A Bayesian approach for exploring person × environment interaction within the environmental sensitivity meta-framework.

BACKGROUND: For investigating the individual-environment interplay and individual differences in response to environmental exposures as captured by models of environmental sensitivity including Diathesis-stress, Differential Susceptibility, and Vantage Sensitivity, over the last few years, a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies. METHOD: In the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman et al. (Psychological Methods, 17, 2012, 615) Nonlinear Least Squares (NLS) approach. RESULTS: Findings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance. CONCLUSIONS: As the body of research exploring the interplay between individual and environmental variables grows, enabling predictions regarding the form of interaction and the extent of effects, the Bayesian approach could emerge as a feasible and readily applicable solution to numerous computational challenges inherent in existing frequentist methods. This approach holds promise for enhancing the trustworthiness of research outcomes, thereby impacting clinical and applied understanding.

Post-selection Inference in Multiverse Analysis (PIMA): An Inferential Framework Based on the Sign Flipping Score Test.

When analyzing data, researchers make some choices that are either arbitrary, based on subjective beliefs about the data-generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can be abused and has been one of the underlying causes of the replication crisis in several fields. Recently, the introduction of multiverse analysis provides researchers with a method to evaluate the stability of the results across reasonable choices that could be made when analyzing data. Multiverse analysis is confined to a descriptive role, lacking a proper and comprehensive inferential procedure. Recently, specification curve analysis adds an inferential procedure to multiverse analysis, but this approach is limited to simple cases related to the linear model, and only allows researchers to infer whether at least one specification rejects the null hypothesis, but not which specifications should be selected. In this paper, we present a Post-selection Inference approach to Multiverse Analysis (PIMA) which is a flexible and general inferential approach that considers for all possible models, i.e., the multiverse of reasonable analyses. The approach allows for a wide range of data specifications (i.e., preprocessing) and any generalized linear model; it allows testing the null hypothesis that a given predictor is not associated with the outcome, by combining information from all reasonable models of multiverse analysis, and provides strong control of the family-wise error rate allowing researchers to claim that the null hypothesis can be rejected for any specification that shows a significant effect. The inferential proposal is based on a conditional resampling procedure. We formally prove that the Type I error rate is controlled, and compute the statistical power of the test through a simulation study. Finally, we apply the PIMA procedure to the analysis of a real dataset on the self-reported hesitancy for the COronaVIrus Disease 2019 (COVID-19) vaccine before and after the 2020 lockdown in Italy. We conclude with practical recommendations to be considered when implementing the proposed procedure.

Hypotony in uveitis: an overview of medical and surgical management.

Hypotony is a well-recognised, sight-threatening complication of uveitis. It can also be the final common endpoint for a multitude of disease entities. Multiple mechanisms underlie hypotony, and meticulous clinical history alongside ocular phenotyping is necessary for choosing the best intervention and therapeutic management. In this narrative review, a comprehensive overview of medical and surgical treatment options for the management of non-surgically induced hypotony is provided. Management of ocular hypotony relies on the knowledge of the aetiology and mechanisms involved. An understanding of disease trajectory is vital to properly educate patients. Both anatomical and functional outcomes depend on the underlying pathophysiology and choice of treatment.

Comparison of colour contrast sensitivity in eyes at high risk of neovascular age-related macular degeneration with and without subsequent choroidal neovascular membrane development.

BACKGROUND: Neovascular age-related macular degeneration (nAMD) is a leading cause of blind registrations in the elderly. Unfortunately, it is difficult to detect the early stage of the disease, when treatment is more likely to be successful. Subjects with very early disease are likely to have abnormal macular function, even in the pre-symptomatic stage. In this study, colour vision was evaluated to establish if subjects at high risk of developing nAMD can be identified, thus allowing earlier diagnosis and possible treatment. METHODS: Colour contrast sensitivity (CCS) was evaluated over time in the fellow unaffected eye of subjects with unilateral nAMD. Participants were divided into Group 1 (182 participants) or Group 2 (15 participants) according to whether nAMD did not or did develop in the study period respectively and the two groups were compared. RESULTS: CCS was increased (i.e. worse colour vision) compared with the age-matched reference range in a high proportion of fellow eyes in both Groups 1 and 2. Global mean CCS values did not show statistically significant differences between the two groups. However, there was a statistically significant difference between mean Group 1 CCS values and the last CCS value prior to nAMD diagnosis from Group 2 subjects. CONCLUSION: This study shows that in patients with unilateral nAMD, colour vision is frequently abnormal in the fellow unaffected eye. Abnormal CCS does not predict the development of nAMD within the 12 month period of the study and therefore it is not a viable screening tool for this pathology.

