week 8 SPSS Introduction updated (1).pdf

It is as per the syllabus

2. x
© LE
Contents & |

+ Statistical Package for the Social
Sciences (SPSS) WE AS
— Data view and Variable views

e How to enter data in

e How to import externa data into SPSS

+ How to clean andhedit data

e How to get descri ptive statistics

y

3. SPSS interface & ,
&

« Data view Ÿ
— The place to enter

¢ Columns: Variables

e Rows: Cas servations
+ Variable view
— The place to enter variables
e Listrof all variables

s Characteristics of all variables y

4. SPSS interface oe
ES

SS
Sy
- Click on PROGRAMS

- Click on srss: INC
- Click on pss 17.0/24.0

- Go to START

5. Opening SPSS

Start — All Programs — SPSS Inc— SPSS 23.0.3 SPSS 23.0

6. i ‚4148

Enter data in SPSS directly

REA IBM SPSS Statistics 22 =

Recent Files:
@ ..\Hospital_Stay_Data sav
2. wiosptal_stay_Data xx
si Open anctner

| Dont show this aiatog i

Modules aná o

Calculation ot new

variables can

Specified in the Database
Wizard and executed in

the database

Learn

&

modules and

Learn howto use
SPSS Statistics to get
the results you need

D

Deliver E:

now be

itself

¡gm SPSS Statistics
[Bu spss Regression

IBM SPSS Advanced Statistics
| IBM SPSS Exact Tests

IBM SPSS Categories

IBM SPSS Missing Values

wrapility extensions

install

introduction

Reading Data

Using the Data Editor |
Examining Summary Statistics for Individual Variable:
Crosstabutation Tables =

y

7. Col H
S
NS

Ÿ

9. Variable View

I
&

Sin à

Ye

a)

“Education
Profession
Marriage
Income

Numeric
Numeric
Numeric
Numeric
Numeric

Width

| Decimals Label
Gender
Education

Profession
ES

Male).

School}
11, Student)
{1, Single}

(1, Under P.

__Missing | Columns ||

None
None
None
None
None

Align |
Æ Right
E Right
3 Right
E Right
= Right

=
ENS
SN
an
SNF
EX

10. Variable View Win

Name
Q The first character of the variable name must
Q Variable names must be unique, and have han 64 characters.

Q Spaces are NOT allowed.

11. ; Type

Q Click on the ‘type’ box.

Variable View Window”
S

Q The two basic types of variables that vou fan use are NUMERIC and

STRING.

Q This column enables you to specify ton of variable.

KE] “Untitled [DataSet0} = SPSS Data Editor C NI
Ble Edt View Data Transform Analyze Graphe Likes) Add-ons Window Help

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Sting

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Type NM | Oscimais| Label D ae
1] VaRooo03 Numeig AO None None ë
A asidble Type E]

IS Processor Is ready |

12. Variable View Window

Width
Q Width allows you to determine the number of characters SPSS will allow to
be the variabQe

14. Variable View Window”

: x
Label S

Q In label, you can specify the details of the variable
Q You can write characters with spaces up Characters

EX] Untitled] [DataSetO] - SPSS Data Editor ~ NS le]
Eile Edit View

Sraffiz Nies Add-ons Window

Data Transform Analyze

Help
SO E ©° =e A HES Rs o.»
Name | Type | WMidth | Decimals Label Values | Mis
1 VARODOD! Numeric E 0 None None [2

al =
Data View

< SPSS Processor is ready |

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15. a

Y

Variable View W

y Values

Q This is used to suggest M06xch

17. ae

Import data from Excel

+ Select File ——> Open ———> Data (Y
+ Choose Excel as file type D
e Select the file you want to import ey

N

18. Open Excel files in S |
S

19. BS

Sr)

E

Add-ons *

_ Continue

a a

View Data Transform Analyze DirectMarketing Grapns Unities

= a 4

ECTS EEES ES

[Msibie: 9 of 9 Variables

wec |

wee |

= DOHS | AGE | sex TEMP
1 5 30

2 10
3 6 2
4 "
5 5
6

7

8

1
30
1

99.0
98.0
99.0

20. Levels of measurement; data
manipulation e

Ss
Measurement Scales:
Q Metric variables (e.g., likert-types: spain, 7-point, 11-point)

Suongly y se
Answer Dawe Disagree — Y [_Neural
Lea 1 re ¡OE

any people thmk of themselves as bons atached ©
ellámown figures or celebrites (eg. singers, politician or

3). Similarly, think aBautsa brand to which you are very
not] attached or À cell phone or shoe brand of

ich you are very Tig@hatiached and would love to [not] buy
se one atte Yollotring Brands: isa
IRRQÈ6 Plus, Coca-Cola, Nike, Samsung

Swongly
= agree

Code

Then “FR the appropriate number to indicate the extent to
whigh Qu agree or disagree with each statement regarding

tioned brands or the brand to which you are very
sed

[ AD (PRN is visuany ansang
| 7 Mvzis goodlooking
¿AD-P | X¥Z looks appealing

wluwluluwlu
al alae} a

nlolnloln

€ D1 | XYZ is hkely to perform well
+02 [AZ seemaro be capable of domgsjor

21. Levels of measurement; data
manipulation 8 ES

SS

Measurement Scales:

Q Non-metric variables (e.g., binary coded 0-1, or 1-2)
S

22. Clean data after import data
files D
WW
S
+ Run cases summaries for all variables. )
+ Run frequency to find out misinglues
+ and Descriptives to find out Méan & SD
+ Check outputs to see if you have variables with wrong values.

