Inferential Statistical Methods Research Articles

Factors predicting self-care behavior of cardiovascular patients during the COVID-19 epidemic

BackgroundThe COVID-19 virus has had wide-ranging effects on all healthcare systems and a direct impact on all areas of human life in all countries around the world. Therefore, it is necessary to take preventive actions to reduce the prevalence and severity of the complications associated with this disease. The purpose of this study was to explain the dimensions of adopting general self-care behaviors (mask-wearing, social distancing, hand hygiene, and home quarantine) for preventing COVID-19 based on the theory of planned behavior (TPB) in cardiovascular patients.MethodsThis was a descriptive-analytical study conducted with the participation of 420 patients referring to health and treatment centers of Ahvaz, southwest of Iran, in 2022. Sampling was done using a non-random (convenience) method. The data collection tool was a questionnaire containing items addressing demographic characteristics, questions related to the TPB, and questions dealing with the adoption of everyday self-care behaviors against contracting COVID-19. Data were analyzed using descriptive and inferential statistical methods (prevalence, mean, standard deviation, Pearson’s correlation coefficient, and linear regression) in SPSS version 25.ResultsThe results of this study showed that the rate of adoption of self-care behaviors against COVID-19 among cardiovascular patients was moderate. The results also showed that among the constructs of the TPB, Perceived behavioral control, Subjective norms, and Perceived behavioral intention were the most important predictors of adopting self-care behaviors among cardiovascular patients with a change variance of 46%.ConclusionsThe results of the present study have implications for health and treatment policy makers as well as planners of educational and behavioral interventions aimed at promoting the adoption of self-care behaviors against COVID-19. In this respect, managing and institutionalizing desirable behaviors among cardiovascular patients could be beneficial from economic, social, and health-related aspects.

Efficient inference of parent-of-origin effect using case-control mother–child genotype data

Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother–child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation–maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation–maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation–maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.

Inference for calendar effects in microstructure noise

We develop a statistical inference procedure for the ubiquitous calendar effects in microstructure noise using high frequency data. This is, to the best of our knowledge, the first inference theory ever built for noise calendar effect under the general semi‐martingale‐plus‐noise setup for prices contaminated with non‐stationary, endogenous, and serially dependent microstructure noise. We devise a noise‐calendar‐effect estimator by an appropriately scaled average of high‐frequency returns that precede a time of day across a large number of trading days. Feasible central limit theorem for the estimator is established under a joint infill and long‐span asymptotics. Monte Carlo simulations corroborate our theoretical findings. An empirical study on the high‐frequency data of the e‐mini S&P 500 futures and a Chinese stock demonstrates that the noise calendar effect has undergone significant changes over time for the latter, yet remains stable for the former.

The Impact of Self-Care Recommendations with and without Tilt-Training on Quality of Life in Children and Adolescents with Vasovagal Syncope: A Randomized Clinical Trial.

Syncope is among the most common paroxysmal disorders in children and adolescents. Vasovagal syncope is the most common syncope in children and adolescents. The aim of this study was to evaluate the impact of self-care recommendations with and without tilt training on the Quality of Life (QoL) of children and adolescents with syncope. This randomized controlled clinical trial was conducted in Isfahan, Iran, from April 2017 to June 2021 and included 120 patients with syncope. Eligible children and adolescents (aged 6-18 years) who met inclusion criteria were recruited by the simple sampling method and then assigned randomly into two groups. The intervention group (n = 60) received routine self-care recommendations such as dietary advice, behaviors to prevent syncope, and counter-pressure maneuvers along with tilt training, while the control group (n = 60) received self-care recommendations without tilt training. The education training included two face-to-face sessions, each of which lasted for 45-60 min. Then, both groups were followed up by telephone (once a month) for six months. A researcher-made self-care questionnaire and Pediatric Quality of Life Inventory (PedsQL™ 4.0) were completed for both groups before and after the intervention. Data were analyzed using descriptive and inferential statistical methods. The Wilcoxon test results showed a significant difference in the mean scores of physical functioning, emotional functioning, social functioning, school functioning, psychosocial functioning, understanding of health, and total QoL in the intervention and control groups before and after the intervention (p < 0.05). Also, the paired t-test results showed a significant difference in the mean scores of self-care domains and total self-care in the intervention and control groups before and after the intervention (p < 0.05). Self-care recommendations with and without tilt training can improve QoL in children and adolescents with syncope.

