WALDEN UNIVERSIT.docx

W
A
L
D
E
N
U
N
I
V
E
R
S
I
T
Y
P
U
B

H
6
0
3
2
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8
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S
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A
L
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R
E
T

A
T
I
O
N
&
A
P
P
O
F
D
A
T
A
T
yp
es of V
ariables &
L
evels of M
easu

rem
en
t
T
yp
es of V
ariables
y
A
variable is an
y factor, trait, con
d
ition
, or d
isease
th
at exists in
d
ifferin
g am
ou
n

ts or typ
es.
y
E
xp
erim
en
tal an
d
observation
al stu
d
ies con
sist of
several typ
es of variables.
y
A
ll stu
d
ies in
volve at least on
e in
d

ep
en
d
en
t variable
(IV
) an
d
on
e d
ep
en
d
en
t variable (D
V
).
{
G
et acqu
ain
ted
w

ith
id
en
tifyin
g th
e IV
an
d
D
V
. It’s th
e first
step
in
d
evelop
in
g a research
qu
estion
.

D
ep
en
d
en
t V
ariable (D
V
)
y
D
isease or h
ealth
ou
tcom
e of in
terest
y
P
resu
m
ed
effect of a stu
d

y
y
Syn
on
ym
s: O
u
tcom
e or C
riterion
variable
y
E
xam
p
les:
{
C
an
cer
{
D
iabetes
{

H
IV
statu
s
{
C
H
D
m
ortality
{
D
en
gu
e in
fection
In
d
ep
en
d
en

t V
ariable (IV
)
y
P
red
ictor or exp
osu
re of in
terest
y
P
resu
m
ed
cau
se of a stu
d
y
y
Syn
on
ym

s: E
xp
erim
en
tal, P
red
ictor, or M
an
ip
u
lated
variable
y
E
xam
p
les:
{
A
ge
{
R
ace

{
In
com
e
{
B
M
I
{
D
ru
g U
se
{
C
on
d
om
U
se
C
on
fou

n
d
in
g V
ariable
y
A
variable th
at obscu
res th
e effects betw
een
th
e IV
an
d
D
V
y
A
variable th
at th

e research
er failed
to con
trol or
elim
in
ate
y
Syn
on
ym
s: C
ovariate, T
h
ird
variable, M
ed
iatin
g
variable
y
E
xam
p

les:
{
C
igarette sm
okin
g con
fou
n
d
s th
e effect betw
een
d
rin
kin
g
coffee (IV
) an
d
h
eart d
isease (D
V
)

{
D
iet, age, an
d
gen
d
er con
fou
n
d
th
e effect betw
een
lack of
exercise (IV
) an
d
w
eigh
t gain
(D
V
)

L
evels of M
easu
rem
en
t
y
E
ach
variable can
be classified
by its level of
m
easu
rem
en
t.
y
W
e w
ill classify accord
in
g to term
s u

sed
in
th
e
G
erstm
an
(20
16
) textbook. H
ere th
ey are listed
in
ord
er of in
creasin
g p
recision
:
{
C
ategorical
{

O
rd
in
al
{
Q
u
an
titative
{
It is im
p
ortan
t to u
n
d
erstan
d
th
e d
ifferen
t levels of
m

easu
rem
en
t as th
ey h
elp
d
eterm
in
e th
e statistical test th
at
sh
ou
ld
be carried
ou
t.
C
ategorical
y

C
lassify observation
s in
to n
am
ed
categories, w
ith
n
o
in
trin
sic ord
erin
g of th
e categories
y
C
ategories are often
assign
ed
n
u
m

erical valu
es as
labels (eg., 0
=
N
o, 1=
Y
es)
y
Syn
on
ym
s: N
om
in
al
y
C
ategorical variables w
ith
2 levels are also called
B
in

ary or D
ich
otom
ou
s
y
E
xam
p
les:
{
G
en
d
er: M
ale/F
em
ale
{
R
ace: B
lack/W
h
ite/O

th
er
{
M
ortality statu
s: D
ead
/A
live
O
rd
in
al
y
C
ategories th
at can
be p
u
t in
ran

k ord
er
y
E
xam
p
les:
{
Stage of can
cer (I, II, III, IV
)
{
Sm
okin
g frequ
en
cy (n
on
-sm
oker, ligh
t sm
oker, m
od
erate

sm
oker, h
eavy sm
oker)
{
Y
ear in
college (fresh
m
an
, sop
h
om
ore, ju
n
ior, sen
ior)
Q
u
an
titative

y
T
ru
e n
u
m
erical valu
es th
at can
be p
u
t on
a n
u
m
ber
lin
e
y
Syn
on
ym
s: C
on

tin
u
ou
s, Scale, an
d
In
terval
y
E
xam
p
les:
{
A
ge (years)
{
B
M
I (kg/m
3)
{
Seru
m
ch

