USING LISREL FOR STRUCTURAL EQUATION MODELING

USING LISREL FOR STRUCTURAL EQUATION MODELING

1. Open data file in SPSS, and generate a correlation matrix:

2. Once you have the output, cut and paste it to the Notepad utility (this will remove all formatting, etc. from the table):

Correlations

info comp arith simil vocab digit pictcomp parang block object coding

info Pearson Correlation 1 .467 .494 .513 .625 .345 .230 .202 .229 .185 .007

Sig. (2-tailed) . .000 .000 .000 .000 .000 .002 .007 .002 .014 .926

N 175 175 175 175 175 175 175 175 175 175 175

comp Pearson Correlation .467 1 .392 .510 .531 .236 .407 .187 .369 .322 .061

Sig. (2-tailed) .000 . .000 .000 .000 .002 .000 .013 .000 .000 .425

N 175 175 175 175 175 175 175 175 175 175 175

arith Pearson Correlation .494 .392 1 .369 .387 .269 .155 .227 .272 .043 .090

Sig. (2-tailed) .000 .000 . .000 .000 .000 .040 .003 .000 .573 .235

N 175 175 175 175 175 175 175 175 175 175 175

simil Pearson Correlation .513 .510 .369 1 .538 .260 .369 .298 .261 .269 -.041

Sig. (2-tailed) .000 .000 .000 . .000 .001 .000 .000 .000 .000 .593

N 175 175 175 175 175 175 175 175 175 175 175

vocab Pearson Correlation .625 .531 .387 .538 1 .294 .285 .132 .297 .185 .100

Sig. (2-tailed) .000 .000 .000 .000 . .000 .000 .081 .000 .014 .188

N 175 175 175 175 175 175 175 175 175 175 175

digit Pearson Correlation .345 .236 .269 .260 .294 1 .075 .148 .073 .035 .173

Sig. (2-tailed) .000 .002 .000 .001 .000 . .322 .050 .339 .648 .022

N 175 175 175 175 175 175 175 175 175 175 175

pictcomp Pearson Correlation .230 .407 .155 .369 .285 .075 1 .249 .382 .363 -.072

Sig. (2-tailed) .002 .000 .040 .000 .000 .322 . .001 .000 .000 .345

N 175 175 175 175 175 175 175 175 175 175 175

parang Pearson Correlation .202 .187 .227 .298 .132 .148 .249 1 .351 .253 .038

Sig. (2-tailed) .007 .013 .003 .000 .081 .050 .001 . .000 .001 .619

N 175 175 175 175 175 175 175 175 175 175 175

block Pearson Correlation .229 .369 .272 .261 .297 .073 .382 .351 1 .399 .107

Sig. (2-tailed) .002 .000 .000 .000 .000 .339 .000 .000 . .000 .159

N 175 175 175 175 175 175 175 175 175 175 175

object Pearson Correlation .185 .322 .043 .269 .185 .035 .363 .253 .399 1 .053

Sig. (2-tailed) .014 .000 .573 .000 .014 .648 .000 .001 .000 . .486

N 175 175 175 175 175 175 175 175 175 175 175

coding Pearson Correlation .007 .061 .090 -.041 .100 .173 -.072 .038 .107 .053 1

Sig. (2-tailed) .926 .425 .235 .593 .188 .022 .345 .619 .159 .486 .

N 175 175 175 175 175 175 175 175 175 175 175

3. Delete all of the extra information from the text file, including the variable names, so that only the correlations are left:

1 .467 .494 .513 .625 .345 .230 .202 .229 .185 .007

.467 1 .392 .510 .531 .236 .407 .187 .369 .322 .061

.494 .392 1 .369 .387 .269 .155 .227 .272 .043 .090

.513 .510 .369 1 .538 .260 .369 .298 .261 .269 -.041

.625 .531 .387 .538 1 .294 .285 .132 .297 .185 .100

.345 .236 .269 .260 .294 1 .075 .148 .073 .035 .173

.230 .407 .155 .369 .285 .075 1 .249 .382 .363 -.072

.202 .187 .227 .298 .132 .148 .249 1 .351 .253 .038

.229 .369 .272 .261 .297 .073 .382 .351 1 .399 .107

.185 .322 .043 .269 .185 .035 .363 .253 .399 1 .053

.007 .061 .090 -.041 .100 .173 -.072 .038 .107 .053 1

4. Save as a text file (.txt)

5. Open the LISREL program from the Start Menu

6. Choose “File-New”, and then select “Path Diagram” (it’s hiding at the bottom of the list – you will have to use the scroll bars to find it):

7. Save the new path diagram (give it a file name and location)

8. Go to the “Setup” menu, and start with “Title and Comments.” At each step, hit “Next” to go on to the next step, until you have completed all four steps:

9. For “Group Labels,” you can just hit “next” if all your variables are I/R level.

10. For “Variables,” name the variables from your original SPSS dataset. They have to be in the same order as they appear in the correlation matrix.

11. use the “Delete” key on the keyboard (not Ctrl-X), to delete the two default variables that LISREL puts in your list, or else rename these variables to the

names of the variables in your data file.

12. create as many latent variables as you want to include in the model, and give them whatever names you want.

13. link the variable names to the data file with the correlation matrix:

• “statistics from” and “matrix to be analyzed” should both be set to

“correlations” from the drop-down menus

• “file type” should be set to “external ASCII data”, and then you can locate the text file with the correlation matrix using the “browse” button.

• Specify your sample size under “number of observations.” This is a key step – if you forget it, you will get a “model does not converge” error at the end of

the process.

14. Hit “OK” to continue, and you will return to the drawing window. Draw your model by dragging the observed and latent variables onto the graph-paper screen.

LISREL will automatically add an error term for each observed variable (unlike

AMOS, where you have to create them manually).

15. To “clean up” the drawing, select objects using the “select” box tool, and then right-click the graph paper screen near these objects (but not on them). Use the

“align” and “even space” tools to clean up the diagram.

16. To specify a covariance between two variables (e.g., between the two latent variables), draw the curving arrow between the error terms on those variables.

17. Under the “Setup” menu, choose “build SIMPLIS syntax.”

18. Once the syntax is generated, click the “run LISREL” button to test the model.

19. The output will show you the path diagram and the chi-square goodness-of-fit test, as well as the RMSEA test. RMSEA of .05 or less indicates a good fit, with

values between .1 and .05 being marginal. Ideally, the chi-square statistic’s p-

value should be greater than .05 (chi-square is used here as a “badness of fit”

statistic), but with large samples this is not always going to happen. RMSEA

adjusts for the complexity of the model and the size of the sample.

20. Additional measures of goodness-of-fit can be obtained by going to “Fit Indices” under the “Output” menu. See notes on AMOS for definitions of the various fit

indices and how to interpret them.

There is also an “output” file created by running the syntax (shrink the path

diagram to see it), which shows you the same goodness-of-fit tests and suggests

additional paths that might improve the model.

21. If you are not satisfied with the level of fit obtained, improve the model by adding or removing variables, changing specified relationships, etc.

22. The easiest way to get the model as an image for a Word document seems to be this: turn off the gridlines (under the “View” menu), move the toolbars out of the

way, and do a print screen (Ctrl-Print Screen key). Then go to Word and paste the

image, and crop out anything but the picture of the path diagram. [There is an

“export to metafile” option under the “File” menu that should save the path

diagram in an image format without doing these manipulations, but I haven’t been

able to get it to work].