This page contains instructions on how to upgrade LISREL 8.50 (June 2001) to 8.51 (October
2001), next 8.51 to 8.52 (June 2002), and next finally 8.52 to 8.53 (December 2002), and finally 8.53 to 8.54 (May 2003).
*Please note that the patch files on this page are only for use with registered copies
of LISREL 8.5 for Windows. It cannot be applied to the rental or student editions, or used on any other platform.*

Depending on your current version, you can skip steps and start with the
8.52 upgrade step below or the
8.53 upgrade step below or the 8.54 upgrade step below.

**Download UPGRADE851.EXE
to update LISREL 8.50 (June 2001) to LISREL 8.51 (October 2001) with the new features and corrections listed
below:**

__New in LISREL 8.51__

**1. Changes in PRELIS**

Since the release of LISREL 8.50 in June 2001 we have added some new features in PRELIS
to improve data screening and other procedures. To explain the differences between the present and new versions
of PRELIS, Prof Joreskog refers to PRELIS 2.50 and PRELIS 2.51 in his second
contribution on the analysis of ordinal data.

**(a) NP Keyword (Optional)**

One can put NP=<number of decimal places> on the OU line in PRELIS syntax. The value
of NP controls the number of printed decimals in the output file. Default: NP=3.

**(b) CRAND(n) Variable (Optional)**

In addition to NRAND and URAND which generate random standard normal and uniform variables,
respectively (see PRELIS 2: User's Reference Guide, p. 191), PRELIS 2.51 has CRAND(n) which generates a random
chi-square with n degrees of freedom. This is useful for generating non-normal variables with different degrees
of skewness. Like NRAND and URAND, CRAND(n) can be used in expressions on NE lines (see PRELIS 2: User's Reference
Guide, pp. 67, 160).

**(c) Changes in the MA keyword**

A listing of the changes that has been made in PRELIS since the release of PRELIS 2.50
is given below. These changes has to do with the specification of MA on the OU line. For further explanation
see Karl's Corner: Analysis
of Ordinal Variables 2: Cross-Sectional Data on our website.

The meaning of MA has been changed as follows:

MA not specified implies data screening but the meaning of data screening has been extended.

MA = CM, MM, AM implies Alternative Parameterization (ordinal variables)

MA = Anything else remains the same.

The output of PRELIS 2.51 is in several ways different from the output of PRELIS 2.50.
For example, "Univariate Marginal Parameters" does not belong in the data screening and will therefore
not be printed if MA is not specified.

Also, if MA is specified the MA matrix
(and means and standard deviations) will in some cases be different, see point (vi) below.

(i) There was a bug when some variables are ordinal if the raw data is saved to a file
and MA is not specified. Then the ordinal variables were wrong on the saved file. This bug has no effect when
all variables are continuous or when MA is specified.

(ii) Data screening (MA not specified) is now done pairwise and a missing data map (number
of variables < 32) is given in the output if there are missing values. The idea is to provide as much information
as possible about the data. But if raw data is saved to a file, the saved file will be produced under the type
of deletion method specified even though the output file contains the pairwise information.

(iii) If all variables are ordinal, PRELIS gives an additional output file called <inputfilename>.freq
listing all response patterns occuring in the sample and the frequency of occurrence of each pattern. The 20
most common response patterns will also be listed in the ordinary output file. The *.freq file should be regarded
as a data file. It gives the data in the most concise form. The *.freq file may be read by PRELIS by specifying
the frequency variable as a weight variable. The reason for the *.freq file having the name of the input file
rather than the name of the data file is that the it depends on the commands in the input file.

(iv) If MA = PM and some variables are ordinal, the bivariate tables will not be given
in the output file but have to be requested by BT on the OU line. Previously these tables appeared automatically
in the output but could be excluded by XB on the OU line. Technically this means that XB is now the default.

