scholarly journals The Impact of Intraclass Correlation on the Effectiveness of Level-Specific Fit Indices in Multilevel Structural Equation Modeling

2016 ◽  
Vol 77 (1) ◽  
pp. 5-31 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Jr-Hung Lin ◽  
Oi-Man Kwok ◽  
Sandra Acosta ◽  
Victor Willson
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Intraclass Correlation ◽  
Monte Carlo Study ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Saturated Model ◽  
Fit Indices ◽  
The Impact ◽  
Multilevel Structural Equation

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific fit indices. Our study proposed to fill this gap in the methodological literature. A Monte Carlo study was conducted to investigate the performance of (a) level-specific fit indices derived by a partially saturated model method (e.g., [Formula: see text] and [Formula: see text]) and (b) [Formula: see text] and [Formula: see text] in terms of their performance in multilevel structural equation models across varying ICCs. The design factors included intraclass correlation (ICC: ICC1 = 0.091 to ICC6 = 0.500), numbers of groups in between-level models (NG: 50, 100, 200, and 1,000), group size (GS: 30, 50, and 100), and type of misspecification (no misspecification, between-level misspecification, and within-level misspecification). Our simulation findings raise a concern regarding the performance of between-level-specific partial saturated fit indices in low ICC conditions: the performances of both [Formula: see text] and [Formula: see text] were more influenced by ICC compared with [Formula: see text] and SRMRB. However, when traditional cutoff values ( RMSEA≤ 0.06; CFI, TLI≥ 0.95; SRMR≤ 0.08) were applied, [Formula: see text] and [Formula: see text] were still able to detect misspecified between-level models even when ICC was as low as 0.091 (ICC1). On the other hand, both [Formula: see text] and [Formula: see text] were not recommended under low ICC conditions.

The Sampling Ratio in Multilevel Structural Equation Models: Considerations to Inform Study Design

2021 ◽  
pp. 001316442110201
Author(s):  
Joseph M. Kush ◽  
Timothy R. Konold ◽  
Catherine P. Bradshaw
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Monte Carlo Simulation Study ◽  
Empirical Survey ◽  
Sampling Ratio ◽  
Group Variation ◽  
Multiple Levels ◽  
Multilevel Structural Equation

Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

Impulsivity, Affect, and Stress in Daily Life: Examining a Cascade Model of Urgency

2019 ◽  
Author(s):  
Brinkley M. Sharpe ◽  
Leonard Simms ◽  
Aidan G.C. Wright
Keyword(s):  
Negative Affect ◽  
Structural Equation ◽  
Psychiatric Patients ◽  
Cascade Model ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Daily Stress ◽  
Daily Diary Study ◽  
Daily Affect ◽  
Multilevel Structural Equation

Using multilevel structural equation modeling, we examined within- and between-person predictors of daily impulsivity, with a particular focus on testing a cascade model of affect and daily stress in a 100-day daily diary study of 101 psychiatric patients with personality disorder diagnoses. On average (i.e., fixed effect), within-person increases in daily stress were associated with increased daily impulsivity, both independently and as accounted for by positive associations with increased negative and positive affect. Higher Personality Inventory for DSM-5 (PID-5) Impulsivity scores were associated with amplified within-person links between impulsivity and daily stress and negative affect, but not the links between daily stress and either positive or negative affect. The results of this cascade model are consistent with the hypothesized link between daily affect and stress and daily impulsivity while providing further evidence for the validity of the PID-5 Impulsivity scale and its ability to predict daily impulsivity above and beyond fluctuations in affect and stress.

scholarly journals Analyzing Organizational Growth in Repeated Cross-Sectional Designs Using Multilevel Structural Equation Modeling

Methodology ◽  
2017 ◽  
Vol 13 (3) ◽  
pp. 83-97 ◽  
Author(s):  
Jan Hochweber ◽  
Johannes Hartig
Keyword(s):  
Structural Equation Modeling ◽  
Growth Curve ◽  
Cross Sections ◽  
Structural Equation ◽  
Organizational Level ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Organizational Growth ◽  
Multilevel Structural Equation ◽  
Level Variable

Abstract. In repeated cross-sections of organizations, different individuals are sampled from the same set of organizations at each time point of measurement. As a result, common longitudinal data analysis methods (e.g., latent growth curve models) cannot be applied in the usual way. In this contribution, a multilevel structural equation modeling approach to analyze data from repeated cross-sections is presented. Results from a simulation study are reported which aimed at obtaining guidelines on appropriate sample sizes. We focused on a situation where linear growth occurs at the organizational level, and organizational growth is predicted by a single organizational level variable. The power to identify an effect of this organizational level variable was moderately to strongly positively related to number of measurement occasions, number of groups, group size, intraclass correlation, effect size, and growth curve reliability. The Type I error rate was close to the nominal alpha level under all conditions.

Sign in / Sign up

Export Citation Format

Share Document