scholarly journals The Structural Relationship between Exercise Frequency, Social Health, and Happiness in Adolescents

2021 ◽  
Vol 13 (3) ◽  
pp. 1050
Author(s):  
HangUk Cheon
Keyword(s):  
Factor Analysis ◽  
High School Students ◽  
Social Relationships ◽  
Sense Of Community ◽  
Subjective Happiness ◽  
Analysis Model ◽  
School Students ◽  
Exercise Frequency ◽  
Factor Analysis Model ◽  
Korean Child

The aim of this study was to verify the relations between exercise frequency, social relationships, sense of community, and subjective happiness among adolescents. The data analysed in the study were from the 11th Korean Child–Adolescent Happiness Index conducted by the Korean Bang Jeong Hwan Foundation in 2019. The data consisted of questionnaire responses from 5094 middle-school and high-school students. Data were analysed using descriptive statistics, exploratory factor analysis, reliability analysis, confirmatory factor analysis, model validity and fit analysis, path analysis, and effects analysis. The results showed that exercise frequency was associated with social relationships, sense of community, and subjective happiness in adolescents, and a comprehensive examination of relations between exercise frequency, a part of social relationships, sense of community, and subjective happiness was confirmed. It can be concluded that adolescents who participate in frequent exercise become more socially adept, which could in turn make them happier. Policymakers and stakeholders, including educational institutions and parents, should therefore promote adolescent participation in exercise and sports.

Development of Affective Self-Assessment Instrument of Chemistry for High School Student as the Daily Assessment Guideline

2022 ◽  
Vol 11 (1) ◽  
pp. 445-456
Author(s):  
Farintis Jihadul ◽  
Widihastuti* Widihastuti*
Keyword(s):  
High School ◽  
High School Students ◽  
High School Student ◽  
Random Sampling ◽  
Affective Domain ◽  
Assessment Instrument ◽  
Analysis Model ◽  
School Students ◽  
Self Assessment ◽  
Factor Analysis Model

<p style="text-align: justify;">The study objectives were (1) developing a valid and reliable Affective Self-assessment Instrument of Chemistry for High School Student and (2) discovering the chemistry affective domain ability trend of high school students based on gender. The current development study utilized 10 non-test instrument development procedures from Mardapi. The study population was all high school students in Yogyakarta Special Region. The sample size was 405 students categorized into two stages and sampling techniques, i.e., the trial stage using cluster random sampling and the measurement stage using simple random sampling. The data analysis techniques were validity test using the Aiken index and construct validity and reliability using the second-order Confirmatory Factor Analysis model. The study findings were (1) the Affective Self-assessment Instrument of Chemistry for High School Student had 15 valid and reliable items and 15 available items to be utilized by teachers to measure students’ affective in the learning process and (2) the chemistry affective domain ability trend of male high school students was dominated by the “good” category and “very good” category for female students.</p>

Noniterative Estimation for the Multiple Battery Factor Analysis Model

1997 ◽  
Vol 24 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Michael W. Browne ◽  
Krishna Tateneni
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Factor Analysis Model

A Factor Analysis model of the instrumented Timed Up and Go test for physical capability assessment

2018 ◽  
Vol 66 ◽  
pp. S11-S12 ◽  
Author(s):  
A. Coni ◽  
S. Mellone ◽  
M. Colpo ◽  
S. Bandinelli ◽  
L. Chiari
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Timed Up And Go ◽  
Factor Analysis Model ◽  
Physical Capability

Reliability and Validity Evidence for the Social Goal Scale-Physical Education (SGS-PE) in High School Settings

2006 ◽  
Vol 25 (2) ◽  
pp. 226-239 ◽  
Author(s):  
Jianmin Guan ◽  
Ron E. McBride ◽  
Ping Xiang
Keyword(s):  
High School ◽  
Factor Analysis ◽  
High School Students ◽  
Physical Education ◽  
Social Responsibility ◽  
Social Relationship ◽  
Social Goal ◽  
School Students ◽  
School Settings ◽  
The Social

Two types of social goals associated with students’ academic performance have received attention from researchers. One is the social responsibility goal, and the other is the social relationship goal. While several scales have been validated for measuring social relationship and social responsibility goals in academic settings, few studies have applied these social goal scales to high school students in physical education settings. The purpose of this study was to assess the reliability, validity, and generalizability of the scores produced by the Social Goal Scale-Physical Education (SGS-PE) in high school settings. Participants were 544 students from two high schools in the southern United States. Reliability analyses, principal components factor analysis, confirmatory factor analysis, and multistep invariance analysis across two school samples revealed that the SGS-PE produced reliable and valid scores when used to assess students’ social goal levels in high school physical education settings.

scholarly journals Non-negative Independent Factor Analysis for single cell RNA-seq

2020 ◽  
Author(s):  
Weiguang Mao ◽  
Maziyar Baran Pouyan ◽  
Dennis Kostka ◽  
Maria Chikina
Keyword(s):  
Factor Analysis ◽  
Single Cell ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Computational Techniques ◽  
Cell Type ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Type Identity ◽  
Wide Range

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the level of individual cells. With the emergence of high-throughput platforms datasets comprising tens of thousands or more cells have become routine, and the technology is having an impact across a wide range of biomedical subject areas. However, scRNA-seq data are high-dimensional and affected by noise, so that scalable and robust computational techniques are needed for meaningful analysis, visualization and interpretation. Specifically, a range of matrix factorization techniques have been employed to aid scRNA-seq data analysis. In this context we note that sources contributing to biological variability between cells can be discrete (or multi-modal, for instance cell-types), or continuous (e.g. pathway activity). However, no current matrix factorization approach is set up to jointly infer such mixed sources of variability.ResultsTo address this shortcoming, we present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that combines features of complementary approaches like Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Non-negative Matrix Factorization (NMF). NIFA simultaneously models uni- and multi-modal latent factors and can so isolate discrete cell-type identity and continuous pathway-level variations into separate components. Similar to NMF, NIFA constrains factor loadings to be non-negative in order to increase biological interpretability. We apply our approach to a range of data sets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA and NMF in terms of cell-type identification and biological interpretability. Studying an immunotherapy dataset in detail, we show that NIFA identifies biomedically meaningful sources of variation, derive an improved expression signature for regulatory T-cells, and identify a novel myeloid cell subtype associated with treatment response. Overall, NIFA is a general approach advancing scRNA-seq analysis capabilities and it allows researchers to better take advantage of their data. NIFA is available at https://github.com/wgmao/[email protected]

Sign in / Sign up

Export Citation Format

Share Document