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Previous work has generally conceptualized emotion regulation as contributing to mental health outcomes, and not vice versa. The present study challenges this assumption by using a prospective design to investigate the directionality of underlying relationships between emotion regulation and mental health in the context of a major population-level stressor. We surveyed a large nationally representative sample of adults (18-91 years, N = 704) at three 1-month intervals across the acute lockdown phase of the COVID-19 pandemic in Australia, using standardized measures of depression and anxiety symptoms. At each time point, we also measured the use of two emotion regulation strategies-cognitive reappraisal and emotional suppression-previously associated with adaptive and maladaptive mental health outcomes, respectively. We found cognitive reappraisal was unrelated to mental health symptoms. In contrast, greater emotional suppression was robustly associated with higher symptom levels for both depression and anxiety. Longitudinal analyses revealed this association reflected bidirectional relationships. Higher symptoms of depression and anxiety each predicted greater subsequent use of emotional suppression, and greater use of emotional suppression predicted higher subsequent symptoms. This bidirectionality suggests emotional suppression is both symptomatic and predictive of psychological distress. The lack of a relationship for cognitive reappraisal is discussed with respect to the pandemic context and evidence that high stress might reduce people's ability to use this strategy effectively. Given the strong emphasis on reappraisal in clinical practice, there is a critical need to understand for whom, what and when this strategy is helpful. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The present study examined behavioral responses during the coronavirus disease 2019 (COVID-19) pandemic and the role of dispositional risk tolerance in the Australian context.The study involved a six-wave longitudinal investigation with a nationally representative sample of Australians (The results revealed a high implementation rate for protective actions when Australia had a peak in the severity of the COVID-19 pandemic and subsequently declined with the easing threat of the pandemic. Individuals' dispositional risk tolerance significantly predicted transition to, and endorsement of, protective actions. Participants who had low risk tolerance were more likely to remain at the state of implementing COVID-19 measures than being in, or transitioning to, other states.The findings suggest that when encouraging protective actions, governments and public authorities should acknowledge variability in the community in responding to risk and consider measures in addition to risk messaging to encourage protective actions among individuals with a high level of risk tolerance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The time and cost to start a business are highly related to the degree of transparency of business information, which strongly impacts the loss due to illicit financial flows. In order to study the distributional characteristics of time and cost to start a business, we introduce right-truncated and left-truncated T-X families of distributions. These families are used to construct new generalized families of continuous distributions. Relationships between the families are investigated. Real data sets including time and cost to start a business are analyzed and the results show that the truncated families perform very well for fitting highly skewed data.

Statistical learning (SL)—sensitivity to statistical regularities in the environment—has been postulated to support language development. While even young infants are capable of using distributional statistics to learn in linguistic and non-linguistic domains, efforts to measure SL at the level of the individual and link it to language proficiency in individual differences designs have been mixed, which has at least in part been attributed to problems with task reliability. In the current study we present the first prospective longitudinal study of the relationship between both non-linguistic SL (measured with visual stimuli) and linguistic SL (measured with auditory stimuli) and language in a group of English-speaking children. One-hundred and twenty-one (N = 121) children in their first two years of formal schooling (Mage = 6;1 years, Range: 5;2 – 7;2) completed tests of visual SL (VSL) and auditory SL (ASL) and several control variables at time 1. Both forms of SL were then measured every 6 months for the next 18 months, and at the final testing session (time 4) their language proficiency was measured using a standardised test. The results showed that the reliability of the SL tasks increased across the course of the study. A series of path analyses showed that both VSL and ASL independently predicted individual differences in language proficiency at time 4. The evidence is consistent with the suggestion that, when measured reliably, an observable relationship between SL and language proficiency exists. Theoretical and methodological issues are discussed.

This article presents techniques for dealing with a form of dependency in data arising when numerical data sum to a constant for individual cases, that is, "compositional" or "ipsative" data. Examples are percentages that sum to 100, and hours in a day that sum to 24. Ipsative scales fell out of fashion in psychology during the 1960s and 1970s due to a lack of methods for analyzing them. However, ipsative scales have merits, and compositional data commonly occur in psychological research. Moreover, as we demonstrate, sometimes converting data to a compositional form yields insights not otherwise accessible. Fortunately, there are sound methods for analyzing compositional data. We seek to enable researchers to analyze compositional data by presenting appropriate techniques and illustrating their application to real data. First, we elaborate the technical details of compositional data and discuss both established and new approaches to their analysis. We then present applications of these methods to real social science data-sets (data and code using R are available in a supplementary document). We conclude with a discussion of the state of the art in compositional data analysis and remaining unsolved problems. A brief guide to available software resources is provided in the first section of the supplementary document. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

