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Psychometrics > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

Meaning of psychometrics

A

Psycho: breath, spirit, soul (greek root)
Metric: measure, size, distance (greek root)

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2
Q

Importance of studying measurement (5 reasons)

A

Minimize subjectivity of judgment
Make more precise statements
Quantify your observations
Can never be sure that measurement is perfect
Assess the degree of error (measurement itself can cause error and participant/researchers can introduce bias)

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3
Q

Our first test done

A

Hospital when we are born
Apgar test
5 categories with a score ranging from 0-2 (Appearance, pulse, grimace, activity, respiration)
7-10 is a normal score

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4
Q

Empirical thinking

A

Knowledge that isn’t based on the bible

Will lead to truth

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5
Q

Francis Galton

A

Founder of psychometrics
Obsessed with observations and measurements
Degree of association between 2 elements (Pearson’s R, correlation)
Recognition of individual differences: understanding the ways in which people differ, how do we calculate those differences, what causes those differences

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6
Q

2 types of individual differences

A

Trait differences

State differences

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7
Q

Trait differences

A

Resistant to change over time
Refers to behavior in general
Often easier to measure with questionnaires
Ex: extraversion, IQ, depression, anxiety

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8
Q

State differences

A

Subject to change over a short period of time
Refers to behavior at the moment
Easy to measure with tasks and questionnaires
Ex: sleepiness, hunger, depression, anxiety

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9
Q

Why we study psychometrics

A

Ensure reliable and valid measures
Application
Questionnaires are good

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10
Q

Ensure reliable and valid measures

A

Essential to sound science
How else can we identify individual or cultural differences
How else can we assess traits

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11
Q

Application

A

Good judgments require good measures

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12
Q

Questionnaires are good

A

They make good dependent variables

Help eliminate errors as covariates, controls, or experimental groups

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13
Q

Methods of measurements

A

Stimulus-centered scaling: psychophysics, the relation of physical, directly measurable, stimuli to perception (ex: sound perception)
Subject-centered scaling: estimating the subjective presence, absence, or degree of a construct

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14
Q

Levels of measurement

A

Nominal
Ordinal
Interval
Ratio

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15
Q

Nominal

A

Numbers are assigned as labels only, doesn’t mean anything
Numbers could easily be words
Ex: coding sex with numbers

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16
Q

Ordinal

A

Numbers are not only to label, they rank individuals
Use numbers in a meaningful way
Degree of change isn’t fixed between numbers
Ex: ranking of height between individuals

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17
Q

Interval

A

Numbers are labels, reflect ranks, and tell us exactly how much more of something we have now
No true zero
Ex: temperature scale

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18
Q

Ratio

A

Numbers are labels, reflect ranks, tell us exactly how much more of something we have now, and we have a true zero
Numbers can’t be negative

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19
Q

Distribution of data

A

Normal distribution

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20
Q

Standards scores can relate to 2 things

A

Common units

Common understanding

21
Q

Common units

A

Compare numbers measured with different units, scaling method standardize to a common (Z) unit

22
Q

Common understanding

A

Compare results across participants we norm reference them

Good is relative, a raw score on a measurement rarely has meaning

23
Q

4 steps to develop standard scores

A

Provide a new measure to a very large sample
Verify that the data represents the full range of scores
Determine the distribution of the scores
Break down scores into psychologically meaningful groups

24
Q

Scales

A

Different measurement system
Survey or questionnaire
Imply a unitary construct

25
Q

Scale development

A

Construct identification: deciding what characteristics are needed, what is the thing you are interested in, how to measure it entirely
Make items: literature review, see what scales already exist, look for potential methods and items, make some questions
Pick a response format: what response format is most effective, only one format for all questions?
Pilot test: find which items work the best, reduce items and repeat, verify the utility of items/methods

26
Q

Making good items

A

Cannot be certain to we make good items
Weight the constructs and make the appropriate number of questions for each
Look for consequences of the questions, sources of errors
Reverse-word questions?
Redundant questions?
4-8 questions for a simple concept, 30 for more complex, 12 is a good number, don’t go over 100