+ Run Cross ta to analyze and compare the
relationship betweèn vo variables

23. Cases summaries <>
(Use Data File ONE) <2

¡EA “Untitled? [DataSet1] - IBM SPSS Statistics Data Editor

a)

id-ons Window Help

Edit View ansform

Data
ENT
a

Direct Marketing Graphs Util

Descriptive Statistics

Tables >
Compare Means So)
Señora Lens Moës WO

FR Report Summaries in Rows.
EX Report Summaries in Columns.

Berg, =
Mixed Models » 2 99.0
Regression NS » 2 98.5
Clagsity > 2 98.6
Den Reduction » = 98:0
Res » 1 98.0

2 97.6
EA 2 BZ
Forecasting » E ace l

SI sanas ; E ss

en ; E

EZ missing Value Analysis.
Muttipte Imputation >
Complex Samples »

[Unicode:ON!

ris ready! |

24. Cases summaries <>
(Use Data File ONE) D
my
PS

Case Processing Summary”

Cases
Included Excluded
Percent Rercent Percent

Gender
Education
Profession
Marriage
Income

a. Limited to first 100 cases

25. Frequencies
Q This analysis produces u. tables showing

frequency counts and >
of individual variables. (©
Descriptives c N
A This analysis shows the maximum, minimum,
mean, and standard deviation of the variables
Cross tabulation

Qis a method to quantitatively analyze the
relationship between multiple variables. £

y
UAlso known as contingency tables or cross tabs 4
25 Pr

tages of the values

26. Quantitative Variables
(Use Data File ONE)

27. Quantitative Variable
(Use Data File ONE) oe

Cumul SS
Frequency Percent Valid Percent Percent

Valid Male 62. 62.9 zo
foo

Female 374
an

Total 100.0
Education

Tomate

Frequency [mPbrednt | valid Percent Percent
Valid School 2 To 1.0
College a2 20.6 21.6
Bachelors e7 47.5 59.1
Master ea 30.9 100.0
Total 204 100.0
Missing System 1
Total 205
Marriage

Frequency

Percent

Valid Percent

Cumulative
Percent

Single
Married

Total

155
50
205

75.6
24.4
1000

75.6
244
100.0

75.6
100.0

28. Oooeo

Descriptive Analysi SS y
E

AS
Descriptives ye
A This analysis shows the
y Se

maximum, S

minimum, vw

mean, and >

standard AO) of the variables
G

N

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29. Descriptive

Ae 22 Bescriptive statistics —..<Descriptives

Descriptive Statistics
AL LL A EEE
Gender 205
Education 204

Profession 205
Marriage 205
Income 205
Valid N (listwise) 204

30. Cross tabulation eS
©
7 N
Cross tabulation x

OCross tabulation is a sl to quantitatively
analyze the relationship SS en multiple variables.

QAlso known as EN. tables or cross tabs

31. Cross tabulation =
ES

~

Cross tabulation SN

OGender * Profession Y
Ne

Q How many males were

O Students >
O Government di

O Private Job

O Own Business

32. Cross tabulation =

S
A OQ
Cross tabulation NN)

Would you rejoin Microinanse loaning program
VS. would you refer/ eae nd to other

af

Table 1: Would you rejoin the program in future % Would you refer program to other as it is now
CrgSstabulation

Would you refer program to other

No Don't know Total
[would you rejointhe Yes
program in future KG

Don't know

Probably,

Only if $éetific changes are
madé,

33. y How to interpret/write ES
À cross tab?

Table 1: Would you rejoin the program in future * Would you refer prograuhto other as it is now
Crosstabulation

Would you refer program to other

Don’t know Total
Would you rejoin the Yes
program in future Ré
Don't know
Probably

Only if specific changes are
made

N,
The finding clearly shows that out 65169 customers (who intended to rejoin the program)
144 customers were also willing to refer program to others. Merely 9 out of 169 did not

show intent to refer program,to others. Out of 169 customers 16 were indifferent to refer
the program to other not,

xo

S

35. (Task 1)

Descriptive statistics
I
p

Conduct descriptive statistics ands“ repare the

ag

following table

Percentage

‘Categories

36. Cross tabulation (Task 2)
A
N

Conduct cross tabulation and interpret the results

OGender * Brand Preference Y
Olncome * Profession NS
Gender * Income CN

Cilncome * Brand Preference

UGender * Edugatieh
OGender * Marriage
OMarriage * Profession

Olncome * Profession