ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}

Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer-mouse, wii, and joysticks, such data provide new insights for analysing the dynamic unfolding of response process. ssMousetrack is a R package for modeling and analysing computerized-tracking data by means of a Bayesian state-space approach. The package provides a set of functions to prepare data, fit the model, and assess results via simple diagnostic checks. This paper describes the package and illustrates how it can be used to model and analyse computerized-tracking data. A case study is also included to show the use of the package in empirical case studies

Mental Health among Former Child Soldiers and Never-Abducted Children in Northern Uganda

The present study aimed to evaluate posttraumatic stress symptoms, psychological distress, and emotional and behavioral problems in former Ugandan child soldiers in comparison with civilian children living in the same conflict setting. Participants included 133 former child soldiers and 101 never-abducted children in northern Uganda, who were interviewed about exposure to traumatic war-related experiences, posttraumatic stress symptoms, psychological distress, and emotional and behavioral problems. Results indicated that former child soldiers had experienced significantly more war-related traumatic events than nonabducted children, with 39.3% of girls having been forced to engage in sexual contact. Total scores on measures of PTSD symptoms, psychological distress, and emotional and behavioral problems were significantly higher among child soldiers compared to their never-abducted peers. Girls reported significantly more emotional and behavioral difficulties than boys. In never-abducted children, more mental health problems were associated with experiencing physical harm, witnessing the killings of other people, and being forced to engage in sexual contact

Entia Non Sunt Multiplicanda … Shall I look for clusters in my cognitive data?

Unsupervised clustering methods are increasingly being applied in psychology. Researchers may use such methods on multivariate data to reveal previously undetected sub-populations of individuals within a larger population. Realistic research scenarios in the cognitive science may not be ideally suited for a successful use of these methods, however, as they are characterized by modest effect sizes, limited sample sizes, and non-orthogonal indicators. This combination of characteristics even presents a high risk of detecting non-existing clusters. A systematic review showed that, among 191 studies published in 2016–2020 that used different clustering methods to classify human participants, the median sample size was only 322, and a median of 3 latent classes/clusters were detected. None of them concluded in favor of a one-cluster solution, potentially giving rise to an extreme publication bias. Dimensionality reduction techniques are almost never used before clustering. In a subsequent simulation study, we examined the performance of popular clustering techniques, including Gaussian mixture model, a partitioning, and a hierarchical agglomerative algorithm. We focused on their ability to detect the correct number of clusters, and on their classification accuracy. Under a reasoned set of scenarios that we considered plausible for the cognitive research, none of the methods adequately discriminates between one vs two true clusters. In addition, non-orthogonal indicators lead to a high risk of incorrectly detecting multiple clusters where none existed, even in the presence of only modest correlation (a frequent case in psychology). In conclusion, it is hard for researchers to be in a condition to achieve a valid unsupervised clustering for inferential purposes with a view to classifying individuals

Factorial validity of the Problematic Facebook Use Scale for adolescents and young adults

Recent research on problematic Facebook use has highlighted the need to develop a specific theory-driven measure to assess this potential behavioral addiction. The aim of the present study was to examine the factorial validity of the Problematic Facebook Use Scale (PFUS) adapted from Caplan’s Generalized Problematic Internet Scale model. Methods A total of 1,460 Italian adolescents and young adults (aged 14–29 years) participated in the study. Confirmatory factor analyses were performed in order to assess the factorial validity of the scale. Results Results revealed that the factor structure of the PFUS provided a good fit to the data. Furthermore, results of the multiple group analyses supported the invariance of the model across age and gender groups. Discussion and conclusions This study provides evidence supporting the factorial validity of the PFUS. This new scale provides a theory-driven tool to assess problematic use of Facebook among male and female adolescents and young adults

PRDA: An R package for Prospective and Retrospective Design Analysis

The paper describes the PRDA package available at https://cran.r-project.org/web/packages/PRDA/ . PRDA is an R package performing prospective or retrospective design analysis (see Gelman & Carlin, 2014 and Altoè et al., 2020) to evaluate inferential risks (i.e., power, Type M error, and Type S error) in a study considering Pearson’s correlation between two variables or mean comparisons (one-sample, paired, two-sample, andWelch’st-test). Prospective Design Analysis is performed in the planning stage of a study to define the required sample size to obtain a given level of power. Retrospective Design Analysis, instead, is performed when the data have already been collected to evaluate the inferential risks associated with the study. PRDA, additionally, offers the possibility to conduct a prospective/retroprospective design analysis taking into account for the uncertainty about the hypothetical value of effect size. In fact, hypothetical effect size can be defined as a single value according to previous results in the literature or experts indications, or by specifying a distribution of plausible values

Effectiveness of digital-based interventions for children with mathematical learning difficulties : A meta-analysis

