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DETAILED PSYCHOMETRICS SUMMARY (FSWP2-052-A)

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Hey guys! This is a detailed summary for psychometrics! It covers ALL of the needed literature for the course (Furr, R. M. (2021). Psychometrics: an introduction, Chapters 4 to 11) I got a grade 8.2 :)

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PPSYDR

,4. TEST DIMENSIONALITY AND FACTOR ANALYSIS

3DIMEUSIORALITY QUESTOUS:


UNIDIMENSIONAL TESTS
the testincludes items thatreflect
When
only a


single attribute of a
person
->
the
responses to the testitems are driven

only by thatone attribute)



test
eg. abt.geometry which
requires true

and ONLY tests
knowledge of geometry
geometry (ne
angebra or stn.)
MULTIDIMENSIONAL TESTS W/
CORRELATED DIMENSIONS
ana tests w/ higher order factors

when testitems reflect more than

one
psychological attribute +

the dimensions correlated
if are




->
only a
single score is computed eg. intelligence tests w/ questions abt
diff facets of intelligence (logic
thinking general knowledge...)
when dimensions are carrelated:person

wI
high score in edimension is
likely
to score
high on other dimension



typically
->
produce more scores,
each subset has its own score
-> when correlated: dimension are

scared to produce a total score by
subsets
combining

, MULTDIMENSIONAL TESTS N/ UnCORRELATED DIMEnSIONS

eg. some personality tests which testfive relatively independent
personcuity attributes
->
test receives five scores which are all treated as if they
were
unidimensional, no total testscore is computed

FACTOR ARALYSIS

this statistical procedure
evaluates the test dimension -




ality

there are two
types:

EXPLORATORY FACTOR ANALYSIS GFA

compute correlations among items and
identify how many dimensions
there really are:



in the
hypothetical correlation matrix
we look for items thatare strongly
correlated w/ each other not
but
other items

=psychological dimension/factor

tallative, assertive, outgoing
-




->
imaginative, creative, intellectual

, EFA STEPS:

1. EXTRACTION METHOD
CHOOSE
which technique? PRINCIPAL AXIS FACTORING (PAF) recommended*

PRIACIPAL COMPONENTS ANALLSIS (PCA)
MAX. LIKELIHOOD FACTOR ANALYSIS



2.IDEUTFY #OF FACTORS +
EXTRACTION
EIGENVALUES
-to
identify # of factors look at


each value represents the potential #of
dimensions reflected among test items




OPTON ONE:
-
than 1.0 then reflects
when itis
greater the item a dimension


eg. if two are 31 then the items reflect I dimensions



OPMON TO:
-

examine relative
the sizes
of the
figenvalues
the 2 and 3
biggestrel, diff. is btwn value



we find the point at which subsequent
differences are relative small (So point3)
and we conclude the test has that

point- 1 dimensions (So 3-1 2)=




we can also plot these values in a SCREf PLOT
wherever the
"leveling off-point"is, then-1




3 1 2D
-
=
D

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