5662336
Description
Mind Map by Akasha Corion, updated more than 1 year ago
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Created by Akasha Corion
almost 8 years ago
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T-Tests- Unrelated (Independent samples) and
Related
- Unrelated T-tests / 2-Sample test /
Independent t-test
- whether two groups of scores have
significantly different means
- Between-subjects--> These involve different participants
taking part in different conditions usually an experimental
group and a control group
- T-scores--> t=(mean of sample 1)-(mean of sample 2) /
standard error of differences between means
- 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=__ .
- Assumes the variance of the two samples are similar
- Can be combined to produce an overall estimate of the
spread of the data--> Levene's test
- Can be combined to produce an overall estimate of the
spread of the data--> Levene's test
- Mann Whitney U-test
- data in one/both conditions are not normally distributed (badly skewed
- data is in the form of ranks
- variances (differences) of the two groups differ
- group sizes differ considerably
- 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=__
- Badly skewed--> non-parametric equivalent
- data in one/both conditions are not normally distributed (badly skewed
- Levene's test for equality of variance
- whether or not the homogeneity (quality of
being the same) of variance
(change/difference) assumption is violated
- if the p-value for the Levene's test is
'non-significant,' then the 'variances are
not significantly different'
- eg, variances are similar between
the two levels of treatment
- Significant--> 'variances not assumed'
- Non-significant--> 'variances assumed'
- eg, variances are similar between
the two levels of treatment
- Significant= Equal variances not assured
Non-Significant= Equal variancesassured
- whether or not the homogeneity (quality of
being the same) of variance
(change/difference) assumption is violated
- Degree of Freedom--> Nsample1 + Nsample2 -2
- whether two groups of scores have
significantly different means
- Related T-tests / Paired Samples T-test
- comparing the means of two related samples of
scores to determine whether the two means
differ significantly
- Within-subjects--> involves the same participants taking part
in 2 or more conditions of the same experiment
- Practice effect- can improve between Condition X and Y.
Fatigue effect- get worse between Condition X and Y
- Counterbalance with X-Y, Y-X or
three conditions
- Counterbalance with X-Y, Y-X or
three conditions
- Carryover effect- earlier condition affects
performance of a subsequent condition
- When tasks differ in difficulty-> change their
behaviour on later tasks
- increase time between conditions
- When tasks differ in difficulty-> change their
behaviour on later tasks
- Practice effect- can improve between Condition X and Y.
Fatigue effect- get worse between Condition X and Y
- T-scores- t=difference in means
/ standard error of the
difference in means
- 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=__ .
- Wilcoxon Matched Pairs Test
- both conditions are not normally distributed
- difference can only be ranked in size
- variances (differences) of the two conditions are very different
- more likely to detect significantly differences
- SPSS: T= smaller of the sum of ranks (positive/negative skewness)
- 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=__.
- Badly skewed--> Non-parametric equivalent
- both conditions are not normally distributed
- Problems surround using a large number of t-tests in one study
- Multiple comparisons and Type 1 error
- Multiple comparisons and Type 1 error
- variances (differences) of the two conditions are very different
- more likely to detect significant differences
- 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.
- comparing the means of two related samples of
scores to determine whether the two means
differ significantly
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