Chapter 3. Psychological Disorders

Statistical Concepts Covered: In this applet, you’ll learn more about relationships by looking at comorbidity or the prevalence of having two disorders at the same time. We will also discuss the importance of operational definitions.

Statistical Lesson. In previous applets we considered correlations and how we use them to look at a relationship between two variables. These variables were sometimes numerical and sometimes categorical. In the case with comorbidity we are looking at categories again because we are evaluating how many people fit into two categories or disorders at the same time. This is what we refer to as comorbidity, the presence of two disorders in an individual at the same time.

When identifying variables within a study it is important that we have operational definitions, which explain how we will manipulate or measure our variables. In the case of comorbidity there might be two ways we can define how we are measuring a person’s experience of two disorders during the same time period.

First, we may choose to operationally define someone as having comorbidity for two disorders if they experience both within a 12 month period, which we will refer to as 12 month prevalence in this applet. Second, we may choose to operationally define someone as having comorbidity if they experience two disorders at any point in their lifetime, which we will refer to as lifetime prevalence.

It is important to distinguish between these two as they can lead to very different interpretations. Be sure to keep this in mind when answering the questions in this applet. Think about how the time frame being used to define the comorbidity may lead us to different conclusions. As a reminder, any analysis of relationships should focus only on that relationship and not imply causality.

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Statistical Lesson. You may have noticed that we are asking about which disorders appear to have higher or lower comorbidity rates than others. When you are performing statistical analysis, you cannot always tell if two points on a graph that show different values are really different from each other, or if the data in the data set just happened to show a difference between the groups due to outside factors. Plenty of formal statistical analyses can be performed to answer this question, but we’ll save learning about those processes until your statistics class. For now, you should recognize that you can get a rough idea of which values may be larger or smaller by eyeballing a graph, but you have to do a bit more work before you can state with confidence that, for example, the lifetime prevalence of PTSD really is higher than the lifetime prevalence of panic disorder.

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