olesterol (m
g/d
L
)
Economics, Ch. 27: Business Cycles, Unemployment, and
Inflation
Read pages 592-594 of Economics.
Consider the following as you read:
· What are primary phases of the business cycle?
· How does inflation affect the economy's level of real output?
Economics, Ch. 28: Basic Macroeconomic Relationships
Read pages 615-622 and 628-634 of Economics.
· How do changes in income affect consumption (and saving)?
· What are factors other than income that can affect
consumption
Economics, Ch. 29: The Aggregate Expenditures Model
· How can changes in real GDP equilibrium occur in the
aggregate expenditures model and how do these changes relate
to the multiplier?
· How do economists integrate the public sector (government
expenditures and taxes) into the aggregate expenditures model?
· Economics, Ch. 30: Aggregate Demand and Aggregate Supply
· What is aggregate demand (AD) and why is its downward
slope the result of the real-balances effect, the interest-rate
effect, and the foreign purchases effect?

· What is aggregate supply (AS) and why does it differ in the
immediate short-run, the short-run, and the long-run
·
W
A
L
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U
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I
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U
B
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3
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8
0
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P
S
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8
0
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R

E
T
A
T
I
O
N
&
A
P
P
O
F
D
A
T
A
D
ata D
iction
ary vs. C
od
ebook

D
A
T
A
D
IC
T
IO
N
A
R
Y
y
Is u
sed
to sh
ow
d
iction
ary in
form
ation

for th
e
cu
rren
t w
orkin
g file
y
Is con
sid
ered
to be a sim
p
lified
cod
ebook
{
C
on
tain
s less in
form
ation

th
an
th
e cod
ebook
D
A
T
A
D
IC
T
IO
N
A
R
Y
y
D
IC
T
IO

N
A
R
Y
IN
F
O
R
M
A
T
IO
N
:
{
A
ll in
form
ation
on
variables
Ù
V
ariable n

am
es
Ù
V
ariable labels –
w
h
at it rep
resen
ts or w
h
at it m
easu
res
Ù
M
easu
rem
en
t scale for variable- n
om
in
al, ord
in

al ,etc.
Ù
M
issin
g valu
es
Ù
H
ow
variable origin
ally record
ed
/rep
orted
in
clu
d
in
g if n
u
m
bers or
strin
gs, h

ow
m
an
y ch
aracters if a strin
g variable or h
ow
m
an
y
d
ecim
als if n
u
m
eric)
Ù
F
or con
tin
u
ou
s or scale d
ata variables: m

easu
rem
en
t u
n
its (in
ch
es,
feet, m
L
, lbs, etc.)
Ù
F
or strin
g or categorical variables: If cod
ed
w
ith
n
u
m
bers (n
ot letters),
w

h
at th
e n
u
m
bers m
ean
p
er rep
resen
tin
g th
e u
n
d
erlyin
g categorical
variable like 1=
m
ale
D
A

T
A
D
IC
T
IO
N
A
R
Y
ST
E
P
S
y
C
lick on
F
ile >
D
isp
la
y D
a

ta
F
ile In
fo
rm
a
tio
n
>
W
o
rk
in
g F
ile
.
y
T
h
e cod
ebook w
ill p
rin

t to th
e O
u
tp
u
t V
iew
er
w
in
d
ow
.
y
A
ll in
form
ation
w
ill be con
tain
ed
in

a sin
gle
aggregate table
D
A
T
A
D
IC
T
IO
N
A
R
Y
E
X
A
M
P
L
E

C
O
D
E
B
O
O
K
y
P
rovid
es all in
form
ation
you
n
eed
on
a variable in
a
sin
gle table

y
C
on
tain
s all th
e d
ata d
iction
ary in
form
ation
an
d
d
escrip
tive statistical in
form
ation
y
C
om

bin
es D
isp
lay for d
iction
ary in
form
ation
, th
e
F
requ
en
cy com
m
an
d
for categorical variables, an
d
th
e D
escrip
tives com

m
an
d
for scale variables.
C
O
D
E
B
O
O
K
y
C
O
D
E
B
O
O

K
IN
F
O
R
M
A
T
IO
N
:
{
A
ll d
isp
lay d
iction
ary in
form
ation
{
D
escrip
tive statistical in

form
ation
Ù
E
ach
variable is sh
ow
n
in
a sep
arate table
Ù
C
ategorical variables w
ill in
clu
d
e cou
n
ts an
d
p
ercen

tages
Ù
Scale variables w
ill in
clu
d
e su
m
m
ary statistics
C
O
D
E
B
O
O
K
ST
E
P

S
y
C
lick on
A
n
a
lyze >
R
ep
o
rts >
C
o
d
eb
o
o
k
.
y
In

V
a
ria
b
les tab: A
d
d
th
e variables you
w
an
t to in
clu
d
e. If you
w
an
t to in
clu
d
e all variables, click in
sid
e th

e V
ariables box, th
en
p
ress C
trl +
A
, an
d
th
en
click arrow
bu
tton
. T
h
en
click O
K
to ru
n
an

d
d
isp
lay you
r cod
ebook.
y
If you
w
an
t to cu
stom
ize you
r cod
ebook, w
h
ich
is op
tion
al, you
can
click on

th
e follow
in
g tabs:
{
O
u
tp
u
t ta
b
: Y
ou
can
ch
oose th
e variable an
d
d
atafile p
rop
erties w
an
t

