scholarly journals Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents

2020 ◽  
Vol 9 (3) ◽  
pp. 698-708
Author(s):  
Ji-Bin Li ◽  
Anise M.S. Wu ◽  
Li-Fen Feng ◽  
Yang Deng ◽  
Jing-Hua Li ◽  
...  
Keyword(s):  
Social Networking ◽  
Large Scale ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Chinese Adolescents ◽  
Screening Tools ◽  
Future Research ◽  
Online Social Networking ◽  
Average Risk ◽  
Latent Profile

AbstractBackground and aimsProblematic online social networking use is prevalent among adolescents, but consensus about the instruments and their optimal cut-off points is lacking. This study derived an optimal cut-off point for the validated Online Social Networking Addiction (OSNA) scale to identify probable OSNA cases among Chinese adolescents.MethodsA survey recruited 4,951 adolescent online social networking users. Latent profile analysis (LPA) and receiver operating characteristic curve (ROC) analyses were applied to the validated 8-item OSNA scale to determine its optimal cut-off point.ResultsThe 3-class model was selected by multiple criteria, and validated in a randomly split-half subsample. Accordingly, participants were categorized into the low risk (36.4%), average risk (50.4%), and high risk (13.2%) groups. The highest risk group was regarded as “cases” and the rest as “non-cases”, serving as the reference standard in ROC analysis, which identified an optimal cut-off point of 23 (sensitivity: 97.2%, specificity: 95.2%). The cut-off point was used to classify participants into positive (probable case: 17:0%) and negative groups according to their OSNA scores. The positive group (probable cases) reported significantly longer duration and higher intensity of online social networking use, and higher prevalence of Internet addiction than the negative group.ConclusionsThe classification strategy and results are potentially useful for future research that measure problematic online social networking use and its impact on health among adolescents. The approach can facilitate research that requires cut-off points of screening tools but gold standards are unavailable.

The latent profile analysis of Chinese adolescents’ anxiety: Examination and validation

2018 ◽  
Vol 59 ◽  
pp. 74-81 ◽  
Author(s):  
Meng Yu ◽  
Gregory S. Chasson ◽  
Mengcheng Wang ◽  
Yawen Zhu ◽  
Qian Xu ◽  
...  
Keyword(s):  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Chinese Adolescents ◽  
Latent Profile

Exploring Science Career Interest: Latent Profile Analysis of High School Occupational Preferences for Science

2018 ◽  
Vol 46 (5) ◽  
pp. 583-598 ◽  
Author(s):  
Sarah L. Ferguson ◽  
Darrell M. Hull
Keyword(s):  
High School ◽  
Gender Differences ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Future Research ◽  
Academic Experience ◽  
Science Interest ◽  
Future Research Directions ◽  
The Impact ◽  
Latent Profile

The present study evaluates high school juniors and seniors ( n = 295) to explore their preference for science as indicated by science motivation, attitude, academic experience, and interest. Latent profile analysis was used to model profiles of preferences for science with a person-centered approach. Then, the impact of self-concept variables was explored and four profiles of science interest were identified. Gender differences were of particular interest due to concerns noted in the literature, and some gender differences were identified in the present study. Covariate analysis indicated vocabulary ability and personality as significantly different for students in the high science interest profile. Implications of these results and future research directions are discussed.

scholarly journals Latent Profile Analysis of the Five Facet Mindfulness Questionnaire in a Sample With a History of Recurrent Depression

Assessment ◽  
2017 ◽  
Vol 27 (1) ◽  
pp. 149-163 ◽  
Author(s):  
Jenny Gu ◽  
Anke Karl ◽  
Ruth Baer ◽  
Clara Strauss ◽  
Thorsten Barnhofer ◽  
...  
Keyword(s):  
Psychological Functioning ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Optimal Number ◽  
Future Research ◽  
Five Facet Mindfulness Questionnaire ◽  
Recurrent Depression ◽  
Mindfulness Skills ◽  
History Of ◽  
Latent Profile

