Latent profile analysis of the three-dimensional model of character strengths to distinguish at-strengths and at-risk populations

2018 ◽  
Vol 27 (11) ◽  
pp. 2983-2990 ◽  
Author(s):  
Wenjie Duan ◽  
Yuhang Wang
2019 ◽  
Vol 27 (3) ◽  
pp. 855-867 ◽  
Author(s):  
Na Zhang ◽  
Jingjing Li ◽  
Zhen Xu ◽  
Zhenxing Gong

Background: The three-dimensional model of nurses’ moral sensitivity has typically been studied using a variable-centered rather than a person-centered approach, preventing a more complete understanding of how these forms of moral sensitivity are expressed as a whole. Latent profile analysis is a person-centered approach that classifies individuals from a heterogeneous population into homogeneous subgroups, helping identify how different subpopulations of nurses use distinct combinations of different moral sensitivities to affect their service behaviors. Objective: Latent profile analysis was used to identify three distinct profiles of nurses’ moral sensitivity. Associations of the profiles with service behaviors were then examined. Methods: Five hundred twenty-five nurses from three tertiary hospitals in China were investigated with Moral Sensitivity Questionnaire and Nurses’ Service Behavior Scale. Latent profile analysis was used to analyze the data. Ethical considerations: Approval was obtained from the Ethics committee for biomedical research of Medical College, the Hebei University of Engineering. Results: A three-profile moral sensitivity model provided the best fit to the data. The resulting profiles were low moral sensitivity, moderate moral sensitivity, and high moral sensitivity. There were significant differences in service behaviors among different profiles of moral sensitivity. Conclusion: The results provide a new and expanded view of nurses’ moral sensitivity, which may be used to monitor nurses’ service behaviors comprehensively and to evaluate nursing ethics management strategies.


2022 ◽  
Author(s):  
Gary Alan Troia ◽  
Heqiao Wang ◽  
Frank R. Lawrence

Our goal in this study is to expand the limited research on writer profiles using the advantageous model-based approach of latent profile analysis and independent tasks to evaluate aspects of individual knowledge, motivation, and cognitive processes that align with Hayes’ (1996) writing framework, which has received empirical support. We address three research questions. First, what latent profiles are observed for late elementary writers using measures aligned with an empirically validated model of writing? Second, do student sociodemographic characteristics—namely grade, gender, English learner status, and special education status—influence latent profile membership? Third, how does student performance on narrative, opinion, and informative writing tasks, determined by quality of writing, vary by latent profiles? A five-profile model had the best fit statistics and classified student writers as Globally Weak, At Risk, Average Motivated, Average Unmotivated, and Globally Proficient. Overall, fifth graders, female students, students without disabilities, and native English speakers had greater odds of being in the Globally Proficient group of writers. For all three genres, other latent profiles were significantly inversely related to the average quality of papers written by students who were classified as Globally Proficient; however, the Globally Weak and At Risk writers were not significantly different in their writing quality, and the Average Motivated and Average Unmotivated writers did not significantly differ from each other with respect to quality. These findings indicate upper elementary students exhibit distinct patterns of writing-related strengths and weaknesses that necessitate comprehensive yet differentiated instruction to address skills, knowledge, and motivation to yield desirable outcomes.


2014 ◽  
Author(s):  
◽  
Yu Bi

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] In this study, we identified patterns of risk factors across developmental contexts during the third year of life using latent profile analysis (LPA). We then examined whether these risk patterns differentially predicted child depression during middle childhood as well as the socioeconomic characters of each identified class. Participants included 688 families. The mean age for the mother participants was 23.4 (SD= 5.8). Over 60% of the sample had household incomes below the poverty line. The racial/ethnic characteristics were 33.5% Pacific Islander, 28% Asian, 12% Caucasian, and 26.5% unknown. The data was collected by the Hawaii's Healthy Start Program (HSP; Duggan et al., 2004). The following variables are included to describe the early environment: infant temperament, child externalizing problems, home educational resources, parent-child attachment, exposure to spouse violence, maternal depression, parenting stress, and insufficient community resources. Research results supported a five-class (i.e. adverse home environment class, low risk class, distressed parents and adverse community class, struggling children and violent spouse class and high risk class) solution. Children's depression scores varied significantly across classes. Results also indicated distinguished demographic factors associated with each class. The results offer important findings to establish a sophisticated model for capturing risk factors across the various ecological systems targeting specific developmental periods. Such findings could guide future prevention efforts by identifying children most at risk for adverse outcomes.


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