!Please note this page is based on an earlier version of Mplus and has not been updated to include new features available in Mplus 6 and 7!
This page was adapted from Mplus for Windows: An Introduction developed by the Consulting group in the Division of Statistics and Data Sciences at UT Austin. We are very grateful to them for their permission to copy and adapt these materials at our web site.
Section 1: Introduction
1. About
this Document
2. Introduction
to SEM and Mplus
3.
Accessing Mplus
4.
Getting Help with Mplus
Section 2: Latent Variable Modeling Using
Mplus
1.
Overview of SEM Assumptions
2.
Categorical Outcomes and Categorical Latent Variables
3.
Should you use Mplus?
Section 3: Using Mplus
1.
Launching Mplus
2.
The Command and Output Windows
3.
Reading Data and Outputting Sample Statistics
Section 4: Exploratory Factor Analysis
1.
Exploratory Factor Analysis with Continuous Variables
2.
Exploratory Factor Analysis with Missing Data
3.
Exploratory Factor Analysis with Categorical Outcomes
Section 5: Confirmatory Factor Analysis
and Structural Equation Models
1.
Confirmatory Factor Analysis with Continuous Variables
2.
Handling Missing Data
3.
Confirmatory Factor Analysis with Categorical Outcomes
4.
Structural Equation Modeling with Continuous Outcomes
Section 6: Advanced Models
1.
Multiple Group Analysis
2.
Multilevel Models
References
Section 1: Introduction
1. About this Document
This document introduces you to Mplus for Windows. It is primarily aimed
at first time users of Mplus who have prior experience with either exploratory
factor analysis (EFA), or confirmatory factor analysis (CFA)
and structural equation modeling (SEM). The document is organized
into six sections. The first section provides a brief introduction to Mplus
and describes how to obtain access to Mplus. The second section briefly
reviews SEM assumptions and describes important and useful model fitting
features that are unique to Mplus. The third section describes how to get
started with Mplus, how to read data from an external data file, and how
to obtain descriptive sample statistics. The fourth section explains how
to fit exploratory factor analysis models for continuous and categorical
outcomes using Mplus. The fifth section of this document demonstrates how
you can use Mplus to test confirmatory factor analysis and structural equation
models. The sixth section presents examples of two advanced models available
in Mplus: multiple group analysis and multilevel SEM. By the end of the
course you should be able to fit EFA and CFA/SEM models using Mplus. You
will also gain an appreciation for the types of research questions well-suited
to Mplus and some of its unique features.
2. Introduction to EFA, CFA, SEM and Mplus
Exploratory factor analysis (EFA) is a method of data reduction
in which you may infer the presence of latent factors that are responsible
for shared variation in multiple measured or observed variables. In EFA
each observed variable in the analysis may be related to each latent factor
contained in the analysis. By contrast, confirmatory factor analysis
(CFA) allows you to stipulate which latent factor is related to any given
observed variable. Structural equation modeling (SEM) is a more
general form of CFA in which latent factors may be regressed onto each
other. Mplus can fit EFA, CFA, and SEM models, among other models.
To effectively use and understand the course material, you should already
know how to conduct a multiple linear regression analysis and compute descriptive
statistics such as frequency tables using SAS, Stata, SPSS, or a similar general
statistical software package. You should also understand how to interpret
the output from a multiple linear regression analysis. This document also
assumes that you are familiar with the statistical assumptions of EFA,
CFA, and SEM, and you are comfortable using syntax-based software programs. If you do not have prior experience with exploratory factor
analysis, we would recommend seeing our Stat Books for
Loan under the section on Factor Analysis and Structural Equation Modeling
for more information about Factor Analysis and SEM. Finally, you should understand
basic Microsoft Windows navigation operations: opening files and folders,
saving your work, recalling previously saved work, etc.
3. Accessing Mplus
You may access Mplus in one of three ways:
Important note: Our Statistical Consulting services are available only to
researchers in the UCLA community. Non-UCLA researchers will find the Muthén
& MuthénWeb
site to be a useful resource; also see the Mplus
Discussion forum for frequently-asked questions and answers. You may
also post your own questions in this forum.
Section 2: Latent Variable Modeling using
Mplus
1. Overview of SEM Assumptions
for Continuous Outcome Data
Before specifying and running a latent variable models, you should give
some thought to the assumptions underlying latent variable modeling with
continuous outcome variables. Several of these assumptions are shown below:
In this sample program, the DATA command uses the FILE subcommand to tell Mplus where to locate the relevant data file. In this case, the file's location is c:\intromplus\grant.dat. The FORMAT subcommand uses the default free option to let Mplus know that the data points appear in order in the data file with the data points separated by commas, tabs, or spaces.TITLE: Grant-White School: Summary Statistics DATA: FILE IS "c:\intromplus\grant.dat" ; FORMAT IS free; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc cubes lozenges paragrap sentence wordmean ; ANALYSIS: TYPE = basic ;
A similar subcommand, USEOBS, allows you to select subsets of cases to be used in a particular analysis. The example below shows how you could limit the analysis to female participants, selecting just those where gender=1. It also shows how you can use the dash notation to specify a group of variables in the USEVARIABLES statement, indicating all of the variables contigously between visperc to wordmean.
TITLE: Grant-White School: Summary Statistics DATA: FILE IS "c:\intromplus\grant.dat" ; FORMAT IS free; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc-wordmean ; USEOBS gender EQ 1 ; ANALYSIS: TYPE = basic ;
The ANALYSIS command specifies the TYPE of analysis to be
performed by Mplus. In this example the type is basic. The basic
model type does not fit any model to the sample data; instead Mplus
will compute sample statistics only. Using basic as the analysis type is useful
during the initial phase of building your command file because you can use the
Mplus sample statistics output to compare Mplus results to results you obtained
using SAS, SPSS, Excel, or other statistical software programs to verify that
Mplus is reading your input data correctly.
Running the program above with the data
grant.dat yields the output from this basic analysis
below. Although Mplus initially returns a copy of the input command file, that
portion of the output has been omitted here in the interest of saving space.
Grant-White School: Summary Statistics SUMMARY OF ANALYSIS Number of groups 1 Number of observations 145 Number of y-variables 6 Number of x-variables 0 Number of continuous latent variables 0 Observed variables in the analysis VISPERC CUBES LOZENGES PARAGRAP SENTENCE WORDMEAN Estimator ML Information matrix EXPECTED Maximum number of iterations 1000 Convergence criterion 0.500D-04 Maximum number of steepest descent iterations 20 Input data file(s) c:\IntroMplus\grant.dat Input data format FREE RESULTS FOR BASIC ANALYSIS SAMPLE STATISTICS Means VISPERC CUBES LOZENGES PARAGRAP SENTENCE ________ ________ ________ ________ ________ 1 29.579 24.800 15.966 9.952 18.848 Means WORDMEAN ________ 1 17.283 Covariances VISPERC CUBES LOZENGES PARAGRAP SENTENCE ________ ________ ________ ________ ________ VISPERC 47.801 CUBES 10.012 19.758 LOZENGES 25.798 15.417 69.172 PARAGRAP 7.973 3.421 9.207 11.393 SENTENCE 9.936 3.296 11.092 11.277 21.616 WORDMEAN 17.425 6.876 22.954 19.167 25.321 Covariances WORDMEAN ________ WORDMEAN 63.163 Correlations VISPERC CUBES LOZENGES PARAGRAP SENTENCE ________ ________ ________ ________ ________ VISPERC 1.000 CUBES 0.326 1.000 LOZENGES 0.449 0.417 1.000 PARAGRAP 0.342 0.228 0.328 1.000 SENTENCE 0.309 0.159 0.287 0.719 1.000 WORDMEAN 0.317 0.195 0.347 0.714 0.685 Correlations WORDMEAN ________ WORDMEAN 1.000
Mplus initially identifies the number of groups and observations in the
analysis, followed by the number of X (predictor) and Y (outcome) variables and
the sample (input) covariances, variances, and means. Once you have verified
that these values are correct, you can turn your attention to fitting your
model(s) of interest. The next section continues with the same example data file
, but describes how to perform an exploratory factor analysis of the continuous
variables in the Grant-White data file using Mplus.
Section 4: Exploratory Factor Analysis
1.
