for this study. However, their data focused primarily on the differences among research institutions and combined non–research-designated institutions (i.e., master’s universities) into a single category. Newhart et al.’s output helped inform the limitations of the observational data collection procedures, such as error in using database search engines. Additionally, this is the first known study to examine observational data of publication trends for CES programs, which might provide an under- or overestimation when compared to selfreported data. Alternatively, the use of self-reported data has often been stated as a limitation because of participant bias, which might inflate the outcomes. Therefore, it would be helpful to compare inferences from both sets of data. An initial comparison of parameter estimates between both studies will inform the trends of publications between Carnegie classifications. For this study, and similar to Newhart et al. (2020), Carnegie classification operated as the predictor variable and number of publications as the outcome variable. The results of the comparison and Bayesian hypothesis testing of data will provide a means to verify self-reported data trends between Carnegie classification using parameter estimates and further information regarding the scholarly productivity