scholarly journals Bayesian mixture structural equation modelling in multiple-trait QTL mapping

2010 ◽  
Vol 92 (3) ◽  
pp. 239-250 ◽  
Author(s):  
XIAOJUAN MI ◽  
KENT ESKRIDGE ◽  
DONG WANG ◽  
P. STEPHEN BAENZIGER ◽  
B. TODD CAMPBELL ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Structural Equation ◽  
Statistical Power ◽  
Bayes Factor ◽  
Causal Structure ◽  
Mapping Method ◽  
Parameter Estimates ◽  
Detection Accuracy ◽  
Multiple Traits ◽  
Multiple Trait

SummaryQuantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis–Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

scholarly journals Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

2017 ◽  
Vol 7 (3) ◽  
pp. 813-822 ◽  
Author(s):  
Riyan Cheng ◽  
R. W. Doerge ◽  
Justin Borevitz
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
Multiple Trait

scholarly journals Statistical power of QTL mapping methods applied to bacteria counts

2001 ◽  
Vol 78 (3) ◽  
pp. 303-316 ◽  
Author(s):  
P. TILQUIN ◽  
W. COPPIETERS ◽  
J. M. ELSEN ◽  
F. LANTIER ◽  
C. MORENO ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
Parametric Method ◽  
Mapping Method ◽  
Point Of View ◽  
Parametric Methods ◽  
Statistical Point ◽  
The Right ◽  
Non Parametric ◽  
Normally Distributed

Most QTL mapping methods assume that phenotypes follow a normal distribution, but many phenotypes of interest are not normally distributed, e.g. bacteria counts (or colony-forming units, CFU). Such data are extremely skewed to the right and can present a high amount of zero values, which are ties from a statistical point of view. Our objective is therefore to assess the efficiency of four QTL mapping methods applied to bacteria counts: (1) least-squares (LS) analysis, (2) maximum-likelihood (ML) analysis, (3) non-parametric (NP) mapping and (4) nested ANOVA (AN). A transformation based on quantiles is used to mimic observed distributions of bacteria counts. Single positions (1 marker, 1 QTL) as well as chromosome scans (11 markers, 1 QTL) are simulated. When compared with the analysis of a normally distributed phenotype, the analysis of raw bacteria counts leads to a strong decrease in power for parametric methods, but no decrease is observed for NP. However, when a mathematical transformation (MT) is applied to bacteria counts prior to analysis, parametric methods have the same power as NP. Furthermore, parametric methods, when coupled with MT, outperform NP when bacteria counts have a very high proportion of zeros (70·8%). Our results show that the loss of power is mainly explained by the asymmetry of the phenotypic distribution, for parametric methods, and by the existence of ties, for the non-parametric method. Therefore, mapping of QTL for bacterial diseases, as well as for other diseases assessed by a counting process, should focus on the occurrence of ties in phenotypes before choosing the appropriate QTL mapping method.

scholarly journals Multitrait Least Squares for Quantitative Trait Loci Detection

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 899-911 ◽  
Author(s):  
Sara A Knott ◽  
Chris S Haley
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Least Squares ◽  
Quantitative Trait ◽  
Testing Procedure ◽  
Mapping Method ◽  
Multiple Trait ◽  
Formal Testing ◽  
Pleiotropic Qtl ◽  
Location Estimate

Abstract A multiple-trait QTL mapping method using least squares is described. It is presented as an extension of a single-trait method for use with three-generation, outbred pedigrees. The multiple-trait framework allows formal testing of whether the same QTL affects more than one trait (i.e., a pleiotropic QTL) or whether more than one linked QTL are segregating. Several approaches to the testing procedure are presented and their suitability discussed. The performance of the method is investigated by simulation. As previously found, multitrait analyses increase the power to detect a pleiotropic QTL and the precision of its location estimate. With enough information, discrimination between alternative genetic models is possible.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

scholarly journals Initial Validation of the Teacher-Created Empowering and Disempowering Motivational Climate Questionnaire in Physical Education

2018 ◽  
Vol 37 (4) ◽  
pp. 340-351 ◽  
Author(s):  
Daniel Milton ◽  
Paul R. Appleton ◽  
Anna Bryant ◽  
Joan L. Duda
Keyword(s):  
Physical Education ◽  
Secondary School ◽  
Structural Equation ◽  
Factor Model ◽  
Motivational Climate ◽  
Equation Modeling ◽  
Physical Education Teachers ◽  
Parameter Estimates ◽  
Two Samples ◽  
Secondary School Pupils

