Statistics 1- 4

Question 1

variance between IV levels is represented by

Answer 1

difference in means between IV levels

Question 2

variance between IV levels is possibly caused by

Answer 2

Manipulation of IV (treatment effects)
Individual differences
Experimental error - Random or Constant error

Question 3

variance within IV levels is possibly caused by

Answer 3

Indiv diff

Experimental error - Random error only

Question 4

why is it so important that constant error is eliminated ( in the context of t-tests)

Answer 4

T tests use the variance Within IV levels to filter out the Variance Between IV levels.
Constant error can only create variance between IV levels and so can not be used by the variance within IV levels and filtered out.
Therefore, Its bias will continue through the T-test results

Question 5

t value close to zero represents

Answer 5

small variance between IV levels relative to within IV levelst

Question 6

value far from zero represents

Answer 6

large variance between IV levels relative to within IV levels

(manipulation of IV has had effect)

Question 7

when is a DF more reliable

Answer 7

when it is higher

Question 8

what does DF represent

Answer 8

degrees of freedom.

no. of pop parameters subtracted from sample size

Question 9

Levenes test is a measure of

Answer 9

difference between variances

Question 10

What result do we want from a Levenes test.
how is this shown

Answer 10

we want the null hypothesis.

because a null hypothesis represents no change in variance.

So don’t violate assumption of equal variance.

this is shown when p is greater than .05

Question 11

if experiment is quasi, it may violate the assumption of independent observation.

How do we check for this and what does it mean

Answer 11

Group A cannot be used to predict Group B scores.

Check for a correlation between Groups. SPSS will show this.

Don’t want a correlation

Question 11

if data seriously violates assumptions of t test , we should

Answer 11

use Mann whitney U test

Question 12

what are the assumptions of a t test

Answer 12

normality: DV normally distributed under each level of IV

Homogeneity of variance: Varience in DV should be equivalent

Equivalent sample size: matters more for smaller samples

Independence of observations: Can group A predict group B .can assume is true if random allocation has occoured.

Question 13

when can normality be assumed in a paired t-test

Answer 13

when n greater than 30

Question 14

what assumptions do we make when conducting paired t test

Answer 14

• Normality (of difference scores of individuals between group (xiA - xi B)
• sample size (only threatened when get drop outs)

Question 15

what parametric test do u use when assumptions are violated for paired t test

Answer 15

wilcoxon t test

Question 16

can 95% confidence intervals be used to assume a difference in IV levels?

what do we take into account

(paired t test)

Answer 16

No. not alone.

Need to look into influence of IV in terms of size AND consistency of effect.

consistancy as in individual participant’s DV score changes at a similar rate between conditions

Question 17

when is an anova test used.

Answer 17

when measuring diff in sample means of an IV that has more than 2 levels

Question 18

how does the F value from ANOVA test relate to t value

Answer 18

F = t ^2

Question 19

if ANOVA tests the same thing as t tests, why not just run three seperate t tests, one between each level?

Answer 19

because in each t test, there is a 5% chance of a type 1 error.
If we run multiple tests (from same population), this chance will increase.

e.g. more likely that will find a difference from chance alone

Question 20

Familywise error rate

Answer 20

the probability that at least one of a family of comparisons, run on the same data, will result in a type 1 error

It provides a corrected significance level (a), expressing the probability of making a Type 1 error.

1 - (1-a)^c

Question 21

what are omnibus tests

Answer 21

tests that control the familywise error rate e.g. ANOVA

Question 22

assumptions for ANOVA test

Answer 22

• Normality
• Homogeneity of variance
• Equivalent sample size
• Independent observations

Question 23

s

Answer 23

s

Question 24

what is a small, medium, large value of eta ^2

Answer 24

small > .01, med > .06, large > .14

Question 25

what does partial eta ^2 test

Answer 25

how much variance in the DV is explained by the manipulation of the IV overall

Question 26

assumptions of RM ANOVA test

Answer 26

• normality of distribution of difference scores under each IV level pair
• Sphericity (homogeneity of Covariance). variance in difference scores under each IV level pair should be reasonably equivalent

Question 27

what stats test do we use to test Sphericity in ANOVA RM

what does H0 state

Answer 27

Mauchly’s test

H0: no diff between covariances under each IV level pair (what we want)

Question 28

what is sensitisation - order effects

Answer 28

p.p works out aim of study

Question 29

what are carry over effects - order effects

Answer 29

effects of exposure to first IV will impact next IV

Question 30

alternatives when counterbalamcing isn’t viable

Answer 30

practice - extensive pre study practice
fatigue - short experiment
sensitisatipn - longer gap between
carry over - include control