Latent class analysis software: Answering the most frequently asked questions
How do I learn more about latent class analysis software?
Book a demo with a Q Research Software expert and learn everything you need to get started (click the button on to the right).
Is latent class analysis better than cluster analysis?
Yes! While both techniques are used for discovering segments in data, latent class analysis outperforms cluster analysis in two ways. First, it can handle many different data types (structures) (e.g., rankings, rating, numeric, categorical, choice models). Cluster analysis can only handle numeric data. Second, it automatically addresses missing values.
What are the limitations of latent class analysis?
A latent class analysis is a lot slower to run than a k-means cluster analysis (even in the best latent class analysis software – Q). This makes it impractical for huge databases.
The latent class analysis algorithm does not assign each respondent to a class. Instead, it computes a probability that a respondent will be in a class. Then, at the end of the analysis, observations are assigned to the segment for which they have the highest probability. This leads to two different ways of computing the sizes of the segments and the mean values of each class. These computations often lead to slightly different answers and this can cause confusion.
When should cluster analysis be used instead of latent class analysis?
In most instances, a latent class analysis should be used instead of cluster analysis. It is a superior way to segment data. However, this does not mean the other techniques are without any merit. Ultimately, a segmentation should be judged by its usefulness in inspiring its users, and there is no guarantee that the segmentation created using the state-of-the-art technique like latent class analysis will be best in this regard.
What is the best latent class analysis tool for market segmentation?
Q research software of course. Book your demo now.