Toolpack: organizational development, surveys, and change

Linkage analysis and driver analysis

linkageOne of the more interesting tools to arrive in recent years has been linkage analysis. One way to describe linkage analysis is through examples:

Statistically, there are many important issues to consider with regard to linkage analysis. Most of the time, people use cross-sectional data - that is, we look at different locations or people at the same time. Generally, I have tried to have the predictors from one year and the outcomes from a later year, but this is not always possible, and is only a partial answer in any case. We are making many assumptions about causality. (There are other statistical assumptions, and many writers have described them in detail. I would like to refer you to StatSoft's site, since they have an on-line textbook that covers much of this).

Note: I am not making a distinction between driver analysis and linkage analysis. Driver analysis can refer to the attempt to find drivers of one factor, such as intention to leave the company, within a single data source, such as an employee survey; linkage analysis is similar but uses more than one data source (hence "linkage").

Some of the methods used are:

One problem which is generally not covered refers to the real life use of linkage. 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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