Exploratory Factor Analysis with Continuous Variables
Once you have read the data into Mplus and verified that the sample statistics
show that the data have been read correctly, you can perform exploratory factor
analysis using Mplus by altering the ANALYSIS command as follows:
This syntax instructs Mplus to perform an exploratory factor analysis of the Grant-White data file. Efa tells Mplus to perform an exploratory factor analysis. The 1 and 2 following the efa specification tells Mplus to generate all possible factor solutions between and including 1 and 2. In this instance, one and two factor solutions will be produced by the analysis. Finally, the ESTIMATOR = ml option has Mplus use the maximum likelihood estimator to perform the factor analysis and compute a chi-square goodness of fit test that the number of hypothesized factors is sufficient to account for the correlations among the six variables in the analysis. This optional specification overrides the default unweighted least-square (uls) estimator.TITLE: Grant-White School: Summary Statistics DATA: FILE IS "c:\intromplus\grant.dat" ; FORMAT IS free ; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc cubes lozenges paragrap sentence wordmean ; ANALYSIS: TYPE = efa 1 2 ; ESTIMATOR = ml ; OUTPUT: sampstat ;
Mplus produces the sample correlations, eigenvalues, and the chi-square
test of the one factor model to the sample data. As you can see from the
results, shown below, the chi-square test is statistically significant,
so the null hypothesis that a single factor fits the data is rejected;
more factors are required to obtain a non-significant chi-square. Since
the chi-square test is sensitive to sample size (such that large samples
often return statistically significant chi-square values) and non-normality
in the input variables, Mplus also provides the Root Mean Square Error
of Approximation (RMSEA) statistic. The RMSEA is not as sensitive
to large sample sizes. According to Hu and Bentler (1999), RMSEA values
below .06 indicate satisfactory model fit. The RMSEA yielded a result of
.162, which was consistent with the chi-square result in suggesting that
the one factor model does not fit the data adequately.
CONTINUOUS VARIABLE CORRELATION MATRIX
VISPERC
CUBES
LOZENGES
PARAGRAP SENTENCE
________
________
________
________ ________
VISPERC
CUBES
.326
LOZENGES
.449 .417
PARAGRAP
.342
.228 .328
SENTENCE
.309
.159
.287 .719
WORDMEAN
.317
.195
.347
.714 .685
EXPLORATORY ANALYSIS WITH 1 FACTOR(S) :
EIGENVALUES FOR SAMPLE CORRELATION MATRIX
1
2
3
4
5
________ ________
________ ________
________
1
3.009
1.225
.656
.530 .311
EIGENVALUES FOR SAMPLE CORRELATION MATRIX
6
________
1 .270
EXPLORATORY ANALYSIS WITH 1 FACTOR(S) :
CHI-SQUARE
VALUE
43.241
DEGREES OF
FREEDOM
9
PROBABILITY
VALUE
.0000
RMSEA (ROOT MEAN SQUARE ERROR OF APPROXIMATION) :
ESTIMATE (90 PERCENT C.I.) IS .162 ( .115
.212)
PROBABILITY RMSEA LE .05 IS .000
Mplus next produces the estimated factor loadings and error
variances. Notice that the visperc,
cubes, and
lozenges
factor loadings are low relative to the other factor loadings
displayed below. See
Factor
Analysis Using SAS PROC FACTOR (courtesy of the Consulting group in the Division of Statistics and Scientific Computation at UT Austin) for more information on interpreting factor loadings.
ESTIMATED
FACTOR LOADINGS
1
________
VISPERC
.415
CUBES
.272
LOZENGES
.415
PARAGRAP
.865
SENTENCE
.818
WORDMEAN
.827
ESTIMATED ERROR VARIANCES
VISPERC
CUBES
LOZENGES
PARAGRAP SENTENCE
________
________
________
________ ________
.828
.926
.828
.252 .330
________
1 .316
The estimated correlation matrix is the correlation matrix reproduced
by Mplus under the assumption that a single factor is sufficient to
explain the sample correlations. From the model fit results shown above,
this is not the case, so it is not surprising that this implied or
model-based correlation matrix differs substantially from the sample
correlation matrix reported above.
ESTIMATED CORRELATION MATRIX
VISPERC
CUBES
LOZENGES
PARAGRAP SENTENCE
________
________
________
________ ________
VISPERC 1.000
CUBES
.113 1.000
LOZENGES
.172
.113 1.000
PARAGRAP
.359
.235
.359 1.000
SENTENCE
.339
.223
.340
.708 1.000
WORDMEAN
.343
.225
.343
.715 .677
WORDMEAN
________
WORDMEAN
1.000
The residuals matrix represents the difference between the
sample correlation matrix and the implied correlation matrix. As noted
above, since the model did not fit the observed data particularly well,
there are some values in this matrix that are non-trivial in size. In
particular, the cubes-visperc, lozenges-visperc, and
lozenges-cubes residual values are high relative to the other
values in the matrix.
RESIDUALS OBSERVED-EXPECTED
VISPERC
CUBES
LOZENGES
PARAGRAP SENTENCE
________
________
________
________ ________
VISPERC
.000
CUBES
.213 .000
LOZENGES
.276
.304 .000
PARAGRAP
-.017
-.007
-.031 .000
SENTENCE
-.030
-.063
-.053
.011 .000
WORDMEAN
-.026
-.030
.004
.000 .009
RESIDUALS OBSERVED-EXPECTED
WORDMEAN
________
WORDMEAN .000
The Root Mean Square Residual (RMR) is another descriptive model
fit
statistic. According to Hu and Bentler (1999), RMR values should be below
.08 with lower values indicating better model fit. The value of .1225
shown below for the one factor solution indicates unacceptably poor model
fit.
ROOT MEAN SQUARE RESIDUAL
IS .1225
In
short,
the one factor solution was a poor fit to the data. In particular, the
model did not account well for the correlations among the visperc,
cubes, and lozenges variables. What about the two factor
solution? Mplus reports the two factor solution following the single
factor model.
The chi-square test of model fit is non-significant,
indicating that the null hypothesis that the model fits the data cannot be
rejected (the model fits the data well). This finding is corroborated by
the RMSEA: Its estimate is zero; it's 90% confidence interval has an upper
bound value of .055, which is below the Hu and Bentler (1999) recommended
cutoff value of .06. The RMSEA estimate and its upper bound confidence
interval value should both fall below .06 to ensure satisfactory model
fit.
EXPLORATORY ANALYSIS WITH 2 FACTOR(S) :
EXPLORATORY ANALYSIS WITH 2 FACTOR(S) :
CHI-SQUARE
VALUE
1.079
DEGREES OF
FREEDOM
4
PROBABILITY
VALUE
.8976
RMSEA (ROOT MEAN SQUARE ERROR OF APPROXIMATION) :
ESTIMATE (90 PERCENT C.I.) IS .000 ( .000
.055)
PROBABILITY RMSEA LE .05 IS .944
For
exploratory factor analysis solutions with two or more factors, Mplus
reports varimax rotated loadings and promax rotated loadings.Varimax loadings assume the two factors are uncorrelated
whereas promax loadings allow the factors to be correlated. Directly below
the promax loadings is the factor intercorrelatrion matrix.
In this
example the two factors are correlated .480. With even a modest
correlation among the two factors, you should choose to interpret the promax rotated loadings. The loadings show that the visperc, cubes, and lozenges variables load onto the first factor
whereas the remaining variables load onto the second factor.
VARIMAX ROTATED LOADINGS
1
2
________ ________
VISPERC
.547 .250
CUBES
.550 .092
LOZENGES
.728 .196
PARAGRAP
.241 .830
SENTENCE
.174 .816
WORDMEAN
.247 .788
PROMAX ROTATED LOADINGS
1
2
________ ________
VISPERC
.540 .112
CUBES
.585 -.063
LOZENGES
.755 -.001
PARAGRAP
.046 .841
SENTENCE
-.025 .846
WORDMEAN
.063 .794
PROMAX FACTOR CORRELATIONS
1
2
________ ________
1 1.000
2
.480 1.000
Mplus
next reports estimated error variances for each observed variable, the
estimated correlation matrix, and the residual correlation matrix. Notice
that unlike the preceding one factor solution, this dual factor solution's
estimated correlation matrix is very close in value to the original sample
correlation matrix. Accordingly, the residual correlation matrix has all
values close to zero and the RMR value of .0092 is well below the Hu and Bentler (1999) recommended cutoff of .08.
