Normality Assumption Research Articles

Performance comparison of alternative volatility models applied to economic indexes: the role of asymmetric effects

ABSTRACT This article examines the empirical relationship between economic indexes – namely the industrial production and the consumer confidence – and their volatility by using the United States data over the periods 1919–2023 and 1960–2023, respectively. To address this topic, several GARCH-family models including volatility feedback and the asymmetric effects are estimated. We find that (i) the industrial production index exhibits asymmetric effects, with significant fitting performance over the standard GARCH model; (ii) the inclusion of t -distributed innovations in the error term improves the fitting performance of the competing models against their counterparts with a normal distribution assumption; (iii) both models tested for the consumer confidence index exhibit similar performance to fit with the data; (iv) by excluding the months during and after the COVID-19 pandemics and the Russia–Ukraine conflict, it leads to the same results and conclusions for both economic indexes. Conditional heteroscedasticity, economic growth, GARCH models, asymmetric effects, volatility feedback

The validity and reliability study of the theory of mind inventory-2 (TOMI-2) Turkish version

This study aims to conduct the Turkish adaptation, validity, and reliability study of the Theory of Mind Inventory-2 (TOMI-2) developed by Hutchins and Prelock (2016) for 3-5-year-old children. The study group consists of 310 mothers with children in the 3-5 age group in Konya city center. Personal Information Form and Theory of Mind Inventory-2 (TOMI-2) were used as data collection tools in the study. After the TOMI-2 was translated into Turkish, the normality assumption was checked with the "Shapiro-Wilk" test. The relationship between two continuous variables was evaluated with the Pearson Correlation Coefficient. Exploratory Factor Analysis, Confirmatory Factor Analysis, Content Validity, Criterion Validity, and Reliability analyses were also used in the study. The findings of the analyses show that the Turkish version of the TOMI-2 is a valid and reliable measurement tool for children aged 3-5, with 60 items in the original form.

Parametric and semiparametric approaches for copula-based regression estimation

Based on the normality assumption on dependent variable, regression analysis is one of the most popular statistical techniques for studying the dependence between response and explanatory variables. However, violation of this assumption in the data makes regression analysis inappropriate in several real life situations. Copula is a powerful tool for modeling multivariate data and have recently been employed in regression analysis. The key concept behind copula-based regression approach is to formulate conditional expectation in terms of copula density and marginal distributions. In this paper, we explore parametric and semiparametric estimations of the copula-based regression function. The maximum likelihood (ML), inference functions for margins (IFM), and pseudo maximum likelihood (PML) techniques are adopted here for estimation purposes. Extensive numerical experiments are performed to illustrate the performance of the proposed copula-based regression estimators under specified and misspecified scenarios of copulas and marginals. Finally, two real data applications are also presented to demonstrate the performance of the considered estimators.

BIOMECHANICAL INFLUENCE OF RUNNING SHOES WITH MIDSOLE HOLLOW STRUCTURE ON LOWER LIMBS DURING RUNNING

The objective was to investigate the effects of running shoes with midsole hollow structure span and height on the biomechanics of the lower limbs during running. We collected 21 adults with running habits who wore two pairs of running shoes with different midsole hollow structures and ran at a speed of 3.3[Formula: see text]m/s on a force-measuring treadmill. The lower limb kinematics, ground reaction force (GRF) and lower limb muscle activation characteristics were simultaneously captured by a motion capture system, a 3D force treadmill, and a surface electromyography (sEMG) system. Paired t-tests were performed on data for the two shoe conditions that fit the normal distribution assumptions; otherwise, Wilcoxon signed-rank tests were used. The statistical parameter mapping (SPM) technology was used for the analysis of 1D parameters of kinematic, dynamic, and sEMG activation characteristics. The result showed that the time to the peak impact force at touchdown of Hollow shoe2 was significantly increased ([Formula: see text]), the maximum loading rate ([Formula: see text]) and average loading rate ([Formula: see text]) were significantly reduced, braking time ([Formula: see text]), push time ([Formula: see text]), contact time ([Formula: see text]) of Hollow shoe2 were significantly increased compared with Hollow shoe1. Hollow shoe2 push phase of the tibialis anterior muscle activation characteristics was significantly lower (SPM, [Formula: see text]) than Hollow shoe1. Our conclusion is that running shoes offer the solution as they have the advantage of the complex structure of the hollow midsole.