(v) If MA = PM and some variables are ordinal, the threshold value for RMSEA has been
changed from 0.05 to 0.1. This affects only the P-value for Close Fit used for testing approximate underlying
bivariate normality (see Analysis
of Ordinal Variables 1: Preliminary Analysis and Analysis
of Ordinal Variables 2: Cross-Sectional Data). Furthermore, if this P-value is less than
0.05 an additional output file called <inputfilename>.bts is produced containing information about the
lack of underlying bivariate normality.

(vi) If MA=CM (or MM or AM) and there are ordinal variables included, the estimation of
the MA matrix is done under the alternative parameterization. This holds generally, i.e., also with ET, FT and
FI (covariates). These cases will be explained carefully in the next three contributions posted on Karl's
Corner.

**2. Changes in LISREL**

**XA Option (Optional)**

One can put XA on the OU line in LISREL syntax or on an Options (or LISREL Output) line
in SIMPLIS syntax. This will have the effect that only C1 (Minimum Fit Function Chi-Square) will be computed.
This works for all methods of estimation (ULS, GLS, ML, DWLS, and WLS) regardless of whether an AC matrix is
read or not. Standard errors are not affected. They will be the same whether XA is used or not.

With XA, C1 is still an asymptotically correct chi-square for GLS, ML, and WLS but not
for ULS and DWLS (without XA, C1 is not given for ULS and DWLS in the output for this reason). The XA option
is only intended for those who have very large models and cannot afford (or do not want) to let the computer
run for an hour or so. An alternative is to use ML without an AC matrix. Because of its scale dependence, ULS
is best applied to a correlation matrix. To get a correct asymptotic chi-square for ULS and DWLS one must use
C2 under normality and C3 or C4 under non-normality. In general, it is a good idea to use ML when an AC matrix
is available and use C3 for decision making (particularly if the sample size is not very large). But for large
models this will take time (unless XA is used).

**3. List of problems corrected**

Since the release of LISREL 8.50 a few problems have been brought to our attention. These
have been corrected, and include:

- For a large number of variables, the SE (select a subset of variables) command was
incorrectly generated.
- A problem with the FO option, which indicates a formatted covariance matrix (CM command),
has been corrected.
- A problem with stacked LISREL syntax files when performing several exploratory factor
analyses has been corrected.
- A problem with the IR command (constrain parameters to a specific interval) has been
resolved.
- When a model has no free parameters the minimum fit chi-square statistic (C1) is shown
in the path diagram instead of C2.
- A problem with the calculation of latent variable scores for a model with a mean structure
has been corrected. These scores are now computed correctly from a LISREL Data System (DSF) file.
- File and folder names that contain a "-" symbol is now supported.
- When a path diagram is displayed, LISREL crashed when the "Select LISREL Outputs"
checkbox on the SIMPLIS Outputs menu (activated by selecting the Output option on the main menu bar) was clicked.
This problem has been corrected.

4. Applying the upgrade
When prompted to save the downloadable file to disk, save it in your LISREL directory
(where liswin32.exe is). To be on the safe side, restart your computer before applying the patch and make sure
that the LISREL 8.50 application is closed.

After downloading the patch file, run UPGRADE851.EXE from the Start Menu. You will be
guided through the patch process.

**Download UPGRADE853.EXE to
update LISREL 8.52 (June 2002) to LISREL 8.53 (December 2002) with the new features and corrections listed below.**
**If you currently have version 8.50 or 8.51, you should upgrade to version 8.52 first:
go
back to the 8.51 upgrade section above or go
back to the 8.52 upgrade section above.**

__New in LISREL 8.53__

**1. Censored Regression**

A censored variable has a large fraction of observations at the minimum or maximum. Because
the censored variable is not observed over its entire range, ordinary estimates of the mean and variance of a
censored variable will be biased. Ordinary least squares (OLS) estimates of its regression on a set of explanatory
variables will also be biased. These estimates are not consistent, *i.e.*, the bias does not get smaller
when the sample size increases. Examples of censored variables are:

- Number of extramarital affairs
- Vacation expenses

Censored variables are also common in biomedical, epidemiological, survival, and duration
studies. For more examples in other fields of application, the reader is referred to the Karl Joreskog (2002)
contribution on censored regression which can be downloaded from www.ssicentral.com/lisrel/column12.htm. Note
that you need version 8.53 of LISREL to run the examples. When upgrading from LISREL 8.52 to 8.53 a folder called
censor containing the examples and datasets will be automatically created.