Abstract The purpose of this work was to meta-analyze empirical evidence about the effectiveness of digital-based interventions for students with mathematical learning difficulties. Furthermore, we investigated whether the school level of the participants and the software instructional approach were decisive modulated factors. A systematic search of randomized controlled studies published between 2003 and 2019 was conducted. A total of 15 studies with 1073 participants met the study selection criterion. A random effects meta-analysis indicated that digital-based interventions generally improved mathematical performance (mean ES = 0.55), though there was a significant heterogeneity across studies. There was no evidence that videogames offer additional advantages with respect to digital-based drilling and tutoring approaches. Moreover, effect size was not moderated when interventions were delivered in primary school or in preschool

developing a simulated environment to study naturalistic decision making processes

Motivation - Combination of qualitative and quantitative methodologies to develop a simulated environment to study decision-making processes from a NDM perspective. Research approach - Discourse analysis to find interpretative repertoires, use of Visual Analog Scales and ANOVA to validate the repertoires. Findings/Design - Usefulness of qualitative and quantitative methodologies to develop the simulation, importance of a rigorous validation. Research limitations/Implications - Need for comparison with a real website in further studies. Originality/Value - Use of NDM perspective to investigate processes that were studied just from a DM perspective. Take away message - It is possible to study decision-making by naturally simulating them with computer technology in a laboratory

The Italian version of the Depression Anxiety Stress Scales-21: Factor structure and psychometric properties on community and clinical samples

Abstract Objective: The Depression Anxiety Stress Scales-21 (DASS-21) is the short version of a self-report measure that was originally developed to provide maximum differentiation between depressive and anxious symptoms. Despite encouraging evidence, the factor structure and other features of the DASS-21 are yet to be firmly established. Method: A community sample of 417 participants and two clinical groups (32 depressive patients and 25 anxious patients) completed the Italian version of the DASS-21 along with several measures of psychopathology. Results: Confirmatory factor analyses suggested that the DASS-21 is a measure of general distress plus three additional orthogonal dimensions (anxiety, depression, and stress). The internal consistency and temporal stability of the measure were good; each DASS-21 scale correlated more strongly with a measure of a similar construct, demonstrating good convergent and divergent validity. Lastly, the DASS-21 demonstrated good criterion-oriented validity. Conclusion: The validity of the Italian DASS-21 and its utility, both for community and clinical individuals, are supported

