92526 Chap.7 Top-Down and Bottom-Up Analysis
Top-Down and Bottom-Up Analysis
The final Week 2 video, Thinking forwards, asks how an analysis should begin: with the problem or with the data already held. The top-down route defines the problem and a plan first, scopes matching data, fixes the design, sample and statistical models before collection, and then collects prospectively until the question is answered or the hypothesis tested.
The bottom-up route starts from existing databases, datasets and repositories, pools them across systems, cleans and explores, mines and models, and consolidates the findings into insight.
This chapter sets the two side by side with the strengths and risks the slides attach to each: control over hidden influences and strategic focus for top-down work, comprehensive use of existing data for bottom-up work, and the matching dangers of missing predictors or over-weighting unimportant variables.
What this chapter covers
- 01
The top-down route from problem to answer
- 02
The bottom-up route from pooled data to insight
- 03
Prospective collection and pre-specified models
- 04
Pooling and mining data across systems
- 05
Strengths and risks of each route
- 06
Latent variables and controlled sampling
- 07
Chaining discovery and confirmation
Worked example · free
Sort six project steps into the top-down and bottom-up routes
- 1Top-down steps are (c), (a) and (e): define the problem and plan, fix the design and model in advance, then collect prospectively.
- 1Bottom-up steps are (b), (d) and (f): pool existing records, mine and model them, then consolidate insight.
- 1The ordering gives the routes away: top-down moves from a defined question to an answer, bottom-up climbs from data already held to insight.
Key terms
- Top-down approach
- An analysis that starts from a defined problem and hypothesis and designs data collection to answer it.
- Bottom-up approach
- An analysis that starts from existing data pooled across systems and works upward towards insight.
- Prospective collection
- Gathering new data going forward, after the design and analysis plan have been fixed.
- Latent variable
- An influence that is not measured directly but can distort a result unless sampling controls for it.
Top-Down and Bottom-Up Analysis FAQ
What is the difference between top-down and bottom-up analysis?
Top-down analysis begins with a problem definition and a plan, scopes data that matches the problem, and fixes the design and models before collecting new data. Bottom-up analysis begins with data already held in databases or repositories, pools it across systems, and explores and models it to find insight. One starts from the question, the other from the data.
When is a top-down approach the better choice?
When there is a specific question with relevant outcomes, such as whether an intervention reduces injuries, and you can define the data that would answer it. The slides describe top-down work as helpful for strategic decisions about sporting concepts, and careful sampling lets you reduce the effect of hidden variables on a hypothesis test.
What are the risks of bottom-up analysis?
The slides name three: it can be hard to interpret what actually mattered in a prediction, too much importance can be given to variables that were included but are not really important, and pooling and preparing data across systems can be expensive and slow to establish.
What does top-down analysis risk missing?
Because the data is scoped tightly to the question, information that would have predicted the outcome can be missed if nobody thought to collect it. The trade-off for that tight scope is control: a designed sample protects the hypothesis test from latent variables that bottom-up data cannot rule out.
Can the two approaches be combined?
Yes, and in practice they often are. A bottom-up search through existing records can propose a candidate warning sign or predictor, and a top-down study can then test it with a pre-specified design and prospective data before a club changes its practice on the strength of it.
Exam move
Draw both routes from memory as two columns, one reading down from the problem and one reading up from the data, and check the order of steps against the slide. Then practise deciding the route for short project descriptions: look for whether a specific hypothesis exists and whether the data already exists. For each route, learn one strength and one risk well enough to apply them to a scenario, not just recite them.
Finally, connect the routes to the lifecycle chapter by noting which lifecycle stages each route meets first, because a real project needs the planning ideas of Weeks 1 and 2 working together.
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