Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences
Notice bibliographique
Résumé
Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences by Brian Everitt is a second-level applied statistics book which is aimed at those who need to build simple models in behavioural sciences. It provides varied sets of real world data so that the reader can gain insights into how these models are relevant to solving real life problems. The book starts with a chapter on data, measurement and models. Whereas most books treat the first chapter as a warm-up to what is to come, Everitt reminds us here of some very important but often neglected principles such as the limitations of significance tests, the importance of highlighting the aspects of the data that are relevant to the substantive arguments, the significance of experiments and the relevance of power in choosing the sample size. The exercises reinforce the principles discussed with the help of real world problems. The second chapter is a preliminary look at the data by using graphic methods. Here the author illustrates the use of less widely used graphics such as dot plots, leaf plots and boxplots, probability plots and various scatter plots followed by a brief discussion of how graphs can be used to mislead the reader. Although these techniques are widely known, the graphic capabilities of R make them much more accessible. The graphic techniques are discussed not so much in the context of presenting the data but in the context of making sense of the data. The next three chapters describe locally weighted linear regression, simple linear regression and its equivalence to analysis of variance. These are followed by logistic regression, survival analysis and linear mixed models for longitudinal analysis. The four subsequent chapters deal with the structure of multivariate data by using interdependent models such as principal components analysis, factor analysis and cluster analysis. The final chapter presents methods to analyse multivariate data drawn from several different populations. In his exposition, Everitt separates the technical aspects from the practical aspects of models. Because technical aspects are presented in self-contained sections, non-technical self-study readers can follow the material without becoming bogged down in formulae. Widely available statistical packages such as SAS, SPSS and Systat make it possible for non-technical readers to implement the models that are described. For those who do not have access to such packages, Everitt provides code in R language, which of course is free. For those who would like access to actual data so that they can practice what they have learnt, several data sets are made available from a companion Web site. Solutions to selected problems appear at the end of the book, which include R code to implement many of the techniques that are described in the book. Clarity and conciseness have always been the hallmarks of Everitt’s writing. This book is no exception. Anyone looking for a clearly written text on the subject that is also practitioner oriented needs to look no further.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».