scholarly journals Quantile regression analysis of the association between parental rearing and interpersonal sensitivity in Chinese adolescents

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yafeng Zhang ◽  
Wei Tian ◽  
Yuqi Xin ◽  
Quan Zhou ◽  
Guangcan Yan ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Influencing Factor ◽  
Ordinary Least Squares ◽  
Interpersonal Sensitivity ◽  
Family Education ◽  
Cross Sectional ◽  
Definitive Evidence ◽  
Maternal Rejection ◽  
Quantile Regression Analysis ◽  
Parental Rearing

Abstract Background Parental rearing is well documented as an important influencing factor of interpersonal sensitivity (IS). However, little research has focused on the extent by which various aspects of parental rearing in fluence IS. This study aimed to analyze the effects of parental rearing on IS, using quantile regression. We analyzed the extent of the influence of parental rearing on IS by quantile regression to provide definitive evidence on the family education of adolescents with IS problems. Methods The multiple cross-sectional studies were conducted among 3345 adolescents from Harbin, China, in 1999, 2006, 2009 and 2016. Furthermore, a multistage sampling method (stratified random cluster) was used to select participants. IS was assessed using a subscale of the Symptom Checklist-90-Revision. Perceived parental rearing was assessed using the Egna Minnen av. Barndoms Uppfostran. The ordinary least squares (OLS) linear regression was used to determine the average effect of parental rearing on IS. The quantile regression was conducted to examine the established associations and to further explain the association. Results Paternal emotional warmth was found to be associated with IS across the quantile, especially after the 0.6 quantiles; however, this association was not found for maternal emotional warmth. Paternal punishment was associated with IS at the 0.22–0.27 and 0.60 quantile; however, maternal punishment had no significant effect on IS. QR method found that paternal overinvolvement was associated with IS at the 0.48–0.65 quantiles, but paternal overprotection was associated with IS across the quantile; however, maternal overinvolvement and overprotection was positively correlated with IS at the 0.07–0.95 quantiles. The correlation between paternal rejection and IS was found at the 0.40–0.75 and > 0.90 quantiles; maternal rejection was associated with IS within the 0.05–0.92 quantiles. Conclusions Parental rearing practices predict different magnitudes of IS at varying levels. This study provides suggestions for parents to assess purposefully and systematically, intervene, and ameliorate adolescent IS problems. We also highlight the role of paternal rearing in children’s IS problems, providing new ideas for family education.

scholarly journals Factors Associated With The Mental Health of Back-To-Wuhan College Students Based On Quantile Regression Model During The COVID-19 Period

2021 ◽  
Author(s):  
Qian Wu ◽  
Lijun Zhuo ◽  
Hao Li ◽  
Ling Zheng ◽  
Guoqing Ma ◽  
...  
Keyword(s):  
Mental Health ◽  
College Students ◽  
Quantile Regression ◽  
Severity Index ◽  
Future Trend ◽  
Cross Sectional ◽  
Impact Of Event Scale ◽  
Event Scale ◽  
Psychological Pressure ◽  
Quantile Regression Analysis

Abstract Background The COVID-19 pandemic has been spreading rapidly in China and other countries since December 2019, which has increased the risk of infection, and brought the unbearable huge psychological pressure on people. Methods A cross-sectional questionnaire survey was conducted from 31 August 2020, to 14 September 2020 by convenience sampling on the back-to-Wuhan college students, which included the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire-9 (PHQ-9), the Insomnia Severity Index-7 (ISI-7), and the revised Impact of Event Scale (IES-R) scales and the basic demographic characteristics. Results The results from 1017 participants suggested that 44%, 47.5%, 37.7%, 57.7% were the prevalence rates of the anxiety, depression, insomnia, and distress respectively. Moreover, quantile regression analysis was used to identify the factors related to the mental health variables of the back-to-Wuhan college students during the COVID-19 period. Conclusion The finding showed that the respondents who were near graduation, discriminated owing to the experience in Wuhan, and worried about the future trend of COVID-19 had a higher risk of becoming negative psychologic status, especially the bottom and median quantile, and might require more psycho-social interventions or support.

Quantile Regression Analysis and Other Alternatives to Ordinary Least Squares Regression

Methodology ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 81-91 ◽  
Author(s):  
Harry Haupt ◽  
Friedrich Lösel ◽  
Mark Stemmler
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Least Squares ◽  
Corporal Punishment ◽  
Ordinary Least Squares ◽  
Study Data ◽  
Physical Punishment ◽  
Least Squares Regression ◽  
Prevention Project ◽  
Quantile Regression Analysis

Data analyses by classical ordinary least squares (OLS) regression techniques often employ unrealistic assumptions, fail to recognize the source and nature of heterogeneity, and are vulnerable to extreme observations. Therefore, this article compares robust and non-robust M-estimator regressions in a statistical demonstration study. Data from the Erlangen-Nuremberg Development and Prevention Project are used to model risk factors for physical punishment by fathers of 485 elementary school children. The Corporal Punishment Scale of the Alabama Parenting Questionnaire was the dependent variable. Fathers’ aggressiveness, dysfunctional parent-child relations, various other parenting characteristics, and socio-demographic variables served as predictors. Robustness diagnostics suggested the use of trimming procedures and outlier diagnostics suggested the use of robust estimators as an alternative to OLS. However, a quantile regression analysis provided more detailed insights beyond the measures of central tendency and detected sources of considerable heterogeneity in the risk structure of father’s corporal punishment. Advantages of this method are discussed with regard to methodological and content issues.

