We'll have a new song or two, plus we'll be singing some "oldies" from SEM the Musical 1, 2, 3, and 4 (just click directly on the numbers to access previous years' lyrics).
SEM Musical FIVE!
Lyrics by Alan Reifman (retread from last year)
(May be sung to the tune of “Let’s Get it Started,” Will Adams et al. for the Black Eyed Peas)
(Softly) The models keep runnin-runnin, and runnin-runnin, and runnin-runnin, and runnin-runnin, and runnin-runnin, and runnin-runnin, and runnin-runnin, and runnin-runnin, and...
We’re back again, to have some fun,
We’re gonna bust some rhyme, have a good time,
We’re gonna sing some songs, about SEM technique,
Access your inner geek, let your voices speak,
SEM is different, your measurement model’s explicit,
The whole model, gets tested for fit,
Is it identified? We know how hard you’ve tried,
Knowns and unknowns, side by side,
It takes you on a ride, finally you’re satisfied,
Your output’s now just fine, you’ve arrived, you can take pride…
NFI, TLI, CFI,
Calculate estimates, let it run, have some fun, yeah…
SEM Musical (FIVE!), SEM Musical (HERE!),
SEM Musical (FIVE!), SEM Musical (HERE!),
SEM Musical (FIVE!), SEM Musical (HERE!),
SEM Musical (FIVE!), SEM Musical (HERE!),
Yeah,
Build your constructs, get this straight,
Make sure the indicators, correlate,
Draw your pathways, residuals too,
Don’t leave out, the fixed 1 value,
Take your time, think it through,
Don’t worry if you’re new, we’ll walk with you,
Step by step, right up the pyramid,
For SEM, we’re really groovin,’
Hope you get an acceptable solution,
Submit your model and get it movin,’
NFI, TLI, CFI,
Calculate estimates, let it run, have some fun, yeah…
SEM Musical (FIVE!), SEM Musical (HERE!),
SEM Musical (FIVE!), SEM Musical (HERE!),
SEM Musical (FIVE!), SEM Musical (HERE!),
SEM Musical (FIVE!), SEM Musical (HERE!),
Yeah…
SEM Pyramid of Success (explanation)
Lyrics by Andrea Swenson
(May be sung to the tune of "Seasons of Love," Jonathan Larson, from the musical "Rent")
(Long opening on piano, about 40 seconds)
One hundred, thirty-nine thousand, two hundred seconds,
One hundred, thirty-nine thousand, moments to learn,
One hundred, thirty-nine thousand, two hundred seconds,
That is, how long, we sit in this class,*
It starts with, correlation,
Regression, and path an-al-y-sis,
E-F-A, builds into,
C-F-A, in time,
One hundred, thirty-nine thousand, two hundred seconds,
How, do you start? And, where do you go?
To get to, S.....E.....M.....
To get to, S.....E.....M.....
To get to, S.....E.....M.....
Measure it well....
Pyramid of.... (slow) success,
Pyramid of.... (slow) success,
One hundred, thirty-nine thousand, two hundred seconds,
One hundred, thirty-nine thousand, moments to learn,
One hundred, thirty-nine thousand, two hundred seconds,
That is, how long, we sit in this class,
Starting with, correlation,
Moving up to, regression,
In exploring, factors,
And confirming them,
It’s time now, to remember,
To bring it all together,
Let's, bring it all together, to do SEM,
Remember the pyramid (Oh you got to you got to remember the pyramid)
Remember the pyramid (You know that SEM is a starts from the r)
Remember the pyramid (regress, factor, SEM)
Assess the model (Learn, learn SEM)
Pyramid of success
Pyramid of success (that’s how we learn, learn SEM)
---
*Number of seconds in the class, based on 29 periods of 80 minutes each.
Hey, Hey, Heywood Cases
Lyrics by Nora "Felix" Phillips
(May be sung to the theme from "The Monkees," Boyce/Hart)
Let it run, the computations go through,
You get an error message, it leaves you feeling blue,
Hey Hey Heywood Cases!
Bringing, my AMOS, model down,
With your, negative variance,
You know that, isn't allowed,
Mis-specification,
Of the model, that you've drawn,
Or maybe, your own sample,
Was just, a tad bit, too small?
Hey Hey Heywood Cases!
I won't let you bring me down,
I can constrain, residuals,
To a, small positive, amount!
---
Nestedness
Lyrics by Alan Reifman
May be sung to the tune of “Yesterday” (Lennon/McCartney)
Nestedness,
It’s the way, models can be compared,
Should new paths be added in or spared?
The delta-test needs nestedness,
Can’t you see?
One model might have simplicity,
But more paths increase fidelity,
Which one to choose, the chi-square’s key,
Inside, the big one, the small one, is self-contained,
One has, extra paths, the other, does not maintain...
Nestedness,
To the baseline, you can only add,
Or only subtract, paths you once had,
You can’t do both, for nestedness,
Inside, the big one, the small one, is self-contained,
One has, extra paths, the other, does not maintain...
Look, shall we?
One model could have, paths “A” and “B,”
They would nest in, model “A/B/C,”
A/B’s contained, in A/B/C…
Maximum Likelihood
Lyrics by Alan Reifman
May be sung to the tune of “Pink Houses” (John Mellencamp)
The computer, runs your model, looking for a solution,
It seeks to maximize, or maybe minimize,
Some function, seen in, a distribution,
You have least squares, which tries to put, the best-fit line near the dots,
But ML, seeks equations, so your findings, will come out on top,
Oh, maximum likelihood, that’s what we use,
Maximum likelihood, it tends to confuse,
Maximum likelihood, underlying values, that make your results, most probable,
And that’s, big news!
Sir Ronald Fisher, statistician,
Developed the, ML perspective,
It will iterate, till it’s really great,
But it’s so, calculation intensive,
For a long time, ML sat there,
Its steps were, so hard to reckon,
But computers, came along, and sped things up,
And now ML, runs in mere seconds,
Oh, maximum likelihood, that’s what we use,
Maximum likelihood, it tends to confuse,
Maximum likelihood, underlying values, that make your results, most probable,
And that’s, big news!
Instrumental
Well there are data, and more data,
What do they show?
With its complex math, on a tricky path,
ML tells you, what you, need to know,
Oh yeah,
Well some data, might be missing,
But there’s no need, for frustration,
’Cause you can, estimate the means, and intercepts,
To get ML, with full, information,
Oh, maximum likelihood, that’s what we use,
Maximum likelihood, tends to confuse,
Maximum likelihood, underlying values, that make your results, most probable,
And that’s, big news!
