There is currently an unprecedented level of doubt regarding the reliability of research findings in psychology. Many recommendations have been made to improve the current situation. This article reports results from PsychDisclosure.org, a novel open-science initiative that provides a platform for authors of recently published articles to disclose four methodological design specification details that are not required to be disclosed under current reporting standards, but which are critical for accurate interpretation and evaluation of reported findings. Grassroots sentiment -- as manifested in the positive and appreciative response to our initiative -- indicates that psychologists want to see changes made at the systemic level regarding disclosure of such methodological details. Almost 50% of contacted researchers disclosed the requested design specifications for the four methodological categories (excluded subjects, non-reported conditions and measures, and sample size determination). Disclosed information provided by participating authors also revealed several instances of questionable editorial practices, which need to be thoroughly examined and redressed. Based on these results, we argue that the time is now for mandatory methods disclosure statements for all psychology journals, which would be an important step forward in improving the reliability of findings in psychology.

 
 
 The imposition of lockdowns in response to the COVID-19 outbreak has underscored the importance of human behavior in mitigating virus transmission. The scientific study of interventions designed to change behavior (e.g., to promote physical distancing) requires measures of effectiveness that are fast, that can be assessed through experiments, and that can be investigated without actual virus transmission. This paper presents a methodological approach designed to deliver such indicators. We show how behavioral data, obtainable through wearable assessment devices or camera footage, can be used to assess the effect of interventions in experimental research; in addition, the approach can be extended to longitudinal data involving contact tracing apps. Our methodology operates by constructing a contact network: a representation that encodes which individuals have been in physical proximity long enough to transmit the virus. Because behavioral interventions alter the contact network, a comparison of contact networks before and after the intervention can provide information on the effectiveness of the intervention. We coin indicators based on this idea Behavioral Contact Network (BECON) indicators. We examine the performance of three indicators: the Density BECON, based on differences in network density; the Spectral BECON, based on differences in the eigenvector of the adjacency matrix; and the ASPL BECON, based on differences in average shortest path lengths. Using simulations, we show that all three indicators can effectively track the effect of behavioral interventions. Even in conditions with significant amounts of noise, BECON indicators can reliably identify and order effect sizes of interventions. The present paper invites further study of the method as well as practical implementations to test the validity of BECON indicators in real data.
 