The Role of Whatsapp Business in Increasing Consumer Engagement by Implementing Dewa Eka Prayoga Marketing Techniques

The development of digital communication technology has changed the marketing paradigm, with WhatsApp Business becoming one of the popular platforms companies use to interact with customers. Effective marketing techniques such as those developed by Dewa Eka Prayoga, a well-known practitioner in the field of marketing, have become a concern in efforts to increase consumer engagement. This research aims to investigate the role of WhatsApp Business in increasing consumer engagement by applying Dewa Eka Prayoga marketing techniques. This research focuses on understanding the extent to which WhatsApp Business and Dewa Eka Prayoga's marketing techniques can increase consumer interaction and engagement. This research uses a quantitative research method with data collection using a questionnaire. The respondents were randomly selected from the target population of WhatsApp Business users who have used the Dewa Eka Prayoga marketing technique. The questionnaire was structured based on consumer involvement dimensions and the effectiveness of marketing techniques, and it was analyzed using descriptive and inferential statistical methods. This research analysis shows that WhatsApp Business significantly increases consumer engagement when applied with the Dewa Eka Prayoga marketing technique. The high level of interaction between companies and consumers via WhatsApp Business positively influences consumer perceptions, as well as increases the level of consumer satisfaction and loyalty. This research concludes that WhatsApp Business is an effective tool for increasing consumer engagement when used together with Dewa Eka Prayoga's marketing techniques. Companies can use this platform to strengthen consumer relationships, expand marketing reach, and increase customer loyalty.

Exploring Contextual Factors Affecting Student Performance in Mathematics: A Sequential Explanatory Research

This study aimed to explore and characterize the contextual factors that affect academic performance in learning mathematics among high school students. The study employed an explanatory sequential mixed method research design, and primary data were collected with the aid of the adopted questionnaire for quantitative data, and the interview was done for qualitative data. The study used descriptive and inferential statistical methods in analyzing and interpreting the gathered data. In addition, qualitative data were analyzed through a thematic analysis approach. Results showed that the performance of students in mathematics is influenced by different factors such as students' attitudes towards mathematics, self-efficacy, parental support, and the learning environment. In addition, a thematized interview with the students supports the quantitative analysis that their performance in mathematics was governed by the said factors. Conclusively, students must be supported in their learning by providing doable tasks and exciting problems in mathematics to boost their attitude and self-efficacy. Parents are also advised to give them the appropriate support to motivate them in their studies. Moreover, teachers must integrate welfare and positive attitudes towards students to have a conducive learning experience.

Analysis of factors affecting purchase decision on freshwater ornamental fish (Case Study: Bandung Ornamental Fish Center, Bandung City, West Java)

Bandung Ornamental Fish Center is one of the freshwater ornamental fish markets in Bandung City. Since its inauguration in 2020, Sentra Ikan Hias Bandung has experienced a decrease in the number of sellers due to the consumer interest on freshwater ornamental fish. This research aims to analyze the consumer characteristics and the factors of consumer behavior and marketing mix (4P) that influence consumer purchasing decisions for freshwater ornamental fish at the Bandung Ornamental Fish Center. This research was conducted from 19 December 2022 to 19 January 2023. The method used in this research is a case study using descriptive analysis for consumer characteristic and statistical inferential methods using PLS-SEM to analyze the factor affecting purchase decision on freshwater ornamental fish. The data collection technique used in this research is a questionnaire and interview. Data analysis in this reaserch uses the PLS-SEM analysis method with a five-point Likert scale using SmartPLS 3 software with nine variables con-sist of personal, psychology, social, culture, price, product, pro-motion, place and purchase decision that listed in Table 1. The analysis results showed that the factors influencing purchase decision of a freshwater ornamental fish at the Bandung Ornamental Fish Center were psychological, social and promotional factors. Researchers suggest the marketers carry out promotions related to consumer psychological and social factors to attract consumer interest.Keywords: Consumer behavior; factor analysis; freshwater ornamental fish; marketing mix; purchase decision; PLS-SEM

Exploring the Nexus: Economics of Happiness and Quality of Life in College Students

