Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Meta-regression is the most frequently used technique for identifying moderators in meta-analysis. In this study, main principles and basic models of meta-analysis and meta-regression were briefly introduced first. Then a Monte Carlo simulation was conducted to investigate the minimum number of the effect size required in meta-regression based on statistical power and estimation precision. The results showed that (1) the Wald-type z test was prone to type I error in meta-regression; (2) at least 20 effect sizes were needed to meet parameter estimation requirements; (3) and inclusion of proper moderators could reduce the number of effect size required. Therefore, it is suggested that (1) meta-analysts should be careful when using the CMA software and the Wald-type z test; (2) at least 20 or more effect sizes are generally needed based on different situations; (3) exploration of moderators is necessary; (4) reviewers can value a meta-analysis research according to the minimum number of effect size required.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Longitudinal cross-lagged models have been widely used to analyze causal relations in behavioral and psychological sciences, and the cross-lagged panel model (CLPM) is common and important. The CLPM and related panel data models usually consist of two kinds of regression relations: (a) the autoregressive effects of a variable from its prior state (an earlier time point) to its current state (present time point) and (b) the cross-lagged effects of one variable at the prior state to another variable at the current state. These effects between the two constructs provide the foundation and crucial information in deciding on their diachronic causation. Importantly, in contrast to general regression models, a CLPM consists of a complex set of regression equations, making it more susceptible to endogeneity-related problems.Endogeneity is a critical concern in regression analyses, which refers to situations when an explanatory variable is correlated with the residual (error) of its regression equation. It will likely lead to over- or under-estimated bias with commonly used estimators. Endogeneity is a critical concern when using regression models to analyze observational data to make causal claims. The CLPM determines diachronic causation based on two kinds of regression effects (autoregressive and cross-lagged paths). Despite its vulnerability to endogeneity, this issue has received little attention and requires systematic analyses.The current study focuses on issues related to endogeneity in the CLPM. We first clarify the main sources of endogeneity problems. Then, we systematically analyze different endogeneity issues in the CLPM. Lastly, we provide an empirical example to illustrate the use of the instrumental variables (IV) method in the CLPM.IV originates from econometrics and refers to the predictor of a predictor. The IV method is probably the most popular approach while dealing with endogeneity. Researchers often incorporate suitable IVs in the model to provide unbiased estimates and alleviate the endogeneity concerns. The model implied IVs (MIIVs) have been frequently used in empirical studies. A MIIV is an IV identified within the model. The MIIVs offer a promising way to deal with endogeneity in longitudinal analyses. Typically, a MIIV is a chronologically prior observation of an exogenous variable in the model. Currently, applications with IV are underutilized in psychological research. This paper tries to illustrate the use of MIIV in the CLPM by an empirical example. To our knowledge, this is the first study to discuss endogeneity issues in the CLPM and explore the performances of MIIV in the longitudinal cross-lagged model. We find that common possible sources of endogeneity in the CLPM are: omitted variables, dynamic panel, and reciprocal relation. The omitted variables are ubiquitous in all empirical research and the omitted variable problem will affect the estimation of cross-lagged analyses. For the dynamic panel, “dynamic” refers to the use of the prior outcome as a predictor. Including the effects of this lagged outcome increases the probability of the explanatory variable being related to the residual. Besides, biases could arise from the reciprocal relation, which is also known as the feedback relation, simultaneity, or simultaneous causality. We conclude, first, there are various types of endogeneity in the CLPM, including the omitted variables, dynamic panel, and reciprocal relation. Second, endogeneity could distort the estimation of cross-lagged effects in the CLPM. Lastly, MIIV is a promising technique to tackle endogeneity issues in the CLPM. For future research, it would be interesting to explore the performance of MIIV in models extended from the CLPM. They may include the Random Intercept-CLPM, the Latent Cure Model with Structured Residuals (LCM-SR), and the Latent Change Score Model (LCS).This paper reviews the main sources of endogeneity in the CLPM to raise applied researchers' awareness of the endogeneity issues in longitudinal research. We recommend the MIIV-CLPM as a solution to deal with the endogeneity issue.