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography.

Purpose: Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype-phenotype correlations. Methods: International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. Results: Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. Conclusions: This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance: Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.

Abnormal visual attention to simple social stimuli in 4-month-old infants at high risk for Autism.

Despite an increasing interest in detecting early signs of Autism Spectrum Disorders (ASD), the pathogenesis of the social impairments characterizing ASD is still largely unknown. Atypical visual attention to social stimuli is a potential early marker of the social and communicative deficits of ASD. Some authors hypothesized that such impairments are present from birth, leading to a decline in the subsequent typical functioning of the learning-mechanisms. Others suggested that these early deficits emerge during the transition from subcortically to cortically mediated mechanisms, happening around 2-3 months of age. The present study aimed to provide additional evidence on the origin of the early visual attention disturbance that seems to characterize infants at high risk (HR) for ASD. Four visual preference tasks were used to investigate social attention in 4-month-old HR, compared to low-risk (LR) infants of the same age. Visual attention differences between HR and LR infants emerged only for stimuli depicting a direct eye-gaze, compared to an adverted eye-gaze. Specifically, HR infants showed a significant visual preference for the direct eye-gaze stimulus compared to LR infants, which may indicate a delayed development of the visual preferences normally observed at birth in typically developing infants. No other differences were found between groups. Results are discussed in the light of the hypotheses on the origins of early social visual attention impairments in infants at risk for ASD.

The longitudinal negative impact of early stressful events on emotional and physical well-being: The buffering role of cardiac vagal development.

Early stressful events negatively affect emotional and physical well-being. Cardiac vagal tone (CVT), which is associated with better emotional and physical well-being, usually gradually increase in early childhood. Nonetheless, children's CVT developmental trajectories are greatly variable, such that CVT can increase or decrease across the years. The present study examines the longitudinal effects of early stressful events and the role of 4 years CVT developmental trajectory on children's emotional and physical well-being. Forty-two 4-year-old children were enrolled. Number of stressful events and resting electrocardiogram (ECG) were collected at T1. ECG was registered again after one (T2), two (T3) and three (T4) years. Children's emotional and physical well-being were assessed at T4 through the Child Health and Illness Profile - Child Edition (CHIP-CE). CVT development was calculated as the angular coefficient, reflecting the developmental trajectory of CVT across the four timepoints. Results yielded that higher experienced stressful events predicted poorer emotional and physical well-being after 4 years. The interaction between the number of stressful events and CVT development emerged on physical well-being. Early stressful events negatively affect long-term children's emotional and physical well-being while a positive CVT development seems to mitigate the negative effects of early stressful events on physical well-being.

Factors Associated with Providers' Work Engagement and Burnout in Homeless Services: A Cross-national Study.

The complexity of homeless service users' characteristics and the contextual challenges faced by services can make the experience of working with people in homelessness stressful and can put providers' well-being at risk. In the current study, we investigated the association between service characteristics (i.e., the availability of training and supervision and the capability-fostering approach) and social service providers' work engagement and burnout. The study involved 497 social service providers working in homeless services in eight different European countries (62% women; mean age = 40.73, SD = 10.45) and was part of the Horizon 2020 European study "Homelessness as Unfairness (HOME_EU)." Using hierarchical linear modeling (HLM), findings showed that the availability of training and supervision were positively associated with providers' work engagement and negatively associated with burnout. However, results varied based on the perceived usefulness of the training and supervision provided within the service and the specific outcome considered. The most consistent finding was the association between the degree to which a service promotes users' capabilities and all the aspects of providers' well-being analyzed. Results are discussed in relation to their implications for how configuration of homeless services can promote social service providers' well-being and high-quality care.

Perinatal psychological well-being in women with zero postnatal anxiety-depressive symptoms scores: a retrospective descriptive study.

Background: A total absence of psychological symptoms during pregnancy or postpartum period is not common. Although there are some considerations on zero scores detected by EPDS, no thorough analysis is currently present in the literature of the eventuality and meaning of a total absence of postpartum symptoms following the compilation of two or more self-report symptom questionnaires.Methods: In a sample of 960 Italian women, three groups of 31 subjects are defined retrospectively by scores on the EPDS-GHQ12: women with 'zero', 'lower', and 'higher' postnatal symptomatology. The psychological well-being of these groups was compared as detected in pregnancy and after childbirth by PWB questionnaire.Results: Higher and excessive scores in Environmental mastery dimension connote the profile of women with a total absence of postpartum anxiety-depression symptoms. Positive relations with other dimension were less characterising, but it should be noted as a distinctive trait in the 'zero' symptoms postnatal symptomatology group. In the same 'zero' group, the scores of the six PWB questionnaire dimensions - except Personal growth - are all higher than the scores obtained by women in the general population.Conclusion: The atypical self-reported perinatal mood condition present in the 'zero' symptoms group have currently unknown clinical significance.

