What is an example of repeated measures design?
In a repeated measures design, each group member in an experiment is tested for multiple conditions over time or under different conditions. For example, a group of people with Type II diabetes might be given medications to see if it helps control their disease, and then they might be given nutritional counseling.
What is an independent measures design?
Independent measures design, also known as between-groups, is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.
Why is an independent measures design used?
An independent measures design is a research method in which multiple experimental groups are used and participants are only in one group. Advantages of independent measures design include less time/money involved than a within subjects design and increased external validity because more participants are used.
What is counterbalanced design?
Counterbalancing is a technique used to deal with order effects when using a repeated measures design. With counterbalancing, the participant sample is divided in half, with one half completing the two conditions in one order and the other half completing the conditions in the reverse order.
What is repeated measures design used for?
Repeated measures design allows conclusions to be drawn using a much smaller group of subjects. It’s a powerful experimental technique, since it limits the variability to one group while also allowing effects to be measured over time.
What are the limitations of independent measures design?
Disadvantages
- researcher cannot control the effects of participant variables (i.e. different characteristics or abilities of each participant). This would cause a confounding variable.
- needs more design than the Repeated Measures Design in order to end up with the same amount of data.
What are the 3 types of experimental design?
There are three primary types of experimental design:
- Pre-experimental research design.
- True experimental research design.
- Quasi-experimental research design.
What are the advantages of repeated measures design?
The primary strengths of the repeated measures design is that it makes an experiment more efficient and helps keep the variability low. This helps to keep the validity of the results higher, while still allowing for smaller than usual subject groups.
What are two advantages to a repeated measures design?
More statistical power: Repeated measures designs can be very powerful because they control for factors that cause variability between subjects. Fewer subjects: Thanks to the greater statistical power, a repeated measures design can use fewer subjects to detect a desired effect size.
When repeated measures are used which assumption is violated?
assumption of sphericity
Unfortunately, repeated measures ANOVAs are particularly susceptible to violating the assumption of sphericity, which causes the test to become too liberal (i.e., leads to an increase in the Type I error rate; that is, the likelihood of detecting a statistically significant result when there isn’t one).
Which is an example of a repeated measures design?
For example, if an independent groups design requires 20 subjects per experimental group, a repeated measures design may only require 20 total. Quicker and cheaper: Fewer subjects need to be recruited, trained, and compensated to complete an entire experiment.
When do you use a repeated measures ANOVA?
Repeated Measures ANOVA. Issues with Repeated Measures Designs. Repeated measures is a term used when the same entities take part in all conditions of an experiment. So, for example, you might want to test the effects of alcohol on enjoyment of a party.
How are sample sizes reduced in repeated measures?
Further sample size reductions are possible because each subject is involved with multiple treatments. For example, if an independent groups design requires 20 subjects per experimental group, a repeated measures design may only require 20 total.
Why do you need fewer subjects for repeated measures?
Requires a smaller number of subjects: Because of the increased power, you can recruit fewer people and still have a good probability of detecting an effect that truly exists. If you’d need 20 people in each group for a design with independent groups, you might only need a total of 20 for repeated measures.
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