Associational Analyses

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From the course by Johns Hopkins University

Managing Data Analysis

2100 ratings

Johns Hopkins University

Managing Data Analysis

2100 ratings

Course 3 of 5 in the Specialization Executive Data Science

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD

From the lesson

Managing Data Analysis

Welcome to Managing Data Analysis! This course is one module, intended to be taken in one week. The course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials that expand on the lecture. I'm excited to have you in the class and look forward to your contributions to the learning community. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

General Framework8:43

Associational Analyses10:24

Prediction Analyses10:30

Meet the Instructors

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

This lecture gives an example of an associational analysis and

how to use formal modeling to challenge your findings.

The basic form of a model in an associational analysis will look

something like this.

You'll have an outcome, y and you'll have a key predictor, x.

And then you have maybe some potential confounder that will call z.

And then you have some independent random error that we call epsilon.

In addition to those factors,

we have a number of parameters that we wanna estimate.

So alpha here is the intercept.

It's the value of y when x and z are zero.

We have beta, which is the change in y associated with a one unit increase in x,

adjusted for z.

And then gamma is the change in y associated with a one-unit increase in z,

adjusting for x.

So, this is a linear model, and

the parameter that we're trying to estimate here is beta.

That's what tells us how our outcome changes along with our key predictor.

Now, there are other parameters, gamma and alpha, that are in the model and

we need to have them in the model for the model to work.

But we're actually not interested in those parameters.

So we sometimes call them nuisance parameters,

because we have to estimate them, but we don't actually care about their value.

So this is what I might consider to be a primary model.

It's very simple, there's a key predictor and there's only one confounder.

And so you may need to consider other things later on, but

this is sometimes good as a primary model.

On the other hand,

sometimes we'll use a primary model that doesn't have any confounders.

And then slowly add things to the mall to see how our results change.

So the example I'm going to use here is going to be an advertising campaign for

a new product.

Imagine you're selling something and you're thinking you're buying ads on

Facebook and you wanna know how effective those ads are gonna be.

So ultimately what you wanna do is sell more products and

make more money from this.

And so one thing you might do is try to pilot a one week experiment where you buy

Facebook ads for a week and see how it does.

So this is a very simple design, you might say look at the one week before

the ad campaign, the one week during the ad campaign.

And then the one week after the ad campaign just to see

how the sales numbers change while you're running the ads.

Then you could compare the total sales for the three weeks during,

the three weeks before and the three weeks after.

And see if there is any reasonable increase in the total sales.

So using this type of design and

this kind of experiment what would you expect to see?

So here's a data set that's not real, but it kind of represents the ideal scenario

for what you might see in an experiment like this.

So in the first seven days you have an average about 200 dollars per day.

Then the next 7 days,

this is during the campaign, you have an average of about $300.

And then after the campaign finishes you have an average of, again, about $200.

So it's possible to tell just from this graph, without doing anything fancy,

that the ad campaign seems to add about $100 per day to the total daily sales.

So, in this case, your primary model might be very simple,

it might look something like this.

Where you have Y, is the outcome, that would be the daily sales.

And then X is just an indicator of whether a given day fell during

the ad campaign or not.

And then still your primary interest is on the coefficient beta

which tells you how your total daily sales increases with the ad campaign in place.

So for example, the data for

this might look something like this in this table here where you have 21 days.

And you have seven days without the campaign, seven days with the campaign,

and seven days without.

I can see that the daily sales change as you go in and out of the campaign.

So it's a very basic setup, very simple, and

this is kinda what you would love to see in your data in an ideal world.

Of course, in reality, you will never see data like this.

There will always be something more complicated.

So here is a picture of what kinda more realistic

data might look like from an experiment like this.

Typically real world data are more noisy.

There are other trends in the background that are kind of messing up your

relationship.

So it makes it harder to analyze the data.

So you'll notice that in this picture there does appear

to be an increase in sales during the campaign period.

But the problem is that it seems like the increase started,

actually before the campaign even started, the sales were kind of going up.

And so you might wanna ask, well are there background trends that for

some reason increase sales over a three week period?

So it's possible that we would've seen higher sales in the product,

even without the ad campaign, just because of these background trends.

And so the question you really wanna know is,

did the ads cause an increase in sales.

Over and above whatever background trends that might of been going on that you

that you're not aware of.

So let's take our primary model,

which is just gonna be a simple model with the outcome and indicator of the campaign.

