analyzing data from independent..

• Independent Group Designs are the most common experimental design in psychological research, used for comparing two (or more) groups which are independent of one another.
• Independent Group Designs can be used for true and quasi-experimental designs.
• In Quasi-experimental designs, people are not assigned to conditions, as they already belong to different groups.
• Three different kinds of dependent variable can be used in Independent Group Designs.
• Advantages of Independent Group Designs include avoiding the problems inherent in repeated measures designs, such as practice effects, sensitization or carry-over effects.
• The Mann-Whitney U Test is used in Study 2 with a sample size of 225 and a total sample size of 32, resulting in a U statistic of 0.167.
• The Mann-Whitney U Test is used in Study 3 with a sample size of 225 and a total sample size of 32, resulting in a U statistic of 0.284.
• Disadvantage of Independent Group Designs is that it involves more people, making it difficult to match the controlled group with the experimental group.
• Statistical Tests for Independent Groups include the Independent Groups t-test, the Mann-Whitney Test (also called the Wilcoxon-Mann Whitney Test), and the Chi-Square Test.
• The Independent Groups t-test, also known as the between subjects t-test or the two-samples t-test, is used when the data are approximately normally distributed within each group and the SD of the two groups are equal.
• A common mistake is to look at the overall distribution of data to determine whether the data are appropriate for an independent samples t-test.
• Cohen’s d is often interpreted according to the following rules: Large effect size: d = 0.8, Medium effect size: d = 0.5, Small effect size: d = 0.3.
• The Independent Groups t-test compares the means of two groups and tells us whether the difference in the means is statistically significant.
• The Mann-Whitney U Test compares two unrelated groups and is used when independent samples t-test can not be used.
• The Mann-Whitney U Test finds the total of each of the columns, calling one of them group A (Schiz) and one of them group B (bipolar).
• The formula for the Mann-Whitney U Test is: U1 = Na x Nb + Na x (Na + 1) / 2 - ΣRa.
• The Mann-Whitney U Test uses the table provided in Appendix 2 to find the statistical significance associated with the Mann-Whitney U Test.
• The Mann-Whitney U Test finds the z-score, which is the probability associated with a z of 1.56, and determines that the result is not statistically significant at the 0.05 level.
• The Mann-Whitney U Test finds the effect size for the Mann-Whitney test using the Greek Letter theta, which is the probability that the group B person’s score will be higher than, or equal, to the person in group A that we choose.
• Sample size must be above some value, such as 6, for the t-test to be valid.
• Sample sizes must be balanced, or similar, for the t-test to be valid.
• The Independent Groups t-test is used when you have approximately equal sample sizes in your two groups.
• The Independent Groups t-test can be used with the Pooled Variance t-test, which makes the assumption of Homogeneity of Variance, or the Unpooled Variance t-test, which does not make this assumption.
• If you do not have approximately equal sample sizes in the two groups, the unpooled variance t-test should be used.
• Levene’s test is used to decide if variances (or SDs) are the same.
• If Levene’s test gives a statistically significant result, this means variances are different from one another and the unpooled variance test should be used.
• A non-significant result does not mean that the variances are the same.
• The tests, such as the Levene test, are dependent on the sample size.
• When the sample size is small, the tests are not very good at detecting differences in the variances.
• It only matters that the variances are the same when the sample size is small.
• Homogeneity of variance does not really matter when the sample sizes are about equal.
• If the sample sizes are unequal, Homogeneity of Variance matters a lot more.
• The Unpooled Variance t-test is a modification of the t-test, which does not make the assumption of equal variances.
• Unequal Sample Sizes can occur for a variety of reasons, such as comparison of two naturally occurring groups, or it may be difficult or expensive for one of the interventions, or there may be an ethical or recruitment issue.
• The effect size for the Independent Groups t-test is often described as Cohen’s d, which is a measure of how far apart the means of the two samples are, in SD units.
• Cohen’s d does not matter what the range of possible scores is – it is interpreted in terms of SD.