**Using M****plus****
to fit and test measurement and structural equation models**

The videos are mp4 files which can be downloaded for viewing. The slides are PDF files.

If you have trouble viewing mp4 files, you could try using VCL or GOM (for example).

Questions or feedback? Email Alan Taylor.

Video 1: ** Introduction to observed and latent
variable models** (30 minutes). Powerpoint slides

Video 2: **A
brief introduction to M*** plus* (22 minutes). Powerpoint slides

Video 3: **Getting data into M*** plus*. Powerpoint slides

Part 1: (28 minutes) Writing a tabbed data file with SPSS, creating an M

Part 2: (20 minutes) Dealing with missing data when writing and reading the data, and using

Video 4: **A multiple regression
analysis with M*** plus*.

Part 1: (2 minutes) Outline of what is covered in video 4. Powerpoint slides

Part 2: (7 minutes) Carrying out a multiple regression, and obtaining standardised results with the

Part 3: (12 minutes) Obtaining a path diagram with

Part 4: (22 minutes) Obtaining graphs with the

data with an SPSS data file. The

Part 5: (10 minutes) A comparison of how M

Part 6: (6 minutes) Using the

Video 5: **A path analysis with
observed variables. **Powerpoint
slides (including a brief description of estimators used by M*plus*)

Part 1: (6 minutes)
Outline of what is covered in video 5.

Part 2:
(16 minutes) Setting up and testing the basic model, using *model
indirect* to test indirect effects, and adding a covariance.

Part 3:
(8 minutes) Using bootstrapping and requesting confidence intervals.

Part 4:
(12 minutes) Including a categorical dependent variable, seeing how M*plus
*automatically chooses estimators, and specifying the

estimator to be used. A postscript on mediation.

Video 6: **A confirmatory factor
analysis - measurement model. **Powerpoint
slides

Part 1: (4 minutes)
Outline of what is covered in video 6.

Part 2:
(6 minutes) Why are we testing a measurement model? (A rambling and
repetitive disquisition for those with spare time.)

Part 3:
(15 minutes) Setting up and running the model, assessing GOF, and
requesting modification indices (with a discussion of MIs).

Part 4:
(4 minutes) Adding a covariance between indicator residuals. Mention
of an article on the importance of taking shared method

variance into account in SEm

Video 7: **A path model with
latent variables. **Powerpoint
slides

Part 1: (2 minutes)
Outline of what is covered in video 7.

Part 2:
(9 minutes) Fitting the model and comparing the results with those for the
same model with observed variables.

Part 3:
(7 minutes) Testing a direct effect and following up an unexpected result.

Part 4:
(8 minutes) Examining the reason for the unexpected direct effect using
the @ operator.

Part 5: (14 minutes) Obtaining MIs and using estimators which are robust to non-normality.

Video 8: **How M plus
handles missing data. **Powerpoint
slides

Part 1: (12 minutes)
Outline of what is covered in video 8, a review of missing data mechanisms, and the M*plus*
approach to

missing data.

Part 2:
(6 minutes) How the missing data were generated, and the use of *type=basic* to obtain information about missing data.

Part 3:
(4 minutes) Investigation of the pattern of missing data for the variables
involved in the analysis, and fitting an

observed-variable regression model with missing data.

Part 4:
(7 minutes) Bringing all variables into the model by requesting a
covariance, and a description of Full Information

Maximum
Likelihood.

Part 5 : (4 minutes) Bringing all variables into the model by requesting variances and means.

Part 6: (5 minutes) A latent variable path model with missing data.

Part 7: (4 minutes) Multiple imputation (MI) and the outline of what is to be covered on MI.

Part 8: (12 minutes)
Producing MI data in SPSS, writing the data out for M*plus* and analysing
the data in M*plus*.

Part 9: (12
minutes) Producing MI data in M*plus* using the H1 (unrestricted)
and H0 (model-based) methods.

Part 10: (19 minutes) Analysing the M*plus*-produced MI data in M*plus*,
and a description of the Fraction of Missing Information index.

Part 11 : (8 minutes) Using auxiliary variables to improve FIML estimates in the presence of missing data, and setting up a simulation.

Part 12:
(10 minutes) Using auxiliary variables in M*plus*.

Video 9: Latent growth models - LGMs**. **Powerpoint
slides

Part 1: (14 minutes) Outline of what is covered in video 9, and an introduction to latent growth models.

Part 2:
(19 minutes) Using M*plus* to fit a LGM to simulated data.

Part 3:
(10 minutes) Using M*plus* to fit a LGM to real data with three time
points, and applying constraints to allow a

quadratic component. Using the* plot* command.

Part 4: (8 minutes) Saving factor scores

Part 5: (17 minutes) Ftting a LGM to data for eight time points and with
missing data. Allowing estimation of the paths to some

time points.