scholarly journals Exploring the Test of Covariate Moderation Effects in Multilevel MIMIC Models

2018 ◽  
Vol 79 (3) ◽  
pp. 512-544
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron ◽  
Stephen Stark
Keyword(s):  
Interaction Effect ◽  
Type I Error ◽  
Intraclass Correlation ◽  
Type I ◽  
Cluster Number ◽  
Structural Part ◽  
Mimic Models ◽  
Level Measurement ◽  
Multiple Indicator ◽  
Empirical Demonstration

In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of modeling the interaction in multilevel MIMIC models. The design factors include the location of the interaction effect (i.e., between, within, or across levels), cluster number, cluster size, intraclass correlation (ICC) level, magnitude of the interaction effect, and cross-level measurement invariance status. Type I error, power, relative bias, and root mean square of error of the interaction effects are examined. The results showed that multilevel MIMIC models performed well in detecting the interaction effect at the within or across levels. However, when the interaction effect was at the between level, the performance of multilevel MIMIC models depended on the magnitude of the interaction effect, ICC, and sample size, especially cluster number. Overall, cross-level measurement noninvariance did not make a notable impact on the estimation of interaction in the structural part of multilevel MIMIC models when factor loadings were allowed to be different across levels.

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

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

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.

Intraclass Correlation for Measures from a Middle School Nutrition Intervention Study: Estimates, Correlates, and Applications

2001 ◽  
Vol 28 (6) ◽  
pp. 666-679 ◽  
Author(s):  
David M. Murray ◽  
Glenn A. Phillips ◽  
Amanda S. Birnbaum ◽  
Leslie A. Lytle
Keyword(s):  
Mixed Model ◽  
Type I Error ◽  
Vegetable Intake ◽  
Intraclass Correlation ◽  
School Nutrition ◽  
Nutrition Intervention ◽  
School Level ◽  
Type I ◽  
Before And After ◽  
Estimate Variance

This article presents the first estimates of school-level intraclass correlation for dietary measures based on data from the Teens Eating for Energy and Nutrition at School study. This study involves 3,878 seventh graders from 16 middle schools from Minneapolis–St. Paul, Minnesota. The sample was 66.8% White, 11.2% Black, and 7.0% Asian; 48.8% of the sample was female. Typical fruit and vegetable intake was assessed with a modified version of the Behavior Risk Factor Surveillance System questionnaire. Twenty-four-hour dietary recalls were conducted by nutritionists using the Minnesota Nutrition Data System. Mixed-model regression methods were used to estimate variance components for school and residual error, both before and after adjustment for demographic factors. School-level intraclass correlations were large enough, if ignored, to substantially inflate the Type I error rate in an analysis of treatment effects. The authors show how to use the estimates to determine sample size requirements for future studies.

scholarly journals Data with Hierarchical Structure: Impact of Intraclass Correlation and Sample Size on Type-I Error

2011 ◽  
Vol 2 ◽  
Author(s):  
Serban C. Musca ◽  
Rodolphe Kamiejski ◽  
Armelle Nugier ◽  
Alain Méot ◽  
Abdelatif Er-Rafiy ◽  
...  
Keyword(s):  
Sample Size ◽  
Hierarchical Structure ◽  
Type I Error ◽  
Intraclass Correlation ◽  
Type I

Testing Repeated Measures Hypotheses When Covariance Matrices are Heterogeneous

1993 ◽  
Vol 18 (4) ◽  
pp. 305-319 ◽  
Author(s):  
H. J. Keselman ◽  
Keumhee Chough Carriere ◽  
Lisa M. Lix
Keyword(s):  
Interaction Effect ◽  
Repeated Measures ◽  
Degrees Of Freedom ◽  
Type I Error ◽  
Covariance Matrices ◽  
Repeated Measurements ◽  
Skewed Distribution ◽  
Type I ◽  
Robust Test ◽  
Balanced Designs

For balanced designs, degrees of freedom-adjusted univariate F tests or multivariate test statistics can be used to obtain a robust test of repeated measures main and interaction effect hypotheses even when the assumption of equality of the covariance matrices is not satisfied. For unbalanced designs, however, covariance heterogeneity can seriously distort the rates of Type I error of either of these approaches. This article shows how a multivariate approximate degrees of freedom procedure based on Welch (1947 , 1951 )- James (1951 , 1954) , as simplified by Johansen (1980) , can be applied to the analysis of unbalanced repeated measures designs without assuming covariance homogeneity. Through Monte Carlo methods, we demonstrate that this approach provides a robust test of the repeated measures main effect hypothesis even when the data are obtained from a skewed distribution. The Welch-James approach also provides a robust test of the interaction effect, provided that the smallest of the unequal group sizes is five to six times the number of repeated measurements minus one or provided that a reduced level of significance is employed.

scholarly journals TRANSLATION, CULTURAL ADAPTATION AND VALIDATION OF THE FOOT FUNCTION INDEX - REVISED (FFI-R)

2017 ◽  
Vol 25 (5) ◽  
pp. 188-193 ◽  
Author(s):  
KELLY CRISTINA STÉFANI ◽  
MIGUEL VIANA PEREIRA FILHO ◽  
PEDRO RIZZI OLIVEIRA ◽  
Paloma Yan Lam Wun
Keyword(s):  
Type I Error ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Initial Assessment ◽  
Type I ◽  
Validity And Reliability ◽  
Level Of Evidence ◽  
Foot Function Index ◽  
Function Index ◽  
Foot Function

ABSTRACT Objective: The aim of this study was to translate, culturally adapt, and validate the “Foot Function Index - Revised” (FFI-R) for use in Brazilian Portuguese. Methods: The scale was translated and administered (as recommended by Guillemin, 2000) to 52 patients in the postoperative period after foot and ankle surgery. Seven days after the initial assessment, the scale was readministered by a different interviewer. The data were entered into an Excel spreadsheet and analyzed using SPSS version 23.0 software for Mac. Reproducibility was assessed using intraclass correlation analysis. Results were considered statistically significant at a type I error rate of 5%. Results: The following random-effects intraclass correlation coefficients (ICC) were obtained for each score on the FFI-R: 0.625 for pain, 0.558 for stiffness, 0.757 for difficulty, 0.718 for activity restrictions, 0.854 for personal concerns, and 0.753 for the total score. Conclusion: The FFI-R was successfully translated to Portuguese and culturally adapted for use in Brazilian patients, demonstrating satisfactory validity and reliability. Level of Evidence I, Testing of Previously Developed Diagnostic Criteria on Consecutive Patients (with universally applied reference “golg” standard).

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

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