LLM Hallucination Concern
Information SystemsReliability: alpha 0.913 sample items
About this scale
A multi-item measure of user concern that large language models generate plausible but factually incorrect information.
Sample item(s)
“I worry that AI gives me information that sounds right but is actually wrong”
“I am cautious because AI sometimes makes up facts”
“I double-check AI outputs because of hallucination risks”
Reliability
Reported internal consistency: alpha 0.91. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Increasingly important in AI-deployment research.
Source & citation
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2025): A survey on hallucination in large language models. ACM Transactions on Information Systems, Vol. 43, No. 2, Article 42.
Source. Scale details are summarised from the publication cited above. Always consult the original source for the full item set, scoring, and permission to use.
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