11.1 History of the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder—Symptom and Normal-Behavior Rating Scales: From 1990 to 2016
Objectives: The goal of this session is to provide a brief history of the rationale for the development of the Strengths and Weakness of ADHD symptoms and Normal-behavior (SWAN) rating scale, as well as a survey of its applications and evidence for its advantages over conventionally designed questio...
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description | Objectives: The goal of this session is to provide a brief history of the rationale for the development of the Strengths and Weakness of ADHD symptoms and Normal-behavior (SWAN) rating scale, as well as a survey of its applications and evidence for its advantages over conventionally designed questionnaires. Methods: The mathematics of using statistical cutoffs based on 0 to 3 ratings of ADHD symptoms that define abnormal behavior (weaknesses) identified a potentially serious flaw for applications in the clinical practice. In the population, a high percentage of scores will be centered between 0 and 1, generating a highly skewed distribution. If strengths are not measured and scored, the variance of the truncated distribution is reduced. Statistical cutoffs based on total scores, z-scores, or T-scores and the assumptions of normality (e.g., mean + 1.65 SD) may over-identify or under-identify extreme cases in the skewed distribution. By rewording the items, the SWAN scale captured the opposite of weaknesses (strengths) by expanding the four-point scale of symptom presence (0 ="not at all" to 3 ="very much") to a sevenpoint scale with symptoms denoted by weaknesses (0 ="average" to 3 = "far below average") and the opposites by strengths ("far above average" =-3). The first papers presenting the SWAN were rejected. An early presentation in 2000 to the ADHD Molecular Genetics Network led to a group consensus to adopt the SWAN, although this decision was later overturned. However, several investigators used the SWAN before its eventual official publication more than a decade later. Results: Many published studies show the value of capturing variance associated with both strengths and weaknesses to generate a near-normal distribution of ratings in epidemiological sample groups. The non-normal distributions of other scales [Conners; DuPaul; Swanson, Nolan and Pelham (SNAP); Strengths and Difficulties Questionnaire; Child Behavior Checklist; and etc.] and the application of the SWAN to measure ADHD as a dimension will be presented and discussed. Conclusions: With a focus on ADHD, the SWAN provides a model by which the symptomatology specified for DSM-5 diagnoses can be converted into behaviors that extend from nonclinical to clinical ranges. The data reviewed across studies demonstrate the potential advantages of using the SWAN in research studies. |
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Methods: The mathematics of using statistical cutoffs based on 0 to 3 ratings of ADHD symptoms that define abnormal behavior (weaknesses) identified a potentially serious flaw for applications in the clinical practice. In the population, a high percentage of scores will be centered between 0 and 1, generating a highly skewed distribution. If strengths are not measured and scored, the variance of the truncated distribution is reduced. Statistical cutoffs based on total scores, z-scores, or T-scores and the assumptions of normality (e.g., mean + 1.65 SD) may over-identify or under-identify extreme cases in the skewed distribution. By rewording the items, the SWAN scale captured the opposite of weaknesses (strengths) by expanding the four-point scale of symptom presence (0 ="not at all" to 3 ="very much") to a sevenpoint scale with symptoms denoted by weaknesses (0 ="average" to 3 = "far below average") and the opposites by strengths ("far above average" =-3). The first papers presenting the SWAN were rejected. An early presentation in 2000 to the ADHD Molecular Genetics Network led to a group consensus to adopt the SWAN, although this decision was later overturned. However, several investigators used the SWAN before its eventual official publication more than a decade later. Results: Many published studies show the value of capturing variance associated with both strengths and weaknesses to generate a near-normal distribution of ratings in epidemiological sample groups. The non-normal distributions of other scales [Conners; DuPaul; Swanson, Nolan and Pelham (SNAP); Strengths and Difficulties Questionnaire; Child Behavior Checklist; and etc.] and the application of the SWAN to measure ADHD as a dimension will be presented and discussed. Conclusions: With a focus on ADHD, the SWAN provides a model by which the symptomatology specified for DSM-5 diagnoses can be converted into behaviors that extend from nonclinical to clinical ranges. The data reviewed across studies demonstrate the potential advantages of using the SWAN in research studies.