**2. Import Data in Free Format**

The Import Data in Free Format option has been changed to enable one to

- Add Variable Names as the first line(s) of the data file
- Read comma separated files (.csv)
- Read Tab-delimited files (.txt)
- Read SPSS for Windows (.sav) files

The SPSS option is included mainly for users of the LISREL student edition. Users of the
full edition should import SPSS data via the "Import External in Other Formats" option.

**3. Export LISREL Data**

Once a PSF file is opened, the new option "Export LISREL Data..." appears on
the file menu. If this option is selected, one can save the contents of the PSF file to one of the following
formats:

- Comma separated files (.csv)
- Tab-delimited files (.txt)
- SPSS for Windows (.sav) files
- ASCII file with no variables on top and fixed format of F15.6

The advantage of the csv file is that one can read it directly with MS EXCEL, whereas the
tab-delimited file can be read by many software packages (*e.g.*, SPSS).

**4. Multiple Imputation**

The Multiple Imputation procedure was changed so that

- All variables are carried over to the imputed file, even if only a subset is selected
for imputation.
- One can choose from one of 3 options which determine how the imputation procedure deals
with those cases where all the variables selected for imputation are missing.

**5. List of problems corrected **

**PRELIS**

- The estimated asymptotic covariance matrix was not produced when there are ordinal
variables present and MA=CM and RP>1. This has been corrected.
- With ET (equal thresholds) lines included and MA=PM, the standard deviations at the
end of the PRELIS output are incorrectly given as 1.000 (although they are correctly given in the beginning
of the output). This has been corrected.
- ET (equal thresholds) does not work with Probit and Logit regression. An error message
is now produced in this case.
- The reference variable solution obtained with exploratory factor analysis is now
based on Sigma-hat instead of S. This makes the solution closer to an ML solution.
- Saved raw data in ASCII or PSF form is now correct without MA specified.
- One can put XU on the OU line to exclude univariate tables in the output.

**LISREL**

- A bug in the standardized solution when PH=ID on the MO line in LISREL syntax has
been corrected.
- A bug that occurs when there are missing values and the LISREL syntax contains CO
lines has been corrected.
- The reference variable solution obtained with exploratory factor analysis is now
based on Sigma-hat instead of S. This makes the solution closer to an ML solution.
- ML is now default even if an asymptotic covariance matrix is read. Previously WLS
was default in this case.

**6. Applying the upgrade
**

When prompted to save the downloadable file to disk, save it in your LISREL directory
(where liswin32.exe is). To be on the safe side, restart your computer before applying the patch and make sure
that the LISREL 8.5 application is closed.

After downloading the patch file, run UPGRADE853.EXE from the Start Menu. You will be
guided through the patch process.

Note that after a successful upgrade the LISREL 8.53 on-line Help file includes a new
book "New in LISREL 8.53". Here you will find more detailed descriptions, with examples, of the new
features.

**Download UPGRADE854.EXE to update LISREL 8.53 (December 2002) to LISREL 8.54 (May 2003) with the new features and corrections listed below.**

**If you currently have version 8.50 or 8.51 or 8.52, you should upgrade to version 8.53 first: go back to the 8.51 upgrade section above or go back to the 8.52 upgrade section above or go back to the 8.53 upgrade section above.**

**1. NP Keyword (Optional) **

One can put NP=<number of decimal places> on the OU PRELIS command line. The value of NP controls the number of printed decimals in the output file. Default: NP=3.

