What makes Measurable Objectives so difficult to get right?

Learning how to define measurable objectives has been more challenging than I thought it would be. I am now realizing first hand how vital a role measurable objectives play. In these trying times for everyone, the gap between how important objectives are - and how often they are overlooked - is getting wider. Now more than ever, there is a need to evaluate training effectiveness and Return on Investment. Evaluation is impossible without a clear, measurable target. Unfortunately, the ineffectiveness of poorly defined objectives is not realized until attempting to evaluate, and by then it's too late.

I found myself asking what makes this so difficult? Why is it so often overlooked?

In this first post I will outline my answers to these questions. In a following post I will share the tricks I have learned to writing measurable objectives.

Writing Measurable Objectives is difficult because:

1) Knowledge or understanding can not be measured. (but we like to think it can)

On some levels I think we get this, but its easy to forget because so much of our culture wants to hold onto the illusion that we can. Throughout the entire education system we are conditioned to believe that our own knowledge is being measured when it isn’t. How much does knowledge weigh? What does it look like? We can only measure what we can observe and knowledge at its very essence is unobservable. What we can observe and therefore measure is behavior. Where we make the mistake is when through measuring a persons behavior we THINK we are measuring their knowledge.

The measurement of behavior can provide valuable insight, but this insight is often used incorrectly by mistaking correlations as cause and effect. Just because its when its snowing outside its freezing, does not mean that when its freezing outside its snowing. Clearly there are additional l variables to consider. The same is true with the link between knowledge and behavior. There are an infinite number of variables that contribute to a persons behavior. Therefore, knowledge does not cause behavior.

Thomas Gilberts Behavioral Engineering Model (BEM) provides 6 main categories in which we can group the many variables that contribute to a persons behavior.
While the BEM can be used to select a strategy for influencing a persons behavior, it can not be used in reverse. The measurement of a persons behavior should not be mistaken as a measurement of anything other than behavior.

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