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This article reports outcomes of a quasi-experimental evaluation of Success for All (SfA), a comprehensive school reform program that has recently been introduced in the Netherlands. The Response to Intervention framework is used to describe how SfA supports students at different Tiers. The effects of SfA on five reading subskills were investigated for first-grade students at risk of reading problems. 299 students from two different cohorts were involved. Multilevel analyses demonstrated a significant effect of SfA on reading comprehension (ES = +0.26) in the first cohort. For the second cohort and the other reading subskills, mostly small positive effects of SfA were found, though these effects were not statistically significant. Furthermore, the relationship between tutoring intensity and reading achievement was examined. In the second cohort, a significant negative association of tutoring intensity with word and text reading skills was found. Implementation issues that may have impacted the outcomes are discussed.

Objectives: De Vet, Mokkink, Mosmuller, and Terwee (2017) discovered that the Spearman-Brown formula (SB formula) can be used to transform certain intraclass correlation coefficients (ICCs) for single measurements into the corresponding ICCs for average measurements, without knowledge of the variance components.Results: A formal proof of the discovery by De Vet et al. is presented. It is also proved that, vice versa, the SB formula can be used to transform certain ICCs for average measurements into the corresponding ICCs for single measurements. Furthermore, it is specified to which type of ICC the transformations can be applied. Finally, several illustrations of the transformations are presented.Conclusion: The discovery by De Vet et al. is quite remarkable because ICCs and the SB formula come from quite different scientific disciplines. (C) 2017 Elsevier Inc. All rights reserved.

Cohen’s kappa and weighted kappa are two standard tools for describing the degree of agreement between two observers on a categorical scale. For agreement tables with three or more categories, popular weights for weighted kappa are the so-called linear and quadratic weights. It has been frequently observed in the literature that, when Cohen’s kappa and the two weighted kappas are applied to the same agreement table, the value of the quadratically weighted kappa is higher than the value of the linearly weighted kappa, which in turn is higher than the value of Cohen’s kappa. This paper considers a sufficient condition for this double inequality.

The kappa coefficient is a popular descriptive statistic for summarizing the cross classification of two nominal variables with identical categories. It has been frequently observed in the literature that combining two categories increases the value of kappa. In this note we prove the following existence theorem for kappa: For any nontrivial k × k agreement table with k ∈ N ≥ 3 categories, there exist two categories such that, when combined, the kappa value of the collapsed ( k − 1 ) × ( k − 1 ) agreement table is higher than the original kappa value. In addition, there exist two categories such that, when combined, the kappa value of the collapsed table is smaller than the original kappa value.

Correspondence analysis is an exploratory technique for analyzing the interaction in a contingency table. Tables with meaningful orders of the rows and columns may be analyzed using a model-based correspondence analysis that incorporates order constraints. However, if there exists a permutation of the rows and columns of the contingency table so that the rows are regression dependent on the columns and, vice versa, the columns are regression dependent on the rows, then both implied orders are reflected in the first dimension of the unconstrained correspondence analysis [Schriever, B.F., 1983. Scaling of order dependent categorical variables with correspondence analysis. International Statistical Review 51, 225-238]. Thus, using unconstrained correspondence analysis, we may still find that the data fit an ordinal stochastic model. Fit measures are formulated that may be used to verify whether the re-ordered contingency table is regression dependent in either the rows or columns. Using several data examples, it is shown that the fit indices may complement the usual geometric interpretation of the unconstrained correspondence analysis solution in low-dimensional space.

Application of Cohen's weighted kappa for inter-rater agreement requires the specification of a weighting matrix. An explicit formula for the determinants of the principal minors of the weighting matrix with Cicchetti-Allison weights is derived. Since all determinants are strictly positive, it follows that the Cicchetti-Allison weighting matrix is positive definite.