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The t Test for Two Related Samples - Coggle Diagram
The t Test for Two Related Samples
KEY Terms
repeated-measures design or within-subjects design
difference scores
estimated standard error for
repeated-measures t statistic
individual differences
order effects
matched-subjects design
In a repeated-measures research study, the same sample of individuals is tested in all the treatment conditions. This design literally repeats measurements on the same subjects.
The repeated-measures t test begins by computing a difference between the first and second measurements for each subject (or the difference for each matched pair). The difference scores, or D scores, are obtained by
The sample mean, , and sample variance, , are used to summarize and describe the set of difference scores.
The formula for the repeated-measures t statistic is
In the formula, the null hypothesis specifies μ=0, and the estimated standard error is computed by
A repeated-measures design may be preferred to an independent-measures study when one wants to observe changes in behavior in the same subjects, as in learning or developmental studies
An important advantage of the repeated-measures design is that it removes or reduces individual differences, which in turn lowers sample variability and tends to increase the chances for obtaining a significant result.
In a matched-subjects design the individuals in one sample are matched one-to-one with individuals in another sample.
The matching is based on a variable (or variables) relevant to the study. The matched-subjects design has elements of an independent-measures study and a repeated-measures study, and is intended to produce the advantages of both designs without the disadvantages.
the quality of a matched-subjects study is limited by the quality of the matching process.
In a matched-subjects design, each individual in one sample is matched with an individual in the other sample.
The matching is done so that the two individuals are equivalent (or nearly equivalent) with respect to a specific variable (or variables) that the researcher would like to control.
The repeated-measures design is especially well suited for studying learning,
A repeated-measures design typically requires fewer subjects than an independent-measures design.
The repeated-measures design uses subjects (or participants) more efficiently because each individual is measured in both of the treatment conditions.
This can be especially important when there are relatively few subjects or participants available (for example, when you are studying a rare species or individuals with a rare disease).
Changes in scores that are caused by participation in an earlier treatment are called order effects and can distort the mean differences found in repeated-measures research studies.
One way to deal with time-related factors and order effects is to counterbalance the order of presentation of treatments.
the participants are randomly divided into two groups, with one group receiving Treatment 1 followed by Treatment 2, and the other group receiving Treatment 2 followed by Treatment 1. The goal of counterbalancing is to distribute any outside effects evenly over the two treatment
The primary disadvantage of a repeated-measures design is that the structure of the design allows for factors other than the treatment effect to cause a participant’s score to change from one treatment to the next
Specifically, in a repeated-measures design, each individual is measured in two different treatment conditions, often at two different times.
it is possible that participation in the first treatment influences the individual’s score in the second treatment.