**2. CRAND(n) variable (Optional) **

In addition to NRAND and URAND which generate random standard normal and uniform variables, respectively (see PRELIS 2: User's Reference Guide, p. 191), PRELIS 2.51 has CRAND(n) which generates a random chi-square with n degrees of freedom. This is useful for generating non-normal variables with different degrees of skewness. Like NRAND and URAND, CRAND(n) can be used in expressions on NE lines (see PRELIS 2: User's Reference Guide, pp. 67, 160).

**3. Changes in the MA keyword **

A listing of the changes that has been made in PRELIS since the release of PRELIS 2.50 is given below. These changes has to do with the specification of MA on the OU line. For further explanation see Karl's paper: Analysis of Ordinal Variables 2: Cross-Sectional Data on our website.

The meaning of MA has been changed as follows:

MA not specified implies data screening but the meaning of data screening has been extended.

MA = CM, MM, AM implies Alternative Parameterization (ordinal variables)

MA = Anything else remains the same.

The output of PRELIS 2.51 is in several ways different from the output of PRELIS 2.50. For example, "Univariate Marginal Parameters" does not belong in the data screening and will therefore not be printed if MA is not specified.

Also, if MA is specified the MA matrix (and means and standard deviations) will in some cases be different, see point (vi) below.

(i) There was a bug when some variables are ordinal if the raw data is saved to a file and MA is not specified. Then the ordinal variables were wrong on the saved file. This bug has no effect when all variables are continuous or when MA is specified.

(ii) Data screening (MA not specified) is now done pairwise and a missing data map (number of variables < 32) is given in the output if there are missing values. The idea is to provide as much information as possible about the data. But if raw data is saved to a file, the saved file will be produced under the type of deletion method specified even though the output file contains the pairwise information.

(iii) If all variables are ordinal, PRELIS gives an additional output file called <inputfilename>.freq listing all response patterns occuring in the sample and the frequency of occurrence of each pattern. The 20 most common response patterns will also be listed in the ordinary output file. The *.freq file should be regarded as a data file. It gives the data in the most concise form. The *.freq file may be read by PRELIS by specifying the frequency variable as a weight variable. The reason for the *.freq file having the name of the input file rather than the name of the data file is that the it depends on the commands in the input file.

(iv) If MA = PM and some variables are ordinal, the bivariate tables will not be given in the output file but have to be requested by BT on the OU line. Previously these tables appeared automatically in the output but could be excluded by XB on the OU line. Technically this means that XB is now the default.

(v) If MA = PM and some variables are ordinal, the threshold value for RMSEA has been changed from 0.05 to 0.1. This affects only the P-value for Close Fit used for testing approximate underlying bivariate normality (see Analysis of Ordinal Variables 1: Preliminary Analysis and Analysis of Ordinal Variables 2: Cross-Sectional Data). Furthermore, if this P-value is less than 0.05 an additional output file called <inputfilename>.bts is produced containing information about the lack of underlying bivariate normality.

(vi) If MA=CM (or MM or AM) and there are ordinal variables included, the estimation of the MA matrix is done under the alternative parameterization. This holds generally, i.e., also with ET, FT and FI (covariates). These cases will be explained carefully in the next three contributions posted on Karl's Corner.

**4. XA Option (Optional) **

One can put XA on the OU line in LISREL syntax or on an Options (or LISREL Output) line in SIMPLIS syntax. This will have the effect that only C1 (Minimum Fit Function Chi-Square) will be computed. This works for all methods of estimation (ULS, GLS, ML, DWLS, and WLS) regardless of whether an AC matrix is read or not. Standard errors are not affected. They will be the same whether XA is used or not.

With XA, C1 is still an asymptotically correct chi-square for GLS, ML, and WLS but not for ULS and DWLS (without XA, C1 is not given for ULS and DWLS in the output for this reason). The XA option is only intended for those who have very large models and cannot afford (or do not want) to let the computer run for an hour or so. An alternative is to use ML without an AC matrix. Because of its scale dependence, ULS is best applied to a correlation matrix. To get a correct asymptotic chi-square for ULS and DWLS one must use C2 under normality and C3 or C4 under non-normality. In general, it is a good idea to use ML when an AC matrix is available and use C3 for decision making (particularly if the sample size is not very large). But for large models this will take time (unless XA is used).

