scholarly journals Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Ye-Mao Xia ◽  
Jian-Wei Gou
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Structural Parameters ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Semiparametric Modeling ◽  
Simulation Based ◽  
Competing Models ◽  
Parametric Families ◽  
Analysis Models

Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent variables and mixed observed responses. These models have been successfully applied to many different fields, including behavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments are constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This leads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric modeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model the distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through the simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For model selection, the logarithm of pseudomarginal likelihood is developed to compare the competing models. Empirical results are presented to illustrate the application of the methodology.

scholarly journals Choosing the number of factors in independent factor analysis model

2005 ◽  
Vol 2 (2) ◽  
Author(s):  
Cinzia Viroli
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Latent Variable ◽  
Information Criteria ◽  
Ratio Test ◽  
Independent Factor ◽  
Analysis Model ◽  
Variable Model ◽  
Factor Analysis Model ◽  
Independent Factor Analysis

Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of hidden independent ones plus a noise term. Despite the peculiarity of its origin the method can be framed within the latent variable model domain and some parallels with the ordinary factor analysis can be drawn. If no prior information on the latent structure is available a relevant issue concerns the correct specification of the model. In this work some methods to detect the number of significant latent variables are investigated. Moreover, since the method defines a probability density function for the latent variables by mixtures of gaussians, the correct number of mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out a likelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulations and empirical results on real data sets are finally presented.

Bayesian proportional hazards model with latent variables

2017 ◽  
Vol 28 (4) ◽  
pp. 986-1002 ◽  
Author(s):  
Deng Pan ◽  
Kai Kang ◽  
Chunjie Wang ◽  
Xinyuan Song
Keyword(s):  
Risk Factors ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Latent Variables ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Hazards Model

We consider a joint modeling approach that incorporates latent variables into a proportional hazards model to examine the observed and latent risk factors of the failure time of interest. An exploratory factor analysis model is used to characterize the latent risk factors through multiple observed variables. In commonly used confirmatory factor analysis, the number of latent variables and their observed indicators are specified prior to analysis. By contrast, the exploratory factor analysis model allows such information to be fully determined by the data. A Bayesian approach coupled with efficient sampling methods is developed to conduct statistical inference, and the performance of the proposed methodology is confirmed through simulations. The model is applied to a study on the risk factors of chronic kidney disease for patients with type 2 diabetes.

scholarly journals Application of Confirmatory Factor Analysis for Correlate of Students Attitude, Self-Belief, Students Engagement in Mathematics Lessons and Mathematics Achievement

2018 ◽  
Vol 7 (2.29) ◽  
pp. 535 ◽  
Author(s):  
Desi Rahmatina
Keyword(s):  
Factor Analysis ◽  
Mathematics Achievement ◽  
Confirmatory Factor Analysis ◽  
Latent Variables ◽  
Negative Relationship ◽  
Mathematics Lesson ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Data Set ◽  
Confirmatory Factor

The study aimed to propose the Confirmatory Factor Analysis via four latent variables : 1) Students Attitude toward mathematics, 2) Self-belief in mathematics, 3) Students engagement in mathematics lessons and 4) Mathematics Achievement  and 19 observed variables and then we conduct to the correlations between latent variables and observed variables. The subjects were 5795 eight grades students from the result of the Trends in International Mathematics and Science Study (TIMSS) 2011 assessment conducted in Indonesia. Data Analysis were undertaken using the Lisrel software to examine the effect of students attitude toward mathematics, students self belief and students engagement in mathematics lesson for mathematics achievement. The proposed Confirmatory Factor Analysis model of the latent variables and observed variables fit well with the empirical data set (RMSEA = 0,071). The results of multivariate analyses has shown a strong negative relationship between student attitude toward mathematics, self-belief in mathematics and their mathematics achievement (t value = -6.32 and t = -6.10, respectively) and a strong positive relationship between students engagement in mathematics lesson with mathematics achievement (t value = 8,28).   

scholarly journals The Noetic Experience and Belief Scale: A validation and reliability study

