based on theory and rich in context that will result in data that makes people excited to take action. More on machine learning can be found in Chapter 6.
Step 9: Collect Retrospective Data Right Away and Take Action
If available, use retrospective data (data used for reporting what has already happened) to determine which predictor variables, of those already being measured, most closely relate to your variable of interest. Have a mathematician, statistician, or data analyst run a correlation table of all the predictor variables. Most organizations employ or contract with mathematicians, statisticians, and/or others who know how to run and read a correlation table using statistics software such as SPSS, SAS, or R. The mathematician, statistician, or analyst will be able to identify all the predictor variables found to have a relationship with the variable of interest and rank them in order of the strength of the relationship. (Many examples of these ranked predictor variables will appear in this book.) This will help start a meaningful conversation about what is being discovered while data on the remaining variables from the measurement model is still being collected manually. If actionable information is discovered during this step, operational changes can be implemented immediately. Further examination of the data will continue, but if what you have discovered by this point makes some opportunities for improvements apparent, do not wait to improve operations!
Step 10: Examine Data Before Analysis
Once the data is collected, it is tempting to proceed directly to the fun part and view the results. How do things relate to one another? What are the high and low scores? Viewing the results that answer questions like these is rewarding; however, prior to viewing any results (even in Step 9, where you may have gathered together some pretty compelling retrospective data), it is critical to ensure that all the data is correct. Have the statistician, analyst, or mathematician use statistics software, or even Excel, to examine the distribution of the scores and look for indications that there is missing data. If the distribution of the data for any predictor variable has a prominent leaning toward low or high scores, with a few outliers, the analyst will need to decide whether the outliers should be removed or whether the data should be weighted. If there are patterns of missing data (e.g. one item from a construct has many missing scores), then the group should discuss why the data is missing and what to do about it. Understanding the distribution and missing data will provide additional insight into the population being studied.
Reviewing the data for accuracy can also begin to give the team a feel for the “personality” of the data.
Reviewing the data for accuracy can also begin to give the team a feel for the “personality” of the data. This data all comes from people, but instead of a coherent conversation or observation of the people contained in the data, it is initially just data. As the data is examined, an understanding of the respondents will begin to emerge.
Step 11: Analyze the Data
For this step, it is important to engage a professional who is trained in analytics so the data can be interpreted accurately. There are several options in today's analytic software, such as SPSS, SAS or R, to aid in the examination of data flaws that are not obvious by merely looking at the dataset or its associated graphic representations. Your organization may have software, such as Tableau, which generates graphs automatically and is dependable for graphic visualization. In this step, it is important to engage an analyst or data scientist who can take advantage of the tools and tests contained in analytic software.
Step 12: Present Data to the People Who Work Directly With the Variable of Interest, and Get Their Interpretation of the Data
Ultimately, the most relevant and useful interpretation will be provided by those who live the clinical experience, because they can validate or refine the interpretation of the data. These people are typically clinicians, not analysts, however, so showing complex data in a format that is accessible to the employee not trained in analytics is important. When possible, use bubble graphs that provide a visual review of overall findings.
Figures 1.2 and 1.3 convey two ways to facilitate understanding of complex mathematics. Figure 1.2 provides a visual representation of the traditional graphic for results from a regression equation which examined three predictor variables related to reduction of central line‐associated blood stream infection (CLABSI). The three predictors—location of the central line insertion, the RN assigned, and the phase of the project—have been tested to see the degree to which each of them explains the frequency of CLABSI.
Similarly, Figure 1.3 is a bubble graph which also shows how much of the outcome variable is explained by the same three predictor variables.
Figure 1.2 Explained variance of CLABSI, traditional graphic.
Figure 1.3 Explained variance of CLABSI, bubble graphic.
Both models reveal that the location of the central line insertion predicted 2.2% of CLABSI, the assigned RN predicted 1.1%, and the phase of the project predicted .4%. It has been the experience of this author that the visual representation in Figure 1.2 does not convey information as quickly as does the visual representation in Figure 1.3, in part because it relies too heavily on statistical symbols and equations to convey the information. In this book, however, you will see that most of the graphic representations of models and their results are expressed in traditional graphs, as many readers are likely to want more information than bubble graphs convey.
Analysts can provide a review of the results, but it is only the staff members who can provide validation or reflections that may suggest the need for secondary analysis to understand the data more deeply. When the data is being presented, pay attention to the listeners' responses. Even people who do not want to speak up may provide useful insight through nonverbal responses such as silence or even a shift in energy in the room. All of these cues can be informative. It is not uncommon for this author to pull listeners aside to discuss the nonverbal cues or silence that was observed. When encouraged to express themselves, these are often people from whom extremely valuable feedback is elicited.
Step 13: Respecify (Correct, Refine, and/or Expand) the Measurement Model
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
