Identity Statuses as Developmental Trajectories. A Five-Wave Longitudinal Study in Early-to-Middle and Middle-to-Late Adolescents
This study tested whether Marcia’s original identity statuses of achievement, moratorium, early closure (a new label for foreclosure), and diffusion, can be considered identity status trajectories.
One Size Does Not Fit All: proposal for a prior-adapted BIC
This paper presents a refinement of the Bayesian Information Criterion (BIC). While the original BIC selects models on the basis of complexity and fit, the so-called prior-adapted BIC allows us to choose among statistical models that differ on three scores: fit, complexity, and model size.
The Effect of Offenders’ Sex on Reporting Crimes to the Police
This article examines the difference in victims’ reporting behavior regarding crimes committed by males and by females. The authors expect that victims of female offenders are less likely to report to the police than victims of male offenders because of differences in the victim–offender relationship as well as in the victim’s sex.
Do Delinquent Young Adults have a High or a Low Level of Self-concept?
This study explored the levels of self-concept of delinquent young adults (n = 873). This question is of theoretical and practical importance, as therapeutic programs addressing the self-concept must be based on clear evidence.
A prior predictive loss function for the evaluation of inequality constrained hypotheses
In many types of statistical modeling, inequality constraints are imposed between the parameters of interest. As we will show in this paper, the DIC (i.e., posterior Deviance Information Criterium as proposed as a Bayesian model selection tool by Spiegelhalter, Best, Carlin, & Van Der Linde, 2002) fails when comparing inequality constrained hypotheses.
Illustrating Bayesian evaluation of informative hypotheses for regression models
In the present article we illustrate a Bayesian method of evaluating informative hypotheses for regression models. Our main aim is to make this method accessible to psychological researchers without a mathematical or Bayesian background.
An introduction to Bayesian model selection for evaluating informative hypotheses
Most researchers have specific expectations concerning their research questions. These may be derived from theory, empirical evidence, or both. Yet despite these expectations, most investigators still use null hypothesis testing to evaluate their data, that is, when analysing their data they ignore the expectations they have.
Directly evaluating expectations or testing the null hypothesis? Null hypothesis testing versus Bayesian model selection
Researchers in psychology have specific expectations about their theories. These are called informative hypothesis because they contain information about reality. Note that these hypotheses are not necessarily the same as the traditional null and alternative hypothesis.
Testing informative hypotheses in SEM increases power: An illustration contrasting classical hypothesis testing with a parametric bootstrap approach
In the present paper, the application of a parametric bootstrap procedure, as described by van de Schoot, Hoijtink, and Deković (2010), will be applied to demonstrate that a direct test of an informative hypothesis offers more informative results compared to testing traditional null hypotheses against catch-all rivals.