pdf/social-movements-and-networks-d77ajpqp42.pdf

Social Movements and Networks

‘Leaders’ Or Brokers? Positions and Influence in Social Movement Networks

https://www.ssc.wisc.edu/~oliver/PROTESTS/NetworkPapers/AnsellBayAreaGroups.pdf

Community embeddedness and collaborative governance in the san francisco bay area environmental movement

The religious economies argument maintains that religious pluralism promotes religious participation. Although this theory has been much debated by sociologists of religion, evaluation has been hindered by the lack of a formal model. This paper offers a formalization that builds on the product-differentiation framework from industrial-organization economics. The model suggests a new measure of religious competition and a new empirical test. One religious market may be regarded as more competitive than another religious market if the set of denominations present in the first contains the set of denominations present in the second. Given this partial order on the set of religious markets, the model predicts that more competitive markets will have higher religious participation. This prediction is examined for two data sets previously considered in the literature-New York towns in 1865 and U.S. counties in 1990. Although the analysis highlights an important theoretical ambiguity (regarding the strategic interdependence of denominational effort choices) and reports a negative empirical finding (that more competitive U.S. counties tend to have lower religious participation), the paper proceeds constructively, attempting to identify viable directions for future research within the religious economies paradigm

A formalization and test of the religious economies model

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record

/pdf/a-comparison-of-latent-class-k-means-and-k-median-methods-48ijv718uo.pdf

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

A three-way three-mode extension of De Boeck and Rosenberg's (1988) two-way two-mode hierarchical classes model is presented for the analysis of individual differences in binary object × attribute arrays. In line with the two-way hierarchical classes model, the three-way extension represents both the association relation among the three modes and the set-theoretical relations among the elements of each model. An algorithm for fitting the model is presented and evaluated in a simulation study. The model is illustrated with data on psychiatric diagnosis. Finally, the relation between the model and extant models for three-way data is discussed.

Indclas: a three-way hierarchical classes model

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed Extensive simulation studies showed that these algorithms succeed quite well in recovering the underlying truth but frequently end in a local minimum In this paper we evaluate whether or not this local minimum problem can be mitigated by means of two common strategies for avoiding local minima in combinatorial data analysis: simulated annealing (SA) and use of a multistart procedure In particular, we propose a generic SA algorithm for hierarchical classes analysis and three different types of random starts The effectiveness of the SA algorithm and the random starts is evaluated by reanalyzing data sets of previous simulation studies The reported results support the use of the proposed SA algorithm in combination with a random multistart procedure, regardless of the properties of the data set under study

http://ppw.kuleuven.be/okp/_pdf/Ceulemans2007TLMPI.pdf

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

In this procedure, a least-squares loss function in terms of discrepancies between D and M is minimized. The present paper describes the original hierarchical classes algorithm proposed by De Boeck and Rosenberg (1988), which is based on an alternating greedy heuristic, and proposes a new algorithm, based on an alternating branch-and-bound procedure. An extensive simulation study is reported in which both algorithms are evaluated and compared according to goodness-of-fit to the data and goodness-of-recovery of the underlying true structure. Furthermore, three heuristics for selecting models of different ranks for a given D are presented and compared. The simulation results show that the new algorithm yields models with slightly higher goodness-of-fit and goodness-of-recovery values.

An Evaluation of Two Algorithms for Hierarchical Classes Analysis

This paper proposes a branch-and-bound algorithm to trace disjunctive (conjunctive) combinations of binary predictor variables to predict a binary criterion variable. The algorithm allows for finding logical classification rules that can be used to derive whether or not a given object belongs to a given category based on the attribute pattern of the object. An objective function is minimized which takes into account both accuracy in prediction and cost of the predictors. A simulation study is presented in which the performance of the algorithm is evaluated.

A Branch-and-bound Algorithm for Boolean Regression

Hierarchical classes models are models for N-way N-mode data that represent the association among the N modes and simultaneously yield, for each mode, a hierarchical classification of its elements. In this paper we present a stochastic extension of the hierarchical classes model for two-way two-mode binary data. In line with the original model, the new probabilistic extension still represents both the association among the two modes and the hierarchical classifications. A fully Bayesian method for fitting the new model is presented and evaluated in a simulation study. Furthermore, we propose tools for model selection and model checking based on Bayes factors and posterior predictive checks. We illustrate the advantages of the new approach with applications in the domain of the psychology of choice and psychiatric diagnosis.

/pdf/bayesian-hierarchical-classes-analysis-554lciiovk.pdf

Bayesian Hierarchical Classes Analysis.

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