**5. Multilevel modeling module **

SSI has also made enhancements to the multilevel modeling module. A brief description of the changes are:

1) The options paragraph may contain all or any of the additional keywords:

SUMMARY=NONE, EFFECTS=YES, NFREE=, and DEVIANCE=<-2log-likelihood of previous model> SUMMARY=NONE requests that the detailed data summary, usually produced as the first part of the output should be suppressed.

EFFECTS=YES requests the print-out of effects sizes for the regression coefficients.

Specification of the NFREE and DEVIANCE values of a previously fitted model results in the printout of a Chi-Square test statistic for testing the current model versus the previous (nested) model. If the previous model is the saturated model, RMSEA statistics are additionally produced.

2) When dummy variables are created via the Response and Fixed variables dialog box, these new variables are added to the current PSF file.

3) Dialog boxes contain addtional instructions to assist the user in building non-standard hierachical linear models.

4) The output produced when testing contrasts has been substantially improved. Output also contains the number of free parameters (NFREE) and -2log(L) is additionally labeled as DEVIANCE.

**6. Independence Chi-square **

With maximum likelihood (ML) estimation of the model there are two different chi-square measures of fit given in the output file. One is the Minimum Fit Function Chi-Square C1 evaluated according to equation (A28) in the "LISREL 8: New Statistical Features" book. The other is the Normal Theory Weighted Least Squares Chi-Square C2. This is N-1 times the minimum value of (A22), where N is the sample size. Alternatively, it can be computed by equation (A29). Under multivariate normality C1 and C2 are asymptotically equivalent but in most samples they are different. Many of the other fit measures given in the output are functions of chi-square, and LISREL then uses C2 rather than C1. One change made in LISREL 8.52 concerns the chi-square for the independence model which is also used in some of the other fit measures. Previously we used C1 for the independence model, which is inconsistent with the use of C2 for the estimated model. In LISREL 8.52 we have changed that so that C2 is used for both the independence model and the estimated model. This change affects all fit measures which depend on the chi-square for the independence model.

**7. Using weighted least squares (WLS) **

Another change made concerns the chi-square for the independence model when the model is estimated by weighted least squares (WLS), also called asymptotically distribution free method (ADF). The independence model specifies that the population covariance matrix is diagonal and it would seem that the most reasonable estimates of the population variances are the sample variances. These are in fact ML, ULS, and DWLS estimates but they are not GLS and WLS estimates. Previously, we have fixed it so that the GLS method gives the "correct" chi-square for the independence model but up through LISREL 8.51 the sample variances were used to compute the independence chi-square in the WLS case. In LISREL 8.52 we have changed that so that the WLS estimates of the variances are used in computing the chi-square for the independence model. This change affects also all fit measures which depend on the chi-square for the independence model.

**8. Censored Regression **

A censored variable has a large fraction of observations at the minimum or maximum. Because the censored variable is not observed over its entire range, ordinary estimates of the mean and variance of a censored variable will be biased. Ordinary least squares (OLS) estimates of its regression on a set of explanatory variables will also be biased. These estimates are not consistent, i.e., the bias does not get smaller when the sample size increases. Examples of censored variables are:

Number of extramarital affairs

Vacation expenses

Censored variables are also common in biomedical, epidemiological, survival, and duration studies. For more examples in other fields of application, the reader is referred to the Karl Joreskog (2002) contribution on censored regression which can be downloaded from www.ssicentral.com/lisrel/column12.htm. Note that you need version 8.53 of LISREL to run the examples. When upgrading from LISREL 8.52 to 8.53 a folder called censor containing the examples and datasets will be automatically created.

**9. Import Data in Free Format **

The Import Data in Free Format option has been changed to enable one to

Add Variable Names as the first line(s) of the data file

Read comma separated files (.csv)

Read Tab-delimited files (.txt)

Read SPSS for Windows (.sav) files

The SPSS option is included mainly for users of the LISREL student edition. Users of the full edition should import SPSS data via the "Import External in Other Formats" option.

