PubMed · 4179264
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R R Hetherington. 1969-02-22. Teaching behavioural science.. https://doi.org/10.1016/s0140-6736(69)91393-2
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The Pioneer Fund was created in 1937 "to conduct or aid in conducting study and research into problems of heredity and eugenics . . . and problems of race betterment with special reference to the people of the United States." The Fund was endowed by Colonel Wickliffe Preston Draper, a New England textile heir, and perpetuates his legacy through an active program of grants, some of the more controversial in aid of research on racial group differences. Those presently associated with the Fund maintain that it has made a substantial contribution to the behavioral and social sciences, but insider accounts of Pioneer's history oversimplify its past and smooth over its more tendentious elements. This article examines the social context and intellectual background to Pioneer's origins, with a focus on Col. Draper himself, his concerns about racial degeneration, and his relation to the eugenics movement. In conclusion, it evaluates the official history of the fund.
Problems such as improper solution, non-convergence, subsets of variables having different distribution, and latent variables with single indicators are common in the practice of structural equation modelling. In such cases, it may be feasible to fix some model parameters at prespecified values while concentrating on estimating some other parameters. This paper formulates such a model fitting process through a model segregation approach. The statistical properties of this procedure are studied using the theory of estimating equations and optimal estimating functions. The dependency of the new parameter estimates on those of the prespecified parameter estimates is characterized for several commonly used estimating equations. A rescaled model fit statistic is proposed. Examples illustrate various applications of this procedure.
In the social and behavioural sciences, structural equation modelling has been widely used to test a substantive theory or causal relationship among latent constructs. Cross-validation (CV) is a valuable tool for selecting the best model among competing structural models. Influential cases or outliers are often present in practical data. Therefore, even the correct model for the majority of the data may not cross-validate well. This paper discusses various drawbacks of CV based on sample covariance matrices, and develops a procedure for using robust covariance matrices in the model calibration and validation stages. Examples illustrate that the CV index based on sample covariance matrices is very sensitive to influential cases, and even a single outlier can cause the CV index to support a wrong model. The CV index based on robust covariance matrices is much less sensitive to influential cases and thus leads to a more valid conclusion about the practical value of a model structure.