submitted time 2023-03-25 Cooperative journals: 《心理科学进展》
Abstract： Longitudinal cross-lagged models have been widely used to analyze causal relations in behavioral and psychological sciences, and the cross-lagged panel model (CLPM) is common and important. The CLPM and related panel data models usually consist of two kinds of regression relations: (a) the autoregressive effects of a variable from its prior state (an earlier time point) to its current state (present time point) and (b) the cross-lagged effects of one variable at the prior state to another variable at the current state. These effects between the two constructs provide the foundation and crucial information in deciding on their diachronic causation. Importantly, in contrast to general regression models, a CLPM consists of a complex set of regression equations, making it more susceptible to endogeneity-related problems.Endogeneity is a critical concern in regression analyses, which refers to situations when an explanatory variable is correlated with the residual (error) of its regression equation. It will likely lead to over- or under-estimated bias with commonly used estimators. Endogeneity is a critical concern when using regression models to analyze observational data to make causal claims. The CLPM determines diachronic causation based on two kinds of regression effects (autoregressive and cross-lagged paths). Despite its vulnerability to endogeneity, this issue has received little attention and requires systematic analyses.The current study focuses on issues related to endogeneity in the CLPM. We first clarify the main sources of endogeneity problems. Then, we systematically analyze different endogeneity issues in the CLPM. Lastly, we provide an empirical example to illustrate the use of the instrumental variables (IV) method in the CLPM.IV originates from econometrics and refers to the predictor of a predictor. The IV method is probably the most popular approach while dealing with endogeneity. Researchers often incorporate suitable IVs in the model to provide unbiased estimates and alleviate the endogeneity concerns. The model implied IVs (MIIVs) have been frequently used in empirical studies. A MIIV is an IV identified within the model. The MIIVs offer a promising way to deal with endogeneity in longitudinal analyses. Typically, a MIIV is a chronologically prior observation of an exogenous variable in the model. Currently, applications with IV are underutilized in psychological research. This paper tries to illustrate the use of MIIV in the CLPM by an empirical example. To our knowledge, this is the first study to discuss endogeneity issues in the CLPM and explore the performances of MIIV in the longitudinal cross-lagged model. We find that common possible sources of endogeneity in the CLPM are: omitted variables, dynamic panel, and reciprocal relation. The omitted variables are ubiquitous in all empirical research and the omitted variable problem will affect the estimation of cross-lagged analyses. For the dynamic panel, “dynamic” refers to the use of the prior outcome as a predictor. Including the effects of this lagged outcome increases the probability of the explanatory variable being related to the residual. Besides, biases could arise from the reciprocal relation, which is also known as the feedback relation, simultaneity, or simultaneous causality. We conclude, first, there are various types of endogeneity in the CLPM, including the omitted variables, dynamic panel, and reciprocal relation. Second, endogeneity could distort the estimation of cross-lagged effects in the CLPM. Lastly, MIIV is a promising technique to tackle endogeneity issues in the CLPM. For future research, it would be interesting to explore the performance of MIIV in models extended from the CLPM. They may include the Random Intercept-CLPM, the Latent Cure Model with Structured Residuals (LCM-SR), and the Latent Change Score Model (LCS).This paper reviews the main sources of endogeneity in the CLPM to raise applied researchers' awareness of the endogeneity issues in longitudinal research. We recommend the MIIV-CLPM as a solution to deal with the endogeneity issue.
Subjects: Psychology >> Statistics in Psychology submitted time 2022-08-08
Abstract：基于交叉滞后结构的追踪模型对于揭示变量间纵向关系具有重要作用，也为因果关系的验证奠定了基础。交叉滞后面板模型在一定条件下可转换为其他形式的模型，如何选择适当的模型是重要的议题。本文对各模型进行概述，并从模型结构、预设轨迹、时间点要求等方面进行比较，最后通过一个实例说明如何选择适当的模型。结果表明，不同模型在变量关系的判断上可能给出很不同的结果，实际运用中应当有模型选择和模型比较的意识。
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2021-10-12
Abstract： Analyzing the interaction effect of latent variables has become an important topic in both theoretical and empirical studies. Standardized estimation plays an important role in model interpretation and effect comparison. Although Wen et al. (2010) has formulated the appropriate standardized estimation for the latent interaction effects, there is no popular commercial software that provides the appropriate standardized estimation before the launch of Mplus 8.2 in 2019. Previous comparisons of methods for estimating latent interaction were based on the original estimation. In this study, through a simulation experiment, the appropriate standardized estimation of latent interaction effects is obtained respectively by four methods: the product indicator (PI) approach, Latent Moderated Structural Equations (LMS), Bayesian method without prior information (BN), and Bayesian method with prior information (BI). Then these estimations are compared in terms of the bias of estimation, the bias of standard error, type Ⅰ error rate and statistical power. The true model in the simulation is based on the structural equation η=0.4ξ_1+0.4ξ_2+γ_3 ξ_1 ξ_2+ζ where the latent variables η, ξ_1, ξ_2 each had three indicators with a standardized factor loading of 0.7. Experiment factors include the distribution of two exogenous latent variables (normal, non-normal), correlation ϕ_12 between two exogenous latent variables (0, 0.3 and 0.7), interaction effect γ_3 (0, 0.2), sample size N (100, 200, and 500) and estimation method (PI, LMS, BN, BI). There are five main findings. (1) the proportion of proper solution of LMS and the two Bayesian methods were close to 100% in all treatments, while PI was almost fully proper when N = 500. (2) Under the normal condition, the bias of standardized estimation of latent interaction obtained by LMS, BI and BN was ignorable, and PI was acceptable when N = 500. Under the non-normal condition, the bias of LMS and Bayesian methods inflated seriously with increasing correlation of two exogenous latent variables, but PI was still acceptable when N = 500. (3) Under both distribution conditions, the bias of standard error of standardized estimation of latent interaction obtained by LMS and BN was small and acceptable, while PI was acceptable when N = 500, and BI tended to overestimate the standard error. (4) Under normal conditions, the type I error rates of LMS were acceptable only when the sample size was large, while the other methods were acceptable in all conditions. Under the non-normal condition, the type I error rates of PI were still acceptable, while the other methods were acceptable only when the sample size was small or the correlation between two exogenous latent variables was low. (5) The statistical power of latent interaction obtained by PI was lower than that by any other method, and a large sample size (e.g., N=500) was required to ensure the PI with statistical power over 80%; LMS and BN had higher statistical power, while BI had the highest one in all conditions. For the latent interaction, the results of comparing different methods in standardized estimation are quite similar to those in the original estimation. Under the normal condition, it is recommended to use LMS to estimate the interaction effect of latent variables, with the caution of Type I error rate and effect size for inference. If accurate prior information can be obtained, Bayesian method is preferred, especially in the case of a small sample. When the variables are not normally distributed, the unconstrained product indicator approach is recommended, which is more robust than the other methods, but the sample size should be large enough (N =500 or above). If the correlation between exogenous latent variables is low (it can be estimated and tested by confirmatory factor analysis), Bayesian method without prior information can be considered for small samples.
Hits
Hits
Downloads
Comment