1. Unit 1
Section 1.3

2. 1.3: Data Collection and Experimental Design
Designing a Statistical Study
Identify the variable(s) of interest and the population of the study.
Develop a detailed plan for collecting data.
If you use a sample, make sure the sample is representative of the population.
Collect the data.
Describe the data, using descriptive statistics techniques.
Interpret the data and make decisions about the population using inferential statistics.
Identify any possible errors.

3. Section 1.3
Observational Study - the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations.
The researcher does not influence the responses.
The researcher observes and measures characteristics of part of a population.
For Example: Motorcycle owners are getting older and richer.
Data was compared based on income of motorcycle owners over a period of time.

4. Section 1.3
Experimental Study (or experiment) – the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables.
The group being manipulated is called the treatment group.
The group not being manipulated is called the control group. Control groups are usually equal in size to treatments groups.
All subjects are known as experimental units.
Often, placebos are given to members of the control group to make them think they are receiving the treatment. Placebos are harmless, fake treatments.
For Example: Drug trials for new medications

5. Section 1.3 Observational Study Advantages: Disadvantages:
Usually occurs in a natural setting (not a lab).
They can be used when it is unethical or dangerous to conduct an experiment.
They also can be used when the researcher cannot manipulate the variables.
Disadvantages:
Definite cause and effect cannot always be determined because other factors may have led to the results.
They can be expensive and time-consuming.
They also can have unreliable data when relying on recordings from the past.

6. Section 1.3 Experimental Study Advantages: Disadvantages:
Researcher has much more control (over who is in groups and manipulating the independent variable).
Disadvantages:
They may occur in unnatural settings such as labs and special classrooms.
This can lead to the Hawthorne effect
They can also have confounding variables.

7. Section 1.3 Data Collection Simulation: Survey:
Mathematical or physical model used to reproduce the conditions of a situation or process.
Often uses computers to collect data.
Used for situations that are impractical or dangerous to recreate.
Often save time and money.
Survey:
Investigates one or more characteristics of a population.
People mostly administer surveys (internet, phone, mail, etc.)
Surveys must be designed to eliminate bias.

8. Section 1.3 Experimental Design
Goal: Produce meaningful and unbiased results.
Three key elements:
Control
Randomization
Replication.

9. Section 1.3
Control
Confounding variable – occurs when an experimenter cannot tell the difference between the effects of different factors on the variable.
For Example: You own a bookstore. You choose to remodel your store at the same time as a new college opens up down the road. Business increases and you can not tell if this is due to the remodel or the new college.
The Hawthorne Effect
Subjects who knew they were participating in an experiment actually changed their behavior. Therefore, the results of the research was affected.

10. Section 1.3 Placebo Effect
Subjects act favorably to a placebo when in fact they have been given a fake treatment.
Blinding is implemented in order to reduce the placebo effect. Blinding is when the subjects do not know whether they have been given the placebo or are receiving treatment. Double blinding is when the experimenter AND the subject do not know whether the subjects have been given the placebo or are receiving treatment.

11. Section 1.3 Randomization
Randomization – process of randomly assigning subjects to different groups.
Completely Randomized Design: subjects are assigned to groups through random selection.
Randomized Block Design: subjects are grouped by similar characteristics (called blocks) and then assigned within each block.
Matched Pair Design: subjects are paired together then assigned into the treatment/control group.

12. Section 1.3
Replication
Replication – repetition of the experiment under the same or similar conditions.

13. Sampling Techniques Section 1.3
Sample – a part of a population used in statistical studies.
An unbiased sample is one where each subject in the population has an equally likely chance of being selected.
A biased sample is one that is non representative of the entire population.
Sampling error is the difference between the results of a sample and those of a population.

14. Section 1.3 Random Sampling Systematic Sampling Stratified Sampling
To obtain unbiased samples statisticians use four basic methods of sampling:
Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling

15. Section 1.3
Random Sampling – every member of a population has a equal chance of being selected.
Previously, tables were used, now calculators are used.
Example: Nursing supervisors are selected using random numbers in order to determine annual salaries. x

16. Creating a Random Number List (on the TI)
Section 1.3
Creating a Random Number List (on the TI)
Press the Math button
Use the arrow key to scroll right until you are on the PRB menu
Select option #5: randInt(
Enter a starting value followed by a comma
Enter an ending value followed by a comma
Enter the number of values you wish to generate followed by a closed parenthesis
Press STO>
Press 2nd then STAT
Select L1
Your random numbers will be stored in L1 for use

17. Section 1.3
Stratified Sampling - selects by dividing the population up into groups by some characteristic that is important to the study, then sample from each group.
The groups are called strata.
Each strata has equal representation in the sample.
Example: Mail carriers in a large city are divided into 4 groups according to gender and whether they walk or drive. Then 10 are selected from each group and interviewed.

18. Section 1.3
Cluster Sampling- selects by dividing the population up into clusters and randomly selecting a few whole clusters as samples.
Clusters are naturally occurring groups.
Different than stratified sampling, members of each cluster are not grouped based on a characteristic.
Example: In a large school district, all teachers from two of the school buildings are interviewed.

19. Section 1.3
Systematic Sampling - selecting every kth subject of the group.
If the group has a population p and you want to sample s subjects, p/s = k .
Example: Every 100th hamburger manufactured is checked to determine its fat content.

20. Other Sampling Methods
Section 1.3
Other Sampling Methods
Convenience Sampling - selects by doing whatever is convenient for the researcher.
Sequential Sampling
Double Sampling
Multi-Stage Sampling

21. Homework
Complete Pgs
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