Mplus version 5.2 was used for these examples.
1.0 Exploratory factor analysis
Mplus has many nice features to assist researchers conducting exploratory
factor analysis. In the example below, we use the
m255_mplus_notes_efa data set, which
contains continuous, dichotomous and ordered categorical variables. Our
data set has missing values on several of the variables that will be used in the
analysis. After declaring the data set, we use the listwise
statement. Unlike many other statistical packages, Mplus does not use
listwise deletion by default. Mplus provides several methods of
handling the missing data: listwise deletion, full information maximum
likelihood (FIML) and FIML with auxiliary variables. (Mplus can also use
multiply imputed data sets, although it will not create multiply imputed data
sets.) In this example, we will use listwise deletion.
If this statement was omitted, Mplus would use FIML to estimate
the EFA with all of the information in the data set. The missing
statement is included to show how it would be used, but in this example, it is
unnecessary. On the categorical statement, we declare all of our
dichotomous and ordered categorical variables. On the analysis
statement, we indicate that we want to run an EFA. After that
specification, two numbers are needed. The first number indicates the
minimum number of factors to extract, and the second number indicates the
maximum number of factors to extract. Mplus will produce solutions for the
number of factors between the minimum and maximum. In our example, we ask
for only three factors (so we have 3 for both the first and the second number).
In the commented out analysis statement, we ask for a minimum of 1 and a
maximum of 3 factors; hence, Mplus will produce a 1, 2 and 3 factor solution.
By default, Mplus provides a geomin rotated solution. (Geomin is an
oblique type of rotation, so the correlations between the factors are given in
the output.) Mplus offers 27
different types of rotations, which are described in the Mplus User’s Guide.
We have commented out an example of using the rotation statement to
request a varimax rotation. Finally, we request a scree plot on the plot statement using type =
plot2. To see the plots requested, click on Graphs and then View
Graphs.
Besides having several options for handling missing data and handling
dichotomous and ordered categorical variables, Mplus can also conduct EFAs with
survey data (data that contain sampling weights, clustering and/or
stratification). As you can see in the output, standard errors are
provided for the factor loadings.
For information on the interpretation of the output, please visit our
Annotated Mplus Output: Exploratory
Factor Analysis page.
title: Exploratory factor analysis with categorical and continuous
variables.
data: file is "f:https://stats.idre.ucla.edu/wp-content/uploads/2016/02/m255_mplus_notes_efa.txt";
listwise is on;
variable: names are facsex facnat facrank studrnk1 grade
salary yrsteach yrsut nstud sample;
usevar are facsex facnat facrank studrnk1 grade
salary yrsteach yrsut nstud;
missing are all (-9);
categorical are facsex facnat facrank studrnk1 grade;
analysis: type = efa 3 3;
! analysis: type = efa 1 3;
! rotation = varimax;
iterations = 100000;
plot: type = plot2;
EXPLORATORY FACTOR ANALYSIS WITH 3 FACTOR(S):
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 184.792*
Degrees of Freedom 12
P-Value 0.0000
Scaling Correction Factor 0.424
for MLR
* The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
for chi-square difference tests. MLM, MLR and WLSM chi-square difference
testing is described in the Mplus Technical Appendices at www.statmodel.com.
See chi-square difference testing in the index of the Mplus User's Guide.
Chi-Square Test of Model Fit for the Baseline Model
Value 3344.873
Degrees of Freedom 36
P-Value 0.0000
CFI/TLI
CFI 0.948
TLI 0.843
Number of Free Parameters 28
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.117
MINIMUM ROTATION FUNCTION VALUE 0.23719
GEOMIN ROTATED LOADINGS
1 2 3
________ ________ ________
FACSEX 0.002 -0.365 0.379
FACNAT -0.002 0.167 0.397
FACRANK 0.459 0.690 0.001
STUDRNK1 -0.067 0.165 0.422
GRADE -0.055 0.036 0.188
SALARY 0.358 0.608 -0.003
YRSTEACH 0.843 0.035 -0.025
YRSUT 0.984 -0.013 0.026
NSTUD -0.299 0.003 -1.078
GEOMIN FACTOR CORRELATIONS
1 2 3
________ ________ ________
1 1.000
2 0.248 1.000
3 0.020 -0.257 1.000
ESTIMATED RESIDUAL VARIANCES
FACSEX FACNAT FACRANK STUDRNK1 GRADE
________ ________ ________ ________ ________
1 0.652 0.849 0.157 0.833 0.965
ESTIMATED RESIDUAL VARIANCES
SALARY YRSTEACH YRSUT NSTUD
________ ________ ________ ________
1 0.393 0.273 0.035 -0.264
S.E. GEOMIN ROTATED LOADINGS
1 2 3
________ ________ ________
FACSEX 0.002 0.051 0.065
FACNAT 0.013 0.073 0.106
FACRANK 0.134 0.067 0.015
STUDRNK1 0.100 0.050 0.046
GRADE 0.050 0.043 0.039
SALARY 0.113 0.052 0.022
YRSTEACH 0.047 0.069 0.031
YRSUT 0.039 0.013 0.019
NSTUD 0.178 0.002 0.092
S.E. GEOMIN FACTOR CORRELATIONS
1 2 3
________ ________ ________
1 0.000
2 0.174 0.000
3 0.151 0.086 0.000
S.E. ESTIMATED RESIDUAL VARIANCES
FACSEX FACNAT FACRANK STUDRNK1 GRADE
________ ________ ________ ________ ________
1 0.062 0.077 0.063 0.035 0.014
S.E. ESTIMATED RESIDUAL VARIANCES
SALARY YRSTEACH YRSUT NSTUD
________ ________ ________ ________
1 0.047 0.050 0.076 0.206
Est./S.E. GEOMIN ROTATED LOADINGS
1 2 3
________ ________ ________
FACSEX 0.922 -7.144 5.879
FACNAT -0.145 2.268 3.732
FACRANK 3.425 10.322 0.074
STUDRNK1 -0.672 3.315 9.170
GRADE -1.087 0.831 4.843
SALARY 3.162 11.765 -0.129
YRSTEACH 17.764 0.514 -0.818
YRSUT 25.214 -0.963 1.411
NSTUD -1.681 1.184 -11.717
Est./S.E. GEOMIN FACTOR CORRELATIONS
1 2 3
________ ________ ________
1 0.000
2 1.426 0.000
3 0.131 -2.999 0.000
Est./S.E. ESTIMATED RESIDUAL VARIANCES
FACSEX FACNAT FACRANK STUDRNK1 GRADE
________ ________ ________ ________ ________
1 10.503 11.031 2.476 23.517 71.234
Est./S.E. ESTIMATED RESIDUAL VARIANCES
SALARY YRSTEACH YRSUT NSTUD
________ ________ ________ ________
1 8.306 5.512 0.462 -1.283
FACTOR STRUCTURE
1 2 3
________ ________ ________
FACSEX -0.081 -0.462 0.473
FACNAT 0.047 0.064 0.354
FACRANK 0.630 0.803 -0.167
STUDRNK1 -0.018 0.040 0.378
GRADE -0.042 -0.026 0.178
SALARY 0.509 0.697 -0.152
YRSTEACH 0.851 0.251 -0.018
YRSUT 0.982 0.225 0.049
NSTUD -0.319 0.205 -1.084
FACTOR DETERMINACIES
1 2 3
________ ________ ________
1 0.984 0.901 1.172
PLOT INFORMATION
The following plots are available:
Eigenvalues for exploratory factor analysis
