Repeated Measures Experiments

Question 1

comparing the scores of individuals in one condition against their scores in another condition

Answer 1

Repeated Measures Design

Question 2

comparing the scores of one group of people taking one condition against the scores of a different group of people in the other condition

Answer 2

Independent Groups Design

Question 3

Types of Experimental Designs

Answer 3

Repeated Measures Design
Independent Groups Design

Question 4

Other name for repeated measures design

Refers to change within a group of individuals, rather than between two groups.

Answer 4

Within-subjects studies / designs

Question 5

Other name for repeated measures design

Refers to people undergoing two different treatments are closely matched, so that the two groups are not independent, rather they are related.

Answer 5

Related groups / design

Question 6

term mainly used in medical research than commonly used in psychology.
People cross-over from one group to the other group.

Answer 6

Cross-over studies / design

Question 7

Modified True or False: it is common to encounter a study where both groups are sufficiently closely matched.

Answer 7

False, it is very rare, not common to encounter a study where both groups are sufficiently closely matched.

Question 8

Advantages of Repeated Measures Design

Answer 8

1. There is no need for many participants
2. Each person acts as their own (perfectly) matched control group. Minimizes external/confounding variables

Question 9

Disadvantages of Repeated Measures Design

Answer 9

1. Practice effects
2. Sensitization

Question 10

Disadvantage of repeated measures design where participants gets better at a task over time.

Answer 10

Practice effects

Question 11

Participants may perceive that a dependency exists between two measures, and deliberately keep their answers similar when we are looking for change. Alternatively, because the participants perceive that the researcher is looking for change, they might change their answers.

Answer 11

Sensitization

Question 12

occurs when something about the previous condition is “carried over” into the next condition.

Answer 12

Carry-over effects

Question 13

Differentiate correlational and repeated measures designs

Answer 13

Correlational
- individual perspective
- without manipulation
- test of relationship
- 2 or more tools of measurement
- 2 or more measured variables

Repeated Measures
- overall perspective
- with manipulation
- test of causality
- 1 tool of measurement used repeatedly
- 1 primary dependent variable that is being measured repeatedly

Question 14

parametric test for continuous data

Answer 14

The Repeated Measures t-test

Question 15

non-parametric test for ordinal data

Answer 15

The Wilcoxon test

Question 16

non-parametric test for categorical data

Answer 16

Sign test

Question 17

Statistical Tests for Repeated Measures Designs

Answer 17

1. The Repeated Measures t-test
2. The Wilcoxon test
3. The Sign Test

Question 18

the most powerful statistical test and most likely to be generalizable and spot significant differences in data

Answer 18

Repeated Measures t-test

Question 19

Statistical test for continuous data such as ratio and interval

Answer 19

Repeated Measures t-test

Question 20

Statistical test for ordinal data

Answer 20

The Wilcoxon test

Question 21

Statistical Tests for nominal, categorical, and/or frequency count

Answer 21

The Sign Test

Question 22

Statistical Test that is easy to understand and calculate

Answer 22

Sign Test

Question 23

Conditions before using repeated measures t-test

Answer 23

1. The data are measured on a continuous (interval) level.
2. The differences between the two scores are normally distributed.

Question 24

Degrees of Freedom formula

Answer 24

N-1

Question 25

3 ways to test statistical significance of t-score

Answer 25

1. Get the p-value of the t-score (probability of getting the score as a result of chance if the NULL is true) 2. Get the t-critical value and compare the t we got. 3. Calculate the Confidence Intervals

Question 26

How to test statistical significance of t-score based on p-value of the t-score

Answer 26

For result to be significant, p-value should be low. It should be equal to or less than the alpha level we use for significance testing (alpha levels could be 0.05, 0.01, 0.001 etc. Choice depends on a researchers tolerance for error)

Question 27

In testing statistical significance of a t-score, why must p-value be low?

Answer 27

because we assume in the first place that the Null Hypothesis is probably true. If the Null hypothesis is probably true, then ideally, there should be no way, or at least there should be a very low probability of us getting the score we got; hence the low p-value we aim for. However, despite the fact that there is a very low probability or chance of us getting the score we have, we got it! Thus, the result is significant.

Question 28

How to test for statistical significance of t-score using t-critical value?

Answer 28

For our t (the t-score we got) to be significant it should be equal to or more than the t-critical value.

Question 29

t-score which has a p-value equal to the alpha level we use

Answer 29

t-critical

Question 30

This is relative to the sample size as exemplified by the use of the concept of degrees of freedom

Answer 30

t-critical

Question 31

How to test for statistical significance of a t-score based on confidence intervals?

Answer 31

The Null Hypothesis then should not be contained within the CI for our result to be significant. If it happens that the Null hypothesis is contained in the CI, the result is not significant.

Question 32

What tells us the likely range of the score in the population, or if the population is measured instead of the sample?

Answer 32

Confidence Intervals

Question 33

summation of difference scores computed from the two groups/conditions

Answer 33

difference score

Question 34

This provides us that estimate by giving us the likely range of values of the difference score if the population is measured

Answer 34

Confidence intervals

Question 35

Non-parametric test where the differences are not normally distributed and the measures are ordinal

Answer 35

Wilcoxon Test

Question 36

makes inferences about population parameters.

Answer 36

Parametric test

Question 37

Able to hypothesize an assumption true for a sample to the population using the confidence interval

Answer 37

Parametric

Question 38

equivalent to Mann-Whitney test which is easier to calculate.

Answer 38

Wilcoxon-rank sum test

Question 39

Referred to when using wilcoxon test

Answer 39

Signed ranks test only

Question 40

used to correct for continuity.

Answer 40

Continuity Correction

Question 41

The test statistic from the sign test is called

Answer 41

S

Question 42

The sign test uses what as the total number of people from whom there was not a tie?

Answer 42

N

Question 43

T - [ (N(N+1)) /4 ] Z = ————————————————— Square root of [ (N(N+1)(2N+1)) /24 ]

Answer 43

Conversion formula of t to z if there are no tied scores and no continuity correction

Question 44

T - [ (N(N+1)) /4 ] - 0.5 Z = ————————————————— Square root of [ (N(N+1)(2N+1)) /24 ]

Answer 44

Conversion formula of Wilcoxon T to z if there are tied scores but no continuity correction

Question 45

z = [ [T - N(N+1)/4] - 0.5 ] / √[ [N(N+1)(2N+1)/24] - [Σ(t³ - t)]/48 ]

Answer 45

Conversion formula of Wilcoxon T to z if there are tied scores and continuity correction

Question 46

z = [ [T - N(N+1)/4] - 0.5 ] / √[ N(N+1)(2N+1) / 24 ]

Answer 46

Conversion formula of Wilcoxon T to z if there no tied scores but with continuity correction