Lecture 20: Factorial Designs ll Flashcards
types of factorial designs
- Pure (between-subjects) factors
- Within-subjects factors
- Mixed design (between + within-subjects factors)
- Higher order factorial designs: factorial designs with 3 or more factors
pure factorial design
- Design in which all factors are being manipulated
- Between-groups designs: different groups of participants are randomly assigned to each cell of the design
advantage of pure factorial designs
Avoids problems with order effects
disadvantages of pure factorial designs
- Can require many participants because all factors are between-subjects
- Individual differences can become confounding variables (as in single-factor between-subjects designs)
when are pure factorial designs best?
when many participants are available, individual differences are relatively small, and order effects might be a problem
within-subjects factorial designs
A single group of participants is in all separate conditions
advantages of a within-subjects factorial design
- Fewer participants are needed
- Reduces individual differences
disadvantages of a within-subjects factorial design
- Many factors means that participants experience many different conditions
- Very time-consuming and the likelihood of attrition is higher
- Increases chances of testing effects (practice/fatigue)
- Makes it difficult to counterbalance orders to control order effects
when are within-subjects factorial designs best?
individual differences are large and order effects will not be a problem
mixed designs
a factorial study that combines both within- and between-subject factors
when are mixed designs used?
- when one factor is expected to threaten validity
- when the experimenter wants the advantage of a between-subjects design for one factor, while a within-subjects design is preferable for the second factor
common breakdown of mixed factorial designs
one between-subjects factor and one within-subjects factor
Pretest-posttest control group designs
example of a two-factor mixed design, where one factor is a between-subjects factor and pretest-posttest is a within-subjects factor
Higher-order factorial designs
more complex designs involving 3 or more factors
In the three-factor design, the researcher evaluates the main effects for each of the three factors, plus three two-way interactions, and one three-way interaction
should you use more than 3 factors?
you should try to avoid more than 3 factors in factorial designs unless you have clear predictions for interactions
advantages of factorial designs
- Highly efficient designs that allow studying the effect of many factors simultaneously, interactions of factors, and replicate and expand upon existing study all in one study
- Instead of reducing individual differences by holding constant (ex. age), can include as another factor in the study
- Complex nature provides real advantages, but also some challenges (especially interpretation)
- High external validity
disadvantages of factorial designs
- More chance of having confounds than single IV designs and the same problems for controlling for them
- Interpretations are no better than correlational studies if the factors are not manipulated
- Too many factors make interpretation confusing
- May require a more stringent alpha level due to multiple statistical tests
statistical analysis of factorial designs depends on whether the factors are:
- Between-subjects
- Within-subjects
- Some mixture of between- and within-subjects
standard procedure for statistical analysis of factorial designs
- Computing the mean for each treatment condition (cell)
- Using ANOVA to evaluate the statistical significance of the mean differences
other uses of factorial designs
- replication
- expanding the design
- using the order of treatments as an additional factor
replication
repeating a previous study and incorporating a new replication factor (first/second replication)
expanding the design
adding factors in the form of new participant characteristics
purpose of expanding the design
to reduce the variance within groups by using the specific variable as a second factor
benefits of expanding the design
- Greatly reduces individual differences within each group
- Does not sacrifice external validity
using the order of treatments as an additional factor
makes it possible to evaluate any order effects that exist in the data
3 possible outcomes of using the order of treatments as an additional factor
- No order effects
- Symmetrical order effects: same order effects across other factors
- Asymmetrical order effects: order interacts with other factors
why does using the order of treatment as an additional factor help reduce variance?
- The randomization of participants to conditions does not always remove bias from unknown extraneous variables
- It is not possible to always randomize condition orders when there are many conditions
