Daniel Fernandez Martinez, I. Liu, A. Preti, J. M. Haro, S. Siddi
{"title":"Launay–Slade Hallucination Scale-Extended: simplifying its interpretation","authors":"Daniel Fernandez Martinez, I. Liu, A. Preti, J. M. Haro, S. Siddi","doi":"10.1080/17522439.2021.1983011","DOIUrl":null,"url":null,"abstract":"ABSTRACT Background The Launay–Slade Hallucination Scale – Extended (LSHS-E) is one of the most used self-reported questionnaires to explore the multidimensionality of hallucinatory-like experiences (HLEs). This scale is defined as a 5-level Likert scale, which goes from 0-“certainly does not apply to me” to 4-“certainly applies to me.” Like any Likert scale, the LSHS-E scale assumes that the ordinal categories are equally spaced among them, which might not be true, giving rise to possible issues in offering a valid interpretation of the responses. Method This study introduces a parametric model: the ordered stereotype model. This model determines the uneven spacing among ordinal responses, dictated by the studied data. Results This work shows that the ordinal categories of the LSHS-E scale are determined both by unequal spacing and by the spacing among the last three adjacent categories, which makes them indistinguishable. Subsequent analysis showed good internal reliability, and also a four-factor structure was maintained. Discussion The current study’s findings suggest that people who suffer from HLEs might not easily disclose their experiences and so give neutral responses for fear of being stigmatized. Further, neutral responses might identify people at risk of psychosis, and individuals during the prodromal stage may not be aware of their transient or fleeting HLEs. Future research should determine the distance among the categories on a Likert scale as a first step before analyzing and understanding the data.","PeriodicalId":46344,"journal":{"name":"Psychosis-Psychological Social and Integrative Approaches","volume":"15 1","pages":"56 - 65"},"PeriodicalIF":1.2000,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychosis-Psychological Social and Integrative Approaches","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/17522439.2021.1983011","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACT Background The Launay–Slade Hallucination Scale – Extended (LSHS-E) is one of the most used self-reported questionnaires to explore the multidimensionality of hallucinatory-like experiences (HLEs). This scale is defined as a 5-level Likert scale, which goes from 0-“certainly does not apply to me” to 4-“certainly applies to me.” Like any Likert scale, the LSHS-E scale assumes that the ordinal categories are equally spaced among them, which might not be true, giving rise to possible issues in offering a valid interpretation of the responses. Method This study introduces a parametric model: the ordered stereotype model. This model determines the uneven spacing among ordinal responses, dictated by the studied data. Results This work shows that the ordinal categories of the LSHS-E scale are determined both by unequal spacing and by the spacing among the last three adjacent categories, which makes them indistinguishable. Subsequent analysis showed good internal reliability, and also a four-factor structure was maintained. Discussion The current study’s findings suggest that people who suffer from HLEs might not easily disclose their experiences and so give neutral responses for fear of being stigmatized. Further, neutral responses might identify people at risk of psychosis, and individuals during the prodromal stage may not be aware of their transient or fleeting HLEs. Future research should determine the distance among the categories on a Likert scale as a first step before analyzing and understanding the data.