scholarly journals Longitudinal measurement invariance in urbanization index of Chinese communities across 2000 and 2015: a Bayesian approximate measurement invariance approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ted C. T. Fong ◽  
Rainbow T. H. Ho
Keyword(s):  
Measurement Invariance ◽  
Factor Structure ◽  
Structural Equation ◽  
Equation Modeling ◽  
Nutrition Survey ◽  
Longitudinal Measurement Invariance ◽  
Longitudinal Measurement ◽  
The One ◽  
Bayesian Structural Equation Modeling ◽  
Urbanicity Scale

Abstract Background The Urbanicity Scale was developed based on the China Health and Nutrition Survey (CHNS) to measure the urbanization index of communities according to 12 components. The present study was designed to systematically investigate the factorial validity, reliability, and longitudinal measurement invariance (LMI) of the Urbanicity Scale. Methods Six waves of CHNS data from 2000 to 2015 were adopted. The factor structure and reliability of the Urbanicity Scale for 301 communities were examined using Bayesian exploratory factor analysis. Metric and scalar LMIs were evaluated using both the conventional exact and a novel approximate LMI approach via Bayesian structural equation modeling across various timeframes. Results The findings verified the one-factor structure for the Urbanicity Scale, with adequate reliability. LMI was established for the Urbanicity Scale only over a shorter timeframe from 2006 to 2009 but not over a longer timeframe from 2000 to 2015. Partial LMI was found in the factor loadings and item intercepts for the Urbanicity Scale over the 2004 to 2011 period. Conclusion Interpretation of the temporal change in urbanicity was supported only for a shorter (2006 to 2009) but not a longer timeframe (2000 to 2015). Adjustments addressing the partial non-invariance of the measurement parameters are needed for the analysis of temporal changes in urbanicity between 2004 and 2011.

scholarly journals A Tutorial in Longitudinal Measurement Invariance and Cross-lagged Panel Models Using Lavaan

2020 ◽  
Author(s):  
Sean P. Mackinnon ◽  
Robin Curtis ◽  
Roisin O'Connor
Keyword(s):  
Measurement Invariance ◽  
Latent Variables ◽  
Structural Equation ◽  
Early Time ◽  
Real Data ◽  
Equation Modeling ◽  
Longitudinal Measurement Invariance ◽  
Panel Models ◽  
Time Points ◽  
Longitudinal Measurement

In longitudinal studies involving multiple latent variables, researchers often seek to predict how iterations of latent variables measured at early time points predict iterations measured at later time points. Cross-lagged panel modeling, a form of structural equation modeling, is a useful way to conceptualize and test these relationships. However, prior to making causal claims, researchers must first ensure that the measured constructs are equivalent between time points. To do this, they test for measurement invariance, constructing and comparing a series of increasingly strict and parsimonious models, each making more constraints across time than the last. This comparison process, though challenging, is an important prerequisite to interpretation of results. Fortunately, testing for measurement invariance in cross-lagged panel models has become easier, thanks to the wide availability of R and its packages. This paper serves as a tutorial in testing for measurement invariance and cross-lagged panel models using the lavaan package. Using real data from an openly available study on perfectionism and drinking problems, we provide a step-by-step guide of how to test for longitudinal measurement invariance, conduct cross-lagged panel models, and interpret the results. Original data source with materials: https://osf.io/gduy4/. Project website with data/syntax for the tutorial: https://osf.io/hwkem/.

scholarly journals Accounting for Differential Item Functioning Using Bayesian Approximate Measurement Invariance

2019 ◽  
Vol 80 (4) ◽  
pp. 638-664 ◽  
Author(s):  
Georgios D. Sideridis ◽  
Ioannis Tsaousis ◽  
Abeer A. Alamri
Keyword(s):  
Measurement Invariance ◽  
Structural Equation ◽  
Equation Modeling ◽  
P Value ◽  
National Examination ◽  
Item Functioning ◽  
Using Data ◽  
Mean Differences ◽  
Bayesian Structural Equation Modeling ◽  
Approximate Measurement

The main thesis of the present study is to use the Bayesian structural equation modeling (BSEM) methodology of establishing approximate measurement invariance (A-MI) using data from a national examination in Saudi Arabia as an alternative to not meeting strong invariance criteria. Instead, we illustrate how to account for the absence of measurement invariance using relative compared to exact criteria. A secondary goal was to compare latent means across groups using invariant parameters only and through utilizing exact and relative evaluative-MI protocol suggested equivalence of the thresholds using prior variances equal to 0.10. Subsequent differences between groups were evaluated using effect size criteria and the prior-posterior predictive p-value (PPPP), which proved to be invaluable in attesting for differences that are beyond zero, some meaningless nonzero estimate, and the three commonly used indices of effect sizes described by Cohen in 1988 (i.e., .20, .50, and .80). Results substantiated the use of the PPPP for evaluating mean differences across groups when utilizing nonexact evaluative criteria.

scholarly journals Factor Structure of the Intelligence and Development Scales–2: Measurement Invariance Across the Dutch and German Versions, Sex, and Age

2021 ◽  
Author(s):  
Silvia Grieder ◽  
Marieke Timmerman ◽  
Linda Visser ◽  
Selma Anne José Ruiter ◽  
Alexander Grob
Keyword(s):  
Measurement Invariance ◽  
Factor Structure ◽  
Visual Processing ◽  
Structural Equation ◽  
Basic Skills ◽  
A Priori ◽  
Selection Procedure ◽  
Equation Modeling ◽  
Confirmatory Factor Analyses ◽  
Final Model

