Exam 3 Material Flashcards

1
Q

What are bivariate correlations?

A

composed of 2 variables
used to determine the relationship between two different variables

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2
Q

What type of variables are found in bivariate correlations?

A

measured variables
1 = continuous
the other may be continuous or categorical
the variables must have greater than or equal to 2 levels

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3
Q

What are 3 limitations to correlational research?

A
  1. can’t randomly assign
  2. prediction as primary goal
  3. can’t manipulate
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4
Q

What is a construct validity concern with correlations?

A

How well was each variable measured?

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5
Q

What is a statistical validity concern with correlations?

A

How well do the data support the state conclusions?

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6
Q

What is the relationship between statistical significance and effect size?

A

Larger effects + larger sample size –> significant p-value
large samples are needed to get sig effects with the effect size is small

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7
Q

What are the implications of larger effect sizes for correlations?

A

more accurate predictions based on correlation

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8
Q

What are the two types of effect sizes?

A

R - 2 continuous variables
d - 2 variables, 1 is categorical

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9
Q

What does measurement error influence?

A

Reliability
unreliability reduces the observed correlation

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10
Q

How does restriction of range influence correlations?

A

It distort observed correlations
gives a biased estimate

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11
Q

What are the two types of restrictions of range?

A

the ceiling effect
the floor effect

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12
Q

What is desirable variability vs. undesirable variability?

A

Desired: we want variability in X and Y

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13
Q

What are 5 statistical validity concerns?

A

Effect size
significance testing
measurement error
restriction of range
outliers

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14
Q

What are the 3 criteria for causation?

A

Covariance
Temporal Precedence
Internal Validity

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15
Q

are multivariate associations better than bivariate?

A

not necessarily, but the do “get closer” to causal inferences

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16
Q

What are the 3 types of longitudinal correlations?

A

Cross-sectional
Autocorrelations
Cross-lag

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17
Q

What are cross-sectional correlations?

A

aka synchronous
time 1 is compared to variable A
time 2 is compared to variable B

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18
Q

What are autocorrelations?

A

aka stability
time 1 is compared to time 2
variable a is compared with variable b

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19
Q

What are cross-lag correlations?

A

time 1 is compared to variable b
time 2 is compare to variable a
tells us which came first
1 sig correlations shows directionality

20
Q

What criteria for causality does multiple regression address?

A

internal validity
rules out 3rd variables

21
Q

What types of variables are in a multiple regression?

A

predictor variables (>2)
outcome variables (1)

22
Q

What is the effect size used in regression?

A

Hierarchical multiple regression: each steps adds a simultaneous regression

23
Q

What is R^2?

A

coefficient of multiple determination
how close the data are to the fitted regression
% of outcome variable variance that is explained by the model

24
Q

What is pattern and parsimony?

A

the results of a variety of correlational studies all support a single, causal theory
or
the most parsimonious explanation for a pattern of results across studies is a causal one

25
Q

What is mediation?

A

WHY 2 variables are associated
implies causation

26
Q

What is moderation?

A

WHEN or FOR WHOM are two variables associated

27
Q

a moderator…

A

…influence the relationship between 2 variables

x <———>y
^
moderator

EXTERNAL VALIDITY

28
Q

a mediator…

A

… explains the relationship between 2 variables

x —> mediator –> y

INTERNAL VALIDITY

29
Q

What is full or partial mediation?

A

full: after considering the mediator variable, there is no x or y relationship
partial: some relationship exists between x and y without mediator variable

30
Q

What is a spurious correlation?

A

when two variables appear to be correlated but are not
icecream and sunburns

31
Q

What types of variables are in an experiment?

A

_> 1 manipulated
_> 1 measured

32
Q

What are 3 potential threats to repeated-measures design? and how to control

A

Order effects - counterbalancing
demand characteristics
design confound - keep everything else constant

33
Q

What is Internal Validity as it related to experiments?

A

Are there any possible alternative reasons for the change in DV other than the change in IV?

34
Q

What is an example of systematic variability?

A

a 3rd variable/confound that regularly/consistently covaries with IV

35
Q

What validity is systematic variability concerned with?

A

Internal validity

36
Q

What are 3 examples of unsystematic variability?

A
  1. measurement error
  2. individual differences
  3. situational noise
37
Q

What validity is unsystematic variability concerned with?

A

Statistical validity

38
Q

When you control for unsystematic variability you…

A

… increase power of the study

39
Q

Why is a manipulation check important?

A

provides empirical evidence of construct validity of the IV

40
Q

What does a pilot study do?

A

It confirms effectiveness of manipulation in a simple first study

41
Q

what test is associated with effect size?

A

Cohen’s d

42
Q

What are null effects?

A

no group/condition difference
the IV did not covary with the dv

43
Q

What is the file drawer problems?

A

when people won’t publish studies that find a null effect

44
Q

What are 3 possible reasons for a null effect?

A
  1. IV does not affect DV
  2. too much within groups variability
  3. too little between groups variability
45
Q

weak manipulations have…?

A

… less power