ANOVA And Repeated Measures ANOVA Designs ANOVA and Repeated Measures ANOVA Designs Use the Online Resources to find two peer-reviewed articles in which th

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ANOVA And Repeated Measures ANOVA Designs ANOVA and Repeated Measures ANOVA Designs
Use the Online Resources to find two peer-reviewed articles in which the authors used ANOVA designs and two peer-reviewed articles in which the authors used repeated measures ANOVA designs. Summarize each article and evaluate whether the design used was logical. Explain your reasoning. Do you think that the design influenced the statistical significance observed? Why or why not? Could this influence the validity of the work?
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Understanding Experimental Designs

Understanding Experimental Designs

Either the Between-Subject or the Within-Subject experimental designs can be used to compare more than two levels of the independent variable. When there are more than two levels of the independent variable, the Between-Subject design is called a simple analysis of variance (ANOVA) and the Within-Subject design is called a repeated-measures ANOVA.

Although experiments are needed to make cause/effect statements, each study design serves a useful role in helping find answers about behavior. As evidence is collected from the different types of designs with a variety of data-collection methods, theories are strengthened in each setting. Evidence from studies converges to build support and you find more plausible and more accurate answers. This is as close as you can get to scientific truth.

Understanding ANOVA and MANOVA

Univariate analysis of variance (ANOVA) tests the significance of differences between groups. For example, a student could use an ANOVA to study how students who drank no coffee, eight ounces of coffee, or sixteen ounces of coffee within the last three hours performed on a written exam.

A multivariate analysis of variance (MANOVA) is similar to ANOVA, but MANOVA tests several dependent variables. In addition, there may be correlations between the dependent variables, and MANOVA controls for this. An example of when to use a MANOVA would be if you were investigating how participation in a group to teach patients to navigate the healthcare system affected their perceptions of using the emergency room (ER). The dependent variables would be number of sessions attended, previous number of visits to the (ER), and perceived quality of care given in the ER.

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Understanding ANOVA and Repeated
Measures

PSY2060 Research methods

2
Understanding ANOVA and Repeated Measures

Understanding experimental designs

Understanding ANOVA and Repeated-Measures

Using the example of caffeine and attention, you could compare how the design would work if
you had three separate groups of participants or one group that gets each level of the independent
variable.

Compare the information for the ANOVA and the Repeated Measures designs. There are some
similarities such as the number of levels and the amount of caffeine each participant is exposed to at each
level. The differences relate to the number of participants needed. The number needed for the ANOVA is
larger. The same participants go from one level to the next in the Repeated Measures group. Although
using the same group of participants reduces the number of participants needed, you can see how
carryover effects can occur.

Independent

Variable

Levels

Amount of

Caffeine
0 mg 90 mg 180 mg

Participants

in Each Group
20 20 20

Independent

Variable

Levels

1 2 3

Amount of

Caffeine
0 mg 90 mg 180 mg

same same

20 20 20

Participants

in Each Group

20 Participants

in sample; each

person gets ever

level of the

independent

va riable.

60 Participants

in Sample

Repeated Measures Example

Independent Variable: Milligrams (mg) of caffeine in coffee Dependent

Variable: Sustained driving ability on a simulated test

ANOVA Example

Independent Variable: Milligrams (mg) of caffeine in coffee Dependent

Variable: Sustained driving ability on a simulated test

1 2 3

PSY2060 Research methods

3
Understanding ANOVA and Repeated Measures

Understanding experimental designs

WSDs take fewer participants and have more statistical power. The major disadvantage is the
possibility of practice effects and carryover effects. Practice and carryover effects occur because the
same group of participants is exposed to all levels of the independent variable. The practice and carryover
effect is the result of memory, residual trace of previous exposure to the testing conditions, or
overexposure to the independent variable. There are different strategies for dealing with these effects.
You can counterbalance the trials or equalize the amount of practice.

Longitudinal studies are a type of WSD that requires the investigator to follow a particular group
over a long period of time.

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