scholarly journals Quantile Regression Analysis of Modifiable and Non-Modifiable Predictors of Stroke among Adults in South Africa

2021 ◽  
Vol 14 (1) ◽  
pp. 409-416
Author(s):  
Delson Chikobvu ◽  
Lyness Matizirofa
Keyword(s):  
South Africa ◽  
Regression Analysis ◽  
Quantile Regression ◽  
Age Groups ◽  
Female Gender ◽  
Public Hospitals ◽  
Cross Sectional ◽  
Black Race ◽  
Modifiable Factors ◽  
Quantile Regression Analysis

Background: Stroke is the second largest cause of mortality and long-term disability in South Africa (SA). Stroke is a multifactorial disease regulated by modifiable and non-modifiable predictors. Little is known about the stroke predictors in SA, particularly modifiable and non-modifiable. Identification of stroke predictors using appropriate statistical methods can help formulate appropriate health programs and policies aimed at reducing the stroke burden. This study aims to address important gaps in stroke literature i.e., identifying and quantifying stroke predictors through quantile regression analysis. Methods: A cross-sectional hospital-based study was used to identify and quantify stroke predictors in SA using 35730 individual patient data retrieved from selected private and public hospitals between January 2014 and December 2018. Ordinary logistic regression models often miss critical aspects of the relationship that may exist between stroke and its predictors. Quantile regression analysis was used to model the effects of each predictor on stroke distribution. Results: Of the 35730 cases of stroke, 22183 were diabetic. The dominant stroke predictors were diabetes, hypertension, heart problems, the female gender, higher age groups and black race. The age group 55-75 years, female gender and black race, had a bigger effect on stroke distribution at the lower upper quantiles. Diabetes, hypertension and cholesterol showed a significant impact on stroke distribution (p < 0.0001). Conclusion: Most strokes are attributable to modifiable factors. Study findings will be used to raise awareness of modifiable predictors to prevent strokes. Regular screening and treatment are recommended for high-risk individuals with identified predictors in SA.

scholarly journals Pricing Mechanism and IPO Initial Return: Evidence from Pakistan Stock Exchange

2021 ◽  
Vol 21 (3) ◽  
pp. 1239-1257
Author(s):  
Waqas Mehmood ◽  
Rasidah Mohd-Rashid ◽  
Abd Halim Ahmad
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Stepwise Regression ◽  
Stock Exchange ◽  
Ordinary Least Squares ◽  
Cross Sectional ◽  
Pricing Mechanism ◽  
Fixed Price ◽  
Initial Return ◽  
Book Building

This study adds to the extent of the literature by examining the impacts of pricing mechanism and premium offered on IPO initial return in Pakistan. Cross-sectional data were gathered using 90 listed IPOs retrieved from Pakistan stock exchange. Accordingly, ordinary least squares, quantile regression, robustness regression, and stepwise regression were employed to assess the factors that influenced initial return. This study describes the intensity of initial return in light of company specific and issue specific variables. Both closing and opening prices to offer price were incorporated to measure the initial return on the initial day of trading. The outcomes showed that after the reform of book building pricing mechanism, the initial return of IPOs increased, when compared to the fixed price offerings in Pakistan. This study concludes that information from book building pricing mechanism and premium had influenced both issuer and investor in subscribing IPO.

scholarly journals Determinants of Poverty in Mexico: A Quantile Regression Analysis

Economies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 60
Author(s):  
Jorge Garza-Rodriguez ◽  
Gustavo A. Ayala-Diaz ◽  
Gerardo G. Coronado-Saucedo ◽  
Eugenio G. Garza-Garza ◽  
Oscar Ovando-Martinez
Keyword(s):  
Quantile Regression ◽  
Household Head ◽  
Elderly Person ◽  
Probit Model ◽  
Ordinary Least Squares ◽  
Indigenous Language ◽  
Poor People ◽  
Explanatory Variables ◽  
Female Head ◽  
Quantile Regression Analysis

Most studies on the determinants of poverty do not consider that the relative importance of each of these determinants can vary depending on the degree of poverty suffered by each group of poor people. For Mexico’s case, the studies carried out so far do not contemplate this approach, even though there is wide variation in the degree of poverty among the different groups of the poor. Investigating these differences is important to design better policies for fighting poverty, which consider how each variable that explains poverty affects each group of people who suffer from poverty differently. This article examines the determinants of poverty for Mexican households. Using data from the Mexican National Household Income and Expenditure Survey (ENIGH) 2018, the study estimates a probit model and a quantile regression model to examine the extent to which the determinants of poverty vary across the poverty spectrum. The results from the probit model indicate that households with more than one member, having a female head, or speaker of an indigenous language are more likely to be poor. The results obtained in the quantile regressions indicate that there are significant differences with the results of the simple ordinary least squares model, especially for households in extreme poverty but also for the other income categories analyzed for several of the explanatory variables used in the models. Households in the categories extremely poor and deeply poor are most affected if they are in the southern region or if the household head speaks an indigenous language or is an elderly person. It is observed that achieving a higher educational level is an effective way to increase income across the poverty spectrum.

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