SEM The Musical 11

(Updated May 5, 2017)

The eleventh annual SEM The Musical was held Thursday, May 4, during our class. We had two new songs this year, one by Dr. Reifman and one by student Derrick Holland. Derrick's song keeps our streak alive of having at least one student-written song every year. We also, of course, sang a bunch of favorites from the previous ten musicals (links: 123456789, 10). See below for this year's new songs...

Why Won’t It Run? 
Lyrics by Alan Reifman
May be sung to the tune of “On the Run” (Lukather/Paich/Waybill for Toto)

SEM involves, some complex math,
Before you go, you need to check, you’ve added each path,
Lots of little details, for you to keep in sight,
Formatting the data, and making sure, your syntax is right,

Have you verified, what you’ll fix to one?
Otherwise, your troubles, have just begun,

Why won’t it run, why all these error signs?
Why won’t it run? Only hints, of what could’ve, gone wrong,
Why won’t it run? Check your steps, line-by-line,
You can put, all your angst, into song!


You never know, what a new model, can bring,
Punctuation, constraints, it could be anything,
Maybe what you have, is a Heywood Case?
You’ll need a sharp eye, to keep things in place,

Maximum likelihood, seeks a minimum to achieve,
Gonna take a miracle, for you to receive,

Oh, oh, oh, why won’t it run, why won’t the steps converge?
Why won’t it run, are the magnitude scales far apart?
Why won’t it run, when will I be, on the verge?
Doing this, can tax your heart!

(Instrumental and Guitar Solo)

Hungry Like a Low Chi-Square
Lyrics by Derrick Holland
May be sung to the tune of "Hungry Like a Wolf" (Duran Duran)

Open the connection, get ready to run,
Make sure variables, are in a dot-dat file,
Do do do do do do do dodo dododo dodo,

List, all your variables, that you will use,
Make sure all missing variables, are -99*,
Do do do do do do do dodo dododo dodo,

All pathways are free,
Unless you fix a path to 1,
The very end goal,
It’s important you know,
And I'm hungry, like a low chi-square,

Straddle the line,
With comparative models,
I'm on the hunt, for a good CFI,
Check your TLI, and RMSEA,
And I'm hungry, like a low chi-square,

You get an error, so you start to freak out,
Mplus tells you, that variables are not defined,
Do do do do do do do dodo dododo dodo,

You get a low CFI, important paths are behind,
You search in theory, for paths that are not benign,
Do do do do do do do dodo dododo dodo,

All pathways are free,
Unless you fix one path to 1,
The very end goal,
It’s important you know,
And I'm hungry, like a low chi-square,

Straddle the line,
With comparative models,
I'm on the hunt, for a good CFI,
Check your TLI, and RMSEA,
And I'm hungry, like a low chi-square,

Searching for paths,
I break from theory,
I'm on the hunt,
But I won’t get pubbed,

Latent constructs, made up of manifest
And I'm hungry like low chi-squares,

Draw many lines,
If you use ONYX,
I'm on the hunt, for a good CFI,
Check your TLI, and RMSEA,
And I'm hungry, like a low chi-square,


*This is a specification in the Mplus program

Partial Least Squares (Small-Sample Alternative to Conventional SEM)

Partial Least Squares (PLS) is a variation on Structural Equation Modeling (SEM). Riou, Guyon, and Falissard (2016) state that, relative to conventional SEM, PLS “is more suitable to … work with smaller sample sizes.” PLS is recommended for exploratory purposes, and is often used with single-indicator constructs. The technique seems to be used predominantly within the field of Management Information Systems (MIS).

Significance testing is done through bootstrapping, with 100 random variations of the original data set being generated and the model rerun in each random data set. An actual path coefficient from one’s model can then be evaluated for extremity, relative to the distribution of the same coefficient estimated 100 times from the bootstrap.

Though PLS may have reputation for making it easier to obtain significant results, this view appears overstated; a simulation study found that “for N = 40, PLS had 3% and 1% higher power than regression for strong and medium effect sizes [and…] the same power as regression at weak effect size” (Goodhue, Lewis, & Thompson, 2006).

Fit indices, such as NFI, CFI, RMSEA, are not available.

