Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Psychotherapy > Quantitative > Flashcards

Quantitative Flashcards

1
Q

Goal of research

A

design studies carefully to make alternative interpretations implausible

Methods are about designing a study so that if a particular finding is obtained we can reach a conclusion

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Illusory Correlation

A

cognitive bias that occurs when we focus on two events that stand out and occur together

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

How do we know things

A

feelings, intuition, AUTHORITY (Expert), reasoning (logic)- assumption has to be true.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

How do we know things part II

A

Empiricism- idea that knowledge is based on observations SCIENCE- empiricism and reasoning

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Process of science

A

Hypothesis-new hypothesis- theory building- body of knowledge

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Goodsteins Evolved Theory of Science

A

1) data play a central role
2) cientists are not alone- observations reported to other scientists and the public
3) science is adversarial- can be falsified or supproted
4) peer reviewed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Tenets of Science

A

Empiricism
Replicability
Falsifiability- can be testable
Parsimony- simple account

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Hypothesis gains support

A

Hypothesis can not be proved

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Extend literature

A

take idea further
remove confounds and improve generalizability

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

behavioral science goals

A

describe behavior, predict behavior, explain behavior, determine the causes of behavior

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

causation

A

temporal, and covariation, and elimination of alternatives

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

causation

A

temporal, and covariation, and elimination of alternatives

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

causation

A

temporal, and covariation, and elimination of alternatives

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Efficacy vs effectiveness

A

does intervention produce expected result in ideal circumstances/// degree of benefit in clinical settings

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Construct Validity

A

adequacy of there operational definition

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Internal Validity

A

Ability to draw conclusions about causal relationships

Integrity of experiment

Ability to draw casual link between IV and DV

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Mediating Variables:

A

psychological processes that mediate
the effects of the situational variable
on a particular response

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

COnstruct vs Variable

A

the idea then what’s used to test it

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Operational Definitions

A

Set of defined and outlined procedures used to measure and manipulate variables

A variable must have operational definition to be studied empirically

Allows others to replicate!

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Construct validity

A

Adequacy of the operational definition of variables

Does the operational definition reflect the true theoretical meaning of the variable?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Nonexperimental method

A

Variables are observed as they occur naturally
If they vary together, there is a relationship (correlation)

Reduction of internal validity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Experimental Control

A

Extraneous variables are kept constant
Every feature of the environment is held constant except the manipulated variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Strong internal validity requires:

A

Temporal precedence
Covariation between the two variables
Elimination of plausible alternative explanations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Issues When Choosing A Method

A

Often the higher the internal validity, the lower the external validity (generalization)
Harder to generalize when strict experimental environment

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Reliability and validity of measurement

A

Not to be confused with internal or external validity of a study

However, reliability and validity of measurement affects internal validity of a study

Measured score = “true” score (real score on the variable) + measurement error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Reliability of Measures

A

Consistency or stability of a measure of behavior
We expect measures to give us the same result each time
Should not fluctuate much

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Test-retest reliability- same individuals at two points in time

A

Practice effects – literally more practiced 2nd time
Maturation – simply that subjects change because time has passed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

Alternate forms reliability

A

Individual takes 2 different forms of the same test
Also at 2 different times

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

Internal consistency reliability

A

Generally measures whether several items that propose to measure the same general construct produce similar scores
Assessed using responses at only one point in time
In general, the more the number of questions, the higher the reliability

3 common measures of internal consistency:
Item total
Split –half reliability
Cronbach’s alpha (α)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

Item-total

A

Correlation between an individual item and the total score without that item
For example, if you had a test that had 20 items, there would be 20-item total correlations. For item 1, it would be the correlation between item 1 and the sum of the other 19 items

Helpful in identifying items to remove
Or in creating a short-from

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

Split –half reliability

A

Correlation of the total score on one half of the test, with the total score on the other half
Randomly divided items
Spearman-Brown split-half reliability coefficient
We want > .80 for adequate reliability
However, for exploratory research, a cutoff as low as .60 is not uncommon

