Keke Lai
Biographic Data
| ID | 6629966 |
|---|---|
| NAME | Keke Lai |
| GIVEN NAMES | Keke |
| FAMILY NAME | Lai |
| SIGNATURE | LAI K |
| AFFILIATIONS | University of California, Merced |
| ORCID | 0000-0002-8501-0210 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
On the Implications of Model Misspecifications and Mixed Indicator Types for Finding Standardized Model Parameters
Model estimations given mixed data types proceed in two stages. Stage 1 obtains a covariance matrix of the continuous indicators and the variables underlying the categorical indicators. Stage 2 fits the researcher’s model to data with weighted, diagonally weighted, or unweighted least squares. For a misspecified model, three problems have been overlooked about defining and estimating population standardized parameters β. First, given the research…
On the Estimation of Fit Indices for the Structural Part of a Model
When a researcher proposes an SEM model to explain the dynamics among some latent variables, the real question in model evaluation is the fit of the model’s structural part. A composite index that lumps the fit of the structural part and measurement part does not directly address that question. The need for more attention to structural-level fit has been noted for a long time, but the literature has been limited to conceptual issues such as how t…
On Selecting Indicators for SEM Models
Fit Difference Between Nonnested Models Given Categorical Data
In structural equation modeling (SEM) studies with categorical data, researchers often use the root mean square error of approximation (RMSEA), comparative fit index (CFI), or standardized root mean squared residual (SRMR) to compare rival models. Model selection based on ΔRMSEA, ΔCFI, or ΔSRMR is meaningful because (a) finding a better model is more scientific and easier than establishing a “good” model, (b) it avoids the problems with cutoffs f…
Using Information Criteria Under Missing Data
The full information maximum likelihood (FIML) and the two-stage (TS) procedure are two popular likelihood-based approaches to SEM model estimation with missing data. After model estimation, one often needs to choose the best model from a group of candidate models. A popular type of model selection tools is information criteria. Because FIML and TS both give consistent model parameter estimates, it is tempting to assume both FIML-based and TS-bas…
Correct Estimation Methods for RMSEA Under Missing Data
The full information maximum likelihood (FIML) and the two-stage (TS) procedure are two popular likelihood-based approaches to estimating structural equation model (SEM) parameters with missing data. Although both FIML and TS can give consistent model parameter estimates and standard errors, the RMSEA point estimate and confidence interval (CI) based on these two procedures are problematic. In this paper, we propose a correct point estimator and …
Bayesian Model Averaging as an Alternative to Model Selection for Multilevel Models
We investigated the Bayesian model averaging (BMA) technique as an alternative method to the traditional model selection approaches for multilevel models (MLMs). BMA synthesizes the information derived from all possible models and comes up with a weighted estimate. A simulation study compared BMA with additional modeling techniques, including the single "best" model approach, Bayesian MLM using informative, diffuse, and inaccurate priors, and res…
Implementing continuous non-normal skewed distributions in latent growth mixture modeling
Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulat…
Estimating Standardized SEM Parameters Given Nonnormal Data and Incorrect Model
When both model misspecifications and nonnormal data are present, it is unknown how trustworthy various point estimates, standard errors (SEs), and confidence intervals (CIs) are for standardized structural equation modeling parameters. We conducted simulations to evaluate maximum likelihood (ML), conventional robust SE estimator (MLM), Huber–White robust SE estimator (MLR), and the bootstrap (BS). We found (a) ML point estimates can sometimes be…
The Problem with Having Two Watches
The root mean square error of approximation (RMSEA) and the comparative fit index (CFI) are two widely applied indices to assess fit of structural equation models. Because these two indices are viewed positively by researchers, one might presume that their values would yield comparable qualitative assessments of model fit for any data set. When RMSEA and CFI offer different evaluations of model fit, we argue that researchers are likely to be conf…
Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation
The root mean square error of approximation (RMSEA) is one of the most widely reported measures of misfit/fit in applications of structural equation modeling. When the RMSEA is of interest, so too should be the accompanying confidence interval. A narrow confidence interval reveals that the plausible parameter values are confined to a relatively small range at the specified level of confidence. The accuracy in parameter estimation approach to samp…
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Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation
