Unique Variable Analysis of Redundancy in ADHD Items from the Conners Teacher Rating Scale - Revised: Short.
peer reviewed ; Attention deficit hyperactivity disorder (ADHD) is a neuropsychiatric disorder interfering with the normal development of the child. The disorder can be screened at school with the Conners Teacher Rating Scale Revised Short (CTRS-R:S). This scale goes beyond the disorder itself and covers a wider construct, that of abnormal child behavior. This can be understood as a complex system of mutually influencing entities. We analyzed a data set of 525 children in French-speaking primary schools from Belgium, and estimated a network structure, as well as to determine the local dependen... Mehr ...
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Dokumenttyp: | journal article |
Erscheinungsdatum: | 2022 |
Verlag/Hrsg.: |
Faculty of Forestry
University of Zagreb |
Schlagwörter: | Attention Deficit Disorder with Hyperactivity/diagnosis/psychology / Belgium / Child / Faculty / Humans / Mass Screening / Schools / Surveys and Questionnaires / Human health sciences / Psychiatry / Sciences de la santé humaine / Psychiatrie |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-28929279 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://orbi.umons.ac.be/handle/20.500.12907/43977 |
peer reviewed ; Attention deficit hyperactivity disorder (ADHD) is a neuropsychiatric disorder interfering with the normal development of the child. The disorder can be screened at school with the Conners Teacher Rating Scale Revised Short (CTRS-R:S). This scale goes beyond the disorder itself and covers a wider construct, that of abnormal child behavior. This can be understood as a complex system of mutually influencing entities. We analyzed a data set of 525 children in French-speaking primary schools from Belgium, and estimated a network structure, as well as to determine the local dependence of items through Unique Variable Analysis. A reduced network was computed including 15 non-locally dependent items. The structural consistency of the network was not affected by redundant items and was structurally sound. The reduction of the number of variables in network studies is important to improve the investigation of network structures as well as better interpret results from inference measures.