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Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables

Myrsini Katsikatsou, Irini Moustaki
2016 Psychometrika  
In this paper, two likelihood ratio test statistics and their asymptotic distributions are derived for testing overall goodness-of-fit and nested models respectively under the estimation framework of pairwise  ...  The proposed test statistics and the model selection criteria have been implemented in the R package lavaan 1 .  ...  The power for the three test statistics for overall fit is investigated under three model misspecifications.  ... 
doi:10.1007/s11336-016-9523-z pmid:27734296 fatcat:s5q26umbnffldedxso45u5d7ke

Advancing formative measurement models

Adamantios Diamantopoulos, Petra Riefler, Katharina P. Roth
2008 Journal of Business Research  
This paper seeks to encourage the thoughtful application of formative models by (a) highlighting the potential consequences of measurement model misspecification, and (b) providing a state-of-the art review  ...  Formative measurement models were first introduced in the literature more than forty years ago and the discussion about their methodological contribution has been increasing since the 1990s.  ...  In contrast, reflective measurement models with three or more indicators are identified and can be estimated (e.g., see Long, 1983) .  ... 
doi:10.1016/j.jbusres.2008.01.009 fatcat:ysg74a37ovfwvf2izz5hsvjeri

Reflections on Partial Least Squares Path Modeling

Cameron N. McIntosh, Jeffrey R. Edwards, John Antonakis
2014 Organizational Research Methods  
path modeling (PLS-PM).  ...  measurement error; (3) can be used to validate measurement models; (4) accommodates small sample sizes; (5) is able to provide null hypothesis tests for path coefficients; and (6) can be employed in an  ...  For this reason, omnibus tests of model fit should be supplemented with local tests of fit on individual constraints to identify the specific sources of model misspecification (Bera & Bilias, 2001; Saris  ... 
doi:10.1177/1094428114529165 fatcat:tvcikwpl7zhn3d4ffiewdaxpea

A Novel Test for Absolute Fit of Evolutionary Models Provides a Means to Correctly Identify the Substitution Model and the Model Tree

2019 Genome Biology and Evolution  
The novel test was found to identify the correct tree topology within a wide range of DNA substitution model misspecifications, indicating the high discriminatory power of the test.  ...  In simulations conducted to evaluate the performance of the test, the test estimator was able to identify both the correct tree topology and substitution model under conditions where the Goldman-Cox test-which  ...  Table 2 Ability of the Presented Test to Identify the Tree Components of the Preferred Evolutionary Models a in the Presence and Absence of Model Misspecification (A) Estimates of Fit b of the Preferred  ... 
doi:10.1093/gbe/evz167 pmid:31368483 pmcid:PMC6736042 fatcat:nwivti5mjvcexfzvmddyf3muou

Formative Vs. Reflective Measurement Model: Guidelines for Structural Equation Modeling Research

2020 International Journal of Analysis and Applications  
Various social sciences researchers have always debated the operationalisation of formative or a reflective measurement in Partial Least Squares Structural Equation Modeling (PLS-SEM).  ...  This paper addresses the issue of measurement misspecification in PLS-SEM assessment by providing logical guidelines for researchers.  ...  Misspecification of measurement models may affect research outcome or mislead future research.  ... 
doi:10.28924/2291-8639-18-2020-876 fatcat:yewtp4opr5hcvlz5vzlsjunkm4

Using PLS path modeling in new technology research: updated guidelines

Jörg Henseler, Geoffrey Hubona, Pauline Ash Ray
2016 Industrial management & data systems  
Findings -PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible.  ...  Purpose -Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences.  ...  More precisely, both measurement model misspecification and structural model misspecification can be detected through the tests of model fit .  ... 
doi:10.1108/imds-09-2015-0382 fatcat:pt6dgow2jbfzlaww3ekfvvhpxe

chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models [article]

Jeremy R. Ash Jacqueline M. Hughes-Oliver
2018 arXiv   pre-print
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of new models.  ...  While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine  ...  Model fitting The ModelTrain function For the model fitting component of chemmodlab, the primary function is ModelTrain, which fits a series of classification or regression models to a data set.  ... 
arXiv:1807.00243v3 fatcat:5ocobm5givhcrhdkg7h7azg5dq

Misspecification Resistant Model Selection Using Information Complexity with Applications [chapter]

