Bias, Precision, and Accuracy of Skewness and Kurtosis Estimators for Frequently Used Continuous Distributions

Translating and establishing the psychometric properties of the Jenkins Sleep Scale for Arabic-speaking individuals

BACKGROUND

The Jenkins Sleep Scale is a widely used self-report questionnaire that assesses sleep quality and disturbances. This study aimed to translate the scale into Arabic and evaluate its psychometric properties in an Arabic-speaking population.

METHODS

The Jenkins Sleep Scale was translated into Arabic using forward and backward translation procedures. The Arabic version was administered to a convenience sample of 420 adults along with the Pittsburgh Sleep Quality Index (PSQI) and Athens Insomnia Scale (AIS) for validation purposes. Reliability was examined using Cronbach's alpha and McDonald's omega coefficients. Confirmatory factor analysis (CFA) was also conducted to test the unidimensional factor structure. Convergent validity was assessed using correlations with PSQI and AIS scores.

RESULTS

The Cronbach's alpha and McDonald's omega values for the Arabic Jenkins Sleep Scale were 0.74 and 0.75, respectively, indicating good internal consistency. The 2-week and 4-week test-retest intraclass correlation coefficients were both 0.94 (p < 0.001), indicating excellent test-retest reliability. The CFA results confirmed the unidimensional factor structure (CFI = 0.99, TLI = 0.96, RMSEA = 0.08). The measurement model had an equivalent factor structure, loadings, intercepts, and residuals across sex, age, and marital status. Significant positive correlations were found between the Arabic Jenkins scale score and the PSQI (r = 0.80, p < 0.001) and AIS (r = 0.74, p < 0.001), supporting convergent validity.

CONCLUSION

The Arabic version of the Jenkins Sleep Scale demonstrated good psychometric properties. The findings support its use as a valid and reliable measure for evaluating sleep quality and disturbances among Arabic-speaking populations.

Dispositional and Social Correlates of Digital Status Seeking Among Adolescents

Social media have transformed peer relationships among adolescents, providing new avenues to attain online status indicators such as likes and followers. This study aimed to explore the associations between various dispositional and social factors and digital status-seeking behaviors among a sample of adolescents (N = 731; Mage = 14.69, 48.7 percent female), as well as explore potential gender differences in the examined associations. Sociometric nominations for digital status-seeking, likeability, and popularity were collected, and participants self-reported their social media use frequency, awareness of social media positivity bias, reward sensitivity, and gender. The findings revealed a positive relationship between sociometric popularity and digital status-seeking, whereas likeability displayed a negative association with digital status-seeking. These results emphasize the importance of distinguishing between different social status indicators in understanding online behaviors. Reward sensitivity did not show a significant link to digital status-seeking, and awareness of social media positivity bias heightened the likelihood of being nominated as a digital status-seeker. These findings underscore the need for further research, especially focusing on girls who appear to be more vulnerable to engaging in digital status-seeking behaviors.

Predicting student-teacher self-directed learning using intrinsic and extrinsic factors: a Theory of Planned Behavior adoption

Introduction

This study set out to develop a model that illustrates the causal relationship between factors influencing Thai student-teacher self-directed learning (SDL). To achieve this, the authors analyzed and applied the Theory of Planned Behavior (TPB) to investigate the role of family support, teacher support, friend support, fellow students, and the university in influencing SDL.

Methods

The research used a sample of 468 student-teachers from five academic majors randomly selected from the King Mongkut's Institute of Technology Ladkrabang (KMITL) Bachelor of Industrial Education Program during the 2021 academic year. The authors developed a questionnaire which was evaluated from both a panel of experts and a 30 student-teacher pilot test which found that item reliability was acceptable. LISREL 9.10 was used to analyze the data from the study's structural equation model path analysis.

Results

The results revealed that all the causal variables in the model positively influenced SDL, explaining the variance of influencing factors on SDL (R2) at 51%. SDL comprised five variables, when ranked in order of importance these were fellow students (FSt), teacher support (TS), family support (FS), friend support (FrS), and their university (Uni). The study also highlighted issues concerning each student-teacher's learning time management ability and their perception of the university's ability/willingness to allow each individual to choose their course, thus enhancing their SDL learning ability skills.

Discussion

It was speculated that some university educators even today perceive SDL as an adult education tool which they perceive is not appropriate at the university level. The authors also felt that for younger student-teachers that the COVID-19 medical emergency limited teacher/student/university interaction, thus contributing to student misconceptions about support availability. This study contributes significantly to the literature by investigating how TPB intrinsic and extrinsic factors impact a university student's self-directed learning.

Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram

The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Applied sensing technology has made it possible for human beings to experience a revolutionary aspect of the science and technology world. Along with many other fields in which this technology is working wonders, human locomotion activity recognition, which finds applications in healthcare, smart homes, life-logging, and many other fields, is also proving to be a landmark. The purpose of this study is to develop a novel model that can robustly handle divergent data that are acquired remotely from various sensors and make an accurate classification of human locomotion activities. The biggest support for remotely sensed human locomotion activity recognition (RS-HLAR) is provided by modern smartphones. In this paper, we propose a robust model for an RS-HLAR that is trained and tested on remotely extracted data from smartphone-embedded sensors. Initially, the system denoises the input data and then performs windowing and segmentation. Then, this preprocessed data goes to the feature extraction module where Parseval’s energy, skewness, kurtosis, Shannon entropy, and statistical features from the time domain and the frequency domain are extracted from it. Advancing further, by using Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)–based feature selection, the system drops the least-informative features and shrinks the feature set by 25%. In the next step, the Yeo–Johnson power transform is applied, which is a maximum-likelihood-based feature optimization algorithm. The optimized feature set is then forwarded to the multilayer perceptron (MLP) classifier that performs the classification. MLP uses the cross-validation technique for training and testing to generate reliable results. We designed our system while experimenting on three benchmark datasets namely, MobiAct_v2.0, Real-World HAR, and Real-Life HAR. The proposed model outperforms the existing state-of-the-art models by scoring a mean accuracy of 84.49% on MobiAct_v2.0, 94.16% on Real-World HAR, and 95.89% on Real-Life HAR. Although our system can accurately differentiate among similar activities, excessive noise in data and complex activities have shown an inverse effect on its performance.

Testing the normality of heart associated variables having neutrosophic numbers

In this paper, tests of skewness and kurtosis are introduced under neutrosophic statistics. The necessary measures and neutrosophic forms of these estimators are introduced. The application of the proposed tests is given using the data associated with heart diseases. From the real example analysis, the proposed tests are quite flexible and informative than the existing tests under classical statistics. In addition, it is concluded from the analysis that the proposed tests give information about the measure of indeterminacy in the presence of uncertainty.

Report Quality of Generalized Linear Mixed Models in Psychology: A Systematic Review

Generalized linear mixed models (GLMMs) estimate fixed and random effects and are especially useful when the dependent variable is binary, ordinal, count or quantitative but not normally distributed. They are also useful when the dependent variable involves repeated measures, since GLMMs can model autocorrelation. This study aimed to determine how and how often GLMMs are used in psychology and to summarize how the information about them is presented in published articles. Our focus in this respect was mainly on frequentist models. In order to review studies applying GLMMs in psychology we searched the Web of Science for articles published over the period 2014–2018. A total of 316 empirical articles were selected for trend study from 2014 to 2018. We then conducted a systematic review of 118 GLMM analyses from 80 empirical articles indexed in Journal Citation Reports during 2018 in order to evaluate report quality. Results showed that the use of GLMMs increased over time and that 86.4% of articles were published in first- or second-quartile journals. Although GLMMs have, in recent years, been increasingly used in psychology, most of the important information about them was not stated in the majority of articles. Report quality needs to be improved in line with current recommendations for the use of GLMMs.

Building a Predictive Model of Social-Emotional Adjustment: Exploring the Relationship between Parenting Self-Efficacy, Parenting Behaviour and Psychological Distress in Mothers of Young Children in Ireland

The purpose of this study was to generate greater understanding of social-emotional difficulties in infants and toddlers in an Irish context. This study compared rates of reported social-emotional difficulties in young children in clinical and non-clinical samples and probed a predictive model of social-emotional adjustment. Data were collected from a cross-sectional sample of 72 mothers of young children aged between 12 and 48 months. Mothers were recruited from waiting lists for child Early Intervention services (clinical sample) and community mother-toddler groups (non-clinical sample). Mothers completed a questionnaire battery which assessed parenting self-efficacy, parenting behaviour, psychological distress and child social-emotional adjustment. The results indicated that 55.5% of young children in the clinical sample and 15% in the non-clinical sample had significant social-emotional problems. Similarly, 55.5% of young children in the clinical sample and 30% in the non-clinical sample had significant delays in the acquisition of social-emotional competencies. Two hierarchical multiple regressions were carried out with social-emotional problems and social-emotional competencies as the respective criterion variables. Clinical or non-clinical group membership, parenting satisfaction and maternal psychological distress were found to be significant predictors of child social-emotional problems in a model which explained 59% of the variance. Task-specific self-efficacy was the only significant predictor of child social-emotional competencies in a model which explained 21% of the variance. The significant rates of social-emotional problems in young children in the current study and the potential negative impact on child health and wellbeing, suggest that the early assessment of social-emotional adjustment should be incorporated into routine clinical assessment for young children. For services to effectively meet the needs of children with social-emotional difficulties and their families, consideration of maternal factors is also necessary.