A happy person will have a more active lifestyle, better career opportunities, and promote a more productive, cohesive, and lasting sustainable society. India is noted for having one of the world’s youngest populations. India has more than fifty percent of the population under 25. The primary objective of this study is to explore the levels of happiness and quality of life among college students in rural and urban areas of Karnataka. The sample comprises college students aged 18 to 25, drawn from different rural and urban colleges in Karnataka using a stratified random sampling method: the Oxford Happiness Questionnaire and the Quality-of-Life Questionnaire for data collection. The collected data underwent analysis using descriptive and inferential statistical methods. It was observed in the present study that there was no significant difference between rural and urban students in Physical and Psychological quality of life. Results also indicate that in Social and Environmental domains urban students are better than rural students. The study revealed a significant relationship between happiness and different quality of life domains among urban and rural college students

Statistical inference of reliability for a K-out-of-N: G system with switching failure under Poisson shocks

Based on the stochastic uncertainty of the system's operating environment, this research presents statistical inferences on the mean time to failure (MTTF) of a K-out-of-N: G non-repairable system model with switching failure under Poisson shocks. The standby component is switched to the operating component when an operating component fails, with a switching failure probability of p. The MTTF of the system is derived by using the Markov process theory and the Laplace transform for two cases where the shock threshold is a constant value or a random variable. The maximum likelihood estimator (MLE) of the MTTF is obtained, and based on this estimator, asymptotic confidence interval estimation and hypothesis testing are performed. Based on the setting of the basic parameter values, the MTTF under two different cases of the shock threshold is compared. The effect of each parameter on the MTTF is analyzed in numerical simulation. The effectiveness of the above statistical inference methods is also verified by numerical simulation.

Modeling of indoor 222Rn in data-scarce regions: an interactive dashboard approach for Bogotá, Colombia

Abstract. Radon (222Rn) is a naturally occurring gas that represents a health threat due to its causal relationship with lung cancer. Despite its potential health impacts, several regions have not conducted studies, mainly due to data scarcity and/or economic constraints. This study aims to bridge the baseline information gap by building an interactive dashboard (http://ircmodelingdashboard.eu.pythonanywhere.com/, last access: 17 April 2024) that uses inferential statistical methods to estimate the spatial distribution of indoor radon concentration (IRC) for a target area. We demonstrate the functionality of the dashboard by modeling IRC in the city of Bogotá, Colombia, using 30 in situ measurements. IRC measured was the highest reported in the country, with a geometric mean of 91±14 Bq m−3 and a maximum concentration of 407 Bq m−3. In 57 % of the residences, RC exceeded the WHO's recommendation of 100 Bq m−3. A prediction map for houses registered in Bogotá's cadaster was built in the dashboard by using a log-linear regression model fitted with the in situ measurements, together with meteorological, geologic and building-specific variables. The model showed a cross-validation root mean squared error of 57 Bq m−3. Furthermore, the model showed that the age of the house presented a statistically significant positive association with RC. According to the model, IRC measured in houses built before 1980 presents a statistically significant increase of 72 % compared to IRC of those built after 1980 (p value = 0.045). The prediction map exhibited higher IRC in older buildings most likely related to cracks in the structure that could enhance gas migration in older houses. This study highlights the importance of expanding 222Rn studies in countries with a lack of baseline values and provides a cost-effective alternative that could help deal with the scarcity of IRC data and get a better understanding of place-specific variables that affect IRC spatial distribution.

Empirical likelihood M‐estimation for the varying‐coefficient model with functional response

AbstractThis work is motivated by a gap in the functional data analysis literature, particularly in the context of neuroimaging, regarding the ability of functional models to robustly accommodate intra‐observation dependence. In response, we propose an M‐estimator based on generalized empirical likelihood for the varying‐coefficient model with a functional response. We develop statistical inference procedures, simultaneous confidence regions, and a global general linear hypothesis test for the model's functional coefficient. Our theoretical results establish the weak convergence of the log‐likelihood ratio process, a nonparametric version of Wilks' theorem for the log‐likelihood ratio, and asymptotic properties of the proposed estimator. Through a simulation study, we show that the proposed confidence sets have close‐to‐nominal coverage probabilities. In a real‐world application to a neuroimaging dataset, we show that mini‐mental state examination score and apolipoprotein E genotype have significant associations with fractional anisotropy, while associations with gender and age are only present at high quantile levels.