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

Cronbach’s alpha

A

How closely related a set of items are as a group
How well all the items “hold together”
Simply put:
Average of all possible split-half reliability coefficients

Expressed as a number between 0 and 1
Generally want > .80, (in practice > .70 considered ok)
By far the most common measure you will see reported

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

Interrater reliability

A

Correlation between the observations of 2 different raters on a measure

Measured by: Cohen’s Kappa
By convention > .70 is considered acceptable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

Construct Validity

A

To what extent does the operational definition of a variable actually reflect the true theoretical meaning of the variable?
Does the measure reflect the operational definition?
Ex. Depression
DSM-5 criteria
BDI sxs

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

Face validity

A

Content of the measure appears to reflect the construct being measured
Very subjective
Easy for a participant to “fake”

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

Content Validity

A

Extent to which a measure represents all facets of a given construct
Subject matter experts may be part of the process

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

Criterion

A

Measures how well one measure predicts an outcome for another measure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

Criterion

A

Predictive Validity
Scores on the measure predict behavior on a criterion measured at a future time
Ex: GRE -> grad school success

Concurrent Validity
Relationship between measure and a criterion behavior at the same time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

Criterion- Convergent Validity

A

Convergent Validity

Scores on the measure are related to other measures of the same construct
i.e., if we test two measures that are supposed to be measuring the same construct and show that they are related

The scores should “converge”
Ex: BDI & CES-D

39
Q

Criterion- Discriminant Validity

A

Scores on the measure are not related to other measures that are theoretically different
i.e., if we test two measures that are not supposed to be related, and show that in fact, they are unrelated

The scores should “discriminate” between constructs
Ex: Narcissism and Self-esteem

40
Q

Reactivity of Measures

A

Measurement is reactive if awareness of being measured changes an individual’s behavior
e.g., self-monitoring, wearing a fitbit

Hawthorne effect / observer effect
Productivity and working conditions

41
Q

sensitivity

A

Ability of a test to correctly identify those with the condition (true positive rate)
Proportion of people with the condition who will have a positive result

42
Q

specificity

A

Ability of the test to correctly identify those without the condition (true negative rate)
Proportion of peoplewithoutthe condition who will have anegativeresult

43
Q

Nominal

A

Categories with no numeric scales

44
Q

Ordinal

A

Rank ordering
Numeric values limited

45
Q

Interval

A

Numeric properties are literal
Assume equal interval between values

46
Q

Ratio

A

Zero indicates absence of variable measured

47
Q

Types of Variables: Discrete/categorical

A

Consist of indivisible categories
i.e., don’t do math on them, only frequency or percent
Answers “how many” questions
Usually naturally occurring groups or categories (not always)

48
Q

Continuous/dimensional

A

Infinitely divisible into whatever units Ex: time or weight

Scores that provide information about the magnitude of differences between participants in terms of the amount of some characteristic

49
Q

Converting variables

A

A continuous variable can be converted into a categorical one
Ex: level of anxiety symptoms (0-100)

50
Q

Controlling extraneous/confounding variables

A

Key to experimental method
Want least ambiguous interpretations of results

Manipulate IV and hold all other variables constant, either by experimental control or randomization

51
Q

Confounding variable

A

Varies along with the independent variable
Cannot determine which variable is responsible for the effect

Ex: exercise vs. video’s effect on mood
Windows vs. window-less room

52
Q

Internal validity

A

Reminder: Internal validity is ability to draw conclusions about causal relationships from the data
Results can be attributed to the effect of the independent variable (IV) and not confounding variables
More we control confounding variables, the more we strengthen internal validity

53
Q

Posttest-only design

A

Obtain two equivalent groups of participants

Introduce the independent variable

Measure the dependent variable
Assume the effect is of the IV on the DV

54
Q

Selection Bias

A

When people selected to conditions differ in an important way
This is why we prefer to recruit all our participants first, and then randomize them!
Basically anything other than randomization may lead to bias in some way
Even when we do randomize, we usually want to make sure the groups are equivalent on important variables…

