Society for Social Work and Research

Sixteenth Annual Conference Research That Makes A Difference: Advancing Practice and Shaping Public Policy
11-15 January 2012 I Grand Hyatt Washington I Washington, DC

62P A Latent Class Analysis of Job Satisfaction Among Caseworkers

Friday, January 13, 2012
Independence F - I (Grand Hyatt Washington)
* noted as presenting author
Alex Redcay, MSW, Doctoral Research Associate, Rutgers University, Piscataway, NJ
Carl F. Siebert, MBA, MS, Statistical Consultant and Research Instructor, Rutgers University, New Brunswick, NJ
Darcy Clay Siebert, PhD, Associate Professor, Rutgers University, New Brunswick, NJ
Purpose: Because job satisfaction is closely associated with intent to quit work, the research literature is replete with studies using variable-centered analytic techniques such as regression and structural equation modeling to examine job satisfaction, typically finding that demographics and locus of control are primary predicting variables. This study, instead, uses a person-centered analytic technique – latent class analysis (LCA) – to explore job satisfaction among caseworkers. By allowing respondents to sort themselves by their levels of satisfaction, it is possible to examine these groups for differences on a wide variety of variables, in an effort to support, or not, the findings of variable-centered techniques.

Method: Three hundred thirty one participants were recruited from caseworkers attending five state-mandated training sessions, covering different topics such as functional assessments and facilitating family team meetings. Participants took paper-and-pencil pretests and/or posttests to evaluate knowledge transfer and training satisfaction. Additional research questions were included in each, and participants were tracked anonymously through self-created IDs.

Measures included professional life satisfaction, a 5-item standardized scale using 5-point Likert-type response options from “strongly disagree” to “strongly agree” on items such as “The conditions of my professional life are close to ideal.” Additional items included demographics, locus of control, and a question asking, “Would you advise your child to work in the public child welfare system?”

An LCA was conducted to identify the most efficient and theoretically sound way to sort the respondents into classes. Next, the class identifiers were used to conduct multinomial regressions and ANOVAs to examine differing characteristics among the identified groups.

Results: The best model fit and theoretical support was for three latent classes/groups (Entropy = 0.834). A graphical comparison of mean responses for the five satisfaction items show distinct groups that represent high (n=126), moderate (n=132), and low (n=73) satisfaction. Using ANOVA, nine different demographic questions were analyzed for group differences that included education, gender, race, job title, household income, and length at current job. Of the nine, only length of professional employment and having enough income to cover their basic needs were significantly different between the high and low satisfaction groups. Unlike variable-centered studies, locus of control was not significantly different. An overwhelming 91.4% of the low-satisfaction respondents would not advise their children to work in public welfare, and even 25.8% of the highly satisfied group would not advise their children to work in public welfare agencies.

Conclusions: In child welfare agencies, turnover is a major threat to the organization, to continuity of care, and to client outcomes. Because job satisfaction is closely linked with turnover, it is imperative that we understand how to improve our caseworkers' professional lives. This study suggests that even those in the highly satisfied group have reservations, given that one quarter of them still would not advise their children to work in the field. It appears that paying our workers adequately could improve their satisfaction, but more education, a heightened internal locus of control, and reducing the number of hours worked will not. Future research will be proposed.