There is minimal knowledge about the impact of large-scale epidemics on community mental health, particularly during the acute phase. This gap in knowledge means we are critically ill-equipped to support communities as they face the unprecedented COVID-19 pandemic. This study aimed to provide data urgently needed to inform government policy and resource allocation now and in other future crises. The study was the first to survey a representative sample from the Australian population at the early acute phase of the COVID-19 pandemic. Depression, anxiety, and psychological wellbeing were measured with well-validated scales (PHQ-9, GAD-7, WHO-5). Using linear regression, we tested for associations between mental health and exposure to COVID-19, impacts of COVID-19 on work and social functioning, and socio-demographic factors. Depression and anxiety symptoms were substantively elevated relative to usual population data, including for individuals with no existing mental health diagnosis. Exposure to COVID-19 had minimal association with mental health outcomes. Recent exposure to the Australian bushfires was also unrelated to depression and anxiety, although bushfire smoke exposure correlated with reduced psychological wellbeing. In contrast, pandemic-induced impairments in work and social functioning were strongly associated with elevated depression and anxiety symptoms, as well as decreased psychological wellbeing. Financial distress due to the pandemic, rather than job loss

There is minimal knowledge about the impact of large-scale epidemics on community mental health, particularly during the acute phase. This gap in knowledge means we are critically ill-equipped to support communities as they face the unprecedented COVID-19 pandemic. This study aimed to provide data urgently needed to inform government policy and resource allocation now and in other future crises. The study was the first to survey a representative sample from the Australian population at the early acute phase of the COVID-19 pandemic. Depression, anxiety, and psychological wellbeing were measured with well-validated scales (PHQ-9, GAD-7, WHO-5). Using linear regression, we tested for associations between mental health and exposure to COVID-19, impacts of COVID-19 on work and social functioning, and socio-demographic factors. Depression and anxiety symptoms were substantively elevated relative to usual population data, including for individuals with no existing mental health diagnosis. Exposure to COVID-19 had minimal association with mental health outcomes. Recent exposure to the Australian bushfires was also unrelated to depression and anxiety, although bushfire smoke exposure correlated with reduced psychological wellbeing. In contrast, pandemic-induced impairments in work and social functioning were strongly associated with elevated depression and anxiety symptoms, as well as decreased psychological wellbeing. Financial distress due to the pandemic, rather than job loss per se, was also a key correlate of poorer mental health. These findings suggest that minimizing disruption to work and social functioning, and increasing access to mental health services in the community, are important policy goals to minimize pandemic-related impacts on mental health and wellbeing. Innovative and creative strategies are needed to meet these community needs while continuing to enact vital public health strategies to control the spread of COVID-19.

Statistical learning (SL) has been a prominent focus of research in developmental and adult populations, guided by the assumption that it is a fundamental component of learning underlying higher-order cognition. In developmental populations, however, there have been recent concerns regarding the degree to which many current tasks reliably measure SL, particularly in younger children. In the current article, we present the results of two studies that measured auditory statistical learning (ASL) of linguistic stimuli in children aged 5–8 years. Children listened to 6 min of continuous syllables comprising four trisyllabic pseudowords. Following the familiarization phase, children completed (a) a two-alternative forced-choice task and (b) a serial recall task in which they repeated either target sequences embedded during familiarization or foils, manipulated for sequence length. Results showed that, although both measures consistently revealed learning at the group level, the recall task better captured learning across the full range of abilities and was more reliable at the individual level. We conclude that, as has also been demonstrated in adults, the method holds promise for future studies of individual differences in ASL of linguistic stimuli.

The literature has shown that different types of moral dilemmas elicit discrepant decision patterns. The present research investigated the role of uncertainty in contributing to these decision patterns. Two studies were conducted to examine participants' choices in commonly used dilemmas. Study 1 showed that participants’ perceived outcome probabilities were significantly associated with their moral choices, and that these associations were independent from the dilemma type. Study 2 revealed that participants had significantly less preference for killing the individual when the outcome probabilities were stated using the modal verb ‘will’ than when they were stated using the numerical phrasing of ‘100%’. Our findings illustrate a discord between experimenter and participant in the interpretation of task instructions.

The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and Merkle (2014) and recently elaborated by Smithson and Shou (2017), considerably expands the variety of distributions available for modeling random variables on the unit interval. This family is especially useful for modeling quantiles, and also sometimes out-performs the other distributions. The distributions are very tractable, with a location and dispersion parameter, explicit probability distribution functions, cumulative distribution functions, and quantiles. They enable a wide variety of quantile regression models with predictors for the location and dispersion parameters, and simple interpretations of those parameters. The R package cdfquantreg (Shou and Smithson 2019) (at least R 3.2.0) presented in this paper includes 36 distributions from the CDF-quantile family. Separate submodels may be specified for the location and for the dispersion parameters, with different or overlapping sets of predictors in each. The package offers maximum likelihood, Bayesian MCMC, and bootstrap estimation methods. Model diagnostics, including the gradient, three types of residuals, and the dfbeta influence measures, are available for evaluating models. The package also provides pseudo-random generators for all of its distributions. Many of its functions and their usage have forms familiar to R users, and the documentation is extensive. We also present a SAS macro for general linear models using the CDF-quantile family that includes many of the same capabilities as the cdfquantreg package. The paper provides examples of applications to real data-sets.

