T-Tests- Unrelated (Independent samples) and Related

Akasha Corion
Mind Map by Akasha Corion, updated more than 1 year ago
Akasha Corion
Created by Akasha Corion over 5 years ago


1 Psychology Mind Map on T-Tests- Unrelated (Independent samples) and Related, created by Akasha Corion on 06/04/2016.

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T-Tests- Unrelated (Independent samples) and Related
  1. Unrelated T-tests / 2-Sample test / Independent t-test
    1. whether two groups of scores have significantly different means
      1. Between-subjects--> These involve different participants taking part in different conditions usually an experimental group and a control group
        1. T-scores--> t=(mean of sample 1)-(mean of sample 2) / standard error of differences between means
          1. Group X [recalled more words correctly] (mean=__, SD=__) than Group Y (mean=__, SD= __). An unrelated t-test indicated that this difference was significant, t(df)=__, p=__ .
            1. Assumes the variance of the two samples are similar
              1. Can be combined to produce an overall estimate of the spread of the data--> Levene's test
              2. Mann Whitney U-test
                1. data in one/both conditions are not normally distributed (badly skewed
                  1. data is in the form of ranks
                    1. variances (differences) of the two groups differ
                      1. group sizes differ considerably
                        1. Participants in Group X were more [accurate] (mean=__, SD=__) than participants in Group Y (mean=__, SD=__). A Mann Whitney U test indicated that this difference was statistically [significant] , U=__,N1=__, N2=__, p=__
                          1. Badly skewed--> non-parametric equivalent
                          2. Levene's test for equality of variance
                            1. whether or not the homogeneity (quality of being the same) of variance (change/difference) assumption is violated
                              1. if the p-value for the Levene's test is 'non-significant,' then the 'variances are not significantly different'
                                1. eg, variances are similar between the two levels of treatment
                                  1. Significant--> 'variances not assumed'
                                    1. Non-significant--> 'variances assumed'
                                    2. Significant= Equal variances not assured Non-Significant= Equal variancesassured
                                    3. Degree of Freedom--> Nsample1 + Nsample2 -2
                                    4. Related T-tests / Paired Samples T-test
                                      1. comparing the means of two related samples of scores to determine whether the two means differ significantly
                                        1. Within-subjects--> involves the same participants taking part in 2 or more conditions of the same experiment
                                          1. Practice effect- can improve between Condition X and Y. Fatigue effect- get worse between Condition X and Y
                                            1. Counterbalance with X-Y, Y-X or three conditions
                                            2. Carryover effect- earlier condition affects performance of a subsequent condition
                                              1. When tasks differ in difficulty-> change their behaviour on later tasks
                                                1. increase time between conditions
                                              2. T-scores- t=difference in means / standard error of the difference in means
                                                1. Participants were [faster to respond] in Condition X (mean__, SD=__) than in Condition Y (mean=__, SD=__). A related t-test indicated that the difference in [reaction time] was statistically significant, t(df)=__, p=__ .
                                                  1. Wilcoxon Matched Pairs Test
                                                    1. both conditions are not normally distributed
                                                      1. difference can only be ranked in size
                                                        1. variances (differences) of the two conditions are very different
                                                          1. more likely to detect significantly differences
                                                            1. SPSS: T= smaller of the sum of ranks (positive/negative skewness)
                                                              1. Participants [were slower to respond] in Condition X (mean=__, SD=__) than in Condition Y (mean=__, SD=__). A Wilcoxon matched-pairs test indicated that the difference [in reaction times] between the two conditions was significant. Z=__, N=__, p=__.
                                                                1. Badly skewed--> Non-parametric equivalent
                                                                2. Problems surround using a large number of t-tests in one study
                                                                  1. Multiple comparisons and Type 1 error
                                                                  2. variances (differences) of the two conditions are very different
                                                                    1. more likely to detect significant differences
                                                                      1. There was a significant association between line-up type and response, c2 = 5.83, df = 1, N = 70, p = .016. With sequential designs, participants were more likely to correctly identify the perpetrator and less likely to not correctly identify him or her. With simultaneous presentations, participants were less likely to correctly identify the perpetrator and more likely not to correctly identify him/her.
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