Trust, ethical climate and nurses’ turnover intention

2020 ◽  
pp. 096973302096485
Author(s):  
Aditya Simha ◽  
Jatin Pandey
Keyword(s):  
Regression Analysis ◽  
Confidence Interval ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Turnover Intention ◽  
Ethical Climate ◽  
Hierarchical Regression ◽  
Bootstrap Confidence Interval ◽  
Ethical Climates ◽  
Upper Level

Background: Nursing turnover is a very serious problem, and nursing managers need to be aware of how ethical climates are associated with turnover intention. Objectives: The article explored the effects of ethical climates on nurses’ turnover intention, mediated through trust in their organization. Methods: A cross-sectional survey of 285 nurses from three Indian hospitals was conducted to test the research model. Various established Likert-type scales were used to measure ethical climates, turnover intention and trust in organization. Hierarchical regression analysis and mediation analysis were used to test the model. Results: Hierarchical regression analysis and mediation analysis were used to test the model. The indirect effect of benevolent ethical climate on turnover intention through trust in organization was –0.20 with a 95% bootstrap confidence interval of lower level = –0.31 and upper level = –0.01. The indirect effect of principled ethical climate on turnover intention through trust in organization was –0.39 with a 95% bootstrap confidence interval of lower level = –0.58 and upper level = –0.17. Ethical considerations: The study adheres to the ethical standards recommended by the American Psychological Association for conducting research with informed consent, confidentiality and privacy. Conclusion: Both benevolent and principled ethical climates decreased turnover intention indirectly through trust in organization. Only principled ethical climates were directly associated with turnover intention. Our results suggest that nurse managers and leaders should try and establish principled and benevolent climates in order to engender trust in organization and to reduce turnover intention.

scholarly journals Correcting the Bias Correction for the Bootstrap Confidence Interval in Mediation Analysis

2021 ◽  
Author(s):  
Tristan Tibbe ◽  
Amanda Kay Montoya
Keyword(s):  
Confidence Interval ◽  
Error Rate ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Bootstrap Confidence Interval ◽  
Type I Error Rates ◽  
Type I Error Rate

The bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for conducting inference on the indirect effect in mediation analysis due to its high power in small samples, but now it is criticized by methodologists for its inflated type I error rates. In its place, the percentile bootstrap confidence interval (PBCI), which does not adjust for bias, is currently the recommended inferential method for indirect effects. This study proposes two alternative bias-corrected bootstrap methods for creating confidence intervals around the indirect effect. Using a Monte Carlo simulation, these methods were compared to the BCBCI, PBCI, and a bias-corrected method introduced by Chen and Fritz (2021). The results showed that the methods perform on a continuum, where the BCBCI has the best balance (i.e., having closest to an equal proportion of CIs falling above and below the true effect), highest power, and highest type I error rate; the PBCI has the worst balance, lowest power, and lowest type I error rate; and the alternative bias-corrected methods fall between these two methods on all three performance criteria. An extension of the original simulation that compared the bias-corrected methods to the PBCI after controlling for type I error rate inflation suggests that the increased power of these methods might only be due to their higher type I error rates. Thus, if control over the type I error rate is desired, the PBCI is still the recommended method for use with the indirect effect. Future research should examine the performance of these methods in the presence of missing data, confounding variables, and other real-world complications to enhance the generalizability of these results.

Research on Moderating Effects of Job Satisfaction in Employee Turnover Intention of Insurance Enterprise

2014 ◽  
Vol 644-650 ◽  
pp. 5934-5938 ◽  
Author(s):  
Wei Ke Chen ◽  
Ming Yu Guo
Keyword(s):  
Job Satisfaction ◽  
Regression Analysis ◽  
Human Resource Management ◽  
Human Resource ◽  
Turnover Intention ◽  
Employee Turnover ◽  
Hierarchical Regression ◽  
Moderating Effects ◽  
Employee Turnover Intention ◽  
Human Cost

With the development of insurance enterprises, the frequent employee turnover increases the human cost in insurance enterprise. It also reduces profit of insurance enterprise. This study conducts hierarchical regression analysis based on the sample of insurance enterprise employees. It will research the regulation of job satisfaction for turnover intention in insurance enterprises. This study will help the human resource management in insurance enterprise.

