BRM
weeks
7&8
AB
designs
-
ANS-Can't
show
replication;
or
verification;
can
not
generalize
the
results
with
one
AB
design
to
other
settings,
other
participants,
and
other
behavior;
case
study
AB
designs/
case
study/
accountability
-
ANS-can
only
tell
prediction
and
affirmation
of
the
consequent
but
no
replications,
can't
show
experimental
control.
ABA
-
ANS-Problematic
ethically
because
we
always
want
to
end
in
an
intervention
ABAB
withdrawal
-
ANS-And
then
we
have
verification
with
our
second
baseline
phase.
We
have
replication
with
our
second
intervention
phase.
allows
us
to
make
our
causal
statements
and
demonstrate
experimental
control,
because
we
have
prediction,
verification,
and
replication.
affirmation
of
the
consequences
-
ANS-The
idea
is
that
we
want
to
see
that
when
the
intervention
is
put
into
place,
the
data
look
different
than
what
our
visual
line
would
have
been.Basically,
we
want
to
predict
what
the
data
would
have
looked
like
without
the
intervention
in
place
and
then
when
we
put
the
intervention
in
place
and
see
data
that
are
different
from
that
original
prediction.
BAB
Design
is
a
type
of
what
category
of
research
design?
-
ANS-withdrawal
design
BAB
variation
(withdrawal
variation)
-
ANS-if
we
needed
to
implement
our
intervention
immediately;
does
not
have
experimental
control;
problematic
for
research
purpose;
we
do
see
replication.
Can't
see
prediction
or
verification.
baseline
logic
-
ANS-the
way
that
we
demonstrate
that
we
have
experimental
control,
that
our
internal
validity
is
found,
and
that
we
can
actually
make
statements
about
whether
or
not
interventions
were
successful.
Baseline
logic
and
how
we
demonstrate
experimental
control
and
internal
validity
within
our
studies.
-
ANS-We
use
our
baseline
to
compare
the
performance
with
and
without
the
intervention
in
place.
In
applied
settings,
the
baselines
are
great
because
it
allows
us
to
show
experimentally
that
there
is
a
need
for
the
intervention.
Baseline
logic:
prediction
-
ANS-anticipated
outcomes
in
single
case
design;
using
trend,
level
and
variability
of
data
to
predict
the
next
point
weeks
7&8
AB
designs
-
ANS-Can't
show
replication;
or
verification;
can
not
generalize
the
results
with
one
AB
design
to
other
settings,
other
participants,
and
other
behavior;
case
study
AB
designs/
case
study/
accountability
-
ANS-can
only
tell
prediction
and
affirmation
of
the
consequent
but
no
replications,
can't
show
experimental
control.
ABA
-
ANS-Problematic
ethically
because
we
always
want
to
end
in
an
intervention
ABAB
withdrawal
-
ANS-And
then
we
have
verification
with
our
second
baseline
phase.
We
have
replication
with
our
second
intervention
phase.
allows
us
to
make
our
causal
statements
and
demonstrate
experimental
control,
because
we
have
prediction,
verification,
and
replication.
affirmation
of
the
consequences
-
ANS-The
idea
is
that
we
want
to
see
that
when
the
intervention
is
put
into
place,
the
data
look
different
than
what
our
visual
line
would
have
been.Basically,
we
want
to
predict
what
the
data
would
have
looked
like
without
the
intervention
in
place
and
then
when
we
put
the
intervention
in
place
and
see
data
that
are
different
from
that
original
prediction.
BAB
Design
is
a
type
of
what
category
of
research
design?
-
ANS-withdrawal
design
BAB
variation
(withdrawal
variation)
-
ANS-if
we
needed
to
implement
our
intervention
immediately;
does
not
have
experimental
control;
problematic
for
research
purpose;
we
do
see
replication.
Can't
see
prediction
or
verification.
baseline
logic
-
ANS-the
way
that
we
demonstrate
that
we
have
experimental
control,
that
our
internal
validity
is
found,
and
that
we
can
actually
make
statements
about
whether
or
not
interventions
were
successful.
Baseline
logic
and
how
we
demonstrate
experimental
control
and
internal
validity
within
our
studies.
-
ANS-We
use
our
baseline
to
compare
the
performance
with
and
without
the
intervention
in
place.
In
applied
settings,
the
baselines
are
great
because
it
allows
us
to
show
experimentally
that
there
is
a
need
for
the
intervention.
Baseline
logic:
prediction
-
ANS-anticipated
outcomes
in
single
case
design;
using
trend,
level
and
variability
of
data
to
predict
the
next
point