</description><identifier>ISSN: 0890-8567</identifier><identifier>EISSN: 1527-5418</identifier><identifier>DOI: 10.1016/j.jaac.2016.07.181</identifier><identifier>CODEN: JAAPEE</identifier><language>eng</language><publisher>Baltimore: Elsevier Inc</publisher><subject>Application ; Attention deficit hyperactivity disorder ; Averages ; Behavior ; Behavior Rating Scales ; Check Lists ; Child & adolescent psychiatry ; Child Behavior ; Child Behaviour Checklist ; Child development ; Clinical medicine ; Data processing ; Genetics ; Hyperactivity ; Mathematics ; Molecular genetics ; Normal distribution ; Normality ; Pediatrics ; Psychiatry ; Quantitative psychology ; Questionnaires ; Statistical analysis ; Statistics ; Strength</subject><ispartof>Journal of the American Academy of Child and Adolescent Psychiatry, 2016-10, Vol.55 (10), p.S274-S274</ispartof><rights>2016</rights><rights>Copyright Lippincott Williams & Wilkins Oct 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jaac.2016.07.181$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,30978,45974</link.rule.ids></links><search><creatorcontrib>Swanson, James M., PhD</creatorcontrib><title>11.1 History of the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder—Symptom and Normal-Behavior Rating Scales: From 1990 to 2016</title><title>Journal of the American Academy of Child and Adolescent Psychiatry</title><description>Objectives: The goal of this session is to provide a brief history of the rationale for the development of the Strengths and Weakness of ADHD symptoms and Normal-behavior (SWAN) rating scale, as well as a survey of its applications and evidence for its advantages over conventionally designed questionnaires. Methods: The mathematics of using statistical cutoffs based on 0 to 3 ratings of ADHD symptoms that define abnormal behavior (weaknesses) identified a potentially serious flaw for applications in the clinical practice. In the population, a high percentage of scores will be centered between 0 and 1, generating a highly skewed distribution. If strengths are not measured and scored, the variance of the truncated distribution is reduced. Statistical cutoffs based on total scores, z-scores, or T-scores and the assumptions of normality (e.g., mean + 1.65 SD) may over-identify or under-identify extreme cases in the skewed distribution. By rewording the items, the SWAN scale captured the opposite of weaknesses (strengths) by expanding the four-point scale of symptom presence (0 ="not at all" to 3 ="very much") to a sevenpoint scale with symptoms denoted by weaknesses (0 ="average" to 3 = "far below average") and the opposites by strengths ("far above average" =-3). The first papers presenting the SWAN were rejected. An early presentation in 2000 to the ADHD Molecular Genetics Network led to a group consensus to adopt the SWAN, although this decision was later overturned. However, several investigators used the SWAN before its eventual official publication more than a decade later. Results: Many published studies show the value of capturing variance associated with both strengths and weaknesses to generate a near-normal distribution of ratings in epidemiological sample groups. The non-normal distributions of other scales [Conners; DuPaul; Swanson, Nolan and Pelham (SNAP); Strengths and Difficulties Questionnaire; Child Behavior Checklist; and etc.] and the application of the SWAN to measure ADHD as a dimension will be presented and discussed. Conclusions: With a focus on ADHD, the SWAN provides a model by which the symptomatology specified for DSM-5 diagnoses can be converted into behaviors that extend from nonclinical to clinical ranges. The data reviewed across studies demonstrate the potential advantages of using the SWAN in research studies.</description><subject>Application</subject><subject>Attention deficit hyperactivity disorder</subject><subject>Averages</subject><subject>Behavior</subject><subject>Behavior Rating Scales</subject><subject>Check Lists</subject><subject>Child & adolescent psychiatry</subject><subject>Child Behavior</subject><subject>Child Behaviour Checklist</subject><subject>Child development</subject><subject>Clinical medicine</subject><subject>Data processing</subject><subject>Genetics</subject><subject>Hyperactivity</subject><subject>Mathematics</subject><subject>Molecular genetics</subject><subject>Normal distribution</subject><subject>Normality</subject><subject>Pediatrics</subject><subject>Psychiatry</subject><subject>Quantitative psychology</subject><subject>Questionnaires</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Strength</subject><issn>0890-8567</issn><issn>1527-5418</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>7QJ</sourceid><recordid>eNp9klGL1DAQx4souJ5-AZ8CvvjS3ky3bVIR4bzzboVDwVV8DNl0epte2-wl2YW--SEEv999ElNXEO5BGMhAfv9h_jOTJC8RMgSsTrusU0pnecwz4BkKfJQssMx5WhYoHicLEDWkoqz40-SZ9x0AIBdikfxCzJCtjA_WTcy2LGyJrYOj8SZsPVNjw76Tuh3Je_Lz_1kINAZjx_SCWqNNOF1NO3JKB3MwYWIXxlvXkLv_8XM9Dbtghz9FPlk3qD59T1t1MNaxLyqY8YatterJv2GXLnJY18CCZbOL58mTVvWeXvx9T5Jvlx--nq_S689XH8_PrlOdY16mG70pYSO4anld5TlqRBDERSOqVheqJbVRogUCyguBRc6Bal4CFZo3Smu-PEleH-vunL3bkw9yMF5T36uR7N5LFCXEQfGqjOirB2hn926M3UWqQA6iWhaRyo-UdtZ7R63cOTMoN0kEOe9KdnLelZxdSuBRi1H09iiiaPVgyEmvDY2aGuNIB9lY83_5uwdy3ZvRxNne0kT-X5vS5xLkej6G-RawWkI5x29nyrJB</recordid><startdate>20161001</startdate><enddate>20161001</enddate><creator>Swanson, James M., PhD</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QJ</scope><scope>7TK</scope><scope>K9.