In multilevel modeling, the relationships between the criterion and predictors are investigated at different levels. Often, the cluster-level predictors are measured by aggregating the individual-level measures. However, the aggregated cluster-level predictors do not always reliably measure the cluster-level regression coefficient, and therefore the context coefficient. This study investigates an alternative approach: estimating cluster-level predictor on the latent cluster mean by using multilevel latent. A comparison is made of the accuracy of the context coefficient and standard error under a wide range of conditions. Results reveal that bias for context effect is small in multilevel latent model. Maximum likelihood (ML) estimator yields more accurate standard error estimation than robust maximum likelihood (MLR) when cluster number is small (less than 50). Very small cluster sample sizes (less than 10) should be avoided because they lack power and empirical sampling variance.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 6, Issue 5) |
| DOI | 10.11648/j.ajtas.20170605.11 |
| Page(s) | 221-227 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Multilevel Latent Model, Context Effect, Parameter Estimate Accuracy, Standard Error, Power
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APA Style
Miao Gao. (2017). Estimating the Context Effect in a Multilevel Latent Model with Small Sample Sizes: A Monte Carlo Simulation Study. American Journal of Theoretical and Applied Statistics, 6(5), 221-227. https://doi.org/10.11648/j.ajtas.20170605.11
ACS Style
Miao Gao. Estimating the Context Effect in a Multilevel Latent Model with Small Sample Sizes: A Monte Carlo Simulation Study. Am. J. Theor. Appl. Stat. 2017, 6(5), 221-227. doi: 10.11648/j.ajtas.20170605.11
AMA Style
Miao Gao. Estimating the Context Effect in a Multilevel Latent Model with Small Sample Sizes: A Monte Carlo Simulation Study. Am J Theor Appl Stat. 2017;6(5):221-227. doi: 10.11648/j.ajtas.20170605.11
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Download@article{10.11648/j.ajtas.20170605.11,
author = {Miao Gao},
title = {Estimating the Context Effect in a Multilevel Latent Model with Small Sample Sizes: A Monte Carlo Simulation Study},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {6},
number = {5},
pages = {221-227},
doi = {10.11648/j.ajtas.20170605.11},
url = {https://doi.org/10.11648/j.ajtas.20170605.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170605.11},
abstract = {In multilevel modeling, the relationships between the criterion and predictors are investigated at different levels. Often, the cluster-level predictors are measured by aggregating the individual-level measures. However, the aggregated cluster-level predictors do not always reliably measure the cluster-level regression coefficient, and therefore the context coefficient. This study investigates an alternative approach: estimating cluster-level predictor on the latent cluster mean by using multilevel latent. A comparison is made of the accuracy of the context coefficient and standard error under a wide range of conditions. Results reveal that bias for context effect is small in multilevel latent model. Maximum likelihood (ML) estimator yields more accurate standard error estimation than robust maximum likelihood (MLR) when cluster number is small (less than 50). Very small cluster sample sizes (less than 10) should be avoided because they lack power and empirical sampling variance.},
year = {2017}
}
TY - JOUR T1 - Estimating the Context Effect in a Multilevel Latent Model with Small Sample Sizes: A Monte Carlo Simulation Study AU - Miao Gao Y1 - 2017/09/04 PY - 2017 N1 - https://doi.org/10.11648/j.ajtas.20170605.11 DO - 10.11648/j.ajtas.20170605.11 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 221 EP - 227 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20170605.11 AB - In multilevel modeling, the relationships between the criterion and predictors are investigated at different levels. Often, the cluster-level predictors are measured by aggregating the individual-level measures. However, the aggregated cluster-level predictors do not always reliably measure the cluster-level regression coefficient, and therefore the context coefficient. This study investigates an alternative approach: estimating cluster-level predictor on the latent cluster mean by using multilevel latent. A comparison is made of the accuracy of the context coefficient and standard error under a wide range of conditions. Results reveal that bias for context effect is small in multilevel latent model. Maximum likelihood (ML) estimator yields more accurate standard error estimation than robust maximum likelihood (MLR) when cluster number is small (less than 50). Very small cluster sample sizes (less than 10) should be avoided because they lack power and empirical sampling variance. VL - 6 IS - 5 ER -
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