The Impact of Omitting Random Interaction Effects in Cross-Classified Random Effect Modeling

Lee, Young Ri; Sehee, Hong

doi:10.1080/00220973.2018.1507985

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…The current study sets the number of feeders at 2, 4, and 6, while most previous studies have set the number of feeders as 2 or 3 (Meyers and Beretvas, 2006 ; Shi et al, 2010 ; Ye and Daniel, 2017 ). Wallace ( 2015 ) varied the number of feeders to 2 and 4, and Lee and Hong ( 2019 ) varied them from 2 to 6. The larger the number of feeders, the more individuals belonging to a j th cluster are randomly distributed to several k th clusters.…”

Section: Methodsmentioning

confidence: 99%

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The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

2021

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The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects. The results showed that ignoring a crossed factor in cross-classified data causes a parameter bias for the random effects of level 2 predictors and a standard error bias for the fixed effects of intercepts, level 1 predictors, and level 2 predictors. Bayesian information criteria generally outperformed Akaike information criteria in detecting the correct model.

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Section: Methodsmentioning

confidence: 99%

“…Luo and Kwok ( 2009 ) manipulated the number of feeders and noted that cross-classified data structures influenced bias. Lee and Hong ( 2019 ) also manipulated the number of feeders from two to six and concluded that the coefficient of the CCREM interaction term is affected by the number of feeder conditions.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

2021

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“…Compared with the research on the impact of model misspecification in traditional single-level SEM, there has been a paucity of research in model misspecification in the ML SEM context. Among the infrequent research about model misspecification in multilevel data, Lee and Hong (2019) examined the impact of omitting interaction of crossed factors on parameter estimates in cross-classified random effect modeling, and they showed that the Level 2 random effects were affected by the omission. In an investigation into the impact of misspecifying the first-level error structure in a twolevel growth models, results demonstrated that the misspecification produced biased estimates of variance parameters but unbiased estimates of fixed effects (Ferron et al, 2002).…”

Section: Model Misspecification In Structural Equation Modelingmentioning

confidence: 99%

“…In an investigation into the impact of misspecifying the withinlevel error structure in a two-level growth models, results demonstrated that the misspecification produced biased estimates of variance parameters but unbiased estimates of fixed effects (Ferron et al, 2002). The studies about the impact of omitting the interaction between crossed factors in cross-classified random effects modeling indicated that coefficient estimates and their associated standard errors were not biased for fixed effects, Level l random effects were not affected, but Level 2 random effects were affected (Lee & Hong, 2019;Shi et al, 2010).…”

mentioning

confidence: 97%

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

Cao

Kim

Chen

et al. 2021

Educational and Psychological Measurement

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This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

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“…The variance component attributed to different factors of the interaction between pathologists and biopsies: 𝜎 ":$ # . Biopsies are cross-classified with pathologists since every pathologist stages every biopsy 32 . However, some biopsies may be easier to stage than others in the sense that pathologists yield more similar ratings for them compared to other biopsies.…”

Section: Modeling Proceduresmentioning

confidence: 99%

Estimating the Inter- and Intra-Rater Reliability for NASH Fibrosis Staging in the Presence of Bridge Ordinal Ratings with Hierarchical Bridge Category Models

Levy

Bobak

Azizgolshani

et al. 2021

Preprint

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The public health burden of non-alcoholic steatohepatitis (NASH), a liver condition characterized by excessive lipid accumulation and subsequent tissue inflammation and fibrosis, has burgeoned with the spread of western lifestyle habits. Progression of fibrosis into cirrhosis is assessed using histological staging scales (e.g., NASH Clinical Research Network (NASH CRN)). These scales are used to monitor disease progression as well as to evaluate the effectiveness of therapies. However, clinical drug trials for NASH are typically underpowered due to lower than expected inter-/intra-rater reliability, which impacts measurements at screening, baseline, and endpoint. Bridge ratings represent a phenomenon where pathologists assign two adjacent stages simultaneously during assessment and may further complicate these analyses when ad hoc procedures are applied. Statistical techniques, dubbed Bridge Category Models, have been developed to account for bridge ratings, but not for the scenario where multiple pathologists assess biopsies across time points. Here, we develop hierarchical Bayesian extensions for these statistical methods to account for repeat observations and use these methods to assess the impact of bridge ratings on the inter-/intra-rater reliability of the NASH CRN staging scale. We also report on how pathologists may differ in their assignment of bridge ratings to highlight different staging practices. Our findings suggest that Bridge Category Models can capture additional fibrosis staging heterogeneity with greater precision, which translates to potentially higher reliability estimates in contrast to the information lost through ad hoc approaches.

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The Impact of Omitting Random Interaction Effects in Cross-Classified Random Effect Modeling

Cited by 3 publications

References 27 publications

The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

Estimating the Inter- and Intra-Rater Reliability for NASH Fibrosis Staging in the Presence of Bridge Ordinal Ratings with Hierarchical Bridge Category Models

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