What is the difference between repeated measures and independent measures?
. For a repeated-measures design, the same group of individuals is tested in both of the treatments. An independent-measures design uses a separate group for each treatment 2. Describe the data used to compute the sample mean and the sample variance for the repeated-measures t statistic.
What is the sample mean of the independent measures study?
An independent-measures study produces sample means of M1 = 35 and M2 = 31 and a pooled variance of 25. For this study, Cohen's d = ______. Two separate samples are being used to estimate the population mean difference between two treatment conditions. Which of the following would produce the widest confidence interval?
What is the m = 75 in an independent measures study?
An independent-measures study with n = 8 in each treatment produces M = 75 for the first treatment and M = 71 for the second treatment with a pooled variance of 9. Construct a 95% confidence interval for the population mean difference.
How do you design an independent-measures design?
An independent-measures design uses a separate group for each treatment 2. Describe the data used to compute the sample mean and the sample variance for the repeated-measures t statistic. 2. The two scores obtained for each individual are used to compute a difference score. The sample of difference scores is used to compute the mean and variance.
Why is the problem eliminated with a repeated-measures design?
The second problem is that individual differences can increase variance. In a repeated-measures design, the individual differences are subtracted out of the variance . 3.
What is a repeated measure design?
2. The repeated-measures design is suited to situations in which a particular type of subject is. not readily available for study. This design is helpful because it uses fewer subjects (only. one sample is needed).
What is matching in research?
1. For a research study comparing two treatment conditions, what characteristic.
How many different conditions can be compared in a single treatment design?
four different conditions ( one of whih may be a no treatment control condition) can be compared effectively within a single pahse of an alternating treatment design, and in many instances only two different treatments can be accommodated.
When is a treatment already in place?
1. when a treatment is already in place. 2. When the behavior analysts has limited time in which to demonstrate practical and socially significant results. Multiple treatment design. Experiments that use reversal design to compare the effects of two or more experimental conditions to baseline/or to one another.
How to alternate treatments?
1. alternated across daily sessions, one treatment in effect each day. 2. in sperate treatment sessions occurring within the same day. 3. implemented each during a portion of the same session.
What is single phase alternating treatment?
1. Single phase alternating treatments design without a no treatment control condition. 2. Single phase design in which two or more conditions, one which is a no treatment control coniditon, are alternated. 3.
What is the null hypothesis for independent measures?
As always, the null hypothesis states that there is no change, no effect, or, in this case, no difference. Thus, in symbols, the null hypothesis for the independent-measures test is
What is the Levene test?
In statistics, Levene's test is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups. Some common statistical procedures assume that variances of the populations from which different samples are drawn are equal. Levene's test assesses this assumption. It tests the null hypothesis that the population variances are equal (called homogeneity of variance or homoscedasticity). If the resulting P-value of Levene's test is less than some significance level (typically 0.05), the obtained differences in sample variances are unlikely to have occurred based on random sampling from a population with equal variances. Thus, the null hypothesis of equal variances is rejected and it is concluded that there is a difference between the variances in the population.
