This methodological synthesis surveys study and instrument quality in L2 pronunciation research by scrutinizing methodological practices in designing and employing scales and rubrics that measure accentedness, comprehensibility, and intelligibility. A comprehensive coding scheme was developed, and searches were conducted in several databases. A total of 380 articles (409 samples) that employed 576 target instruments and appeared in peer-reviewed journals from 1977 to 2023 were synthesized. Results demonstrated, among other findings, strengths in reporting several listener and speaker characteristics. Areas in need of improvement include (a) more thorough evaluation and reporting of interrater reliability and instrument validity and (b) greater adherence to methodological transparency and open science practices. We conclude by discussing the implications of these findings for researchers and researcher trainers; by raising awareness of methodological and ethical challenges in psychometric research on L2 speech perception; and by providing recommendations for advancing the quality of instruments in this domain.

One of the most significant challenges in research related to nutritional epidemiology is the achievement of high accuracy and validity of dietary data to establish an adequate link between dietary exposure and health outcomes. Recently, the emergence of artificial intelligence (AI) in various fields has filled this gap with advanced statistical models and techniques for nutrient and food analysis. We aimed to systematically review available evidence regarding the validity and accuracy of AI-based dietary intake assessment methods (AI-DIA). In accordance with PRISMA guidelines, an exhaustive search of the EMBASE, PubMed, Scopus and Web of Science databases was conducted to identify relevant publications from their inception to 1 December 2024. Thirteen studies that met the inclusion criteria were included in this analysis. Of the studies identified, 61·5 % were conducted in preclinical settings. Likewise, 46·2 % used AI techniques based on deep learning and 15·3 % on machine learning. Correlation coefficients of over 0·7 were reported in six articles concerning the estimation of calories between the AI and traditional assessment methods. Similarly, six studies obtained a correlation above 0·7 for macronutrients. In the case of micronutrients, four studies achieved the correlation mentioned above. A moderate risk of bias was observed in 61·5 % (n 8) of the articles analysed, with confounding bias being the most frequently observed. AI-DIA methods are promising, reliable and valid alternatives for nutrient and food estimations. However, more research comparing different populations is needed, as well as larger sample sizes, to ensure the validity of the experimental designs.

Trust in the validity of published work is of fundamental importance to scientists. Confirmation of validity is more readily attained than addressing the question of whether fraud was involved. Suggestions are made for key stakeholders - institutions and companies, journals, and funders as to how they might enhance trust in science, both by accelerating the assessment of data validity and by segregating that effort from investigation of allegations of fraud.

A prominent paradigm demonstrates many White Americans respond negatively to information on their declining population share. But this paradigm considers this “racial shift” in a single hierarchy-challenging context that produces similar status threat responses across conceptually distinct outcomes, undercutting the ability to both explain the causes of Whites’ social and political responses and advance theorizing about native majorities’ responses to demographic change. We test whether evidence for Whites’ responses to demographic change varies across three distinct hierarchy-challenging contexts: society at large, culture, and politics. We find little evidence any racial shift information instills status threat or otherwise changes attitudes or behavioral intentions, and do not replicate evidence for reactions diverging by left- versus right-wing political attachments. We conclude with what our well-powered (n = 2100) results suggest about a paradigm and intervention used prominently, with results cited frequently, to understand native majorities’ responses to demographic change and potential challenges to multiracial democracy.

Systematic searches of published literature are a vital component of systematic reviews. When search strings are not “sensitive,” they may miss many relevant studies limiting, or even biasing, the range of evidence available for synthesis. Concerningly, conducting and reporting evaluations (validations) of the sensitivity of the used search strings is rare, according to our survey of published systematic reviews and protocols. Potential reasons may involve a lack of familiarity or inaccessibility of complex sensitivity evaluation approaches. We first clarify the main concepts and principles of search string evaluation. We then present a simple procedure for estimating a relative recall of a search string. It is based on a pre-defined set of “benchmark” publications. The relative recall, that is, the sensitivity of the search string, is the retrieval overlap between the evaluated search string and a search string that captures only the benchmark publications. If there is little overlap (i.e., low recall or sensitivity), the evaluated search string should be improved to ensure that most of the relevant literature can be captured. The presented benchmarking approach can be applied to one or more online databases or search platforms. It is illustrated by five accessible, hands-on tutorials for commonly used online literature sources. Overall, our work provides an assessment of the current state of search string evaluations in published systematic reviews and protocols. It also paves the way to improve evaluation and reporting practices to make evidence synthesis more transparent and robust.

Psychopathology assessed across the lifespan often can be summarized with a few broad dimensions: internalizing, externalizing, and psychosis/thought disorder. Extensive overlap between internalizing and externalizing symptoms has garnered interest in bifactor models comprised of a general co-occurring factor and specific internalizing and externalizing factors. We focus on internalizing and externalizing symptoms and compare a bifactor model to a correlated two-factor model of psychopathology at three timepoints in a large adolescent community sample (N = 387; 55 % female; 83% Caucasian; M age = 12.1 at wave 1) using self- and parent-reports. Each model was tested within each time-point with 25–28 validators. The bifactor models demonstrated better fit to the data. Child report had stronger invariance across time. Parent report had stronger reliability over time. Cross-informant correlations between the factors at each wave indicated that the bifactor model had slightly poorer convergent validity but stronger discriminant validity than the two-factor model. With notable exceptions, this pattern of results replicated across informants and waves. The overlap between internalizing and externalizing pathology is systematically and, sometimes, non-linearly related to risk factors and maladaptive outcomes. Strengths and weaknesses to modeling psychopathology as two or three factors and clinical and developmental design implications are discussed.

P. M. Bentler has shown that Rao's canonical factor analysis is in effect a psychometric analysis, leading to factors that are maximally assessible from the data. He contrasts this with Kaiser and Caffrey's alpha factor analysis that leads to factors that maximally represent the true factors in the content domain. Noting the problems associated with factors that may be highly assessible, but not very representative, or vice versa, Bentler suggests the need for a technique that would, insofar as possible, be optimal with respect to both criteria. Such a technique is presented here, and is shown to resolve into a traditional scaling method, which in turn acquires a richer psychometric interpretation.

Parrots are popular companion animals but show prevalent and at times severe welfare issues. Nonetheless, there are no scientific tools available to assess parrot welfare. The aim of this systematic review was to identify valid and feasible outcome measures that could be used as welfare indicators for companion parrots. From 1,848 peer-reviewed studies retrieved, 98 met our inclusion and exclusion criteria (e.g. experimental studies, captive parrots). For each outcome collected, validity was assessed based on the statistical significance reported by the authors, as other validity parameters were rarely provided for evaluation. Feasibility was assigned by considering the need for specific instruments, veterinary-level expertise or handling the parrot. A total of 1,512 outcomes were evaluated, of which 572 had a significant P-value and were considered feasible. These included changes in behaviour (e.g. activity level, social interactions, exploration), body measurements (e.g. body weight, plumage condition) and abnormal behaviours, amongst others. Many physical and physiological parameters were identified that either require experimental validation, or veterinary-level skills and expertise, limiting their potential use by parrot owners themselves. Moreover, a high risk of bias undermined the internal validity of these outcomes, while a strong taxonomic bias, a predominance of studies on parrots in laboratories, and an underrepresentation of companion parrots jeopardised their external validity. These results provide a promising starting point for validating a set of welfare indicators in parrots.