Journal of Data Science

Abstract: A crucial problem in knowledge space theory, a modern psy chological test theory, is the derivation of a realistic knowledge structure representing the organization of knowledge in an information domain and examinee population under reference. Often, one is left with the problem of selecting among candidate competing knowledge structures. This article proposes a measure for the selection among competing knowledge structures. It is derived within an operational framework (prediction paradigm), and is partly based on the unitary method of proportional reduction in predictive error as advocated by the authors Guttman, Goodman, and Kruskal. In particular, this measure is designed to trade off the (descriptive) fit and size of a knowledge structure, which is of high interest in knowledge space theory. The proposed approach is compared with the Correlational Agreement Coef ficient, which has been recently discussed for the selection among competing surmise relations. Their performances as selection measures are compared in a simulation study using the fundamental basic local independence model in knowledge space theory

Abstract: Despite the availability of software for interactive graphics, current survey processing systems make limited use of this modern tool. Interactive graphics offer insights, which are difficult to obtain with traditional statis tical tools. This paper shows the use of interactive graphics for analysing survey data. Using Labour Force Survey data from Pakistan, we describe how plotting data in different ways and using interactive tools enables analysts to obtain information from the dataset that would normally not be possible using standard statistical methods. It is also shown that interacative graphics can help the analyst to improve data quality by identifying erroneous cases.