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1741
Author(s):  
Helané Wahbeh ◽  
Garret Yount ◽  
Cassandra Vieten ◽  
Dean Radin ◽  
Arnaud Delorme
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Internal Consistency ◽  
Latent Variables ◽  
Convergent Validity ◽  
The United States ◽  
Divergent Validity ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor

Background: Belief in the paranormal is widespread worldwide. Recent surveys suggest that subjective experiences of the paranormal are common. A concise instrument that adequately evaluates beliefs as distinct from experiences does not currently exist. To address this gap, we created the Noetic Experiences and Beliefs Scale (NEBS) which evaluates belief and experience as separate constructs. Methods: The NEBS is a 20-item survey with 10 belief and 10 experience items rated on a visual analog scale from 0-100. In an observational study, the survey was administered to 361 general population adults in the United States and a subsample of 96 one month later. Validity, reliability and internal consistency were evaluated. A confirmatory factor analysis was conducted to confirm the latent variables of belief and experience. The survey was then administered to a sample of 646 IONS Discovery Lab participants to evaluate divergent validity and confirm belief and experience as latent variables of the model in a different population. Results: The NEBS demonstrated convergent validity, reliability and internal consistency (Cronbach’s alpha Belief 0.90; Experience 0.93) and test-retest reliability (Belief: r = 0.83; Experience: r = 0.77). A confirmatory factor analysis model with belief and experience as latent variables demonstrated a good fit. The factor model was confirmed as having a good fit and divergent validity was established in the sample of 646 IONS Discovery Lab participants. Conclusions: The NEBS is a short, valid, and reliable instrument for evaluating paranormal belief and experience.

scholarly journals Dynamic Fit Index Cutoffs for Confirmatory Factor Analysis Models

2020 ◽  
Author(s):  
Daniel McNeish ◽  
Melissa Gordon Wolf
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Specific Model ◽  
Central Component ◽  
Size Factor ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor ◽  
Fit Index ◽  
Analysis Models

Model fit assessment is a central component of evaluating confirmatory factor analysis models. Fit indices like RMSEA, SRMR, and CFI remain popular and researchers often judge fit based on suggestions from Hu and Bentler (1999), who derived cutoffs that distinguish between fit index distributions of true and misspecified models. However, methodological studies note that the location and variability of fit index distributions – and, consequently, cutoffs distinguishing between true and misspecified fit index distributions – are not fixed but vary as a complex interaction of model characteristics like sample size, factor reliability, number of items, and number of factors. Many studies over the last 15 years have cautioned against fixed cutoffs and the faulty conclusions they can trigger. However, practical alternatives are absent, so fixed cutoffs have remained the status quo despite their shortcomings. Criticism of fixed cutoffs stem primarily from the fact that they were derived from one specific confirmatory factor analysis model and lack generalizability. To address this, we propose dynamic cutoffs such that derivation of cutoffs is adaptively tailored to the specific model and data being evaluated. This creates customized cutoffs that are designed to distinguish between true and misspecified fit index distributions in the researcher’s particular context. Importantly, we show that the method does not require knowledge of the “true” model to accomplish this. As with fixed cutoffs, the procedure requires Monte Carlo simulation, so we provide an open-source, web-based Shiny application that automates the entire process to make the method as accessible as possible.

scholarly journals Measuring extraordinary experiences and beliefs: A validation and reliability study

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1741
Author(s):  
Helané Wahbeh ◽  
Garret Yount ◽  
Cassandra Vieten ◽  
Dean Radin ◽  
Arnaud Delorme
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Internal Consistency ◽  
Latent Variables ◽  
Convergent Validity ◽  
The United States ◽  
Divergent Validity ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor

