CH_09: Basic Between-Subjects Designs Flashcards
A design where different subjects take part in each condition of the experiment
Between-subjects design
A process of randomization that first creates treatment blocks containing one random order of the conditions in the experiment; subjects are then assigned to fill each successive treatment block
Block randomization
A condition in which subjects receive a zero value of the independent variable
Control condition
The subjects in a control condition
Control group
A treatment condition in which the researcher applies a particular value of an independent variable to subjects and then measures the dependent variable; in an experimental group-control group design, the group that receives some value of the independent variable
Experimental condition
The general structure of an experiment (but not its specific content)
Experimental design
The subjects in an experimental condition
Experimental group
A between-subjects design with one IV, in which there are more than two treatment conditions
Multiple-groups design
The most commonly used multiple-groups design in which the subjects are assigned to the different treatment conditions at random
Multiple-independent-groups design
A mini-experiment using only a few subjects to pretest selected levels of an independent variable before conducting the actual experiment
Pilot study
In drug testing, a control condition in whih subjects are treated exactly the same as subjects who are in the experimental group, except for the presence of the actual drug; the prototype of a good control group
Placebo group
Creating pairs whose subjects have identical scores on the matching variable
Precision matching
The technique of assigning subjects to treatments so that each subject has an equal chance of being assigned to each treatment condition
Random assignment
Creating pairs of subjects whose scores on the matching variable fall within a previously specified range of scorres
Range matching
Creating matched pairs by placing subjects in order of their scores on the matching varibale; subjects with adjacent scores become pairs
Rank-ordered matching
A design in which two groups of subjects are exposed to different levels of the independent variable
Two-experimental-groups design
The simplest experimental design, used only when two treatment conditions are needed
Two-group design
An experimental design in which subjects are placed in each of two treatment conditions through random assignment
Two-independent-groups design
An experimental design with two treatment conditions and with subjects who are matched on a subject variable thought to be highly related to the dependent variable
Two-matched-groups design
T/F: An experiment must have at least two treatment conditions.
TRUE.
THE IV is manipulated such that at least two levels or treatment conditions are created.
What are the two variations of the two-group design?
Two-independent-groups design and two-matched-groups design.
T/F: In an experiment for TIGD, subjects are always randomly assigned to treatment conditions.
TRUE.
Even when not possible to select subjects entirely at random, the TIG design can still be used as long as subjects are randomly assigned to each group.
T/F: In a TIG design, the makeup of one group has an effect on that of the other.
F.
The makeup of one group has no effect on that of the other.
T/F: Random assignment gives us better chances of forming groups that are roughly the same on all the extraneous variables that might affect our DV.
T.
Random assignment controls for subject variables and guards against the possibility that subjects’ characteristics will vary systemically along with the IV.
What type of design is the experiment where testing if a highly violent music video vs a mildly violent music video would produce more aggressiveness?
Two-experimental-groups design
T/F: The more subjects we have available to assign to treatment conditions, the better the chances are that randomization will lead to equivalent groups of subjects.
T.
T/F: When we use the TIGS, we assume that randomization is succesful.
T.
T/F: Randomization always guarantees that treatment groups will be comparable on all the relevant extraneous subject variables.
F.
It is not always succesful.
What are the three types of matching procedures?
- Precision matching
- Range matching
- Rank-order matching
(Used after the experiment is conducted and scores are collected)
T/F: Statistical procedures are the same for those in matched groups and in independent groups.
F.
They are different.
What design is usually needed in situations in which the amount or degree of the IV is important?
Multiple-groups design, where there are more than two groups of subjects and each group is run through a different treatment condition
What type(s) of design is/are under multiple-groups design?
Multiple-independent groups design, in which the subjects are assigned to the different treatment conditions at random.
What is a random number table used for?
Assigning subjects.
What are the three factors the researcher considers to decide on the experimental design?
- number of IVs in the hypothesis
- number of treatment conditions needed to make a fair test of the hypothesis
- whether the same or diff subjects are used in each of the treatment conditions
T/F: Effect size of the IV can affect the number of participants needed in an experiment?
T.
Bonus: How?
What is the simplest experimental design?
Two-group design
How can matching be an advantage?
One can guarantee that groups start out the same on variables that matter AND if the matching variable is strongly related to the DV, matching can make treatment effects easier to detect statistically.
T/F: Block randomization can be used to ensure that each condition has an equal number of subjects.
T
T/F: It is valuable to always try to do multiple-groups design.
F.
Although we can get additional information from this, it is not always practical or necessary to do so. For some experimental hypotheses, just two values of the IV is sufficient.
T/F: While a between-subjects design has fewer threats to internal validity, it also requires more participants for high statistical power compared to a within-subjects design.
T.
How do you counter subject variables that confound the dependent variable in random assignment?
Use matching.
