The Effects of Selection Strategies for Bivariate Loglinear Smoothing Models on NEAT Equating Functions

In this study, eight statistical strategies were evaluated for selecting the parameterizations of loglinear models for smoothing the bivariate test score distributions used in nonequivalent groups with anchor test (NEAT) equating. Four of the strategies were based on significance tests of chi-square statistics (Likelihood Ratio, Pearson, Freeman-Tukey, and Cressie-Read) and four additional strategies were based on different evaluations of the Likelihood Ratio Chi-Square statistic (Akaike Information Criterion, Bayesian Information Criterion, Consistent Akaike Information Criterion, and an index traced to Goodman). The focus was the implications of the selection strategies' selection tendencies for the accuracy of chained and poststratification equating functions. The results differentiated the strategies in terms of their tendencies to select models with particular bivariate parameterizations and the implications of these tendencies for equating bias and variability .     

Determinants of sales and price at auction for three Australian Indigenous artists: to pool or not to pool?

Albert Namatjira (1902–1959), Emily Kame Kngwarreye (c1910–1996) and Rover (Julama) Thomas (c1926–1998) are three of Australia’s best-known Indigenous artists. Each is known for one style of painting—Namatjira for watercolor on paper, Kngwarreye for acrylic (synthetic polymer) on canvas and Thomas for natural earth pigments on canvas. We estimate a sample selectivity model using data from the Australian Art Sales Digest to study the determinants of sales and the hammer price of artworks offered at auction from these artists. The results show that pre-sale information on the artwork and auction effects are significant and that the three artists studied are very different from each other. Thus, data should not be pooled when estimating these models.