 
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In S. Kanazawa's evolutionary theory of general intelligence (g), g is presented as a species-typical information-processing mechanism. This conceptualization of g departs radically from the accepted conceptualization of g as a source of individual differences that is manifest in the positive manifold. Kanazawa's theory is thus problematic in the sense that it concerns a purely hypothetical, and empirically unsupported, conceptualization of g. The authors argue that an evolutionary account of g should address it as a source of individual differences--that is, in a manner that is consistent with the empirical support for g.
In this article, a distinction is made between absolute and relative measurement. Absolute measurement refers to the measurement of traits on a group-invariant scale, and relative measurement refers to the within-group measurement of traits, where the scale of measurement is expressed in terms of the within-group position on a trait. Relative measurement occurs, for example, if an item induces a within-group comparison in respondents. These distinctions are discussed within the framework of measurement invariance, differentiating between absolute and relative forms of measurement invariance and bias. It is shown that items for relative measurement will produce bias as classically defined if the mean and/or variance of the trait distribution differ between groups. This form of bias, however, does not result from multidimensionality but from the fact that measurement is on a relative scale. A logistic regression procedure for the detection of relative measurement invariance and bias is proposed, as well as a model that allows for the incorporation of items for relative measurement in test analysis. Implications of the distinction between absolute and relative measurement are discussed and prove to be especially relevant for the domain of personality research.
In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self‐sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.
In the present article, we discuss the role that quantitative genetic methodology may play in assessing and understanding the dimensionality of psychological (psychometric) instruments. Specifically, we study the relationship between the observed covariance structures, on the one hand, and the underlying genetic and environmental influences giving rise to such structures, on the other. We note that this relationship may be such that it hampers obtaining a clear estimate of dimensionality using standard tools for dimensionality assessment alone. One situation in which dimensionality assessment may be impeded is that in which genetic and environmental influences, of which the observed covariance structure is a function, differ from each other in structure and dimensionality. We demonstrate that in such situations settling dimensionality issues may be problematic, and propose using quantitative genetic modeling to uncover the (possibly different) dimensionalities of the underlying genetic and environmental structures. We illustrate using simulations and an empirical example on childhood internalizing problems.
Major depressive disorder (MDD) is a heterogeneous condition in terms of symptoms, course, and underlying disease mechanisms. Current classifications do not adequately address this complexity. In novel network approaches to psychopathology, psychiatric disorders are conceptualized as complex dynamic systems of mutually interacting symptoms. This perspective implies that a more densely connected network of symptoms is indicative of a poorer prognosis, but, to date, no previous study has examined whether network structure is indeed associated with the longitudinal course of MDD.To examine whether the baseline network structure of MDD symptoms is associated with the longitudinal course of MDD.In this prospective study, in which remittent and persistent MDD was defined on the basis of a follow-up assessment after 2 years, 515 patients from the Netherlands Study of Depression and Anxiety with past-year MDD (established with the Composite International Diagnostic Interview) and at least moderate depressive symptoms (assessed with the Inventory of Depressive Symptomatology [IDS]) at baseline were studied. Baseline starting and ending dates were September 1, 2004, through February 28, 2007. Follow-up starting and ending dates were September 1, 2006, through February 28, 2009. Analysis was conducted August 2015. The MDD was considered persistent if patients had at least moderate depressive symptoms (IDS) at 2-year follow-up; otherwise, the MDD was considered remitted.Sparse network structures of baseline MDD symptoms assessed via IDS were computed. Global and local connectivity of network structures were compared across persisters and remitters using a permutation test.Among the 515 patients, 335 (65.1%) were female, mead (SD) age was 40.9 (12.1) years, and 253 (49.1%) had persistent MDD at 2-year follow-up. Persisters (n = 253) had a higher baseline IDS sum score than remitters (n = 262) (mean [SD] score, 40.2 [8.9] vs 35.1 [7.1]; the test statistic for the difference in IDS sum score was 22 027; P < .001). The test statistic for the difference in network connectivity was 1.79 (P = .01) for the original data, 1.55 for data matched on IDS sum score (P = .04), and 1.65 for partialed out data (P = .02). At the symptom level, fatigue or loss of energy and feeling guilty had the largest difference in importance in persisters' network compared with that of remitters (Cohen d = 1.13 and 1.18, respectively).This study reports that symptom networks of patients with MDD are related to longitudinal course: persisters exhibited a more densely connected network at baseline than remitters. More pronounced associations between symptoms may be an important determinant of persistence in MDD.
In his response to our article, Keith Markus argues that our recommendation that psychologists adopt correspondence truth is not supported by sound argument. In our rejoinder, we show that Markus’s critique only has force against a minor part of our article. Additionally, we show that he does not accurately reconstruct our argument in favor of correspondence truth. Moreover, we rebut two of his arguments for taking up alternative theories of truth. We maintain that the ability of correspondence truth to explain important features of scientific practice is yet to be demonstrated for rival theories of truth.
Background/Introduction Heart failure (HF) continues to be a leading cause of morbidity, decreased quality of life (QoL) and mortality worldwide. Contributing to decreased QoL is the high rehospitalization rate, which is often related to non-cardiac causes, highlighting the important multimorbidity of the HF population. Detailed clinical and outcome data for HF admissions in Belgium over time are only scarcely available. Purpose We aimed to evaluate and compare two groups of patients admitted with HF ten years apart, regarding clinical characteristics, comorbidities, use of HF medication and devices, as well as rehospitalization rate and all-cause mortality three months post-discharge. Methods This is a monocentric retrospective observational cohort study conducted in Belgium. Two groups of patients admitted for acute decompensated HF who were discharged alive were included and compared: 340 patients during 2011-2012 and 319 patients during 2021-2022. Results The two cohorts did not differ significantly regarding age (77.3 vs. 78.4 years), gender (47.4 vs. 44.5% women), number of patients with HF with reduced ejection fraction (47.4 vs. 48.6%) and NT-proBNP on admission (8514 vs. 7398 pg/mL). Similarly, comorbidities including diabetes, chronic obstructive pulmonary disease, peripheral artery disease, stroke and renal failure were comparable in both groups. Concerning medical therapy at discharge, mineralocorticoid receptor antagonists (MRA) were used significantly more frequently in the latest cohort. However, there was no change in administration of beta-blockers or renin angiotensin aldosterone system antagonists. As part of the implementation of new therapies for heart failure with reduced ejection fraction, 29% of patients were prescribed sodium-glucose cotransporter-2 (SGLT-2) inhibitors, while sacubitril/valsartan was given in 13% of the cases. The use of internal defibrillators (ICD) and resynchronization devices (CRT) doubled from 2011-2012 to 2021-2022 (3.9 vs. 9.6%, p = 0.002). In-hospital length of stay decreased significantly over a ten-year period across all ejection fraction subgroups from 7 to 5 days (figure 1). The combined outcome of rehospitalization for HF and all-cause mortality three months post-discharge remained constant at approximately 20% (figure 2). Conclusion Clinical characteristics and comorbidities of patients admitted with acute decompensated HF did not change significantly between 2011 and 2022. Uptake of evidence-based medicine for HF increased, especially for MRA, SGLT-2 inhibitors and sacubitril/valsartan, as did the use of ICD and CRT, but still with considerable potential for improvement. Although in-hospital length of stay decreased significantly over time, rehospitalization rates for HF and all-cause mortality within 90 days post-discharge remained high and did not improve. These sobering results illustrate the need for continuing efforts to improve the care and outcome of HF patients.
This paper comments on an article by Schmidt and Hunter [Intelligence 27 (1999) 183.], who argue that the correction for attenuation should be routinely used in theory testing. It is maintained that Schmidt and Hunter's arguments are based on mistaken assumptions. We discuss our critique of Schmidt and Hunter in terms of two arguments against a routine use of the correction for attenuation within the classical test theory framework: (1) corrected correlations do not, as Schmidt and Hunter claim, provide correlations between constructs, and (2) corrections for measurement error should be made using modern test theory models instead of the classical model. The arguments that Schmidt and Hunter advance in favor of the correction for attenuation can be traced to an implicit identification of true scores with construct scores. First, we show that this identification confounds issues of validity and issues of reliability. Second, it is pointed out that equating true scores with construct scores is logically inconsistent with the classical test theory model itself. Third, it is argued that the classical model is not suited for detecting the dimensionality of test scores, which severely limits the interpretation of the corrected correlation coefficients. It is concluded that most measurement problems in psychology concern issues of validity, and that the correction for attenuation within classical test theory does not help in solving them.
