Session 9: Before the session

Can you measure something you cannot observe?

Thierry Warin, PhD

HEC Montréal

Outline · Before class

  1. What to do, and how long it takes
  2. Read the paper
  3. Get BOTH datasets
  4. The course data
  5. Self-check, and what the session will ask of you
  6. Before you run anything

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

Before the session

Can you measure something you cannot observe?

What to do, and how long it takes

1 Read the paper 45–60 min
2 Get both datasets — the paper’s, and your own angle 10 min
3 Attempt the self-check 15 min

The lecture assumes all three are done. The practice assumes the data is already cached on your machine.

1 · Read the paper

Bennani & Romelli (2024), Exploring the informativeness and drivers of tone during committee meetings

Read the article

Read for the argument, not for coverage. Annotate four things:

1 The research question, in one sentence
2 The method or design, and why they chose it
3 The strongest single piece of evidence
4 One limitation you would raise as a referee

2 · Get BOTH datasets

The practice uses two: the paper’s data, to reproduce one of its results, and your own angle, to apply the method. Have both before you arrive.

a) The paper’s replication package — Harvard Dataverse

10.7910/DVN/TZEN38

Download it from the terminal — no Dataverse account, no browser. From the repository root:

macOS and Linux

mkdir -p 09-structural-equation-modelling/data/replication && cd 09-structural-equation-modelling/data/replication
curl -L -o replication.zip \
  "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/TZEN38"
unzip -q replication.zip && rm replication.zip && cd -

Windows PowerShell

mkdir 09-structural-equation-modelling/data/replication; cd 09-structural-equation-modelling/data/replication
curl.exe -L -o replication.zip `
  "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/TZEN38"
Expand-Archive replication.zip .; Remove-Item replication.zip; cd -

data/replication/ is git-ignored, so nothing large is committed. Keep the authors’ own structure — do not tidy it.

2b · The course data

import qmib

data = qmib.load("efa")     # what the lecture uses
core = qmib.load("core")                # the spine, shared by every group
mine = qmib.load("angle_c_country")     # REPLACE with your own angle

print(data.shape, core.shape, mine.shape)

Run this before you arrive. It downloads once and caches as parquet, so the practice works whatever the room’s wifi is doing.

angle_c_country is an example, not your angle. Yours is named in RESEARCH-MANDATES.md.

A 14-item questionnaire on purchase decisions, plus semopy’s bundled examples · everything available: qmib.catalog()

3 · Self-check, and what the session will ask of you

Answer these on paper. If you cannot, that is what the lecture is for.

  1. In one sentence: what does this session’s method let you claim that the previous one did not?
  2. What must be true of your data for it to apply?
  3. Which claim in the paper rests on this method — and how hard does it lean on it?

By the end of the session you should be able to:

  • Distinguish the measurement model from the structural model
  • Write a model description and read =~ as “is measured by”
  • Interpret a standardised loading, and say when an indicator is weak
  • Report CFI, TLI and RMSEA, and say what each would have to be
  • State why good fit is not evidence that the model is correct

Arrive with: the paper read and annotated · the replication package unzipped · qmib.load() run at least once · your self-check answers on paper.

Before you run anything

Everything runs in the VS Codium terminal, from the repository root, with the project environment active. Activate it in every new terminal, not only the first:

source .venv/bin/activate        # macOS and Linux
.venv\Scripts\activate           # Windows (PowerShell)

Your prompt must begin with (.venv). Without it python and pip are your system’s, and nothing the course installed is visible to them.

ModuleNotFoundError means the packages went somewhere else, or were never installed. Check the prompt first, then pip install -r requirements.txt — from the repository root, with the environment active. Building the environment from scratch is Session 01, step 1.4.

Session 09: README.md · the lecture: 01-lecture/