Extending previous research, we applied latent profile analysis in a sample of adults with a history of recurrent depression to identify subgroups with distinct response profiles on the Five Facet Mindfulness Questionnaire and understand how these relate to psychological functioning. The sample was randomly divided into two subsamples to first examine the optimal number of latent profiles (test sample; n = 343) and then validate the identified solution (validation sample; n = 340). In both test and validation samples, a four-profile solution was revealed where two profiles mapped broadly onto those previously identified in nonclinical samples: “high mindfulness” and “nonjudgmentally aware.” Two additional subgroups, “moderate mindfulness” and “very low mindfulness,” were observed. “High mindfulness” was associated with the most adaptive psychological functioning and “very low mindfulness” with the least adaptive. In most people with recurrent depression, mindfulness skills are expressed evenly across different domains. However, in a small minority a meaningful and replicable uneven profile indicating nonjudgmental awareness is observable. Current findings require replication and future research should examine the extent to which profiles change from periods of wellness to illness in people with recurrent depression and how profiles are influenced by exposure to mindfulness-based intervention.

scholarly journals Latent Profile Analysis of the Physical Self-Description Among Chinese Adolescents

2014 ◽  
Vol 34 (2) ◽  
pp. 282-293 ◽  
Author(s):  
Chee Keng John Wang ◽  
Yanlin Sun ◽  
Woon Chia Liu ◽  
Jiaxin Yao ◽  
Do Young Pyun
Keyword(s):  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Chinese Adolescents ◽  
Latent Profile

Using Latent Profile Analysis to Harness the Heterogeneity of Nonretained College Students

2016 ◽  
Vol 18 (4) ◽  
pp. 457-473
Author(s):  
Tracey N. Sulak ◽  
Jennifer Massey ◽  
David Thomson
Keyword(s):  
Large Scale ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Retention Rates ◽  
First Year ◽  
First Year Students ◽  
Private Research ◽  
Key Variables ◽  
The University ◽  
Latent Profile

Universities struggle to raise retention rates among first-year students. Traditional analyses have not only focused on large-scale issues and addressed the needs of the majority but also done little to change overall retention numbers. The current study demonstrates the benefit of using a person-centered approach to retention research. Latent profile analysis was used to examine all nonretained, first-year students ( n = 515) from the 2011 cohort at a private, research-intensive university. The larger population of nonretained first-year students appeared to contain several smaller, subpopulations, and these smaller groups differed on key variables collected by the university. The differences in the subpopulations indicate a need for greater specificity in retention programming.

A Latent Profile Analysis of Living a Calling, Burnout, Exploitation, and Work–Life Imbalance

2021 ◽  
pp. 089484532199416
Author(s):  
Joo Yeon Shin ◽  
Eunseok Kim ◽  
Jina Ahn
Keyword(s):  
Job Satisfaction ◽  
Organizational Support ◽  
Latent Profile Analysis ◽  
Psychological Capital ◽  
Profile Analysis ◽  
Physical Symptoms ◽  
Work Life ◽  
Future Research ◽  
Work Related ◽  
Latent Profile

Research has predominantly focused on the positive aspects of living a calling (LC), hence more attention needs to be given to its potentially negative aspects. The current study examined profiles of 237 South Korean working adults, defined by individuals’ scores on LC, burnout, exploitation, and work–life imbalance from a person-centered perspective. Then, we examined the role of psychological capital, organizational support, and adequate compensation in predicting profile membership. Lastly, we examined mean differences across class membership in the levels of job satisfaction and work-related psychological and physical symptoms. Latent profile analysis identified three distinct profiles of individuals: the adaptive, average, and maladaptive. Psychological capital, organizational support, and adequate compensation predicted a higher likelihood of membership into the adaptive group, compared to the average group. The adaptive group showed the highest job satisfaction and the lowest work-related psychological symptoms. Implications for calling-related interventions and directions for future research are discussed.

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