This article is concerned with measures of fit of a model. Two types of error involved in fitting a model are considered. The first is error of approximation which involves the fit of the model, wi...

Alternative Ways of Assessing Model Fit

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Attention and Effort

A framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the relationship of confidence intervals to a framework for hypothesis testing. The approach allows for testing null hypotheses of not-good fit, reversing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit. The approach also allows for direct estimation of power, where effect size is defined in terms of a null and alternative value of the root-mean-square error of approximation fit index proposed by J. H. Steiger and J. M. Lind (1980). It is also feasible to determine minimum sample size required to achieve a given level of power for any test of fit in this framework. Computer programs and examples are provided for power analyses and calculation of minimum sample sizes.

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Power analysis and determination of sample size for covariance structure modeling.

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Arthur Jensen argues that the failure of recent compensatory education efforts to produce lasting effects on children's IQ and achievement suggests that the premises on which these efforts have been based should be reexamined.

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How Much Can We Boost IQ and Scholastic Achievement

A pesar de la relativamente corta historia de la Psicologia como ciencia, existen pocos constructos psicologicos que perduren 90 anos despues de su formulacion y que, aun mas, continuen plenamente vigentes en la actualidad. El factor «g» es sin duda alguna uno de esos escasos ejemplos y para contrastar su vigencia actual tan solo hace falta comprobar su lugar de preeminencia en los modelos factoriales de la inteligencia mas aceptados en la actualidad, bien como un factor de tercer orden en los modelos jerarquicos o bien identificado con un factor de segundo orden en el modelo del recientemente desaparecido R.B.Cattell.

The g Factor: The Science of Mental Ability

Illuminating detailed methods for assessing bias in commonly used I.Q., aptitude, and achievement tests, Jensen argues that standardized tests are not biased against Englishspeaking minority groups and describes the uses of such tests in education and employment.

Arthur R. Jensen

Papers

How Much Can We Boost IQ and Scholastic Achievement

The g Factor: The Science of Mental Ability

Bias in Mental Testing

Abilities: Their Structure, Growth, and Action

The stroop color-word test: A review