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Titlebook: Distributions of Correlation Coefficients; Hoben Thomas Book 1989 Springer-Verlag New York Inc. 1989 Estimator.Evaluation.Likelihood.corre

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書(shū)目名稱(chēng)Distributions of Correlation Coefficients
編輯Hoben Thomas
視頻videohttp://file.papertrans.cn/283/282024/282024.mp4
圖書(shū)封面Titlebook: Distributions of Correlation Coefficients;  Hoben Thomas Book 1989 Springer-Verlag New York Inc. 1989 Estimator.Evaluation.Likelihood.corre
描述An important problem in personnel psychology, namely, the psychometric problem known as "validity generalization" is addressed in this volume. From a statistical point of view, the problem is how to make statements about a population correlation coefficient based on inferences from a collection of sample correlation coefficients. The first part of the book examines the largely ad hoc procedures which have been used to determine validity generalization. The second part develops a new model formulated from the perspective of finite mixture theory and, in addition, illustrates its use in several applications.
出版日期Book 1989
關(guān)鍵詞Estimator; Evaluation; Likelihood; correlation; psychology; test theory
版次1
doihttps://doi.org/10.1007/978-1-4684-6366-8
isbn_softcover978-0-387-96863-6
isbn_ebook978-1-4684-6366-8
copyrightSpringer-Verlag New York Inc. 1989
The information of publication is updating

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Estimation, and generally unidentified model. The explanation may be simple. In effect, there has been estimation without a model. Recall that Schmidt and Hunter (1977) sketched only an idea: no model was presented, but they nonetheless provided an estimation scheme. This same trend has continued. In fact, the
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Summary and Discussion of Validity Generalization,y take on values ., ., and . is an observed correlation coefficient; . is a latent variable of population correlation coefficients, and . is a latent error variable. . may be viewed as a product of other latent variables that influence testing situations.
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A Conditional Mixture Model for Correlation Coefficients,pulation correlation coefficients underlying such histograms. That appears achievable. In addition, estimates can be given of the number of different . that a given data set can support. Once having determined the number of different ., maximum likelihood point estimates of each . can be obtained.
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Artifact Corrections and Model Assumptions,del parameters . and their estimates are viewed. They may be regarded as a product of other parameters if desired. However, in many practical testing settings, measurement unreliability and range restriction are important considerations and cannot be regarded as negligible. The obvious question aris
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generalization. The second part develops a new model formulated from the perspective of finite mixture theory and, in addition, illustrates its use in several applications.978-0-387-96863-6978-1-4684-6366-8
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Book 1989statistical point of view, the problem is how to make statements about a population correlation coefficient based on inferences from a collection of sample correlation coefficients. The first part of the book examines the largely ad hoc procedures which have been used to determine validity generaliz
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