Meta-analysis of the factor structure of the Brief Symptom Inventory (BSI-18) using an aggregated co-occurrence matrix approach

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Dissociating the impact of alexithymia and impaired self-awareness on emotional distress and aggression after traumatic brain injury.

“…The reliability of GSI has been reported as 0.91, and each subdimension is greater than 0.7 (Alzahrani, 2016). A recent meta-analysis also found consistency in the BSI-18’s factor structure (Govindasamy et al, 2020).…”

“…A recent meta-analysis also found consistency in the BSI-18's factor structure (Govindasamy et al, 2020).…”

Measurement invariance of maternal depressive symptoms across the first 2 years since birth and across racial group, education, income, primiparity, and age.

“…The BSI-18 is a frequently used measure of psychological distress comprising three factors of anxiety, depression, and somatization (Govindasamy et al, 2020). Previous studies have demonstrated high internal consistency of Depressive Symptoms subscale scores (Derogatis, 2017;Houghton et al, 2013;Prinz et al, 2013) and high convergent validity of subscale scores with scores from other measures of depressive symptoms (Prinz et al, 2013), supporting the use of subscale scores for making valid interpretations about depressive symptoms.…”

Consistency check of power fault multi-source heterogeneous big data under common factor structure

“…The algorithm consists of two parts: time update and measurement update [1]. The time update is responsible for obtaining the a priori estimate of the state value at the next time and the estimate of the covariance of the estimation error [2], while the measurement update is responsible for updating the a priori estimate using the observations to obtain the a posteriori estimate of the state value. Assume that the discrete model of the linear power fault system is :…”