Do Cooperatives with Better Economic–Financial Indicators also have Better Socioeconomic Performance?

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The overall performance of cooperatives is related to economic–financial results and actions focused on benefits for...     

AN EXPERIMENTAL STUDY OF KIN AND ETHNIC FAVORITISM

Ethnic and kinship ties have long been viewed as potential catalysts for favoritism, and hence corruption. In experiments conducted in three countries, we recruit siblings, coethnics and strangers and vary the relationship(s) between the players of a game to observe how kin and ethnic ties influence the willingness of two players to benefit one another at the expense of a third party. We see universal sibling favoritism, but ethnic favoritism, and favoritism toward other in-group members (friends) varies. We argue this may be driven in part by kinship institutions, since favoritism is more common in societies with denser kin networks. ( JEL D9, C9, D73, J12)     

The Dynamics of Social Indicator Research for California’s Central Valley in Transition

How can social indicator research improve understanding of community health as well as inform stakeholders about the assets disadvantaged communities have for coping with disparities? This paper describes the development and evolution of the Partnership for Assessment of Communities (PAC) and its best practices for social indicator research. The PAC will be of interest to researchers across multiple disciplines for a number of reasons. First, PAC is a working model of best practices for multidisciplinary scholarly inquiry. Second, it has developed an integrated model of quantitative and qualitative methodology to define and measure community health as compared to traditional quality-of-life indicators. Third, it serves as an example of “action research,” in that the findings have the potential to make an impact on community stakeholders and policy outcomes in the greater Central San Joaquin Valley of California, a region characterized by deep social and economic disparities.     

Life Satisfaction in Brazil: Testing the Psychometric Properties of the Satisfaction With Life Scale (SWLS) in Five Brazilian Samples

The aim of this paper was to validate the Brazilian-Portuguese version of the Satisfaction With Life Scale (SWLS), presenting evidence of its reliability and construct and criterion-related validity. A large Brazilian sample (2,180 participants), from five different populations (undergraduate and high school students, general population, elementary school teachers, and physicians), was considered. The results confirmed the single factorial structure and reliability (0.77 < Cronbach’s α < 0.88, mean α = 0.81) of the SWLS. Supporting its criterion-related validity, the SWLS correlated positively with positive affect and negatively with both negative affect and psychological distress across all five samples. The findings indicate that the Brazilian-Portuguese version of the SWLS is a valid instrument to use with diverse Brazilian samples.     

Marital Fertility and its Determinants in Rural and in Urban Spain, 1887–1930

Based primarily on census data between 1887 and 1920, the present article explores the basic structure of the social and economic factors that influenced marital fertility levels during the early part of the fertility transition in Spain. Multiple regression analysis is applied to ecological models based on separate rural and urban data. While some of the conclusions from the Princeton European Fertility Project have been corroborated, a number of the results have been quite noteworthy. Not the least of these are the sharply differing structures of causality in rural and urban areas, and the surprising and consistent role played by literacy as a stimulant of marital fertility. In the discussion of the results, we have insisted on the importance of interpreting different demographic, economic, and cultural indicators within concrete historical contexts.     

The sample size debate: response to Norman Blaikie

In his detailed response to our paper on sample size in qualitative research, Norman Blaikie raises important issues concerning conceptual definitions and taxonomy. In particular, he points out the problems associated with a loose, generic application of adjectives such as ‘qualitative’ or ‘inductive’. We endorse this concern, though we suggest that in some specific contexts a broad categorization may be more appropriate than a more nuanced distinction – provided that it is clear in which sense the terms are employed. However, other concepts, such as saturation, do not lend themselves to generic use, and require a more detailed conceptualization. Blaikie’s analysis also makes it clear that meaningful discussion of sample size in qualitative research cannot occur with reference to an undifferentiated conception of the nature of qualitative research; clear distinctions need to be made within this approach in terms of methodology, ontological and epistemological assumptions and broader research paradigms.     

The double sampling S2 chart with estimated process variance

This paper proposes useful exact bounds for the parameters of the double sampling S² chart with known process variance and it also investigates the properties of the double sampling S² chart with estimated process variance, in terms of the average run length, the standard deviation of the run length and the average sample size, providing a numerical comparison with the known process variance case. It also provides guidelines to systematically design the double sampling S² chart both with known and estimated process variance and proposes two optimal design procedures with estimated process variance, for (a) minimizing the out-of-control average run length and (b) minimizing the out-of-control average sample size.     

Procedure to identify outliers through cumulative distribution of extremes in a Gamma response model

This work aimed at proposing a procedure based on the cumulative distribution of maximums and minimums to identify outliers in generalized Gamma-response models. In order to validate such method, we used simulations scenarios defined by the combination of different samples, contamination rate and distributions with different degrees of asymmetry. In this context, probabilities related to errors in classification and accuracy were obtained by carrying by Monte Carlo simulations. Using cumulative distribution of extremes to identify outliers in a Gamma-response model is recommended, since it is not likely to present errors and was highly accurate in all assessed scenarios.