If we use that model, what you'll see is that we'll estimate beta,

the increase in the daily sales due to the ad campaign to be $44.75, okay?

Now, now let's suppose we add a background trend into our model.

So instead of the primary model where we just had the key predictor and

the outcome, we fit the following model.

Which has a quadratic trend for time, so this allows for kinda a little curvature,

and allows for kind of rising and falling of the daily total sales.

Okay, so if we use this model, and we still try to estimate beta,

what we get is that the, our estimate of beta is $39.86.

So that's somewhat less than the beta that we estimated for the primary model.

And recall that this is the increase in sales due to the ad campaign.

So just for comparison, we can fit actually even more complex model.

We could have a fourth order polynomial to model the trend,

just in case there might be something more complicated going on in the background.

And then if we were to fit that model, this gives us a beta of $49.10 per day.

So under this model, the ad campaign added an extra $49.10 per day to our sales.

So we have three models.

One primary model and two secondary models.

And so, model one tells us that our estimate of beta is $44.75.

Model two tells us it's $39.86, and model three tells us it's $49.10.

So, what do we do with this?

So there are a number of ways to evaluate your formal modeling and

your examination of primary and secondary models.

The first thing you want to look at is the effect size.

So the effect size essentially is what is the value of beta that you estimate.

And is it big or is it small, or

how do they compare to each other from the different models?

So the three different models represent a range of going from roughly $39 to $49.

And so you might wanna ask yourself is that a big range?

Do you care about those differences?

It's a little hard to answer that question without knowing kind

of what the context is and what your situation is.

For example, if the cost of the ad campaign were really low, and

it didn't really matter how much you made as long as you made something.

Then maybe you don't care whether it's $39 or $49.

As long as you make something back, then the ad campaign's worth it.

On the other hand, if the ad campaign were really expensive, so

maybe it's $20 a day to run these ads.

Then you might care which end of the range you fall on when you

run this ad campaign, because maybe $39 is not worth it, but $49 would be worth it.

So ultimately,

the question you're asking is is it worth the risk of buying these ads.

Given the evidence that you've seen from these different models showing you that

there might be a range of say, $39 $49 increase in total sales

during the period of the ad campaign.

The second factor you wanna think about is plausibility.

So even though we fit many different models,

not all of them are equally plausible, okay?

So the primary model may not be plausible, because it didn't incorporate

the possibility of any sort of background trend, okay?

And likely with real world data,

there's gonna be lots of things going on in the background.

And you don't want to just forget about them because they could affect how your

sales go, and you may think your campaign is having a big effect.

When actually there's something just going on in the background that's moving your

sales numbers.

Now the third model that we did which had this kind of fourth order polynomial

was a relatively complex model to capture a trend.

And it may be too complex or

more than we need to capture a very simple smooth trend in the background.

And so model two seems kind of reasonable.

We allow for this background trend.

It's a polynomial model.

But it's not very complicated, or

at least not overly complicated, to try to capture this trend.

So whether a model could be considered more or

less plausible will depend on your knowledge of the subject.

And your ability to map real world events to mathematical formulations of the model.

The last concept to think of is parsimony.

When different models tell the same story, for example,

if you don't care about the range of $39 to $49, then it's

sometimes better to choose the simpler model or the model with fewer parameters.

And the reason is it's usually easier to tell a story about the data

when you have fewer parameters and so this makes the model more useful.

And then finally a simpler model would be more efficient

because you're using more data to estimate fewer parameters and so

that's generally good from a statistical standpoint.

That will afford you to have less uncertainty about those

parameter estimates because you're able to use more data for each of the parameters.

So in this example, this was an associational analysis, and

it focused on estimating the association between two features.

Total sales and the ad campaign, while adjusting for other confounding factors,

like this potential background trend.

The primary models kind of capture the basic relationship.

While the secondary models are used to kind of adjust for

different factors in different ways.

So we had two different models kind of adjusting for the background trends.

So what conclusion you make ultimately may depend on outside factors like cost,

or kind of timing issues.

And so you have to factor all this in when you kind of ultimately draw your

conclusions about what to do or what decisions to make.

And also, you may have to think about the plausibility of the various

models that you've chosen.

To determine what evidence you're gonna weight more heavily than other evidence.

So this is the basic kinda outline of an associational analysis.

Obviously there'll often be many more things that you're gonna wanna do.

But they'll generally follow this framework, and you're gonna wanna iterate

between kinda fitting primary models and secondary models.

And so you get to an answer that you can use or can make a decision on.

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