Changes in older Dutch adults’ role networks after moving

Using the convoy model (R. L. Kahn & T. C. Antonucci, 1980), this study examined the differential impact of relocation, depending on the distance moved, on the size of 3 types of role networks. A total of 890 Dutch nonmovers and 445 movers (aged 55–86 years) were selected from the Longitudinal Aging Study Amsterdam. Results of analyses of variance showed that the neighbor networks changed most after relocation. Long‐distance movers discontinued the largest number of relationships with fellow club members. As expected, moving did not affect coworker networks. The findings show that, consistent with the convoy model, role networks proved to be unstable. Older adults, however, restored their partial networks at the second observation by starting new relationships.     

Generating items during testing: Psychometric issues and models

On-line item generation is becoming increasingly feasible for many cognitive tests. Item generation seemingly conflicts with the well established principle of measuring persons from items with known psychometric properties. This paper examines psychometric principles and models required for measurement from on-line item generation. Three psychometric issues are elaborated for item generation. First, design principles to generate items are considered. A cognitive design system approach is elaborated and then illustrated with an application to a test of abstract reasoning. Second, psychometric models for calibrating generating principles, rather than specific items, are required. Existing item response theory (IRT) models are reviewed and a new IRT model that includes the impact on item discrimination, as well as difficulty, is developed. Third, the impact of item parameter uncertainty on person estimates is considered. Results from both fixed content and adaptive testing are presented.This article is based on the Presidential Address Susan E. Embretson gave on June 26, 1999 at the 1999 Annual Meeting of the Psychometric Society held at the University of Kansas in Lawrence, Kansas. —Editor     

Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation

An additive multilevel item structure (AMIS) model with random residuals is proposed. The model includes multilevel latent regressions of item discrimination and item difficulty parameters on covariates at both item and item category levels with random residuals at both levels. The AMIS model is useful for explanation purposes and also for prediction purposes as in an item generation context. The parameters can be estimated with an alternating imputation posterior algorithm that makes use of adaptive quadrature, and the performance of this algorithm is evaluated in a simulation study.     

A Multicomponent Latent Trait Model for Diagnosis

This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait model (MLTM; Whitely in Psychometrika, 45:479–494, 1980; Embretson in Psychometrika, 49:175–186, 1984) to be applicable to measures of broad traits, such as achievement tests, in which component structure varies between items. Conditions for model identification are described and marginal maximum likelihood estimators are presented, along with simulation data to demonstrate parameter recovery. To illustrate how MLTM-D can be used for diagnosis, an application to a large-scale test of mathematics achievement is presented. An advantage of MLTM-D for diagnosis is that it may be more applicable to large-scale assessments with more heterogeneous items than are latent class models.     

The continued search for nonarbitrary metrics in psychology

H. Blanton and J. Jaccard examined the arbitrariness of metrics in the context of 2 current issues: (a) the measurement of racial prejudice and (b) the establishment of clinically significant change. According to Blanton and Jaccard, although research findings are not undermined by arbitrary metrics, individual scores and score changes may not be meaningfully interpreted. The author believes that their points are mostly valid and that their examples were appropriate. However, Blanton and Jaccard's article does not lead directly to solutions, nor did it adequately describe the scope of the metric problem. This article has 2 major goals. First, some prerequisites for nonarbitrary metrics are presented and related to Blanton and Jaccard's issues. Second, the impact of arbitrary metrics on psychological research findings are described. In contrast to Blanton and Jaccard (2006), research findings suggest that metrics have direct impact on statistics for group comparisons and trend analysis.     

A multidimensional latent trait model for measuring learning and change

A latent trait model is presented for the repeated measurement of ability based on a multidimensional conceptualization of the change process. A simplex structure is postulated to link item performance under a given measurement condition or occasion to initial ability and to one or more modifiabilities that represent individual differences in change. Since item discriminations are constrained to be equal within a measurement condition, the model belongs to the family of multidimensional Rasch models. Maximum likelihood estimators of the item parameters and abilities are derived, and an example provided that shows good recovery of both item and ability parameters. Properties of the model are explored, particularly for several classical issues in measuring change.