Exploratory and confirmatory factor analysis for validating a distributed leadership inventory for secondary schools in Ethiopia

This study aimed to validate the Ethiopian version of the distributed leadership inventory (DLI-Eth) and evaluate its psychometric properties. The survey was administered to 714 randomly selected teachers. The data were subjected to data cleaning and preliminary analysis for normality and multivariate assumptions using SPSS version 23 with an extension of AMOS. Exploratory and confirmatory factor analyses were performed to identify the cluster of items and test model fit. The principal component analysis extraction method with varimax rotation revealed a significant four-factor solution. The four-factor structure accounted for 70.04% of the cumulative variance. The results showed that the proposed model met the acceptable model fit. The study concluded that the DLI-Eth is a good tool for measuring distributed leadership functions in Ethiopia. Moreover, this paper makes a practical contribution to future leadership investigators by providing a validated inventory to broaden our understanding of distributed leadership in a non-Western context.

Spatial clustering based on geographically weighted multivariate generalized gamma regression

Geographically Weighted Regression (GWR) is one of the local statistical models that can capture the effects of spatial heterogeneity. This model can be used for both univariate and multivariate responses. However, it should be noted that GWR models require the assumption of error normality. To overcome this problem, we propose a GWR model for generalized gamma distributed responses that can capture the phenomenon of some special continuous distributions. The proposed model is known as Geographically Weighted Multivariate Generalized Gamma Regression (GWMGGR). Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method optimized with the Bernt-Hall-Hall-Haussman (BHHH) algorithm. To determine the significance of the spatial heterogeneity effect, a hypothesis test was conducted using the Maximum Likelihood Ratio Test (MLRT) approach. We made a spatial cluster based on the estimated model parameters for each response using the k-means clustering method to interpret the obtained results. Some highlights of the proposed method are:•A new model for GWR with multivariate generalized gamma distributed responses to overcome the assumption of normally distributed errors.•Goodness of fit test to test the spatial effects in GWMGGR model.•Spatial clustering of districts/cities in Central Java based on three dimensions of educational indicators.

Government Management for Sustainable Economic Development and Innovation

Objective: The objective of this study is to analyze whether efficient and effective government management promotes sustainable economic growth and innovation. It seeks to understand how the income generated through GDP per capita is related to the effectiveness and efficiency with which the government of the State of Nayarit performs. Theoretical Framework: Effective and efficient government management, which uses innovative technologies to improve public administration, is the basis of the research. The relevance of economic diversification for resilience to external events is highlighted, based on the endogenous growth theory, which emphasizes the role of innovation and human capital in economic development. Transparency and citizen participation, which are fundamental for a modern and effective government, are promoted by open government innovation. Method: A non-experimental research of quantitative type was established, in which information was collected on the study variables (Hernández et al., 2014). Subsequently, a correlational type of research was conducted, considering four socioeconomic development indicators of the Mexican Institute for Competitiveness A.C. The research was conducted in Nayarit, Mexico, considering the periods from 2021 to 2023. Subsequently, Spearman's Rho correlation was performed because the Shapiro-Wilk normality test did not meet the normality assumption. Results and Discussion: The findings reveal important relationships and implications in the theory of efficient and effective government administration; first, it shows that improvements in tax collection efficiency are directly related to economic growth and diversification in Nayarit, Mexico. On the other hand, economic growth is driven by internal factors such as investment in human capital and innovation, further suggesting that greater economic complexity can lead to lower own revenues and ultimately to a lack of technological infrastructure or adequate public policies. Implications of the research: The practical and theoretical implications of this study highlight the importance of efficient and strategic government management, the need for economic diversification policies, and the re-evaluation of economic theory in specific contexts. The differences and limitations observed suggest areas for future research and improvements in data collection. Originality/Value: The present research contributes to the line of public administration and economic development, since the results found motivate the generation of sustainable growth and economic diversification policies. The above, adds to the content on state level income and GDP, in this particular case to the state of Nayarit, Mexico.

Exploring the Depths of the Autocorrelation Function: Its Departure from Normality

In this article, we study the autocorrelation function (ACF), which is a crucial element in time series analysis. We compare the distribution of the ACF, both from a theoretical and empirical point of view. We focus on white noise processes (WN), i.e., uncorrelated, centered, and identically distributed variables, whose ACFs are supposed to be asymptotically independent and converge towards the same normal distribution. But, the study of the sum of the sample ACF contradicts this property. Thus, our findings reveal a deviation of the sample ACF from normality beyond a specific lag. Note that this phenomenon is observed for white noise of varying lengths, and evenforn the residuals of an ARMA(p,q) model. This discovery challenges traditional assumptions of normality in time series modeling. Indeed, when modeling a time series, the crucial step is to validate the estimated model by checking that the associated residuals form white noise. In this study, we show that the widely used portmanteau tests are not completely accurate. Box–Pierce appears to be too conservative, whereas Ljung–Box is too liberal. We suggest an alternative method based on the ACF for establishing the reliability of the portmanteau test and the validity of the estimated model. We illustrate our methodology using money stock data in the USA.

