Measurement invariance, selection invariance, and fair selection revisited.

This note contains a corrective and a generalization of results by Borsboom et al. (2008), based on Heesen and Romeijn (2019). It highlights the relevance of insights from psychometrics beyond the context of psychological testing.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis.

Background: Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

Intervention and Identifiability in Latent Variable Modelling.

We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal models.

PSYCHIATRIC COMORBIDITY DOES NOT ONLY DEPEND ON DIAGNOSTIC THRESHOLDS: AN ILLUSTRATION WITH MAJOR DEPRESSIVE DISORDER AND GENERALIZED ANXIETY DISORDER.

Background: High rates of psychiatric comorbidity are subject of debate: To what extent do they depend on classification choices such as diagnostic thresholds? This paper investigates the influence of different thresholds on rates of comorbidity between major depressive disorder (MDD) and generalized anxiety disorder (GAD).

Methods: Point prevalence of comorbidity between MDD and GAD was measured in 74,092 subjects from the general population (LifeLines) according to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria. Comorbidity rates were compared for different thresholds by varying the number of necessary criteria from ≥ 1 to all nine symptoms for MDD, and from ≥ 1 to all seven symptoms for GAD.

Psychiatric comorbidity: fact or artifact?

The frequent occurrence of comorbidity has brought about an extensive theoretical debate in psychiatry. Why are the rates of psychiatric comorbidity so high and what are their implications for the ontological and epistemological status of comorbid psychiatric diseases? Current explanations focus either on classification choices or on causal ties between disorders. Based on empirical and philosophical arguments, we propose a conventionalist interpretation of psychiatric comorbidity instead.

Data-driven subtypes of major depressive disorder: a systematic review.

Background: According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability.

Psychiatric comorbidity and causal disease models.

In psychiatry, comorbidity is the rule rather than the exception. Up to 45% of all patients are classified as having more than one psychiatric disorder. These high rates of comorbidity have led to a debate concerning the interpretation of this phenomenon.

The humble Bayesian: model checking from a fully Bayesian perspective.

Gelman and Shalizi (2012) criticize what they call the 'usual story' in Bayesian statistics: that the distribution over hypotheses or models is the sole means of statistical inference, thus excluding model checking and revision, and that inference is inductivist rather than deductivist. They present an alternative hypothetico-deductive approach to remedy both shortcomings. We agree with Gelman and Shalizi's criticism of the usual story, but disagree on whether Bayesian confirmation theory should be abandoned.

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. We conclude that Aristotle was actually interested in evaluating informative hypotheses.

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.

The all-too-flexible abductive method: ATOM's normative status.

The author discusses the abductive theory of method (ATOM) by Brian Haig from a philosophical perspective, connecting his theory with a number of issues and trends in contemporary philosophy of science. It is argued that as it stands, the methodology presented by Haig is too permissive. Both the use of analogical reasoning and the application of exploratory factor analysis leave us with too many candidate theories to choose from, and explanatory coherence cannot be expected to save the day.

Measurement invariance versus selection invariance: is fair selection possible?

This article shows that measurement invariance (defined in terms of an invariant measurement model in different groups) is generally inconsistent with selection invariance (defined in terms of equal sensitivity and specificity across groups). In particular, when a unidimensional measurement instrument is used and group differences are present in the location but not in the variance of the latent distribution, sensitivity and positive predictive value will be higher in the group at the higher end of the latent dimension, whereas specificity and negative predictive value will be higher in the group at the lower end of the latent dimension. When latent variances are unequal, the differences in these quantities depend on the size of group differences in variances relative to the size of group differences in means.