6/1/2015

Recall

When we study populations:

  • We are usually interested in some number or parameter that describes the population.
  • We estimate parameters with statistics, which are calculated from samples
  • Samples are randomly drawn from the population of interest
  • If our sample is representative, our statistics will be good estimators of the parameters.

Experiments and Observational Studies

There are two major types of studies:

  • Experiments
  • Observational Studies

Observational studies can be further broken down into:

  • Prospective Studies
  • Retrospective Studies

Case Study

Consider the following study:

  • To examine the effect of music programs on academic performance, a sample of students music students and non-music students were collected at a particular high school.

They found the following average GPAs:

  • Music students: 3.59
  • Non-Music Students: 2.91

What does this tell us?

  • Should every student be forced to play an instrument?

Observational Studies

Key characteristics:

  • Researchers simply observe the subjects
  • Various measurements or values are recorded for each subject
  • Good for describing or discovering relationships, especially in large populations
  • Cannot prove cause-and-effect, only provide evidence
  • Handling lurking or confounding variables can be tough

Case Study

For our students:

  • This was an observational study
  • Did they prove that playing an instrument increases grades?
  • Other factors could be at play
  • Families with more money can afford musical instruments and pay for tutors
  • If you have to work after school, you don't have time for band practice or homework

Retrospective Studies

Retrospective studies collect data on events that already occured

  • This is the most common type of retrospective study

They can be unreliable:

  • Rely on subjects' memories
  • Historical records can be incomplete
  • We only have information for people "in the system"

Examples:

  • Medical Records
  • Customer History
  • Academic Records

Prospective Studies

Prospective studies identify subjects first, then make observations as time goes on

  • This is commonly used in genetics or family studies

Pros:

  • Better for finding causal relationships
  • Can record any variables we want, not just what's already in records

Cons:

  • Takes a long time (often years)
  • Can be expensive
  • Rare events require very large samples

Case Study

The study on music and academic performance could have been either type.

Retrospective:

  • Choose a random sample of students in a given year
  • Examine academic records of these students
  • Calculate GPA

Prospective:

  • Choose a random sample of kindergarten students
  • Record their performance as they go through school and if/when they start in a music program

Case Study

Which would have been better?

Retrospective:

  • Can be done very quickly at any point in time
  • Very cheap – just need to get records

Prospective:

  • We can see if performance changed for each student as they picked up an instrument
  • We can also track if they pick up a part-time job or have any family problems

Experiments

While observational studies have researchers passively observing, researchers actively impose treatments on the subjects.

Examples:

  • Clinical trials
  • Scientific experiments
  • Marketing trials
  • Psychological experiments

Experiments are the only type of study that can prove cause-and-effect.

Treatments

In experiments, we call the explanatory variables factors and their possible values levels. A treatment is a unique combination of factor levels.

Say we wanted to test the affects of medicine \(X\) taken every 2 hours or every 4 hours, compared to a placebo.

  • The factor medicine has levels: "X", "Placebo"
  • The factor interval has levels: "2 hours", "4 hours"
  • We get four treatments: "X, 2 hours"; "X, 4 hours"; "Placebo, 2 hours"; "Placebo, 4 hours"

Performing Experiments

Designing an experiment is essentially just following the scientific method

  1. Come up with a hypothesis (question)
  2. Identify the factors
  3. Identify the response
  4. Select the experimental units (subjects, participants, classroom, petri dishes, etc.)
  5. Decide on the levels of the factors and make treatments
  6. Assign treatments to experimental units
  7. Analyze the results and compare treatments

Assigning Participants to Treatments

For any experiment (especially those with humans), decided who gets which treatment is important.

  • Never let the subjects choose (or even know)
  • Don't assign people to treatments based on what you think they need (giving the sickest the new drug)
  • In fact, never assign treatments yourself
  • Give each participant a label and let software randomly assign treatments

There's one catch

  • Experiments can introduce ethical dilemmas

Case Study

Say we want to study the relationship between Post-Traumatic Stress Disorder (PTSD) and suicide in veterans. What would the different studies look like?