PENGARUH INDUSTRI RUMAHAN KERAJINAN SANGKAR BURUNG TERHADAP PEREKONOMIAN MASYARAKAT SANGGRAAGUNG SOCAH BANGKALAN

This study was motivated by the researcher's curiosity about the influence of the home industry of bird cage crafts on improving society's economy in the village of Sanggra Agung, which has been doing these crafts for generations. Although they continuously produce bird cages, it seems that the economy of the bird cage craftsmen is still not growing. The study was carried out using a quantitative non-sampling method because the number of craftsmen was still below 100, and the data were obtained from the responses to the craftsmen's questionnaire and observations in the field about the home industry of bird cage crafts. From the results of the validity and reliability instrument tests, as well as the data normality assumption test, no deviations were found in the instrument or questionnaire data. Based on a simple regression analysis, it shows that the variable in the home industry of bird cage crafts affects the community economy, although the effect is not very significant. The coefficient correlation value is 0.211 or 4.5%, and the remaining 95.5% is influenced by other variables not discussed in this study.

A distribution-free adaptive CUSUM-sign chart for monitoring shifts in the location of unknown industrial process

In most of the manufacturing industry, the distributional assumption of the online working processes is customarily unknown or hard to meet. In such situations, parametric charting mechanisms generally designed under normality assumptions of the model yield more false alarms and invalid out-of-control (OOC) comparisons. The nonparametric (distribution-free) charts are a better choice for practitioners in such cases as their in-control (IC) run length (RL) profiles remain the same. The study intends to develop a new distribution-free adaptive CUSUM sign (NPACUSUM-SN; named hereafter) chart for monitoring the process location. The proposed NPACUSUM-SN chart estimates the unknown process mean shift using an unbiased function and updates adaptively the reference parameter of the CUSUM statistic. The IC robustness of the conventional adaptive CUSUM and the proposed NPACUSUM-SN charts under the symmetric, skewed, and contaminated normal (CN) distributions have been computed using Monte Carlo simulations. The OOC RL profiles of the proposed study have been assessed for the initial state and the shift delay (steady state) in the processes. The proposed NPACUSUM-SN chart shows more resistance against non-normality and effective behaviour as compared to its conventional competitor. The proposed NPACUSUM-SN chart provides uniformly efficient RL characteristics as compared to its counterparts. Implementation of the proposal is made by using a manufacturing industry dataset along with an artificial dataset.

Small‐sample properties of robust willingness‐to‐pay estimators

AbstractThe small‐sample properties of robust, binary choice willingness‐to‐pay (WTP) estimators are analyzed. A Monte Carlo simulation compares the bias and mean‐squared error of marginal and expected WTP estimates from probit, zero‐centered (“robit”), logit, and a generalized Tukey lambda distribution called a “pregibit” regression under normal and nonnormal distributional assumptions. Robust binary choice estimators allow for variation in tail thickness (the robit and pregibit) or tail asymmetry (the pregibit). No previous studies have compared the performance of these WTP estimators. The findings will interest researchers who use contingent validation methods to estimate WTP for nonmarket or hypothetical goods.

Treatment with robot-assisted gait trainer Walkbot along with physiotherapy vs. isolated physiotherapy in children and adolescents with cerebral palsy. Experimental study