55
Q

Pretest-posttest design

A

Pretest is given to each group
Help assure that groups are equivalent at the beginning of the experiment

56
Q

Pros to adding a pre-test

A

Can look at change in DV from pre to post
Can assure the randomization worked
i.e., we started with “equivalent groups”
Compare the pre measures between the groups
Especially helpful if have a small sample size
Sometimes used to select participants for the experiment
Like screening for people who score above a cutoff on depressive symptoms
Then they get randomized to groups

57
Q

Pre test and attrition

A

May have started with equivalent groups, but then people start dropping out….

Drop out may be random…
but what if it is due to something non-random, i.e. systematic?
Like patients with “worse” symptoms dropped out

Pretest allows us to see if those who dropped out were different in some way from those who remained in the study

58
Q

Cons to adding a pre-test

A

Time consuming
Might sensitizes participants to what is being studied
Affects the way participants react to manipulation/intervention
Practice effects
When same pretest is used to post-test (measure DV)

Disguise pretest if possible
Different form
Embed in other questions

59
Q

Solomon 4-group design

A

A design that tests for effects of a pretest

Half receive posttest only
Other half receive pretest and posttest

60
Q

Between-subjects design/Independent groups design:

A

> = 2 groups/conditions
Participants participate in only one group
Comparisons are made between different group/condition of participants

61
Q

Within-subjects design /Repeated measures design:

A

Participants experience all groups/conditions
Comparisons are made within the same group of participants

62
Q

Within types

A

Time
Pre-post
i.e. how did the group of people do from pre to post?
T1 T2 T3 T4
Pre, post, 6-month follow-up, 1-year follow-up
Condition
All participants go through all conditions
Ex. Taste test challenge
Condition: 1) Pepsi, 2) Coke, 3) RC Cola
Participant tastes all three.

63
Q

Repeated Measures Design: Pros

A

Fewer participants
- Each participant serves as their own “control”
- Extremely sensitive to statistical differences (between-group tends to have more random error)
- When you use the same participant, you automatically are controlling for a large amount of potential confounding factors: such as any demographic/historical differences

64
Q

Repeated Measures Design: Cons

A

Order effects:
Order of presenting the conditions/treatments affects the dependent variable
Practice (learning) effects:
Performance improves because of the practice gained from previous tasks, i.e., repeated measurement of the DV

65
Q

Counterbalancing

A

All possible orders of intervention/condition are included
Can help with order and practice effects (depending on measurement)

66
Q

Repeated Measures Design: Cons (cont)

A

Fatigue effect:
Performance deteriorates because the participant becomes tired, bored, or distracted from previous tasks
Carryover effect:
Effect of the previous treatment carry over to influence the response of the next treatment

67
Q

Time Between Repeated Measures

A

Longer time interval between measures
Sometimes called “washout”
Helps with fatigue and potential carryover effects

However longer intervals can lead to more attrition….

68
Q

Matched Pair Design

A

Method of assigning participants to conditions in an experiment based on a participant characteristic

Goal is to achieve the same equivalency of groups

Participants are grouped together into pairs based on some variable they “match” on e.g., age, gender, SES
Then, within each pair, participants are randomly assigned to different treatment groups

69
Q

Populatoin

A

All possible individuals of interest

70
Q

sample

A

The group of people you actually study that are drawn from the population

71
Q

Reasoning for sampling

A

How you select your sample affects external validity

In general, the larger the sample, the closer it is to estimating the population
Sample size matters
Must consider the cost/benefit of increasing sample size

71
Q

Inferential statistics

A

inferences and predictions about a population based on a sample of data taken from the population in question

Because a sample is typically only a part of the whole population, sample data provide only limited information about the population.
This is why sampling is so important!

