Zusammenfassung
Summary 2.5C Psychometrics
- Kurs
- Hochschule
A summary of the learning goals of course 2.5 Psychometrics. It is sumarized per learning goal.
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Inhaltsvorschau
PsychometricsLearning goal 1: Challenges to measurement
Participant reactivity The act of measurement itself can influence people. Their true psychological states cannot be observed because they change their
behaviour.
• Demand characteristics: Showing the behaviour you think the researcher wants to see, instead of being natural.
• Social desirability: Changing your behaviour to impress the person doing the measurements.
• Malingering: Changing your behaviour to leave a poor impression on the person doing the measurements.
Objectivity The expectations or biases of the researcher influence how they are collecting and interpreting the data.
Composite scores Composite scores are scores that consist of multiple items.
Score sensitivity A measure needs to be sufficiently sensitive to discriminate between meaningful amount of what it is measuring (e.g., if
depression is becoming less, you need more answers than just a feeling good or feeling bad option).
Lack of awareness of Testing is often conducted with no though to the quality of the test and how it is supposed to be done.
important psychometric
information
Learning goal 2: Understanding and explaining classical test theory
Observed score. Observed scores are scores that are obtained from measuring a characteristic. They are the scores that you get from a test.
True score. True scores are the scores that reflect the true amount of a characteristic present in a person.
True scores could also be defined as the average score someone would obtain when doing a test an infinite number of times.
True scores could also be seen as the score that would be obtained if the test perfectly measured what it intends to measure
,Reliability Reliability is the extent to which differences in observed scores can be attributed to differences in true scores, and not to the
influence of other characteristics.
𝑠𝑖𝑔𝑛𝑎𝑙
𝑟𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =
𝑠𝑖𝑔𝑛𝑎𝑙 + 𝑛𝑜𝑖𝑠𝑒
The true scores are determined by observed score plus measurement error.
Classical test theory assumes that error occurs as if it is random.
There are four ways to think about reliability in CTT. There are two distinctions in conceptualizing reliability. One such is whether
reliability should be conceptualized in terms of correlations or in terms of proportion of variance. The second distinction is
whether it is conceptualized in terms of observed scores to true scores or observed scores to measurement errors.
The most common way is defining reliability as the portion of observed score variance that is attributable to true score variance.
A second method is defining reliability as the lack of measurement error. That is one minus the proportion of error variance from
observed variance.
Reliability can also be the squared correlation between the observed score and the true scores.
It can also in how far error scores are not correlated to true scores.
However, the reliability coefficient does not reflect the degree of error scores in the measurement.
To determine this, we have the standard deviation of error scores, the standard error of measurement. It gives you the average
size of the measurement errors.
𝑠𝑒𝑚 = 𝑠𝑜2 √1 − 𝑅𝑥𝑥
Systematic measurement Systematic measurement error is the error that is systematic. This error can be the influence of the location on the responder, or
error. anything like that.
Systematic measurement error is often not included in the reliability and error measurement part of the equation, because it
remains the same over all items and questions and thus is lobbed in with the true scores/
Random measurement Measurement errors occur as if they are random. It is by chance that another characteristic influences your score.
error.
Measurement error is also independent from the true score, meaning that the error can influence everyone the same.
The error also tends to cancel itself out over respondents.
, The correlation between true scores and error scores is 0.
The error masks the true differences in peoples scores because the error scores are independent from the true scores.
It is assumed that the variance in the observed scores is the variance from the true scores plus the variance from the errors.
Because true scores and errors are uncorrelated, there is no correlation part in the formula.
𝑠02 = 𝑠𝑡2 + 𝑠𝑒2
Learning goal 3: Reliability
Test-retest reliability. Is measured by having someone take the same test on two separate occasions. You can use the correlation as a measure of
reliability.
You do need to meet the assumption of parallel tests, which in this case means that you should be able to assume that the true
scores do not change over time. The second assumption needed is that the error variances are equal.
There are a few factors which affect the stability assumption:
- Some psychological attributes are more stable than others. E.g., mood states fluctuate, meaning you cannot measure them
twice and expect the same results. Trait-like attributes on the other hand, should be relatively stable.
- The length of the interval also influences the stability. The longer the interval, the more likely that the psychological attribute
changes.
- The period during which the interval occurs also influences the stability. There are more likely to be changes in some periods
of life than in other periods. These changes also don’t occur at the same rate for everyone.
This score is also referred to as the stability coefficient, due to the stability assumption.
Parallel test. Two tests are parallel when:
- Error measurement is independent from the true scores.
- The error cancels itself out over different respondents.
- The two tests have the same error variance.
- The intercept in the formula linking the two true scores is 0.
- The slope linking the true scores on the two tests is 1.
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