Hamparsum Bozdogan, J. Andrew Howe, Suman Katragadda, Caterina Liberati
2012 Studies in Classification, Data Analysis, and Knowledge Organization  
The second issue we address is that of model misspecification -specifically that of an incorrect assumed functional form.  ...  Then, we use the genetic algorithm with the multivariate Gaussian regression model to identify the best subset regression model.  ...  In most statistical modeling problems, we almost always fit a wrong model to the observed data. This can introduce bias into the model due to model misspecification.  ... 
doi:10.1007/978-3-642-28894-4_20 fatcat:cuj6sqie7nczlkne5p42cvhy2m

Structural Equation Modeling in Psychology: The History, Development and Current Challenges

Leila Karimi, Denny Meyer
2014 International Journal of Psychological Studies  
SEM has attracted attention primarily because it lends itself to effectively studying problems or models that are hard to assess using other procedures.  ...  Structural Equation Modeling (SEM) represents a series of cause-effect relationships between variables combined into composite testable models (Shipley, 2000) .  ...  Acknowledgments The idea of using a diagram to show the history of SEM came from a personal conversation with Professor Peter Bentler in 2012.  ... 
doi:10.5539/ijps.v6n4p123 fatcat:fxy6v4o6aze3bbvrmejwomhvd4

Multinomial logit random effects models

J. Hartzel, A. Agresti, B. Caffo
2001 Statistical Modelling  
We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models.  ...  For cases in which this is computationally infeasible, we generalize a Monte Carlo EM algorithm.  ...  The authors appreciate many helpful comments from two referees, in particular pointing out several references and software of which we were unaware.  ... 
doi:10.1191/147108201128104 fatcat:b7wytn66orerbf3rjslickv4ty

Multinomial logit random effects models

Jonathan Hartzel, Alan Agresti, Brian Caffo
2001 Statistical Modelling  
We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models.  ...  For cases in which this is computationally infeasible, we generalize a Monte Carlo EM algorithm.  ...  The authors appreciate many helpful comments from two referees, in particular pointing out several references and software of which we were unaware.  ... 
doi:10.1177/1471082x0100100201 fatcat:idkt3id5sbdoxjz5fpnwbyx6jy

Five Common Mistakes for Using Partial Least Squares Path Modeling (PLS-PM) in Management Research

Asyraf Afthanorhan, Zainudin Awang, Nazim Aimran
2020 Contemporary Management Research  
As far as this method concerned, many researchers are misused or overuse the application of PLS-PM without understanding the basic knowledge in structural equation modeling.  ...  The value of Partial Least Squares Path Modeling (PLS-PM) in management research has now been acknowledged, although the PLS-PM was developed for a reason.  ...  That is, the interpretation model must be include model testing, model validation, and model fit.  ... 
doi:10.7903/cmr.20247 fatcat:jdjlkquqifaojovp3eustrguou

chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models

Jeremy R Ash, Jacqueline M Hughes-Oliver
2018 Journal of Cheminformatics  
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models.  ...  While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine  ...  Availability of data and materials The AID 364 dataset analyzed in this study is available on the PubChem repository [9].  ... 
doi:10.1186/s13321-018-0309-4 pmid:30488298 pmcid:PMC6755574 fatcat:yqqcass35rehta3bt4vbvnfiru

Partial least squares path modeling: Time for some serious second thoughts

Mikko Rönkkö, Cameron N. McIntosh, John Antonakis, Jeffrey R. Edwards
2016 Journal of Operations Management  
Partial least squares (PLS) path modeling is increasingly being promoted as a technique of choice for various analysis scenarios, despite the serious shortcomings of the method.  ...  We show that although the PLS technique is promoted as a structural equation modeling (SEM) technique, it is simply regression with scale scores and thus has very limited capabilities to handle the wide  ...  The chi-square test of exact fit strongly rejected the model χ 2 (74) =173.718 (p <.001), indicating that misspecification was present.  ... 
doi:10.1016/j.jom.2016.05.002 fatcat:md3eyb2ovvbf3nrlzk4sdhkxda

Looking for Darwin in Genomic Sequences: Validity and Success Depends on the Relationship Between Model and Data [chapter]

Christopher T. Jones, Edward Susko, Joseph P. Bielawski
2019 Msphere  
These include: (1) model misspecification, (2) low information content, (3) the confounding of processes, and (4) phenomenological load, or PL.  ...  We contend that if complex CSMs continue to be developed for testing explicit mechanistic hypotheses, then additional analyses such as those described in here (e.g., penalized LRTs and estimation of PL  ...  Model misspecification, which can result in biased parameter estimates; 2.  ... 
doi:10.1007/978-1-4939-9074-0_13 pmid:31278672 fatcat:ilgtxcishvdgbm45wuigbvhsp4
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