scholarly journals Climate, species richness, and body size drive geographical variation in resource specialization of herbivorous butterflies

2020 ◽  
Author(s):  
Ryosuke Nakadai ◽  
Tommi Nyman ◽  
Koya Hashimoto ◽  
Takaya Iwasaki ◽  
Anu Valtonen
Keyword(s):  
Body Size ◽  
Structural Equation ◽  
Spatial Scales ◽  
Equation Modeling ◽  
Butterfly Species ◽  
Host Use ◽  
Resource Specialization ◽  
The Japanese Archipelago ◽  
Bayesian Structural Equation Modeling ◽  
General Prediction

AbstractRevealing drivers of variation in resource specialization is a long-standing goal in ecological and evolutionary research. As a general prediction, the degree of resource specialization increases towards lower latitudes. Although herbivorous insects are one of the best-studied consumer groups, factors determining the degree of specialization on large spatial scales are poorly understood. Herein, we focused on the fundamental host breadth of 246 herbivorous butterfly species distributed across the Japanese archipelago. Using Bayesian Structural Equation Modeling based on information of pooled geographical occurrence and host use, we show that local butterfly communities tend to become more specialized towards higher latitudes, a pattern that is opposite to predictions from classical hypotheses. We also found that the pattern is mainly driven by factors related to climate, butterfly diversity, and body size in each community. Our results re-emphasize the importance of current climate as a regulating factor for butterfly host breadth and morphology.

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

scholarly journals Psychometric properties of the Chinese version of Five Facet Mindfulness Questionnaire—short form in cancer patients: a Bayesian structural equation modeling approach

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Ted C. T. Fong ◽  
Adrian H. Y. Wan ◽  
Venus P. Y. Wong ◽  
Rainbow T. H. Ho
Keyword(s):  
Psychometric Properties ◽  
Cancer Patients ◽  
Structural Equation ◽  
Short Form ◽  
Equation Modeling ◽  
Five Facet Mindfulness Questionnaire ◽  
Health Measures ◽  
Acting With Awareness ◽  
Colorectal Cancer Patients ◽  
Bayesian Structural Equation Modeling

Abstract Background Mindfulness has emerged as an important correlate of well-being in various clinical populations. The present study evaluated the psychometric properties of the 20-item short form of the Five Facet Mindfulness Questionnaire (FFMQ-SF) in the Chinese context. Methods The study sample was 127 Chinese colorectal cancer patients who completed the FFMQ-SF and validated physical and mental health measures. Factorial validity of the FFMQ-SF was assessed using Bayesian structural equation modeling (BSEM) via informative priors on cross-loadings and residual covariances. Linear regression analysis examined its convergent validity with the health measures on imputed datasets. Results The five-factor BSEM model with approximate zero cross-loadings and one residual covariance provided an adequate model fit (PPP = 0.07, RMSEA = 0.06, CFI = 0.95). Satisfactory reliability (ω = 0.77–0.85) was found in four of the five facets (except nonjudging). Acting with awareness predicted lower levels of perceived stress, negative affect, anxiety, depression, and illness symptoms (β = − 0.37 to − 0.42) and better quality of life (β = 0.29–0.32). Observing, nonjudging, and nonreacting did not show any significant associations (p > .05) with health measures. Acting with awareness was not significantly correlated (r < 0.15) with the other four facets. Conclusion The present findings provide partial support for the psychometric properties of the FFMQ-SF in colorectal cancer patients. The nonjudging facet showed questionable validity and reliability in the present sample. Further studies with larger sample sizes are needed to elucidate the viability of FFMQ-SF as a measure of mindfulness facets in cancer patients.

scholarly journals Attitudinal and normative responses to advertising stimuli and vaping intentions

2020 ◽  
pp. 147078532092920
Author(s):  
Abraham Brown ◽  
Seamus Allison
Keyword(s):  
Structural Equation ◽  
Cross Sectional Study ◽  
Equation Modeling ◽  
Cross Sectional ◽  
Gas Stations ◽  
Cigarette Advertising ◽  
Retail Outlets ◽  
Former Smokers ◽  
Never Smokers ◽  
Bayesian Structural Equation Modeling

Empirical data to show whether exposure to e-cigarette advertising stimuli may influence former- and never-smokers to consider vaping is lacking. We examined whether former- and never-smokers’ cognitive, affective, and normative responses to e-cigarette stimuli in retail outlets will predict their vaping intention. A repeat cross-sectional study recruited 876 participants aged 18–24 years at Waves 1 and 2 in the United Kingdom. Bayesian structural equation modeling tested mediation and moderation effects of the variables of interest. Results from Waves 1 and 2 revealed that the association of salience of e-cigarette advertising in stores and gas stations with vaping intention was mediated by affect and subjective norms among former smokers. Cognitive attitudes of never smokers mediated the relationship between salience of e-cigarette advertising in retail outlets and vaping intention at Waves 1 and 2. Former smokers were more likely to hold stronger affect toward vaping than never smokers at Wave 2. Our study supports the need for stronger policies to restrict e-cigarette portrayals in retail outlets, as advertising messages can trigger strong thoughts, feelings, and norms of vaping. Interventions may benefit from including attitudinal and normative components to promote pro-social behavior.

scholarly journals Tonic immobility is a measure of boldness toward predators: an application of Bayesian structural equation modeling

2012 ◽  
Vol 23 (3) ◽  
pp. 619-626 ◽  
Author(s):  
Pim Edelaar ◽  
David Serrano ◽  
Martina Carrete ◽  
Julio Blas ◽  
Jaime Potti ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Tonic Immobility ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling

scholarly journals Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling

2016 ◽  
Vol 8 (11) ◽  
pp. 1204 ◽  
Author(s):  
Hashem Salarzadeh Jenatabadi ◽  
Peyman Babashamsi ◽  
Datis Khajeheian ◽  
Nader Seyyed Amiri
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Bayesian Structural Equation Modeling ◽  
New Framework

scholarly journals Prior sensitivity analysis in default Bayesian structural equation modeling

2017 ◽  
Author(s):  
Sara van Erp ◽  
Joris Mulder ◽  
Daniel L. Oberski
Keyword(s):  
Sensitivity Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Empirical Bayes ◽  
Mean Squared Error ◽  
Small Samples ◽  
Equation Modeling ◽  
Model Parameters ◽  
Ml Estimation ◽  
Bayesian Structural Equation Modeling

Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models while solving some of the issues often encountered in classical maximum likelihood (ML) estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error (MSE), bias, coverage rates, and quantiles of the estimates.In this paper, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors, with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature and all code for conducting the prior sensitivity analysis is made available in the online supplemental material.

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