Designing your investigation

Research Methods — Week 5

Welcome to Phase 2

The shift

Phase 1 (done ✓)

Scaffolded mini-project.

Same data, same question.

Formative.

Phase 2 (starts now)

Your investigation.

Your question, your data.

Summative — 30% of module grade.

• [5 min] Set the tone. Phase 2 is where students take ownership. The skills are the same; the stakes are higher.
• Briefly outline the assessment: individual 4-page policy report based on group project work.

Questions?

Submit questions anonymously:

PollEv.com/geol

text geol to 07480 781235

• Open the Poll Everywhere Q&A activity before the session and leave it active throughout.
• Questions appear in your Poll Everywhere presenter view; upvoted questions rise to the top.
• Address during transitions or at the end of concept blocks, not mid-explanation.
• Reset (archive) the activity between sessions so questions don’t carry over from the previous week.

The assessment

Individual 4-page policy report.

• Based on your group’s investigation
• Same structure as Phase 1: summary → evidence → caveats → conclusions
• Due after Week 10

Your commit history matters — it shows your individual contribution.

• The report is individual even though the project is group-based. Students need to understand: the group collects and analyses data together; each person writes their own report.

What makes a good experiment?

🎓 Concept block 1

The logic of comparison

You can’t know if something is big unless you measure something else.

• [20 min block] Experimental design fundamentals.

Controls

What you hold constant so you can isolate the variable of interest.

Example: comparing wind farm output across sites.

Control for	Why
Turbine model	Different models have different capacities
Terrain	Ridgetop vs valley affects wind speed
Grid curtailment	Some farms are told to stop generating

What can’t you control? Weather. Maintenance schedules.

• Geoscience angle: in field sciences we rarely get true experiments. We observe natural variation and try to isolate factors. This makes design more important, not less.

Treatment vs control groups

Lab sciences

Treatment group gets the intervention.

Control group doesn’t.

Random assignment.

Field sciences

“Treatment” is often a natural condition.

“Control” is a comparison group.

You can’t randomise geology.

• Because we can’t randomise, we need to think harder about what might confound our comparisons. That’s next.

HolmesCo’s site investigation

💬✏️ Exercise 1

The scenario

HolmesCo

“Geological Solutions Since 2019”

Ground Investigation Report — Proposed Wind Farm, County Durham

HolmesCo drilled 3 boreholes in the valley where access roads already exist. All three encountered competent sandstone.

Conclusion: “Bedrock across the site is suitable for turbine foundations.”

What’s wrong with this? Work in pairs — 5 minutes.

• [15 min] Expected problems: (a) boreholes are in the valley, turbines are on ridgetops; (b) 3 is tiny; (c) convenience sampling;
  4. no control site. What would a better investigation look like?

The problems

• Selection bias: sampled where it was convenient, not representative
• Wrong location: boreholes in the valley, turbines on the ridgetops
• Tiny sample: 3 boreholes for a multi-km² site
• No comparison: how do we know this is unusually good (or bad)?

Confounding variables

🎓 Concept block 2

What is a confounder?

A variable that is correlated with both the treatment and the outcome.

You can’t tell which caused the effect.

• [20 min block] Confounders are the hidden trap in observational studies.

Classic examples

Observation	Apparent cause	Real cause (confounder)
Ice cream sales ↑, drowning ↑	Ice cream causes drowning?	Hot weather
More solar panels → higher GDP	Solar causes wealth?	Latitude, governance, investment
Deeper boreholes → higher temp	Depth causes heat?	Location (geothermal area)

Correlation is not causation — because of confounders.

How to handle confounders

1. Randomisation — when possible (rare in geoscience)
2. Stratification — compare within subgroups
3. Matching — pair similar observations
4. Acknowledgement — when all else fails, be honest about what you can’t control

• Option 4 is the most common in geoscience and policy analysis. Honesty about confounders is itself a research skill.

Spot the confounder

💬✏️ Exercise 2

Three scenarios

For each: identify the confounder and suggest how to address it.

1. “Communities near wind farms report more headaches than communities without wind farms.”

Confounders: rural/urban, age, awareness/nocebo effect, reporting bias.

Scenario 2

2. “Countries with higher nuclear capacity have lower carbon emissions per capita.”

Confounders: GDP, industrialisation stage, energy mix decisions driven by geography and politics.

Scenario 3

3. “Students who use AI assistants score higher on coding assignments.”

Confounders: prior coding experience, motivation, time spent on assignments.

• [15 min total] Quick discussion for each. Don’t linger — the point is pattern recognition, not exhaustive analysis.

Sampling strategies

🎓 Concept block 3

How to choose your sample

Strategy	How	When
Random	Every unit has equal probability	Gold standard; often impractical
Stratified	Sample within each subgroup	Ensures representation
Systematic	Every nth unit	Regular grids, monitoring stations
Convenience	Whatever’s easiest	HolmesCo’s default

• [20 min block] Practical sampling. The key message: your sampling strategy is a decision you must justify.

The HolmesCo default

“We sampled these because they were there.”

Honest — but weak.

Your sampling strategy is a decision you must justify in your report.

• This connects directly to Phase 2: students will need to explain why they chose their data sources and whether their sample is representative.

Design sketch

✏️💬 Integrative exercise

Choose your topic

Browse the available project topics.

Form provisional groups (3–4 people).

Pick a candidate topic. Then sketch:

1. Question — what are you investigating?
2. Data — what would you need?
3. Comparison — what’s your control?
4. Confounders — what should you worry about?

• [15 min] Groups form and discuss. Each group shares one sentence. Topics don’t need to be final — that happens in the app session.

Wrap-up

Key points

1. Good design = clear comparison + controlled confounders
2. Sampling is a choice you must justify
3. HolmesCo’s mistakes are easy to make — watch for them in your own work

Next time

Application session: “Your question, your plan”

• Finalise groups and topics
• Set up your group GitHub repo
• Write a one-page research plan

• Students should come to the application session with a topic preference and at least a tentative group.