The Society for Social Work and Research

2014 Annual Conference

January 15-19, 2014 I Grand Hyatt San Antonio I San Antonio, TX

Examining Strengths Among Systems of Care Youth: A Latent Class Analysis

Sunday, January 19, 2014: 11:15 AM
HBG Convention Center, Room 003A River Level (San Antonio, TX)
* noted as presenting author
Theda Rose, PhD, Research Assistant Professor, University of Maryland at Baltimore, Baltimore, MD
Bethany Lee, PhD, Associate Professor, University of Maryland at Baltimore, Baltimore, MD
Maria Jose Horen, MS, MPH, Research Supervisor, University of Maryland at Baltimore, Baltimore, MD
Background and Purpose:

Increased attention has been directed towards measuring well-being of youth from a strengths perspective. The Behavioral and Emotional Rating Scale (BERS) is one of the most widely known and utilized measures of youth strengths, with strong psychometric properties.  Using a positive youth development framework, the purpose of this paper is to identify distinct groups of youth based on their constellation of strengths as measured with the BERS. We will also explore how membership in these strength classes is associated with other youth characteristics.


The sample data are from 83 caregiver reports of families participating in a Systems of Care (SOC) project in a mid-Atlantic state. The sample consists of youth with serious emotional disturbances who are at risk of placement or placement disruption. Youth ranged in age from 5 to 20, with an average age of 14. The majority of youth were Black or African American (56%) and 48% male. Strengths were assessed using the BERS, which has 57 items in 6 subscales. Each item is scaled on 4-point responses ranging from 0 (not at all like your child) to 3 (very much like your child).  In addition to the strengths measure, demographic data and problem behavior scores from the Child Behavior Checklist (CBCL) were included in the analysis.

To identify distinct groups of youth by strengths, latent class analysis was conducted with the 57 BERS items using the 6 mean subscale scores. Then bivariate relationships between most likely group membership and youth demographics and CBCL problem behavior scores were compared.


A four-class Strengths model best fit the data. Specifically, the emerging latent classes included: low strengths (n=13), borderline low strengths (n=42), average socio-emotional strengths (n=11), and many strengths (n=17). The many strengths class was the oldest (mean age=14.18) and had the smallest proportion of youth with CBCL scores in the clinical range (31%). For the youth in the low strengths class, all of these youth scored in the clinical range of the externalizing and total problems score scales for the CBCL. Interestingly, in the average socio-emotional strengths class, all youth had clinical levels of total problem behaviors, suggesting that their limitations in the family and school domains override their average socio-emotional strengths.

Conclusions and Implications:

These results show that assessment of problem behaviors only does not provide a complete picture of youth functioning in diverse domains.  For example, we identified a class of youth who had strengths in several areas, but still scored in clinical ranges for problem behaviors. Strengths assessment is especially important for populations of youth receiving services as it reflects the core values and principles of SOC. Once identified, strengths can be used to guide treatment planning for youth and their families.