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1.
J Safety Res ; 73: 133-142, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32563385

RESUMO

INTRODUCTION: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. RESULTS: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.


Assuntos
Condução de Veículo/estatística & dados numéricos , Autorrelato/estatística & dados numéricos , Inquéritos e Questionários/estatística & dados numéricos , Adulto , Idoso , Idoso de 80 Anos ou mais , Brasil , Análise Fatorial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Portugal , Análise de Componente Principal , Adulto Jovem
2.
J Dairy Sci ; 103(2): 1642-1650, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31759604

RESUMO

This research aimed to estimate genetic parameters for milk yield and type traits [withers height (WH), croup height (CH), body length (BL), croup length (CL), iliac width (ILW), ischial width (ISW), and thoracic circumference] in Murrah buffaloes and to identify genomic regions related to type traits by applying a single-step genome-wide association study. Data used to estimate the genetic parameters consisted of 601 records of milk yield in the first lactation and the aforementioned type traits. For the single-step genome-wide association study, 322 samples genotyped with a 90K Axiom Buffalo Genotyping array (Thermo Fisher Scientific, Santa Clara, CA) were used. Bivariate analysis revealed that heritability for milk yield (kg) at 305 d was 0.31 ± 0.11, whereas it ranged from 0.22 ± 0.07 to 0.34 ± 0.09 for the studied conformation traits. Based on the percentages of genetic variance explained by windows of 10 markers, there were 16 genomic regions explaining more than 0.5% of the variance for WH, CH, BL, CL, ILW, ISW, and thoracic circumference. Between those regions, 4 were associated with more than 1 trait, suggesting pleiotropic roles for some genes of Bos taurus autosome (BTA) 12 on CL and WH, BTA13 on ISW and ILW, BTA23 on CH and BL, and BTA28 on ISW and BL. Most of these regions coincide with known quantitative trait loci for milk traits. Thus, further studies based on sequence data will help to validate the association of this region with type traits and likely identify the causal mutations.


Assuntos
Búfalos/genética , Estudo de Associação Genômica Ampla/veterinária , Leite , Animais , Pesos e Medidas Corporais/veterinária , Búfalos/anatomia & histologia , Bovinos , Indústria de Laticínios , Feminino , Genótipo , Lactação/genética , Locos de Características Quantitativas
3.
Acta investigación psicol. (en línea) ; 5(2): 2047-2061, abr. 2015. tab
Artigo em Espanhol | LILACS | ID: biblio-949403

RESUMO

Resumen: El presente estudio se planteó como objetivo identificar correlatos psicosociales del consumo nocivo de alcohol en estudiantes mexicanos, así como probar comparativamente la validez y capacidad explicativa de tres modelos teóricos y de medición de estos factores, a fin de apoyar la investigación del problema y dar pauta al desarrollo de acciones preventivas teórica y empíricamente sustentadas. Estos tres modelos son: la teoría de las expectativas, la teoría del aprendizaje social de R. Akers y la teoría de la conducta planificada. Los hallazgos indican que los tres modelos tienen una buena capacidad predictiva del consumo nocivo de alcohol en la población de estudio. El mayor peso corresponde a las expectativas positivas asociadas a los efectos del alcohol, seguidas por el uso de alcohol entre pares, una baja percepción de riesgo y, como factor protector, la capacidad percibida para controlar la cantidad que se bebe. Los hallazgos reflejan la conveniencia de aplicar un modelo multivariado compuesto por variables provenientes de los tres modelos.


Abstract: The aim of this study was to identify psychosocial correlates of harmful alcohol use among young Mexican students, and to comparatively prove the predictive validity of three theoretical models, each one capable to support the research of the problem and to guide the design of theoretically and empirically grounded preventive and treatment programs: a) the alcohol expectancies theory, b) the social learning theory by R. Akers, and c) the planned behavior theory. Findings indícate that these three models are appropriate predictive models for harmful alcohol use in the studied group. The highest predictive weight belongs to positive expectancies associated to the effects of alcohol, followed by alcohol use among friends, low risk perception, and self-control of alcohol drinking as a protective factor. Findings suggest the convenience of the application of a composed multi-varied algorithm, integrated by factors and variables extracted from the three models.

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