LECTURE 5

Question 1

Parametric stats are used to analyze ____data

Answer 1

quantitative

Question 2

true/false: data needs to be normalized for parametric stats

Answer 2

true
based on t, F, chi-square distribution

Question 3

-test, ANOVA, Pearson correlation, linear regression
examples of

Answer 3

Parametric statistics

Question 4

Spearman rho, Mann-Whitney U, Friedman’s ANOVA, Wilcoxon-signed ranks
examples of…

Answer 4

non-parametric stats

Question 5

This is our option when we have violated assumptions, or we have nominal or ordinal data.

Answer 5

non-parametric stats

Question 6

Parametric Assumptions for t-test/one way ANOVA

Answer 6

1. IR data
2. normality
3. homogeneity of variance
4. free of extreme outliers
5. independence of observations

Question 7

if you knew that the population is normally distributed…even small samples (n<30) will meet this assumption.
In practical terms, as long as your sample is fairly large, outliers are a much more pressing concern than ______

Answer 7

normality! (need 30 bc of CLT)

Question 8

how do you check for normality?

Answer 8

histograms
look at skew/kurtosis (greater than 2 or smaller than -2)
shapiro-wilk test (want it to be non-significant at 0.05-greater than 0.05)

Question 9

for skewness and kurtosis, we are looking for

Answer 9

Is data more than 2, less than -2?
IF SO, NOT NORMAL

Question 10

How would you test for HOV?

Answer 10

Levene’s Test

Question 11

What is Levene’s Test?

Answer 11

Tests if variances in different groups are the same.
Want this test to be not significant. You want them to show “no difference.”
Set alpha at .05.

Question 12

In t-test/ANOVA, what will influential outlier do to data?

Answer 12

pull mean to outlier

Question 13

For regression tests, what will influential outliers do?

Answer 13

pull best fit line towards outliers

Question 14

How can you look for influential outliers?

Answer 14

1. histograms
2. skewness/kurtosis
3. boxplots
4. regression: COOK’S DISTANCE GREATER THAN 1

Question 15

-Data must be Independent
-scores must not follow a pattern over time
-scores from one participant can’t influence another participant’s scores.

What assumption for parametric stats is being met?

Answer 15

independence of observations
(scores shouldn’t trend over time
data can’t be from same body parts of same person…)

Question 16

regression assumptions

Answer 16

1. linearity
2. homoscedasticity
3. outlier testing in regression

Question 17

In correlational/relationship analyses (ex/ regression), the variance of the outcome variable must be about the same at all levels of the predictor variable.
If the variance is not evenly distributed, it’s called having

Answer 17

heteroscedasticity

Question 18

what is a residual?

Answer 18

distance between observed score and predicted score in linear model

Question 19

An outlier will have a large _____

Answer 19

residual

Question 20

to have _____, all of the residuals in a scatterplot should be similar

Answer 20

homoscedasticity

Question 21

Whats the goal of a hypothesis test?

Answer 21

rule out sampling error (chance)

Question 22

The null hypothesis states there is -_____in the pop before and after intervention

Answer 22

NO CHANGE IN POP

Question 23

The alt hypothesis states there is _______ in population following an intervention

Answer 23

A CHANGE

Question 24

ALPHA LEVEL DETERMINES RISK OF

Answer 24

type 1 error!

Question 25

test statistic t is calculated as

Answer 25

difference between pre and post means / amount of diff one would expect without any treatment effect (STAND ERROR)

Question 26

If alpha is 0.05, there is a 5% chance of committing a type ___ error

Answer 26

type 1 ERROR

Question 27

actual probability that the results occurred just because of sampling error.

Answer 27

p-value

Question 28

when values are in tails, it is

Answer 28

significant! p-value is smaller or equal to alpha level

Question 29

HOW TO INCREASE POWER

Answer 29

1. Increase the ES….increase the difference between the groups or decrease the variability.
2. Increase the sample size
3. Increase the alpha (.01 to .05)
   OR
4. Use a 1-tail test

Question 30

independent t-test

Answer 30

compares two means, based on independent data
*data from diff groups of people

Question 31

dependent t-test

Answer 31

compares 2 means based on related data
-data from same ppl measured diff times (pre/post)
-data from matched samples/twins

Question 32

df is smaller if ___ is not met

Answer 32

HOV is not met
Levene’s is smaller than 0.05 = significant

Question 33

when do we use Repeated Measures t-tests?

Answer 33

Comparing twins or matched pairs
Studies that measure the same participants twice (ex/ pre/post)

Question 34

You are more likely to find significance with a RM test. Why?

Answer 34

Less error when you use the same person and test them twice. However, this isn’t always the case.

This is more likely to be true if the degrees of freedom (number of participants) for the two t-tests are approximately the same… RM will be more powerful.

Question 35

Mann-Whitney U data is based on

Answer 35

rankings! look at mean ranking

Question 36

if t (test stat) is in critical region, then difference is ___

Answer 36

significant (reject null!)

Question 37

effect size tells you

Answer 37

how meaningful the relationship between variables or the difference between groups is. PRACTICAL SIGNIFICANCE

Question 38

small effect size can be significant if sample is

Answer 38

very large (hypothesis test is dependent on sample size)

Question 39

effect size
r=0.1, d=0.2
r=0.3, d=0.5
r=0.5, d=0.8

Answer 39

small
medium
large

Question 40

r=0.3 explains

Answer 40

9% of total variance between two samples (medium effect size)

r=used for correlational data

Question 41

power is defined as

Answer 41

probability that stat test will reject the null when treatment does have an effect!