BackgroundImproving walking ability is a key objective in the treatment of children and adolescents with cerebral palsy, since it directly affects their activity and participation. In recent years, robotic technology has been implemented in gait treatment, which allows training of longer duration and repetition of the movement. To know the effectiveness of a treatment with the robotic-assisted gait trainer Walkbot combined with physiotherapy compared to the isolated physiotherapy treatment in children and adolescents with cerebral palsy, we carried out a clinical trial.Methods23 participants, were divided into two groups: experimental and control. During 5 weeks, both groups received their physiotherapy sessions scheduled, in addition experimental group received 4 sessions per week of 40 min of robot. An evaluation of the participants was carried out before the intervention, at the end of the intervention, and at follow-up (two months after the end of the intervention). Gait was assessed with the Gross Motor Function Measure-88 dimensions D and E, strength was measured with a hydraulic dynamometer, and range of motion was assessed using the goniometer. A mixed ANOVA was performed when the assumptions of normality and homoscedasticity were met, and a robust mixed ANOVA was performed when these assumptions were not met. Statistical significance was stipulated at p < 0.05. For the effect size, η2 was calculated.ResultsSignificant differences were found regarding the time x group interaction in the Gross Motor Function Measure-88 in dimension D [η2 = 0.016], in the flexion strength of the left [η2 = 0.128] and right [η2 = 0.142] hips, in the extension strength of the right hip [η2 = 0.035], in the abduction strength of the left hip [η2 = 0.179] and right [η2 = 0.196], in the flexion strength of the left knee [η2 = 0.222] and right [η2 = 0.147], and in the range of motion of left [η2 = 0.071] and right [η2 = 0.053] knee flexion.ConclusionsCompared to treatments without walking robot, physiotherapy treatment including Walkbot improves standing, muscle strength, and knee range of motion in children and adolescents with cerebral palsy.Trial registrationClinicalTrials.gov: NCT04329793. First posted: April 1, 2020.

Diabetic retinopathy incidence, predictors and its association with longitudinal fasting blood sugar level changes among diabetes mellitus patients in Ethiopia: joint model.

Diabetes mellitus (DM) is a global public health problem characterized by an elevated blood glucose level. Monitoring blood sugar levels is vital for effective diabetes management and preventing complications. However, the association between longitudinal biomarkers and the incidence of diabetic complications is often overlooked. Therefore, this study aimed to assess the incidence of diabetic retinopathy, predictors, and association with longitudinal fasting blood sugar level changes among diabetes mellitus patients in Ethiopia. A multicenter retrospective follow-up study was carried out in referral hospitals in Amhara region, Ethiopia. A random sample of 462 newly diagnosed DM patients was selected. The proportional hazard assumption was checked for the survival sub-model, and for the longitudinal sub-model, the normality assumption was checked. Then the joint modeling with time-dependent lagged parameterizations was fitted. Model assumptions and comparisons were checked. Finally, the hazard ratio with a 95% confidence interval (CI) with a corresponding P-value<0.05 was used to identify predictors. In this study, Overall, 54 patients developed DR, and the incidence rate was 2.33 per 1000 person-months over the follow-up period, with a 95% CI of [1.78, 3.05]. Rural residence (AHR = 2.21, 95% CI: [1.21, 4.05]), hypertension co-morbidity (AHR = 3.01, 95% CI: [1.85, 6.53]), and longer duration of DM (>5 years) (AHR = 2.28, 95% CI: [1.91, 5.15]) were important predictors for the incidence of DR. In addition, the incidence of DR was substantially correlated with the time-dependent lagged value of FBS change (AHR = 4.20, 95% CI [1.62, 10.85]). In this study, the incidence of diabetic retinopathy was somewhat high when compared to prior similar studies in Ethiopia. A joint model of longitudinal fasting blood sugar level changes was significantly associated with an increased risk of DR. Besides, being rural residence, hypertension co-morbidity, and a longer duration of DM were significant predictors for the incidence of DR. Therefore, public awareness, an integrated care approach, and prioritizing glycemic control are highly recommended.

Covariate-adjusted generalized pairwise comparisons in small samples.

Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds.

Performance evaluation of nursing homes using finite mixtures of logistic models and M-quantile regression for binary data

Evaluating the performance of health care institutions is of paramount interest and it is often conducted using generalized linear mixed models. In this paper, we focus on the evaluation of Nursing Homes for elderly residents in a region of Italy and concentrate on binary outcomes (death and worsening). We propose to use a routinely assessed covariate such as the Resource Utilization Group to account for case-mix. We fit finite mixtures of logistic models to check the assumption of normality of the random effects in the generalized linear mixed model approach and to obtain a clustering of the Nursing Homes with respect to their performance. Since the distribution of the random effects is very skew, we propose to use scores based on robust M-Quantile regression for binary data and estimate their standard error using block-bootstrap. A sensitivity analysis is also conducted to evaluate the assumption of missing at random for non-observed data on discharged residents.

