Distinguishing Hope from Optimism and Related Affective States

Three studies examined the conceptual and psychological differences between hope and related mental states. In Study 1, participants provided definitions of hope as well as optimism, want, desire, wish, and the non-anticipatory state of joy; in Study 2, participants wrote about a time when they had experienced each of these states. These definitions and stories were coded for a number of psychological features that were then used to distinguish the different states. Study 3 mapped the differences among the six mental states into a multidimensional conceptual space. Overall, hope is most closely related to wishing but distinct from it. Most important, hope is distinct from optimism by being an emotion, representing more important but less likely outcomes, and by affording less personal control. The importance of combining a folk-conceptual perspective with a more traditional analysis of appraisal for understanding differences among psychological constructs is discussed.     

Role of Stressful Life Events in Homelessness: An Intragroup Analysis

The present paper describes the results of an analysis of individual differences—using multidimensional strategies—in stressful life event data collected from a representative sample of homeless people (N = 289) in Madrid, Spain, which revealed the existence of three subgroups within the sample. Each subgroup can be defined by the following differentiating characteristics: The first cluster (n = 124) was characterized by economic problems; the second (n = 80) by health problems, alcohol abuse, and death of one or both parents; and the third cluster (n = 50) by an accumulation of stressful life in childhood events and alcohol abuse. In order to define the subgroups, the article also examines the differences found in other variables such as total duration of homelessness, mental and physical health status, and social support availability, among others. The existence of such subgroups within the homeless population emphasizes the importance of designing different interventions for each of these groups, adapted to their diverse needs.     

V.A. Smirnov’s Results in the Field of Modern Formal Logic

This paper is a survey of V.A. Smirnovs main results in modern logic.     

A dual process item response theory model for polytomous multidimensional forced-choice items

The use of multidimensional forced-choice (MFC) items to assess non-cognitive traits such as personality, interests and values in psychological tests has a long history, because MFC items show strengths in preventing response bias. Recently, there has been a surge of interest in developing item response theory (IRT) models for MFC items. However, nearly all of the existing IRT models have been developed for MFC items with binary scores. Real tests use MFC items with more than two categories; such items are more informative than their binary counterparts. This study developed a new IRT model for polytomous MFC items based on the cognitive model of choice, which describes the cognitive processes underlying humans' preferential choice behaviours. The new model is unique in its ability to account for the ipsative nature of polytomous MFC items, to assess individual psychological differentiation in interests, values and emotions, and to compare the differentiation levels of latent traits between individuals. Simulation studies were conducted to examine the parameter recovery of the new model with existing computer programs. The results showed that both statement parameters and person parameters were well recovered when the sample size was sufficient. The more complete the linking of the statements was, the more accurate the parameter estimation was. This paper provides an empirical example of a career interest test using four-category MFC items. Although some aspects of the model (e.g., the nature of the person parameters) require additional validation, our approach appears promising.     

A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

In the distance approach to nonlinear multivariate data analysis the focus is on the optimal representation of the relationships between the objects in the analysis. In this paper two methods are presented for including weights in distance-based nonlinear multivariate data analysis. In the first method, weights are assigned to the objects while the second method is concerned with differential weighting of groups of variables. When each analysis variable defines a group the latter method becomes a variable weighting method. For objects the weights are assumed to be given; for groups of variables they may be given, or estimated. These weighting schemes can also be combined and have several important applications. For example, they make it possible to perform efficient analyses of large data sets, to use the distance-based variety of nonlinear multivariate data analysis as an addition to loglinear analysis of multiway contingency tables, and to do stability studies of the solutions by applying the bootstrap on the objects or the variables in the analysis. These and other applications are discussed, and an efficient algorithm is proposed to minimize the corresponding loss function.This study is funded by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the PIONEER project Subject Oriented Multivariate Analysis to the third author.     

Conditional covariance structure of generalized compensatory multidimensional items

