Ming-Chi Tseng
Biographic Data
| ID | 6629988 |
|---|---|
| NAME | Ming-Chi Tseng |
| GIVEN NAMES | Ming-Chi |
| FAMILY NAME | Tseng |
| SIGNATURE | TSENG M |
| AFFILIATIONS | National University of Tainan |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
The Construction of a Growth Model with Residual Structure Equation Modeling
The present study employs simulation to demonstrate that, in conducting growth model analyses, it is essential not only to specify a growth model at the between-individual but also to estimate the within-individual residual structure using residual structure equation modeling. This approach serves to mitigate any potential biases in parameter estimates that may arise from assuming residual independence, particularly in the variances of random int…
Residual Structural Equation Modeling with Nonnormal Distribution
This study primarily investigates the impact of ignoring nonnormal distributions in RSEM models on the estimation of parameters in the second residual structure. The results of the simulation studies demonstrate that when the RSEM model follows a nonnormal distribution, it is crucial to test and estimate the nonnormal distribution while constructing mixture RI-AR or mixture RI-CLPM models. This approach guarantees the unbiased estimation of autor…
Non-normal GMM with Covariates
Latent Profile Transition Analysis with Random Intercepts (RI-LPTA)
The primary objective of this investigation is the formulation of random intercept latent profile transition analysis (RI-LPTA). Our simulation investigation suggests that the election between LPTA and RI-LPTA for examination has negligible impact on the estimation of transition probability parameters when the population parameters are generated based on the LPTA model. However, an estimation bias in the transition probabilities is observed in al…
Fitting Cross-Lagged Panel Models with the Residual Structural Equations Approach
This study simplifies the seven different cross-lagged panel models (CLPMs) by using the RSEM model for both inter-individual and intra-individual structures. In addition, the study incorporates the newly developed dynamic panel model (DPM), general cross-lagged model (GCLM) and the random intercept auto-regressive moving average (RI-ARMA) model. Then, using a longitudinal study of self-esteem and depression, ten different CLPMs are analyzed usin…
Latent Interaction Effect in the CLPM Model
This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within × within and between × within latent interaction under the CLPM model framework is compared between the one-step Bayesian LMS method and the two-step multiple imputation analysis through a simulation study. The analysis show that the two-step multiple imputation analysis can provide unbiased estimat…
No prominent works on this page.
Latent Profile Transition Analysis with Random Intercepts (RI-LPTA)
The primary objective of this investigation is the formulation of random intercept latent profile transition analysis (RI-LPTA). Our simulation investigation suggests that the election between LPTA and RI-LPTA for examination has negligible impact on the estimation of transition probability parameters when the population parameters are generated based on the LPTA model. However, an estimation bias in the transition probabilities is observed in al…
Fitting Cross-Lagged Panel Models with the Residual Structural Equations Approach
This study simplifies the seven different cross-lagged panel models (CLPMs) by using the RSEM model for both inter-individual and intra-individual structures. In addition, the study incorporates the newly developed dynamic panel model (DPM), general cross-lagged model (GCLM) and the random intercept auto-regressive moving average (RI-ARMA) model. Then, using a longitudinal study of self-esteem and depression, ten different CLPMs are analyzed usin…
Latent Interaction Effect in the CLPM Model
This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within × within and between × within latent interaction under the CLPM model framework is compared between the one-step Bayesian LMS method and the two-step multiple imputation analysis through a simulation study. The analysis show that the two-step multiple imputation analysis can provide unbiased estimat…
Residual Structural Equation Modeling with Nonnormal Distribution
This study primarily investigates the impact of ignoring nonnormal distributions in RSEM models on the estimation of parameters in the second residual structure. The results of the simulation studies demonstrate that when the RSEM model follows a nonnormal distribution, it is crucial to test and estimate the nonnormal distribution while constructing mixture RI-AR or mixture RI-CLPM models. This approach guarantees the unbiased estimation of autor…
Non-normal GMM with Covariates
The Construction of a Growth Model with Residual Structure Equation Modeling
The present study employs simulation to demonstrate that, in conducting growth model analyses, it is essential not only to specify a growth model at the between-individual but also to estimate the within-individual residual structure using residual structure equation modeling. This approach serves to mitigate any potential biases in parameter estimates that may arise from assuming residual independence, particularly in the variances of random int…
Econometrics (3 works) · Mathematics (3 works) · Residual (3 works) · Computer Science (2 works) · Mixture model (2 works) · Psychology (2 works) · Statistics (2 works) · Advanced Statistical Modeling Techniques (1 work) · Algorithm (1 work) · Autoregressive model (1 work)