Detecting Symptom Overreporting – Equivalence of the Dutch and German Self-Report Symptom Inventory

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Abstract. The Self-Report Symptom Inventory (SRSI) intends to measure symptom overreporting. To assess the Dutch and German SRSI equivalence, both versions were split into two half-forms. Forty bilingual participants were randomly allocated to two groups that completed the first half in German and the second half in Dutch or vice versa. Each group completed the SRSI honestly and then under feigning instructions. For both conditions, the Dutch and German SRSI did not statistically significantly differ within and across the two groups. For most comparisons, the Bayes factor was ≥ 3, indicating moderate evidence favoring the equivalence of language versions and half-forms. Genuine and pseudosymptoms endorsement was significantly higher in the feigning than in the honest condition (both Zs = 5.44, r _rb = 1.00). The SRSI standard cut score correctly identified honest responding and detected 80% of feigned responses. Our results align with Giger and Merten’s (2019) German and French SRSI equivalence study.

Related collections

Most cited references 41

Record: found
Abstract: found
Article: not found

Translation, adaptation and validation of instruments or scales for use in cross-cultural health care research: a clear and user-friendly guideline.

Valmi D. Sousa, Wilaiporn Rojjanasrirat (2011)

The diversity of the population worldwide suggests a great need for cross-culturally validated research instruments or scales. Researchers and clinicians must have access to reliable and valid measures of concepts of interest in their own cultures and languages to conduct cross-cultural research and/or provide quality patient care. Although there are well-established methodological approaches for translating, adapting and validating instruments or scales for use in cross-cultural health care research, a great variation in the use of these approaches continues to prevail in the health care literature. Therefore, the objectives of this scholarly paper were to review published recommendations of cross-cultural validation of instruments and scales, and to propose and present a clear and user-friendly guideline for the translation, adaptation and validation of instruments or scales for cross-cultural health care research. A review of highly recommended methodological approaches to translation, adaptation and cross-cultural validation of research instruments or scales was performed. Recommendations were summarized and incorporated into a seven-step guideline. Each one of the steps was described and key points were highlighted. Example of a project using the proposed steps of the guideline was fully described. Translation, adaptation and validation of instruments or scales for cross-cultural research is very time-consuming and requires careful planning and the adoption of rigorous methodological approaches to derive a reliable and valid measure of the concept of interest in the target population. © 2010 Blackwell Publishing Ltd.

0 comments Cited 652 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

The JASP guidelines for conducting and reporting a Bayesian analysis

Johnny van Doorn, Don van den Bergh, Udo Böhm … (2020)

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.