72
Q

Probability sampling

A

utilizes some form of random selection
assures that a member of your population has equal probability of being chosen

73
Q

Nonprobability sampling

A

Just about all other methods
Also known as a “convenience sample”

A sample that is not drawn randomly
Usually consists of participants who are readily available to the researcher

74
Q

Stratified Sampling

A

Stratified sampling
Used to ensure that the proportional representation of groups in the sample is the same as in the population.

Example:
Population of psychology grad students may be 88% female,10% male, 2% non-binary. How would you sample?

75
Q

Sampling bias

A

Systematic differences between the characteristics of a sample and a population

Leads to underrepresentation of many types of people
Limits the potential generalizability of results

76
Q

Sampling Error

A

The discrepancy between a sample statistic and its population parameter

77
Q

Straightforward manipulations

A

Ex:
Assignment to groups/conditions
Treatment vs. control: therapy or pill
2 different signs in the bathroom
Different instructions to participants

78
Q

Staged manipulations

A

Might be when trying to create some psychological state (like anger, anxiety)
Frequently use a confederate or accomplice
Frequently uses deception
Ex:
Milgram obedience study
Sneezing confederate

79
Q

Strong IV manipulations

A

Maximizes the difference between groups and increases the chances that the IV will have an effect that is statistically significant
Early stages of research
Want to show that a relationship exists
External validity?
Might create situations which never naturally occur

80
Q

Measuring the Dependent Variable- Self-report measures:

A

Simple ask them!
Used to measure aspects of human thought and behavior
Attitudes, judgements, emotional states, intended behaviors, etc.
Ex: How often do you exercise? How often? How long?

81
Q

Measuring the Dependent Variable Behavioral measures:

A

Direct observations of behavior
Ex: Observe how much exercise one does
FitBit – accelerometer/pedometer data
the fitbit observes for you!

82
Q

Measuring the Dependent Variable
Physiological measures:

A

Recordings of responses of the body
Ex: FitBit – Heart rate monitor data
blood lactate concentration

83
Q

Measuring the Dependent Variable
Physiological measures:

A

Recordings of responses of the body
Ex: FitBit – Heart rate monitor data
blood lactate concentration

84
Q

Measuring the Dependent Variable
Ceiling effect:

A

Maximum level is quickly reached
Task too easy?
Scale of 1-5 -> most responses are 4 or 5
Maybe would have seen a better spread if scale was 1-10

85
Q

Floor effect:

A

Reduces variability in data and difficult to detect any differences – just not captured by the DV measure

86
Q

Floor and ceiling

A

This also happens when there isn’t much room for improvement in your sample
Ex:
Low depression scores to start, no where to go
A1C
Stress management
1550 SAT score

87
Q

Measuring the DV: Likert scale

A

Most people use “Likert-like” or “Likert-type” scales
“True” Likert scale
containsseveral items
response levels are arranged horizontally and anchored with consecutiveintegers and verbal labels
connotemore-or-lessevenly-spaced gradations, bivalentandsymmetricalabout a neutral middle

88
Q

Participant expectations-Demand characteristics

A

Participants respond/behave how they think is expected
Especially problematic if they know the hypothesis

Control by
deception, filler items, asking about perception of purpose of research, blinding!

89
Q

Placebo effect

A

Used to control placebo effect
i.e., just taking a pill makes a difference
Compare placebo pill with active pill effects – needs to above and beyond placebo response

90
Q

Balanced-placebo design

A

When specifically looking for effect of expectations

91
Q

Nocebo

A

Anoceboresponse occurs when participant’s symptoms are worsened by the administration of an inert, sham, or dummy (simulator) treatment due to negative expectations of treatment or prognosis

92
Q

Experimenter

A

Subtle biases of how experimenter interprets and records behaviors, or how they interact with participants

93
Q

Controlling the expectancy problem

A

Train experimenters
Run all participants at some time so experimenter’s behavior is the same
Take the human out of the equation - Automate procedures
print, video directions

94
Q

Controlling demand characteristics and expectancy problem

A

Single and double blind

Psychotherapy (4 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions