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      A Network Analysis of the Relationship among Reading, Spelling and Maths Skills

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          Abstract

          Background. Skill learning (e.g., reading, spelling and maths) has been predominantly treated separately in the neuropsychological literature. However, skills (as well as their corresponding deficits), tend to partially overlap. We recently proposed a multi-level model of learning skills (based on the distinction among competence, performance, and acquisition) as a framework to provide a unitary account of these learning skills. In the present study, we examined the performance of an unselected group of third- to fifth-grade children on standard reading, spelling, and maths tasks, and tested the relationships among these skills with a network analysis, i.e., a method particularly suited to analysing relations among different domains. Methods. We administered a battery of reading, spelling, and maths tests to 185 third-, fourth-, and fifth-grade children (103 M, 82 F). Results. The network analysis indicated that the different measures of the same ability (i.e., reading, spelling, and maths) formed separate clusters, in keeping with the idea that they are based on different competences. However, these clusters were also related to each other, so that three nodes were more central in connecting them. In keeping with the multi-level model of learning skills, two of these tests (arithmetic facts subtest and spelling words with ambiguous transcription) relied heavily on the ability to recall specific instances, a factor hypothesised to underlie the co-variation among learning skills. Conclusions. The network analysis indicated both elements of association and of partial independence among learning skills. Interestingly, the study was based on standard clinical instruments, indicating that the multi-level model of learning skills might provide a framework for the clinical analysis of these learning skills.

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          Regression Shrinkage and Selection Via the Lasso

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            Estimating psychological networks and their accuracy: A tutorial paper

            The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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              qgraph: Network Visualizations of Relationships in Psychometric Data

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Brain Sci
                Brain Sci
                brainsci
                Brain Sciences
                MDPI
                2076-3425
                18 May 2021
                May 2021
                : 11
                : 5
                : 656
                Affiliations
                [1 ]Department of Psychology, University of Roma “Sapienza”, 00185 Rome, Italy
                [2 ]Neuropsychology Unit, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
                [3 ]Department of History, Society and Human Studies, Lab of Applied Psychology and Intervention, University of Salento, 73100 Lecce, Italy; paola.angelelli@ 123456unisalento.it (P.A.); chiaravaleria.marinelli@ 123456unisalento.it (C.V.M.); daniele.romano@ 123456unimib.it (D.L.R.)
                [4 ]Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy
                [5 ]Department of Psychology, NeuroMi Milan Centre of Neuroscience, University of Milano-Bicocca, 20126 Milan, Italy
                Author notes
                [* ]Correspondence: pierluigi.zoccolotti@ 123456uniroma1.it ; Tel.: +39-06-4991-7597
                Author information
                https://orcid.org/0000-0002-6351-2455
                https://orcid.org/0000-0002-6366-8433
                Article
                brainsci-11-00656
                10.3390/brainsci11050656
                8157862
                34069961
                fd1617a4-cb3e-452c-ba1b-3ec7df192448
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 18 April 2021
                : 13 May 2021
                Categories
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                reading,spelling,maths
                reading, spelling, maths

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