ESTIMATED ERROR VARIANCES
VISPERC
CUBES
LOZENGES PARAGRAP SENTENCE
________
________
________
________ ________
1
.638
.689
.431
.253 .304
ESTIMATED ERROR VARIANCES
WORDMEAN
________
1 .318
ESTIMATED CORRELATION MATRIX
VISPERC
CUBES
LOZENGES PARAGRAP SENTENCE
________
________
________
________ ________
VISPERC 1.000
CUBES
.324 1.000
LOZENGES
.448
.419 1.000
PARAGRAP
.339
.209
.338 1.000
SENTENCE
.299
.170
.286
.719 1.000
WORDMEAN
.332
.208
.334
.714 .686
ESTIMATED CORRELATION MATRIX
WORDMEAN
________
WORDMEAN
1.000
RESIDUALS OBSERVED-EXPECTED
VISPERC
CUBES
LOZENGES PARAGRAP SENTENCE
_______
________
________
________ ________
VISPERC
.000
CUBES
.002 .000
LOZENGES
.001
-.002 .000
PARAGRAP
.002
.019
-.010 .000
SENTENCE
.010
-.011
.000
.000 .000
WORDMEAN
-.015
-.013
.013
.001 -.001
RESIDUALS OBSERVED-EXPECTED
WORDMEAN
________
WORDMEAN .000
ROOT MEAN SQUARE RESIDUAL
IS .0092
This
example assumes that the Grant-White data file is complete. In other words,
there are no missing cases in the Grant-White data file . What if some cases
had missing values? Often data files have cases with incomplete data. The
next section describes a feature unique to Mplus: exploratory factor
analysis of a data file with incomplete cases.
2. Exploratory Factor Analysis
with Missing Data
Suppose you altered the Grant-White data file so
that cases with visperc scores that exceed 34 have missing
cubes scores and that cases with wordmean scores of 10 or
below have missing sentence values. In this instance the missing
cubes and setence completion data are said to be missing at random
(MAR) because the patterns of missing data are explainable by the values
of other variables in the data file , visual perception and word meaning.
Ordinarily, if you do not specify a missing data analysis in Mplus, Mplus
performs listwise or casewise deletion of cases with any
missing data. That is, any case with one or more missing data points is
omitted entirely from analyses. However, for exploratory factor analysis,
confirmatory factor analysis, and structural equation modeling with
continuous variables, Mplus features a missing data option that
outperforms the default listwise deletion method. The optional method that
offers superior performance is called full information maximum likelihood
(FIML); details on FIML can be found in the UT Austin Statistical Services General FAQ #25: Handling missing or incomplete Data.
Regardless of whether you choose to use FIML or listwise data deletion
to handle missing data, if you have missing data in your input data file ,
you must tell Mplus how the missing values for each variable are
represented in the data file . You use the MISSING subcommand of the VARIABLE command to accomplish this task. In this example, missing
values for cubes and sentence are represented by -9, so the MISSING subcommand reads:
The all keyword tells Mplus that all variables in the analysis use -9 to represent missing values. If your data file contains blanks to represent missing values, you may use the specificationMISSING ARE all (-9) ;
Similarly, you may useMISSING = blank ;
if your data file contains period symbols to represent missing values. Other missing value specifications are available; see the Mplus User's Guide for specifics.MISSING ARE . ;
TITLE: Grant-White School: EFA with Missing Data DATA: FILE IS "c:\intromplus\grant-missing.dat" ; VARIABLE: NAMES ARE cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc - wordmean; MISSING ARE all (-9) ; ANALYSIS: TYPE = efa 1 2; ESTIMATOR = ml ;
Selected output from the analysis appears below.
Grant-White School: Exploratory Factor Analysis with Missing
Data
SUMMARY OF ANALYSIS
Number of
groups
1
Number of
observations
79
Number of
y-variables
6
Number of
x-variables
0
Number of continuous latent
variables
0
Notice that Mplus considers the data file to contain 79 usable cases rather than the original 145 cases.
EXPLORATORY ANALYSIS WITH 1 FACTOR(S) :TITLE: Grant-White School: EFA with Missing Data DATA: FILE IS "c:\intromplus\grant-missing.dat" ; VARIABLE: NAMES ARE cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc - wordmean; MISSING ARE all (-9) ; ANALYSIS: TYPE = missing efa 1 2 ; ESTIMATOR = ml ;Run the analysis and consider the results, shown below.
First, you must change the names of the variables in the NAMES and USEVARIABLES subcommands of the DATA command. Next, you tell Mplus which variables are categorical with the CATEGORICAL subcommand of the DATA command, like this:TITLE: Grant-White School: EFA with categorical outcomes DATA: FILE IS "a:\grantcat.dat" ; VARIABLE: NAMES ARE viscat cubescat lozcat paracat sentcat wordcat ; USEVARIABLES ARE viscat - wordcat ; CATEGORICAL ARE viscat - wordcat ; ANALYSIS: TYPE = efa 1 2; ESTIMATOR = wlsmv ; OUTPUT: sampstat ;
CATEGORICAL ARE vizcat - wordcat ;
You should also change the ESTIMATOR option for the ANALYSIS command. The default is unweighted least-squares (uls), which is fast and is useful for exploratory work, but a more optimal choice for categorical outcomes, based on the work of Muthén, DuToit, and Spisic (1997), is weighted least-squares with mean and variance adjustment, wlsmv.
ANALYSIS: TYPE = efa 1 2; ESTIMATOR = wlsmv ;
Selected output from the analysis
appears below. Notice that the categorical nature of the data precludes
computation of the descriptive model fit statistics such as the RMSEA,
though Mplus does produce the familiar chi-square test of overall model
fit.
EXPLORATORY ANALYSIS WITH 2 FACTOR(S) :
CHI-SQUARE
VALUE
2.823
DEGREES OF
FREEDOM
4
PROBABILITY
VALUE
.5875
The chi-square result for the two factor model is not
significant, which indicates that two factors are sufficient to explain
the intercorrelations among the six observed variables. The varimax and
promax rotated factor loadings appear below. The pattern and values
obtained from this analysis are consistent with the results of the first
exploratory factor analysis of the completely continuous data discussed
previously.
VARIMAX ROTATED LOADINGS
1
2
________ ________
VISCAT
.571 .332
CUBESCAT
.700 .117
LOZCAT
.667 .244
PARACAT
.473 .642
SENTCAT
.235 .847
WORDCAT
.206 .858
PROMAX
ROTATED LOADINGS
1
2
________ ________
VISCAT
.559 .159
CUBESCAT
.777 -.137
LOZCAT
.698 .022
PARACAT
.347 .550
SENTCAT
.005 .876
WORDCAT
-.031 .899
PROMAX
FACTOR CORRELATIONS
1
2
________ ________
1 1.000
2
.557 1.000
Although Mplus does not produce the RMSEA descriptive model fit
statistic for categorical outcomes, it does output the standardized root
mean residual, RMR:
ROOT MEAN SQUARE RESIDUAL
IS .0310
The value
of .031 suggests an excellent fit of the two factor model to the observed
data. (Please note that as of version 4.2, Mplus does give the RMSEA.)
There are several notes worth keeping in mind when you perform exploratory factor analysis with categorical outcome variables.
TITLE: Grant-White School: Summary Statistics DATA: FILE IS "c:\intromplus\grant.dat" ; FORMAT IS free ; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc cubes lozenges paragrap sentence wordmean ; ANALYSIS: TYPE = general ; MODEL: visual BY visperc@1 cubes lozenges ; verbal BY paragrap@1 sentence wordmean ; visual WITH verbal ; OUTPUT: standardized sampstat ;
The general analysis type tells Mplus that you are fitting
a general structural equation model rather than specific model such as
an exploratory factor analysis. The model is general in the sense that
you must define what parameters are estimated; all other parameters are
assumed to be fixed. In the exploratory factor analysis context, Mplus
already knows the specifics of that model, so specifying the model is handled
automatically by Mplus. By contrast, in the confirmatory factor analysis
and structural equation modeling context each hypothesized model is unique,
so you must tell Mplus how the model is constructed. The MODEL command
allows you to specify the parameters of your model.
The first line of the MODEL command shown above defines a latent
factor called visual. The BY keyword (an abbreviation for
"measured by") is used to define the latent variables; the latent variable
name appears on the left-hand side of the BY keyword whereas the
measured variables appear on the right-hand side of the BY keyword.
It has three observed indicator variables: visperc, cubes,
and lozenges. Similarly, in the second line of the MODEL command a latent factor called verbal has three indicators: paragrap, sentence,
and wordmean. The third line of MODEL command uses the WITH keyword to correlate the visual latent factor with the verbal
latent factor.
