Journal of East China Normal University(Educational Sciences) ›› 2023, Vol. 41 ›› Issue (1): 1-15.doi: 10.16382/j.cnki.1000-5560.2023.01.001
Zhonglin Wen1, Jinyan Xie1, Huihui Wang2
Online:
2023-01-01
Published:
2022-12-29
Zhonglin Wen, Jinyan Xie, Huihui Wang. Principles, Procedures and Programs of Latent Class Models[J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 1-15.
陈宇帅, 温忠麟, 顾红磊. (2015). 因子混合模型: 潜在类别分析与因子分析的整合. 心理科学进展, 23 (3), 529- 538. | |
何妍, 袁柯曼, 张明明, 边玉芳. (2023). 父母控制亚型及其对青少年适应的影响. 华东师范大学学报(教育科学版), 41 (1), 25- 39. | |
黄菲菲, 张敏强, 崔雪平, 黄熙彤, 甘露. (2018). 家校关系类型对小学生学业成绩的影响: 基于潜在剖面分析. 教育研究与实验, (2), 88- 91. | |
黄声华, 尹弘飚, 靳玉乐. (2023). 家长教育卷入类型特征及其与中学生学科素养的关系: 基于PISA 2018中国香港及澳门数据的潜在类别分析. 华东师范大学学报(教育科学版), 41 (1), 50- 59. | |
廖友国, 王峥, 陈建文, 张妍, 张本钰. (2021). 初中生外化问题行为的潜在类别及其影响因素. 中国临床心理学杂志, 29 (2), 297- 300+305. | |
刘红云. (2019). 高级心理统计. 北京: 中国人民大学出版社. | |
邱皓政. (2008). 潜在类别模型的原理与技术. 北京: 教育科学出版社. | |
孙思雨, 许添舒, 孔企平. (2022). 基于潜在类别分析的小学生早期代数思维水平研究. 数学教育学报, 31 (1), 52- 58. | |
王碧瑶, 张敏强, 张洁婷, 胡俊. (2015). 基于转变矩阵描述的个体阶段性发展: 潜在转变模型. 心理研究, 8 (4), 36- 43.
doi: 10.3969/j.issn.2095-1159.2015.04.006 |
|
王孟成, 毕向阳. (2018a). 回归混合模型: 方法进展与软件实现. 心理科学进展, 26 (12), 2272- 2280. | |
王孟成, 毕向阳. (2018b). 潜变量建模与Mplus应用•进阶篇. 重庆: 重庆大学出版社. | |
王孟成, 邓俏文, 毕向阳, 叶浩生, 杨文登. (2017). 分类精确性指数Entropy在潜剖面分析中的表现: 一项蒙特卡罗模拟研究. 心理学报, 49 (11), 1473- 1482. | |
温聪聪, 朱红. (2021). 随机截距潜在转变分析(RI-LTA)——个案自我转变与个案间差异的分离. 心理科学进展, 29 (10), 1773- 1782. | |
温忠麟. (2016). 心理与教育统计(第二版). 广州: 广东高等教育出版社. | |
温忠麟, 方杰, 陈虹熹, 叶宝娟, 蔡保贞. (2022). 新世纪20年国内测验信度研究. 心理科学进展, 30 (8), 1682- 1691. | |
吴旻, 宋文琦, 梁丽婵. (2023). 农村小学生同伴攻击−受侵害类型及其学校适应: 基于潜在剖面分析. 华东师范大学学报(教育科学版), 41 (1), 40- 49. | |
尹奎, 彭坚, 张君. (2020). 潜在剖面分析在组织行为领域中的应用. 心理科学进展, 28 (7), 1056- 1070. | |
臧蓓蕾, 张俊. (2017). 基于潜在类别分析的方法探究3~5岁儿童心理数线发展的特点. 学前教育研究, (7), 49- 60. | |
张洁婷, 焦璨, 张敏强. (2010). 潜在类别分析技术在心理学研究中的应用. 心理科学进展, 18 (12), 1991- 1998. | |
张文明, 陈嘉晟. (2022). 中小学生肥胖问题研究: 校际差异及时间分配表征. 华东师范大学学报(教育科学版), 40 (2), 43- 56. | |
赵雪艳, 游旭群, 秦伟. (2023). 中学教师情绪劳动策略与职业幸福感指标的关系: 基于潜在剖面分析. 华东师范大学学报(教育科学版), 41 (1), 16- 24. | |
Asparouhov, T. & Muthén, B. O. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling:A Multidisciplinary Journal, 21 (3), 329- 341.
