Quality home visits: Activities to promote meaningful interactions

Examination of and support for specific practices that promote high-quality home visiting are essential as family support programs continue to expand across the country. The current study used direct observation of 91 home visits across 41 home visitors to examine relations among interaction partners, content of the interactions, the home-visitors’ activities, and quality of home-visitors’ practices and family-members’ engagement within programs funded by the Maternal, Infant, and Early Childhood Home Visiting program. More time spent in triadic interactions focused on child-related content, as measured by the Home Visit Rating Scale-Revised, was related to higher quality of family engagement in home visits, as measured with the Home Visit Observation Rating Scales. Time spent in adult-focused interactions and administrative tasks, however, was related to lower quality of home-visiting practices and family engagement. Implications for research and practice are discussed.     

Impacts of school-wide positive behaviour support: Results from National Longitudinal Register Data

Problem behaviour in schools may have detrimental effects both on students' well-being and academic achievement. A large literature has consistently found that school-wide positive behaviour support (SWPBS) successfully addresses social and behavioural problems. In this paper, we used population-wide longitudinal register data for all Norwegian primary schools and a difference-in-difference (DiD) design to evaluate effects of SWPBS on a number of primary and secondary outcomes, including indicators of externalising behaviour, school well-being, pull-out instruction, and academic achievement. Indications of reduced classroom noise were found. No other effects were detected. Analyses revealed important differences in outcomes between the intervention and control schools, independent of the implementation of SWPBS, and that a credible design like DiD is essential to handle such school differences.     

Enhancing frequency recording by developmental disabilities treatment staff

We evaluated a staff training and management package for increasing accuracy of recording frequency of problem behavior in a residential care facility. A multiple baseline design across the first and second work shifts showed that 2 of 8 participants increased their accuracy following in-service training, and all 8 improved during a condition with supervisor presence and feedback. Improvements were maintained when feedback was removed and generalized to activity periods when neither supervisor presence nor feedback was provided. Other staff behavior was not adversely affected by the intervention package.     

The Short Mood and Feelings Questionnaire (SMFQ): A Unidimensional Item Response Theory and Categorical Data Factor Analysis of Self-Report Ratings from a Community Sample of 7-through 11-Year-Old Children

Item response theory (IRT) and categorical data factor analysis (CDFA) are complementary methods for the analysis of the psychometric properties of psychiatric measures that purport to measure latent constructs. These methods have been applied to relatively few child and adolescent measures. We provide the first combined IRT and CDFA analysis of a clinical measure (the Short Mood and Feelings Questionnaire—SMFQ) in a community sample of 7-through 11-year-old children. Both latent variable models supported the internal construct validity of a single underlying continuum of severity of depressive symptoms. SMFQ items discriminated well at the more severe end of the depressive latent trait. Item performance was not affected by age, although age correlated significantly with latent SMFQ scores suggesting that symptom severity increased within the age period of 7–11. These results extend existing psychometric studies of the SMFQ and confirm its scaling properties as a potential dimensional measure of symptom severity of childhood depression in community samples.     

A two-stage logistic regression model for analyzing inter-rater agreement

Studies of agreement commonly occur in psychiatric research. For example, researchers are often interested in the agreement among radiologists in their review of brain scans of elderly patients with dementia or in the agreement among multiple informant reports of psychopathology in children. In this paper, we consider the agreement between two raters when rating a dichotomous outcome (e.g., presence or absence of psychopathology). In particular, we consider logistic regression models that allow agreement to depend on both rater- and subject-level covariates. Logistic regression has been proposed as a simple method for identifying covariates that are predictive of agreement (Coughlin et al., 1992). However, this approach is problematic since it does not take account of agreement due to chance alone. As a result, a spurious association between the probability (or odds) of agreement and a covariate could arise due entirely to chance agreement. That is, if the prevalence of the dichotomous outcome varies among subgroups of the population, then covariates that identify the subgroups may appear to be predictive of agreement. In this paper we propose a modification to the standard logistic regression model in order to take proper account of chance agreement. An attractive feature of the proposed method is that it can be easily implemented using existing statistical software for logistic regression. The proposed method is motivated by data from the Connecticut Child Study (Zahner et al., 1992) on the agreement among parent and teacher reports of psychopathology in children. In this study, parents and teachers provide dichotomous assessments of a child's psychopathology and it is of interest to examine whether agreement among the parent and teacher reports is related to the age and gender of the child and to the time elapsed between parent and teacher assessments of the child.The authors thank the Associate Editor and the referees for their helpful comments and suggestions. We also thank Gwen Zahner for use of data from the Connecticut Child Study, which was conducted under contract to the Connecticut Department of Children and Youth Services. This research was supported by grants HL 69800, AHRQ 10871, HL52329, HL61769, GM 29745, MH 54693 and MH 17119 from the National Institutes of Health.     

1. On note taking

In this paper the author explores the theoretical and technical issues relating to taking notes of analytic sessions, using an introspective approach. The paper discusses the lack of a consistent approach to note taking amongst analysts and sets out to demonstrate that systematic note taking can be helpful to the analyst. The author describes his discovery that an initial phase where as much data was recorded as possible did not prove to be reliably helpful in clinical work and initially actively interfered with recall in subsequent sessions. The impact of the nature of the analytic session itself and the focus of the analyst's interest on recall is discussed. The author then describes how he modified his note taking technique to classify information from sessions into four categories which enabled the analyst to select which information to record in notes. The characteristics of memory and its constructive nature are discussed in relation to the problems that arise in making accurate notes of analytic sessions.     

Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units

A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a constrained three-mode factor analysis and it can be seen as a generalization of the REDKM model proposed by De Soete and Carroll for two-mode data. The least squares fitting problem is mathematically formalized as a constrained problem in continuous and discrete variables. An iterative alternating least squares algorithm is proposed to give an efficient solution to this minimization problem in the crisp and fuzzy classification context. The performances of the proposed methodology are investigated by a simulation study comparing our model with other competing methodologies. Different procedures for starting the proposed algorithm have also been tested. A discussion of some interesting differences in the results follows. Finally, an application to real data illustrates the ability of the proposed model to provide substantive insights into the data complexities.