The visperc and paragrap variables are each followed by @1.
The @ sign tells Mplus to fix the factor loading (regression weight)
of the visual-visperc relationship to the value that follows the @,
1.00. Similarly, the verbal-paragrap relationship is also fixed
to 1.00. The reason you fix these two parameters is to provide a scale
for the visual and verbal latent variables' variances. If you ever need
to supply starting values for a particular parameter in Mplus, you can
specify its number after an asterisk, like this: sentence*.5. Omitting
the asterisks when you do not specify starting values is the default. Note
that each variable is separated from the other variables in the analysis
by at least one space.
Finally, the OUTPUT command contains an added keyword, standardized.
This option instructs Mplus to output standardized parameter estimate values
in addition to the default unstandardized values. Selected output from
the analysis appears below.
Grant-White School: Confirmatory Factor Analysis
SUMMARY OF ANALYSIS
Number of groups
1
Number of observations
145
Number of y-variables
6
Number of x-variables
0
Number of continuous latent variables
2
Observed variables in the analysis
VISPERC CUBES
LOZENGES PARAGRAP SENTENCE WORDMEAN
Continuous latent variables in the analysis
VISUAL VERBAL
The summary of analysis information tells you that there are six continuous
observed variables in the analysis and two latent factors, visual
and verbal. Mplus then displays the input covariance matrix generated
from the six observed variables:
SAMPLE STATISTICS
Covariances/Correlations/Residual
Correlations
VISPERC CUBES
LOZENGES PARAGRAP
SENTENCE
________ ________
________ ________
________
VISPERC 47.801
CUBES 10.012
19.758
LOZENGES 25.798
15.417 69.172
PARAGRAP 7.973
3.421 9.207
11.393
SENTENCE 9.936
3.296 11.092
11.277 21.616
WORDMEAN 17.425
6.876 22.954
19.167 25.321
Covariances/Correlations/Residual
Correlations
WORDMEAN
________
WORDMEAN 63.163
Mplus next reports the results of fitting the hypothesized model to
the sample data.
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value
3.663
Degrees
of Freedom
8
P-Value
.8861
Loglikelihood
H0 Value
-2575.128
H1 Value
-2573.297
Information Criteria
Number of
Free Parameters
13
Akaike (AIC)
5176.256
Bayesian
(BIC)
5214.954
Sample-Size
Adjusted BIC 5173.817
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate
.000
90 Percent
C.I.
.000 .046
Probability RMSEA <= .05 .957
As was the case for the exploratory factor analysis of these data, Mplus
reports the chi-square goodness-of-fit test and the RMSEA descriptive model
fit statistic. The chi-square test of model fit is not significant and
the RMSEA value is well below the value of .06 recommended by Hu and Bentler (1999) as an upper boundary, so you can conclude that the proposed model
fits the data well. Mplus also reports the Akaike Information Criterion
(AIC) and the Bayesian Information Criterion (BIC). These are descriptive
indexes of model fit that you can use to compare the goodness of model
fit of two or more competing models. Smaller values indicate better model
fit.
Mplus also outputs the unstandardized coefficients (Estimates
in the output), the standard errors (abbreviated S.E. in the output),
the estimates divided by their respective standard errors (Est./S.E.),
and two standardized coefficients for each estimated parameter in the model
(Std and StdYX). The estimate divided by the standard error
tests the null hypothesis that the parameter estimate is zero in the population
from which you drew your sample. An unstandardized estimate divided by
its standard error may be evaluated as a Z statistic, so values
that exceed +1.96 or fall below -1.96 are significant below p =
.05.
MODEL RESULTS
Estimates S.E. Est./S.E.
Std StdYX
VISUAL BY
VISPERC
1.000 .000
.000 4.358 .632
CUBES
.542 .116 4.658
2.360 .533
LOZENGES
1.392 .272 5.112
6.064 .732
VERBAL BY
PARAGRAP
1.000 .000
.000 2.920 .868
SENTENCE
1.309 .115 11.352
3.821 .825
WORDMEAN
2.247 .197 11.402
6.560 .828
VISUAL WITH
VERBAL
6.784 1.720 3.943
.533 .533
In this example, each of the estimated parameters has an estimate to
standard error ratio greater than +1.96, so each factor loading is statistically
significant, as well as the correlation between the visual and verbal
latent factors (Z = 3.943). The variance components of the two factors,
shown in the output appearing below, are also statistically significant,
indicating that the amount of variance accounted for by each factor is
significantly different from zero.
Each unstandardized estimate represents the amount of change in the outcome
variable as a function of a single unit change in the variable causing
it. In this example, you assume that the latent variables, in addition
to some measurement error (shown below), are responsible for the scores
on the six observed variables. For instance, for each single unit change
in the verbal latent factor, sentence scores increase by
1.309 units.
Different measures often have different scales, so you will often find
it useful to examine the standardized coefficients when you want to compare
the relative strength of associations across observed variables that are
measured on different scales. Mplus provides two standardized coefficients.
The first, labeled Std on the output, standardizes using the latent
variables' variances whereas the second type of standardized coefficient, StdYX,
standardizes based on latent and observed variables' variances. This standardized
coefficient represents the amount of change in an outcome variable per
standard deviation unit of a predictor variable. In this output, you can
see clearly that the standardized coefficients of paragrap,
sentence,
and wordmean are larger than those of visperc,
cubes,
and lozenges. This finding suggests that the verbal latent
factor does a better job at explaining the shared variance among paragrap,
sentence,
and wordmean than does the visual
latent factor for its three
indicator variables, visperc, cubes, and lozenges.
This assertion is corroborated by the residual variances output by Mplus.
The standardized coefficients for the first three indicators are larger
than those for the remaining three indicators.
Residual Variances
Grant-White School: Confirmatory Factor Analysis
Estimates S.E. Est./S.E.
Std StdYX
VISPERC
28.485 4.739 6.011
28.485 .600
CUBES
14.050 1.978 7.105
14.050 .716
LOZENGES
31.933 7.269 4.393
31.933 .465
PARAGRAP
2.791 .584 4.775
2.791 .247
SENTENCE
6.869 1.164 5.900
6.869 .320
WORDMEAN
19.695 3.385 5.819
19.695 .314
Variances
VISUAL
18.989 5.582 3.402
1.000 1.000
VERBAL
8.525 1.376 6.196
1.000 1.000
R-SQUARE
Observed
Variable R-Square
VISPERC .400
CUBES
.284
LOZENGES .535
PARAGRAP .753
SENTENCE .680
WORDMEAN .686
Finally, the r-square output illustrates that only modest amounts of
variance are accounted for in the first three indicators whereas much larger
amounts of variance are accounted for in the final three indicators. As
is the case with exploratory factor analysis of continuous outcome variables,
you may want to use the mlm or mlmv estimators
in lieu of the default ml estimator if your input data are
not distributed joint multivariate normal by using the ESTIMATOR = option on the ANALYSIS command. The mlm option provides
a mean-adjusted chi-square model test statistic whereas the mlmv option produces a mean and variance adjusted chi-square test of model fit;
both options also induce Mplus to produce robust standard errors displayed
in the model results table that are used to compute Z tests of significance
for individual parameter estimates. An added advantage of the mlm option is that its chi-square test and standard errors are equivalent to
those produced by EQS in its ML;ROBUST method. Muthén and Muthén have placed formulas
on their Web site that allow you to use mlm-produced
chi-square values in nested model comparisons.
2. Handling Missing Data
It is often the case that you have missing data in the context of confirmatory
factor analysis and structural equation modeling. Using Mplus, you can
employ the optimal Full Information Maximum Likelihood (FIML) approach
to handling missing data that was described above in the section Exploratory
Factor Analysis with Missing Data in Section 4.
Consider once again the same modified data file, grant-missing.dat,
containing incomplete cases that was used in the earlier exploratory factor
analysis with missing data. As in the previous example, define the missing
value code to be -9 for all variables using the MISSING subcommand
in the VARIABLE command, copy the MODEL syntax from the previous
confirmatory factor analysis example into the Mplus input window, and then
modify the ANALYSIS command so that it reads as follows (with the changed
part in italics for emphasis).
