Transforming the time scale in linear multivariate growth curve models

Latente Wachstumskurven-Modelle zeigen wiederholte Messungen von Ergebnis-Variablen als Funktionen von aufeinanderfolgenden Zeitpunkten und anderen Maßen. Einige Autoren bemerkten bereits, dass die Beziehung zwischen dem anfänglichen Status und der Wachstumsrate von der Zeitskala abhängt, die mit dem Modell verbunden ist. Verschiedene Zeitskalen führen zu verschiedenen Schätzungen dieser beiden Wachstumsparameter, wie auch ihre Varianzen und Ko-Varianzen. Im vorliegenden Beitrag betrachtet der Autor ein multivariates Wachstumskurven-Modell, in dem die Beziehung zwischen den Mustern des Wandels durch mehr als eine Ergebnis-Variable modelliert werden kann. Es wird gezeigt, dass die Abhängigkeit auch im multivariaten Fall in Erscheinung tritt. Es wird ein mathematischer Beweis erbracht, in dessen Rahmen eine Verbindung von anfänglichem Status und Wachstumsrate mit einer ausgewählten Zeitskala hergestellt wird. Das Wesen dieser Verbindung wird anhand von Modellen mit einer verschiedenen Zeitskala für dieselben empirischen Daten veranschaulicht. (ICIÜbers)'Latent growth curve models represent repeated measures of outcome variables as functions of consecutive time points and other measures. Already a few authors noticed that the relationship between the initial status and growth rate depends on the time scale involved in the model. Different time scales lead to different estimates of these two growth parameters, as well as their variances and covariances. In this article the author's consider the multivariate growth curve model, in which the relationship between patterns of change of more than one outcome variable can be modeled. The author's will show that the dependency also occurs in the multivariate case. Mathematical evidence will be presented in which the relationship will be established of initial status and growth rate with the selected time scale. The nature of the relationship will be illustrated an models with a different time scale for the same empirical data.' (author's abstract

Decision support for using mobile rapid DNA analysis at the crime scene

Mobile Rapid DNA technology is close to being incorporated into crime scene investigations, with the potential to identify a perpetrator within hours. However, the use of these techniques entails the risk of losing the sample and potential evidence, because the device not only consumes the inserted sample, it is also is less sensitive than traditional technologies used in forensic laboratories. Scene of Crime Officers (SoCOs) therefore will face a ‘time/success rate trade-off’ issue when making a decision to apply this technology. In this study we designed and experimentally tested a Decision Support System (DSS) for the use of Rapid DNA technologies based on Rational Decision Theory (RDT). In a vignette study, where SoCOs had to decide on the use of a Rapid DNA analysis device, participating SoCOs were assigned to either the control group (making decisions under standard conditions), the Success Rate (SR) group (making decisions with additional information on DNA success rates of traces), or the DSS group (making decisions supported by introduction to RDT, including information on DNA success rates of traces). This study provides positive evidence that a systematic approach for decision-making on using Rapid DNA analysis assists SoCOs in the decision to use the rapid device. The results demonstrated that participants using a DSS made different and more transparent decisions on the use of Rapid DNA analysis when different case characteristics were explicitly considered. In the DSS group the decision to apply Rapid DNA analysis was influenced by the factors “time pressure” and “trace characteristics” like DNA success rates. In the SR group, the decisions depended solely on the trace characteristics and in the control group the decisions did not show any systematic differences on crime type or trace characteristic. Guiding complex decisions on the use of Rapid DNA analyses with a DSS could be an important step towards the use of these devices at the crime scene

Predicting growth curves of early childhood externalizing problems: Differential susceptibility of children with difficult temperament

Using an accelerated longitudinal design, the development of externalizing problems from age 2 to 5 years was investigated in relation to maternal psychopathology, maternal parenting, gender, child temperament, and the presence of siblings. The sample consisted of 150 children selected at age 2-3 years for having high levels of externalizing problems. Parenting was measured using observational methods, and maternal reports were used for the other variables. Overall, mean levels of externalizing problems decreased over time, and higher initial levels (intercept) were related to a stronger decrease (negative slope) in externalizing problems. Results showed that higher levels of maternal psychopathology were related to less decrease in early childhood externalizing problems. Parental sensitive behavior predicted a stronger decrease in externalizing problems, but only for children with difficult temperaments. A stronger decrease of externalizing problems in children with older siblings also pertained only to children with difficult temperaments. Thus, temperamentally difficult children appear to be more susceptible to environmental influences on the development of externalizing behaviors. Our results indicate that the role of siblings in early childhood externalizing problems deserves more research attention, and that intervention efforts need to take into account temperamental differences in children's susceptibility to environmental influences. © 2009 Springer Science+Business Media, LLC

Bias Among Forensic Document Examiners: Still a Need for Procedural Changes

In 1984, Miller published the paper: Bias among forensic document examiners: A need for procedural changes, with the intent to elicit some concern about the amount of cognitive bias among forensic document examiners. There is a need for the development of procedures regarding how a document examiner can minimize the amount of cognitive bias that may lead to erroneous conclusions by the examiner. Such procedures would serve to demonstrate that a conscientious effort was made by the examiner and the submitting agency to control extraneous variables that could bias the results of the examination. Some 28 years after Miller1 the forensic sciences are confronted with serious criticism with respect to cognitive bias (e.g. Risinger et al.2, and the NAS report3). It appears that not much of Millers suggestions have been applied in practice. No good general procedures have been implemented for minimizing the risk of cognitive bias in most institutes. In this paper we address the main issues raised in the 1984 paper, and describe the current state of affairs with respect to minimizing cognitive bias in the forensic sciences. There is still a need for procedural changes in the forensic sciences

A note on testing perfect correlations in SEM

This article is concerned with the seemingly simple problem of testing whether latent factors are perfectly correlated (i.e., statistically indistinct). In recent literature, researchers have used different approaches, which are not always correct or complete. We discuss the parameter constraints required to obtain such perfectly correlated latent factors in the context of 4 commonly used models: (a) the oblique factor model, (b) the hierarchical factor model, (c) models in which the factors are predicted by a covariate, and (d) models in which the factors are predictors of a dependent variable. It is shown that the necessary constraints depend on the choice of scaling. We illustrate testing the indistinctiveness of factors with 2 real data examples