Ù
V
a
ria
b
le d
isp
la
y o
rd
er: If you
w
an
t to ch
an
ge th
e ord
er of you
r variables in
th
e
d
ata file, you
can

ch
an
ge to m
ake th
em
alp
h
abetical or by m
easu
rem
en
t level, etc..
{
S
ta
tistics ta
b
: Y
ou
can
ch
oose th
e statistics you
w

an
. If n
o ch
an
ges are m
ad
e,
th
e d
efau
lt settin
g in
clu
d
es, cou
n
ts an
d
p
ercen
tages for n
om
in
al an

d
ord
in
al
variab
les; an
d
m
ean
an
d
stan
d
ard
d
eviation
for scale variab
les.
{
W
h
en
fin
ish

ed
, click O
K
.
C
O
D
E
B
O
O
K
E
X
A
M
P
L
E

R
E
F
E
R
E
N
C
E
S
y
H
orber, E
. (20
16
). C
od
ebook. R
etrieved
from
h
ttp
://w

w
w
.u
n
ige.ch
/ses/sococ/cl///sp
ss/cm
d
/cod
eb
ook.h
t
m
l?
y
H
orber, E
. (20
16
). D
isp
lay d
iction

ary. R
etrieved
from
h
ttp
://w
w
w
.u
n
ige.ch
/ses/sococ/cl//sp
ss/cm
d
/d
isp
lay.h
tm
l
y
K
en

t State U
n
iversity. (20
14
). C
reatin
g a C
od
ebook - SP
SS
T
u
torials. R
etrieved
from
h
ttp
://libgu
id
es.library.ken
t.ed
u
/SP

SS/C
od
ebooks
y
U
C
L
A
: Statistical C
on
su
ltin
g G
rou
p
. (20
16
). SP
SS
learn
in
g
m

od
u
le. R
etrieved
from
h
ttp
://w
w
w
.ats.u
cla.ed
u
/stat/sp
ss/m
od
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les/labels.h
tm
© 2014 Laureate Education, Inc. Page 1 of 2

Module 2 Application Data Set
This data set on the following page does not contain actual
patient information.
However, relationships have been built into the measures that
may reflect actual
associations found in health.
The invented background for this data is that of a sample of
blood pressures and BMIs
measured for clinic patients who were then followed for up to a
20-year period. These
patients were followed until their deaths, and their final age was
recorded. A data
dictionary describing the measures follows the data.
Data
ID
Num
Age
BP

sex
Systolic
BP
Diastolic
BP
BMI
Death
age
diabetic
Place
1 68 0 139 81 25 84 0 1
2 57 1 170 110 41 64 1 3
3 41 1 151 109 36 59 1 3
4 79 0 140 85 30 81 0 2
5 49 0 165 100 39 69 1 2
6 68 0 141 82 29 86 0 1
7 69 0 138 81 25 89 0 1
8 70 1 149 85 32 78 0 2
9 39 1 189 100 48 47 1 3
10 42 1 152 88 35 60 1 3
11 51 1 150 90 38 70 1 3
12 66 0 153 89 36 73 0 2

13 70 1 145 85 30 81 0 2
14 75 0 139 79 23 92 0 1
15 72 0 144 86 29 91 0 1
16 42 1 171 91 44 61 1 3
17 71 1 150 90 35 74 0 2
18 58 0 141 87 30 75 0 1
19 40 0 175 93 44 60 1 2
20 25 1 180 90 45 40 1 3
21 48 0 146 85 29 67 0 1
22 29 1 160 97 39 49 1 3
23 73 0 137 81 25 92 0 1
24 66 1 167 95 40 68 1 3
25 55 0 190 99 50 65 1 2
© 2014 Laureate Education, Inc. Page 2 of 2
Data Dictionary
Name Type Label Values Missing Measure
ID_Num
Numeric

A unique and encrypted ID
number for a patient
None
Nominal
Age_BP
Numeric
Initial visit age in years when
a patient’s blood pressure
was recorded

None
Scale
sex
Numeric
Sex of patient
0 = female,
1 = male
None
Nominal

Systolic_BP
Numeric
Systolic blood pressure at
initial visit
None
Scale
Diastolic_BP
Numeric
Diastolic blood pressure at
initial visit
None

Scale
BMI Numeric Body Mass Index
None Scale
Death_age
Numeric
Follow-up age at death in
whole years
None
Scale
diabetic

Numeric
Diabetic status of patient
0 =
nondiabetic,
1 = diabetes
None
Nominal

Place
Numeric
Census designation for
patient’s residence
1 = rural, 2
= suburban,
3 = urban
None

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