Can Mentoring Promote Self-esteem and School Connectedness? An Evaluation of the Mentor-UP Project / ¿Puede la mentoría fomentar la autoestima y la conexión escolar? Evaluación del proyecto Mentor-UPP

Research in the United States has shown that youth mentoring is a promising strategy for increasing self-esteem and school connectedness in at-risk youth. There has been little confirmation of those findings internationally. The current study evaluates the impact of mentoring by trained university students on children's self-esteem and school connectedness compared to schoolmates not involved in the program. Mentor-UP is a school- and community-based weekly mentoring program implemented in northern Italy over a period of seven months. Participants (209 students - 34 in the experimental group and 175 in the comparison group - aged between 11 and 13, 56% male, 27% immigrants) reported their levels of self-esteem and school connectedness at the beginning and at the end of the program. Results showed a significant increase in mentees' self-esteem compared to the control group, while the difference in school-connectedness was nonsignificant. The findings support the effectiveness of Mentor-UP in nurturing youth's self-esteem

La investigación en EE. UU. ha demostrado que la mentoría juvenil es una estrategia prometedora para aumentar la autoestima y la conexión escolar en jóvenes en situación de riesgo. Sin embargo, ha habido escasa confirmación de estos hallazgos a nivel internacional. El estudio actual evalúa el impacto de la mentoría por parte de estudiantes universitarios capacitados en autoestima y conexión escolar de los niños en comparación con los compañeros de escuela que no participaron en el programa. Mentor-UP es un programa de mentoría semanal llevado a cabo en la escuela y la comunidad que se implementó en el norte de Italia durante un período de siete meses. Los participantes (209 estudiantes, 34 en el grupo experimental y 175 en el grupo de comparación de edades comprendidas entre 11 y 13 años, 56% hombres, 27% inmigrantes) informaron de su nivel de autoestima y conexión escolar al principio y al final del programa. Los resultados mostraron un aumento significativo en la autoestima de los niños mentorizados en comparación con el grupo de control, mientras que la diferencia en la conexión escolar no fue significativa. Los hallazgos respaldan la efectividad de Mentor-UP para fomentar la autoestima de los jóvenes

Colour contrast sensitivity in eyes at high risk of neovascular age-related macular degeneration.

PURPOSE: To generate the first published reference database of colour contrast sensitivity in eyes at high risk of neovascular age-related macular degeneration and to explore this important feature in quality of vision. BACKGROUND: Quality of vision depends on many factors. Changes in chromatic contrast sensitivity remain largely unexplored in eyes at high risk of neovascular age-related macular degeneration; they may however not only be relevant for quality of life but also an early indicator of the onset of the disease, so it is important to have a means to evaluate any variation in colour contrast sensitivity, especially in view of the likely increase in neovascular age-related macular degeneration as the population ages. METHODS: This prospective longitudinal study evaluated colour contrast sensitivity along the protan and tritan colour axes in 145 eyes at high risk of neovascular age-related macular degeneration. RESULTS: Colour contrast sensitivity showed statistically significant correlations with age and visual acuity, but not gender nor laterality (i.e. whether the right or left eye was being tested). There was significant variability among individuals, especially for the tritan axis, with some subjects well within normal limits for age and others with very poor colour contrast sensitivity. CONCLUSION: This study has generated the first published colour contrast sensitivity reference database for eyes at high risk of neovascular age-related macular degeneration. It has also shown a high inter-individual variability of colour contrast sensitivity in eyes at high risk of neovascular age-related macular degeneration, but the significance of this is unclear. Further work is required to establish if eyes with high colour contrast sensitivity thresholds (i.e. poor colour vision) have a higher risk of developing neovascular age-related macular degeneration over time, and this is the subject of ongoing work.

Less is more: Morphometric and psychological differences between low and high reappraisers.

