Contents
What is an advantage of using repeated measures?
More statistical power: Repeated measures designs can be very powerful because they control for factors that cause variability between subjects. Fewer subjects: Thanks to the greater statistical power, a repeated measures design can use fewer subjects to detect a desired effect size.
What are the strengths and weaknesses of repeated measures?
2. Repeated Measures:
- Pro: As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
- Con: There may be order effects.
- Pro: Fewer people are needed as they take part in all conditions (i.e. saves time).
What is a 2×2 repeated measures design?
A two-way repeated measures ANOVA (also known as a two-factor repeated measures ANOVA, two-factor or two-way ANOVA with repeated measures, or within-within-subjects ANOVA) compares the mean differences between groups that have been split on two within-subjects factors (also known as independent variables).
How are reliability and validity of results measured?
Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through various statistical methods depending on the types of validity, as explained below:
When does a measure have good reliability and internal consistency?
When a measure has good test-retest reliability and internal consistency, researchers should be more confident that the scores represent what they are supposed to. There has to be more to it, however, because a measure can be extremely reliable but have no validity whatsoever.
Can a measurement be reliable but not valid?
A measurement can be highly reliable and yet not valid. For example, an alarm clock that is set for 7AM but rings every morning at 6:30AM is reliable, but not valid
Why do we need a repeated measure design?
Like all repeated measures designs, this reduces the chance of variation between individuals skewing the results and also requires a smaller group of subjects. It also reduces the chance of practice or fatigue effects influencing the results because, presumably, it will be the same for both groups and can be removed by statistical tests.