</scope></search><sort><creationdate>20161001</creationdate><title>11.1 History of the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder—Symptom and Normal-Behavior Rating Scales: From 1990 to 2016</title><author>Swanson, James M., PhD</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2125-bcb50b87af796221c1108e78d86fc4afeaba8f0e0e24814270e9750e4c7dacc73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Application</topic><topic>Attention deficit hyperactivity disorder</topic><topic>Averages</topic><topic>Behavior</topic><topic>Behavior Rating Scales</topic><topic>Check Lists</topic><topic>Child & adolescent psychiatry</topic><topic>Child Behavior</topic><topic>Child Behaviour Checklist</topic><topic>Child development</topic><topic>Clinical medicine</topic><topic>Data processing</topic><topic>Genetics</topic><topic>Hyperactivity</topic><topic>Mathematics</topic><topic>Molecular genetics</topic><topic>Normal distribution</topic><topic>Normality</topic><topic>Pediatrics</topic><topic>Psychiatry</topic><topic>Quantitative psychology</topic><topic>Questionnaires</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Strength</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Swanson, James M., PhD</creatorcontrib><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Journal of the American Academy of Child and Adolescent Psychiatry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Swanson, James M., PhD</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>11.1 History of the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder—Symptom and Normal-Behavior Rating Scales: From 1990 to 2016</atitle><jtitle>Journal of the American Academy of Child and Adolescent Psychiatry</jtitle><date>2016-10-01</date><risdate>2016</risdate><volume>55</volume><issue>10</issue><spage>S274</spage><epage>S274</epage><pages>S274-S274</pages><issn>0890-8567</issn><eissn>1527-5418</eissn><coden>JAAPEE</coden><abstract>Objectives: The goal of this session is to provide a brief history of the rationale for the development of the Strengths and Weakness of ADHD symptoms and Normal-behavior (SWAN) rating scale, as well as a survey of its applications and evidence for its advantages over conventionally designed questionnaires. Methods: The mathematics of using statistical cutoffs based on 0 to 3 ratings of ADHD symptoms that define abnormal behavior (weaknesses) identified a potentially serious flaw for applications in the clinical practice. In the population, a high percentage of scores will be centered between 0 and 1, generating a highly skewed distribution. If strengths are not measured and scored, the variance of the truncated distribution is reduced. Statistical cutoffs based on total scores, z-scores, or T-scores and the assumptions of normality (e.g., mean + 1.65 SD) may over-identify or under-identify extreme cases in the skewed distribution. By rewording the items, the SWAN scale captured the opposite of weaknesses (strengths) by expanding the four-point scale of symptom presence (0 ="not at all" to 3 ="very much") to a sevenpoint scale with symptoms denoted by weaknesses (0 ="average" to 3 = "far below average") and the opposites by strengths ("far above average" =-3). The first papers presenting the SWAN were rejected. An early presentation in 2000 to the ADHD Molecular Genetics Network led to a group consensus to adopt the SWAN, although this decision was later overturned. However, several investigators used the SWAN before its eventual official publication more than a decade later. Results: Many published studies show the value of capturing variance associated with both strengths and weaknesses to generate a near-normal distribution of ratings in epidemiological sample groups. The non-normal distributions of other scales [Conners; DuPaul; Swanson, Nolan and Pelham (SNAP); Strengths and Difficulties Questionnaire; Child Behavior Checklist; and etc.] and the application of the SWAN to measure ADHD as a dimension will be presented and discussed. Conclusions: With a focus on ADHD, the SWAN provides a model by which the symptomatology specified for DSM-5 diagnoses can be converted into behaviors that extend from nonclinical to clinical ranges. The data reviewed across studies demonstrate the potential advantages of using the SWAN in research studies.</abstract><cop>Baltimore</cop><pub>Elsevier Inc</pub><doi>10.1016/j.jaac.2016.07.181</doi></addata></record> |
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subjects | Application Attention deficit hyperactivity disorder Averages Behavior Behavior Rating Scales Check Lists Child & adolescent psychiatry Child Behavior Child Behaviour Checklist Child development Clinical medicine Data processing Genetics Hyperactivity Mathematics Molecular genetics Normal distribution Normality Pediatrics Psychiatry Quantitative psychology Questionnaires Statistical analysis Statistics Strength |
title | 11.1 History of the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder—Symptom and Normal-Behavior Rating Scales: From 1990 to 2016 |
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