A comparative analysis of comparison of latent variables, manifest variables and Bayesian mediation effect models

XU Honglyu; SU Yingzhen; TAO Jian; GUO Jichang; TAO Fangbiao

doi:10.16462/j.cnki.zhjbkz.2023.08.013

Volume 27 Issue 8

Aug. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(8): 946-954

XU Honglyu, SU Yingzhen, TAO Jian, GUO Jichang, TAO Fangbiao. A comparative analysis of comparison of latent variables, manifest variables and Bayesian mediation effect models[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 946-954. doi: 10.16462/j.cnki.zhjbkz.2023.08.013

Citation:

XU Honglyu, SU Yingzhen, TAO Jian, GUO Jichang, TAO Fangbiao. A comparative analysis of comparison of latent variables, manifest variables and Bayesian mediation effect models[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 946-954. doi: 10.16462/j.cnki.zhjbkz.2023.08.013

Citation:

XU Honglyu, SU Yingzhen, TAO Jian, GUO Jichang, TAO Fangbiao. A comparative analysis of comparison of latent variables, manifest variables and Bayesian mediation effect models[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 946-954. doi: 10.16462/j.cnki.zhjbkz.2023.08.013

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A comparative analysis of comparison of latent variables, manifest variables and Bayesian mediation effect models

doi: 10.16462/j.cnki.zhjbkz.2023.08.013

1.
Department of Clinical Medicine, School of Medicine, Kunming University, Kunming 650214, China
2.
School of Educational Science, Minzu Normal University of Xingyi, Xingyi 562400, China
3.
Department of Maternal, Child and Adolescent Health Care, School of Public Health, Anhui Medical University, Hefei 230032, China

Funds:

National Natural Science Foundation of China 82160622

The Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities Association 202101BA070001-117

Introduced Talents Scientific Research Project of Kunming University YJL2103

More Information

Corresponding author: TAO Fangbiao, E-mail: fbtao@ahmu.edu.cn
Received Date: 2022-03-11
Rev Recd Date: 2022-09-20

Available Online: 2023-09-02

Publish Date: 2023-08-10

Abstract

Abstract

Objective The study aimed to compare the mediating effects and model fitting of the latent variable, the manifest variable and the Bayesian mediating effect model. Methods Data from a previous specialized survey on college students′ behavior and health were utilized. Six beverage consumption scores served as independent variables, seven dimensions of the Pittsburgh Sleep Quality Index Scale were mediating variables, and nine patient health questionnaire assessment depressive symptom scores were dependent variables. The mediating effects of sleep quality on the association between beverage consumption and depressive symptoms were analyzed using latent variable, manifest variable, and Bayesian mediation effect models. Results The mediating effect values of the above three models were 0.12, 0.06, and 0.06, respectively. The mediating effect accounted for 71%, 43%, and 43% of the total effect, and the ratio of the mediating effect to the direct effect were 2.49, 0.76, and 0.76, respectively. The mediating effects of the manifest variable mediation effect model and the Bayesian mediation effect model were nearly identical. The estimated value of the mediating effect, the ratio of the mediating effect to the total effect, and the ratio of the mediating effect to the direct effect of the latent variable mediation effect model were 2.00 times, 1.65 times, and 3.27 times those of the other two models, respectively. Conclusions While the mediating effects of the three models were consistent, the latent variable mediation effect model estimated a higher mediating effect value, and the Bayesian mediation effect model provided a more comprehensive evaluation index for model fitting.
- Mediating effect,
- Structural equation model,
- Manifest variable,
- Latent variable,
- Bayesian

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References(39)

