No abstract is provided for this article.
Construct validity theory holds that (a) validity is a property of test score interpretations in terms of constructs that (b) reflects the strength of the evidence for these interpretations. In this paper, we argue that this view has absurd consequences. For instance, following construct validity theory, test score interpretations that deny that anything is measured by a test may themselves have a high degree of construct validity. In addition, construct validity theory implies that now defunct test score interpretations, like those attached to phlogiston measures in the 17th century, ‘were valid’ at the time but ‘became invalid’ when the theory of phlogiston was refuted. We propose an alternative view that holds that (a) validity is a property of measurement instruments that (b) codes whether these instruments are sensitive to variation in a targeted attribute. This theory avoids the absurdities of construct validity theory, and is broadly consistent with the view, commonly held by working researchers and textbook writers but not construct validity theorists, that a test is valid if it measures what it should measure. Finally, we discuss some pressing problems in psychological measurement that are salient within our conceptualization, and argue that the time has come to face them.
[Correction Notice: An Erratum for this article was reported in Vol 29(3) of
Forbes, Wright, Markon, and Krueger (2017) state that “psychopathology networks have limited replicability” and that “popular network analysis methods produce unreliable results”. These conclusions are based on an assessment of the replicability of four different network models for symptoms of major depression and generalized anxiety across two samples; in addition, Forbes et al. (2017) analyze the stability of the network models within the samples using split-halves. Our re-analysis of the same data with the same methods led to results directly opposed to those of Forbes et al. (2017): All network models replicate very well across the two datasets and across the split-halves. We trace the differences between Forbes et al.’s (2017) results and our own to the fact that they did not appear to accurately implement all network models, and used debatable metrics to assess replicability. In particular, Forbes et al. (2017) deviate from existing estimation routines for relative importance networks, do not acknowledge the fact that the skip-structure used in the interviews strongly distorted correlations between symptoms, and incorrectly assume that network structures and metrics should not only be expected to be the same across the different samples, but also across the different network models used. In addition to a comprehensive re-analysis of the data, we end with a discussion of best practices concerning future research into the replicability of psychometric networks.
In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.
Cognition played a pivotal role in the acceleration of technological innovation during the Industrial Revolution. Growing affluence may have provided favourable environmental conditions for a boost in cognition, enabling individuals to tackle more complex (industrial) problems. Dynamical systems thinking may provide useful tools to describe sudden transitions like the Industrial Revolution, by modelling the recursive feedback between psychology and environment.
Descriptives per condition. We implemented experimental conditions in eight time slots. SDSs were only handed out during these times. Six experimental conditions lasted two hours, while two lasted one hour. Except in the buzzer conditions, the SDS was covered in a black bag so the flashing light was invisible.
In this article the author discusses professor Stephen M. Humphry's critical attitude with respect to psychometric modeling. The author criticizes Humphry's model stating that the model is theoretically interesting but cannot be tested as it is not identified. The author also states that Humphry's model faces the same conceptual problems faced by Rasch model and states that his model's concept reflects the realm of traditional psychometrics..