TITLE: Grant-White School: CFA with missing data DATA: FILE IS "c:\intromplus\grant-missing.dat" ; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc - wordmean ; MISSING ARE all (-9) ; ANALYSIS: TYPE = general missing h1 ; MODEL: visual BY visperc@1 cubes lozenges ; verbal BY paragrap@1 sentence wordmean ; visual WITH verbal ; OUTPUT: standardized sampstat ;
The missing keyword alerts Mplus to activate the FIML missing data handling feature. The additional h1 keyword
tells Mplus to output the chi-square goodness-of-fit test in addition to
the typical summary statistics, missing data pattern information, parameter
estimates, and standard errors obtained in an analysis. Mplus requires
that you specify the h1 keyword because large models with
many missing data patterns can take a long time to converge. If this describes
your situation, you may want to omit the h1 option on the TYPE
= line to verify that you have specified your model correctly
before
invoking the h1 option to produce the chi-square test of
model fit. If you elect to remove the h1 option from the ANALYSIS
TYPE = command, be sure to omit the sampstat option from
the OUTPUT line, as well. If sampstat is included
on the OUTPUT line, Mplus automatically assumes the h1ANALYSIS option and computes the chi-square test of model fit, even if h1 is not included on the ANALYSIS TYPE = line.
The chi-square test of model fit for the confirmatory factor analysis
with missing data shows that the hypothesized model fit the data well:
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value
2.777
Degrees
of Freedom
8
P-Value
.9476
Loglikelihood
H0 Value
-2376.312
H1 Value
-2374.923
Information Criteria
Number of
Free Parameters
19
Akaike (AIC)
4790.623
Bayesian
(BIC)
4847.181
Sample-Size
Adjusted BIC 4787.058
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate
.000
90 Percent
C.I.
.000 .011
Probability RMSEA <= .05 .982
The Mplus parameter estimates, standard errors, and standardized parameter
estimates are similar to those found in the preceding confirmatory factor
analysis example. The only substantial difference is the inclusion of an
additional section that contains means and intercepts for the latent factors
and observed variables. These means and intercepts are required to be estimated
by the FIML missing data handling procedure, but are otherwise not a part
of the tested model.
MODEL RESULTS
Estimates S.E. Est./S.E.
Std StdYX
VISUAL BY
VISPERC
1.000 .000
.000 4.377 .635
CUBES
.469 .127 3.679
2.051 .473
LOZENGES
1.373 .294 4.673
6.010 .725
VERBAL BY
PARAGRAP
1.000 .000
.000 2.914 .866
SENTENCE
1.187 .114 10.376
3.460 .821
WORDMEAN
2.247 .206 10.888
6.547 .827
VISUAL WITH
VERBAL
7.014 1.800 3.896
.550 .550
Residual Variances
VISPERC
28.354 5.037 5.629
28.354 .597
CUBES
14.589 2.340 6.234
14.589 .776
LOZENGES
32.642 7.938 4.112
32.642 .475
PARAGRAP
2.824 .627 4.507
2.824 .250
SENTENCE
5.781 1.070 5.401
5.781 .326
WORDMEAN
19.872 3.578 5.554
19.872 .317
Variances
VISUAL
19.158 5.859 3.270
1.000 1.000
VERBAL
8.493 1.393 6.099
1.000 1.000
Intercepts
VISPERC
29.579 .572 51.673
29.579 4.291
CUBES
24.616 .421 58.431
24.616 5.678
LOZENGES
15.965 .689 23.184
15.965 1.925
PARAGRAP
9.952 .279 35.620
9.952 2.958
SENTENCE
19.054 .366 52.057
19.054 4.522
WORDMEAN
17.283 .658 26.274
17.283 2.182
Finally, Mplus produces the r-square values for the observed variables.
Once again, these are similar to those obtained from the original data file
with complete cases.
R-SQUARE
Observed
Variable R-Square
VISPERC .403
CUBES
.224
LOZENGES .525
PARAGRAP .750
SENTENCE .674
WORDMEAN .683
If you elect to use Mplus's FIML approach to handling missing data,
be aware that the only available estimator is the maximum likelihood option, ml.
If you suspect that your data are non-normally distributed, remember that
the chi-square test of model fit may be affected by the non-normality problem.
Depending on the severity of the non-normality problem and the amount of
missing data you have, you may want to explore other ways of handling the
missing data problem prior to performing analyses using Mplus; see see the UT
Austin Statistical Services General
FAQ #25: Handling missing or incomplete data.
3.
Confirmatory Factor Analysis with Categorical Outcomes
Confirmatory factor analysis with dichotomous and polytomous categorical
outcomes, or confirmatory factor analysis with mixed categorical and continuous
outcomes is also possible using Mplus. Recall the grantcat.dat data file used in the example Exploratory
Factor Analysis with Categorical Outcomes in Section
4. Using the same data file that replaces the six continuous observed variables
with a dichotomous variables, you can use the confirmatory factor analysis
syntax from the example Confirmatory
Factor Analysis With Continuous Variables with the following modifications.
First, add the CATEGORICAL ARE vizcat ... wordcat ; statement
to the DATA command. Mplus will now treat the six observed variables
as categorical in the analysis. The entire command syntax is shown here.
TITLE: Grant-White School: CFA with categorical outcomes DATA: FILE IS "c:\intromplus\grantcat.dat" ; VARIABLE: NAMES ARE viscat cubescat lozcat paracat sentcat wordcat ; USEVARIABLES ARE viscat - wordcat ; CATEGORICAL ARE viscat - wordcat ; ANALYSIS: TYPE = general ; MODEL: visual BY viscat@1 cubescat lozcat ; verbal BY paracat@1 sentcat wordcat ; visual WITH verbal ; OUTPUT: sampstat standardized ;
Selected results from the analysis appear below.
Chi-Square Test of Model Fit
Value
7.463*
Degrees
of Freedom
6**
P-Value
.2800
* The chi-square value for MLM, MLMV, WLSM and WLSMV cannot
be used for
chi-square difference tests.
** The degrees of freedom for MLMV and WLSMV are estimated according
to
formula 109 (page 281) in the Mplus User's Guide.
The chi-square test of model fit is once again non-significant, suggesting
that the specified model fits the data adequately. The default estimator
for models that contain categorical outcomes is the mean and variance-adjusted
weighted least-squares method, wlsmv. Optional estimators
you may choose are weighted least-squares (wls) and mean-adjusted
weighted least-squares (wlsm). As is the case in the exploratory
factor analysis of categorical data example, there are no descriptive model
fit statistics produced by Mplus when it analyzes categorical outcomes.
Mplus also produces a note alerting you not to use the MLMV, WLSM, and
WLSMV chi-square values in nested model comparisons (the warning about
the MLM chi-square is not relevant as long as you use the formulas
shown on the Mplus Web site for nested model MLM chi-square comparisons
when you use the MLM estimator in the analysis of continuous outcomes).
You should not use the MLM estimator for the analysis of intrinsically
categorical outcome variables.
Mplus then outputs the model results:
MODEL RESULTS
Estimates S.E. Est./S.E.
Std StdYX
VISUAL BY
VISCAT
1.000 .000
.000 .729 .729
CUBESCAT
.831 .212 3.922
.606 .606
LOZCAT
.975 .230 4.248
.710 .710
VERBAL BY
PARACAT
1.000 .000
.000 .814 .814
SENTCAT
1.058 .134 7.920
.861 .861
WORDCAT
1.038 .127 8.154
.844 .844
VISUAL WITH
VERBAL
.397 .087 4.592
.670 .670
Variances
VISUAL
.531 .162 3.273
1.000 1.000
VERBAL
.662 .117 5.661
1.000 1.000
Thresholds
VISCAT$1
.095 .104 .913
.095 .095
CUBESCAT$1
.271 .105 2.571
.271 .271
LOZCAT$1
-.043 .104 -.415
-.043 -.043
PARACAT$1
.009 .104 .083
.009 .009
SENTCAT$1
.183 .105 1.743
.183 .183
WORDCAT$1
.043 .104 .415
.043 .043
This output is similar to that of a confirmatory factor analysis with
continuous outcomes, with one notable exception: Mplus now produces
threshold information for each categorical variable. A threshold is
the expected value of the latent variable or factor at which an individual
transitions from a value of 0 to a value of 1.00 on the categorical outcome
variable when the continuous underlying latent variable's score is zero.
There are only two categorical values for each outcome variable, so there
is only one threshold per variable. For any categorical outcome variable
with K levels, Mplus will output
K-1 threshold values. For
example, a five-point Likert scale item would contain four threshold values.
