Using Creative Reflection to Reduce Social Desirability Bias in Research on Sensitive Topics: From Task Design to Data Analysis

Measuring attitudes on sensitive topics such as immigration, prejudice, and multiculturalism is a persistent methodological challenge because standard self-report instruments are prone to social desirability bias. This bias—the tendency for participants to provide socially acceptable answers rather than their true beliefs—can distort both predictors and outcomes, severely compromising the rigor of research. Statistical control via social desirability scales offers only partial remediation, and when the same bias affects all measured variables, statistical control alone is often insufficient or even counterproductive. This tutorial introduces a novel methodological tool that mitigates social desirability bias by eliciting responses through creative reflection—an open-ended, low-stakes task that prompts participants to express their views indirectly. We detail the process of designing such tasks, coding and analyzing the resulting data, and integrating these methods into broader research designs. Our analysis applies a mixed-methods framework that integrates quantitative survey responses from 454 in-service teachers with qualitative creative narratives from 413 in-service teachers. Step-by-step code, annotated examples, and the full dataset are provided to enable replication and adaptation. While illustrated with a study of teachers’ multicultural attitudes, the approach is applicable to other sensitive psychological and educational research topics.