WarpPLS (Kock, 2015) is a program I've found useful and that has a three-month free trial version. Note that the probabilities given in WarpPLS output are one-tailed, so that if you want to report two-tailed p-values, you must double the printed value (e.g., p = .02 one-tailed represents p = .04 two-tailed).

Discussion of the pros and cons of PLS, and of the circumstances for which it may -- or may not -- be appropriate, is available in Goodhue, Thompson, and Lewis (2013); Marcoulides, Chin, and Saunders (2009); McIntosh, Edwards, and Antonakis (2014); and other sources. See also this discussion piece by Kock.


Goodhue, D., Lewis, W., & Thompson, R. 2006. “PLS, small sample size and statistical power in MIS research,” in Proceedings of the 39th Hawaii International Conference on System Sciences, R. Sprague Jr. (ed.), Los Alamitos, CA: IEEE Computer Society Press. (link)

Goodhue D. L., Thompson R. L., & Lewis W. (2013). Why you shouldn’t use PLS: Four reasons to be uneasy about using PLS in analyzing path models. In 46th Hawaii International Conference on System Sciences (pp. 4739–4748). Wailea, HI: HICSS.

Kock, N. (2015). WarpPLS 5.0 User Manual. Laredo, TX: ScriptWarp Systems. (link)

Marcoulides, G. A., Chin, W. W., & Saunders, C. (2009). A critical look at partial least squares modeling. MIS Quarterly, 33(1), 171-175. (link)

McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17, 210-251. (abstract)

Riou, J., Guyon, H., & Falissard, B. (2016). An introduction to the partial least squares approach to structural equation modelling: A method for exploratory psychiatric research. International Journal of Methods in Psychiatric Research, 25, 220-231. Published online first at doi: 10.1002/mpr.1497.

Reminder of Terminology in an SEM

To the beginning SEM practitioner, terms such as "parameter," "factor loading," and "directional path" may be confusing. Here's a drawing on the whiteboard (with some touch-ups in PowerPoint) to help clarify proper usage. Thanks to the students who photographed the board!

Introduction to ONYX

ONYX is a free SEM package developed in Germany. We will use it for Assignment 1, a CFA on the Hendrick and Hendrick love styles. ONYX is a graphic-arts-based program (like the commercial product AMOS), so your first experience designing a structural equation model will involve what I hope is an intuitive approach of drawing a picture (before we switch to the more technical, but more broadly applicable, Mplus for later assignments).  Here are some tips I have come up with for using ONYX, given its differences from other SEM programs:

1. Everything is done through right-clicking to bring up menus.

2. You can use an SPSS data file or a plain-text (tab-delimited) ".dat" file saved from an SPSS data file. The ONYX user manual lists available options for designating missing data. Once you've drawn your model, you can use "Load Data" to connect to the .dat file, yielding what's called a "Data Panel."

3. Use the "Create Variable" option to generate either latent or observed variables.

4. You should name latent variables (in ALL CAPITALS) via the right-clicking. However, but you’ll have to drag in the measured variables from the "Data Panel" to the variables' respective boxes in the model. By hovering over the measured-variable boxes, you can verify that the data have been linked.

5. By right-clicking on top of a variable, you can use the "Add Path" tool (the default is to draw unidirectional "causal" paths, whereas holding down the Shift key while using "Add Path" yields dual-headed correlational arrows).

6. All unstandardized factor loadings start out fixed at 1; you should free all of them (i.e., letting them take on freely estimated values). To identify the model (i.e., make sure you're not estimating more quantities than you have information for), construct variances should be fixed to 1.*

7. The default settings yield an unstandardized solution, whereas usually we're interested in a standardized one. You can obtain a standardized solution by right-clicking on each indicator’s box and selecting “z-score Transform.”

8. Covariances (correlations between factors) are also fixed and should be freed.

9. Unlike other programs, which have you submit a "job" or a "run," ONYX is constantly running in the background and responds to changes you make in model specifications. Right-clicking and selecting “Show Estimate Summary” will show current results.

*Fixing (or constraining) variables, underidentification, and degrees of freedom are discussed here. I have edited the linked document to distinguish between suggestions for when the AMOS program is used vs. ONYX.