 Factor analysis extracts underlying dimensions from the data and answers the question, what indicators have in common. Sometimes factor analysis nevertheless doesn't give you the solution that you expect and then you have to understand why that would happen. To do so you have to understand what exactly the factor analysis is doing. In this video I will provide a conceptual explanation of exploratory factor analysis. The idea of factor analysis is that there are different variance components in the data. Typically if we have a measurement occasion there is variance caused by the construct. So we have indicators A1, A2 and A3 that are supposedly valid measures of construct A. And we have B1, B2 and B3 that are supposedly valid measures of construct B. Then each indicator also has this random noise unreliability and some unique aspects. So if these are survey questions then the survey questions measure the construct. They could measure something else and then there is unreliability. In factor analysis we add one or more latent variables to this model. So these are observed variables and we try to explain the correlation between the observed variables by using a smaller number of latent variables. For example we add one factor here that we think explains the inter-correlations between these items. And there were two strategies, exploratory analysis where we allow the computer to specify the factors and confirm the analysis where we specify the factor structure ourselves. The factor analysis model also it's a statistical model so it's a set of equations and here is the model. So we are saying that all these indicators A1, A2, A3, B1, B2, B3 are function of the factor times factor loading for which we use the Greek letter lambda plus some error that we don't observe. So it's a regression equation basically. The only difference is that we only observe the dependent variable, we don't observe the key independent variable. So this is a latent variable. If it was observed variable then we could just regress all indicators on the factor but we can't because the factor is not observed. So these were the factor loadings and these were the item uniqueness. It's important to note the factor analysis cannot separate unreliability from some other unique variables. So if the A1 indicator has some unique aspect Q then you cannot separate it from unreliability. And that's basically with any reliability statistic this applies. So if your indicators have variation that is unique from other indicators but still reliable so it's not random knows it's some variation then it cannot be a distinguished form of reliability. We assume that all variants that can't be explained by the other items all unique variants is unreliability so that's the workaround for that limitation. So we had exploratory analysis and confirmatory analysis. The idea of exploratory factor analysis is that the computer first tries to explain the data with one factor. So it estimates one factor model, one factor explains all correlations between the indicators. Then we eliminate the variance explained by that factor from the data and then we fit the same single factor model again on the residual variance. And we repeat this until there is no more covariance between indicators to explain. So what does the process look like? We have the data here so we have this A variance here to the A construct, B variance to the B construct. And we want to know how much of the variation of these indicators are due to the A construct and the B construct. We first fit a single factor model and let's say that this single factor now picks up all the A variance. So all the A variance goes to the factor and the remaining variance will go to the error term. So we take apart the variation in the observed variables. We assign some to the factors and some to the error terms. And this model doesn't fit really well because these errors are assumed to be uncorrelated. But we can see here that because this error term takes the B variance and this as well they are actually correlated. So the factor analysis wouldn't stop here because there is evidence that there is still correlations after this factor. So we take the A variance, we put it aside here and then from the remaining data we fit another factor. It picks up the B variance here and the B variance here and then the remaining indicators here are uncorrelated. In which case the factor analysis stops. So that's how factor analysis works. We pick up some variation then we continue with the remaining. We pick up some variation until all the remaining indicators are uncorrelated. In which case the factor analysis said I have discovered two factors. These two factors explain the inter-correlation with the variables completely. The remaining variation in the data are simply unique features of these indicators or unreliability. So that's the conceptual idea. We extract variation then we do it over and over until there is no more covariance to extract.