Q1:
Quasi
Experiment
✨
Quasi
vs
experiment
-
Random
or
non-random
-
Control
group
needed
or
not
-
Treatment
are
designed
or
pre-existing
groups
✨
Disadvantages
1-
An
important
drawback
of
pre-experimental
designs
is
that
they
are
subject
to
numerous
threats
to
their
validity
.
2-
Often
do
not
include
any
control
or
comparison
group
.
Without
something
to
compare
it
to,
it
is
difficult
to
assess
the
significance
of
an
observed
change
in
the
case.
✨
The
designs:
a-
non-equivalent
control
group
design
b-
static
group
comparison
design
c-
single
subject
design
d-
multiple
baseline
subject
e-
factorial
design
a)
non-equivalent
contr ol
group
design
In
nonequivalent
group
design,
the
researcher
chooses
existing
groups
that
appear
similar,
but
where
only
one
of
the
groups
experiences
the
treatment.
In
a
true
experiment
with
random
assignment
,
the
control
and
treatment
groups
are
considered
equivalent
in
every
way
other
than
the
treatment.
But
in
a
quasi-experiment
where
the
groups
are
not
random,
they
may
differ
in
other
ways—they
are
nonequivalent
groups
.
Similar
to
pretest
post-test
control
group
design.
Participants
Assigned to
the
Experimental
Group
Pretest
Treatment
Post-test
Participants
Assigned to
the
Control
Group
Pretest
No
Treatment
Post-test When
using
this
kind
of
design,
researchers
try
to
account
for
any
confounding
variables
by
controlling
for
them
in
their
analysis
or
by
choosing
groups
that
are
as
similar
as
possible.
Example:
You
hypothesize
that
a
new
after-school
program
will
lead
to
higher
grades.
You
choose
two
similar
groups
of
childr en
who
attend
differ ent
schools,
one
of
which
implements
the
new
program
while
the
other
does
not.
By
comparing
the
childr en
who
attend
the
program
with
those
who
do
not,
you
can
find
out
whether
it
has
an
impact
on
grades.
b)
static
group
comparison
design
-
no
pretest
Experimental
Group
Treatment
Post-test
Control
Group
No
Treatment
Post-test
Example:
In
2011,
Friedber g
and
Sinderman
wanted
to
study
if
the
CDI,
a
tool
to
measur e depression
in
outpatients,
can
also
classify
inpatient
childr en
as
having
depressive
disor ders
or not.
So
they
brought
data
from
the
archive
of
a
medical
center ,
and
selected
childr en
whose records
contained
a
depression
diagnosis
at
admission
and
a
CDI
score.
Patients
wher e
divided into
2
groups:
-
the
treatment
group
:
contains
children
diagnosed
with
any
type
of
depressive
spectrum
at admission.
-
the
control
group
:
contains
children
diagnosed
with
any
other
psychiatric
problem -
This
is
a
retrospective
study,
so
randomization
was
not
an
option. -
The
intervention
(a.k.a.
treatment)
was
the
depression
diagnosis
at
admission,
so
the treatment
was
the
disease! -
Collecting
a
pre-intervention
measurement
was
impossible
since
the
researchers
had
no access
to
any
data
before
the
admission c)
Single
Subject
Design
-Looks
at
cause
and
effect
relationships
in
single
individuals
rather
than
in
groups
-Common
in
behavioral
analysis
and
special
education
-Based
on
behavioral
view
of
development
-
I
n
single
subject
research
the
participant
serves
as
both
the
control
and
treatment
group.
-
The
researcher
uses
line
graphs
to
show
the
effects
of
a
particular
intervention
or
treatment.
-
An
important
factor
of
single
subject
research
is
that
only
one
variable
is
changed
at
a
time.
Single
subject
research
designs
are
“
weak
when
it
comes
to
external
validity
-
most
have
good
internal
validity
-
AB,
ABA
or
ABAB
❖
AB
measure
baseline
and
impose
treatment
In
a
single
subject
design
using
an
AB
measure,
the
baseline
(A)
refers
to
the
initial
phase
where
the
subject's
behavior
is
observed
without
any
intervention.
Then,
the
treatment
or
intervention
(B)
is
introduced,
and
the
behavior
is
measured
again
to
see
if
there
is
any
change.
For
example,
let's
consider
a
scenario
wher e
we
are
studying
the
effectiveness
of
a
new
study
technique
on
a
student's
test
scores.
In
the
baseline
phase
(A),
the
student
takes
three
math
tests
without
any
additional
study
technique.
Their
average
score
is
recorded.
Next,
in
the
treatment
phase
(B),
the
student
is
taught
a
new
study
technique
and
then
takes
three
more
math
tests.
The
average
score
of
these
tests
is
also
recorded.
By
comparing
the
average
scores
from
the
baseline
(A)
phase
to
the
treatment
(B)
phase,
we
can
evaluate
if
the
new
study
technique
had
a
positive
impact
on
the
student's
test
scores.
This
design
allows
for
a
direct
comparison
within
the
same
individual,
making
it
a
powerful
tool
in
resear ch.
❖
ABA
(reversal)
remove
treatment
In
a
single
subject
design
using
an
ABA
(reversal)
design,
the
baseline
phase
(A)
is
followed
by
the
treatment
phase
(B),
and
then
the
treatment
is
removed
to
observe
if
the
behavior
reverts
back
to
its
original
state
during
the
second
baseline
phase
(A).
Example
:
Imagine
a
study
on
the
effects
of
caffeine
consumption
on
a
person's
sleep
quality .
In
the
baseline
phase
(A),
the
individual's
sleep
quality
is
measur ed
over
a
week
without
consuming
any
caffeine.
