Chat with us, powered by LiveChat Jane, a military psychologist, wants to examine two types of treatments for depression in a group of military personnel who h - School Writers

Jane, a military psychologist, wants to examine two types of treatments for depression in a group of military personnel who h

Jane, a military psychologist, wants to examine two types of treatments for depression in a group of military personnel who have suffered the loss of their legs. She has only 20 men to work with.

  • What would be the best research design for the study and why?
  • What are some issues that Jane needs to consider before starting the study?
  • What is a longitudinal study? What are the benefits and challenges associated with a longitudinal study?
  • Using the South University Online Library find two peer-reviewed articles (one that has used a between study design and one that has used a within study design) Summarize both of these articles. Make sure you discuss the research design specifically.
  • Explain what practice and carryover effects are in the context of the within subjects design study that you found. What steps did the researchers take to reduce these effects?

Justify your answers with appropriate reasoning and research

No plagerism

Understanding Experimental Designs.html

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.

Additional Materials

View the PDF transcript for Understanding ANOVA and Repeated Measures

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media/transcripts/SUO_PSY2060 Understanding ANOVA and Repeated Measures.pdf

Understanding ANOVA and Repeated Measures

PSY2060 Research methods

©2016 South University

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

©2016 South University

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.

© 2016 South University

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Considerations in Choosing a Sample.html

Considerations in Choosing a Sample

Considerations Before Choosing an Experimental Design

You are conducting a study on caffeine to see how it alters alertness. For this type of study, you could choose to use either design. The choice of design might be made as a result of answers to several questions.

Question:  How many participants are available?

Consider:  If you do not have a large number of participants, you may need to use a Within-Subject design.

Question: How many participants are needed to find a statistical difference?

Consider: Finding a statistical difference is related to the size of the sample. If the behavioral measure is robust, then it may show up with a smaller group size.  If the effect is difficult to detect, more participants will be needed.

Question:  What kind of independent variable is being manipulated?

Consider: The study on caffeine and alertness could work with a Within-Subject design if you allowed enough time between the testing sessions for the previous caffeine exposure to wear off.

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Between- and Within Designs Overview.html

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