Many applications of sequence analysis demand an overall procedure in which the same set of analyses is done for each case (subject, dyad, family, group) in our study. Most of our book is designed with such an application in mind. For example, we will usually want to know, for all the married couples in the study, which one “best” order of the Markov chain to pick, and, in general, how many segments are meaningful for dividing conversations in a stationarity analysis. This is the usual practice, because results would be difficult to report a case at a time.

However, in part to illustrate the flexibility of the methods we have mentioned, we will present a worked everyday example of the analysis of one couple using log-linear methods. We selected a case that has limitations in the amount of data available and that illustrates how much artfulness is needed to select a best model.

This chapter is best thought of as: (1) a worked example of log-linear methods; and as (2) an approach to hypothesis generating with sequential methods.

A Six-Step Procedure

Remember that the goal is to find the simplest model smaller than the fully saturated model that fits, that is, produces nonsignificant chi square. For example, if a Markov (1) (first-order Markov) model produces a significant chi square, this is evidence of the inadequacy of the model. The example will also illustrate how to cope with practical problems that can emerge from a model-fitting procedure.