Lecture 10, 11: Analyzing Data

Question 1

To look for the effects of the IV we examine the ______ for the DV

Answer 1

means

*if the independent variable had an effect on the DV the means of the experimental conditions should differ

Question 2

The problem with only observing the means of experimental conditions is what? and why?

whats a solution

Answer 2

means can differ while the IV had no effect

confounding variables may be present

error variance = random influences of variables that remain unidentified in a study

estimate the means difference when the IV has no effect –> if the variance observed after IV is larger than that of the estimated variance without the IV then difference can be attributed to the IV

Question 3

What are the three stat tools researchers use to estimate the probability that differences is due to error variance to help decide if a difference in means was a result of a real effect of the IV?

Answer 3

significance testing: determine the probability that the difference between the means is due to error variance

effect sizes: examine the size of the difference to see if its noteworthy

confidence intervals: judge the difference between means relative to the precision of the data

Question 4

Significance testing/ null hypothesis testing

Answer 4

used to determine if the observed effect is “real”/due to the IV or due to error variance

Question 5

What does the null hypothesis assume?

Answer 5

there is no effect in the population

the IV had no effect on the DV

Question 6

what does it mean to reject the null hypothesis?

Answer 6

the IV did have a statistically significant effect

the difference between means of the experimental group was larger than expected given the amount of error variance in the data

Question 7

If you fail to reject the null hypothesis you are claiming that…?

Answer 7

the IV had no effect on the DV

the difference between means reflects nothing more than the influence or error variance

Question 8

What does a p value of 1 indicate?

Answer 8

the difference between the means is exactly what one would expect based on the amount of error variance

Question 9

Type I error

Answer 9

researcher rejects the null hypothesis that is true and concludes IV has had an effect on the EV when it has not

Question 10

Type II error

Answer 10

Researcher fails to reject the null hypothesis when it is false. Concludes the IV did not have an effect when it did

Question 11

How do researchers try to reduce the likelihood of making a type I error?

Answer 11

they try to design experiments with high power

power = a studies ability to detect any effect of the IV that occurs

studies with low power may fail to detect the IVs effect on the DV

Question 12

T tests are used to

you report them in the following format:

Answer 12

test the difference between two means

italicized lowercase t(df)=t-value, p<0.05, 0,01 or p=n.s.

Question 13

what is a t value ? what is a p value? how do they relate to one another?

Answer 13

t value is a comparison of differences between two groups to an index of error variance

a p value is the probability that the differences between means in the population is zero

as t value increases p value tends to decrease / there is association with lower p values and larger t values –> this makes sense because the larger the difference between the means the more likely this variance is not due to error variance

Question 14

what is a way of preventing a type I error ? what is its downside?

Answer 14

Bonferroni adjustment = desired alpha level is divided by the number of tests run and then only consider something significant if it has a p value less than this new value

while it reduces a type I error it increases the chance of a type II error

Question 15

What is an ANOVA?

Answer 15

analysis of variance between more than two conditions means

it is based on the F-test

Question 16

describe the F-test

Answer 16

its a ratio of the between group variance to within group error variance

if the IV has no effect the between group and within group variance will be about the same and F roughly = 1.00

the larger the variance between group as compared to within group variance the larger the F value and the more likely differences detected are due to the independent variable

Question 17

how do F value and P value relate

Answer 17

the larger the F value the lower the P value tends to be

Question 18

what is the format for reporting an F test/ ANOVA results

Answer 18

upercade italisized F(df,df error)= f-stat, p<0.05, 0.01 or p= n.s.

Question 19

what is a post hoc test? why/when are they used?

Answer 19

they are used after significant findings from an Ftest are revealed.This is because a significant anova finding will reveal that differences in means occured but will not inform where they occured. Thus post hoc test inform researchers where the differences in means occured / which conditions means differed

Question 20

What information does an ANOVA provide in a factorial design with more than two IV ?

Answer 20

the error variance

the main effect of IV A

the main effect of IV B

the interaction between IV A and IV B

Question 21

simple main effect

Answer 21

the effect of one IV at a particular level of another IV

Question 22

Paired t-test

Answer 22

used for two groups

Question 23

within-subjects ANOVA

Answer 23

experiements that involve more than 2 conditions including factorial designs

Question 24

MANOVA

Answer 24

tests for differences between means of two or more conditions on two or more DV simultaneously

the DV measured all tap into the same concept

they help control type I error

Question 25

the calculated value of F is based on the ratio of between groups variance to _____ variance

Answer 25

error