Power, Conflict, and Effectiveness

2000 ◽  
Vol 5 (1) ◽  
pp. 28-33 ◽  
Author(s):  
M. Afzalur Rahim ◽  
David Antonioni ◽  
Krum Krumov ◽  
Snejana Ilieva
Keyword(s):  
United States ◽  
Regression Analysis ◽  
Interpersonal Conflict ◽  
The United States ◽  
Hierarchical Regression ◽  
Hierarchical Regression Analysis ◽  
Referent Power ◽  
Legitimate Power ◽  
Power Base ◽  
Effectiveness Data

This study investigated the relationships of bases of leader power (coercive, reward, legitimate, expert, and referent) and styles of handling interpersonal conflict (integrating, obliging, dominating, avoiding, and compromising) to subordinates' effectiveness. Data for this study were collected with questionnaires from the United States and Bulgaria and analyzed with hierarchical regression analysis for each country. Results indicated that in the United States referent power base of supervisors and integrating style of handling conflict of subordinates were positively associated with effectiveness. In Bulgaria, legitimate power base of supervisors was positively associated with effectiveness, but the subordinates' conflict styles were not associated with effectiveness.

Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

2019 ◽  
Author(s):  
Amanda Kay Montoya ◽  
Andrew F. Hayes
Keyword(s):  
Confidence Intervals ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Analytic Approach ◽  
Analytic Framework ◽  
Hypothesis Tests ◽  
Bootstrap Confidence Intervals ◽  
Complex Models ◽  
Path Analytic ◽  
Statistical Mediation Analysis

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.

scholarly journals Spotting Loneliness at School: Associations between Self-Reports and Teacher and Peer Nominations

2021 ◽  
Vol 18 (3) ◽  
pp. 971
Author(s):  
Flore Geukens ◽  
Marlies Maes ◽  
Antonius H. N. Cillessen ◽  
Hilde Colpin ◽  
Karla Van Leeuwen ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Children And Adolescents ◽  
Social Behaviors ◽  
Peer Status ◽  
Hierarchical Regression ◽  
Moderate Correlation ◽  
Hierarchical Regression Analysis ◽  
Teacher Nominations ◽  
Self Reports ◽  
Peer Nominations

In two independent studies, we aimed to examine the extent to which teacher and peer nominations of loneliness are associated with children’s and adolescents’ self-reported loneliness, respectively. Additionally, we examined whether loneliness nominations from teachers and peers were informative above and beyond peer status and social behaviors associated with loneliness. In Study 1 (N = 1594, Mage = 9.43 years), teacher nominations of loneliness showed a small to moderate correlation with children’s self-reported loneliness as assessed using the Loneliness and Social Dissatisfaction Questionnaire (LSDQ). The results of a hierarchical regression analysis showed that teacher nominations of loneliness predicted children’s self-reported loneliness above and beyond teacher nominations of peer status and social behaviors. In Study 2 (N = 350, Mage = 13.81 years), peer nominations of loneliness showed a small to moderate correlation with adolescents’ self-reported loneliness as assessed using the peer-related loneliness subscale of the Loneliness and Aloneness Scale for Children and Adolescents (LACA). The results of a hierarchical regression analysis showed that peer nominations of loneliness predicted adolescents’ self-reported loneliness above and beyond peer nominations of peer status and social behaviors. We conclude that loneliness nominations are valuable, but caution is needed when they are used exclusively to identify lonely children and adolescents.

scholarly journals Acculturation, Communication Competence, and Family Functioning in Mexican–American Mother–Daughter Dyads

2021 ◽  
Author(s):  
Becky Marquez ◽  
Tanya Benitez ◽  
Zephon Lister
Keyword(s):  
Family Functioning ◽  
Relationship Quality ◽  
Communication Skills ◽  
Adult Children ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Mexican American ◽  
Communication Competence ◽  
Well Being ◽  
Actor Partner Interdependence Model

