Repeated-Measures and Multilevel Latent Class Analysis in Child Welfare Research
Methods: The sample for the pathways to residential care study includes youth, age 13 to 16, who entered residential care for the first time under the auspices of a Midwestern state public child welfare agency (N=7,187). Based on administrative data records of placement moves, runaway episodes, placements in detention, and psychiatric hospitalizations during the year preceding placement into residential care, repeated-measures latent class analysis (Lanza & Collins, 2006) is used to develop a typology of pre-residential care placement pathways.
The sample for the supervisory relationship study includes child welfare caseworkers (N = 1,460) from 56 private and public agencies in Illinois who completed a web-based survey between August and October, 2010 (91% response rate). Respondents were queried about their experiences as child welfare workers, the characteristics of the organizational and resource environments in which they worked, and the nature of their relationships with their immediate supervisors. Using a variant of latent class analysis (LCA) suitable for multilevel data (Vermunt, 2003), we develop a typology of supervisory climate based on the nature of individual caseworker-supervisor relationships.
Results: Results of the pathways to residential care study suggest the existence of placement distinct pathways characterized by notable discontinuities and strong interrelationships between the sequence and timing of different pre-residential placement events. Results of the supervisory relationship study suggest the existence of distinct supervisory climates composed of distinct worker-supervisor relationship types.
Implications: These studies illustrate the utility of using LCA when examining phenomena of interest to child welfare researchers. These phenomena, which are often complex and not directly observable (latent), are also often poorly conceptualized and measured. However, recent advances in mixture models (including LCA), now afford researchers the opportunity to faithfully summarize this complexity.