A Comparison of Anchor-Item Designs for the Concurrent Calibration of Large Banks of Likert-Type Items

Current interest in measuring quality of life is generating interest in the construction of computerized adaptive tests (CATs) with Likert-type items. Calibration of an item bank for use in CAT requires collecting responses to a large number of candidate items. However, the number is usually too large to administer to each subject in the calibration sample. The concurrent anchor-item design solves this problem by splitting the items into separate subtests, with some common items across subtests; then administering each subtest to a different sample; and finally running estimation algorithms once on the aggregated data array, from which a substantial number of responses are then missing. Although the use of anchor-item designs is widespread, the consequences of several configuration decisions on the accuracy of parameter estimates have never been studied in the polytomous case. The present study addresses this question by simulation, comparing the outcomes of several alternatives on the configuration of the anchor-item design. The factors defining variants of the anchor-item design are (a) subtest size, (b) balance of common and unique items per subtest, (c) characteristics of the common items, and (d) criteria for the distribution of unique items across subtests. The results of this study indicate that maximizing accuracy in item parameter recovery requires subtests of the largest possible number of items and the smallest possible number of common items; the characteristics of the common items and the criterion for distribution of unique items do not affect accuracy.

Importance of Ecological Factors and Colony Handling for Optimizing Health Status of Apiaries in Mediterranean Ecosystems

We analyzed six apiaries in several natural environments with a Mediterranean ecosystem in Madrid, central Spain, in order to understand how landscape and management characteristics may influence apiary health and bee production in the long term. We focused on five criteria (habitat quality, landscape heterogeneity, climate, management and health), as well as 30 subcriteria, and we used the analytic hierarchy process (AHP) to rank them according to relevance. Habitat quality proved to have the highest relevance, followed by beehive management. Within habitat quality, the following subcriteria proved to be most relevant: orographic diversity, elevation range and important plant species located 1.5 km from the apiary. The most important subcriteria under beehive management were honey production, movement of the apiary to a location with a higher altitude and wax renewal. Temperature was the most important subcriterion under climate, while pathogen and Varroa loads were the most significant under health. Two of the six apiaries showed the best values in the AHP analysis and showed annual honey production of 70 and 28 kg/colony. This high productivity was due primarily to high elevation range and high orographic diversity, which favored high habitat quality. In addition, one of these apiaries showed the best value for beehive management, while the other showed the best value for health, reflected in the low pathogen load and low average number of viruses. These results highlight the importance of environmental factors and good sanitary practices to maximize apiary health and honey productivity.