Toolpack Consulting: research-based change including surveys

Linkage analysis and driver analysis

linkageOne of the more interesting tools is linkage analysis. One way to describe linkage analysis is through examples:

There are many important issues to consider with regard to linkage analysis. First, people use cross-sectional data—for example, we look at different locations at the same time. It is often better to have the predictors from one time and the outcomes from a later time, but this is not always possible. Regardless, we are making many assumptions about causality, since correlations and regressions cannot really show cause and effect—only whether, as one thing goes up, another one goes up (or down).

While the methods are basically the same, most people use the term “driver analysis” for trying to find causes (“drivers”) of an outcome within a single survey or other dataset; while “linkage analysis” uses more than one data source, linking them together.

Some of the methods used are:

Often, corporations and other organizations use the "linkage map" to focus their actions on issues which will have the highest impact on some outcome - customer service, profit, turnover, etc. If this is the end goal, then most methods, which are concerned with the "truth" (or goodness of fit) between the whole model and the data, may not be appropriate, because you really want to know what you can do to get the most change in the outcome. Sometimes, it pays to ignore the issue of shared variance and work on the major contributors to an outcome individually.

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