Emotion regulation plays a crucial role in an individual's well-being, as it is known that deficits in regulating emotions can lead to psychological and psychiatric disorders. Cognitive reappraisal is widely considered to be an adaptive and effective emotion-regulation strategy. People are more or less able to apply it, but it is still not clear how reappraisal affects brain structures and the psychological profile of individuals. In our study we thus aimed to explore the impact of applying reappraisal at both the neural and the psychological level. Source-based morphometry (SBM), a whole-brain multivariate method based on the Independent Component Analysis that extracts patterns of covariation of gray matter ("independent networks"), was applied to the MRI images of 37 participants. In order to enrich their psychological profiles, we measured their experienced affectivity (PANAS) and their empathic abilities (IRI). Based on the frequency of applying reappraisal (ERQ), participants were divided into low and high reappraisers (18 vs. 19). An independent source of gray matter emerged as being different between the groups: specifically, low reappraisers showed more gray matter volume concentration in a network including the frontal, temporal, and parietal regions as compared to high reappraisers. At the psychological level, low reappraisers reported a more strongly experienced negative affect, while no difference among reappraisers emerged with regard to empathic abilities. Capitalizing on a multivariate method for structural analysis that is innovative in this field, this study extends previous observations on individual differences in the ability to regulate emotions, and it describes a plausible impact of reappraisal on brain structures and affectivity.

Are children with developmental dyslexia all the same? A cluster analysis with more than 300 cases.

Reading is vital to every aspect of modern life, exacerbated by reliance of the internet, email, and social media on the written medium. Developmental dyslexia (DD) characterizes a disorder in which the core deficit involves reading. Traditionally, DD is thought to be associated with a phonological impairment. However, recent evidence has begun to suggest that the reading impairment in some individuals is provoked by a visual processing deficit. In this paper, we present WISC-IV data from more than 300 Italian children with a diagnosis of DD to investigate the manifestation of phonological and visual subtypes. Our results indicate the existence of two clusters of children with DD. In one cluster, the deficit was more pronounced in the phonological component, while both clusters were impaired in visual processing. These data indicate that DD may be an umbrella term that encompasses different profiles. From a theoretical perspective, our results demonstrate that dyslexia cannot be explained in terms of an isolated phonological deficit alone; visual impairment plays a crucial role. Moreover, general rather than specific accounts of DD are discussed.

Measuring Distribution Similarities Between Samples: A Distribution-Free Overlapping Index.

Every day cognitive and experimental researchers attempt to find evidence in support of their hypotheses in terms of statistical differences or similarities among groups. The most typical cases involve quantifying the difference of two samples in terms of their mean values using the t statistic or other measures, such as Cohen's d or U metrics. In both cases the aim is to quantify how large such differences have to be in order to be classified as notable effects. These issues are particularly relevant when dealing with experimental and applied psychological research. However, most of these standard measures require some distributional assumptions to be correctly used, such as symmetry, unimodality, and well-established parametric forms. Although these assumptions guarantee that asymptotic properties for inference are satisfied, they can often limit the validity and interpretability of results. In this article we illustrate the use of a distribution-free overlapping measure as an alternative way to quantify sample differences and assess research hypotheses expressed in terms of Bayesian evidence. The main features and potentials of the overlapping index are illustrated by means of three empirical applications. Results suggest that using this index can considerably improve the interpretability of data analysis results in psychological research, as well as the reliability of conclusions that researchers can draw from their studies.

Investigating the potential of Zernike polynomials to characterise spatial distribution of macular pigment.

It has been postulated that particular patterns of macular pigment (MP) distribution may be associated with the risk for eye diseases such as age-related macular degeneration (AMD). This work investigates the potential of Zernike polynomials (ZP) to characterise the level and distribution of MP, and their suitability as a representation for analysis of the effects of age and AMD on MP patterns. As the case study, MP distribution maps computed using an experimental method based on fundus reflectance (MRIA) were obtained for ninety volunteers representing three groups: under-fifty without AMD, fifty and over without AMD, and fifty and over with AMD. ZP with 105 coefficients were fitted to the maps using least-squares optimisation and found to represent MP maps accurately (RMSE<10-1). One-way MANOVA analysis carried out on ZP representations showed that the three subject groups have significantly different means (Wilk's Lambda 0.125, p<0.0001). Linear discriminant analysis with leave-one-out scheme resulted in accuracy, sensitivity and specificity of classification according to, respectively, disease status regardless of age (81% all); disease status in the age-matched groups (87%, 88%, 86%); age irrespective of disease status (81%, 83%, 73%); and age for subjects without AMD (83%, 88%, 80%). Mean MP distributions computed from ZP coefficients for the three groups showed more elevated and more peaked MP for the healthy under-fifty group; more irregular and more elevated peripheral levels in over-fifty AMD group than in over-fifty non-AMD group; and moderate radial asymmetry in non-AMD over-50 group. The results suggest that ZP coefficients are capable of accurately representing MP in a way that captures certain spatial patterns of its distribution. Using the ZP representation MP maps could be classified according to both age and disease status with accuracy significantly greater than chance, with peak elevation, pattern irregularity and radial asymmetry identified as important features.

