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Purpose The purpose of this study is to examine the relationships among a learning organization, knowledge and financial performance using the Dimensions of the Learning Organization Questionnaire and its abbreviated version. Design/methodology/approach This study used a secondary data set and performed second-order factor analysis and structural equation modeling for testing the proposed relationships. Findings The study found that a learning organization has a positive effect on knowledge performance; knowledge performance has a positive effect on financial performance; and knowledge performance fully mediates the relationship between a learning organization and financial performance. Research limitations/implications This study contributes to validating the current dimensionality of the theoretical framework of a learning organization proposed by Watkins and Marsick (1993, 1996) and offers a valid conceptual framework of the relationship among the learning culture and organizational performance dimensions. Practical implications This study re-stresses the significance of the learning and knowledge generated by the human resources of an organization and developed by human resource development practitioners. Originality/value This study is valuable to human resource development scholars and practitioners interested in improving and measuring organizational performance.

There is a large body of research on the effectiveness of rater training methods in the industrial and organizational psychology literature. Less has been reported in the measurement literature on large‐scale writing assessments. This study compared the effectiveness of two widely used rater training methods—self‐paced and collaborative frame‐of‐reference training—in the context of a large‐scale writing assessment. Sixty‐six raters were randomly assigned to the training methods. After training, all raters scored the same 50 representative essays prescored by a group of expert raters. A series of generalized linear mixed models were then fitted to the rating data. Results suggested that the self‐paced method was equivalent in effectiveness to the more time‐intensive and expensive collaborative method. Implications for large‐scale writing assessments and suggestions for further research are discussed.

Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, U.S. Department of Labor, 1997). A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed.

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