PSYC311

Question 1

What is the dependent variable?

Answer 1

The numerical variable that measures what we are interested in.

Question 2

What is an independent variable? What are they also called?

Answer 2

The condition that is manipulated by researchers to look for an effect. Also called factors.

Question 3

What is between-subjects?

Answer 3

When subjects are exposed to one level of the factor.

Question 4

What is within-subjects?

Answer 4

When subjects are exposed to all levels of the factors.

Question 5

What is a mixed design?

Answer 5

When there are factors that are within-subjects and other factors that are between-subjects?

Question 6

What is a pure within/between-subjects desgin?

Answer 6

When all factors in the study are all within/between-subjects.

Question 7

What is a null hypothesis?

Answer 7

A prediction that the factor will have no effect on the population of interest.

Question 8

What is a type I error?

Answer 8

When you reject the null hypothesis but should have accepted it.

Question 9

What is a type II error?

Answer 9

When you support the null hypothesis but you should have rejected it.

Question 10

If H0 was rejected, what two results could we end up getting?

Answer 10

A true positive (null is really false).

A false positive or type 1 error (null is actually correct).

Question 11

If H0 was not rejected, what two results could we get?

Answer 11

A true negative (null is true).

A false negative or type II error (null should be rejected).

Question 12

What does ANOVA test?

Answer 12

How the average of a dependent variable differs across categories.

Question 13

What does correlation and regression analysis test?

Answer 13

How a dependent variable is related to another numerical value/s.

Question 14

What does dummy variable regression and analysis of covariance (DVR & ANCOVA) test?

Answer 14

How does the relationship of one numerical DV to another numerical variables change across groups, conditions etc?

Question 15

How do you find out the number of cells in the experiment?

Answer 15

Multiply the number of levels of all factors.

Question 16

What are the 4 goals of the GLM?

Answer 16

1. Representing the structure of the experiment.
2. Measure the size of each factors effect.
3. Measure the size of the sampling errors effect.
4. Decide if factors are too large to be due to error.

Question 17

What is the GLM model for a 1-factor between-ss design?

Answer 17

Y = baseline + A + S(A)
Y = baseline + effect of factor A + error

Question 18

What does the first letter in a Y subscript indicate?

Answer 18

Indicates the level of factor A.

Question 19

What does the second letter in a Y subscript indicate?

Answer 19

The subject number for a 1 factor design or the level number for factor B

Question 20

What does a dot in the subscript mean?

Answer 20

Average.

Question 21

How to work out the baseline for estimation equations?

Answer 21

The average of all of the scores.

Question 22

How to work out the baseline for estimation equations?

Answer 22

The average of all of the scores.

Question 23

How to calculate the sums of squares.

Answer 23

Square every number in the column of decomposition matrix and add them together.

Question 24

What does a large vs small sum of squares mean?

Answer 24

If there is a large sum of squares, there is a large effect of that variable. If there is a small sum of squares, the effect of that variable is small on the overall effect.

Question 25

How to calculate df total.

Answer 25

Number of subjects x number of Y values per subject.

Question 26

What is degrees of freedom for baseline?

Answer 26

1.

Question 27

How to calculate df for any factor.

Answer 27

Number of levels of that factor - 1.

Question 28

How to calculate the df for error.

Answer 28

Number of subjects - number of groups.

Question 29

How do you calculate the mean square for a given term?

Answer 29

Sum of squares of that term / degrees of freedom of that term.

Question 30

How do you calculate the F observed value?

Answer 30

Mean square of the given value / mean square of error.

Question 31

When do we reject the null hypothesis in terms of F?

Answer 31

When the observed F value is larger than the critical F value.

Question 32

What are the three equivalent versions of a null hypothesis? In english, in terms of the model, and in terms of comparison between models.

Answer 32

English: the true means are the same. Model: the true population values of (insert term) are all zero. Comparison: the model including (insert term) is just as good as the model without it.

Question 33

What do we need to draw a causal conclusion? 3 steps.

Answer 33

Needs to be observed differences too large to be due to chance, subjects must be randomly assigned, and there must be no confounding variables.

Question 34

What are we doing when observing a 2-way interaction?

Answer 34

Observing how the effects of A differ across levels of B (or vice versa).

Question 35

State the model for a 2-factor between-ss design.

Answer 35

Yij = baseline + Ai + Bj + ABij + S(AB)ijk

Question 36

How do we work out estimation equation for error?

Answer 36

Take the individual score and remove all other factors (baseline, factors, interactions). It is whatever is left over that has not been previously explained.

Question 37

How to calculate the degrees of freedom for an interaction.

Answer 37

Multiply the degrees of freedom of all factors involved in the interaction (dfA x dfB x dfC).

Question 38

Give a template for how to state an interaction effect between two factors.

Answer 38

The advantage of (insert two levels of one factor) is larger/smaller for (insert two levels of other factor).

Question 39

What is the main thing to remember to show within an interpretation of an interaction?

Answer 39

Show how one factor differs over levels of another factor.

Question 40

What happens if you ignore certain factors of the equation?

Answer 40

Error for each of the values is raised and we cannot get an accurate mean square or observed F.

Question 41

What do unambiguous data patterns tell us? What are they?

Answer 41

When interaction effects go in opposite directions. Provides good evidence of dynamic interrelation for the effects of A and B, even with indirect (proxy) DVs.

Question 42

What do somewhat ambiguous data patterns tell us about relationships between data? What are these patterns?

Answer 42

Interactions with an effect vs no effect. Suggests medium evidence for an interrelationship with proxy DVs, but strong evidence with direct DVs.

Question 43

What do very ambiguous data patterns tell us about the relationship between factors? What is this pattern?

Answer 43

Interactions with small vs large effect and different starting levels. Provides weak evidence for interrelationship with proxy DVs, but somewhat strong evidence with direct DVs.

Question 44

How can we tell from graphs if there is an interaction effect?

Answer 44

When the lines cross over, they diverge from one another, or there is more space between the lines at one end of the graph than the other.

Question 45

What does a three-way interaction look at?

Answer 45

How a 2-way interaction changes across levels of the third factor.

Question 46

How can we tell if there is a three-way interaction?

Answer 46

When the 2-way interactions look different in graph one compared to graph two (which shows the different levels of the third factor).

Question 47

State the model for a 3-factor between-subjects design.

Answer 47

Yijkl = baseline + Ai + Bj + Ck + ACik + ABij + BCjk + ABCijk + S(ABC)ijkl.

Question 48

What do the different subscripts in 'Yijkl' mean?

Answer 48

``` i = level of factor A. j = level of factor B. k = level of factor C. l = subject no. ```

Question 49

How do you calculate two-factor marginal means?

Answer 49

Look at the means of two levels of certain two factors over the levels of the third factor.

Question 50

How do you calculate one-factor marginal means?

Answer 50

Take the means for the individual levels of each factor.

Question 51

How do we calculate the error terms in a shortcut for estimation equations? (For each individual score).

Answer 51

Take the individual cell score and subtract the cell mean.