A Manager’s Interpretation of Cross Tabulation Survey Data

This practitioner’s guide is based on examples which highlight the usefulness of cross tabulation as a tool for investigating cause‐effect relationships in the business environment. Although cross‐tabulations are common in statistical analysis, many managers need a better understanding of the data being presented. We consider a soft drinks application where a third factor helps uncover an association not readily apparent, in addition to different cases where a third factor causes refinement of initial conclusions based on two‐factor analysis. Because conclusions are always subject to modification with introduction of the “right” factors, we are always in the position of inferring only that an association exists. This is the rationale for why the accumulation of research studies, rather than a single result, supporting a single relationship is so important to understanding the forces driving a business.