The effect of stimulus variability on children's judgments of quantity

The concept of variability (i.e., dispersion of observed data) has a central role in statistics and in quantitative decisions in everyday life. In the educational literature, however, scholars have only recently directed their attention to the study of how reasoning about variability develops (Garfield & Ben-Zvi, 2005). From a developmental cognitive perspective, much research has been conducted on children's quantity judgments since Piaget’s seminal work, but few studies have systematically investigated the development of children's reasoning about variability. To address these gaps, the present study investigated development of the ability to make quantity judgments in the presence of variability. In the first Chapter, we define statistical reasoning and describe its importance in common life situations as well as in empirical research. We then introduce a psychological perspective on statistical reasoning based on the work of Kahneman and Tversky. We focus on the role of variability in statistical reasoning and define the concept of variability and its related measures in statistical terms. In the second Chapter, we illustrate the crucial role of variability across different statistical domains. We describe the most relevant difficulties students encounter in understanding variability. Finally, a brief literature review of educational studies is provided. Since reasoning about variability is always related to the presence of quantity, in the third Chapter we present a critical overview of the psychological literature concerning the development of children’s ability to make quantity judgments. In the fourth Chapter we describe the results of our first experiment. Two-hundred forty-one children aged 4, 5, 6, 8, and 12 years and 82 university students were assessed using a computerized task in which they were asked to compare two sets of five chocolate bars. The mean and variability of the chocolate bar lengths were held constant in one set and manipulated in the other set. Participants indicated which set contained more chocolate or that the amounts of chocolate were equal. Overall, the judgments were surprisingly difficult even for adults, who responded incorrectly to 29% of the stimuli. Quantity judgment performance significantly increased with age, with mean performance increasing monotonically between 4 and 12 years. In particular, 8-year-olds performed significantly better than their younger counterparts, and 12-year-olds performed significantly better compared to all the other children, but not compared to adults. The performance of 12-year-olds and adults decreased as stimulus variability increased, whereas a different pattern emerged for 4, 5, and 6-year-olds. In these age groups, performance was highest for intermediate levels of variability, resulting in a surprisingly inverted U-shaped effect of variability on performance. The effect of variability for 8-year-olds was intermediate between that observed among younger children and adults. Taken together, these results suggest that a developmental shift occurs between the ages of 8 and 12 in the ability to make quantity judgments in the presence of variability. One of the most salient results emerging from Experiment 1 was the presence of an age-related effect of variability on participants’ performance. In Chapter 5, we describe a control experiment conducted to further validate this finding by taking possible biases related to the specific experimental design into account (i.e., younger children apparently showed a response bias against selecting the equal response). In this experiment 64 children (30 six-year-olds, 34 eight-year-olds) performed the same task, but all stimuli with equal quantities and the equal response alternative were eliminated. Overall, the results of this study confirmed the findings of Experiment 1. Chapter 6 briefly summarizes and discusses our findings. To conclude, judging quantity in the presence of variability is a relevant and difficult task. Understanding development of the ability to make quantity judgments in the presence of variability may suggest innovative teaching strategies and prevent possible reasoning biases in adults.Il concetto di variabilità, intesa come dispersione dei dati osservati, ha un ruolo centrale nelle scienze statistiche e nelle decisioni quantitative della vita quotidiana. Tuttavia, le ricerche in ambito educativo si sono focalizzate solo recentemente sullo studio del modo in cui si sviluppano le abilità di ragionamento riguardanti la variabilità (Garfield & Ben-Zvi, 2005). Da una prospettiva cognitivo-evolutiva, sono state condotte molte ricerche sul giudizio di quantità nei bambini a partire dai primi lavori di Piaget, ma pochi studi hanno indagato lo sviluppo del ragionamento sulla variabilità nei bambini in maniera sistematica. Sulla base di queste lacune, il presente studio ha l’obiettivo di indagare lo sviluppo della capacità di formulare dei giudizi di quantità in presenza di variabilità. Nel primo capitolo viene proposta una definizione di ragionamento statistico e si descrive l’importanza di questa abilità in situazioni di vita comuni, nonché nella ricerca empirica. Si introduce poi la prospettiva psicologica sul ragionamento statistico facendo riferimento ai lavori di Kahneman e Tversky. Successivamente ci focalizziamo sul ruolo della variabilità nel ragionamento statistico e definiamo in termini statistici il concetto di variabilità e le misure ad esso associate. Nel secondo capitolo viene illustrato il ruolo cruciale della variabilità in diversi ambiti della statistica. Si descrivono le difficoltà più rilevanti incontrate dagli studenti nella comprensione della variabilità, e si conclude con una breve rassegna della letteratura sugli studi condotti in ambito educativo. Poiché ragionare sulla variabilità implica sempre la presenza di quantità, nel terzo capitolo presentiamo una panoramica della letteratura psicologica riguardante lo sviluppo dell’abilità di formulare dei giudizi di quantità nei bambini, evidenziandone gli aspetti critici. Nel quarto capitolo vengono descritti i risultati del primo esperimento. 241 bambini di 4, 5, 6, 8 e 12 anni e 82 studenti universitari hanno partecipato a un compito al computer, in cui veniva chiesto di confrontare due set contenenti 5 barre di cioccolata ciascuno. In un set, la media e la variabilità (intesa come deviazione standard) della lunghezza delle barre venivano mantenute costanti, mentre nel secondo set sono state manipolate. Ai partecipanti veniva chiesto di indicare quale set contenesse più cioccolata, o se la quantità fosse equivalente nei due set. Complessivamente i giudizi erano sorprendentemente difficili anche per gli adulti, che hanno fornito risposte non corrette al 29% degli stimoli. Più specificamente, dai risultati è emerso che 1) la performance nei giudizi di quantità aumenta significativamente con l’età, mostrando un aumento monotonico tra i 4 e i 12 anni. In particolare, i bambini di 8 anni hanno prestazioni significativamente migliori dei bambini più piccoli, e i dodicenni mostrano una performance migliore di quella di tutti gli altri bambini, ma non degli adulti; 2) nei dodicenni e negli adulti, la performance peggiora all’aumentare della variabilità dello stimolo, mentre un pattern diverso è emerso nei bambini di 4, 5 e 6 anni. In questi ultimi, le prestazioni migliori sono state rilevate in presenza di livelli intermedi di variabilità, dando luogo ad un effetto “a U rovesciata” piuttosto inaspettato. Nei bambini di 8 anni, l’effetto della variabilità era di tipo intermedio tra quello osservato nei bambini più piccoli e negli adulti. Complessivamente, questi risultati suggeriscono la presenza di un cambiamento evolutivo nell’abilità di formulare giudizi di quantità in presenza di variabilità tra gli 8 e i 12 anni. Uno dei risultati più salienti emersi dall’Esperimento 1 è l’effetto della variabilità sulla performance dei partecipanti in funzione dell’età. Nel capitolo 5 descriviamo un esperimento di controllo condotto per validare ulteriormente tale risultato, prendendo in considerazione dei possibili bias insiti nella procedura sperimentale (i bambini più piccoli apparentemente mostravano un bias nella risposta “uguale”, da loro quasi mai utilizzata). L’esperimento è stato somministrato a 64 bambini (30 di 6 anni, 34 di 8 anni) eliminando tutti gli stimoli contenenti quantità uguali nonché l’opzione di risposta “uguale”. I risultati di questo esperimento hanno confermato quanto già rilevato nell’esperimento principale. Nel capitolo 6 riassumiamo brevemente e discutiamo i risultati della nostra ricerca. Per concludere, il giudizio di quantità in presenza di variabilità costituisce un compito rilevante e difficile. Riteniamo che la comprensione del modo in cui si sviluppa la capacità di formulare giudizi di quantità in presenza di variabilità può essere utile per implementare delle strategie d’insegnamento innovative e per prevenire la formazione di possibili bias di ragionamento negli adulti