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This is also the work of the data analyst, but it is done in close collaboration with staff members. This step includes refinement and possible expansion of the model to make it an even more sensitive model to detect predictors accurately. During presentation of the data to staff members, new variables will be identified, or variables that could not be measured in the first round but belong in the overall model will be addressed. Respecify the model to include anything that could not be included in the initial analysis or was identified in the interpretation as missing, and delete anything that was determined in the analysis to be unimportant. In one real‐life example, we were measuring the performance of charge nurses as the variable of interest, and we had proposed that our three predictor variables were (a) demographics of the charge nurse, (b) attending the charge nurse program, and (c) the preceptor who trained the charge nurse into the role. The originally specified model looked like Figure 1.4.

After Model 1 was used to examine this variable of interest, and after the data was presented to unit managers, charge nurses, and staff members from the unit, those who attended the presentation reported that Model 1 was missing two influential predictor variables: (a) mentoring of the unit manager and (b) resources available on the job for charge nurses to execute their required role. These influential variables were added to a respecified model (Figure 1.5), and the study was conducted again to see whether analysis of the respecified model could further explain what was influencing performance of charge nurses.

Figure 1.4 Model 1 to measure new charge nurse performance.

Figure 1.5 Model 2, respecified with new predictor variables to measure new charge nurse performance.

Note also that in structural models such as Figures 1.4 and 1.5, we have rectangles that look like they are representing one variable, when in many cases they represent multiple variables. For example, the rectangle labeled “Demographics” in both figures might be representing a dozen or so variables. These smaller, more compact models, which appear throughout this book, are called over‐aggregated structural models. Remember when you see them that what looks like a model testing three or four variables is actually testing dozens of variables at the same time.