lecture 2 Flashcards

1
Q

what is a conceptual variable

A

something abstract and broad (like “memory”)

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2
Q

what is operationalizing a variable

A

picking a specific way to look at your variable
- ex; instead of memory, we look specifically at free recall

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3
Q

frequency claims

A

describe a level/degree of a single variable and involve one measured variable

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4
Q

association variable

A

says one level of a variable is likely to be associated w a level of another variable

supported by studies with 2 variables

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5
Q

causal claims

A

argues one variable changes in the level of another variable

supported by experiments

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6
Q

can a “zero association” help us make predictions?

A

no

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7
Q

what are the 3 common types of measures

A

self report
observational
physiological

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8
Q

list n explain scales of measurement + the types

A

categorical:
- categorical (lvls are categories)
- quantitative (lvls are meaningful nmbrs)
- ordinal (fixed order for how the data is presented)

quantitative:
- ordinal scale (order, and dont know if distance between each variable is same)
- interval scale (numbers, and each lvl has same distance between them)

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9
Q

what r 3 types of reliability

A

test retest
interrater
internal

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10
Q

test retest reliability

A

consistent scores every time the measure is used

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11
Q

interrater reliability

A

consistent scores regardless of who does the measuring

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12
Q

internal reliability

A

participant provides consistent pattern of responses, regardless of how researcher has phrased question

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13
Q

what is average inter item correlation (AIC)?

A

the average of all correlations between diff items

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14
Q

face validity

A

looks like what you want it to measure?

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15
Q

content validity

A

measure contains all parts that your theory says it should contain

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16
Q

criterion validity

A

does it correlate with key behaviours?

17
Q

list the 9 questionable research practices

A

limbo
overselling
post hoc story telling
p value fishing
creative use of outliers
plagiarism
non publication of data
partial publication of data
inventing data

18
Q

why are scatterplots useful

A

can evaluate reliability
can show interrater agreement or disagreement

19
Q

can a measure be less valid than it is reliable?

A

yes

20
Q

can a measure be more valid then it is reliable?

A

no