When should cluster analysis be used instead of factor analysis?
Cluster analysis and factor analysis have different objectives. The usual objective of factor analysis is to explain correlation in a set of data and relate variables to each other, while the objective of cluster analysis is to address heterogeneity in each set of data.
Is cluster analysis A data reduction method?
Like factor analysis, cluster analysis may be thought of as a data reduction technique. We seek to reduce the n original observations into g groups, where 1 ≤ g ≤ n.
What is Q type factor analysis?
a type of factor analysis used to understand the major dimensions or “types” of people by identifying how they perceive different variables.
Can I do the factor analysis for Likert type question?
Likert scale is most ideal for Factor analysis. You need to have a set of interrelated items with good facial and construct validity and with a cronbach Alpha value above 0.7. As far as I know, the issue of how many items one needs to construct a measurement model has no agreed-upon answer.
What are the problems with the Likert scale?
Many problems arise in EFA and latent factor modeling of Likert scale ratings. Likert scales produce ordinal (i.e., categorical, polytomous, ordered) data, not continuous data.
How many questionnaires do you need for factor analysis?
When using factor analysis, the recommendation is to collect at least 2.5 times as many completed questionnaires as you have items. So with a total of 23 items you would need about 58 completed questionnaires for the purposes of factor analysis.
Do you use Pearson correlations in factor analysis?
However, most people use Pearson correlations on their Factor analyses. Not only that, but they also use statistical tests on their total scores, even if the items are dichotomous, as if those are continuous. This is in my mind a serious flaw of those researchers.