A Hierarchical Regression Analysis of Factors Influencing the Participation Persistence Intention among College Athletes

2020 ◽  
Vol 80 ◽  
pp. 261-270
Author(s):  
Seo-Jin Youn ◽  
Tai-Seok Chang
Keyword(s):  
Regression Analysis ◽  
College Athletes ◽  
Hierarchical Regression ◽  
Hierarchical Regression Analysis ◽  
Factors Influencing

Why We Retweet: Factors Influencing Intentions to Share Sport News on Twitter

2015 ◽  
Vol 8 (2) ◽  
pp. 212-232 ◽  
Author(s):  
Jan Boehmer ◽  
Edson C. Tandoc
Keyword(s):  
Social Media ◽  
Regression Analysis ◽  
Human Behavior ◽  
Hierarchical Regression ◽  
Hierarchical Regression Analysis ◽  
Qualitative And Quantitative ◽  
Content Sharing ◽  
Source Characteristics ◽  
Factors Influencing ◽  
Perceived Relevance

The current study explored factors influencing content sharing on Twitter in the context of sport news. It employed a 2-step text-based analysis combining qualitative and quantitative approaches and found that 3 main categories of factors are influencing retweeting decisions: characteristics of the source, characteristics of the message, and characteristics of the user. A subsequent hierarchical-regression analysis revealed that factors related to a user’s encounter of a Tweet are the best predictor of retweeting intentions. More specifically, interest in the exact topic of the tweet, the perceived relevance that the tweet might have for the user’s own followers, and similarity in opinion play important roles. Implications for communication practitioners, as well as research investigating human behavior on social media, are discussed.

scholarly journals Factors influencing online purchase intention of smartphones: A hierarchical regression analysis

2018 ◽  
Vol 5 (1) ◽  
pp. 1496612 ◽  
Author(s):  
Rex P. Bringula ◽  
Shirley D. Moraga ◽  
Annaliza E. Catacutan ◽  
Marilou N. Jamis ◽  
Dionito F. Mangao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Purchase Intention ◽  
Hierarchical Regression ◽  
Hierarchical Regression Analysis ◽  
Online Purchase ◽  
Factors Influencing

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.

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.

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.

scholarly journals Factors Affecting Secondary Traumatic Stress of Nurses Caring for COVID-19 Patients in South Korea

2021 ◽  
Vol 18 (13) ◽  
pp. 6843
Author(s):  
Mee Sun Lee ◽  
Sujin Shin ◽  
Eunmin Hong
Keyword(s):  
Social Support ◽  
Regression Analysis ◽  
Traumatic Stress ◽  
Secondary Traumatic Stress ◽  
Direct Care ◽  
Hierarchical Regression ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Affect Patient Care ◽  
Significant Factors

The secondary traumatic stress (STS) of nurses caring for COVID-19 patients is expected to be high, and it can adversely affect patient care. The purpose of this study was to examine the degree of STS of nurses caring for COVID-19 patients, and we identified various factors that influence STS. This study followed a descriptive design. The data of 136 nurses who had provided direct care to COVID-19 patients from 5 September to 26 September 2020 were collected online. Hierarchical regression analysis was conducted to identify the factors influencing STS. Participants experienced moderate levels of STS. The regression model of Model 1 was statistically significant (F = 6.21, p < 0.001), and the significant factors influencing STS were the duration of care for patients with COVID-19 for more than 30 days (β = 0.28, p < 0.001) and working in an undesignated COVID-19 hospital (β = 0.21, p = 0.038). In Model 2, the factor influencing STS was the support of a friend in the category of social support (β = −0.21, p = 0.039). The nurses caring for COVID-19 patients are experiencing a persistent and moderate level of STS. This study can be used as basic data to treat and prevent STS.

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