{"title":"Meta-analysis of the factor structure of the Brief Symptom Inventory (BSI-18) using an aggregated co-occurrence matrix approach","authors":"Priyalatha Govindasamy, K. Green, Antonio Olmos","doi":"10.1108/mhrj-05-2020-0028","DOIUrl":null,"url":null,"abstract":"The Brief Symptom Inventory-18 (BSI-18) is a tool used to measure clinically relevant psychological symptoms to support clinical decision-making at intake and during the course of treatment in various settings. The BSI-18 has frequently been evaluated for construct validity via analysis of its structure. However, these studies showed mixed results of the factor solutions and no consensus on the dimensionality. Therefore, the purpose of this paper is to synthesize the empirical findings about the factor structure to reach an overall conclusion about the factor structure of the BSI-18.,A meta-analysis of factor analysis results using an aggregated co-occurrence matrix approach was conducted to synthesize the factor structure. The item factor loading information from seven published studies is gathered, combined and summarized to conclude the factor structure of the instrument. Multidimensional scaling (MDS) was used to quantify the similarity between the underlying factor structures of BSI-18 from different empirical articles.,The perceptual map from MDS-found items was clustered into three distinctive factors matching the original intent. The findings highlight the consistency of the BSI-18’s factor structure. However, the findings should be used with caution owing to the small sample size and conclusions made from visual representation.,This original study contributes to research in the provision of empirically tested measures that take a focus on factor analysis and the use of meta-analysis technique to account for an understanding of the factor structure.","PeriodicalId":45687,"journal":{"name":"Mental Health Review Journal","volume":"25 1","pages":"367-378"},"PeriodicalIF":1.0000,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/mhrj-05-2020-0028","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mental Health Review Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/mhrj-05-2020-0028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 2
Abstract
The Brief Symptom Inventory-18 (BSI-18) is a tool used to measure clinically relevant psychological symptoms to support clinical decision-making at intake and during the course of treatment in various settings. The BSI-18 has frequently been evaluated for construct validity via analysis of its structure. However, these studies showed mixed results of the factor solutions and no consensus on the dimensionality. Therefore, the purpose of this paper is to synthesize the empirical findings about the factor structure to reach an overall conclusion about the factor structure of the BSI-18.,A meta-analysis of factor analysis results using an aggregated co-occurrence matrix approach was conducted to synthesize the factor structure. The item factor loading information from seven published studies is gathered, combined and summarized to conclude the factor structure of the instrument. Multidimensional scaling (MDS) was used to quantify the similarity between the underlying factor structures of BSI-18 from different empirical articles.,The perceptual map from MDS-found items was clustered into three distinctive factors matching the original intent. The findings highlight the consistency of the BSI-18’s factor structure. However, the findings should be used with caution owing to the small sample size and conclusions made from visual representation.,This original study contributes to research in the provision of empirically tested measures that take a focus on factor analysis and the use of meta-analysis technique to account for an understanding of the factor structure.