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# PSYCHOLOGICAL ASSESSMENT by suchenfz

VIEWS: 24 PAGES: 12

• pg 1
```									LECTURES 2008

Norms
• Relative position
• Instruments measuring same variable
 Differ in content
 Different scale units
 Composition of standardisation sample
• National norms; specific norms; local norms
• Fixed reference group
• Item response theory

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LECTURES 2008

Definition of norms
• Normative sample from population
• Many characteristics are assumed to be normally
distributed in the population
• Standard normal distribution mean = 0, standard
deviation = 1
• Raw scores as such, not very informative – they can be
transformed to norms to determine a respondent‟s
„relative position‟
• “A norm is a measurement against which the individual‟s
raw score is evaluated so that the individual‟s position
relative to that of the normative sample can be
determined.”

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LECTURES 2008

Types of norms
• Developmental scales – some characteristics increase
progressively with age and experience (vocabulary, job
experience)
• Mental age scales; determine basal age = highest age at
and below which all items were passed; mental age =
basal age + additional months of credit eared at higher
age levels
• Grade equivalents: educational achievement often
equivalent
• Scale units not necessarily equal; represent median

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LECTURES 2008

Percentiles
• Refers to the percentage of people in a
scores lower than a specific raw score e.g. if an
individual obtained a raw score of 32 and this
score corresponds with a percentile score of 45
(i.e. 32 has a percentile rank of 45), it means
45% of the normative population received raw
scores below 32
• Disadvantages: inequality of scale units; ordinal
level measures; percentile ranks of different
variables and different populations not directly
comparable

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LECTURES 2008

Standards scores
• z-scores = (X – Mean)/SD; positive z-scores – above
average performance, negative below average –
distribution of z-scores same as raw scores on which
they based – disadvantages: half z-scores negative;
limited ranges (about -3 to +3)
• Linearly transformed standard scores – to eliminate
disadvantages of z-scores – multiply z by constant A (to
compensate for limited range) and add constant B (to
eliminate negative scores): transformed ziT = ziA + B –
disadvantages: do not look like z-scores and statistically
more complex, less useful

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LECTURES 2008

Normalised standard scores
• Standard scores transformed to fit normal distribution – if attribute
normal in population – advantage: assessment scores correspond to
normal distribution; disadvantages same as z-scores and
normalised scores change form of original raw score distribution
 McCall‟s T score: T = zN10 + 50
 Stanine scale: ranges from 1 to 9, mean = 5, SD = 1.96 –
advantages: scale units equal; reflects individual‟s position relative
to normative sample; performance in rank order is evident;
comparable across groups; allow statistical manipulation – only 9
scale units
 Sten scale: 10 scale units, mean = 5.5, SD = 2 – advantages and
 Deviation IQ scale – used intelligence scales – mean = 100, SD =
15 – easy to understand – used for age levels above 18 – not
directly comparable to scales with different means, SDs

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LECTURES 2008

Setting standards and cut-off
scores
• Norm score gives individual‟s position relative to the
norm group
• Instead of comparison with a norm group, can compare
with a standard/criterion score – e.g. 50% to pass an
exam; x% to get a driver‟s licence
• Cut-off scores on criterion determined by complex
process – issues: legal, ethical, professional,
psychometric, accepted policies
• Can use expectancy tables (based on past experience,
psychometric properties of measure) – advantage:
provide way of interpreting relationship between
assessment and probable success on criterion

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LECTURES 2008

Item response theory (IRT)
• Also called latent trait models
• Item analysis based on the probability that a
person with a specified ability („latent trait‟)
succeeds on an item of specified difficulty.
• No implication that such latent traits/underlying
abilities exist in a physical/physiological sense
nor that it can cause behaviour – latent traits are
statistical constructs derived mathematically
from empirically observed relations among
responses to test items
• Models used to establish „sample-free‟ scale of
measurement
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LECTURES 2008

• Four interpretations of fairness relate to equality
i.t.o. being selected on the basis of one‟s
predicted criterion performance (irrespective of
group membership), equality in the outcomes of
testing for different groups, equitable treatment
in the testing process, and equality of
opportunity to learn the material contained in the
test.
• Adverse impact when it discriminates against
specific group so that members of the group
have a smaller probability than other groups of
being selected
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LECTURES 2008

Difference between bias and fairness
• Predictive bias a statistical concept – can
be addressed with statistical procedures or
fixing instrument
• Fairness in sense of equality in testing
outcomes seen as socio-political issue that
refers to proportions of the various
subgroups selected (in USA coined term
disproportionate rate of selection of –
usually minority - subgroups)               10
LECTURES 2008

Interpretation
• If intended interpretation of assessment is shown to be
valid and free of bias, and assessees have been treated
fairly in assessment process, fairness has prevailed,
regardless of whether instrument yields equal group
results
• Thus, if different selection rates are due solely to an
unequal distribution of the construct in the relevant
subgroups (i.e. the groups actually differ), and if the
construct has a strong relationship with job performance,
differential subgroup results do not imply unfairness – in
SA may be a problem?

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LECTURES 2008

Assignment 2
• Case study: Plant Manager at Dynamo
(on website)

• Due date: 25 August

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