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Batterers' resistance to traditional intervention programs has been well documented. Within a Transtheoretical Model of Change (stage of change) framework, a measure of processes of resistance was developed and administered to 346 adult male domestic violence offenders in treatment. The study yielded a 38-item measure that assesses eight dimensions of resistance: (a) System Blaming, (b) Problems with Partner, (c) Problems with Alliance, (d) Social Justification, (e) Hopelessness, (f) Isolation, (g) Psychological Reactance, and (h) Passive Reactance. The relationship between resistance and stage of change, time in treatment, and partner aggression are reported. Results suggest that we look beyond the most common forms of resistance (e.g., denial and victim-blaming) to identify and address other forms of resistance that may be more internally based and difficult to detect. The processes of resistance measure provides a tool for measuring those types of resistance. [ABSTRACT FROM AUTHOR]

It has been suggested that individuals with autism are more vulnerable to, and less able to cope with, the effects of stressors than the general population. However, very little work has been done to develop tools such as surveys or questionnaires that assess reactions to stressors in individuals with developmental disabilities. In the present investigation, the Stress Survey Schedule for Persons with Autism and Other Developmental Disabilities (SSS; Groden et al., 2001) was completed for 180 individuals with autism, varying in gender, verbal ability, and age. The results demonstrate that the SSS is a valid tool for identifying which dimensions of stress are perceived to be the most and least stressful for subgroups of persons with autism. [ABSTRACT FROM AUTHOR]

Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated time-series data of length 100 were generated for 50 different conditions representing five levels of autocorrelation, two levels of slope, and five levels of proportion of missing data. Methods were compared with respect to the accuracy of estimation for four parameters (level, error variance, degree of autocorrelation, and slope). The choice of method had a major impact on the analysis. The maximum likelihood very accurately estimated all four parameters under all conditions tested. The mean of the series was the least accurate approach. Statistical methods such as the maximum likelihood procedure represent a superior approach to missing data. [ABSTRACT FROM AUTHOR]