The linear utility model for optimal selection

A linear utility model is introduced for optimal selection when several subpopulations of applicants are to be distinguished. Using this model, procedures are described for obtaining optimal cutting scores in subpopulations in quota-free as well as quota-restricted selection situations. The cutting scores are optimal in the sense that they maximize the overall expected utility of the selection process. The procedures are demonstrated with empirical data.     

Assessment of dental anxiety: A facet approach

Dental anxiety is a complicated phenomenon, and its multicomponent nature is often not acknowledged in existing measurement instruments. Using a facet design, a new Dental Anxiety Inventory (DAI) was constructed. Facets chosen were: time (made up of four elements: at home, on your way to the denstist, in the denstist's waiting room, in the dental chair), situation (three elements: introductory aspects, dentist-patient interaction, actual dental treatment), and reaction (three elements: subjective feelings, physical reactions, cognitive reactions). Psychometric qualities of the DAI are good. The internal structure of the DAI was studied by linear and nonlinear techniques, and was partially recovered from the data. The construct and criterion validity of the DAI was supported in several studies.     

The concept of validity

This article advances a simple conception of test validity: A test is valid for measuring an attribute if (a) the attribute exists and (b) variations in the attribute causally produce variation in the measurement outcomes. This conception is shown to diverge from current validity theory in several respects. In particular, the emphasis in the proposed conception is on ontology, reference, and causality, whereas current validity theory focuses on epistemology, meaning, and correlation. It is argued that the proposed conception is not only simpler but also theoretically superior to the position taken in the existing literature. Further, it has clear theoretical and practical implications for validation research. Most important, validation research must not be directed at the relation between the measured attribute and other attributes but at the processes that convey the effect of the measured attribute on the test scores.     

Functional Thought Experiments

The literature on thought experiments has been mainly concernedwith thought experiments that are directed at a theory, be it in aconstructive or a destructive manner. This has led somephilosophers to argue that all thought experiments can beformulated as arguments. The aim of this paper is to drawattention to a type of thought experiment that is not directed ata theory, but fulfills a specific function within a theory. Suchthought experiments are referred to as functional thoughtexperiments, and they are routinely used in applied statistics. An example is given from frequentist statistics, where a thoughtexperiment is required to establish the probability space. It isconcluded that (a) not all thought experiments can be formulated asarguments, and (b) the role of thought experiments is more generaland more important to scientific reasoning than has previouslybeen recognized.     

The theoretical status of latent variables

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized.     

On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Investigating sources of within- and between-group differences and measurement invariance (MI) across groups is fundamental to any meaningful group comparison based on observed test scores. It is shown that by placing certain restrictions on the multigroup confirmatory factor model, it is possible to investigate the hypothesis that within- and between-group differences are due to the same factors. Moreover, the modeling approach clarifies that absence of measurement bias implies common sources of within- and between-group variation. It is shown how the influence of background variables can be incorporated in the model. The advantages of the modeling approach as compared with other commonly used methods for group comparisons is discussed and illustrated by means of an analysis of empirical data.