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.
Moving beyond traditional null hypothesis testing: evaluating expectations directly
This mini-review illustrates that testing the traditional null hypothesis is not always the appropriate strategy. Half in jest, we discuss Aristotle’s scientific investigations into the shape of the earth in the context of evaluating the traditional null hypothesis.
Evaluating expectations about negative emotional states of aggressive boys using Bayesian model selection
Researchers often have expectations about the research outcomes in regard to inequality constraints between, e.g., group means. Consider the example of researchers who investigated the effects of inducing a negative emotional state in aggressive boys.
Can at-risk young adolescents be popular and anti-social? Sociometric status groups, anti-social behavior, gender and ethnic background
This study aimed to extend the understanding of anti-social behaviour and its association with popularity and sociometric status in a sample of at-risk adolescents from diverse ethnic backgrounds (n = 1491, average age 14.7 years).
Informative hypotheses: How to move beyond classical null hypothesis testing
Almost all researchers in psychology have specific expectations about their theories in the form of hypothesized order constraints between statistical parameters. For example: the mean of group 1 is larger than the mean of group 2 which in turn is larger than the mean of group 3.
Prolonged Grief Disorder, depression, and posttraumatic stress-disorder are distinguishable syndromes
This study examined the distinctiveness of symptoms of Prolonged Grief Disorder (PGD), depression, and posttraumatic stress disorder (PTSD). We compared the fit of a one-factor model with the fit of four hierarchical models in which symptoms formed three distinct correlated higher-order dimensions, and PTSD-items were modeled in different ways.
On the Progression and Stability of Adolescent Identity Formation. A Five-Wave Longitudinal Study in Early-to-Middle and Middle-to-Late Adolescence
This study examined identity development in a 5-wave study of 923 early-to-middle and 390 middle-to-late adolescents thereby covering the ages of 12–20.
Testing inequality constrained hypotheses in SEM Models
Researchers often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model. It is currently not possible to test these so-called informative hypotheses in structural equation modeling software.
Ph.D. Trajectories and Labour Market Mobility. A survey of recent doctoral recipients at four universities in the Netherlands
The results of a survey of recent doctoral recipients at four universities in The Netherlands.
Bayesian model selection of informative hypotheses for repeated measurements
When analyzing repeated measurements data, researchers often have expectations about the relations between the measurement means. The expectations can often be formalized using equality and inequality constraints between (i) the measurement means over time, (ii) the measurement means between groups,