The Effect of Movement Correction Exercises with and Without Mirror Therapy on Painand Disability with Chronic Low Back Patients: A Randomized Control Trial

Background: There is evidence supporting the effectiveness of mirror therapy (MT) in improving pain and function in the upper limbs. However, there are limited studies that validate its effectiveness in enhancing function and reducing pain among individuals with chronic back pain. Objectives: This study aimed to assess the effect of motor correction exercises with and without MT on pain intensity and disability in patients with chronic low back. Methods: This randomized clinical trial study was conducted on 72 participants with chronic back pain referring to physiotherapy centers affiliated with Jundishapur University of Medical Sciences, Ahvaz. Specific exercises using MT were performed in the intervention group (36 participants) and without it in the control group (36 participants) for 10 weeks. The condition of the patients before and after the intervention and 3 months after the intervention was evaluated using a chronic pain questionnaire, pain intensity, and Oswestry index. The data were analyzed using SPSS version 22, employing descriptive and inferential statistical methods. A significance level of P ≤ 0.05 was used. Results: A significant difference was observed between the groups for the variables that showed pain intensity after treatment (P = 0.001) and the difference remained in the follow-up (P = 0.003) Chronic pain after treatment and the difference at follow-up [-0.41 (-3.45 to 2.47), P = 0.001] and disability remained after treatment [-2.6 (-5.6 to 0.5); P = 0.02] which indicates the effectiveness of MT. Conclusions: Based on the results, MT was effective in reducing the pain intensity and disability experienced by patients with chronic back pain. As a result, considering its affordability and ease of access, patients can perform the exercises independently after receiving proper instruction and guidance.

An investigation of academic resilience, resilience, and empathy in pharmacy students: Implications for pharmacy education

Background: Resilience supports pharmacists and students in thriving in challenging healthcare and academic environments. Empathy is necessary to provide patient-centred care. Knowledge of how these attributes influence pharmacy students is limited, and correlations between academic resilience, empathy, and academic success continue to be explored. Methods: This study explores the relationships between levels of academic resilience, resilience, and empathy with the academic achievements of pharmacy students at a Malaysian university. Participants were pharmacy students enrolled from 2020/2021 to 2022/2023. Academic resilience was assessed using the Academic-Pharmacy-Resilience-Scale-16, resilience through the Brief-Resilience-Scale, and empathy with the Kiersma-Chen Empathy-Scale. Scores were related to students’ academic grades. Both descriptive and inferential statistical methods were utilised to analyse associations among variables. Results: Responses were received from 359 students. The levels of resilience of students were deemed to be moderate. Small, statistically significant variations were found in academic resilience scores across cohorts, resilience scores, and empathy scores in relation to gender. Academic resilience showed a correlation to academic grades. Conclusion: Academic resilience positively correlated with academic success and was positively influenced by work experience. Strengthening students’ resilience in pharmacy education may support their academic achievement, particularly during times of change like the COVID-19 pandemic.

Impact of the Product Master Data Quality on the Logistics Process Performance

Background: The importance of up-to-date product master data in the digital age should not be underestimated. However, companies still struggle to ensure high-quality product data, especially in the field of logistics. Hence, the focus of our research lies in the disregard of the importance of product data quality to the performance of logistics processes. Methods: The analysis of the influence of product data on the performance of logistics processes was carried out using data from two fast-moving consumer goods (FMCG) distribution and retail companies. Data were gathered via interviews, while process activities were timed using a stopwatch, and interruptions were documented. The significance of the impact was determined using inferential statistical procedures based on the variable and the measurement scale type employed. Results: The quality of product master data has a significant impact on the performance of logistics processes; while managers are aware of the complications, they lack the motivation to detect and analyse such inaccuracies. Conclusion: The findings enhance comprehension of the obstacles generated by inadequate product data in logistics, which obstruct optimisation, and offer numerical proof of the impact of product data quality on logistics performance, thus expanding the current body of research.

Inference in Coarsened Time Series via Generalized Method of Moments

We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated.

