Estimating and Comparing Specific Mediation Effects in Complex Latent Variable Models
By Rebecca S. Lau, Gordon W. Cheung. 2012
401 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3e8a3aa1502ac5bc33ab6ddb945b95f4b806ea12
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Rex Bryan Kline
- Rebecca S. Lau
- Gordon W. Cheung
- Won‐Moo Hur
- Sohail S. Chaudhry
- Mingyao Hu
- Taewon Moon
- Seongho Kang
In Sociology and Political Science
- Robin Nunkoo
- D. Gursoy
- Haywantee Ramkissoon
- Won‐Moo Hur
- Taewon Moon
- Sohail S. Chaudhry
- Mingyao Hu
- Rebecca S. Lau
In Statistics and Probability
- Won‐Moo Hur
- Sohail S. Chaudhry
- Mingyao Hu
- Taewon Moon
- Seongho Kang
- Inyong Shin
- G. Castaño
- Yongzhan Li
Citations in the corpus
- Use of Structural Equation Modeling in Tourism Research (2013, background): “SEM provides researchers the flexibility of including multiple causes and mediators in one single model (Lau and Cheung 2012; MacKinnon et al. 2002).”
- The Mediation Myth (2015, background): “…are ways to measure the relative sizes of direct versus indirect effects of both observed and latent variables in standardized metrics that are directly comparable across different studies (Lau & Cheung, 2012; Preacher & Kelley, 2011), when it is appropriate to analyze standardized effect size.”
- Linking leadership styles to work engagement: The role of psychological capital among Chinese knowledge workers (2018, methodology): “Given the relatively strong function of bootstrap analysis in examining indirect effects (Hayes, 2013; Lau and Cheung, 2012), we further adopted this analysis in Amos with 5000 bootstrap samples so as to examine indirect effect in M1.”