The relation between information and variance analyses

Analysis of variance and uncertainty analysis are analogous techniques for partitioning variability. In both analyses negative interaction terms due to negative covariance terms that appear when non-orthogonal predictor variables are allowed may occur. Uncertainties can be estimated directly from variances if the form of distribution is assumed. The decision as to which of the techniques to use depends partly on the properties of the criterion variable. Only uncertainty analysis may be used with a non-metric criterion. Since uncertainties are dimensionless (using no metric), however, uncertainty analysis has a generality which may make it useful even when variances can be computed.The work of the senior author was supported by Contract N5ori-166, Task Order 1, between the U.S. Office of Naval Research and The Johns Hopkins University. This is Report No. 166-I-192, Project Designation No. NR 145-089, under that contract.     

General problems of questioning research

Summary This paper outlines certain minimum conditions for setting up experimental research on questioning. A discussion is given of the problems involved in using the same kinds of question as in natural interactions, creating a naturalistic exchange between questioner and respondent, and enabling people to use other inquiry strategies besides questioning. It is argued that these issues merit attention not only because of methodological concerns, but also because they are likely to lead to important insights about questioning.     

Complex Reaction Time and General Intelligence: A Refinement

College students (N = 28) completed a reaction-time task that yielded decision times for simple and complex conditions. Each student was also administered the revised edition of the Wechsler Adult Intelligence Scales (WAIS-R). Decision-time parameters generally correlated negatively with WAIS subscales. These correlations reached significance, however, mainly with timed performance subscales. The results were consistent with Vernon's (1983) results.     

Consistency in catecholamine excretion in laboratory and natural settings: correlational and variance components analyses

Data from 20 healthy male subjects were used to evaluate the consistency in catecholamine excretion in both laboratory and natural situations during approximately a 48-hr period. Correlational analyses showed high interindividual consistency in adrenaline excretion under laboratory conditions regardless of degree and kind of activity and of the interval at which measures were taken. In non-standardized natural situations the consistency was low. For nor adrenaline the pattern was less regular. Analysis of variance components showed that absolute consistency was high in both amines in situations with similar activity content and low activity level. It was found that the more similar the situations the more of variation in catecholamine excretion was due to interindividual differences and the more dissimilar the situations the more of the variation was due to interaction. It is concluded that the degree of consistency in catecholamine excretion varies with situational characteristics.     

Comparison of alternative estimation methods in confirmatory factor analyses of the General Health Questionnaire

This paper examines the implications of violating assumptions concerning the continuity and distributional properties of data in establishing measurement models in social science research. The General Health Questionnaire-12 uses an ordinal response scale. Responses to the GHQ-12 from 201 Hong Kong immigrants on arrival in Australia showed that the data were not normally distributed. A series of confirmatory factor analyses using either a Pearson product-moment or a polychoric correlation input matrix and employing either maximum likelihood, weighted least squares or diagonally weighted least squares estimation methods were conducted on the data. The parameter estimates and goodness-of-fit statistics provided support for using polychoric correlations and diagonally weighted least squares estimation when analyzing ordinal, nonnormal data.     

Synthesis of variance

The distribution of a linear combination of two statistics distributed as is Chi-square is studied. The degree of approximation involved in assuming a Chi-square distribution is illustrated for several representative cases. It is concluded that the approximation is sufficiently accurate to use in many practical applications. Illustrations are given of its use in extending the Chi-square, the Student t and the Fisher z tests to a wider range of problems.     

Nonlinear epigenetic variance: review and simulations

We present a review of empirical evidence that suggests that a substantial portion of phenotypic variance is due to nonlinear (epigenetic) processes during ontogenesis. The role of such processes as a source of phenotypic variance in human behaviour genetic studies is not fully appreciated. In addition to our review, we present simulation studies of nonlinear epigenetic variance using a computational model of neuronal network development. In each simulation study, time series for monozygotic and dizygotic twins were generated and analysed using conventional behaviour genetic modelling. In the results of these analyses, the nonlinear epigenetic variance was subsumed under the non-shared environmental component. As is commonly found in behaviour genetic studies, observed heritabilities and unique environmentabilities increased with time, whereas common environmentabilities decreased. The fact that the phenotypic effects of nonlinear epigenetic processes appear as unsystematic variance in conventional twin analyses complicates the identification and quantification of the ultimate genetic and environmental causes of individual differences. We believe that nonlinear dynamical system theories provide a challenging perspective on the development of individual differences, which may enrich behaviour genetic studies.     

Inequality constrained analysis of variance: a Bayesian approach

Researchers often have one or more theories or expectations with respect to the outcome of their empirical research. When researchers talk about the expected relations between variables if a certain theory is correct, their statements are often in terms of one or more parameters expected to be larger or smaller than one or more other parameters. Stated otherwise, their statements are often formulated using inequality constraints. In this article, a Bayesian approach to evaluate analysis of variance or analysis of covariance models with inequality constraints on the (adjusted) means is presented. This evaluation contains two issues: estimation of the parameters given the restrictions using the Gibbs sampler and model selection using Bayes factors in the case of competing theories. The article concludes with two illustrations: a one-way analysis of covariance and an analysis of a three-way table of ordered means.