The first threshold would represent the expected value at which an individual
would be most likely to transition from a value of 0 to a value of 1.00
on the Likert outcome variable. The second threshold would represent the
expected value at which an individual would be most likely to transition
from a value of 1.00 to a value of 2.00 on the outcome variable, and so
on through the fourth threshold, which represents the expected value at
which an individual would transition from 3.00 to 4.00 on the outcome variable.
Finally, Mplus produces the r-square table output. The r-square values
are computed for the continuous latent variables underlying the categorical
outcome variables rather than the actual outcome variables as is the case
in analyses that contain continuous outcome variables. Note that the r-square
values for the categorical outcomes cannot be interpreted as the proportion
of variance explained as is the case in the analysis of continuous outcomes.
Therefore, examining the sign and significance of the estimated coefficients
shown in the model results table above is generally more informative than
interpreting r-square values.
R-SQUARE
Observed Residual
Variable Variance R-Square
VISCAT
.469 .531
CUBESCAT .633
.367
LOZCAT
.495 .505
PARACAT
.338 .662
SENTCAT
.259 .741
WORDCAT
.287 .713
The r-square table's residual variance output is, however, useful for
computing expected probabilities. You can use threshold and coefficient
information shown above with the residual variance information from the
r-square table to compute the expected probability of case having a value
of 0 or 1.00. Consider following formula for computing the
conditional probability of a Y = 0 response given the factor eta.:
P(Y_ij
= 0|eta_ij) = F[(tau_j - lambda_j*eta_i )*(1/square root of theta_jj)]
where:
eta
is the factor's value
F
is the culmulative normal distribution fuction
tau
is
the measured item's threshold
lambda
is the item's factor loading
theta
is the residual variance of the measured item
Suppose you want to obtain the estimated probability for sentcat
= 0 at eta = 0. Using the formula, shown above, you can compute
this value:
P(Y_ij|eta_ij)
= F[(.183 - 0)*(1/square root of .259)]
= F[.183*1.9649437]
= F[.3595847]
You can look up the value of .3595847 in a Z table in a statistics
textbook, or you can supply the computed value of .3595847 to the PROBNORM function in SAS to obtain the correct probability value. The PROBNORM function returns the value from a cumulative normal distribution for the
inputted value. A simple SAS program such as the one shown below enables
you to obtain the final expected probability value of .64.
DATA one ; p = PROBNORM(.3595847) ; RUN ; PROC PRINT DATA = one ; RUN ;
You may substitute other values of eta and lambda to obtain different expected probability values. In general, the same cautions and limitations that were discussed above in the section Exploratory Factor Analysis with Categorical Variables section also apply to the analysis of categorical outcomes in the confirmatory factor analysis and structural equation modeling contexts. In addition, the following point is worth considering:
Educ - Education level
SEI - Socioeconomic index
Anomia67 - Anomie in 1967
Anomia71 - Anomie in 1971
Powles67 - Powerlessness in 1967
Powles71 - Powerlessness in 1971
One of the fitted structural equation models features a latent factor,
SES,
that influences Educ and SEI scores. The SES latent variable in turn influences
two additional latent variables: Alien67 and Alien71. Alien67 represents
self-perceived alienation in 1967 and it influences responses on the anomie and
powerlessness variables measured in 1967. Similarly, Alien71 represents
self-perceived alienation in 1971 and it influences responses on the anomie and
powerlessness variables measured in 1971. SES influences both Alien67 and
Alien71 and Alien67 also influences Alien71.
The dataset,
wheaton-generated.dat,
is used in the analysis that follows:
TITLE: Wheaton et al. Example 1: Full SEM DATA: FILE IS "c:\intromplus\wheaton-generated.dat" ; VARIABLE: NAMES ARE educ sei anomia67 powles67 anomia71 powles71 ; USEVARIABLES ARE educ - powles71 ; ANALYSIS: TYPE = general ; MODEL: ses BY educ@1 sei ; alien67 BY anomia67@1 powles67 ; alien71 BY anomia71@1 powles71 ; alien67 ON ses ; alien71 ON ses alien67 ; OUTPUT: standardized sampstat ;
The syntax for this analysis is similar to that of the confirmatory
factor analysis example shown in subsection
1 above. The only noteworthy difference is the use of the ON keyword in the MODEL command to specify the regression relationships
among the latent variables; the WITH keyword is used to specify
correlations or covariances among variables. In this example, the alien67
latent variable is regressed on the SES latent variable. Similarly,
the alien71 latent variable is regressed on both the SES
and alien67 latent variables. The model fit statistics appear below:
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value
76.184
Degrees
of Freedom
6
P-Value
.0000
<some output deleted to save space>
RMSEA (Root Mean Square Error Of Approximation)
Estimate
.112
90 Percent
C.I.
.090 .135
Probability
RMSEA <= .05 .000
The statistically significant chi-square test of absolute model fit
coupled with the poor RMSEA fit statistic value suggest that this model
may need some modification before it fits the data well. The model fit
and r-square tables appear below.
MODEL RESULTS
Estimates S.E. Est./S.E.
Std StdYX
SES BY
EDUC
1.000 .000
.000 2.420 .784
SEI
.592 .043 13.694
1.433 .683
ALIEN67 BY
ANOMIA67
1.000 .000
.000 2.929 .816
POWLES67
.823 .038 21.734
2.409 .793
ALIEN71 BY
ANOMIA71
1.000 .000
.000 2.989 .843
POWLES71
.825 .039 21.305
2.465 .778
ALIEN67 ON
SES
-.759 .062 -12.235
-.627 -.627
ALIEN71 ON
SES
-.172 .064 -2.689
-.139 -.139
ALIEN67
.710 .056 12.609
.696 .696
Residual Variances
EDUC
3.677 .416 8.839
3.677 .386
SEI
2.345 .172 13.651
2.345 .533
ANOMIA67
4.301 .364 11.807
4.301 .334
POWLES67
3.422 .260 13.150
3.422 .371
ANOMIA71
3.637 .369 9.849
3.637 .289
POWLES71
3.951 .289 13.681
3.951 .394
ALIEN67
5.201 .495 10.516
.606 .606
ALIEN71
3.352 .382 8.781
.375 .375
Variances
SES
5.854 .557 10.515
1.000 1.000
R-SQUARE
Observed
Variable R-Square
EDUC
.614
SEI
.467
ANOMIA67 .666
POWLES67 .629
ANOMIA71 .711
POWLES71 .606
Latent
Variable R-Square
ALIEN67 .394
ALIEN71
.625
There are several noteworthy features of these tables. First, the model
results table contains residual variance estimates for the alien67
and alien71 latent variables. These variables are predicted by the
SES
latent variable, so it makes sense that the residual or unexplained variance
is due to factors other than SES in the model. Because
SES
is not predicted by any other variables, its variance is estimated independently
and is shown in the Variances section of the model results table.
The path coefficients from SES to alien67, from SES
to alien71, and from alien67 to alien71 and their
associated standard errors, tests of significance, and standardized coefficients
also appear in the same table.
The r-square table contains r-square values for each of the predicted
latent variables, alien67 and alien71, as well as the observed
variables. Taken as a whole, these results suggest that the model is capturing
the observed variables' variances fairly well, though the prediction of
alienation in 1967 is somewhat weak as is the variance accounted for in
the SEI variable. The model may be modified, however. When all variables
are continuous, Mplus can print modification indices that can provide an
empirical basis to aid your decision to free additional paths, means, intercepts,
or variance components to be estimated in your model. A modification
index provides the expected drop in model fit chi-square if a parameter
that is currently not free is in fact allowed to be estimated. As always,
theory should be your first guide in the decision to modify your model.
To request modification indices, add the following keywords to the OUTPUT line:
TITLE: Wheaton et al. Example 1: Full SEM DATA: FILE IS "c:\intromplus\wheaton-generated.dat" ; VARIABLE: NAMES ARE educ sei anomia67 powles67 anomia71 powles71 ; USEVARIABLES ARE educ - powles71 ; ANALYSIS: TYPE = general ; MODEL: ses BY educ@1 sei ; alien67 BY anomia67@1 powles67 ; alien71 BY anomia71@1 powles71 ; alien67 ON ses ; alien71 ON ses alien67 ; OUTPUT: standardized sampstat modindices (4) ;
The number shown in the parentheses is the amount of chi-square reduction
necessary for Mplus to print any given modification index. The critical
chi-square statistic is 3.84 for 1 degree of freedom at p = .05,
so this example sets the cutoff to print modification indices at 4.00.
