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      Which Data Do Elementary School Teachers Use to Determine Reading Difficulties in Their Students?

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          Abstract

          Small-group interventions allow for tailored instruction for students with learning difficulties. A crucial first step is the accurate identification of students who need such an intervention. This study investigated how teachers decide whether their students need a remedial reading intervention. To this end, 64 teachers of 697 third-grade students from Germany were asked to rate whether a reading intervention for their students was “not necessary,” “potentially necessary,” or “definitely necessary.” Independent experimenters tested the students’ reading and spelling abilities with standardized tests, and a subsample of 370 children participated in standardized tests of phonological awareness and vocabulary. Findings show that teachers’ decisions with regard to students’ needing a reading intervention overlapped more with results from standardized spelling assessments than from reading assessments. Hierarchical linear models indicated that students’ spelling abilities, along with phonological awareness and vocabulary, explained variance in teachers’ ratings over and above students’ reading skills. Teachers thus relied on proximal cues such as spelling skills to reach their decision. These findings are discussed in relation to clinical standards and educational contexts. Findings indicate that the teachers’ assignment of children to interventions might be underspecified, and starting points for specific teacher training programs are outlined.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Judgment under Uncertainty: Heuristics and Biases.

            This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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              The measurement of observer agreement for categorical data.

              This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
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                Author and article information

                Journal
                J Learn Disabil
                J Learn Disabil
                LDX
                spldx
                Journal of Learning Disabilities
                SAGE Publications (Sage CA: Los Angeles, CA )
                0022-2194
                1538-4780
                15 January 2021
                September 2021
                : 54
                : 5 , Special Series: Data-Based Instruction and Decision making: An International Perspective–Part 2
                : 349-364
                Affiliations
                [1 ]DIPF—Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany
                [2 ]Goethe University Frankfurt am Main, Germany
                Author notes
                [*]Alexandra M. A. Schmitterer, PhD, DIPF—Leibniz Institute for Research and Information in Education, Rostocker Str. 6, Frankfurt am Main 60323, Germany. Email: schmitterer@ 123456dipf.de
                Author information
                https://orcid.org/0000-0002-6142-3539
                Article
                10.1177_0022219420981990
                10.1177/0022219420981990
                8358535
                33448247
                c99c5f51-482e-446b-8486-10a734e49215
                © Hammill Institute on Disabilities 2021

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

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                September/October

                diagnostics,teachers,reading intervention
                diagnostics, teachers, reading intervention

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