Data underlie understanding of processes and prediction of the future. However, things change; data from one population at one time may have uncertain relevance for modeling or prediction in another population or at another time. Data-based prediction in a changing world requires two complementary capabilities: versatile modeling, integrated with management of uncertainty. We develop a response to this challenge. We focus on statistical models of bounded random variables, associated with additional non-probabilistic uncertainties. We employ CDF-quantile distributions to model the probabilistic aspects of these phenomena. Non-probabilistic uncertainties in parameter values and in the shapes of probability distributions are modeled and managed with the method of robust satisficing from info-gap theory. The robustness to uncertainty is evaluated for alternative estimators. We show that putatively optimal estimators may be less robust than sub-optimal estimators, suggesting preference for a sub-optimal estimator in some circumstances. These two attributes –statistical accuracy and info-gap robustness –trade off against one another. The info-gap robustness function quantifies this trade off. Generic propositions specify the robustness functions and their trade offs, and characterize a class of situations in which preference for sub-optimal estimators can occur. Three examples are discussed.

This paper presents an investigation of a relatively unstudied approach to modeling lower and upper subjective probabilities. It is based on the fact that every cumulative distribution function (CDF) with support (0,1) has a “dual” CDF that obeys the conjugacy relation between coherent lower and upper probabilities. A new 2-parameter family of “CDF-Quantile” distributions with support (0,1) is extended via a third parameter for the purpose of modeling lower–upper probabilities via this approach. The extension exploits certain properties of the CDF-Quantile family, and the fact that continuous CDFs on (0,1) random variables form an algebraic group that is closed under composition. This extension also yields methods for testing specific models of lower–upper probability assignments. Finally, the new models are applied to real data-sets, and compared with alternative approaches for their relative advantages and drawbacks.

Overweight/obesity, sleep disturbance, night eating, and a sedentary lifestyle are common co-occurring problems. There is a tendency for them to co-occur together more often than they occur alone. In some cases, there is clarity as to the time course and evolution of the phenomena. However, specific mechanism(s) that are proposed to explain a single co-occurrence cannot fully explain the more generalized tendency to develop concurrent symptoms and/or disorders after developing one of the phenomena. Nor is there a clinical theory with any utility in explaining the development of co-occurring symptoms, disorders and behaviour and the mechanism(s) by which they occur. Thus, we propose a specific mechanism—dysregulation of core body temperature (CBT) that interferes with sleep onset—to explain the development of the concurrences. A detailed review of the literature related to CBT and the phenomena that can alter CBT or are altered by CBT is provided. Overweight/obesity, sleep disturbance and certain behaviour (e.g. late-night eating, sedentarism) were linked to elevated CBT, especially an elevated nocturnal CBT. A number of existing therapies including drugs (e.g. antidepressants), behavioural therapies (e.g. sleep restriction therapy) and bright light therapy can also reduce CBT. An elevation in nocturnal CBT that interferes with sleep onset can parsimoniously explain the development and perpetuation of common co-occurring symptoms, disorders and behaviour including overweight/obesity, sleep disturbance, late-night eating, and sedentarism. Nonetheless, a significant correlation between CBT and the above symptoms, disorders and behaviour does not necessarily imply causation. Thus, statistical and methodological issues of relevance to this enquiry are discussed including the likely presence of autocorrelation. Level V, narrative review.

Extensive but separate bodies of research in education concern the constructs of school climate and school connectedness/belonging. In the interests of advancing a more integrated approach, a new measurement tool is developed– the School Climate and School Identification Measure–Student (SCASIM-St). This scale builds on the Moos (1973) framework which assesses relationships, personal growth, and system management in schools. The social identity approach to group processes (Tajfel & Turner, 1979; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987) is used to extend work on school connectedness and belonging through the inclusion of a measure of social identification. A range of methods across three studies are designed to assess the reliability and validity of SCASIM-St (N = 7209, Australian grades 7–10 students). These include confirmatory factor analysis, test-retest analysis, and convergent validity (Study 1 and 2). Additionally measurement invariance tests for student sub-groups regarding gender, grade level, and non-English language, were employed in Study 3. It also included criterion validity analysis using multilevel models for the key outcome measures of students’ academic achievement, well-being, and aggressive behaviors. All of these analyses indicate that SCASIM-St is an effective measure. Theoretical and practical implications as well as future directions are outlined.