References

[1]	Wang YB, Chen Z, Goldstein JM, et al. A Bayesian regularized mediation analysis with multiple exposures[J]. Stat Med, 2019, 38(5): 828-843. DOI: 10.1002/sim.8020.
[2]	温忠麟, 叶宝娟. 中介效应分析: 方法和模型发展[J]. 心理科学进展, 2014, 22(5): 731-745. DOI: 10.3724/SP.J.1042.2014.00731. Wen ZL, Ye BJ. Analyses of mediating effects: the development of methods and models[J]. Adv Psychol Sci, 2014, 22(5): 731-745. DOI: 10.3724/SP.J.1042.2014.00731.
[3]	温忠麟, 张雷, 侯杰泰, 等. 中介效应检验程序及其应用[J]. 心理学报, 2004, 36(5): 614-620. DOI: 10.3969/j.issn.1671-6981.2003.01.021. Wen ZL, Zhang L, Hou JT, et al. Testing and application of the mediating effects[J]. Acta Psychol Sin, 2004, 36(5): 614-620. DOI: 10.3969/j.issn.1671-6981.2003.01.021.
[4]	Henderson KA, Obeid N, Buchholz A, et al. Coping in adolescents: a mediator between stress and disordered eating[J]. Eat Behav, 2022, 47: 101626. DOI: 10.1016/j.eatbeh.2022.101626.
[5]	Xu H, Guo J, Wan Y, et al. Association between screen time, fast foods, sugar-sweetened beverages and depressive symptoms in Chinese adolescents[J]. Front Psychiatry, 2020, 11: 458. DOI: 10.3389/fpsyt.2020.00458.
[6]	Rijnhart JJM, Twisk JWR, Chinapaw MJM, et al. Comparison of methods for the analysis of relatively simple mediation models[J]. Contemp Clin Trials Commun, 2017, 7: 130-135. DOI: 10.1016/j.conctc.2017.06.005.
[7]	VanderWeele TJ, Tchetgen Tchetgen EJ. Mediation analysis with time varying exposures and mediators[J]. J R Stat Soc Series B Stat Methodol, 2017, 79(3): 917-938. DOI: 10.1111/rssb.12194.
[8]	Lee H, Herbert RD, McAuley JH. Mediation Analysis[J]. JAMA, 2019, 321(7): 697-698. DOI: 10.1001/jama.2018.21973.
[9]	方杰, 温忠麟, 张敏强, 等. 基于结构方程模型的多层中介效应分析[J]. 心理科学进展, 2014, 22(3): 530-539. DOI: 10.3724/SP.J.1042.2014.00530. Fang J, Wen ZL, Zhang MQ, et al. Analyzing multilevel mediation using multilevel structural equation models[J]. Adv Psychol Sci, 2014, 22(3): 530-539. DOI: 10.3724/SP.J.1042.2014.00530.
[10]	Stamps JA, Frankenhuis WE. Bayesian models of development[J]. Trends Ecol Evol, 2016, 31(4): 260-268. DOI: 10.1016/j.tree.2016.01.012.
[11]	Sint K, Rosenheck R, Lin H. Latent class mediator for multiple indicators of mediation[J]. Stat Med, 2021, 40(12): 2800-2820. DOI: 10.1002/sim.8929.
[12]	方杰, 温忠麟. 三类多层中介效应分析方法比较[J]. 心理科学, 2018, 41(4): 962-967. DOI: 10.16719/j.cnki.1671-6981.20180430. Fang J, Wen ZL. A comparison of three methods for testing multilevel mediation[J]. J Psychol Sci, 2018, 41(4): 962-967. DOI: 10.16719/j.cnki.1671-6981.20180430.
[13]	Rijnhart JJM, Lamp SJ, Valente MJ, et al. Mediation analysis methods used in observational research: a scoping review and recommendations[J]. BMC Med Res Methodol, 2021, 21(1): 226. DOI: 10.1186/s12874-021-01426-3.
[14]	侯杰泰, 温忠麟, 成子娟. 结构方程模型及其应用[M]. 北京: 教育科学出版社, 2004: 32-38. Hou JT, Wen ZL, Cheng ZJ. Structural equation model and its application[M]. Beijing: Education Science Press, 2004: 32-38.
[15]	徐洪吕, 伍晓艳, 陶舒曼, 等. 大学生饮料消费睡眠质量和抑郁症状的关系[J]. 中国学校卫生, 2020, 41(1): 16-20. DOI: 10.16835/j.cnki.1000-9817.2020.01.005. Xu HL, Wu XY, Tao SM, et al. Beverages consumption, sleep quality and depressive symptoms in Chinese university students: a latent variable mediation model[J]. Chin J Sch Health, 2020, 41(1): 16-20. DOI: 10.16835/j.cnki.1000-9817.2020.01.005.
[16]	Vrublevska J, Trapencieris M, Rancans E. Adaptation and validation of the Patient Health Questionnaire-9 to evaluate major depression in a primary care sample in Latvia[J]. Nord J Psychiatry, 2018, 72(2): 112-118. DOI: 10.1080/08039488.2017.1397191.
[17]	Ju M, Tao Y, Lu Y, et al. Evaluation of sleep quality in adolescent patients with osteosarcoma using Pittsburgh Sleep Quality Index[J]. Eur J Cancer Care (Engl), 2019, 28(4): e13065. DOI: 10.1111/ecc.13065.
[18]	Du Y, Du J, Liu X, et al. Multiple-to-multiple path analysis model[J]. PLoS One, 2021, 16(3): e0247722. DOI: 10.1371/journal.pone.0247722.
