A latent class analysis of job satisfaction and turnover among practicing pharmacists.

Department of Public Health, College of Medicine, Nova Southeastern University, Lauderdale, FL 33328, USA.
Research in Social and Administrative Pharmacy (Impact Factor: 2.35). 03/2010; 6(1):32-8. DOI: 10.1016/j.sapharm.2009.03.002
Source: PubMed

ABSTRACT Research on job satisfaction and turnover using latent class analysis (LCA) has been conducted in other disciplines. LCA has seldom been applied to social pharmacy research and may be especially useful for examining job situation constructs in pharmacy organizations.
The objective of the study was to determine the probability of turnover among practicing pharmacists using LCA.
Using a cross-sectional descriptive design, 2400 randomly selected pharmacists with active licenses in Florida were surveyed. A model was created using LCA, then fit indices were used to determine whether underlying "job satisfaction clusters" were present. Once identified, these clusters along with the covariate practice site were modeled on a distal outcome turnover.
A 5-class model appeared to best fit the data: a "pseudo-satisfied" class that contained 8% of the sample, a "career-goal" class that contained 11% of the sample, a "satisfied class" that contained 44% of the sample, a "job-expectation" class that contained 3% of the sample, and an "unsatisfied class" that contained 17% of the sample. In terms of predicting the distal outcome "turnover," the calculated odds ratios indicate that compared with class 3 or the satisfied group, class 2 was 14 times more likely, class 4 was 17 times more likely, and class 5 was 26 times more likely to state that they do not intend to be employed with their current employer 1 year from now.
The LCA method was found to be effective for finding relevant subgroups with a heterogeneous at-risk population for turnover. Results from the analysis indicate that job satisfaction may be parsed into smaller, more interpretable and useful subgroups. This result holds great promise for practitioners and researchers, alike.

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