Perceived social support and mental health outcomes among perinatally HIV positive children in Gondar healthcare centers, North-Western Ethiopia

ABSTRACT Children with chronic illness are found to be at greater risk for psychiatric problems, including depression, anxiety and feelings of isolation. Particularly, HIV positive children in underdeveloped nations are more likely experiencing psychological disorders and live with socially alienated social support systems. In present study, institution-based cross-sectional research design was employed to assess the prevalence of mental health distress among HIV positive children 115(F = 69) in Ethiopia. Systematic random sampling technique was used to select participants. Quantitative data were collected with standardized instruments. Nurse Counsellors and case managers from the children’s ART department participated in sample selection and data collection under the close supervision of a researcher. Descriptive and inferential statistical methods were used to analyze the quantitative data. The key findings of this study revealed that 57.4% HIV positive children reported high level in measures of anxiety and 52.2% had high depression level, while 47.8% had low self-esteem. Children’s ages, parental conditions, and perceived social support shown significant association with participants’ mental health condition. Perceived social support was a powerful set of predictors of children level of anxiety F = 1, 113 24.526, p < .001 and self-esteem F = 1, 113 6.494, p < .0.05. Furthermore, children’s age was a strong predictor of mental health status. The present study finding asserted that HIV positive children require a lifetime of continuous treatment, care and support to ensure their physical and mental development. This study reminds families, caregivers and healthcare institutions of the development of appropriate prevention and intervention program to alleviate stressful life events and improve the psychological well-being of HIV positive children.

In-service teacher trainees experience with and preference for online learning environments during Covid-19 pandemic

The Covid-19 pandemic has forced the educational sector to quickly adapt to the crisis and shift to online learning environments (OLEs). Therefore, a critical area for research is assessing the readiness of teachers in terms of their familiarity and preference for OLEs. In this study, we aimed to improve the experience and preference of in-service teacher trainees with OLEs during the Covid-19 pandemic in Ethiopia. To achieve this goal, questionnaires were used to gather data from in-service teacher trainees. Descriptive and inferential statistical methods, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), Pearson correlation, and one-way ANOVA, were used to analyze the data and determine the participants' opinions and experiences with OLEs. The results showed that while in-service teacher trainees had less experience in knowledge and skills compared to cooperation and interaction, they preferred online learning platforms over online learning strategies. Additionally, correlations were found between cooperation and interaction, knowledge and skills; cooperation and interaction, online learning platforms; cooperation and interaction, online learning strategies; and online learning platforms and online learning strategies. Interestingly, no statistically significant correlation was found between knowledge and skills and online learning platforms. In conclusion, Ethiopian in-service teacher trainees favored online learning platforms over traditional online learning strategies, despite having less experience in knowledge and skills. Further research is needed to explore the experiences and preferences of in-service teacher trainees in various ecological aspects.

A Comprehensive Survey on Community Detection With Deep Learning.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

Nursing students' experiences of the clinical learning environment at Hafr Al Batin University: A cross-sectional study

The clinical learning environment plays a crucial role in nursing education, significantly affecting student learning. This research aimed to examine the learning experiences of nursing students at the University of Hafr Al Batin, specifically within their clinical learning environments. The study used the Clinical Learning Environment, Supervision, and Nurse Teacher (CLES+T) scale to assess these experiences in hospital placements. Participants included nursing students in their second year and beyond who had completed at least one clinical placement in hospitals. The research adopted a quantitative approach, utilizing descriptive and inferential statistical methods for data analysis. Results indicated that final-year students were less satisfied with certain aspects, including the physical environment of the ward, supervisory relationships, and the involvement of nurse teachers. In contrast, second and third-year students generally viewed the clinical learning environment more positively. These outcomes are consistent with findings from international studies. The study suggests the importance of enhancing the roles of nurse teachers and supervisors to improve nursing students' learning experiences. Further qualitative research is recommended to gain a deeper understanding of these issues.

Randomized multiarm bandits: An improved adaptive data collection method

AbstractIn many scientific experiments, multiarmed bandits are used as an adaptive data collection method. However, this adaptive process can lead to a dependence that renders many commonly used statistical inference methods invalid. An example of this is the sample mean, which is a natural estimator of the mean parameter but can be biased. This can cause test statistics based on this estimator to have an inflated type I error rate, and the resulting confidence intervals may have significantly lower coverage probabilities than their nominal values. To address this issue, we propose an alternative approach called randomized multiarm bandits (rMAB). This combines a randomization step with a chosen MAB algorithm, and by selecting the randomization probability appropriately, optimal regret can be achieved asymptotically. Numerical evidence shows that the bias of the sample mean based on the rMAB is much smaller than that of other methods. The test statistic and confidence interval produced by this method also perform much better than its competitors.