If you do not specify a cutoff value, Mplus supplies 10.00 as the default
value. The modification indices from this model appear below.
MODEL MODIFICATION INDICES
Minimum M.I. value for printing the modification index
4.000
M.I. E.P.C. Std E.P.C. StdYX E.P.C.
WITH Statements
POWLES67 WITH EDUC
8.381 -.574 -.574
-.061
ANOMIA71 WITH EDUC
5.626 .533
.533 .049
ANOMIA71 WITH ANOMIA67 62.098
2.091 2.091
.164
ANOMIA71 WITH POWLES67 48.629
-1.546 -1.546
-.144
POWLES71 WITH ANOMIA67 54.470
-1.693 -1.693
-.149
POWLES71 WITH POWLES67 41.262
1.233 1.233
.128
In addition to the raw modification index value (M.I.), Mplus
also prints the unstandardized expected parameter change (E.P.C.)
and standardized versions of the expected parameter change.
You can draw several immediate conclusions about the model from this
table. First, the largest raw modification indicies are associated with
correlating the residuals of the anomie and powerlessness variables, indicating
that freeing these parameters to be estimated will result in the largest
improvement in model fit. Second, the StdYX expected parameter change values
are comparable with each other because they are standardized coefficients.
The largest of these is the correlation of anomia67 with anomia71
(.164). The next largest value is the correlation of anomia67 with
powles71
(-.149). However, you must ask yourself, "Is this modification theoretically
sensible and meaningful?" about any modification you plan to undertake.
You can make a case for correlating anomia67 and anomia71,
and powles67 and powles71, because these measures are identical
instruments measured on the same people at two different time points. It
is conceivable that some method or instrument variance is shared across
time on the same measurement instruments, but not across two distinct measurement
instruments.
With this information, suppose you change the MODEL command to
add two residual covariances via the WITH statement:
anomia67
with anomie71, and powles67 and powles71. The Mplus
syntax for this model is shown below, with the added part shown in italics for
emphasis.
TITLE: Wheaton et al. Example 1: Full SEM DATA: FILE IS "c:\intromplus\wheaton-generated.dat" ; VARIABLE: NAMES ARE educ sei anomia67 powles67 anomia71 powles71 ; USEVARIABLES ARE educ - powles71 ; ANALYSIS: TYPE = general ; MODEL: ses BY educ@1 sei ; alien67 BY anomia67@1 powles67 ; alien71 BY anomia71@1 powles71 ; alien67 ON ses ; alien71 ON ses alien67 ; anomia67 WITH anomia71 ; powles67 WITH powles71 ; OUTPUT: standardized sampstat modindices (4) ;
Consider the result of this modification on the model fit statistics.
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value
7.826
Degrees
of Freedom
4
P-Value
.0978
...output deleted...
RMSEA (Root Mean Square Error Of Approximation)
Estimate
.032
90 Percent
C.I.
.000 .065
Probability
RMSEA <= .05 .782
The chi-square test of overall model fit is not signicant and the RMSEA
value is well below the recommended .06 cutoff that indicates good model
fit, so you conclude that your modified model fits the data well (the value
of .065 for the upper bound of the 90 percent confidence interval for the
RMSEA suggests that the model could be improved even more if you wished
to pursue further model modifications). If you use them properly, model
modification indices are a powerful tool in your analytic toolbox. The
following points about model modification indices are worth considering:
Section 6: Advanced Models
Although Mplus can fit many standard models and it contains some useful features lacking in other SEM programs at the time of this writing (e.g., FIML missing data handling with exploratory factor analysis, modification indices with FIML missing data handling for structural equation and confirmatory factor analysis models), Mplus advanced modeling features are its most distinctive trademark. A full treatment of Mplus's advanced modeling features is beyond the scope of this tutorial, but several representative examples appear below.
1. Multiple Group Analysis
Recall the first confirmatory factor analysis example that features data from 145 students from the Grant-White School contained in the data file grant.dat. 72 of those students are male whereas 73 students are female. Suppose you decide to investigate the equality of the factor structure across the two groups of students. You can use Mplus to perform one or more multiple group analyses in which the parameters of your choosing are stipulated to be equal across the two groups of children. For instance, suppose you wanted to test the equality of the factor loading and factor variances and covariance values for males and females. The Mplus command file shown below performs this test.
TITLE: Grant-White School: Multiple Group CFA DATA: FILE IS "c:\intromplus\grant.dat" ; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc - wordmean ; GROUPING = gender (1=males 2=females); ANALYSIS: TYPE = mgroup ; MODEL: visual BY visperc@1 cubes lozenges ; verbal BY paragrap@1 sentence wordmean ; visual (1) ; verbal (2) ; visual WITH verbal (3) ; OUTPUT: standardized sampstat ;
Several new elements of this program are immediately apparent. First,
the GROUPING = option for the VARIABLE command tells Mplus
which variable in the data file contains the information about group membership.
For each value of the grouping variable, you supply a name that Mplus uses
to define separate groups in the analysis. The ANALYSIS command
contains an mgroup keyword that lets Mplus know you are specifying
a multiple group analysis. Use the GROUPING = option for raw data;
use the mgroupANALYSIS keyword when you input summary data
such as covariance matrices for each group. Both multiple group specification
methods are included in this example for illustrative purposes, though
only the GROUPING = option is required to run the command file because
you input raw data.
By default Mplus assumes that the following specified parameter estimates
are equal across multiple groups:
TITLE: Grant-White School: Multiple Group CFA DATA: FILE IS "c:\intromplus\grant.dat" ; VARIABLE: NAMES ARE visperc cubes lozenges paragrap sentence wordmean gender ; USEVARIABLES ARE visperc - wordmean ; GROUPING = gender (1=males 2=females); ANALYSIS: TYPE = mgroup ; MODEL: visual BY visperc@1 cubes lozenges ; verbal BY paragrap@1 sentence wordmean ; visual (1) ; verbal (2) ; visual WITH verbal (3) ; MODEL males: visperc - wordmean (4); OUTPUT: standardized sampstat ;
This model constrains the residual variance values of the six observed
variables for males to be equal, but the females' residual variances are
allowed to remain unique for each measured variable.
For more information on multiple group analysis, including cautionary
notes regarding multiple group analysis, see the UT Austin Statistical Services AMOS
FAQ #3: Multiple group analysis.
2. Multilevel Models
Investigators often draw data from sources that feature a hierarchical
or multilevel structure such as students nested within classrooms,
patients residing in hospitals, children grouped within a family, individuals
grouped within couples, etc. In recent years, specialized software such
as HLM and MLwiN
have been developed to fit regression and related-models (e.g., ANOVA,
ANCOVA, MANOVA, and MANCOVA) to such data files because many statistical
software packages such as SPSS and SAS assume every observation is independent
of the observations that precede and follow it (some exceptions to this
general rule are the MIXED procedure in SAS and the LISREL multilevel module,
both of which may be used to fit multilevel regression models). In
situations where individuals are members of some type of larger aggregate
or cluster (e.g., families, couples, classrooms), this independence
assumption can be and often is violated. Violations of the independence
assumption can seriously degrade the results from an analysis conducted
on multilevel data.
Although specialized software products such as HLM and related programs
permit multilevel regression analyses, Mplus features a latent variable-based
approach to multilevel modeling that has the following benefits:
TITLE: Multilevel latent growth model (based on Mplus example program) DATA: FILE IS "c:\intromplus\comp.dat"; VARIABLE: NAMES ARE g1 g2 cluster g3 y11-y14 y21-y24 x1-x5; USEOBS = (x1 EQ 1 AND g1 EQ 2); MISSING = ALL (999); USEVAR = y11-y14 ; CLUSTER = cluster; DEFINE: y11 = y11/5; y12 = y12/5; y13 = y13/5; y14 = y14/5; ANALYSIS: TYPE = twolevel; MODEL: %BETWEEN% level1b BY y11-y14@1; trend1b BY y11@0 y12@1 y13*2.5 y14*3.5; [y11-y14@0]; [level1b-trend1b]; level1b WITH trend1b ; %WITHIN% level1w BY y11-y14@1; trend1w BY y11@0 y12@1 y13*2.5 y14*3.5; level1w WITH trend1w ; OUTPUT: sampstat standardized ;
In the interests of conserving space, this program makes use of several Mplus
shortcuts. First, the DATA command illustrates the use of the FORTRAN FORMAT statement to read the variables from the large data file efficiently,
as recommended by the Mplus manual. The USEOBS command limits the observations to the subset of cases of interest for
this analysis.
