Young children living with intimate partner violence (IPV) are often also exposed to harsh parenting. Both forms of violence increase children’s risk for clinically significant disruptive behavior, which can place them on a developmental trajectory associated with serious psychological impairment later in life. Although it is hypothesized that IPV behaviors may spillover into harsh parenting, and thereby influence risk for disruptive behavior, relatively little is known about these processes in families with young children. The current study examines the overlap of the quality and frequency of psychological and physical forms of IPV and harsh parenting, and tests whether harsh parenting mediates the relationship between IPV and child disruptive behavior in a diverse cross-sectional sample of 81 children ages 4 to 6 years. Results suggest that mothers reporting a greater occurrence of psychologically aggressive IPV (e.g., yelling, name-calling) more often engage in psychological and physical aggression toward their children (odds ratios [ORs] = 4.6–9.9). Mothers reporting a greater occurrence of IPV in the form of physical assault more often engage in mild to more severe forms of physical punishment with potential harm to the child (ORs = 3.8–5.0). Psychological and physical forms of IPV and harsh parenting all significantly correlated with maternal reports of child disruptive behavior (r = .29–.40). Psychological harsh parenting partially mediated the association between psychological IPV and child disruptive behavior. However, a significant direct effect of psychological IPV on preschool children’s disruptive behavior remained. Implications for child welfare policy and practice and intervention, including the need for increased awareness of the negative impact of psychological IPV on young children, are discussed.

It is possible to accurately identify LI in English language learners once they use English 40% of the time or more. However, for children with high Spanish experience, more information about development and patterns of impairment is needed to positively identify LI.

Research studies in psychology and education often seek to detect changes or growth in an outcome over a duration of time. This research provides a solution to those interested in estimating latent traits from psychological measures that rely on human raters. Rater effects potentially degrade the quality of scores in constructed response and performance assessments. We develop an extension of the hierarchical rater model (HRM), which yields estimates of latent traits that have been corrected for individual rater bias and variability, for ratings that come from longitudinal designs. The parameterization, called the longitudinal HRM (L-HRM), includes an autoregressive time series process to permit serial dependence between latent traits at adjacent timepoints, as well as a parameter for overall growth. We evaluate and demonstrate the feasibility and performance of the L-HRM using simulation studies. Parameter recovery results reveal predictable amounts and patterns of bias and error for most parameters across conditions. An application to ratings from a study of character strength demonstrates the model. We discuss limitations and future research directions to improve the L-HRM.

Many large‐scale assessments are designed to yield two or more scores for an individual by administering multiple sections measuring different but related skills. Multidimensional tests, or more specifically, simple structured tests, such as these rely on multiple multiple‐choice and/or constructed responses sections of items to generate multiple scores. In the current article, we propose an extension of the hierarchical rater model (HRM) to be applied with simple structured tests with constructed response items. In addition to modeling the appropriate trait structure, the multidimensional HRM (M‐HRM) presented here also accounts for rater severity bias and rater variability or inconsistency. We introduce the model formulation, test parameter recovery with a focus on latent traits, and compare the M‐HRM to other scoring approaches (unidimensional HRMs and a traditional multidimensional item response theory model) using simulated and empirical data. Results show more precise scores under the M‐HRM, with a major improvement in scores when incorporating rater effects versus ignoring them in the traditional multidimensional item response theory model.

Applying thermodynamics to realistic systems requires a knowledge of the thermodynamic properties of mixtures. Functions of mixing and excess functions provide a useful approach. The concepts are simple and their application straightforward, but students often fail to apply them correctly when they are given only a theoretical explanation. We discuss some typical mistakes and some problems we have found useful for overcoming them.