Factorial ANOVAs

Question 1

based on analysis that is based on 2 IVs (factors) or more =

Answer 1

2 way factorial ANOVA

Question 2

how do factorial ANOVAs allow us to look at the interaction between factors?

Answer 2

they test the differences between conditions for more than 1 categorical independent variable

Question 3

mean difference is constant across all levels of factor (IV) combinations =

Answer 3

no interaction

Question 4

different combinations of factors and levels give different means =

Answer 4

interaction

Question 5

the pattern of responses for each level is different depending on the factor it is paired with. this is called an _______

Answer 5

interaction

Question 6

factorial ANOVAs produce a ____ _____ for each factor as well as an interaction

Answer 6

main effect

Question 7

outcome that can show consistent difference between levels of a factor =

Answer 7

main effect

Question 8

what do main effects indicate?

Answer 8

whether there are significant differences between the levels of 1 factor

Question 9

what does the output for a 2 way factorial ANOVA include?

Answer 9

2x main effects, 1x interaction, mean values for each group (these means describe the interaction)

Question 10

what is generated from each main effect?

Answer 10

a marginal mean

Question 11

what do marginal means tell us?

Answer 11

which level of each factor resulted in better performance

Question 12

where do you find the marginal mean in the SPSS output?

Answer 12

in the ‘Mean’ box

Question 13

what table has the values you need to report the main effects and the interaction?

Answer 13

Tests of Between-Subjects effects

Question 14

for each factor how do you report the main effect and the interaction between the 2 factors?

Answer 14

F(effect df, error df) = [F value], p = [p value], np2 = [np2 value]

Question 15

if there is a significant main effect you need to include the _____ in the write up

Answer 15

means

Question 16

if there is a significant interaction what is required?

Answer 16

follow up tests called SIMPLE EFFECTS TESTS

Question 17

what are simple effects tests?

Answer 17

basically bonferroni corrected t tests

Question 18

what stats are reported from simple effects tests?

Answer 18

mean, SD, t value, df, p value

Question 19

how do you calculate the bonferroni correction?

Answer 19

divide alpha 0.05 by the number of t tests you do

Question 20

what else is needed in the report of simple effects tests?

Answer 20

a table of means

Question 21

list the steps to run the descriptive stats for a between factorial design

Answer 21

DATA > SPLIT FILE > ORGANISE OUTPUT BY GROUPS. put one of the factors into the box ‘groups based on’. ANALYSE > DESCRIPTIVE STATS > EXPLORE. move DV to dependent list and move factor that was not moved to ‘groups based on’ into the factor list. before running the data you must unsplit it again: DATA > SPLIT FILE > RESET > OK

Question 22

list the steps to run a factorial ANOVA

Answer 22

ANALYSE > GENERAL LINEAR MODEL > UNIVARIATE. move DV to ‘dependent variable box’ and both factors to ‘fixed factors box’. select plots. move 1 factor to horizontal axis and other into separate lines box > add. repeat this but swap the factors around > continue. choose correct ‘options’. select EM means. move 2 factors to ‘display means for’ box > continue > ok.

Question 23

what options need to be selected for the factorial ANOVA?

Answer 23

descriptive stats, estimates of effect size, homogeneity tests

Question 24

what does the ANOVA produce in the SPPS output?

Answer 24

descriptive stats, Levene’s test of error variances, table of between subjects effects, estimated marginal means for each factor (main effect) and 2 line plots

Question 25

list the steps in running simple effects tests

Answer 25

DATA > SPLIT FILE. organise output by groups. move 1 factor across to ‘groups based on’ > ok. ANALYSE > COMPARE MEANS > INDEPENDENT SAMPLES T TEST. move other factor that wasn’t split into grouping variable. define groups > 1+2. DV into test variables. then reset split data file and swap factors around. (repeat this whole process with the factors also swapped around)