Equivalence of msmt model pt3 2Nov12 - Flash (Large) - 20121102 02.45.48PM
X
Manual multi-group analysis
Using the manual procedure, replicate this model
What has changed?
Using the manual procedure, replicate this model
What has changed?
So how do we feel about fit?
Statistical tests of invariance
So how do we feel about fit?
Statistical tests of invariance
Slide 28
Statistical tests of invariance
So how do we feel about fit?
Statistical tests of invariance
So how do we feel about fit?
Statistical tests of invariance
Slide 28
The logic of quest for the noninvariant source
Step 1: Test for invariance of factor loadings relative to each subscale, separately
Step 1: Test for invariance of factor loadings relative to each subscale, separately
Slide 31
Slide 32
Slide 33
00:00
/
00:00
CC
Manual
multi-group
analysis
Constrain
measurement
model
parameters
to
be
equal
by
right-clicking
on
the
parameters.
Label
the
regression
weight
B/c
we’ve
already
constrained
the
weight
between
item
1
and
the
factor
to
be
“1”
Start
with
L2
(loading
of
item
on
its
factor)
Checking
the
“All
Groups”
box
is
what
constrains
the
2
panels
for
the
invariance
test
Use
a
sensible
labeling
system
11/2/2012
23
Using
the
manual
procedure,
replicate
this
model
11/2/2012
24
What
has
changed?
11/2/2012
25
Parameters
for
Invariance
for
Measurement
model
(now)
Parameters
for
Invariance
for
Configural
model
(earlier)
MCj01377830000[1]
Using
the
manual
procedure,
replicate
this
model
11/2/2012
24
What
has
changed?
11/2/2012
25
Parameters
for
Invariance
for
Measurement
model
(now)
Parameters
for
Invariance
for
Configural
model
(earlier)
MCj01377830000[1]
So
how
do
we
feel
about
fit?
11/2/2012
26
Statistical
tests
of
invariance
ΔΧ2:
The
difference
between
Χ2
values
for
the
configural
and
other
models
in
which
the
equality
constraints
have
been
imposed.
The
difference
is
distributed
with
Χ2
df
equal
to
the
difference
in
df.
Invariance
is
supported
with
ns
results
Noninvariance
is
claimed
if
the
ΔΧ2
is
significant
(and
researchers
would
follow
up
with
tests
exploring
for
which
parameters
account
for
the
noninvariance.
Chi-Square
Distribution
table.
Because
Monte
Carlo
tests
have
supported
the
idea
that
the
ΔΧ2
is
too
strict…
ΔCFI:
invariance
is
shown
when
ΔCFI
<
.01
11/2/2012
27
So
how
do
we
feel
about
fit?
11/2/2012
26
Statistical
tests
of
invariance
ΔΧ2:
The
difference
between
Χ2
values
for
the
configural
and
other
models
in
which
the
equality
constraints
have
been
imposed.
The
difference
is
distributed
with
Χ2
df
equal
to
the
difference
in
df.
Invariance
is
supported
with
ns
results
Noninvariance
is
claimed
if
the
ΔΧ2
is
significant
(and
researchers
would
follow
up
with
tests
exploring
for
which
parameters
account
for
the
noninvariance.
Chi-Square
Distribution
table.
Because
Monte
Carlo
tests
have
supported
the
idea
that
the
ΔΧ2
is
too
strict…
ΔCFI:
invariance
is
shown
when
ΔCFI
<
.01
11/2/2012
27
ΔΧ2
value
is
statistically
significant,
p
<
.025,
suggesting
model
is
noninvariant
ΔCFI
<
.01,
suggesting
that
the
model
is
completely
invariant
Discrepant
findings.
Byrne
said
the
researcher
would
decide.
She
felt
it
instructive
to
investigate
the
source
of
noninvariance.
11/2/2012
28
Statistical
tests
of
invariance
ΔΧ2:
The
difference
between
Χ2
values
for
the
configural
and
other
models
in
which
the
equality
constraints
have
been
imposed.
The
difference
is
distributed
with
Χ2
df
equal
to
the
difference
in
df.
Invariance
is
supported
with
ns
results
Noninvariance
is
claimed
if
the
ΔΧ2
is
significant
(and
researchers
would
follow
up
with
tests
exploring
for
which
parameters
account
for
the
noninvariance.
Chi-Square
Distribution
table.
Because
Monte
Carlo
tests
have
supported
the
idea
that
the
ΔΧ2
is
too
strict…
ΔCFI:
invariance
is
shown
when
ΔCFI
<
.01
11/2/2012
27
So
how
do
we
feel
about
fit?
11/2/2012
26
Statistical
tests
of
invariance
ΔΧ2:
The
difference
between
Χ2
values
for
the
configural
and
other
models
in
which
the
equality
constraints
have
been
imposed.
