Native Hawaiian / other Pacific Islander

The IPS Fidelity scale was applied to each site. Data was collected and scored consistent with the Fidelity Scale directions.

An ex post facto design, using data mining as a statistical analysis strategy. Data was taken from the RSA-911 report for all the persons with SCI closed by State Vocational Rehabilitation agencies in 2001. A chi-squared automatic interaction detector (CHAID) based data mining analysis was used to identify the strongest associations between predictors (VR services) and the outcome variable (employment outcomes).

The specific variables measured in this study included: the influence of duration of community-based training (CBT) that included on-the-job training, on the-job training as a subset of CBT, the least restrictive environment (LRE), or the degree of integration with non-disabled peers during the school day, demographics(gender, ethnicity, home setting, behavior problems, physical disability and mental ability) as measured by
intelligence quotient (I.Q.).Data was collected by structured interview with teachers and administrators, record review and on site observations. Correlations were used to examine predictive relationships between the independent variables and the dependent variable of post-school integrated employment. Cross tabulations and chi-square analysis of correlated variables were then used to identify significance of specific variables on employment outcome.

The data consisted of patient clinical records related to HIV/AIDS treatment and symptoms and services delivered. Statistical analyses consisted of descriptive statistics and analysis of variance (ANOVA).

Data was collected on a number of variables using the RSA 911 database. This included: disability, wages earned, hours worked, and cost of services. Vocational rehabilitation counselors are required to keep this type of documentation. The rate of employment was determined by dividing the number of individuals who had their case closed due to an employment outcome by the total number of job seekers in that group.

Data on demographics and vocational services were analyzed. Multiple discriminant analysis was used to identify different services used by cancer survivors who were unemployed and those who were employed.

A data mining technique, exhaustive chi-squared automatic interaction detector (CHAID), was used to analyze. In the current study, exhaustive CHAID was used to build classification trees. This technique uses a systematic algorithm to detect the strongest association between predictors and the outcome variable (e.g., employment outcomes) through a comprehensive search of the predictors and the levels of predictors from the entire set that show the most differentiation on the employment outcome variable. The degree of differentiation is depicted sequentially in a decision tree format to show the optimally split predictors. Thus, homogeneous groups of vocational rehabilitation clients could be identified based on their demographic characteristics, work disincentives, and service patterns in terms of their observed levels on the outcome variable. The statistical software SPSS Answer Tree 2.0 was used to conduct the exhaustive CHAID analysis. The alpha level for all statistical tests was 0.01. This study corrected for the number of statistical tests within each predictor using a Bonferroni feature in the Answer Tree software.

The measures included the Addiction severity index-multimedia version (ASI-MV), Social adjustment scale-self report (SAS-SR), Employment questionnaire, Treatment services review and Satisfaction questionnaire. Around 75% of the sample completed 3 or more CD-ROM sessions. These participants completed a satisfaction questionnaire after each session and at the end entire course. Follow up assessment was scheduled 6 month from baseline. Subjects completed SAS-SR and Employment Questionnaire. They were also contacted by telephone to complete a follow up to the ASI.

A number of variables were examined in the study. This included: successful employment outcome, hours worked and wages and the cost of services. Data was pulled for all of the United States. Washington DC and US territories were excluded. Data from each state and year was reported for each study question. In order to identify trends data from 2002 to 2006 was compared to 2007 to 2011 and regression analysis was used to evaluate the data. Mean data for the total transition population served by VR was compared to the ASD group and t test statistics were used to compare outcomes between the two groups.

The service had a comprehensive business information system that tracked all clients throughout the contract. Records of clients(n=49) aged 16–25 years were retrospectively examined. Data analysis was conducted by the team leader using Microsoft Excel 2003, guided by the outcome variables identified from published reports of comparable services. The comprehensive data management system enabled most variables of interest to be reported. Data quality was governed by the contract that required 100% accuracy and supporting evidence for all employment outcomes. All client records were reviewed monthly by an employment specialist, an administrator and by the team leader for the term of the contract. The records were also subject to regular internal review and intensive external auditing as a condition of the contract.