Obstructive sleep apnea syndrome and olfactory perception: An OERP study.

Obstructive Sleep Apnea Syndrome (OSA) is characterized by snoring associated with repeated apnea and/or obstructive hypopnea. The nasal airways of OSA patients, measured via acoustic rhinometry, could be significantly narrower than healthy subjects and this reduced nasal structure can impair olfactory function. The relationship between nasal structure and olfactory function, assessed via behavioral test results, indicates that there is a high prevalence of nasal airflow problems. Based on these assumptions, the purpose of this study was to carry out an assessment of olfactory perception in OSA patients through the Chemosensory Event-Related Potentials (CSERP), investigating the N1 component and the Late Positive Component (LPC). Twelve OSA patients, non-smokers, were recruited in the Pulmonary Rehabilitation Unit, scored with the Epworth Sleepiness Scales, after Polygraphic Recording, Apnea Hypopnea Index and Body Mass Index evaluation. The control group consisted of twelve healthy controls, non-smokers, recruited as volunteers. Subjects, during an EEG recording, performed an oddball olfactory recognition task based on two scents: rose and eucalyptus. Main results highlighted differences in N1 and LPC between OSA and controls. OSA patients presented faster N1 latencies and greater amplitude. The same trend was found in LPC, where OSA showed decreased latency and increased amplitude during rose stimulation, in the right inferior frontal cortex. and faster latencies in left centroparietal cortex OERP results can suggest an impairment in endogenous components. This result could be the consequence of the exogenous perceptual difficulty highlighted in N1 component. The increased arousal could also be related to the respiratory activity involved during the olfactory task.

A Maximum Entropy Procedure to Solve Likelihood Equations.

In this article, we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy (ME) approach. Unlike standard procedures that require equating the score function of the maximum likelihood problem at zero, we propose an alternative strategy where the score is instead used as an external informative constraint to the maximization of the convex Shannon's entropy function. The problem involves the reparameterization of the score parameters as expected values of discrete probability distributions where probabilities need to be estimated. This leads to a simpler situation where parameters are searched in smaller (hyper) simplex space. We assessed our proposal by means of empirical case studies and a simulation study, the latter involving the most critical case of logistic regression under data separation. The results suggested that the maximum entropy reformulation of the score problem solves the likelihood equation problem. Similarly, when maximum likelihood estimation is difficult, as is the case of logistic regression under separation, the maximum entropy proposal achieved results (numerically) comparable to those obtained by the Firth's bias-corrected approach. Overall, these first findings reveal that a maximum entropy solution can be considered as an alternative technique to solve the likelihood equation.

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data.

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them.

Enhancing Statistical Inference in Psychological Research via Prospective and Retrospective Design Analysis.

In the past two decades, psychological science has experienced an unprecedented replicability crisis, which has uncovered several issues. Among others, the use and misuse of statistical inference plays a key role in this crisis. Indeed, statistical inference is too often viewed as an isolated procedure limited to the analysis of data that have already been collected. Instead, statistical reasoning is necessary both at the planning stage and when interpreting the results of a research project. Based on these considerations, we build on and further develop an idea proposed by Gelman and Carlin (2014) termed "prospective and retrospective design analysis." Rather than focusing only on the statistical significance of a result and on the classical control of type I and type II errors, a comprehensive design analysis involves reasoning about what can be considered a plausible effect size. Furthermore, it introduces two relevant inferential risks: the exaggeration ratio or Type M error (i.e., the predictable average overestimation of an effect that emerges as statistically significant) and the sign error or Type S error (i.e., the risk that a statistically significant effect is estimated in the wrong direction). Another important aspect of design analysis is that it can be usefully carried out both in the planning phase of a study and for the evaluation of studies that have already been conducted, thus increasing researchers' awareness during all phases of a research project. To illustrate the benefits of a design analysis to the widest possible audience, we use a familiar example in psychology where the researcher is interested in analyzing the differences between two independent groups considering Cohen's d as an effect size measure. We examine the case in which the plausible effect size is formalized as a single value, and we propose a method in which uncertainty concerning the magnitude of the effect is formalized via probability distributions. Through several examples and an application to a real case study, we show that, even though a design analysis requires significant effort, it has the potential to contribute to planning more robust and replicable studies. Finally, future developments in the Bayesian framework are discussed.