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Statistics & Data Analysis > t-test > Flashcards

t-test Flashcards

1
Q

t-test

A
  • when we have 1 IV with 2 levels
  • estimates whether the population means under the 2 levels are different
  • estimates based on difference between the measured sample means
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2
Q

independent t-test

A

between participants / independent groups

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3
Q

paired t-test

A

within participants/ repeated measures

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4
Q

true experimental

A

random allocation

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5
Q

variance between IV levels

A

variance we assume is accounted for by manipulation of IV

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6
Q

variance within IV levels

A

difference between participants within the groups (levels)
- reflected by standard deviation

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7
Q

how does variance between IV levels arise

A
  • manipulation of IV
  • individual differences
  • experimental error
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8
Q

experimental error

A

random or constant

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9
Q

random error

A

chance fluctuation in measurement
e.g. hitting stop watch too early or late

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10
Q

constant error

A

confounds that influence measurement of DV between IV levels
- bias
e.g. giving one group practice and one group not

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11
Q

sources of variance within IV levels

A
  • individual differences
  • experimental error (random not including constant)
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12
Q

null hypothesis

A
  • no difference between the population means and sample means
  • H0: u1-u2=0
    or
    u1=u2
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13
Q

what does t-distribution represent

A
  • distribution of sampled mean differences when the null hypothesis is true
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14
Q

features of t-distributions

A
  • mean of 0
  • ## s.d. = s.e
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15
Q

s.e.

A

standard error
the extent to which an individual sampled mean difference deviates from 0

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16
Q

t-value

A

difference between sample means
reflected in standard error units

(don’t need to memorize formula)

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17
Q

ESEd

A

s=variance
n=sample size

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18
Q

t-value closer to 0

A

small variance between IV levels relative to within IV levels

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19
Q

t-value further from 0

A

larger between IV levels than within IV levels
- shows difference of manipulation of IV

20
Q

in order to claim value of t is significant…

A

it must fall outside of the 95% bounds, in the 2.5% tails

21
Q

if t > critical value

A

reject the null

22
Q

larger degrees of freedom

A

more reliable the estimate

23
Q

d.f. fro 2 sample independent t-test

A
24
Q

what is t-distribution mediated by

A

degrees of freedom

25
Q

assumptions of independent t-tests

A
  • normality
  • homogeneity of variance
  • equivalent sample sizes
  • independence of observations
26
Q

normality - independent

A

DV should be normally distributed, under each level of IV

27
Q

homogeneity of variance

A

the variance in the DV, under each level of the IV, should be reasonably equivalent
- check using Levene’s test in SPSS

28
Q

Levene’s test

A

SPSS
- we want a non-significant result
- under H0: no difference between variances under each level of IV (homogeneity)
- but it p<0.05 we reject null - heterogeneity
- use row below is violated

29
Q

independence of observtions

A

scores under each IV level should be independent

30
Q

what do we do if we violate assumptions of independent t-test

A

Mann-Whitney U test

31
Q

p<a

A

reject null

32
Q

how to get a t-test result from SPSS output

A
33
Q

paired t-test

A
  • related/ dependent t-test
  • used for within-subjects/ repeated measures design
34
Q

t calculation for paired

A

don’t need to memorize

35
Q

what contributes to variance for within-subjects design

A
  • experimental error
  • manipulation of IV
36
Q

assumptions of paired t-test

A
  • normality
  • sample size roughly equal
37
Q

normality - paired

A
  • distribution of difference scores between IV levels should be aprox. normal
  • ok to assume if n>30
38
Q

non-parametric equivalent of paired t-test

A

Wilcoxon T test
- if assumptions are violated

39
Q

paired t-test: SPSS output

A
40
Q

degrees of freedom for paired t-test

A

df = ( n - 1)

41
Q

effect size measure

A

Cohen’s d

42
Q

Cohen’s d

A

the magnitude of difference between two IV level means, expressed in standard deviation units

43
Q

Cohen’s d formula

A

… ignore the sign (remove negative sign)
- express to 2 d.p

44
Q

interpreting Cohen’s d effect size

A

e.g. bigger effect size, further apart population means, less overlap

45
Q

t

A

magnitude of difference between two IV level means, expressed in ESE units

46
Q

why t not Cohen’s d

A

takes sample size into account

Statistics & Data Analysis (4 decks)

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