doi: 10.1080/10705511.2014.915181 |
|
Asparouhov, T. , & Muthen, B. (2021). Auxiliary Variables in Mixture Modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes: No. 21. Los Angeles, CA: Muthén & Muthén. | |
Bakk, Z., Oberski, D. L., & Vermunt, J. K. (2016). Relating latent class membership to continuous distal outcomes: Improving the LTB approach and a modified three-step implementation. Structural Equation Modeling:A Multidisciplinary Journal, 23 (2), 278- 289.
doi: 10.1080/10705511.2015.1049698 |
|
Bakk, Z, & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling:A Multidisciplinary Journal, 23 (1), 20- 31.
doi: 10.1080/10705511.2014.955104 |
|
Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L., & Rathouz, P. R. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92 (440), 1375- 1386.
doi: 10.1080/01621459.1997.10473658 |
|
Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12 (1), 3- 27.
doi: 10.1093/pan/mph001 |
|
Clark, S. L. , & Muthén, B. O. (2009). Relating latent class analysis results to variables not included in the analysis. Retrieved from https://www.statmodel.com/download/relatinglca.pdf. | |
Collins, L. M. , & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Hoboken: John Wiley & Sons. | |
Gibson, W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis. Psychometrika, 24 (3), 229- 252.
doi: 10.1007/BF02289845 |
|
Hagenaars, J. A. , & McCutcheon, A. L. (2002). Applied Latent Class Analysis. United Kingdom: Cambridge University Press. | |
Huang, G. H., Wang, S. M., & Hsu, C. C. (2011). Optimization-based model fitting for latent class and latent profile analyses. Psychometrika, 76 (4), 584- 611.
doi: 10.1007/s11336-011-9227-3 |
|
Jung, T., & Wickrama, K. A. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2 (1), 302- 317.
doi: 10.1111/j.1751-9004.2007.00054.x |
|
Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling:A Multidisciplinary Journal, 20 (1), 1- 26.
doi: 10.1080/10705511.2013.742377 |
|
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent Structure Analysis. Boston, MA: Houghton Mifflin. | |
Lubke, G., & Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling:A Multidisciplinary Journal, 14 (1), 26- 47.
doi: 10.1080/10705510709336735 |
|
McLachlan, G. J.. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture model. Journal of the Royal Statistical Society, Series C, Applied Statistics, 36 (4), 318- 324. | |
Muthén, B. O, & Asparouhov, T. (2022). Latent transition analysis with random intercepts (RI-LTA). Psychological Methods, 27 (1), 1- 16.
doi: 10.1037/met0000370 |
|
Muthén, B. O, & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism:Clinical and Experimental Research, 24 (6), 882- 891.
doi: 10.1111/j.1530-0277.2000.tb02070.x |
|
Muthén, L. K. , & Muthén, B. O. (1998−2022). Mplus User's Guide. Los Angeles, CA: Muthén & Muthén. | |
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural Equation Modeling:A Multidisciplinary Journal, 14 (4), 535- 569.
doi: 10.1080/10705510701575396 |
|
Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling:A Multidisciplinary Journal, 20 (4), 640- 657.
doi: 10.1080/10705511.2013.824781 |
|
Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18 (4), 450- 469.
doi: 10.1093/pan/mpq025 |
[1] | Shenghua Huang, Hongbiao Yin, Yule Jin. The Types of Parental Involvement and Secondary School Students’ Subject Literacy: A Latent Class Analysis Based on the Data of Hong Kong and Macao in PISA 2018 [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 50-59. |
[2] | Min Wu, Wenqi Song, Lichan Liang. Types of Aggressive Victims of Rural Pupils and Their School Adaptation: Based on Latent Profile Analysis [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 40-49. |
[3] | Yan He, Keman Yuan, Mingming Zhang, Yufang Bian. The Profiles of Parental Control and its Influence on Adolescents’ Adaptation: Based on Latent Transition Analysis [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 25-39. |
[4] | Xueyan Zhao, Xuqun You, Wei Qin. The Latent Profile Analysis of Middle School Teachers’ Emotional Labor Strategies and the Relationship with Vocational Well-being [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 16-24. |
Viewed | ||||||||||||||||||||||||||||||||||||||||||||||||||
Full text 7508
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
Abstract 3676
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||