27
Q

Reverse wording

A

Items that reflect the opposite of our chosen construct
Reverse-score to break the habitual response pattern
Can be problematic because the opposite can be hard to define
Can introduce biases in answers

28
Q

Redundant

A

Use to strengthen a scale
Rephrasing items will bring out the common cause, reduce idiosyncrasies in how a particular wording may reflect the construct
Avoid using bad redundancy: rephrasing are completely different, differences in grammatical structure

29
Q

Pick a response format

A

Decides how a participant gives you data and what kind it will be
Dichotomous: 2 parts, 2 options (true or false)
Semantic differential: using adjectives, opposite adjectives at the ends of the scale, 5 discrete options, no in-between

30
Q

Thurstone scale (1928)

A

Construct: Parental aspirations for children’s success
Law of comparative judgment: tries to produce proper interval data
Difference between different options, an equal amount of change, carefully calibrated, suppose to only agree with one, problem when you agree with more because the score is supposed to be only 1, can’t use the data if that happens

31
Q

Guttman scale (1944)

A

Construct: Parental aspirations for children’s success
Tried to make it a little easier to construct questions
Possible to get uninterpretable questions for participants
Cam agree with more than one statement, response og first item will influence the answers of the other items
Change in responses: looking for the change, follow a pattern to figure out where the change is

32
Q

Analog scale

A

Precursor to digital scale
High level of detail
Easier to use that scale on your computer, harder to measure when directly on paper
Similar to semantic differential

33
Q

Likert scale (1932)

A

Write questions to agree or disagree
5 options
Tell them what the description of the level is

34
Q

Likert-type scale

A

More than 5 options
Not with agree or disagree
Frequency scale

35
Q

Writing good items

A

Never use double negatives because it can be confusing (bad for construct validity) and sometimes not implicit in the wording

36
Q

Double-barreled questions

A

Asking 2 questions at the same time is bad because can’t know to which question the participant is answering
Can be converted into 2 items

37
Q

Leading questions

A

Introduce bias in the answers (reduce variability and increase systematic error)
Confusion and bias are bad for validity

38
Q

Participant’s biases

A

Yea-saying: agrees with everything
Social desirability: answer in a way that makes you look like a better person
Malingering: answer to make you look like a bad person

39
Q

Balancing the scales

A

When we use Likert and Likert-type, we assume (incorrectly) that we have equal intervals
The assumption is at least approximately true
Differences between different levels is not always the same, individual variability, can introduce bias depending on how we label a scale

40
Q

Useful caveats

A

Best to put low-value responses (disagree) on the left even if there is a left-side response bias
Overall evaluation questions often do not apply in psychology and are not something you should consider a critical part of a good questionnaire

41
Q

Costs of poor measurements

A

Using a poor measurement can be worst than having no measurement at all
Validity: will be poor
Correlations: hard to find the expected degree of association between 2 things, leading to potential mistakes

42
Q

Boredom proneness scale

A

A 28-item questionnaire that measures negative experiences of boredom as it arises when the situation seems to lack meaning, interest, or challenge, self-regulatory problem
Originally believed to be a unitary construct measured with true or false
Recently believed to be 2 sub-constructs (internal and external) and measured with a 7-point Likert-type scale

43
Q

What do we do with questionnaires

A

Rarely measure constructs with a single term
Combine each item to get a single, more comprehensive, raw score
Total: sum of the individual’s response
Mean: average of individual item response
Raw scores have little interpretative value

44
Q

Norm-referencing

A

Way to improve the interpretation of raw scores and give better feedback on performance
Good performance is a relative term (relative to people completing the same measure)
Examine the distribution

45
Q

Frequency distributions

A

How many people got that score
Group de scores
Can become a histogram

46
Q

Percentile ranks

A

Percentile: normative way of describing performance on a test
How many people would score lower than you on that measure
Not equal intervals (no equal interval between)

47
Q

Calculating percentiles

A

Within a sample
Across a sample
5th percentile: 5% are below you

48
Q

How to talk about percentiles

A

Important to describe accurately
60th percentile: 60% of people would have obtained a lower score than you
90th percentile: score was in the top 10%, well-above-average

Psychometrics (1 decks)

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