The
difference
is
distributed
with
Χ2
df
equal
to
the
difference
in
df.
Invariance
is
supported
with
ns
results
Noninvariance
is
claimed
if
the
ΔΧ2
is
significant
(and
researchers
would
follow
up
with
tests
exploring
for
which
parameters
account
for
the
noninvariance.
Chi-Square
Distribution
table.
Because
Monte
Carlo
tests
have
supported
the
idea
that
the
ΔΧ2
is
too
strict…
ΔCFI:
invariance
is
shown
when
ΔCFI
<
.01
11/2/2012
27
So
how
do
we
feel
about
fit?
11/2/2012
26
Statistical
tests
of
invariance
ΔΧ2:
The
difference
between
Χ2
values
for
the
configural
and
other
models
in
which
the
equality
constraints
have
been
imposed.
The
difference
is
distributed
with
Χ2
df
equal
to
the
difference
in
df.
Invariance
is
supported
with
ns
results
Noninvariance
is
claimed
if
the
ΔΧ2
is
significant
(and
researchers
would
follow
up
with
tests
exploring
for
which
parameters
account
for
the
noninvariance.
Chi-Square
Distribution
table.
Because
Monte
Carlo
tests
have
supported
the
idea
that
the
ΔΧ2
is
too
strict…
ΔCFI:
invariance
is
shown
when
ΔCFI
<
.01
11/2/2012
27
ΔΧ2
value
is
statistically
significant,
p
<
.025,
suggesting
model
is
noninvariant
ΔCFI
<
.01,
suggesting
that
the
model
is
completely
invariant
Discrepant
findings.
Byrne
said
the
researcher
would
decide.
She
felt
it
instructive
to
investigate
the
source
of
noninvariance.
11/2/2012
28
The
logic
of
quest
for
the
noninvariant
source
Test
for
invariance
of
all
factor
loadings
comprising
each
subscale,
separately.
If
noninvariance
at
the
subscale
level
is
found,
Then,
test
for
invariance
of
each
factor
loading
(relating
only
to
the
factor
in
question),
separately
As
factor-loading
parameters
are
found
to
be
invariant
across
groups,
their
equality
constraints
are
maintained,
cumulatively.
11/2/2012
29
http://www.netharuka.com/wp-content/uploads/2009/01/monty-python.jpg
Step
1:
Test
for
invariance
of
factor
loadings
relative
to
each
subscale,
separately
Good
news!
ΔΧ2
is
NS
ΔCFI
is
NS
Now
add
DP
to
the
mix
11/2/2012
30
Step
1:
Test
for
invariance
of
factor
loadings
relative
to
each
subscale,
separately
Good
news!
ΔΧ2
is
NS
ΔCFI
is
NS
Now
add
DP
to
the
mix
11/2/2012
30
More
good
news!
ΔΧ2
is
NS
ΔCFI
is
NS
We
now
know
the
source
of
invariance
is
in
PA
Because
we’ve
isolated
PA,
add
one
factor
loading
at
a
time.
11/2/2012
31
Still
ns
Keep
going
to
find
out
which
items
contribute
to
noninvariance
11/2/2012
32
11/2/2012
33
Model
description
Comparison
Χ2
df
ΔΧ2
Δdf
Statistical
significance
CFI
ΔCFI
Configural
model;
no
equality
constraints
imposed
___
1962.345
330
___
___
___
.919
___
Measurement
model
Model
A:
All
factor
loadings
constrained
equal
2A
versus
1
1998.257
349
35.912
19
p
<
.025
.918
.001
Model
B:
Factor
loadings
constrained
for
only
EE
2B
versus
1
1969,117
339
6.173
9
NS
.919
.000
Model
C:
Factor
loadings
constrained
for
EE
and
DP
only
2C
versus
1
1977.807
343
15.462
13
NA
.918
.001
Model
C:
Factor
loadings
constrained
for
EE,
DP
and
Item
7
(on
PA)
2D
versus
1
1980.466
344
18.121
14
NS
.918
.001
Model
C:
Factor
loadings
constrained
for
EE,
DP,
and
Items
7,
9
(on
PA)
2E
versus
1
Model
C:
Factor
loadings
constrained
for
EE,
DP,
and
Items
7,
9,
17
(on
PA)
2F
versus
1
Model
C:
Factor
loadings
constrained
for
EE,
DP,
and
Items
7,
9,
18
(on
PA)
2G
versus
1
Model
C:
Factor
loadings
constrained
for
EE,
DP,
and
Items
7,
9,
19
(on
PA)
2H
versus
1
Model
C:
Factor
loadings
constrained
for
EE,
DP,
and
Items
7,
9,
19,
21
(on
PA)
2I
versus
1
You
can
just
type
your
answers
right
into
this
table!
(please
take
the
time
to
do
this
completely;
think
of
it
as
your
homework
)