Bivariate Logit Models with Dummy Endogenous Regressors Using Copulas

Measuring the impact of a binary treatment on a binary response variable is of great interest in many medical, social and economic applications. Estimating such effect is very important when the endogeneity problem occurs. This research proposes a bivariate logit model to control endogeneity when the structural errors of the two equations are correlated. The copula approach will be applied to estimate the dependence between the binary treatment and the binary response; and hence, the joint normality assumption of the structural error is irrelevant. For estimation, the maximum likelihood method will be applied to estimate the model parameters. The performance of the copula bivariate logit model in estimating the dependence between the binary treatment variable and the binary response variable is assessed by the Average Treatment Effect (ATE) criterion in both simulation study and real medical data.

Investigation of the Effect of Sports Participation Motivations of Students of Faculty of Sports Sciences on Their Leadership Orientations

In this study, it was aimed to determine the relationship between motivation to participate in sports and leadership orientations of students of the faculty of sports sciences. A relational survey model was used in this study. The study group consisted of students studying in the Faculty of Sports Sciences. The questionnaire part of the study consists of three parts. Firstly, includes demographic information. Secondly, the “Leadership Orientations Questionnaire”, which was developed by Bolman and Deal (1990) and adapted into Turkish by Dereli (2003), is explored. Thirdly, consists of the Participation Motivation Questionnaire, which was developed by Gill et al. (1983) and adapted into Turkish by Oyar et al. (2001). For the determine normality assumption skewness and kurtosis values was used. Skewness and kurtosis values were found to be between ±2 and it was determined that the data had a normal distribution (George and Malley, 2003). Afterward, an Independent samples t-test was used between paired groups, and a one-way analysis of variance was used for paired variables. Moreover, reliability coefficient, frequency and percentage and mean and standard deviation calculations were conducted. Simple regression analysis was used to determine the effect between the dependent and independent variables. The findings, a statistically significant difference was found in the variables of leadership orientation and age, income level, and frequency of weekly sports participation in sports participation motivation. As a result, it was determined that there was a low-level significant and negative relationship between the participants' motivation to participate in sports and their leadership orientations.Keywords: Leadership, Motivation, Sports.

Pengaruh Pemahaman Kode Etik Akuntan, Kecerdasan Spiritual Dan Kecintaan Pada Uang Terhadap Persepsi Mahasiswa Akuntansi Mengenai Perilaku Etis Akuntan

his research aims to test the understanding of the accountant's code of ethics, spiritual intelligence, love of money on the ethical perceptions of accounting students regarding the ethical perceptions of accountants. The population was Stiem Bongaya accounting students class 2020, 2021 and 2022. The sampling technique used was Purposive Sampling. Thus, the number of samples used in this research was 175 accounting students. The analysis technique used is multiple linear regression. The results of the questionnaire data have been tested with classical instruments and assumptions in the form of normality, heteroscedasticity and multicollinearity assumptions. The data analysis method uses multiple linear regression techniques. The results of this research show that understanding the professional code of ethics for accountants has a positive effect, while spiritual intelligence and love of money have a negative effect on students' ethical behavior.

Pengaruh Time Budget Pressure dan Risiko Audit Terhadap Penghentian Prematur Atas Prosedur Audit Pada Kantor Akuntan Publik di Kota Makassar

The purpose of this study is to determine the effect of Time Budget Pressure and Audit Risk on Premature Termination of Audit Procedures at Public Accounting Firms in Makassar City. Data collection uses primary data obtained from questionnaires. Determination of the sample using the saturated sample method, so that the sample in this study were 40 respondents. The results of the questionnaire have been tested for validity and reliability, and have also been tested for classic assumptions in the form of normality assumptions, heteroscedasticity assumptions, and multicollinearity assumptions. Methods of data analysis using multiple regression analysis. The research results show that Time Budget Pressure has a positive and significant effect on Premature Termination of Audit Procedures and Audit Risk does not have a positive and significant effect on Premature Termination of Audit Procedures.

Efficient and distribution-free charts for monitoring the process location for individual observations

Sudden and sequential variations are crucial in industrial and production processes. To track these consistent changes in process parameters, effective charting methods are needed. The generally weighted moving average (GWMA) chart outperforms the exponentially weighted moving average (EWMA) chart in detecting small changes under various design parameters. However, its application relies on process distribution normality assumptions. This study presents new distribution-free GWMA control charts for individual measurements when the central limit theorem doesn't apply under simple random sampling. The charts' robustness and performance are evaluated under symmetric, skewed, and contaminated process environments, using run length properties, relative mean index (RMI), and extra quadratic loss (EQL) for overall assessment. The proposed chart outperforms existing charts in detecting specific and over-the-range shifts with appropriate design parameter choices. It's been applied to an electronics dataset where voltage on constant capacitance serves as a key quality characteristic, validating the theoretical findings.