The expansion of religious affiliation: An explanation of the growth of church participation in the United States 1850–1930

This paper focuses on the period in U.S. history that experienced the most rapid rate of increase of church membership—the decades between 1850 and 1930—in order to explain synchronic and diachronic variation in those rates. Using pooled cross-sectional time series analysis, different predictions are derived and tested from theories of secularization/social control, comparative disadvantage, resource mobilization, and pluralism. The effects of spatial diffusion and the momentum of religious tradition also are estimated. Our conclusion is that religious monopoly—not diversity—fuels religious expansion. This finding is bolstered by the complementary result that ethnic homogeneity is also conducive to religious expansion. Together these results highlight the importance for mobilization of religious and ethnocultural dominance in a particular niche.     

Game-theoretic analyses of coalition behavior

Coalitions are frequently more visible than payoffs. The theory of n-person games seeks primarily to identify stable allocations of valued resources; consequently, it gives inadequate attention to predicting which coalitions form. This paper explores a way of correcting this deficiency of game-theoretic reasoning by extending the theory of two-person cooperative games to predict both coalitions and payoffs in a three-person game of status in which each player seeks to maximize the rank of his total score. To accomplish this, we analyze the negotiations within each potential two-person coalition from the perspective of Nash's procedure for arbitrating two-person bargaining games, then assume that players expect to achieve the arbitrated outcome selected by this procedure and use these expectations to predict achieved ranks and to identify players' preferences between alternative coalition partners in order to predict the probability that each coalition forms. We test these payoff and coalition predictions with data from three laboratory studies, and compare the results with those attained in the same data by von Neumann and Morgenstern's solution of two-person cooperative games, Aumann and Maschler's bargaining set solution for cooperative n-person games, and an alternative model of coalition behavior in three-person sequential games of status.

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Computation of exact and approximate probability values for a matched pairs permutation test

An algorithm is presented for computing the probability value associated with a recently-developed test of statistical inference for matched pairs. The exact probability value is provided for small samples; otherwise, an approximate probability value is computed.     

Scale estimators for symmetric stable distributions

This paper reports on a simulation comparison of scale estimators for symmetric stable distributions in terms of their ability to identify the population with the greater scale. The modified geometric mean is found to be superior to the sample standard deviation and the Fama-Roll estimator for the larger values of the characteristic exponent, while the Fama-Roll estimator is judged superior for the smaller values. Further, this study sheds some light on the question of the appropriate sample size for discriminating risk measurement in investment analysis when the samples are taken from symmetric stable distributions.     

An extended class of permutation techniques for matched pairs

A class of matched-pairs permutation techniques based on distances between each pair of observed signed values is considered. Although many commonly-used inference techniques for matched pairs are members of this class, some of the more appealing inference techniques among this class have received very little attention. Two new simple rank tests of this class jointly possess both intuitive properties and location-alternative power characteristics which appear more appealing than the corresponding characteristics of either the sign test or the Wllcoxon signed-ranks test. In particular, power comparisons based on slmula-tions indicate that these new rank tests are jointly as good or even vastly superior to the sign test or the Wilcoxon signed-ranks test for location alternatives involving five symmetric distributions. The five distributions selected for these com-parisons include the Laplace, logistic, normal, uniform and a U-shaped distribution     

Non-random shuffling strategies in blackjack

Numerous playing and betting strategies for the game of twenty-one have been computed assuming the deck or decks are randomly shuffled. In practice, dealers do not spend the time necessary (it takes too long) to completely randomly shuffle the decks used. Hence, there is information not only from the current round of play, but potentially from the previous round of play. We present a model for a non-random shuffle and assert ways in which this information can be used. Rules are derived using a normal approximation which updates the current strategies utilizing information from a non-random shuffle.     

A test for second order stochastic dominance

In this paper we propose a test for second order stochastic dominance (SSD), for the case where both distribution functions are unknown. This is a generalization of a test proposed by Deshpande and Singh (1985), who compare a new random prospect with a known distribution function. We then show that our test is based on comparing the mean minus one half of Gini's mean difference of the distributions, which is known to be a necessary condition for SSD, as developed in the economics literature (Yitzhaki, 1982).     

Factor Analysis Revisited – How Many Factors are There?

We suggest a procedure to improve the overall performances of several existing methods for determining the number of factors in factor analysis by using alternative measures of correlation: Pearson's, Spearman's, Gini's, and a robust estimator of the covariance matrix (MCD). We examine the effect of the choice of the covariance used on the number of factors chosen by the KG rule of one, the 80% rule, the Minimum average partial (MAP), and the Parallel Analysis Methodology (PAM). Extensive simulations show that when the entire (or part) of the data come from heavy-tail (lognormal) distributions, ranking the variables which come from non symmetric distributions improves the performances of the methods. In this case, Gini is slightly better than Spearman. The PAM and MAP procedures are qualitatively superior to the KG and the 80% rules in determining the true number of factors. A real example involving data on document authorship is analyzed.