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Research Methods in Psych > Ch 3: Interrogation tools > Flashcards

Ch 3: Interrogation tools Flashcards

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1
Q

Variable

A
  • something that changes/ varies

- must have at least two levels

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2
Q

levels

A

values within a variable

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3
Q

constant

A

something that doesn’t vary- has only one level (in this case)

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4
Q

measured variable

A

variable is observed and recorded by researcher an as it occurs naturally

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5
Q

example of a measured variable

A

age, IQ, gender

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6
Q

How do you measure abstract variables?

A

using sets of questions to represent different levels

  • ex: stress, depression
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7
Q

manipulated variable

A
  • a variable that a researcher controls

- usually by assigning participants to levels

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8
Q

example of a manipulated variable

A

assign some to take a test in a full room vs alone

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9
Q

can all variables be manipulated or measured?

A
  • some can only be measured
    ( ex: age)
  • some unethical to manipulate
    ( ex: assigning low quality vs high quality schooling)
  • some can be manipulated or measured
    (ex: measure kids taking music or drama, or assign kids to music or drama)
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10
Q

Conceptual Variable (or Constructs)

A
  • abstract theoretical concepts
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11
Q

example of a conceptual variable

A
  • infant temperment

- anxiety

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12
Q

conceptual definitions

A

defining conceptual variables at a theoretical level

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13
Q

operationalizations (or operational variable)

A
  • turning a conceptual definition into a measured or manipulated variable so it can be tested
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14
Q

operational definition

A
  • operationalizing/ defining a conceptual variable in the terms of the exact procedures used to measure or manipulate it
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15
Q

what are some ways “school achievement” could be operationalized?

A
  • self-report questionnaire
  • checking school records
  • teacher observations
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16
Q

claim

A

an argument someone is trying to make (about variables)

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17
Q

what are the 3 types of claims?

A
  • frequency
  • association
  • causal
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18
Q

frequency claim

A
  • describes a particular rate/ degree of a single variable of interest
  • describes how frequent/ common something is
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19
Q

is the variable in frequency claims measured or manipulated?

A

in frequency claims, the variable is always measured

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20
Q

association claim

A
  • involve two variables and their relationship to each other
  • one level of a variable is likely associated to a level of another variable
  • both measured variables
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21
Q

3 types of associations

A
  • positive
  • negative
  • zero
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22
Q

positive association

A

high (scores on variables) goes with high, low with low

  • ex: partners who express gratitude 3x more likely to stay together
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23
Q

Negative association

A

High scores on variables goes with low of another, low with high

  • ex: people who multitask most are the worst at it
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24
Q

zero association

A
  • no association btwn variables
  • no trend to data points
  • ex: childhood obesity not linked to autism
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25
Q

how can association claims be useful

A

useful because they help make predictions

  • if we know a level of a variable we can predict the level of the other
  • the stronger the association between the variables, the more likely the prediction is accurate
26
Q

predicting

A

using associations to make an estimate more accurate

27
Q

Causal claims

A

argues one variable is responsible for changing another

  • one of the variables is measured and the other manipulated
28
Q

3 criteria for causal claims

A
  • they start as association claim
  • temporal precedence (causal variable must come before the outcome variable)
  • no other explanations (confounds- internal validity)
29
Q

where else can claims come from (non research)?

A
  • popular press
  • intuition
  • experience
  • authority
30
Q

what does it mean to say something is valid?

A
  • reasonable
  • accurate
  • justifiable
31
Q

What are the 4 validities?

A
  • construct validity
  • external validity
  • statistical validity
  • internal validity
32
Q

what is construct validity?

A
  • how well conceptual variables are operationalized

- how well the variables are measured or manipulated

33
Q

External Validity

A
  • How well do the participants represent the intended population?
  • what context?
34
Q

Statistical Validity

A

Extent to which the data supports the conclusions

35
Q

Internal Validity

A
  • Only relevant in in causal claims

- How confident are we that the first variable causes the second, and that that is the only explanation ( no confounds)

36
Q

How is Construct Validity used in frequency claims?

A

ask how was the conceptual variable measured?

ex: 80% college students depressed last year
- how was depression operationally defined?

37
Q

How is External Validity used in Frequency claims?

A

ask how the participants were chosen, how well they represent the intended population

ex: 72% of the world smiled yesterday
- only urban areas? how were participants chosen

38
Q

How is statistical Validity used in frequency claims?

A

percentage is usually accompanied by a margin of error

  • ex: 41% teenagers text while driving- report included “+/- 2.6% margin of error
39
Q

margin of error

A
  • statistical figure based on sample size that indicates where the true value in the population probably lies
  • helps describe how well the sample estimates the true percentage
40
Q

Correlational Studies

A
  • studies to support association claims

- measures 2 variables

41
Q

How is construct validity used in association claims? (+ example)

A
  • how well were the two conceptual variables operationalized to be measured?
  • ex: people who multitask more are worse at it
    • how is frequency of multitasking measured? - self report or observation?
    • how is ability to multitask measured? - self report or scored exercise?
42
Q

How is external validity used in association claims?

A

can the claim generalize to other populations, contexts, times, places?

43
Q

How is statistical validity used in association claims?

A
  • how strong is the relationship between the two variables?

- how statistically significant is the relationship?

44
Q

statistical significance

A
  • means result is probably not due to chance of sampling error
  • if you had access to whole population of interest we would probably see same pattern of results
45
Q

what are the mistaken conclusions that can come about in association claims?

A
  • Type I error (false positive): study might mistakenly conclude from sample an association between variables when there is no association in the population
  • type II error (miss): study might mistakenly conclude from sample no association when there is an association in the full population
46
Q

Sampling error:

A

natural discrepancy that happens between population and a sample used to represent the population

47
Q

what are the 3 criteria for causation?

A
  • covariance
  • temporal precedence
  • internal validity
48
Q

covariance

A

two variables are related/ associated

49
Q

temporal precedence

A

one variable must come before the other in time

50
Q

internal validity/ third variable criteria:

A

study should be able to eliminate external alternate explanations

  • want to be confident that variable A leads to change in variable B, and nothing else changed
51
Q

experiment

A

study in which one variable is manipulated and the second is measured

52
Q

independent variable

A

a manipulated variable that we believe will cause a change in the dependent variable

53
Q

dependent variable

A

in a causal claim- the measured variable which we believe will be effected by the independent variable

54
Q

random assignment

A

method used to assign participants to different levels or conditions of the independent variable in a way that each person is equally likely to be assigned to any condition

55
Q

how does random assignment increase internal validity

A

it controls for potential alternate explanations due to variables within participants

56
Q

Music lessons enhance IQ experiment- what are the variables, and the levels?

A

independent variable: music lessons

dependent variable: IQ

variable levels of music lessons:

  • keyboard
  • voice
  • drama
  • none

results: kids with music lessons gained

57
Q

Music lessons enhance IQ experiment- what are the results?

A
  • kids with music lessons gain avg. 3.7 IQ points
  • statistically significant
  • establishes covariance
  • method establishes temporal precedence and internal validity
58
Q

How is Construct Validity used in Causal claims?

A

need to see if manipulated and measured variables were operationalized well

59
Q

Relationship between internal and external validities

A

Internal Validity is emphasized in experiments- as experimental control increases, externally validity decreases

60
Q

How is statistical validity used in causal claims?

A
  • what is the strength of the relationship between the independent and dependent variable?
  • statistical significance
61
Q

How do researchers prioritize validities?

A
  • best research programs employ multiple types of methods to address their research questions to emphasize different validities in each of their studies

Research Methods in Psych (14 decks)

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