Research Topics:

Data Analysis | Statistical Analysis

Content Type:

Glossary

Cross-Validity Definition

A method of comparing predicted and observed values that involves using comparable data (resampling) to check the validity of an original estimation.

Cross-validity is the assessment of generalizability and consistency of research findings across segments, populations or contexts. This method of comparing predicted and observed values uses comparable data, also known as resampling, to check the validity of an original estimation. It ensures that conclusions drawn from a study are not limited to a specific group or situation. Rather, it can be applied more broadly. It involves examining whether results are true when the research is conducted on various samples or situations. Cross-validity can enhance the reliability and generalizability of research findings. It demonstrates that the patterns, trends and insights identified in studies are not chance occurrences.

Who relies on cross-validity?

Marketing researchers, analysts and professionals use cross-validity to make certain their findings and insights are robust and applicable across various market segments, demographics, geographic locations or time spans.

Why should I care about cross-validity?

Cross-validity validates the credibility and relevance of marketing research. Understanding cross-validity helps marketing professionals ensure that their insights are far-reaching and not just limited to a specific situation. What more, the method provides evidence to make decisions that can have a meaningful impact on target audiences or markets.