ABSTRACT. Representational measurement theory is the dominant theory of measurement within the philosophy of science; and the area in which the the-ory of conjoint measurement was developed. For many years it has been argued the Rasch model is conjoint measurement by several psychometri-cians. This paper critiques this argument from the perspective of representa-tional measurement theory. It concludes that the Rasch model is not conjoint measurement as the model does not demonstrate the existence of a represen-tation theorem between an empirical relational structure and a numerical relational structure. Psychologists seriously interested in investigating traits for quantitative structure should use the theory of conjoint measurement itself rather than the Rasch model. This is not to say, however, that empirical rela-tionships between conjoint measurement and the Rasch model are precluded. The paper concludes by suggesting some relevant research avenues. KEY WORDS: axiomatic conjoint measurement, homomorphism, Rasch model, real numbers, representation theorem

Knowledge-based systems are computer programs achieving expert-level competence in solving problems for specific task areas. This chapter is a tutorial on the implementation of this kind of systems in the framework of credal networks. Credal networks are a generalization of Bayesian networks where credal sets, i.e., closed convex sets of probability measures, are used instead of precise probabilities. This allows for a more flexible model of the knowledge, which can represent ambiguity, contrast and contradiction in a natural and realistic way. The discussion guides the reader through the different steps involved in the specification of a system, from the evocation and elicitation of the knowledge to the interaction with the system by adequate inference algorithms. Our approach is characterized by a sharp distinction between the domain knowledge and the process linking this knowledge to the perceived evidence, which we call the observational process. This distinction leads to a very flexible representation of both domain knowledge and knowledge about the way the information is collected, together with a technique to aggregate information coming from different sources. The overall procedure is illustrated throughout the chapter by a simple knowledge-based system for the prediction of the result of a football match.

a b s t r a c t In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like 'depression' is not seen as a latent variable that underlies symptoms like 'lack of sleep' or 'fatigue', and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep / fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real. Ó 2011 Elsevier Ltd. All rights reserved. Current theorizing and research in psychology is dominated by two conceptualizations of the relationship between psychological attributes (e.g., 'neuroticism') and observable variables (e.g., 'worries about things going wrong '; In the present paper, we argue that the dichotomy of reflective/formative models does not exhaust the possibilities that can be used to connect psychological attributes and observable variables. We advocate an alternative conceptualization, in which psychological attributes are conceptualized as networks of directly related observables. We discuss the possibilities that this addition to the psychometric arsenal offers, the inferential techniques that it allows for, and the consequences it has for the ontology of psychopathological constructs and the epistemic status of validation strategies. The structure of this paper is as follows. First, we discuss the ideas that underlie reflective and formative models. Second, we highlight important problems that the models face. Third, we discuss the network approach. Fourth, we touch on the ramifications that this approach has in the context of validity theory. 1. Reflective and formative models Reflective models In reflective models, observed indicators (e.g., item or subtest scores) are modeled as a function of a common latent variable (i.e., unobserved) and item-specific error

Abstract: Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences.

Cognitive neuroscience involves the simultaneous analysis of behavioral and neurological data. There is nothing more practical than a good theory. -

Abstract. Bayesian networks are probabilistic graphical models widely employed in AI for the implementation of knowledge-based systems. Standard inference algorithms can update the beliefs about a variable of interest in the network after the observation of some other variables. This is usually achieved under the assumption that the observations could reveal the actual states of the variables in a fully reliable way. We propose a procedure for a more general modeling of the observations, which allows for updating beliefs in different situations, including various cases of unreliable, incomplete, uncertain and also missing observations. This is achieved by augmenting the original Bayesian network with a number of auxiliary variables corresponding to the observations. For a flexible modeling of the observational process, the quantification of the relations between these auxiliary variables and those of the original Bayesian network is done by credal sets, i.e., convex sets of probability mass functions. Without any lack of generality, we show how this can be done by simply estimating the bounds for the likelihoods of the observations. Overall, the Bayesian network is transformed into a credal network, for which a standard updating problem has to be solved. Finally, a number of transformations that might simplify the updating of the resulting credal network is provided. 1

Context: Generalization is a critical concept in all research designed to generate knowledge that applies to all elements of a unit (population) while studying only a subset of these ele-ments (sample). Commonly applied criteria for generalizing focus on experimental design or representativeness of samples of the population of units. The criteria tend to neglect popula-tion diversity and targeted uses of knowledge generated from the generalization. Objectives: This article has two connected purposes: (a) to articulate the structure and dis-cuss limitations of different forms of generalizations across the spectrum of quantitative and qualitative research and (b) to argue for considering population heterogeneity and future uses of knowledge claims when judging the appropriateness of generalizations. Research Design: In the first part of the paper, we present two forms of generalization that rely on statistical analysis of between-group variation: analytic and probabilistic generaliza-tion. We then describe a third form of generalization: essentialist generalization. Essentialist generalization moves from the particular to the general in small sample studies. We discuss limitations of each kind of generalization. In the second part of the paper, we propose two additional criteria when evaluating the validity of evidence based on generalizations from

Abstract not found

Abstract. Many have criticized the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), and few regard it as a vehicle of truth, yet its most serious limitation is that its frank operationism in defining manifest categories has distracted attention from theories about what is going on at the latent level. We sketch a Generalized Interpersonal Theory of Person-ality and Psychopathology and apply it to interpersonal aspects of depres-sion to illustrate how structural individual differences combine with functional dynamic processes to cause interpersonal behavior and affect. Such a causal account relies on a realist ontology in which manifest diagnoses are only a means to learning about the latent distribution, whether categorical or dimensional. Comorbidity of DSM diagnoses sug-gests that dimensionality will be the rule, not the exception, with internal-ization and externalization describing common diagnoses.

Structural equation models are commonly used to analyze 2-mode data sets, in which a set of objects is measured on a set of variables. The underlying structure within the object mode is evaluated using latent variables, which are measured by indicators coming from the variable mode. Additionally, when the objects are measured under different conditions, 3-mode data arise, and with this, the simultaneous study of the correlational structure of 2 modes may be of interest. In this article the authors present a model with a simultaneous latent structure for 2 of the 3 modes of such a data set. They present an empirical illustration of the method using a 3-mode data set (person by situation by response) exploring the structure of anger and irritation across different interpersonal situations as well as across persons.