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How employees react to an organization's ethical/social initiatives has little support in terms of empirical evidence. We examine employee perceptions about organizational social commitment (OSC) and its association with employee well-being (WB). The sample consists of 289 participants of a healthcare organization in Colombia. We use a comprehensive methodology for mining psychological/managerial constructs in R comprising six processes (observe, explore, confirm, explain, predict, and report). We provide information concerning the scales' plausibility, reliability, convergent/discriminant validity, and equity. We contrast the relationship between OSC and WB by using structural equation modelling with bootstrap approaches. We examine the capability of OSC to predict WB by using machine learning methods. We found a positive relationship between the constructs, which shows that OSC is a valuable strategy for contributing to employee objectives from a 'being well together' perspective. The paper stimulates/facilitates future research and teaching-learning initiatives in latent variable analysis using the R language. [ABSTRACT FROM AUTHOR]

Estimating the parameters of a multiple linear model is a common task in all areas of sciences. In order to obtain conjugate distributions, the Bayesian estimation of these parameters is usually carried out using noninformative priors. When informative priors are considered in the Bayesian estimation an important problem arises because techniques are required to extract information from experts and represent it in an informative prior distribution. Elicitation techniques can be used for such purpose even though they are more complex than the traditional methods. In this paper, we propose a technique to construct an informative prior distribution from expert knowledge using hypothetical samples. Our proposal involves building a mental picture of the population of responses at several specific points of the explanatory variables of a given model and indirectly eliciting the mean and the variance at each of these points. In addition, this proposal consists of two steps: the first step describes the elicitation process and the second step shows a simulation process to estimate the model parameters. [ABSTRACT FROM AUTHOR]

In the inferential process of Principal Component Analysis (PCA), one of the main challenges for researchers is establishing the correct number of components to represent the sample. For that purpose, heuristic and statistical strategies have been proposed. One statistical approach consists in testing the hypothesis of the equality of the smallest eigenvalues in the covariance or correlation matrix using a Likelihood-Ratio Test (LRT) that follows a X² limit distribution. Different correction factors have been proposed to improve the approximation of the sampling distribution of the statistic. We use simulation to study the significance level and power of the test under the use of these different factors and analyze the sample size required for an adequate approximation. The results indicate that for covariance matrix, the factor proposed by Bartlett offers the best balance between the objectives of low probability of Type I Error and high Power. If the correlation matrix is used, the factors W B and c X² d are the most recommended. Empirically, we can observe that most factors require sample sizes 10 or 20 times the number of variables if covariance or correlation matrices, respectively, are implemented. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Medicina de la Universidad Nacional de Colombia is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Facultad de Medicina de la Universidad Nacional de Colombia is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Facultad de Medicina de la Universidad Nacional de Colombia is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)