1. Topic 2: Types of Statistical Studies

2. Observational Studies

3. Observational Study
When data is collected only by monitoring what occurs, we call this an observational study. For example, to study the relationship between cell phone use and brain cancer, researchers might monitor the health of those who regularly use cell phones and those who do not.

5. Association and Causation
An association between two variables exists if a value of one variable is more likely to occur with certain values of another variable. A causation between two variables exists if a value of one variable tends to cause certain values of another variable. An observational study can help establish an association between variables, but not a causation.

7. The breakfast-slimness study
This is an observational study since the researchers merely observed the behavior of the girls (as opposed to assigning which girls ate breakfast and which didn’t).
What is the conclusion of the study? There is an association between girls eating breakfast and being slimmer.

8. Three possible explanations
Eating breakfast causes girls to be thinner.
Being thin causes girls to eat breakfast.
A third (unconsidered) variable is responsible for both. This is called a lurking variable or (confounding variable).

9. Distinguishing variables
“Girls who regularly ate breakfast were slimmer than those who skipped the morning meal.” If we suspect eating breakfast affects weight, then we call eating breakfast the explanatory variable and weight the response variable. explanatory variable response variable Warning: Labeling variables as explanatory and response does not guarantee the relationship between them is causal, even if we identify an association. In fact the labeling has no effect on the statistical analysis at all.
might affect

10. Distinguishing variables (continued)
Sometimes there is no clear choice between two variables, and we do not indicate one as the explanatory and the other as the response. Consider the following: If homeownership is lower than the national average in one county, will the percent of multi-unit structures in that county likely be above or below the national average?

11. Obtaining good samples
Recall that the population of interest is often too large to collect data from, so we choose a sample instead. Ideal situation: You can enumerate every individual in the population and randomly select (using a computer algorithm) the number of individuals you want in your sample. This is called a simple random sample. There are other random sampling techniques that are often used. The most common are stratified and cluster sampling.

12. Simple random sample
Randomly select subjects from the population, where there is no implied connection between the subjects chosen.

13. Stratified sample
Strata are made up of similar observation. We take a simple random sample from each stratum.

14. Cluster sample
Clusters do not usually consist of homogeneous observations, and we take a simple random sample from a random sample of clusters.

15. Experiments

16. Experiment
Studies where researchers assign treatments to subjects are called experiments. For example, to study the relationship between cell phone use and brain cancer, researchers might take 200 mice and randomly split them into two groups of 100. One group is exposed to the same radiation transmitted from a cell phone, and the other group is not exposed. After eighteen months, the mice are checked for cancer development. It is possible for a well-designed experiment to help establish a causation.

18. Let’s design an experiment
We want to investigate if energy gels make people run faster.
Treatment: energy gel
Control: no energy gel

19. Let’s design an experiment
We want to investigate if energy gels make people run faster.
Treatment: energy gel
Control: no energy gel
It is suspected that energy gels may affect pro and amateur athletes differently, so we block for pro status.

20. Let’s design an experiment
We want to investigate if energy gels make people run faster.
Treatment: energy gel
Control: no energy gel
It is suspected that energy gels may affect pro and amateur athletes differently, so we block for pro status.
Divide the sample by pro and amateur

21. Let’s design an experiment
We want to investigate if energy gels make people run faster.
Treatment: energy gel
Control: no energy gel
It is suspected that energy gels may affect pro and amateur athletes differently, so we block for pro status.
Divide the sample by pro and amateur
Randomly assign pro athletes to treatment and control groups

22. Let’s design an experiment
We want to investigate if energy gels make people run faster.
Treatment: energy gel
Control: no energy gel
It is suspected that energy gels may affect pro and amateur athletes differently, so we block for pro status.
Divide the sample by pro and amateur
Randomly assign pro athletes to treatment and control groups
Randomly assign amateur athletes to treatment and control groups

23. Distinguishing variables
“Do energy gels make people run faster?” If we suspect consuming energy gels affects speed, then we call energy gel the explanatory variable and speed the response variable. explanatory variable response variable
might affect

24. More experimental-design terminology
Placebo: fake treatment, often used as the control group for medical studies.
Placebo effect: experimental subjects showing improvement simply because they believe they are receiving a special treatment.
Blinding: when experimental subjects do not know whether they are in the control or treatment group.
Double-blind: when both the experimental subjects and the researchers who interact with the patients do not know who is in the control and who is in the treatment group.

25. Replication experiments
A replication experiment is a repeat of a previous experiment using the same methods, but with different subjects. When random sampling is not practical for an experiment, causation can only be established for the sample. By replicating the experiment using different samples, causation can be established for a larger group of subjects.

26. Treating chronic fatigue syndrome: revisited
Objective. Evaluate the effectiveness of cognitive-behavior therapy for chronic fatigue syndrome.
Participant pool. 142 patients who were recruited from referrals by primary care physicians and consultants to a hospital clinic specializing in chronic fatigue syndrome
Actual participants. Only 60 of the 142 referred patients entered the study. Some were excluded because they didn't meet the diagnostic criteria, some had other health issues, and some refused to be a part of the study.
Deale, et. al Cognitive behavior therapy for chronic fatigue syndrome: A randomized controlled trial. The American Journal of Psychiatry 154:3.

27. Recall: generalizing the results of the chronic fatigue syndrome case study
Are the results of this study generalizable to all patients with chronic fatigue syndrome? No. These patients had specific characteristics and volunteered to be a part of this study, therefore they may not be representative of all patients with chronic fatigue syndrome. Later replication experiments may be able to strengthen the assertion that cognitive-behavior therapy is an effective treatment for chronic fatigue syndrome.

28. Random assignment vs. random sampling