**10. Export LISREL Data **

Once a PSF file is opened, the new option "Export LISREL Data..." appears on the file menu. If this option is selected, one can save the contents of the PSF file to one of the following formats:

Comma separated files (.csv)

Tab-delimited files (.txt)

SPSS for Windows (.sav) files

ASCII file with no variables on top and fixed format of F15.6

The advantage of the csv file is that one can read it directly with MS EXCEL, whereas the tab-delimited file can be read by many software packages (e.g., SPSS).

**11. Multiple Imputation **

The Multiple Imputation procedure was changed so that

All variables are carried over to the imputed file, even if only a subset is selected for imputation.

One can choose from one of 3 options which determine how the imputation procedure deals with those cases where all the variables selected for imputation are missing.

**12. Factor Analysis by MINRES **

The MINRES (MINimum RESiduals) method for exploratory factor analysis is based on the direct minimization of least squares rather than the ULS minimization in Joreskog (1977), which is based on eigenvalues and eigenvectors of the reduced correlation matrix. However, it can be shown that, up to an orthogonal transformation, the two methods are equivalent.

MINRES can be used with small samples even when the number of variables is large and when the correlation matrix is not positive definite for other reasons (for example, this might be the case for a matrix of tetrachoric or polychoric correlations). It is particularly suited for exploratory factor analysis when only parameter estimates (and not standard error estimates and chi-square values) are of interest.

For a detailed discussion of this topic and interpretation of the output, the reader is referred to the contribution on MINRES by Karl Joreskog, which can be downloaded from www.ssicentral.com/lisrel/column13.htm.

The examples discussed are based on data and syntax files contained in the lis850ex and ls8ex subdirectories of LISREL 8.54 for Windows.

**13. Mean Structures in Multilevel Structural Equation Models **

A multivariate level-2 model consists of a fixed part (the population means) and a random part (the between-clusters and within-clusters covariance matrices).

Several examples of structural equation models imposed on the between-clusters and the within-clusters covariance matrices are given in the MSEMEX subfolder of the folder in which LISREL 8.5 for Windows is installed. It is interesting to note that these SEM models are fitted by assuming that Group1 refers to the between-clusters variation whereas Group2 refers to the within-clusters variation. In spite of this method for setting up the model, observations from clustered data cannot be split into two mutually exclusive groups. Furthermore, there is only one set of means.

In longitudinal studies, it is often the case that the trend in means is of interest. For example, cholesterol levels of patients at 60 hospitals are measured on 4 occasions. Patients are randomly assigned to a control and a treatment group. Using the treatment group data, we may want to test the hypothesis that there is no change in the mean cholesterol level over time. On the other hand, it may be evident that the treatment results in a decrease in cholesterol level over time. In this case, we may be interested in testing the hypothesis that the cholesterol levels decrease (or increase) linearly over time. A third type of mean structure that may be of interest is to assume that variables such as treatment group (in which case the control group data is also used), gender and age influence the values of, for example, the intercept parameter, but not the slope parameter of a latent trait model.

For practical applications and more detailed information, the user should consult the LISREL 8.54 for Windows Help file. Examples illustrating the imposition of structured means are available in the MSEMEX folder. These examples are clearly distinguished by the addition of "_means" or "_trend" descriptions to the syntax filenames.

**14. List of problems corrected **

Since the release of LISREL 8.50 a few problems have been brought to our attention. These have been corrected, and include:

For a large number of variables, the SE (select a subset of variables) command was incorrectly generated.

A problem with the FO option, which indicates a formatted covariance matrix (CM command), has been corrected.

A problem with stacked LISREL syntax files when performing several exploratory factor analyses has been corrected.

A problem with the IR command (constrain parameters to a specific interval) has been resolved.

When a model has no free parameters the minimum fit chi-square statistic (C1) is shown in the path diagram instead of C2.