Expertise in research integration and implementation is an essential but often overlooked component of tackling complex societal and environmental problems. We focus on expertise relevant to any complex problem, especially contributory expertise, divided into ‘knowing-that’ and ‘knowing-how.’ We also deal with interactional expertise and the fact that much expertise is tacit. We explore three questions. First, in examining ‘when is expertise in research integration and implementation required?,’ we review tasks essential (a) to developing more comprehensive understandings of complex problems, plus possible ways to address them, and (b) for supporting implementation of those understandings into government policy, community practice, business and social innovation, or other initiatives. Second, in considering ‘where can expertise in research integration and implementation currently be found?,’ we describe three realms: (a) specific approaches, including interdisciplinarity, transdisciplinarity, systems thinking and sustainability science; (b) case-based experience that is independent of these specific approaches; and (c) research examining elements of integration and implementation, specifically considering unknowns and fostering innovation. We highlight examples of expertise in each realm and demonstrate how fragmentation currently precludes clear identification of research integration and implementation expertise. Third, in exploring ‘what is required to strengthen expertise in research integration and implementation?,’ we propose building a knowledge bank. We delve into three key challenges: compiling existing expertise, indexing and organising the expertise to make it widely accessible, and understanding and overcoming the core reasons for the existing fragmentation. A growing knowledge bank of expertise in research integration and implementation on the one hand, and accumulating success in addressing complex societal and environmental problems on the other, will form a virtuous cycle so that each strengthens the other. Building a coalition of researchers and institutions will ensure this expertise and its application are valued and sustained., Palgrave Communications, 6 (1)

Many quantitative variables in psychological research, assessment, and testing have bounds, but boundedness often is ignored by researchers. Ignoring bounds can result in miss-estimation, miss-specified models, and improper statistical inference. This tutorial introduces concepts and models for analyzing quantitative random variables that have one or more bounds. These variables fall into two groups: Those where the bounds are “absolute”, and “limited” variables whose bounds are “censored” or “truncated”. This tutorial explains which techniques are suited to dealing with specific types of bounded variables and how to deal with boundary cases, and provides a guide to resources for using these techniques effectively.

Previous studies of age-related macular degeneration (AMD) report impaired facial expression recognition even with enlarged face images. Here, we test potential benefits of caricaturing (exaggerating how the expression's shape differs from neutral) as an image enhancement procedure targeted at mid- to high-level cortical vision. Experiment 1 provides proof-of-concept using normal vision observers shown blurred images as a partial simulation of AMD. Caricaturing significantly improved expression recognition (happy, sad, anger, disgust, fear, surprise) by ∼4%-5% across young adults and older adults (mean age 73 years); two different severities of blur; high, medium, and low intensity of the original expression; and all intermediate accuracy levels (impaired but still above chance). Experiment 2 tested AMD patients, running 19 eyes monocularly (from 12 patients, 67-94 years) covering a wide range of vision loss (acuities 6/7.5 to poorer than 6/360). With faces pre-enlarged, recognition approached ceiling and was only slightly worse than matched controls for high- and medium-intensity expressions. For low-intensity expressions, recognition of veridical expressions remained impaired and was significantly improved with caricaturing across all levels of vision loss by 5.8%. Overall, caricaturing benefits emerged when improvement was most needed, that is, when initial recognition of uncaricatured expressions was impaired.

We argue that the way ambiguity has been operationalized throughout the literature on ambiguity effects has an important limitation, insofar as ambiguity in outcomes has been neglected. We report two studies where judges do encounter ambiguity in the sampled outcomes and find evidence that ambiguity aversion is not less than when judges are given a range of outcomes without reference to ambiguous outcomes themselves. This result holds regardless of whether people are presented with a sample all at once or sample outcomes sequentially. Our experiments also investigate the effects of conflicting information about outcomes, finding that conflict aversion also does not decrease. Moreover, ambiguity and conflict aversion do not seem to arise as a consequence of judges ignoring uncertain outcomes and thereby treating outcome sets as reduced samples of unambiguous (or unconflicting) information. Instead, ambiguity and conflict aversion are partly explained by more pessimistic outcome forecasts by judges. This pessimism, in turn, may be due to the judges' uncertainty about how the chance of a desirable outcome from an ambiguous or conflictive alternative compares with an equivalent risky alternative. Both studies used hypothetical scenarios, and no incentives were provided for participants' decisions.

This paper discusses largely ignored issues regarding moderation of effect-sizes. We show that, under commonly-occurring conditions, popular alternatives for effect-size measures in ANOVA and multiple regression are not moderated identically across independent samples. Effects may appear to be unmoderated according to one effect-size measure but not according to another, or may even be moderated in opposite directions. We identify the conditions under which differential effect-size moderation can occur, and show that they are commonplace. We then review techniques for detecting and dealing with differential moderation of alternative effect-size measures. Finally, we discuss implications for research practice, reporting, replication, and meta-analysis.