AbstractLittle is known of how intergenerational acculturation discrepancy relates to communication skills differences that may influence relationship quality among parents and adult children. Mexican–American mother–daughter dyads (n = 59) were studied using the Actor Partner Interdependence Model to examine dyadic associations of acculturation and communication competence with family functioning and mediation analysis to determine the indirect effect of acculturation discrepancy on family functioning through communication competence differences. Communication competence of mothers exerted significant actor and partner effects on daughter-perceived cohesion and closeness. Higher acculturation discrepancy predicted greater communication competence difference which in turn was associated with lower cohesion and closeness. There was a significant indirect effect of acculturation discrepancy on daughter-perceived cohesion through communication competence difference. Communication competence of mothers impacts their own as well as their daughters’ perceptions of dyad cohesion and closeness. Intergenerational discrepant acculturation contributes to discordant communication skills that impair family functioning, which has implications for psychological well-being.

scholarly journals Association of three missense mutations in the homocysteine-related MTHFR and MTRR gene with risk of polycystic ovary syndrome in Southern Chinese women

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wanqin Feng ◽  
Yan Zhang ◽  
Yuan Pan ◽  
Yi Zhang ◽  
Minjuan Liu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Polycystic Ovary Syndrome ◽  
Mediation Analysis ◽  
Homocysteine Level ◽  
Polycystic Ovary ◽  
Linear Regression Analysis ◽  
Dependent Manner ◽  
Missense Mutations ◽  
Ovary Syndrome ◽  
Southern Chinese

Abstract Background The etiology between homocysteine and polycystic ovary syndrome (PCOS) is unclear. In humans, the level of homocysteine is mainly affected by two enzymes: methylene tetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR). While the activity of these two enzymes is mainly affected by three missense mutations, namely C677T (MTHFR), A1298C (MTHFR), and A66G (MTRR). This study aims to examine the association between the three missense mutations and PCOS and investigate whether the three missense mutations exerted their effect on PCOS by affecting the homocysteine level. Methods A case-control study was designed, comprising 150 people with PCOS and 300 controls. Logistic regression analysis was used to assess the association between the three missense mutations and PCOS. Linear regression analysis was used to assess the association between the three missense mutations and the homocysteine level. Mediation analysis was used to investigate whether the three missense mutations exerted their effect on PCOS by affecting the homocysteine level. Results Following adjustments and multiple rounds of testing, MTHFR A1298C was found to be significantly associated with PCOS in a dose-dependent manner (compared to AA, OR = 2.142 for AC & OR = 3.755 for CC; P < 0.001). MTRR A66G was nominally associated with PCOS. Mutations in MTHFR A1298C and MTRR A66G were significantly associated with the homocysteine level. Mediation analysis suggested the effect of MTHFR A1298C on PCOS was mediated by homocysteine. Conclusions MTHFR A1298C and MTRR A66G were associated with PCOS, and MTHFR A1298C might affect the risk of PCOS by influencing the homocysteine level.

Predictors of professional commitment for Chinese new generation cohorts in a sample of seafaring officers

2021 ◽  
pp. 1-17
Author(s):  
Xiaohui Jia
Keyword(s):  
Regression Analysis ◽  
Questionnaire Survey ◽  
Important Predictor ◽  
Professional Commitment ◽  
Hierarchical Regression ◽  
Dynamic Features ◽  
Hierarchical Regression Analysis ◽  
Generation Cohorts ◽  
New Generation

Abstract This study aims to examine the predictors of professional commitment of the Chinese new generation, divided into post-80s and post-90s cohorts. A questionnaire survey was employed to collect data from Chinese seafaring officers of these cohorts. The results through hierarchical regression analysis present the two cohorts as having both similar and different predictors. The main difference is that company management and policies positively drive the post-80s cohort to work at sea but not the post-90s. The main similarity is that the work itself is the most important predictor for both cohorts. This study contributes to helping maritime companies to estimate the possibility of Chinese new generation officers quitting sea jobs and can be of value to both managers and authorities as they seek to comprehend the generational dynamic features to better cope with the problem of shortage of younger qualified officers.

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