Some nonparametric dimensionality assessment procedures, such as DIMTEST and DETECT, use nonparametric estimates of item pair conditional covariances given an appropriately chosen subtest score as their basic building blocks. Such conditional covariances given some subtest score can be regarded as an approximation to the conditional covariances given an appropriately chosen unidimensional latent composite, where the composite is oriented in the multidimensional test space direction in which the subtest score measures best. In this paper, the structure and properties of such item pair conditional covariances given a unidimensional latent composite are thoroughly investigated, assuming a semiparametric IRT modeling framework called a generalized compensatory model. It is shown that such conditional covariances are highly informative about the multidimensionality structure of a test. The theory developed here is very useful in establishing properties of dimensionality assessment procedures, current and yet to be developed, that are based upon estimating such conditional covariances.In particular, the new theory is used to justify the DIMTEST procedure. Because of the importance of conditional covariance estimation, a new bias reducing approach is presented. A byproduct of likely independent importance beyond the study of conditional covariances is a rigorous score information based definition of an item's and a score's direction of best measurement in the multidimensional test space.This paper is based on a chapter of the first author's doctoral dissertation, written at the University of Illinois and supervised by the second author. Part of this research has been presented at the annual meeting of the National Council on Measurement in Education, San Francisco, April 1995.The authors would like to thank Jeff Douglas, Xuming He and Ming-mei Wang for their comments and suggestions. The research of the first author was partially supported by an ETS/GREB Psychometric Fellowship, and by Educational Testing Service Research Allocation Project 884-01. The research of the second author was partially supported by NSF grant DMS 97-04474.     

Is the heart preadapted to hypoxia? Evidence from fractal dynamics of heartbeat interval fluctuations at high altitude (5,050 m)

The dynamics of heartbeat interval time series over large time scales were studied by a modifed random walk analysis introduced recently asDetrended Fluctuation Analysis. In this analysis, the intrinsic fractal long-range power-law correlation properties of beat-to-beat fluctuations generated by the dynamical system (i.e., cardiac rhythm generator), after decomposition from extrinsic uncorrelated sources, can be quantified by the scaling exponent (α) which, in healthy subjects, for time scales of ∼10⁴ beats is ∼1.0. The effects of chronic hypoxia were determined from serial heartbeat interval time series of digitized twenty-four-hour ambulatory ECGs recorded in nine healthy subjects (mean age thirty-four years old) at sea level and during a sojourn at 5,050 m for thirty-four days (Ev-K2-CNR Pyramid Laboratory, Sagarmatha National Park, Nepal). The group averaged α exponent (±SD) was 0.99±0.04 (range 0.93–1.04). Longitudinal assessment of α in individual subjects did not reveal any effect of exposure to chronic high altitude hypoxia. The finding of α∼1 indicating scale-invariant long-range power-law correlations (1/f noise) of heartbeat fluctuations would reflect a genuinely self-similar fractal process that typically generates fluctuations on a wide range of time scales. Lack of a characteristic time scale along with the absence of any effect from exposure to chronic hypoxia on scaling properties suggests that the neuroautonomic cardiac control system is preadapted to hypoxia which helps prevent excessive mode-locking (error tolerance) that would restrict its functional responsiveness (plasticity) to hypoxic or other physiological stimuli. Editorial Note: A philosopher is, by definition, a lover of wisdom. In the following article, Dr. Dimitrov, as a philosopher, examines the principles that underlie being and thinking as performed by the structures of the brain. He reports his thoughts in a “virtual context.” Virtual, a word enjoying great popularity today, is derived from the Latin word for man; it describes manliness and implies apparent, but not actual, power. In his analysis of how information is dealt with by the human being, the author uses theoretical physics and mathematics that may seem arcane to some readers. A major aspect of his thesis declares that the effective performance of information systems relies not on the structures involved, but on the way they interact.     

Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features

Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple or single. The single transformations consist of three types: nominal, ordinal, and numerical. The corresponding OVERALS computer program minimizes a least squares loss function by using an alternating least squares algorithm. Many existing linear and nonlinear multivariate analysis techniques are shown to be special cases of OVERALS. An application to data from an epidemiological survey is presented.This research was partly supported by SWOV (Institute for Road Safety Research) in Leidschendam, The Netherlands.     

A minimum-cost network-flow solution to the case V thurstone scaling problem

This paper presents an approach for determining unidimensional scale estimates that are relatively insensitive to limited inconsistencies in paired comparisons data. The solution procedure, shown to be a minimum-cost network-flow problem, is presented in conjunction with a sensitivity diagnostic that assesses the influence of a single pairwise comparison on traditional Thurstone (ordinary least squares) scale estimates. When the diagnostic indicates some source of distortion in the data, the network technique appears to be more successful than Thurstone scaling in preserving the interval scale properties of the estimates.My special thanks go to Alvin Silk, Thomas Magnanti, and Roy Welsch for their support and advice throughout the formative stages of this paper, and to V. Srinivasan for his helpful comments on a later draft of this paper. I also wish to thank the Editor, Associate Editor, and two reviewers for their constructive suggestions.James M. Lattin is Associate Professor of Marketing and Management Science and the James and Doris McNamara Faculty Fellow for 1988-1989.