Reliability, factor structure, and validity of the German version of the Trauma Symptom Checklist for Children in a sample of adolescents

Abstract

Background

The Trauma Symptom Checklist for Children (TSC-C) is the most widely used self-report scale to assess trauma-related symptoms in children and adolescents on six clinical scales. The purpose of the present study was to develop a German version of the TSC-C and to investigate its psychometric properties, such as factor structure, reliability, and validity, in a sample of German adolescents.

Method

A normative sample of N=583 and a clinical sample of N=41 adolescents with a history of physical or sexual abuse aged between 13 and 21 years participated in the study.

Results

The Confirmatory Factor Analysis on the six-factor model (anger, anxiety, depression, dissociation, posttraumatic stress, and sexual concerns with the subdimensions preoccupation and distress) revealed acceptable to good fit statistics in the normative sample. One item had to be excluded from the German version of the TSC-C because the factor loading was too low. All clinical scales presented acceptable to good reliability, with Cronbach's α's ranging from .80 to .86 in the normative sample and from .72 to .87 in the clinical sample. Concurrent validity was also demonstrated by the high correlations between the TSC-C scales and instruments measuring similar psychopathology. TSC-C scores reliably differentiated between adolescents with trauma history and those without trauma history, indicating discriminative validity.

Conclusions

In conclusion, the German version of the TSC-C is a reliable and valid instrument for assessing trauma-related symptoms on six different scales in adolescents aged between 13 and 21 years.

• Posttraumatic stress disorder
• trauma
• children
• adolescents
• self-report
• assessment

For the abstract or full text in other languages, please see Supplementary files under ‘Article Tools’

For the abstract or full text in other languages, please see Supplementary files under ‘Article Tools’

Acknowledgements

The authors thank Giulia Capudi, Magdalena Eich, Hanna Fray, Corinna Jung, Lena Löffler, Susan Manzke, Verena Noel, Sarah Seitz, and Ebru Sümen for their help with data collection.

Notes

For the abstract or full text in other languages, please see Supplementary files under ‘Article Tools’