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Association between categorical variables in contingency tables is analyzed using the information identities based on multivariate multinomial distributions. A scheme of geometric decompositions of the information identities is developed to identify indispensable predictors and interaction effects in the construction of concise log-linear and logit models; it suggests a new approach for selecting parsimonious log-linear and logit models which would facilitate the search for the minimum AIC models as a byproduct. The proposed constructive schemes are illustrated along with the analysis of a contingency data table collected in a study on the risk factors of ischemic cerebral stroke., Comment: This article sets up a new construction methodology for selecting the most parsimonious log-linear and logit models in any finite-dimensional categorical data table using the analysis of information identity. Please refer this article to arXiv: 1801.01003 [stat.ME] and Cheng et al. (JASA, 2010). Email your comments and questions to the corresponding author

The Akaike information criterion (AIC) is commonly used to select a logistic regression model for optimal prediction of a binary response by a specified family of models. It however lacks a convincing method of prescribing a proper family of models using the desired predictors and their interaction effects. For an alternative approach to model selection, we propose a direct selection scheme which first identifies the indispensable regressors as main-effect predictors, then examines significant interaction effects between the selected predictors such that a logistic model is constructed. The two-step selection scheme is formulated by testing for valid information identity between the response and the predictors, from which the most parsimonious logistic model is derived from the least set of indispensable predictors and interaction effects. As a byproduct, the minimum AIC model is easily found in a neighborhood of the selected model. The scheme is employed to yield the logistic model for predicting the acquisition of professional licenses in a survey of employed youth workers., Comment: Following the JASA article (2010) by Cheng et al., this article introduces a constructive scheme for selecting parsimonious models for a multivariate contingency table using logistic regression (i.e., a well-defined target variable plus several predictors). Please email your comments and questions to the corresponding author

This research derives simplified formulas for computing the standard error of the frequency estimation method for equating score distributions that are continuized using a uniform or Gaussian kernel function (P. W. Holland, B. F. King, and D. T. Thayer, 1989; Holland and Thayer, 1987). The simplified formulas are applicable to equating both the observed- and smoothed-score distributions (P. R. Rosenbaum and D. Thayer, 1987). Two empirical studies investigated the use of the simplified formulas. The first dataset was scores on 2 forms of an English test, each of which had 55 items, taken by 719 examinees, who answered 25 common items. The second dataset was reading data from the National Assessment of Educational Progress for 9,229 examinees. Results from the empirical studies indicate that the simplified formulas work reasonably well for samples with moderate sizes, such as 1,000 examinees. (Contains 9 figures and 17 references.) (Author/SLD)

Different data imputation techniques that are useful for equipercentile equating are discussed, and empirical data are used to evaluate the accuracy of these techniques as compared with chained equipercentile equating. The kernel estimator, the EM algorithm, the EB model, and the iterative moment estimator are considered. (SLD)

Simplified formulas are proposed for computing the standard errors of equipercentile equating for continuous and discrete test scores. These formulas are easily extended to more complicated equating designs. Results from a study of 719 subjects taking an English test indicated that the formulas work reasonably well for moderate-size samples. (SLD)

A recursive equation is proposed for computing higher order derivatives of elementary symmetric functions in the Rasch model. A simulation study indicates a small loss in accuracy for the proposed formula compared to Gustafsson's sum algorithm (1980) for computing higher order derivatives when tests contain 60 items or less. (SLD)

Accuracy of three exact person tests for assessing model-data fit in the Rasch model was investigated in a simulation study. Empirical Type I error rates and statistical power of the person tests were computed. The exact person test conditioned on total score is a promising tool for assessing consistency of response patterns with the Rasch model. (SLD)