Step 6: Take Action Farrokh Alemi Ph.D. I am professor Alemi and this lecture discusses how you can use causal analysis to take action. This research was funded by Grant RO1 HL 084767 from the National Heart Blood and Lung Institute.

Continues from a previous section Step 6: Take Action Continues from a previous section This lecture continues from a previous section on analyzing causal diaries.

What helps Thinking through it Keeping data Understanding the data The analytical process of thinking through causes of exercise and listing them by itself is beneficial.  It leads to new insights.  The thought experiments we presented earlier help you understand what might be a false cause.  At some point, you arrive at a list of reasonable causes and constraints, then a 2-3 week of data collection help you sort out which of the causes are the most influential one.  Through out this process you gain insight into what is keeping you from success and what is enabling you to move forward. 

What Helps? In the example we have been following, some causes such as planning to bike to work has a larger impact on our success than sleeping early. Armed with this new insight we can take action so that we are ready to bike to work more often.

Analysis not Paralysis Precision is not needed Focus on improvement No blame Analysis is limited Analysis not Paralysis Keep in mind that the purpose of putting numbers on the impact of causes is to help produce insights.  Given the short data collection you will not have much precision in the conclusions you arrive at. You do not need it. The numbers are helping your intuitions about what is working for you. They are not replacing it.

Analysis not Paralysis Precision is not needed Focus on improvement No blame Analysis is limited Analysis not Paralysis Also keep in mind that the focus should be on improvement and not measurement.

Analysis not Paralysis Precision is not needed Focus on improvement No blame Analysis is limited Analysis not Paralysis The data are presented without any blame or exhortation for more effort. The purpose is to understand how the environment affects exercise and not to put more effort or be more motivated.

Analysis not Paralysis Precision is not needed Focus on improvement No blame Analysis is limited Analysis not Paralysis Keep in mind that the purpose of putting numbers on the impact of causes is to help produce insights.  Given the short data collection you will not have much precision in the conclusions you arrive at. You do not need it. The number are helping your intuitions about what is working for you. They are not replacing it. Also keep in mind that the focus should be on improvement and not measurement. The data are presented without any blame or exhortation for more effort.  The purpose is to understand how the environment affects exercise and not to put more effort or be more motivated. Also keep in mind that any analysis is limited,  You could not possibly look at all causes.  At best you have focus on a handful of causes.  Furthermore, the cause of your behavior may change over time.  Given these limitations, the resulting analysis should be used with caution.  If you do not jump to unsupported conclusions, some information is better than no information at all.  After you gain insight into causes and constraints on your behavior, then act to enable the causes and remove the constraints (if that is possible).  Continue with your data collection and see if the changes you have introduced have in fact increased the probability of success.

Analysis not Paralysis Precision is not needed Focus on improvement No blame Analysis is limited Analysis not Paralysis Also keep in mind that any analysis is limited,  You could not possibly look at all causes.  At best you have focus on a handful of causes.  Furthermore, the cause of your behavior may change over time.  Given these limitations, the resulting analysis should be used with caution.  If you do not jump to unsupported conclusions, some information is better than no information at all.  After you gain insight into causes and constraints on your behavior, then act to enable the causes and remove the constraints (if that is possible).  Continue with your data collection and see if the changes you have introduced have in fact increased the probability of success.

Take Home Lesson New Insights Causal analysis can help you gain new insights into your behavior.