Task 8 subject design Flashcards
Within-subject design
- A single group of subjects is exposed to all of the treatments, one treatment at a time
- Looking at the performance of each subject across treatments
Between-subject design
Each treatment is administered to a different group of subjects
The randomized two-group design
randomly assign your subjects to two groups and expose them to two different levels of independent variable, and take steps to keep the extraneous variables constant
→Compare the two means to determine whether they differ
Parametric design (The randomized multi-group design)
systematic variation of the amount of the independent variable (quantitatively e.g. different dose of medicine)
non-parametric design
The randomized multi-group design
manipulate your independent variable qualitatively (e.g. different kinds of medicine)
multiple control group design
The randomized multi-group design
when a single control group is not adequate to rule out alternative explanations
Matched group design
Matched pairs design
Determine a characteristic and test your participants for them now pair these who have the same results and assign them to a different group
Matched multi group design
requires finding matched subjects for each of your groups (so 4 groups = 4 matched subjects)
Mixed design
• A field was divided into several plots, and different plots received different levels of a given treatment. Each plot was then divided into subplots and each subplot received a different second treatment, thus each plot received all levels of fertilizer, but only one level of pesticide. Each plot are groups who all receive the same level of between-subjects variable. The subplots receive different levels of within-subjects variable to which all members of that group are exposed.
Advantages of within-subject designs
- Matching is on a high level so any differences across treatment cannot due to error variance arising from such differences (closest matching because you compare somebody to himself)
- More sensitive to the effect of your independent variable
- You can use fewer subjects
Disadvantages of within-subject design
- Require time to complete →might be difficult to find participants for this
- You could split up the experiment to different times but than you have the problem that some might leave the experiment because they wont show up to following treatments
- Carryover effect: When a treatment changes the behaviour of a subject these carry over all treatment and changes the whole performance of the subject (matched groups are a solution for this problem)
Advantages of between-subject design
+safes time and money
+relatively easy to analyse statistically
Disadvantages of between-subject design
- provides a limited amount of information about the effect of the independent variable (you can only compare the average)
- you don’t learn much about the nature of the relationship and function, in view of your independent and dependent variable
- sensitivity is limited to the effect of the independent variable (characteristics can influence the outcome so you can’t really determine the effect of the independent variable)
error variance
the variability among scores caused by other variable than your independent variable (e.g. age, gender, personality)
Interferential statistics
Show you the probability with which error variance alone would produce between group differences on your dependent measure at least as large as those you actually observed in your data. When its low it is statistically significant