Pocket guide to Exploratory and Confirmatory Factor Analysis in R

Last updated on Aug 13, 2023 1 min read R, Rblog, psychology, psychometrics

One single common factor

i1i2i3i4
i11.000.640.640.64
i20.641.000.640.64
i30.640.641.000.64
i40.640.640.641.00
%0 1 F 2 i1 2->1 0.8 3 i2 3->1 0.8 4 i3 4->1 0.8 5 i4 5->1 0.8

Two uncorrelated common factors

i1i2i3i4
i11.000.640.000.00
i20.641.000.000.00
i30.000.001.000.64
i40.000.000.641.00
%0 1 F1 2 F2 3 i1 3->1 0.8 4 i2 4->1 0.8 5 i3 5->2 0.8 6 i4 6->2 0.8

Two correlated common factors

i1i2i3i4
i11.000.640.410.41
i20.641.000.410.41
i30.410.411.000.64
i40.410.410.641.00
%0 1 F1 2 F2 1:s->2:s 0.64 3 i1 3->1 0.8 4 i2 4->1 0.8 5 i3 5->2 0.8 6 i4 6->2 0.8

In Confirmatory Factor Analysis, relationships among factors may be specified, giving rise to the ability to test higher-order structures. For example, the correlation between specific factors F1 and F2 could be accounted for by a higher-order general factor F.

%0 1 F1 2 F2 3 F 1->3 0.8 2->3 0.8 4 i1 4->1 0.8 5 i2 5->1 0.8 6 i3 6->2 0.8 7 i4 7->2 0.8
R psychology psychometrics psychlab lavaan EFA CFA
Claudiu-Cristian Papasteri
Claudiu-Cristian Papasteri
Psychologist, Psychotherapist, Researcher

Related