A problem with the calculation of latent variable scores for a model with a mean structure has been corrected. These scores are now computed correctly from a LISREL Data System (DSF) file.

File and folder names that contain a "-" symbol is now supported.

When a path diagram is displayed, LISREL crashed when the "Select LISREL Outputs" checkbox on the SIMPLIS Outputs menu (activated by selecting the Output option on the main menu bar) was clicked. This problem has been corrected.

Parameter specifications for fixed elements of the covariance LISREL parameter matrices are correctly reported.

Modification indices for fixed parameters are computed and reported.

SIMPLIS syntax for equal error covariances are processed correctly.

Correct standardized indirect effects are computed and reported for FIML estimation.

The GF keyword on the OU command line for FIML estimation has been updated.

Correct Chi-square test statistic values are produced in the case of a nonpositive definite estimated asymptotic covariance matrix.

The name of the file containing the estimated asymptotic covariance matrix used within a Data System File (DSF) may include blank spaces.

Large bootstrap samples do not require the -SIZE parameter.

Correct Z-scores for univariate tests for skewness and kurtosis are produced.

Correct estimated asymptotic variances are produced.

The correct bootstrap asymptotic covariance matrix and means are produced.

The correct augmented moment matrix is produced.

Data values written to a PSF-file when recoding ordinal variables are now correct.

Chi-square statistic when testing a set of simultaneous contrasts for multilevel models is now correct.

The LISREL ProJect (LPJ) and SIMPLIS ProJect (SPJ) dialog boxes are operating correctly.

The estimated asymptotic covariance matrix was not produced when there are ordinal variables present and MA=CM and RP>1. This has been corrected.

With ET (equal thresholds) lines included and MA=PM, the standard deviations at the end of the PRELIS output are incorrectly given as 1.000 (although they are correctly given in the beginning of the output). This has been corrected.

ET (equal thresholds) does not work with Probit and Logit regression. An error message is now produced in this case.

The reference variable solution obtained with exploratory factor analysis is now based on Sigma-hat instead of S. This makes the solution closer to an ML solution.

Saved raw data in ASCII or PSF form is now correct without MA specified.

One can put XU on the OU line to exclude univariate tables in the output.

A bug in the standardized solution when PH=ID on the MO line in LISREL syntax has been corrected.

A bug that occurs when there are missing values and the LISREL syntax contains CO lines has been corrected.

The reference variable solution obtained with exploratory factor analysis is now based on Sigma-hat instead of S. This makes the solution closer to an ML solution.

ML is now default even if an asymptotic covariance matrix is read. Previously WLS was default in this case.

**15. Applying the upgrade**

When prompted to save the downloadable file to disk, save it in your LISREL directory (where liswin32.exe is). To be on the safe side, restart your computer before applying the patch and make sure that the LISREL 8.5 application is closed.

The P-value for the null hypothesis of a close model fit for multiple group analyses is now based on the RMSEA recommendations in Steiger, J.H. (1998). A Note on Multiple Sample Extensions of the RMSEA Fit Index. Structural Equation Modeling, 5, 411-419.

The default estimation method is now always Maximum Likelihood (ML) even if an estimated asymptotic covariance matrix is specified. Please note that the estimation method should be specified on the OU command line of the FIRST group in a multiple group LISREL syntax file.

The correct imputed data set is produced if imputation by matching for ordinal variables is used.

The correct decision table for the number of factors is reported in the exploratory factor analysis results if the number of factors is not specified.

SIMPLIS syntax files with equal error covariances (off-diagonal elements of PSI) are now processed correctly.

Spaces are now permissible in the name of the file for the estimated asymptotic covariance matrix specified in a Data System File (DSF).

The estimated asymptotic covariance matrix of the parameter estimators of the parameters of multilevel structural equation models is now printed correctly in the file specified in the EC option on the LISREL OU command line.

The RP command is now available for the Full Information Maximum Likelihood (FIML) method for data with missing values.

The calculation of the degrees of freedom for the equal thresholds hypothesis test has been corrected (ET command in PRELIS).