Chapter 4 Gathering data Learn …. How to gather “good” data About Experiments and Observational Studies

Should We Experiment or Should we Merely Observe? Section 4.1 Should We Experiment or Should we Merely Observe?

Population, Sample and Variables Population: all the subjects of interest Sample: subset of the population - data is collected on the sample Response variable: measures the outcome of interest Explanatory variable: the variable that explains the response variable

Observational Studies Types of Studies Experiments Observational Studies

Experiment A researcher conducts an experiment by assigning subjects to certain experimental conditions and then observing outcomes on the response variable The experimental conditions, which correspond to assigned values of the explanatory variable, are called treatments

Observational Study In an observational study, the researcher observes values of the response variable and explanatory variables for the sampled subjects, without anything being done to the subjects (such as imposing a treatment)

Example: Does Drug Testing Reduce Students’ Drug Use? Headline: “Student Drug Testing Not Effective in Reducing Drug Use” Facts about the study: 76,000 students nationwide Schools selected for the study included schools that tested for drugs and schools that did not test for drugs Each student filled out a questionnaire asking about his/her drug use

Example: Does Drug Testing Reduce Students’ Drug Use?

Example: Does Drug Testing Reduce Students’ Drug Use? Conclusion: Drug use was similar in schools that tested for drugs and schools that did not test for drugs

Example: Does Drug Testing Reduce Students’ Drug Use? What were the response and explanatory variables?

Example: Does Drug Testing Reduce Students’ Drug Use? Was this an observational study or an experiment?

Advantages of Experiments over Observational Studies We can study the effect of an explanatory variable on a response variable more accurately with an experiment than with an observational study An experiment reduces the potential for lurking variables to affect the result

Experiments vs Observational Studies When the goal of a study is to establish cause and effect, an experiment is needed There are many situations (time constraints, ethical issues,..) in which an experiment is not practical

Good Practices for Using Data Beware of anecdotal data Rely on data collected in reputable research studies

Example of a Dataset General Social Survey (GSS): Observational Data Base Tracks opinions and behaviors of the American public A good example of a sample survey Gathers information by interviewing a sample of subjects from the U.S. adult population Provides a snapshot of the population

What Are Good Ways and Poor Ways to Sample? Section 4.2 What Are Good Ways and Poor Ways to Sample?

Setting Up a Sample Survey Step 1: Identify the Population Step 2: Compile a list of subjects in the population from which the sample will be taken. This is called the sampling frame. Step 3: Specify a method for selecting subjects from the sampling frame. This is called the sampling design.

Random Sampling Best way of obtaining a representative sample The sampling frame should give each subject an equal chance of being selected to be in the sample

Simple Random Sampling A simple random sample of ‘n’ subjects from a population is one in which each possible sample of that size has the same chance of being selected

Example: Sampling Club Officers for a New Orleans Trip The five offices: President, Vice-President, Secretary, Treasurer and Activity Coordinator The possible samples are: (P,V) (P,S) (P,T) (P,A) (V,S) (V,T) (V,A) (S,T) (S,A) (T,A)

The possible samples are: (P,V) (P,S) (P,T) (P,A) (V,S) (V,T) (V,A) (S,T) (S,A) (T,A) What are the chances the President and Activity Coordinator are selected? 1 in 5 1 in 10 1 in 2

Selecting a Simple Random Sample Use a Random Number Table Use a Random Number Generator

Methods of Collecting Data in Sample Surveys Personal Interview Telephone Interview Self-administered Questionnaire

How Accurate Are Results from Surveys with Random Sampling? Sample surveys are commonly used to estimate population percentages These estimates include a margin of error

Example: Margin of Error A survey result states: “The margin of error is plus or minus 3 percentage points” This means: “It is very likely that the reported sample percentage is no more than 3% lower or 3% higher than the population percentage” Margin of error is approximately:

Be Wary of Sources of Potential Bias in Sample Surveys A variety of problems can cause responses from a sample to tend to favor some parts of the population over others

Types of Bias in Sample Surveys Sampling Bias: occurs from using nonrandom samples or having undercoverage Nonresponse bias: occurs when some sampled subjects cannot be reached or refuse to participate or fail to answer some questions Response bias: occurs when the subject gives an incorrect response or the question is misleading

Poor Ways to Sample Convenience Sample: a sample that is easy to obtain Unlikely to be representative of the population Severe biases my result due to time and location of the interview and judgment of the interviewer about whom to interview

Poor Ways to Sample Volunteer Sample: most common form of convenience sample Subjects volunteer for the sample Volunteers are not representative of the entire population

A Large Sample Does Not Guarantee An Unbiased Sample Warning: A Large Sample Does Not Guarantee An Unbiased Sample

What Are Good Ways and Poor Ways to Experiment? Section 4.3 What Are Good Ways and Poor Ways to Experiment?

