Chapter 3

Question 1

3 types of claims

Answer 1

frequency
association
causal

Question 2

frequency claims

Answer 2

describe a rate or degree of only ONE measured variable
-percentage, ratio, etc.

Question 3

association claims

Answer 3

-one level of a variable is LIKELY to be associated with a level of another variable
-at least 2 measured variables

Question 4

what type of study are scatterplots used for?

Answer 4

correlational

Question 5

what is the slope of a zero association?

Answer 5

zero

Question 6

which type of associations dont help with predictions?

Answer 6

zero associations

Question 7

is “uncertain” language still okay? ex: “may” increase, etc…

Answer 7

yes, it can still indicate a causal relationship

Question 8

3 criteria for causation

Answer 8

covariance
temporal precedence
internal validity

Question 9

validity

Answer 9

appropriateness of a conclusion/decision

Question 10

4 big validities

Answer 10

internal
external
construct
statistical

Question 11

what do the 4 big validities do?

Answer 11

interrogate evidence and claims

Question 12

construct validity

Answer 12

how well is the variable(s) operationalized/measured?

Question 13

external validity

Answer 13

how well does the study represent the people outside of the study/how well would the results hold up if you included everyone/not just a sample?

Question 14

statistical validity

Answer 14

extent to which the study’s stats are reasonable, precise and replicable

Question 15

what is the point estimate in relation to the interval estimate?

Answer 15

point estimate is the middle value of the interval estimate.

Question 16

what 3 validities are important for frequency claims?

Answer 16

construct, external and statistical

Question 17

confidence interval

Answer 17

the range that the true population value falls in x% of the time

Question 18

margin of error

Answer 18

range of values DEVIATING from the PI

Question 19

what does statistical significance imply?

Answer 19

the result is probably not due to chance based on that sample

Question 20

what 3 validities are important for association claims?

Answer 20

construct, external and statistical

Question 21

covariance

Answer 21

A and B are related

Question 22

temporal precedence

Answer 22

A comes before B in time

Question 23

role of internal validity in causal claims

Answer 23

eliminate any alternative explanations

Question 24

which validity should causal claims prioritize but which should they also refer to

Answer 24

internal & refer to all

Question 25

which validities have a heavy trade off?

Answer 25

internal and external

Question 26

what may stay the same in one study but could vary in another?

Answer 26

a constant

Question 27

how many levels does a variable have?

Answer 27

at least 2

Question 28

measured vs manipulated variables

Answer 28

measured: levels are observed/recorded
manipulated: controlled by researchers

Question 29

in what type of study are variables manipulated?

Answer 29

causal/experiments.

Question 30

2 reasons why some variables can only be measured and not manipulated

Answer 30

naturally occurring attributes (age, gender, etc.)
ethics

Question 31

most of this type of variable can be both measured and manipulated

Answer 31

physiological

Question 32

4 types of “roles” of variables

Answer 32

subject variables (self-esteem)
context variables (privacy)
stimulus variables (something presented that provokes a response)
response variables (test performance)

Question 33

2 data types of variables

Answer 33

quantitative: levels differ in amount
qualitative: levels differ in quality/type

Question 34

where do categorical, ordinal and nominal variables belong to?

Answer 34

categorical and nominal - qualitative
ordinal - quantitative

Question 35

main difference between conceptual definition and operational definition of a variable

Answer 35

the operational definition defines the method that a variable is measured/manipulated; a conceptual definition is just an abstract/theoretical statement about a variable.
ex: hunger: number of hours of food deprivation (operational)
hunger: desire for food (conceptual)

Question 36

3 reasons why operational definition are important

Answer 36

-replication
-forces researchers to clarify ideas
-objectivity and public verification

Question 37

relation of variables in experimental studies vs non-experimental studies

Answer 37

experimental: IV is manipulated, DV is measured
non-experimental: predictor variable is assumed IV, criterion variable is assumed DV

Question 38

difference in IV in experimental vs non-experimental studies

Answer 38

experimental: manipulated
non-experimental: measured