Session 9: Practice

What is the construct behind your indicators?

Thierry Warin, PhD

HEC Montréal

Outline · The workshop

  1. Ninety minutes
  2. Before you start
  3. Reproduce — 20 min
  4. Apply to your own angle — 35 min
  5. Break one assumption — 15 min
  6. Write it down — 20 min
  7. Submitting
  8. What loses marks
  9. Two-minute report
  10. After class, if you want it

What session 9 is for

The mathematics

  • Distinguish the measurement model from the structural model
  • Read =~ as “is measured by”, and write a model description
  • Interpret a standardised loading, and say when an indicator is weak
  • Report CFI, TLI and RMSEA, and say what each would have to be
  • Decompose a mediation into direct and indirect paths

The Python

  • Look at the correlation matrix with .corr() and plt.imshow before fitting anything
  • Turn a drawing into a model description, fit it with semopy.Model, and read .inspect()
  • Pull fit indices with semopy.calc_stats and report them all
  • Fit two models with opposite arrows and watch the fit not decide between them

The international business

  • Measure a construct — trust, capability, institutional quality — you cannot observe
  • State why good fit is not evidence that the model is correct
  • Name the alternative model you rejected, and on what grounds

Your Python so far

Everything you need to type is on one sheet — how to start Python in VS Codium on your platform, how to make a file and run it, the DataFrame basics, and every name from sessions 1 to 9, each with a line showing it in use.

Python Cheatsheet · Session 09 (PDF) · in the repository at 09-…/PYTHON-CHEATSHEET.pdf

One sheet per package, functions explained with pictures: pandas · NumPy · matplotlib · semopy · SciPy

Print it, or keep it open beside the editor. Nothing on it is examinable; all of it is assumed.

Practice

What is the construct behind your indicators?

Ninety minutes

00–20 Reproduce one result from the paper you read
20–55 Apply the method, to your own angle
55–70 Break one assumption, deliberately
70–90 Write, and report 250 words, then two minutes to the room

The presenter is drawn at random when your group is called. All three of you prepare.

Before you start

Everyone pushes at least once. Replace XX with your group number — group 07 uses group-07.

git checkout -b group-XX
mkdir -p groups/A2026/group-XX/session-09      # macOS and Linux
git checkout -b group-XX
mkdir groups/A2026/group-XX/session-09         # Windows — no -p

PowerShell’s mkdir creates the intermediate folders already, and rejects -p. The git log is the participation record. It is not a formality.

1 · Reproduce — 20 min

Take the result the paper rests on, and reproduce it. The package you downloaded is at 09-structural-equation-modelling/data/replication/.

import qmib

data = qmib.load("efa")     # the session's own data

Paper: Bennani & Romelli (2024), Exploring the informativeness and drivers of tone during committee meetings · 10.7910/DVN/TZEN38

A failed reproduction that you diagnose earns full marks. One you do not attempt earns none.

“It did not work” is not a diagnosis. Where did it stop, and what did you check?

2 · Apply to your own angle — 35 min

core = qmib.load("core")
mine = qmib.load("angle_c_country")     # your angle — see your dictionary
df   = core.merge(mine, on=["geo", "time"], how="inner")

Fit the session’s method on your data, and report what the lecture said to report — in the units of your own variables.

A coefficient without units is not a finding.

3 · Break one assumption — 15 min

Every method in this course rests on something.

Find the assumption this one leans on hardest. Break it deliberately. Record what happened to your answer.

This is the part that carries the marks. A group that shows how its result falls apart understands it; a group that only shows it holding does not yet know.

4 · Write it down — 20 min

250 words in your submissions folder:

  • What you found, in units
  • Which assumption you broke, and what it did
  • What this result does not license you to claim

And the standing rule: one LLM output you identified as wrong, and how you established that.

Submitting

git add groups/A2026/group-XX/session-09
git commit -m "Session 09 practice — group XX"
git push -u origin group-XX

Deliverable. A measurement model for one construct in your project, with the loadings reported, the fit indices stated, and a note on the indicators you would drop and why.

What loses marks

  • Reporting fit indices without saying which threshold you applied
  • Treating good fit as confirmation of the causal structure
  • Adding correlated residuals until the model fits
  • Naming a latent variable without defending the name

And everywhere in this course: an association described in causal language.

Two-minute report

One number with its units and its uncertainty
One assumption the one you broke, and what happened
One limit what you cannot claim from this

Listen for traceable evidence, not confidence of delivery.

After class, if you want it

The QMIB Lab has a knowledge check on what you have just done: warin.ca/qmib-labs

Optional, never a prerequisite for the next lecture, and never marked for correctness. A completed report counts because attempting it is the engagement being measured — it is one of the three routes to the participation mark.

What is required before the next session: the article, its Dataverse package, and the self-check. That is the pre-session deck, not this.