The first multilevel analysis command is the CLUSTER command.
The CLUSTER command identifies which variable in the data file denotes
group or cluster membership. In this example, the variable's name is cluster.
Following the CLUSTER command is the DEFINE command. DEFINE allows you to rescale the observed variables so that Mplus is more likely
to converge when it fits the multilevel model to the data file (multilevel
models often have more difficultly converging than single-level models).
The ANALYSIS command defines the type of analysis as twolevel.
This option tells Mplus that you are fitting a two-level model to the data.
At present, Mplus can only fit multilevel models with a single clustering
variable, though Mplus can fit some three-level models if you consider
the third level of the model to consist of equally-spaced repeated measurements
of the observed variables. As mentioned previously, you may use ml, mlm,
or mlmv as estimator options for multilevel models. If you
select the ml estimator, Mplus produces RMSEA model fit statistics
in addition to the familiar chi-square test of model fit. Use the ml estimator option only if cluster sizes are equal and it is reasonable to
assume joint multivariate normality of the model residuals; otherwise,
use the default mlm estimator or the optional mlmv estimator.
The MODEL command contains the model specification statements
for the between and within-cluster components of the model. The between-cluster
model specification is listed under the %BETWEEN% subcommand. Notice
that any mean and intercept structure specifications occur here; these
occur at the between level only. The %WITHIN% subcommand then lists
the model specification for the within-cluster model for individuals in
the dataset.
The output from this analysis appears below, with some output deleted
in the interest of conserving space. The first displayed output is the
summary of data, which displays the number of clusters and the ID numbers
contained within clusters of a given size. For instance, two clusters contain
seven cases each. These clusters are cluster number 103 and cluster number
132.
SUMMARY OF DATA
Number of
clusters
50
Size
(s)
Cluster ID with Size s
2
114
3
136
6
304
7
103 132
9
102 109
10
305
11
111
14
134
15
116 106
16
118 138 110 105
17
101 128
18
133 131 122
19
303 124 146
20
147 137 307
21
129 141 145
22
144 127 142 143
23
139 308
24
119
25
120 121 112 123
26
140
27
301 108 117
29
135
34
104
35
115
40
302
41
309
Average cluster
size 19.609
Mplus also displays the intraclass correlations of the observed variables.
The intraclass correlation assesses the level of variance in the observed
variable that is attributable to membership in its cluster. Even small
intraclass correlations suggest the need for a multilevel analysis. In
this analysis, the amount of variance attributable to cluster membership
ranges from 15% to 20%, suggesting that a multilevel analysis is required.
Estimated Intraclass
Correlations for the
Y Variables
Intraclass
Intraclass
Intraclass
Variable Correlation
Variable Correlation Variable Correlation
Y11
.206 Y12
.150 Y13
.167
Y14
.165
The overall test of model fit is satisfactory, as is the RMSEA information.
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value
7.561*
Degrees
of Freedom
4
P-Value
.1087
The model results appear below. The results are divided by level. Mplus
first outputs the results for the between-cluster portion of the model:
MODEL RESULTS
Estimates S.E. Est./S.E.
Std StdYX
Between Level
LEVEL1B BY
Y11
1.000 .000
.000 .687 .923
Y12
1.000 .000
.000 .687 .914
Y13
1.000 .000
.000 .687 .842
Y14
1.000 .000
.000 .687 .764
TREND1B BY
Y11
.000 .000 .000
.000 .000
Y12
1.000 .000
.000 .027 .036
Y13
2.432 .173 14.026
.065 .080
Y14
3.458 .256 13.519
.092 .103
LEVEL1B WITH
TREND1B
.038 .011 3.369
2.077 2.077
Residual Variances
Y11
.082 .031 2.668
.082 .148
Y12
.016 .013 1.264
.016 .029
Y13
.005 .010 .509
.005 .007
Y14
.065 .028 2.337
.065 .080
Variances
LEVEL1B
.472 .087 5.450
1.000 1.000
TREND1B
.001 .003 .282
1.000 1.000
Means
LEVEL1B
10.557 .114 92.953
15.368 15.368
TREND1B
.522 .046 11.427
19.561 19.561
Intercepts
Y11
.000 .000 .000
.000 .000
Y12
.000 .000 .000
.000 .000
Y13
.000 .000 .000
.000 .000
Y14
.000 .000 .000
.000 .000
Mplus then displays the corresponding model results for the within-cluster
level of the model:
Within Level
LEVEL1W BY
Y11
1.000 .000
.000 1.447 .897
Y12
1.000 .000
.000 1.447 .863
Y13
1.000 .000
.000 1.447 .785
Y14
1.000 .000
.000 1.447 .689
TREND1W BY
Y11
.000 .000 .000
.000 .000
Y12
1.000 .000
.000 .193 .115
Y13
2.709 .826 3.281
.524 .284
Y14
4.237 1.417 2.991
.820 .390
LEVEL1W WITH
TREND1W
.082 .033 2.466
.294 .294
Residual Variances
Y11
.507 .052 9.791
.507 .195
Y12
.516 .038 13.567
.516 .183
Y13
.580 .045 12.885
.580 .171
Y14
.943 .167 5.646
.943 .214
Variances
LEVEL1W
2.093 .109 19.199
1.000 1.000
TREND1W
.037 .027 1.390
1.000 1.000
Though this analysis produced similar findings for the between and within-cluster
components of the model, this is not always the case. It is often the case
that you will need different model specifications for the between versus
the within-cluster sections of the model's specification.
It is also worth noting that despite the congruence between the within
and the between-cluster components of this model, if you fit the model
as a single level model (using the mlm estimator option),
you obtain the following results:
MODEL RESULTS
Estimates S.E. Est./S.E.
Std StdYX
LEVEL BY
Y11
1.000 .000
.000 1.606 .903
Y12
1.000 .000
.000 1.606 .870
Y13
1.000 .000
.000 1.606 .797
Y14
1.000 .000
.000 1.606 .708
TREND BY
Y11
.000 .000 .000
.000 .000
Y12
1.000 .000
.000 .227 .123
Y13
2.451 .130 18.812
.556 .276
Y14
3.496 .195 17.901
.793 .350
LEVEL WITH
TREND
.124 .031 4.000
.341 .341
Residual Variances
Y11
.582 .055 10.593
.582 .184
Y12
.528 .044 12.071
.528 .155
Y13
.565 .045 12.614
.565 .139
Y14
1.061 .112 9.443
1.061 .206
Variances
LEVEL
2.580 .137 18.881
1.000 1.000
TREND
.051 .012 4.317
1.000 1.000
Means
LEVEL
10.557 .057 184.473
6.572 6.572
TREND
.517 .032 15.958
2.280 2.280
Intercepts
Y11
.000 .000 .000
.000 .000
Y12
.000 .000 .000
.000 .000
Y13
.000 .000 .000
.000 .000
Y14
.000 .000 .000
.000 .000
Although the chi-square model fit test for this model indicates the
model fits the data well (chi-square = 3.697 with 3 DF, p = .295),
you can see that all variance estimates are statistically significant.
This finding does not take into account the non-independence of individuals
who are grouped within the same cluster; it thus stands in contrast to
the more appropriate multilevel model that shows a non-significant variance
component for the trend latent variable on both the between and within-cluster
levels.
The following notes are worth considering before you specify a multilevel
model and fit it to your data using Mplus.
Hu, L., & Bentler, P.M. (1999). Cutoff criteria in fix indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
Muthén, B. (1997). Latent variable modeling with longitudinal and multilevel data. In A. Raftery (ed.), Sociological Methodology 1997 (pp. 453-480). Boston: Blackwell Publishers.
Muthén, B., du Toit, S.H.C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Accepted for publication in Psychometrika.
Muthen, L.K. and Muthen, B.O. (1998). Mplus User's Guide. Los Angeles: Muthen & Muthen.
Wheaton, B., Muthén, B., Alvin, D., & Summers, G. (1977). Assessing reliability and stability in panel models. In D.R. Heise (Ed.): Sociological Methodology. San Francisco: Jossey-Bass.
This page was adapted from Mplus for Windows: An Introduction developed by the Consulting group in the Division of Statistics and Scientific Computation at UT Austin. We are very grateful to them for their permission to copy and adapt these materials at our web site.
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