We examined the factor structure of the intelligence and basic skills domains of the German and Dutch versions of an international test battery with 13 representative national standardizations (among others, Italian, Polish, U.K.)—the Intelligence and Development Scales–2 (IDS-2)—with confirmatory factor analyses (CFA) of the standardization samples. This included measurement invariance analyses across the Dutch and German versions and sex using multiple-group CFA, and across age using local structural equation modeling (LSEM). We tested several a priori theoretically (mostly following the Cattell–Horn–Carroll and verbal–perceptual–image rotation models) and empirically (with EFA) determined models and found a second-order model with six first-order factors best represented the Dutch IDS-2 structure. Five IDS-2 factors were confirmed, but Visual Processing and Abstract Reasoning and the intelligence and basic skills domains were not separable. This final model displayed full invariance across the Dutch and German versions and partial scalar invariance across sex, and it was largely invariant across ages 7 to 20 years. Thus, scores derived according to this final model will be comparable across these language versions, sex, and age. The strong general intelligence factor and weak broad ability factors suggest clinical interpretation should mainly be based on the full-scale IQ. We discuss the importance of testing multiple plausible models and adhering to a strict model selection procedure in CFA and implications for intelligence theory and clinical practice.

scholarly journals Further insights on the French WISC–IV factor structure through Bayesian structural equation modeling.

2013 ◽  
Vol 25 (2) ◽  
pp. 496-508 ◽  
Author(s):  
Philippe Golay ◽  
Isabelle Reverte ◽  
Jérôme Rossier ◽  
Nicolas Favez ◽  
Thierry Lecerf
Keyword(s):  
Structural Equation Modeling ◽  
Factor Structure ◽  
Structural Equation ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling

scholarly journals 0551 Longitudinal Measurement Invariance of the Insomnia Severity Index in Veterans with Sleep Apnea

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A211-A211
Author(s):  
W Wohlgemuth ◽  
A Fins ◽  
J Tutek ◽  
A Gonzalez ◽  
A Martinez-Garcia ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Measurement Invariance ◽  
Factor Model ◽  
Severity Index ◽  
Equation Modeling ◽  
Longitudinal Measurement Invariance ◽  
Insomnia Severity Index ◽  
Longitudinal Measurement ◽  
Insomnia Severity ◽  
Over Time

Abstract Introduction The Insomnia Severity Index is a commonly used instrument to assess the presence of insomnia symptoms as well as an outcome measure following an intervention. Longitudinal measurement invariance is a necessary property of an assessment instrument when it is repeated over time. The validity of conclusions regarding change in the construct ‘insomnia severity’ depend on scale equivalence at each measurement timepoint. Assessment of measurement invariance of the ISI in sleep apnea patients has never been performed. Methods Veterans with sleep apnea (n=654; AHI=36±28; 93% male; age=52±12; BMI=33±6) completed the ISI on the night of their overnight PSG and again when they picked up their PAP device. Invariance was determined by imposing a series of more restrictive equivalence constraints on a 2-factor model of the ISI. The series of constraints tested for configural, weak, strong and strict invariance. Invariance testing was modeled with exploratory structural equation modeling in Mplus (v. 7.0). Results The 2-factor model that emerged from the analysis showed items relating to nighttime symptoms loading on factor 1 and daytime symptoms loading on factor 2. The sleep ‘satisfaction’ item, however, had weak but similar loadings on both factors. The increasingly restrictive constraints imposed on the model revealed no decrement in model fit (RMSEA=.039 to.043; CFI=.987 to .980; TLI=.981-.977; SRMR=.027-.041). Conclusion The ISI met strict criteria for longitudinal measurement invariance demonstrating that it is a valid instrument to be used in repeated measures study designs of insomnia in sleep apnea patients. Change over time on the ISI is not due to the changing measurement characteristics of the ISI but to true changes in the ‘insomnia severity’ construct. Support None

Factor Structure of the PANAS With Bayesian Structural Equation Modeling in a Chinese Sample

2021 ◽  
pp. 016327872199679
Author(s):  
Minsun Kim ◽  
Ze Wang
Keyword(s):  
High School Students ◽  
Structural Equation Modeling ◽  
Factor Structure ◽  
Structural Equation ◽  
Model Fit ◽  
Self Report ◽  
Equation Modeling ◽  
School Students ◽  
Chinese Sample ◽  
Bayesian Structural Equation Modeling

The Positive and Negative Affect Schedule (PANAS) is the most widely used self-report instrument for assessing affect. However, there are inconsistent findings regarding the factor structure of the PANAS. In this study, we applied Bayesian structural equation modeling (BSEM) to investigate the structure of the PANAS using data from a sample of 893 Chinese middle and high school students. Four models, the orthogonal two-, the oblique two-, the three-, and the bi-factor models were tested with prior specifications including approximately zero cross-loadings and residual covariances. The results indicated that the orthogonal two-factor model specified with informative priors for both cross-loadings and residual correlations has the best model fit. Confirmatory factor analysis with the maximum likelihood estimator (ML-CFA) based on modifications from BSEM analysis showed improved model fit compared to ML-CFA based on frequentist analysis, which is the evidence for the merit of BSEM for addressing misspecifications.

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