[19]	方绮雯, 刘振球, 袁黄波, 等. 结构方程模型的构建及AMOS软件实现[J]. 中国卫生统计, 2018, 35(6): 958-960. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201806045.htm Fang QW, Liu ZQ, Yuan HB, et al. Construction of structural equation model and realization of AMOS software[J]. Chin J Health Statistics, 2018, 35(6): 958-960. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201806045.htm
[20]	侯杰泰, 温忠麟, 成子娟. 结构方程模型及其应用[M]. 北京: 教育科学出版社, 2004: 43-48. Hou JT, Wen ZL, Cheng ZJ. Structural equation model and its application[M]. Beijing: Education Science Press, 2004: 43-48.
[21]	王卫东. 结构方程模型原理与应用[M]. 北京: 中国人民大学出版社, 2010: 17-24. Wang WD. Principles and applications of structural equation models[M]. Beijing: Renmin University of China Press, 2010: 17-24.
[22]	王孟成. 潜变量建模与Mplus的应用(基础篇)[M]. 重庆: 重庆大学出版社, 2014: 205-207. Wang MC. Latent variable modeling and the application of mplus (the basics)[M]. Chongqing: Chongqing University Press, 2014: 205-207.
[23]	王孟成. 潜变量建模与Mplus的应用(进阶篇)[M]. 重庆: 重庆大学出版社, 2018: 291-293. Wang MC. Latent variable modeling and the application of Mplus (advanced)[M]. Chongqing: Chongqing University Press, 2018: 291-293.
[24]	Rindskopf D. Overview of Bayesian statistics[J]. Eval Rev, 2020, 44(4): 225-237. DOI: 10.1177/0193841X19895623.
[25]	Miočević M, MacKinnon DP, Levy R. Power in Bayesian mediation analysis for small sample research[J]. Struct Equ Modeling. 2017, 24(5): 666-683. DOI: 10.1080/10705511.2017.1312407.
[26]	Miočević M, Golchi S. Bayesian mediation analysis with power prior distributions[J]. Multivariate Behav Res, 2022, 57(6): 978-993. DOI: 10.1080/00273171.2021.1935202.
[27]	Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations[J]. J Pers Soc Psychol, 1986, 51(6): 1173-1182. DOI: 10.1037//0022-3514.51.6.1173.
[28]	Zhao X, Lynch JG, Chen Q. Reconsidering baron and Kenny: myths and truths about mediation analysis[J]. J Consum Res, 2010, 37(2): 197-206. DOI: 10.1086/651257.
[29]	Enders CK, Fairchild AJ, Mackinnon DP. A Bayesian approach for estimating mediation effects with missing data[J]. Multivariate Behav Res, 2013, 48(3): 340-369. DOI: 10.1080/00273171.2013.784862.
[30]	Williams J, Mackinnon DP. Resampling and distribution of the product methods for testing indirect effects in complex models[J]. Struct Equ Modeling, 2008, 15(1): 23-51. DOI: 10.1080/10705510701758166.
[31]	Mackinnon DP, Lockwood CM, Williams J. Confidence limits for the indirect effect: distribution of the product and resampling methods[J]. Multivariate Behav Res, 2004, 39(1): 99. DOI: 10.1207/s15327906mbr3901_4.
[32]	方杰, 张敏强. 中介效应的点估计和区间估计: 乘积分布法、非参数Bootstrap和MCMC法[J]. 心理学报, 2012, 44(10): 1408-1420. DOI: 10.3724/SP.J.1041.2012.01408. Fang J, Zhang MQ. Assessing point and interval estimation for the mediating effect: distribution of the product, nonparametric Bootstrap and Markov chain Monte Carlo Methods[J]. Acta Psychol Sin, 2012, 44(10): 1408-1420. DOI: 10.3724/SP.J.1041.2012.01408.
[33]	Lefebvre G, Samoilenko M, Boucoiran I, et al. A Bayesian finite mixture of bivariate regression model for causal mediation analyses[J]. Stat Med, 2018, 37(25): 3637-3660. DOI: 10.1002/sim.7835.
[34]	Huang J, Yuan Y. Bayesian dynamic mediation analysis[J]. Psychol Methods, 2017, 22(4): 667-686. DOI: 10.1037/met0000073.
[35]	Miocevic M, Mackinnon DP, Levy R. Power in Bayesian mediation analysis for small sample research[J]. Struct Equ Modeling, 2017, 24(5): 666-683. DOI: 10.1080/10705511.2017.1312407.
[36]	Sakai H, Murakami K, Kobayashi S, et al. Food-based diet quality score in relation to depressive symptoms in young and middle-aged Japanese women[J]. Br J Nutr, 2017, 117(12): 1674-1681. DOI: 10.1017/S0007114517001581.
[37]	Roberts RE, Duong HT. The prospective association between sleep deprivation and depression among adolescents[J]. Sleep, 2014, 37(2): 239-244. DOI: 10.5665/sleep.3388.
[38]	Rindskopf D. Reporting Bayesian results[J]. Eval Rev, 2020, 44(4): 354-375. DOI: 10.1177/0193841X20977619.
[39]	陶秋山, 詹思延, 李立明. 流行病学研究中的病因与病因推断[J]. 中华流行病学杂志, 2004, 25(11): 86-89. DOI: 10.3760/j.issn:0254-6450.2004.11.021. Tao QS, Zhan SY, Li LM. Etiology and etiological inference in epidemiological research[J]. Chin J Epidemiol, 2004, 25(11): 86-89. DOI: 10.3760/j.issn:0254-6450.2004.11.021.