Understanding evidence-based research methods: confirmatory and exploratory analysis.

This contribution to the methodology series focuses on understanding when to use two different factor analytic techniques: confirmatory and exploratory factor analysis. The author welcomes comments from all readers who have suggestions about the way information is presented or ques- tions about the content of this column. It is not comprehensive and does not replace information found in textbooks or peer-reviewed articles. The author used textbooks as sources and recommends that you also refer to them for more detailed explanations of what is discussed in this column.SCENARIO: Allan and Gwen have decided to create a new survey to mea- sure patient perceptions of built environment factors because they view existing measures as deficient in some way. Their conversation about how to analyze a new survey follows.GWEN: Allan, I am glad we decided to create this new survey. Initial reports from participants who completed the survey indicate that the survey was easy to understand and measured important issues. Now our challenge will be deter- mining whether the survey items we created load onto unique factors like we posited when creating our survey.ALLAN: Is that difficult to do? Will we know whether we were right or wrong about what we previously posited?GWEN: This is not too difficult and we will know whether we were right or wrong.ALLAN: What happens if we discover that we were wrong? Do we need to start over?GWEN: Not necessarily. Let's discuss what our analytical plan should be and then discuss what we do if we find contradictory results to what we expected. How does that sound, Allan?ALLAN: Perfect, Gwen.GWEN: As you will recall, we decided to measure three new constructs: (1) incor- porating nature into the built environment; (2) appropriate balance of natural and artificial light; and (3) room comfort. For each of these constructs we wrote seven new survey items, resulting in 21 total new items. There are two ways to analyze this data. One way is to conduct a confirmatory factor analysis (CFA). We conduct a CFA when we have a specific hypothesis we want to test. The other approach is an exploratory factor analysis (EFA), which is an analytic approach we use when we are unsure of a specific hypothesis or to help us understand our data better when our hypothesis is rejected. Please remember this latter point later in our discussion, as I will discuss it again.ALLAN: Ok. But how do we know whether we have a hypothesis? I thought we just cre- ated 21 items and then sent them out to patients to complete so we could understand their thoughts about our questions. What am I missing?GWEN: Great question! Remember, Allan, when we created the 21 items in the first place, we wrote specific items for each of our three constructs, right?ALLAN: Yes. So, are you saying that we need to test a hypothesis about whether the seven items we wrote to measure " incorporating nature into the built environment" actually measure that construct?GWEN: That is exactly right, Allan. And we also want to test hypotheses about the items we wrote for the other two constructs.ALLAN: Sounds like we want to conduct a CFA then. What are the steps involved in conducting a CFA? Can we conduct a CFA using any software program?GWEN: We need a special software program to conduct a CFA, Allan, because not all programs are able to do this. Examples of software programs able to do this are AMOS, LISREL, MPLUS, and R. For our analysis, I will use A MOS, which includes a graphical component in the program that allows me to show you a visual representation of the model that we will test. See Figure 1 for an example of what our "incorporating nature into the built environment" model looks like.ALLAN: What do the different notations represent?GWEN: The circular "Incorporate" represents our latent variable. The squares with I1 to I7 represent the seven survey items we created to measure this variable and the circles with D1 to D7 represent disturbance terms associated with each item. …