Background: Belief in the paranormal is widespread worldwide. Recent surveys suggest that subjective experiences of the paranormal are common. A concise instrument that adequately evaluates beliefs as distinct from experiences does not currently exist. To address this gap, we created the Noetic Experiences and Beliefs Scale (NEBS) which evaluates belief and experience as separate constructs. Methods: The NEBS is a 20-item survey with 10 belief and 10 experience items rated on a visual analog scale from 0-100. In an observational study, the survey was administered to 361 general population adults in the United States and a subsample of 96 one month later. Validity, reliability and internal consistency were evaluated. A confirmatory factor analysis was conducted to confirm the latent variables of belief and experience. The survey was then administered to a sample of 646 IONS Discovery Lab participants to evaluate divergent validity and confirm belief and experience as latent variables of the model in a different population. Results: The NEBS demonstrated convergent validity, reliability and internal consistency (Cronbach’s alpha Belief 0.90; Experience 0.93) and test-retest reliability (Belief: r = 0.83; Experience: r = 0.77). A confirmatory factor analysis model with belief and experience as latent variables demonstrated a good fit. The factor model was confirmed as having a good fit and divergent validity was established in the sample of 646 IONS Discovery Lab participants. Conclusions: The NEBS is a short, valid, and reliable instrument for evaluating paranormal belief and experience.

scholarly journals The Noetic Experience and Belief Scale: A validation and reliability study

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1741 ◽  
Author(s):  
Helané Wahbeh ◽  
Garret Yount ◽  
Cassandra Vieten ◽  
Dean Radin ◽  
Arnaud Delorme
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Internal Consistency ◽  
Latent Variables ◽  
Convergent Validity ◽  
The United States ◽  
Divergent Validity ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor

Background: Belief in the paranormal is widespread worldwide. Recent surveys suggest that subjective experiences of the paranormal are common. A concise instrument that adequately evaluates beliefs as distinct from experiences does not currently exist. To address this gap, we created the Noetic Experiences and Beliefs Scale (NEBS) which evaluates belief and experience as separate constructs. Methods: The NEBS is a 20-item survey with 10 belief and 10 experience items rated on a visual analog scale from 0-100. In an observational study, the survey was administered to 361 general population adults in the United States and a subsample of 96 one month later. Validity, reliability and internal consistency were evaluated. A confirmatory factor analysis was conducted to confirm the latent variables of belief and experience. The survey was then administered to a sample of 646 IONS Discovery Lab participants to evaluate divergent validity and confirm belief and experience as latent variables of the model in a different population. Results: The NEBS demonstrated convergent validity, reliability and internal consistency (Cronbach’s alpha Belief 0.90; Experience 0.93) and test-retest reliability (Belief: r = 0.83; Experience: r = 0.77). A confirmatory factor analysis model with belief and experience as latent variables demonstrated a good fit. The factor model was confirmed as having a good fit and divergent validity was established in the sample of 646 IONS Discovery Lab participants. Conclusions: The NEBS is a short, valid, and reliable instrument for evaluating paranormal belief and experience.

scholarly journals Dynamic Fit Index Cutoffs for Confirmatory Factor Analysis Models

2020 ◽  
Author(s):  
Daniel McNeish ◽  
Melissa Gordon Wolf
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Specific Model ◽  
Central Component ◽  
Size Factor ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor ◽  
Fit Index ◽  
Analysis Models

Model fit assessment is a central component of evaluating confirmatory factor analysis models. Fit indices like RMSEA, SRMR, and CFI remain popular and researchers often judge fit based on suggestions from Hu and Bentler (1999), who derived cutoffs that distinguish between fit index distributions of true and misspecified models. However, methodological studies note that the location and variability of fit index distributions – and, consequently, cutoffs distinguishing between true and misspecified fit index distributions – are not fixed but vary as a complex interaction of model characteristics like sample size, factor reliability, number of items, and number of factors. Many studies over the last 15 years have cautioned against fixed cutoffs and the faulty conclusions they can trigger. However, practical alternatives are absent, so fixed cutoffs have remained the status quo despite their shortcomings. Criticism of fixed cutoffs stem primarily from the fact that they were derived from one specific confirmatory factor analysis model and lack generalizability. To address this, we propose dynamic cutoffs such that derivation of cutoffs is adaptively tailored to the specific model and data being evaluated. This creates customized cutoffs that are designed to distinguish between true and misspecified fit index distributions in the researcher’s particular context. Importantly, we show that the method does not require knowledge of the “true” model to accomplish this. As with fixed cutoffs, the procedure requires Monte Carlo simulation, so we provide an open-source, web-based Shiny application that automates the entire process to make the method as accessible as possible.

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