The root mean square error of approximation (RMSEA) is one of the most widely reported measures of misfit/fit in applications of structural equation modeling. When the RMSEA is of interest, so too should be the accompanying confidence interval. A narrow confidence interval reveals that the plausible parameter values are confined to a relatively small range at the specified level of confidence. The accuracy in parameter estimation approach to samp…
The Problem with Having Two Watches
The root mean square error of approximation (RMSEA) and the comparative fit index (CFI) are two widely applied indices to assess fit of structural equation models. Because these two indices are viewed positively by researchers, one might presume that their values would yield comparable qualitative assessments of model fit for any data set. When RMSEA and CFI offer different evaluations of model fit, we argue that researchers are likely to be conf…
Estimating Standardized SEM Parameters Given Nonnormal Data and Incorrect Model
When both model misspecifications and nonnormal data are present, it is unknown how trustworthy various point estimates, standard errors (SEs), and confidence intervals (CIs) are for standardized structural equation modeling parameters. We conducted simulations to evaluate maximum likelihood (ML), conventional robust SE estimator (MLM), Huber–White robust SE estimator (MLR), and the bootstrap (BS). We found (a) ML point estimates can sometimes be…
Implementing continuous non-normal skewed distributions in latent growth mixture modeling
Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulat…
Fit Difference Between Nonnested Models Given Categorical Data
In structural equation modeling (SEM) studies with categorical data, researchers often use the root mean square error of approximation (RMSEA), comparative fit index (CFI), or standardized root mean squared residual (SRMR) to compare rival models. Model selection based on ΔRMSEA, ΔCFI, or ΔSRMR is meaningful because (a) finding a better model is more scientific and easier than establishing a “good” model, (b) it avoids the problems with cutoffs f…
Using Information Criteria Under Missing Data
The full information maximum likelihood (FIML) and the two-stage (TS) procedure are two popular likelihood-based approaches to SEM model estimation with missing data. After model estimation, one often needs to choose the best model from a group of candidate models. A popular type of model selection tools is information criteria. Because FIML and TS both give consistent model parameter estimates, it is tempting to assume both FIML-based and TS-bas…
Correct Estimation Methods for RMSEA Under Missing Data
The full information maximum likelihood (FIML) and the two-stage (TS) procedure are two popular likelihood-based approaches to estimating structural equation model (SEM) parameters with missing data. Although both FIML and TS can give consistent model parameter estimates and standard errors, the RMSEA point estimate and confidence interval (CI) based on these two procedures are problematic. In this paper, we propose a correct point estimator and …
Bayesian Model Averaging as an Alternative to Model Selection for Multilevel Models
We investigated the Bayesian model averaging (BMA) technique as an alternative method to the traditional model selection approaches for multilevel models (MLMs). BMA synthesizes the information derived from all possible models and comes up with a weighted estimate. A simulation study compared BMA with additional modeling techniques, including the single "best" model approach, Bayesian MLM using informative, diffuse, and inaccurate priors, and res…
On the Estimation of Fit Indices for the Structural Part of a Model
When a researcher proposes an SEM model to explain the dynamics among some latent variables, the real question in model evaluation is the fit of the model’s structural part. A composite index that lumps the fit of the structural part and measurement part does not directly address that question. The need for more attention to structural-level fit has been noted for a long time, but the literature has been limited to conceptual issues such as how t…
On Selecting Indicators for SEM Models
On the Implications of Model Misspecifications and Mixed Indicator Types for Finding Standardized Model Parameters
Model estimations given mixed data types proceed in two stages. Stage 1 obtains a covariance matrix of the continuous indicators and the variables underlying the categorical indicators. Stage 2 fits the researcher’s model to data with weighted, diagonally weighted, or unweighted least squares. For a misspecified model, three problems have been overlooked about defining and estimating population standardized parameters β. First, given the research…
Mathematics (10 works) · Statistics (10 works) · Econometrics (9 works) · Psychometric Methodologies and Testing (8 works) · Structural equation modeling (7 works) · Computer Science (6 works) · Statistical Methods and Bayesian Inference (5 works) · Estimator (4 works) · Multi-Criteria Decision Making (4 works) · Confidence interval (3 works)