Welcome
to
this
poster
session.
My
name
is
Renita
Washburn,
and
my
colleague
and
I,
Dr.
Mary
Jean Amon.
For
this
project,
we
examine
the
relationship
between
legislative
restrictions
on
the
use
of
public
resources
for
abortions
and
their
impact
on
fertility
rates
based
on
maternal
age
in
the
United
States.
We
believe
that
by
examining
these
regulations,
we
could
offer
insight
into
potential
impacts
of
future
legislative
changes,
thus
aiding
and
understanding
the
dynamics
and
potential
consequences
of
such
policy
shifts
on
fertility
rates.
I'll
start
by
quickly
discussing
our
data
sources,
and
then
I'll
demonstrate
how
we
use
JMP
to
perform
a
two-way
ANOVA
and
a
late-in
class
analysis
to
investigate
these
relationships.
There
were
two
data
sources
used
for
the
study.
The
first
was
legislation
that
was
obtained
from
a
law,
ATLAS
policy
surveillance
program
dataset.
The
secondly,
we
got
the
2021
fertility
rates
from
CDC's
WNDYR
database.
The
data
sets
were
combined
together
based
on
the
mother's
state
of
residence
in
the
year,
with
a
one
year
lag
in
the
birth
status
so
that
the
legislation
was
in
effect
when
the
pregnancy
began.
Our
first
objective
was
to
identify
impacts
of
restrictions
on
public
resources
on
fertility
rates.
We
broke
the
legislation
down
into
three
buckets,
whether
the
state
had
no
restrictions,
whether
they
had
restrictions
but
excluded
Medicaid,
and
then
whether
they
were
restrictions,
including
Medicaid.
A
two-way
analysis
of
variance
was
performed
to
determine
whether
there
was
a
statistically
significant
difference
between
the
mean
fertility
rates
based
on
these
three
buckets
in
the
maternal
age.
We
first
started
by
visualizing
these
three
categories
with
the
map
that
we
made
with
JMP's
Graph
Builder.
From
there,
we
observed
that
there
was
an
uneven
number
of
states
in
each
of
the
restriction
categories.
We
used
a
common
practice
of
running
the
analysis
with
the
rate
transform
data
to
avoid
any
challenges
from
the
imbalance
data.
I'll
demonstrate
first
how
we
add
this
ranking
to
the
data
set,
and
then
we'll
go
through
the
two-way
ANOVA.
First,
you
start
with
Analyze,
Distribution.
We're
going
to
put
in
the
variable
that
we
want
to
do
the
rank
transformation
on,
which
is
fertility
rates
and
to
Y
and
hit
OK.
We
go
down
to
our
lovely
red
triangle
then
to
Save
and
Ranks.
That's
going
to
save
the
ranking
of
from
lowest
to
highest
of
the
fertility
rates
onto
our
main
data
set.
We're
just
checking
that
it's
there.
Next,
with
the
results
of
the
rank
fertility
rates,
we're
going
to
use
that
to
do
our
two-way
ANOVA.
Back
to
Analyze
Fit
Model,
we're
going
to
add
in
not
the
original
fertility
rates,
but
that
ranked
fertility
rate
that
we
just
created
to
our
Y.
We're
going
to
add
in
our
two
independent
variables
in
full
factorial,
so
that
we
can
do
that
two-way
for
the
maternal
age
and
that
restriction
category
will
hit
Run.
Then
it
gives
us
the
same
outputs
that
we
would
obviously
expect,
but
it's
using
those
rank
transformations.
We
can
go
down
and
look
at
the
effect
summary
to
see
that
there
are
statistically
significant
interactions
going
on
there.
The
two-way
ANOVA
identify
that
there
were
statistically
significant
differences
in
those
fertility
rates
based
on
maternal
age
and
the
presence
of
restrictions.
Specifically,
we
observed
that
women
ages
15
to
29
had
lower
fertility
rates
when
there
were
no
restrictions
present
in
their
state.
The
next
objective
was
to
identify
patterns
in
the
state
abortion
restriction
composition.
We
use
a
late-in-class
analysis
because
we
had
these
binary,
these
yes
or
no
indicators
for
six
different
categories
of
interest.
They
were
related
to
government
funds,
government
facilities,
and
other
various
programs
like
state
insurance
programs
for
state
employees.
We'll
demo
how
we
use
JMP's
clustering
to
group
those
states
together
based
on
these
six
different
categories,
and
then
how
we
use
Graph
Builder
to
help
us
display
the
results
and
interpret
them
a
little
easier.
To
run
the
late-in
class
analysis,
we're
going
to
analyze
clustering,
late-in
class
analysis.
We're
going
to
put
in
our
six
binary
indicators
into
our
Y.
Then
we
could
adjust
the
number
of
clusters
and
it
would
run
more
than
just
three,
but
we're
just
going
to
use
three
for
our
purposes
to
keep
it
simple.
After
running
it,
we
have
this
high-level
characterization
about
each
one
of
the
clusters.
But
let's
create
an
additional
visualization
that'll
start
helping
us
interpret
how
the
states
fell
out
in
each
one
of
the
three
clusters.
To
do
that,
we're
going
to
go
to
Graph
Builder.
We're
going
to
take
our
state
of
residence
and
put
it
down
on
map
shape
that
lets
JMP
know
that
we're
wanting
to
make
a
map.
Then
we're
going
to
put
our
most
likely
cluster,
which
is
added
to
the
data
set
after
we
run
that
LCA,
and
we're
going
to
use
that
to
actually
color
it.
This
gives
us
a
really
quick
look
at
how
each
of
our
states
that
actually
had
restrictions,
which
of
the
three
clusters
they
fell
out
into.
Then
you're
able
to
use
Graph
Builder
again
to
create
other
visualizations
like
we
did
that
allow
you
to
compare
each
of
the
states'
composition
of
their
restrictions
and
their
fertility
rates
using
the
three
clusters
that
we
created
with
the
LCA.
When
we
did
that,
what
we
observed
is
that
when
there
were
restrictions
on
multiple
types
of
public
resources,
it
was
often
associated
with
higher
fertility
rates
for
those
states.
I
just
want
to
thank
you
for
viewing
the
poster
session.
The
goal
was
really
to
demonstrate
how
we
used
JMP
to
examine
the
relationships
between
abortion
restrictions
that
were
targeting
public
resources
and
fertility
rates
across
multiple
maternal
age
groups.
By
performing
the
two-way
ANOVA
on
that
rank
transform
data,
we
observed
that
women
ages
15
to
29
had
lower
fertility
rates
when
there
were
no
restrictions.
Through
the
use
of
late-in-class
analysis
and
visually
analyzing
the
results,
we
observed
that
restrictions
on
multiple
categories
of
public
restrictions
were
associated
with
higher
fertility
rates.
Thank
you
again.
