Body misperception plays an important role in the development of weight and dietary disorders among children and adolescents.A school-based health promotion program(2014-2015)was conducted to promote the school health...Body misperception plays an important role in the development of weight and dietary disorders among children and adolescents.A school-based health promotion program(2014-2015)was conducted to promote the school health education and improve the teenagers'physical health among Chinese children and adolescents.Based on this program,we intended to examine weight status and weight misperception among Chinese children and adolescents and to explore the relationship between weight misperception and lifestyle behaviors.A total of 10708 Chinese children and adolescents in 3rd and 7th grade from Shandong and Qinghai province participated in the program.The participants,dietary and activity patterns were clustered by latent class analysis(LCA).Logistic regression analysis was undertaken to explore the relationship between weight perception and demographic factors or dietary and activity patterns.Given the gender-specific difference of children and adolescents,analyses were separately conducted among boys and girls.The total prevalence of weight misperception was 44.50%.Boys,especially those in higher grade and living in wealthier district,were more likely to misperceive body weight.Girls were more likely to overestimate their weight(26.10%)while boys tended to underestimate the weight(28.32%).Three latent dietary and activity patterns including obesogenic pattern,malnourished pattern and healthy pattern were derived.The participants who had weight misperception were more likely to choose unhealthy dietary and exercise activities.The high prevalence of weight misperception was closely related to the unhealthy weight pattern and unhealthy dietary or exercise patterns.Our research found that most children and adolescents failed to perceive their weight correctly and boys tended to underestimate their weight while girls were subjected to overestimation.So,comprehensive intervention programs should focus on improving self-weight awareness,and appropriate guidance should be made to lead the adolescents to more healthy weight pattern.展开更多
Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping pr...Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.展开更多
Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software pack...Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.展开更多
基金This study was supported by grants from the National Natural Science Foundation of China(No.81573262)the Fundamental Research Funds for the Central Universities,HUST(No.2016YXZD042).
文摘Body misperception plays an important role in the development of weight and dietary disorders among children and adolescents.A school-based health promotion program(2014-2015)was conducted to promote the school health education and improve the teenagers'physical health among Chinese children and adolescents.Based on this program,we intended to examine weight status and weight misperception among Chinese children and adolescents and to explore the relationship between weight misperception and lifestyle behaviors.A total of 10708 Chinese children and adolescents in 3rd and 7th grade from Shandong and Qinghai province participated in the program.The participants,dietary and activity patterns were clustered by latent class analysis(LCA).Logistic regression analysis was undertaken to explore the relationship between weight perception and demographic factors or dietary and activity patterns.Given the gender-specific difference of children and adolescents,analyses were separately conducted among boys and girls.The total prevalence of weight misperception was 44.50%.Boys,especially those in higher grade and living in wealthier district,were more likely to misperceive body weight.Girls were more likely to overestimate their weight(26.10%)while boys tended to underestimate the weight(28.32%).Three latent dietary and activity patterns including obesogenic pattern,malnourished pattern and healthy pattern were derived.The participants who had weight misperception were more likely to choose unhealthy dietary and exercise activities.The high prevalence of weight misperception was closely related to the unhealthy weight pattern and unhealthy dietary or exercise patterns.Our research found that most children and adolescents failed to perceive their weight correctly and boys tended to underestimate their weight while girls were subjected to overestimation.So,comprehensive intervention programs should focus on improving self-weight awareness,and appropriate guidance should be made to lead the adolescents to more healthy weight pattern.
文摘Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.
基金supported by the Yonsei University Research Fund of 2021(2021-22-0060).
文摘Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.