Studies on the Geometrical Design of Spider Webs for Reinforced Composite Structures

Spider silk is an astonishingly tough biomaterial that consists almost entirely of large proteins. Studying the secrets behind the high strength nature of spider webs is very challenging due to their miniature size. In spite of their complex nature, researchers have always been inspired to mimic Nature for developing new products or enhancing the performance of existing technologies. Accordingly, the spider web can be taken as a model for optimal fiber orientation for composite materials to be used in critical structural applications. In this study an attempt is made to analyze the geometrical characteristics of the web construction building units such as spirals and radials. As a measurement tool, we have used a developed MATLAB algorithm code for measuring the node to node of rings and radials angle of orientation. Spider web image samples were collected randomly from an ecological niche with black background sample collection tools. The study shows that the radial angle of orientation is 12.7 degrees with 5 mm distance for the spirals’ mesh size. The extracted geometrical numeric values from the spider web show moderately skewed statistical data. The study sheds light on spider web utilization to develop an optimized fiber orientation reinforced composite structure for constructing, for instance, shell structures, pressure vessels and fuselage cones for the aviation industry.

Pressure Fluctuations in the Spatial Hydraulic Jump in Stilling Basins with Different Expansion Ratio

Pressure fluctuations are a key issue in hydraulic engineering. However, despite the large number of studies on the topic, their role in spatial hydraulic jumps is not yet fully understood. The results herein shed light on the formation of eddies and the derived pressure fluctuations in stilling basins with different expansion ratios. Laboratory tests are conducted in a horizontal rectangular flume with 0.5 m width and 10 m length. The range of approaching Froude numbers spans from 6.4 to 12.5 and the channel expansion ratios are 0.4, 0.6, 0.8, and 1. The effects of approaching flow conditions and expansion ratios are thoroughly analyzed, focusing on the dimensionless standard deviation of pressure fluctuations and extreme pressure fluctuations. The results reveal that these variables show a clear dependence on the Froude number and the distance to the hydraulic jump toe. The maximum values of extreme pressure fluctuations occur in the range 0.609<X<3.385, where X is dimensionless distance from the toe of the hydraulic jump, which makes it highly advisable to reinforce the bed of stilling basins within this range.

Multivariate Gamma Regression: Parameter Estimation, Hypothesis Testing, and Its Application

Gamma distribution is a general type of statistical distribution that can be applied in various fields, mainly when the distribution of data is not symmetrical. When predictor variables also affect positive outcome, then gamma regression plays a role. In many cases, the predictor variables give effect to several responses simultaneously. In this article, we develop a multivariate gamma regression (MGR), which is one type of non-linear regression with response variables that follow a multivariate gamma (MG) distribution. This work also provides the parameter estimation procedure, test statistics, and hypothesis testing for the significance of the parameter, partially and simultaneously. The parameter estimators are obtained using the maximum likelihood estimation (MLE) that is optimized by numerical iteration using the Berndt–Hall–Hall–Hausman (BHHH) algorithm. The simultaneous test for the model’s significance is derived using the maximum likelihood ratio test (MLRT), whereas the partial test uses the Wald test. The proposed MGR model is applied to model the three dimensions of the human development index (HDI) with five predictor variables. The unit of observation is regency/municipality in Java, Indonesia, in 2018. The empirical results show that modeling using multiple predictors makes more sense compared to the model when it only employs a single predictor.

Self-Concept in China: Validation of the Chinese Version of the Five-Factor Self-Concept (AF5) Questionnaire

The principle of invariance is a mandatory methodological requirement for the psychological measures, even when items such as self-concept measures frequently present asymmetric heavy-tailed distributions. Few validated self-concept instruments can be applied in Eastern–Western cross-cultural studies. The Five-Factor Self-Concept Questionnaire (AF5) is one of the few psychometrically sound instruments used to assess multidimensional self-concept in Spanish-speaking samples. The availability of the AF5 in Spanish and Chinese would facilitate cross-cultural research. To validate the Chinese version of the AF5, we used multisample confirmatory factor analysis with transformed dichotomous scales from the median to compare four alternative theoretical models. The sample consisted of 2507 participants (52.3% women) from China (n = 1298) and Spain (n = 1209), ranging in age from 19 to 35. Analyses confirmed the five-factor structure of the Chinese AF5 (i.e., academic, social, emotional, family, and physical) compared to the Spanish sample. Moreover, the Chinese version of the AF5 was found to be invariant in terms of item-factor weights, factor variance, and between-factor covariance, compared to the original Spanish version. The findings from this first validation study indicate that the Chinese version of the AF5 is an acceptable measure for use with Chinese-speaking adolescents and young adults.

Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods

Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability.