final exam

Question 1

correlation (r)

Answer 1

reflects the strength and direction of a relation between two continuous variables

Question 2

When are correlations stronger?

Answer 2

They can be from 1 to -1, correlations closer to 1 are stronger than correlations closer to 0

Question 3

What do negative correlations tell us?

Answer 3

the variables have a different relation between them than positive correlations

Question 4

Examples of correlations

Answer 4

strong - r= .80
weak- r=. 10
positive- r= .80, .10
negative- r= -.80, -.10

Question 5

Correlation does not mean what?

Answer 5

Causation

Question 6

regression coefficient (b)

Answer 6

reflects how well one of those continuous variables predicts the other

Question 7

predict

Answer 7

means we can see what happens with one variable and predict what will happen with the other

Question 8

Regression equation

Answer 8

y= a + b (x)

Question 9

What do the variables mean in regression equation?

Answer 9

x= predictor variable
y= criterion variable (score we are predicting)
b= regression coefficient
a= regression constant (where we start)

Question 10

b in the formula means

Answer 10

for every 1 raw unit increase in x their is a b unit increase in y

Question 11

a in the formula means

Answer 11

the predicted value of y when x equals zero

Question 12

conceptual interpretation

Answer 12

For every one raw unit increase in [x -> hours slept last night] there is a [b -> 1] unit increase in [y -> happy mood]

Question 13

Substantive interpretation

Answer 13

For every additional hour of sleep people are predicted to be one point happier

Question 14

multiple regression

Answer 14

more than one predictor
Does income and sleep predict happiness?

Question 15

simple regression

Answer 15

one predictor
Does income predict happiness?

Question 16

multiple regression equation

Answer 16

y= a + b(1)x(1) + b(2)x(2) and so on depending on how many predictors

Question 17

b 1.2

Answer 17

partial regression coefficient for X1

Question 18

b 2.1

Answer 18

partial regression coefficient for X2

Question 19

partial out

Answer 19

to remove shared credit from other predictions

Question 20

conceptual interpretation for partial regression

Answer 20

for every raw unit increase in X there is a b 1.2 unit increase in Y partialing out the other predictions.

Question 21

substantive interpretation for partial regression

Answer 21

for every one raw unit increase in income their is a 0.5 unit increase in happiness partialing out sleep.

Question 22

R is tested with what?

Answer 22

F test

Question 23

b is tested with?

Answer 23

t test

Question 24

R squared reflects what?

Answer 24

the proportion of variation in Y examined by our set of predictors

Question 25

what does b show us?

Answer 25

strength and direction of prediction

Question 26

hierarchical regression

Answer 26

sets of predictors Set A -> R squared age education gender Set B -> triangle r squared money sleep stress

Question 27

logistic regression

Answer 27

allows to model each value in your categorical outcome probability of guilty verdict = .8 probability of not guilty verdict = .2 odds ratio of guilty verdict= .8/.2 we add predictors to the model after

Question 28

Effect sizes

Answer 28

express magnitude and sometimes direction of effect or estimate, they are standardized

Question 29

d index

Answer 29

standardized mean difference, focuses on the standardized difference between two group averages

Question 30

Why are standardized units beneficial

Answer 30

because they remove raw units, we use standard deviation to remove raw units

Question 31

SD pooled

Answer 31

standard deviation from both participants

Question 32

conceptual interpretation of d

Answer 32

there is a (d) standard deviation unit difference between the average (y) for group 1 and group 2

Question 33

substantive interpretation of d

Answer 33

there is a 2.5 standard deviation unit difference between the average salaries for men and women in business

Question 34

benchmarking

Answer 34

d = .20 -small d = .50 -medium d= .80 -large

Question 35

conventional benchmarking is too?

Answer 35

general

Question 36

empirical benchmarking

Answer 36

compares d-index to other similar studies

Question 37

confidence interval

Answer 37

interval or numbers whose length reflects the precision of an estimate Given a sample from a population, the CI indicates a range in which the population mean is believed to be found. Usually expressed as a 95% CI, indicating the lower and upper boundaries. M= 200k, 95% CI (150k, 250k)

Question 38

M= 200k, 95% CI (150k, 250k) this starts with a?

Answer 38

lower limit of 150 and high limit of 250

Question 39

The width of a confidence interval reflects?

Answer 39

how precise or accurate our estimate is TOO WIDE IS LESS PRECISE

Question 40

misinterpretation of confidence intervals

Answer 40

1. 95% confidence interval has a 95% chance of containing the population parameter of interest 2. a 95% confidence interval predicts that 95% of sample estimates from future studies will fail within it (no)

Question 41

data cleaning

Answer 41

preparing data involves cleaning it which ensures the "dataset" on which you conduct analyses is complete, correct and consistent

Question 42

complete

Answer 42

has all the data been recorded or transferred into data set

Question 43

data entry

Answer 43

recording all data in the form of scores from participants in your study

Question 44

transcription error

Answer 44

researchers entered data incorrectly

Question 45

reporting error

Answer 45

participants enter data incorrectly

Question 46

downloading error

Answer 46

sometimes data isn't downloading correctly

Question 47

cleaning data

Answer 47

ensures its integrity and trustworthy

Question 48

assumptions

Answer 48

the things we hold true for an inferential test to operate the way we think it should

Question 49

inferential tests

Answer 49

they only work right if our assumptions are true, they can handle moderate or minor departures

Question 50

when statistical assumptions are violated the probability of a test statistic may be

Answer 50

inaccurate

Question 51

normality

Answer 51

outcome variable scores are normally distributed in the population

Question 52

homogeneity of variance

Answer 52

all groups have the same variance in the population (focuses on width not shape)