Retrospective:

  • Examine medical records of soldiers and compare suicide rates of those with and without PTSD

Prospective:

  • Track soldiers as they get discharged and watch their mental health

Experiment:

  • Randomly assign soldiers to high-pressure combat situations where they'd be likely to develop PTSD and see if they commit suicide in the following years

Experiments: Major Principles

There are four major principles in experimental design:

  1. Control
  2. Randomization
  3. Replication
  4. Blocking

Control

Control in an experiment means regulating as many conditions as possible, like:

  • Temperature
  • Time of Day
  • Classroom conditions

Why?

  • Controlling allows us to isolate the treatments we want to study
  • This eliminates (or at least mitigates) the effects of lurking variables

Randomization

Randomizing the assignment of treatments helps us by:

  • Equalizing the effects of uncontrollable variation across the sample
  • Distributes uncontrollable effects (lurking variables) evenly across the groups
  • The effects of lurking variables will "average out" so they don't matter

Generally, we control what we can and randomize what we can't.

Replication

When we replicate, we apply a treatment to more than once

  • We assign multiple subjects the same treatment
  • Ideally, we repeat the entire experiment on a new sample

Why?

  • This helps us mitigate the chances of our results being a coincidence

Blocking

When we block, we group similar observations together and apply all treatments within the block.

Why block?

  • By blocking, we get more precise results by eliminating variation between the groups

Examples

  • Blocking by gender if we expect men and women to react differently
  • Blocking by age if we think it has a large effect

Case Study

A certain researcher thinks that taking an herbal supplement will help insomniacs get to sleep.

What is the hypothesis?

  • Taking the supplement decreases the time it takes for insomniacs to fall asleep

What is the factor?

  • Take the herbal supplement or don't take it

What is the response?

  • The time it takes to fall asleep

Case Study

What are our experimental units?

  • Individuals with insomnia

What are our treatments?

  • Placebo or supplement (or various doses)

How do we assign treatments?

  • Randomly, and without the subjects knowing

Analyzing

  • Did the people who got the supplement fall asleep faster, on average?

Case Study

Should we block here?

  • People in different lines of work will have different stress levels
  • Stress level can affect sleeping habits, so we should consider blocking on industry
  • The supplement might affect men and women differently, so we might want to block on gender
  • People often sleep less as they get older, so blocking by age range might be a good idea

The Placebo Effect

The control group is a group that doesn't get a treatment. Unfortunately, there's a complication called the placebo effect

  • People subconsciously react when they're told they might react to something.
  • This means that we can't just give some people pills and not give anything to the others – our results might just be due to the placebo effect.
  • To get around this, we give everyone a treatment, but some people get an inert treatment called a placebo

This isn't just limited to subjects

  • If the researcher knows who gets the drug in a medical trial, they might act more hopeful when examining them
  • The researcher can unknowingly influence the results of the trial

Control Groups

We can mitigate the effects of the placebo effect using blinding

Single blinding:

  • The subjects don't know whether they get the treatment or a placebo

Double blinding:

  • Neither the subject nor the researcher know whether the subject got the treatment or a placebo

Lurking and Confounding

Thus far, we've used lurking and confounding interchangeably when a third variable affects the relationship between two that we are interested in. There is a subtle difference.

Say we are interested in how \(X\) affects \(Y\), but there is a third factor \(W\)

Lurking Variable:

  • \(W\) affects both \(X\) and \(Y\)
  • \(X\) and \(Y\) are not related
  • This can make it look like \(X\) and \(Y\) are associated
  • Forest fires and ice creams sales are both linked to temperature

Confounding Variables

  • \(W\) directly affects \(X\)
  • \(W\) may also be related to \(Y\)
  • If we don't know about \(W\), it will make it look like \(X\) causes \(Y\) exclusively
  • Drinking habits and age of death might be related
  • Drinking habits might also be related to social class
  • Social class can also affect the age of death
  • If we control for social class, we might not see an association between drinking and age of death

Summary

  • Observational studies are when researchers simply observe their subjects. They can be retrospective or prospective.
  • In experiments, researchers actively impose a treatment on the subjects
  • We can control for the placebo effect using blinding
  • We can control for lurking and confounding variables with randomization, replication, and blocking