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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).

In short, an observational study merely observes rather than experiments with the study subjects. An experimental study assigns to each subject a treatment and then observes the outcome on the response variable.

Type of Study: Observational Study

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A sample survey selects a sample of people from a population and interviews them to collect data.

A sample survey is a type of observational study.

A census is a survey that attempts to count the number of people in the population and to measure certain characteristics about them.

Observational Study – Sample Survey

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A headline read: “Student Drug Testing Not Effective in Reducing Drug Use” in a news release from the University of Michigan.

Facts about the study:

76,000 students nationwide 497 high schools and 225 middle schools 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: Drug Testing and Student Drug Use

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Questions:1. What were the response and explanatory variables?2. Was this an observational study or an experiment?

Conditional Proportions on Drug Use

Example: Drug Testing and Student Drug Use

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This study was an observational study.

In order for it to be an experiment, the researcher would had to have assigned each school to use or not use drug testing rather than leaving this decision to the school.

Example: Drug Testing and Student Drug Use

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The sampling frame is the list of subjects in the population from which the sample is taken, ideally it lists the entire population of interest.

The sampling design determines how the sample is selected.

Sampling Frame and Sampling Design

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Random Sampling is the best way of obtaining a sample that is representative of the population.

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.

A simple random sample is often just called a random sample. The “simple” adjective distinguishes this type of sampling from more complex random sampling designs presented in Section 4.4.

Simple Random Sampling, (SRS)

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To select a simple random sample: number the subjects in the sampling frame using

numbers of the same length (number of digits).

select numbers of that length from a table of random numbers or using a random number generator.

include in the sample those subjects having numbers equal to the random numbers selected.

SUMMARY: Using Random Numbers to select a SRS

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Sample surveys are commonly used to estimate population percentages.

These estimates include a Margin of Error which tells us how well the sample estimate predicts the population percentage.

When a SRS of n subjects is used, the margin of error is approximately equal to

Accuracy of the Results from Surveys with Random Sampling

1100%

n

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Convenience Sample: a type of survey sample that is easy to obtain.

Unlikely to be representative of the population.

Often severe biases result from such a sample.

Results apply ONLY to the observed subjects.

Poor Ways to Sample

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It is not always possible to conduct an experiment so it is necessary to have well designed, informative studies that are not experimental, e.g., sample surveys that use randomization.

Simple Random Sampling

Cluster Sampling

Stratified Random Sampling

Sample Surveys: Random Sampling Designs

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Cluster Random Sample

Preferable sampling design if a reliable sampling frame is unavailable, or the cost of selecting a SRS is excessive

Disadvantage Usually need a larger sample size than with a SRS

in order to achieve a particular margin of error.

Sample Surveys: Cluster Random Sample

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Stratified Random Sample Advantage is that you can include in your sample

enough subjects in each group (stratum) you want to evaluate.

Disadvantage is that you must have a sampling frame and know the stratum into which each subject belongs.

Sample Surveys: Stratified Random Sample

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Comparing Random Sampling Methods

Figure 4.2 Ways of Randomly Sampling 40 Students. The figure is a schematic for a simple random sample, a cluster random sample of 8 clusters of students who live together,and a stratified random sample of 10 students from each class (Fresh., Soph., Jnr., Snr.).Question: What’s the difference between clustering and stratifying?

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Comparison of different random sampling methods. A good sampling design ensures that each subject in a population has an opportunity to be selected. The design should incorporate randomness. Table 4.2 summarizes the random sampling methods we’ve presented.

Comparing Random Sampling Methods