Gudmund R. Iversen

Bio: Gudmund R. Iversen is an academic researcher. The author has contributed to research in topics: Mixed-design analysis of variance & One-way analysis of variance. The author has an hindex of 2, co-authored 2 publications receiving 1483 citations.

Analysis of Variance

Abstract: The authors have improved on their widely used first edition by providing updated examples, adding material on how to do ANOVA using statistical packages for microcomputers, linking the use of ANOVA to regression analysis, and enchancing their discussion on using ANOVA for experimentally gathered data.

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

Abstract: G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of thet, F, and χ2 test families. In addition, it includes power analyses forz tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

Generalized Linear Models

Abstract: The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log- likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components).

Specification Tests in Econometrics

Abstract: Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but asymptotically inefficient estimator, specification tests are devised for a number of model specifications in econometrics. Local power is calculated for small departures from the null hypothesis. An instrumental variable test as well as tests for a time series cross section model and the simultaneous equation model are presented. An empirical model provides evidence that unobserved individual factors are present which are not orthogonal to the included right-hand-side variable in a common econometric specification of an individual wage equation.