Imputación múltiple de valores perdidos en el análisis factorial exploratorio de escalas multidimensionales: estimación de las puntuaciones de rasgos latentes

Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants’ scores on the corresponding latent traits. Our approach uses the following steps: (1) multiple imputation creates several copies of the data, in which the missing values are imputed; (2) each copy of the data is subject to independent factor analysis, and the same number of factors is extracted from all copies; (3) all factor solutions are simultaneously orthogonally (or obliquely) rotated so that they are both (a) factorially simple, and (b) as similar to one another as possible; (4) latent trait scores are estimated for ordinal data in each copy; and (5) participants’ scores on the latent traits are estimated as the average of the estimates of the latent traits obtained in the copies. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses and a simulation study based on artificial datasets. The results show that our approach was able to compute factor score estimates even for participants that have missing data.Los investigadores con frecuencia se enfrentan a la difícil tarea de analizar las escalas en las que algunos de los participantes no han respondido a todos los ítems. En este artículo nos centramos en el análisis factorial exploratorio de escalas multidimensionales (es decir, escalas que constan de varias de subescalas), donde cada subescala se compone de una serie de ítems de tipo Likert, y el objetivo del análisis es estimar las puntuaciones de los participantes en los rasgos latentes correspondientes. En este contexto, se propone un nuevo enfoque para hacer frente a las respuestas faltantes que se basa en (1) la imputación múltiple de las respuestas faltantes y (2) la rotación simultánea de las muestras de datos imputados. Se ha aplicado el método en una muestra de datos reales en que las respuestas que faltantes fueron introducidas artificialmente siguiendo un patrón real de respuestas faltantes, y un estudio de simulación basado en conjuntos de datos artificiales. Los resultados muestran que nuestro enfoque (en concreto, Hot-Deck de imputación múltiple seguido de rotación Consensus Promin) es capaz de calcular correctamente la puntuación factorial estimada incluso para los participantes que tienen valores perdidos

Young offenders caught in the act:A population-based cohort study comparing internationally adopted and non-adopted adolescents

Empirical research has shown an elevated risk for externalizing behavior problems in international adoptees. To address the extent to which this risk exists for more serious externalizing problems we compared the rates of registered criminal offending of internationally adopted adolescents with those of non-adopted adolescents in the Netherlands. In a large population-based cohort study (N = 3,758,506 including n = 10,563 international adoptees) on Dutch youth with ages up to 19 years we examined registrations in the program on juvenile crime and in the national police system from 2005 to 2013. Controlling for time lapse and background variables we found that international adoptees had been in contact with the criminal justice system more frequently than non-adoptees. However, the findings differed across region of adoption: Adoptees from South America and from Africa had been in contact with the criminal justice system most frequently (and more often than non-adoptees), whereas adoptees from China (total n = 4569) had the least contacts (and less often than non-adoptees). The percentages of criminal offending of adoptees ranged between 1.16% and 15.83% across regions of adoption (versus 10.86% in non-adoptees). The large majority of adoptees – including those from South America and Africa – were not involved in criminal acts. We hypothesize that the higher and lower risks of criminal offending found for adoptees from certain countries are associated with the varying levels of pre-adoption adversity (e.g., neglect and abuse) that the adoptees have experienced

Intergenerational transmission of child maltreatment using a multi-informant multi-generation family design.

In the current study a three-generational design was used to investigate intergenerational transmission of child maltreatment (ITCM) using multiple sources of information on child maltreatment: mothers, fathers and children. A total of 395 individuals from 63 families reported on maltreatment. Principal Component Analysis (PCA) was used to combine data from mother, father and child about maltreatment that the child had experienced. This established components reflecting the convergent as well as the unique reports of father, mother and child on the occurrence of maltreatment. Next, we tested ITCM using the multi-informant approach and compared the results to those of two more common approaches: ITCM based on one reporter and ITCM based on different reporters from each generation. Results of our multi-informant approach showed that a component reflecting convergence between mother, father, and child reports explained most of the variance in experienced maltreatment. For abuse, intergenerational transmission was consistently found across approaches. In contrast, intergenerational transmission of neglect was only found using the perspective of a single reporter, indicating that transmission of neglect might be driven by reporter effects. In conclusion, the present results suggest that including multiple informants may be necessary to obtain more valid estimates of ITCM

A randomized controlled trial of an Internet-based intervention for eating disorders and the added value of expert-patient support: study protocol

Background: E-mental health has become increasingly popular in interventions for individuals with eating disorders (EDs). It has the potential to offer low-threshold interventions and guide individuals to the needed care more promptly. Featback is such an Internet-based intervention and consists of psychoeducation and a fully automated monitoring and feedback system. Preliminary findings suggest Featback to be (cost-)effective in reducing ED symptomatology. Additionally, e-mail or chat support by a psychologist did not enhance the effectiveness of Featback. Support by an expert patient (someone with a lived experience of an ED) might be more effective, since that person can effectively model healthy behavior and enhance self-efficacy in individuals struggling with an ED. The present study aims to replicate and build on earlier findings by further investigating the (cost-)effectiveness of Featback and the added value of expert-patient support. Methods: The study will be a randomized controlled trial with a two-by-two factorial design with repeated measures. The four conditions will be (1) Featback, in which participants receive automated feedback on a short monitoring questionnaire weekly, (2) Featback with weekly e-mail or chat support from an expert patient, (3) weekly support from an expert patient, and (4) a waiting list. Participants who are 16 years or older and have at least mild self-reported ED symptoms receive a baseline measure. Subsequently, they are randomized to one of the four conditions for 8 weeks. Participants will be assessed again post-intervention and at 3, 6, 9, and 12 months follow-up. The primary outcome measure will be ED psychopathology. Secondary outcome measures are experienced social support, self-efficacy, symptoms of anxiety and depression, user satisfaction, intervention usage, and help-seeking attitudes and behaviors. Discussion: The current study is the first to investigate e-mental health in combination with expert-patient support for EDs and will add to the optimization of the delivery of Internet-based interventions and expert-patient support. Trial registration: Netherlands Trial Register, NTR7065. Registered on 7 June 2018

Neurobiological correlates of antisociality across adolescence and young adulthood: a multi-sample, multi-method study

Background Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined. Methods We harmonized data from 1489 participants (9-27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood. Results Three dimensions of antisociality were uncovered: 'callous-unemotional (CU)/manipulative traits', 'intentional aggression/conduct', and 'reactivity/impulsivity/irritability'. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age. Conclusions Across a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations

Much Ado About Nothing: Multiple Imputation to Balance Unbalanced Designs for Two-Way Analysis of Variance

In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type III sum of squares in two-way ANOVA. In the current simulation study we studied four pooled statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data, and compared these statistics with Type III sum of squares. Statistics D₀ and D₂ generally performed best regarding Type-I error rates, and had power rates closest to that of Type III sum of squares. However, none of the statistics produced power rates higher than Type III sum of squares. The results lead to the conclusion that for multiply imputed datasets D₀ and D₂ may be the best methods for pooling the results of multiparameter estimates in multiply imputed datasets, and that for unbalanced data, Type III sum of square is to be preferred over using multiple imputation in obtaining ANOVA results