Quasi
Experiment
✨
Quasi
vs
experiment
-
Random
or
non-random
-
Control
group
needed
or
not
-
Treatment
are
designed
or
pre-existing
groups
✨
Disadvantages
1-
An
important
drawback
of
pre-experimental
designs
is
that
they
are
subject
to
numerous
threats
to
their
validity
.
2-
Often
do
not
include
any
control
or
comparison
group
.
Without
something
to
compare
it
to,
it
is
difficult
to
assess
the
significance
of
an
observed
change
in
the
case.
✨
The
designs:
a-
non-equivalent
control
group
design
b-
static
group
comparison
design
c-
single
subject
design
d-
multiple
baseline
subject
e-
factorial
design
a)
non-equivalent
contr ol
group
design
In
nonequivalent
group
design,
the
researcher
chooses
existing
groups
that
appear
similar,
but
where
only
one
of
the
groups
experiences
the
treatment.
In
a
true
experiment
with
random
assignment
,
the
control
and
treatment
groups
are
considered
equivalent
in
every
way
other
than
the
treatment.
But
in
a
quasi-experiment
where
the
groups
are
not
random,
they
may
differ
in
other
ways—they
are
nonequivalent
groups
.
Similar
to
pretest
post-test
control
group
design.
Participants
Assigned to
the
Experimental
Group
Pretest
Treatment
Post-test
Participants
Assigned to
the
Control
Group
Pretest
No
Treatment
Post-test When
using
this
kind
of
design,
researchers
try
to
account
for
any
confounding
variables
by
controlling
for
them
in
their
analysis
or
by
choosing
groups
that
are
as
similar
as
possible.
Example:
You
hypothesize
that
a
new
after-school
program
will
lead
to
higher
grades.
You
choose
two
similar
groups
of
childr en
who
attend
differ ent
schools,
one
of
which
implements
the
new
program
while
the
other
does
not.
By
comparing
the
childr en
who
attend
the
program
with
those
who
do
not,
you
can
find
out
whether
it
has
an
impact
on
grades.
b)
static
group
comparison
design
-
no
pretest
Experimental
Group
Treatment
Post-test
Control
Group
No
Treatment
Post-test
Example:
In
2011,
Friedber g
and
Sinderman
wanted
to
study
if
the
CDI,
a
tool
to
measur e depression
in
outpatients,
can
also
classify
inpatient
childr en
as
having
depressive
disor ders
or not.
So
they
brought
data
from
the
archive
of
a
medical
center ,
and
selected
childr en
whose records
contained
a
depression
diagnosis
at
admission
and
a
CDI
score.
Patients
wher e
divided into
2
groups:
-
the
treatment
group
:
contains
children
diagnosed
with
any
type
of
depressive
spectrum
at admission.
-
the
control
group
:
contains
children
diagnosed
with
any
other
psychiatric
problem -
This
is
a
retrospective
study,
so
randomization
was
not
an
option. -
The
intervention
(a.k.a.
treatment)
was
the
depression
diagnosis
at
admission,
so
the treatment
was
the
disease! -
Collecting
a
pre-intervention
measurement
was
impossible
since
the
researchers
had
no access
to
any
data
before
the
admission c)
Single
Subject
Design
-Looks
at
cause
and
effect
relationships
in
single
individuals
rather
than
in
groups
-Common
in
behavioral
analysis
and
special
education
-Based
on
behavioral
view
of
development
-
I
n
single
subject
research
the
participant
serves
as
both
the
control
and
treatment
group.
-
The
researcher
uses
line
graphs
to
show
the
effects
of
a
particular
intervention
or
treatment.
-
An
important
factor
of
single
subject
research
is
that
only
one
variable
is
changed
at
a
time.
Single
subject
research
designs
are
“
weak
when
it
comes
to
external
validity
-
most
have
good
internal
validity
-
AB,
ABA
or
ABAB
❖
AB
measure
baseline
and
impose
treatment
In
a
single
subject
design
using
an
AB
measure,
the
baseline
(A)
refers
to
the
initial
phase
where
the
subject's
behavior
is
observed
without
any
intervention.
Then,
the
treatment
or
intervention
(B)
is
introduced,
and
the
behavior
is
measured
again
to
see
if
there
is
any
change.
For
example,
let's
consider
a
scenario
wher e
we
are
studying
the
effectiveness
of
a
new
study
technique
on
a
student's
test
scores.
In
the
baseline
phase
(A),
the
student
takes
three
math
tests
without
any
additional
study
technique.
Their
average
score
is
recorded.
Next,
in
the
treatment
phase
(B),
the
student
is
taught
a
new
study
technique
and
then
takes
three
more
math
tests.
The
average
score
of
these
tests
is
also
recorded.
By
comparing
the
average
scores
from
the
baseline
(A)
phase
to
the
treatment
(B)
phase,
we
can
evaluate
if
the
new
study
technique
had
a
positive
impact
on
the
student's
test
scores.
This
design
allows
for
a
direct
comparison
within
the
same
individual,
making
it
a
powerful
tool
in
resear ch.
❖
ABA
(reversal)
remove
treatment
In
a
single
subject
design
using
an
ABA
(reversal)
design,
the
baseline
phase
(A)
is
followed
by
the
treatment
phase
(B),
and
then
the
treatment
is
removed
to
observe
if
the
behavior
reverts
back
to
its
original
state
during
the
second
baseline
phase
(A).
Example
:
Imagine
a
study
on
the
effects
of
caffeine
consumption
on
a
person's
sleep
quality .
In
the
baseline
phase
(A),
the
individual's
sleep
quality
is
measur ed
over
a
week
without
consuming
any
caffeine.