An Experiment Assign each subject (called an experimental unit ) to an experimental condition, called a treatment Observe the outcome on the response variable Investigate the association – how the treatment affects the response

Elements of a Good Experiment Primary treatment of interest Secondary treatment for comparison Comparing the primary treatment results to the secondary treatment results help to analyze the effectiveness of the primary treatment

Control Group Subjects assigned to the secondary treatment are called the control group The secondary treatment could be a placebo or it could be an actual treatment

Randomization in an Experiment It is important to randomly assign subjects to the primary treatment and to the secondary (control) treatment Goals of randomization: Prevent bias Balance the groups on variables that you know affect the response Balance the groups on lurking variables that may be unknown to you

Blinding the Study Subjects should not know which group they have been assigned to – the primary treatment group or the control group Data collectors and experimenters should also be blind to treatment information

Example: A Study to Assess Antidepressants for Quitting Smoking Design: 429 men and women Subjects had smoked 15 cigarettes or more per day for the previous year Subjects were highly motivated to quit

Example: A Study to Assess Antidepressants for Quitting Smoking Subjects were randomly assigned to one of two groups: One group took an antidepressant daily Second group did not take the antidepressant (this group is called the placebo group)

Example: A Study to Assess Antidepressants for Quitting Smoking The study ran for one year At the end of the year, the study observed whether each subject had successfully abstained from smoking or had relapsed

Example: A Study to Assess Antidepressants for Quitting Smoking Results after 1 year: Treatment Group: 55.1% were not smoking Placebo Group: 42.3% were not smoking Results after 18 months: Antidepressant Group: 47.7% not smoking Placebo Group: 37.7% not smoking Results after 2 years: Antidepressant Group: 41.6% not smoking Placebo Group: 40% not smoking

Example: A Study to Assess Antidepressants for Quitting Smoking Question to Think About: Are the differences between the two groups statistically significant or are these differences due to ordinary variation?

What Are Other Ways to Conduct Experimental and Observational Studies? Section 4.4 What Are Other Ways to Conduct Experimental and Observational Studies?

Multifactor Experiments Multifactor Experiments: have more than one categorical explanatory variable (called a factor).

Example: Do Antidepressants and/or Nicotine Patches Help Smokers Quit?

Matched-Pairs Design Each subject serves as a block Both treatments are observed for each subject

Subject Drug Placebo 1 Relief No Relief 2 3 Example: A Study to Compare an Oral Drug with a Placebo for Treating Migraine Headaches Subject Drug Placebo 1 Relief No Relief First matched pair 2 3

Blocks and Block Designs Block: collection of experimental units that have the same (or similar) values on a key variable Block Design: identifies blocks before the start of the experiment and assigns subjects to treatments with in those blocks

Experiments vs Observational Studies An Experiment can measure cause and effect An observational study can yield useful information when an experiment is not practical An observational study is a practical way of answering questions that do not involve trying to establish causality

Observational Studies A well-designed and informative observational study can give the researcher very useful data. Sample surveys that select subjects randomly are good examples of observational studies.

Random Sampling Schemes Simple Random Sample: every possible sample has the same chance of selection

Random Sampling Schemes Cluster Random Sample: Divide the population into a large number of clusters Select a sample random sample of the clusters Use the subjects in those clusters as the sample

Random Sampling Schemes Stratified Random Sample: Divide the population into separate groups, called strata Select a simple random sample from each strata

Observational Studies Well-designed observational studies use random sampling schemes

Retrospective and Prospective Studies Retrospective study: looks into the past Prospective study: follows its subjects into the future

Case-Control Study A case-control study is an observational study in which subjects who have a response outcome of interest (the cases) and subjects who have the other response outcome (the controls) are compared on an explanatory variable

Example: Case-Control Study Response outcome of interest: Lung cancer The cases have lung cancer The controls did not have lung cancer The two groups were compared on the explanatory variable: Whether the subject had been a smoker