The experience of receiving scholarship aid and its effect on future giving: a listening guide analysis

Why are there so many people who receive scholarship aid who do not give back to the schools that gave it? Social exchange theories grounded in the assumption of direct reciprocity have long been the framework used within higher education to understand donative behavior. As a result, conventional wisdom within higher education holds that recipients of scholarship aid will later donate money to the institution that provided it. However, the philanthropic data show that most aid recipients do not, in fact, give back. This study probes the underlying factors for this phenomenon by using the Listening Guide, a voice-centered relational method of discovery, to analyze and interpret interviews with 10 scholarship recipients. By providing a more robust understanding of the experience of receiving, the findings challenge some of the most basic psychological and sociological assumptions about giving and generosity within higher education.     

A review of the feminist pedagogy literature: The neglected child of feminist psychology

Feminist psychologists have introduced feminist values, critiques, and methods into the science and practice of psychology. On the topic of teaching, feminist psychologists have done a thorough job of addressing curricula issues, but they have been involved marginally in developing the literature on feminist pedagogy. A comprehensive review of the feminist pedagogy literature was compiled based on a review of 60 articles, 25 chapters from books, 10 books, and 4 bibliographies. The review covers major aspects of the legacy of patriarchy in schools as well as feminist transformations of traditional educational practice. Explanations are posed for the minimal involvement of feminist psychologists in the development of the feminist pedagogy literature. Reciprocal benefits are identified for both the field of psychology and the feminist pedagogy scholars if more bridges are built between the two.     

An analysis and synthesis of multiple correspondence analysis,optimal scaling,dual scaling,homogeneity analysis and other methods for quantifying categorical multivariate data

We discuss a variety of methods for quantifying categorical multivariate data. These methods have been proposed in many different countries, by many different authors, under many different names. In the first major section of the paper we analyze the many different methods and show that they all lead to the same equations for analyzing the same data. In the second major section of the paper we introduce the notion of a duality diagram, and use this diagram to synthesize the many superficially different methods into a single method.The ideas in this paper were worked out by the first author, with some suggestions provided by the second. The current version of this paper has evolved from three previous versions, the first two written by the first author.     

Principals versus osmod: A comment on saito and otsu

Saito and Otsu (1988) compared their OSMOD method of nonmetric principal-component analysis to an early and incorrect implementation of the PRINCIPALS algorithm of Young, Takane, and de Leeuw (1978). In this comment we present results from the current, correct implementations of the algorithm.     

Coin value and perceived size: a longitudinal study.

Decimalization of the Irish currency presented an opportunity to study the perceived size of coins as they were introduced or withdrawn and to examine the accentuation of differences between coins. Estimates were obtained of the sizes of 4 coins from 478 subjects in 3 groups tested at different times: just after decimalization, 2, or 3 yr. later. A newly introduced coin was significantly underestimated in size at first but came to be significantly overestimated later, while all familiar coins were always significantly overestimated. The amount of overestimation of a coin depended on its value, not its size, leading to an accentuation of the difference between any two coins if the larger coin was also the more valuable but sometimes to the opposite if the smaller coin were of higher value than the larger member of the pair.     

Interactive scaling with individual subjects

A metric multidimensional scaling (MDS) procedure based on computer-subject interaction is developed, and an experiment designed to validate the procedure is presented. The interactive MDS system allows generalization of current MDS systems in two directions: (a) very large numbers of stimuli may be scaled; and (b) the scaling is performed with individual subjects, facilitating the investigation of individual as well as group processes. The experiment provided positive support for the interactive MDS system. Specifically, (a) individual data are amenable to meaningful interpretation, and they provide a tentative basis for quantitative investigation; and (b) grouped data provide meaningful interpretive and quantitative results which are equivalent to results from standard paired-comparisons methods.This report was supported in part by a PHS research grant, No. M-10006, from the National Institute of Mental Health, in part by a Science Development grant, No. GU-2059, from the National Science Foundation, both granted to the Psychometric Laboratory at the University of North Carolina, and in part by a PHS research grant, No. MH-16474, from the National Institute of Mental Health, Public Health Service, granted to the second author. The major portion of this research was performed while the second author was the L. L. Thurstone Distinguished Fellow at the Psychometric Laboratory of the University of North Carolina while on leave from the University of Southern California. The authors are indebted to Amnon Rapoport and Thomas S. Wallsten for their critical evaluations of an earlier version of this report. While this paper was entirely a cooperative effort on the part of both authors, the first author was primarily responsible for the algorithms, and the second for developing the mathematical model.     

Nonmetric multidimensional scaling: Recovery of metric information

The degree of metric determinancy afforded by nonmetric multidimensional scaling was investigated as a function of the number of points being scaled, the true dimensionality of the data being scaled, and the amount of error contained in the data being scaled. It was found 1) that if the ratio of the degrees of freedom of the data to that of the coordinates is sufficiently large then metric information is recovered even when random error is present; and 2) when the number of points being scaled increases the stress of the solution increases even though the degree of metric determinacy increases.This report was supported in part by a PHS research grant No. M-10006 from the National Institute of Mental Health, Public Health Service, and in part by a Science Development grant No. GU-2059, from the National Science Foundation. The author is indebted to Charles R. Sherman for his assistance in gathering the data and for his critical re-writing of sections of this report. The assistance of Lyle V. Jones in his critical readings and comments is also deeply appreciated.     

Component models for three-way data: An alternating least squares algorithm with optimal scaling features

A review of the existing techniques for the analysis of three-way data revealed that none were appropriate to the wide variety of data usually encountered in psychological research, and few were capable of both isolating common information and systematically describing individual differences. An alternating least squares algorithm was proposed to fit both an individual difference model and a replications component model to three-way data which may be defined at the nominal, ordinal, interval, ratio, or mixed measurement level; which may be discrete or continuous; and which may be unconditional, matrix conditional, or row conditional. This algorithm was evaluated by a Monte Carlo study. Recovery of the original information was excellent when the correct measurement characteristics were assumed. Furthermore, the algorithm was robust to the presence of random error. In addition, the algorithm was used to fit the individual difference model to a real, binary, subject conditional data set. The findings from this application were consistent with previous research in the area of implicit personality theory and uncovered interesting systematic individual differences in the perception of political figures and roles.This paper is part of a Thesis performed by Richard Sands under the direction of Forrest Young at the L. L. Thurstone Psychometric Laboratory, University of North Carolina at Chapel Hill. Thanks are extended to Drs. Charles Schmidt and Andrea Sedlak for the use of their political role data set.