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Objective: This research examines the psychometric properties of two subscales of the Association of University Centers on Disabilities (AUCD) Multicultural Council's Organizational Cultural Competence Assessment, which together have been used to assess organizational cultural competence in child welfare agencies. Method: Confirmatory factor analysis was utilized to analyze data on 478 child welfare workers. Results: Six models were generated based upon the theoretical literature. The best-fitting model was unidimensional (?[superscript 2] = 13.18, df = 9, p = 0.15; root mean square error of approximation = 0.03; comparative fit index = 0.99; Tucker-Lewis Index = 0.99). Discussion and applications to social work: Factors that are more proximal to client interaction were stronger indicators of organizational cultural competence than those that were more distal. Those wishing to examine organizational cultural competence in voluntary child welfare settings should consider using the validated form of the AUCD instrument.

A number of proxies have been used in child welfare workforce research to represent actual turnover; however, there have been no psychometric studies to validate a scale specifically designed for this purpose. The Intent to Leave Child Welfare Scale is a proxy for actual turnover that measures workers' intention to leave. This scale was validated in the current study by a CFA. The resulting factors were compared to actual turnover. Nearly two in three workers who indicated that they had considered looking for a job in the past year actually left their agencies (60.0%). A confirmatory factor analysis was conducted to determine the validity of the Intent to Leave Child Welfare Scale. The best fitting model consisted of three factors with acceptable fit statistics (X[superscript 2] = 28.6, p = 0.04; RMSEA = 0.05, 90% RMSEA CI = 0.01-0.08; CFI = 0.99; TLI = 0.98). Identified latent factors included "thinking," which included observed variables related to workers thinking about leaving their current jobs; "looking," which included observed variables related to workers searching for a new job; and "acting," which included observed variables related to workers actually taking physical steps to seek a new job. Once a good fitting model was identified, binary logistic regression was conducted to determine odds ratios to predict who actually left their agencies. Each of the identified latent factors was significantly predictive of actual leaving (thinking: OR = 1.24, p = 0.00; looking: OR = 1.25, p = 0.00; acting: OR = 1.28, p = 0.01).

The need for statistical analysis in single-subject designs presents a challenge, as analytical methods that are applied to group comparison studies are often not appropriate in single-subject research. "SSD for R" is a robust set of statistical functions with wide applicability to single-subject research. It is a comprehensive package of statistical tests and produces accompanying graphs for almost every function. These include, among others, tests of autocorrelation, t-tests, effect sizes, median and celeration chi[superscript 2] analyses, and control charts. All functions and graphs are described in a series of tables. Since "SSD for R" is open source, it is freely available and can be accessed on the authors' website, www.ssdanalysis.com. Additional support, including video tutorials and sample data are also available at no charge. (Contains 7 figures and 4 tables.)

This article focuses on the connection between social work practice and prevention in public health by presenting a study on newborns who may have hearing impairments. Newborn hearing screening records from six hospitals in a large suburban health system were analyzed. Binary logistic regression was used to predict factors related to being fit with hearing aids or loss-to-follow-up. Children with unilateral or conductive losses were less likely to be fit with hearing aids as were children with Medicaid coverage and families living 25 or more miles away from the hearing center. Implications for social work practice are discussed. [ABSTRACT FROM AUTHOR]