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As part of developing a comprehensive strategy for structural equation model building and assessment, a large‐scale Monte Carlo study of 7,200 covariance matrices sampled from 36 population models was conducted. This study compared maximum likelihood with the much simpler centroid method for the confirmatory factor analysis of multiple‐indicator measurement models. Surprisingly, the contribution of maximum likelihood to model analysis is limited to formal evaluation of the model. No statistically discernible differences were obtained for the bias, standard errors, or mean squared error (MSE) of the estimated factor correlations, and empirically obtained maximum likelihood standard errors for the pattern coefficients were only slightly smaller than their centroid counterparts. Further supporting the recommendations of Anderson and Gerbing (1982), the considerably faster centroid method may have a useful role in the analysis of these models, particularly for the analysis of large models with 50 or more input variables. These results encourage the further development of a comprehensive research paradigm that exploits the relative strengths of both centroid and maximum likelihood as complementary estimation procedures along an integrated exploratory‐confirmatory continuum of model specification, revision, and formal evaluation. 相似文献
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As part of the development of a comprehensive strategy for structural equation model building and assessment, a Monte Carlo study evaluated the effectiveness of different exploratory factor analysis extraction and rotation methods for correctly identifying the known population multiple‐indicator measurement model. The exploratory methods fared well in recovering the model except in small sample sizes with highly correlated factors, and even in those situations most of the indicators were correctly assigned to the factors. Surprisingly, the orthogonal varimax rotation did as well as the more sophisticated oblique rotations in recovering the model, and generally yielded more accurate estimates. These results demonstrate that exploratory factor analysis can contribute to a useful heuristic strategy for model specification prior to cross‐validation with confirmatory factor analysis. 相似文献
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Medical care in sport comprises a variety of treatments, from scientifically proven biomedicine to complementary and alternative medicine. Information and knowledge about these diverse treatment options is spread by different sources. Thus, athletes encounter information of varying content, quality and background. This exploratory pilot study addresses athletes' medical opinions, their health-related information seeking behaviour and the knowledge sources they utilise. Questionnaires were used to examine n = 110 German athletes (nmale = 69, nfemale = 41; meanage = 24.28 ± 4.97 years) at high performance levels (national team and/or European championship and/or World championship n = 22; first national league and/or German championship n = 51, second national league and/or State championship n = 37) from various Olympic sports. A cluster analysis regarding the athletes' attitudes towards sport medicine exhibited four different types of athletes: ‘the autonomous athlete’, ‘the open-minded athlete’, ‘the functionalistic athlete’ and ‘the conservative athlete’. In general, our findings show that the most used and trusted information sources are physicians and physiotherapists. However, medical information is trusted the most if it is experience- and field-tested, and comes from the athletes' sport-specific network. Our findings also suggest that professional medical knowledge management in competitive sport is needed. 相似文献
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