Dynasty Formation in Eight Imaginary Societies*

De quelle maniere les aspirations populaires et les mecanismes de controle de l'elite permettent-ils l'etablissement de dynasties ? Quelles sont les differences qui existent entre les societes dynastiques et les societes non-dynastiques dans les possibilites reelles de mobilite sociale intergenerationnelle ? Cet article tente d'apporter une reponse a ces questions par une analyse mecanographique simulee de huit societes fictives dont les effectifs demographiques sont modeles a l'image de l'Ontario. Nos analyses revelent que les societes dynastiques possedent les caracteristiques suivantes: (a) une tres forte probabilite d'heriter un statut d'appartenance a l'elite; (b) une faible chance d'entrer dans l'elite en provenance d'une classe inferieure; (c) les individus mobiles se retrouvent dans un nombre limite de classes; (d) une probabilite mediane relativement elevee d'une mobilite ascen-dante moderee (une a deux classes); et (e) un faible risque median d'une mobility descendante. Ces resultats sont expliques et verifies, par apres, en utilisant des donnees provenant de societes contemporaines. On discute, en dernier lieu, a la lumiere de ces resultats, les avantages des societes dynastiques et non-dynastiques. How do popular aspirations and elite controls contribute to the formation of dynasties? How do the opportunities for intergenerational social mobility differ in dynastic and non-dynastic societies? This paper attempts to answer these questions through a computer simulation of eight reasonably realistic “imaginary societies” modelled demographically after that of Ontario. The simulations reveal that dynastic societies are characterized by (a) a high probability of inheritance of elite status; (b) a low probability of entering the elite from below; (c) the restriction of mobile persons to a limited number of classes; (d) a relatively high median probability of moderate (one or two class) upward mobility; and (e) a relatively low median risk of downward mobility. These findings are explained and then verified by data from actual societies. The relative advantages of dynastic and non-dynastic societies are discussed in view of these findings.     

Hepatitis C psychoeducation curriculum topics generated by group members

ABSTRACT

Educational programs for hepatitis C (HCV) have been created by professionals. There has not been a focus on whether what clients want to know about HCV differs from what providers want patients to know. This study addresses this gap by examining topics selected by participants in an education/support group model. Data were collected as part of a randomized psychoeducation trial for patients with HCV. Topics were systematically generated by the members uniquely for each group. Topics and rank order for each group were generated and a combined variable was created. Twenty-eight different topics were identified across 12 groups. The total number of topics was 102, averaging 8.5 per group. Topics are divided into three separate conceptual groups, members’ personal relationship to HCV, medical aspects of HCV, and unique concerns. A patient-centered approach that addresses the needs of HCV patients first provides an optimal starting point to discuss HCV disease management.     

Bounds on deviations of estimates arising in finite population regression models

Bounds for the maximum deviation between parameters of a finite population and their corresponding sample estimates are found in the multiple regression model. The parameters considered are the vector of regression coefficients and the value ofthe regression function for given values of the independent variable (or variables). Applications are considered to several widely employed sampling methods.     

Nonparametric prediction analysis for binary data

A nonparametric inference algorithm developed by Davis and Geman (1983) is extended problem. The algorithm and applied to a medical prediction employs an estimation procedure for acquiring pairwise statistics among variables of a binary data set, allows for the data-driven creation of interaction terms among the variables, and employs a decision rule which asymptotically gives the minimum expected error. The inference procedure was designed for large data sets but has been extended via the method of cross-validation to encompass smaller data sets.     

The two-sample t test versus satterthwaite's approximate f test

A comparison between the two-sample t test and Satterthwaite's approximate F test is made, assuming the choice between these two tests is based on a preliminary test on the variances. Exact formulas for the sizes and powers of the tests are derived. Sizes and powers are then calculated and compared for several situations.