Purpose: Guided by Duda’s hierarchical conceptualization of the motivational climate that draws from self-determination and achievement goal theories, this study provides initial evidence of the psychometric properties of the Empowering and Disempowering Motivational Climate Questionnaire in physical education (EDMCQ-PE). Method: Questionnaire based with two samples of Welsh secondary school pupils. Results: Exploratory structural equation modeling provided a better fit of the data to the hypothesized model than confirmatory factor analysis. Moreover, a two-factor composite (i.e., empowering and disempowering) lower-order model provided an acceptable fit and clear parameter estimates. This two-factor model also demonstrated scalar gender measurement invariance. Discussion: The evidence from this study suggests the EDMCQ-PE is a promising scale for the assessment of secondary school pupils’ perceptions of the empowering and disempowering features of the motivational climate created by their physical education teachers. Conclusion: Moving forward, the statistical approach employed in this paper can inform future studies that develop questionnaire methodology in physical education and from an applied perspective; the EDMCQ-PE can be used by researchers and teachers to assess the motivational climate in PE and help inform the pedagogy underpinning teachers’ classes.

Assessment of A Mobile Educational Coaching App

2018 ◽  
Vol 14 (1) ◽  
pp. 22-43
Author(s):  
Niousha Shahidi ◽  
Vesselina Tossan ◽  
Silvia Cacho-Elizondo
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Online Survey ◽  
Causal Structure ◽  
Convenience Sample ◽  
Equation Modelling ◽  
Educational Coaching ◽  
Acceptance Model ◽  
Type Of School

This article explores which antecedents explain intentions to adopt a mobile coaching app. To that end, this study describes a coaching service designed to guide/encourage students throughout their studies in order to validate a new model of planned behavior based on the Technology Acceptance Model and the Goal-Directed Behavioral theory. The methodology included a short qualitative study and an online survey to examine the theoretical model which is based on scales tested in previous studies. The convenience sample is composed of students (Bachelor and Master/MBA) with the results analyzed using structural equation modelling to test the proposed model's causal structure. The results show different adoption patterns by gender and type of school.

scholarly journals CB-SEM latent interaction: Unconstrained and orthogonalized approaches

2020 ◽  
Author(s):  
JH Cheah ◽  
MA Memon ◽  
James Richard ◽  
H Ting ◽  
TH Cham
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Interaction Analysis ◽  
Marketing Research ◽  
Interaction Effects ◽  
Parameter Estimates ◽  
Exploratory Research ◽  
Analysis Techniques ◽  
Guidelines And Recommendations ◽  
Latent Variable Interaction

© 2020 Australian and New Zealand Marketing Academy Covariance Based – Structural Equation Modelling (CB-SEM) is often used to investigate moderation and latent interaction effects. This study illustrates and compares the application of constrained, unconstrained and orthogonalized CB-SEM approaches to latent variable interaction analysis using AMOS. Although all three techniques provided similar parameter estimates, the orthogonalized approach provided reduced standard errors resulting in identifying a significant latent interaction, suggesting the orthogonalized approach may be better suited for exploratory research. The illustrated example demonstrates three CB-SEM techniques, and the simplicity of the three approaches to test for interaction effects. The three approaches can be comfortably implemented in available software programs. Guidelines and recommendations for the use of the three approaches are identified with a step-wise process of assessing the latent interaction effect in CB-SEM. As far as we are aware this is the first investigation comparing and recommending specific CB-SEM latent variable moderation analysis techniques in marketing research.

scholarly journals Cross-Cultural Effects in Adoption Patterns of a Mobile Coaching Service for studies

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Technology Acceptance ◽  
Structural Equation ◽  
Mobile Apps ◽  
Causal Structure ◽  
Perceived Usefulness ◽  
Developed Country ◽  
Cultural Effects ◽  
Adoption Model ◽  
Pioneer Effort ◽  
Acceptance Model

This study applied an adoption model, inspired by the Technology Acceptance Model (TAM) and Multipurpose Information Appliances Adoption Model (MIAAM), to compare key variables explaining adoption patterns of a mobile coaching app that guides and encourages students via a technology-based platform. This article constitutes a pioneer effort to compare adoption behaviors across a developed country and an emerging country (France and Mexico) with differences in level of use of mobile apps. A multi-group structural equation modelling approach was used to test the causal structure of the conceptual model. Results confirmed significant differences and similarities across samples and identified critical factors. Perceived usefulness was found to be the most important driver with mediating effects. Organizations implementing coaching services with an improved perceived usefulness could boost their adoption rates.

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