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SPSS .

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SPSS

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SPSS

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SPSS

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I-III

SPSS................................1

11 .....................................................1

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: ................................................6

: .......................................6

: .............................................6

: ..............................................7

: ........................................7

: ................................................8

: ...............................................9

:.....................................................9

13 Data Editor.........11

14 ...............................13

View Data.....................................16

21 View ................................................16

22 Data................................................20

1. Define Date..................20

2.Insert Variable........................................21

3.Insert Case............................................21

4.Go to Case............................................21

5.Sort Cases.............................................21

6.Transpose.............................................23

7. Merge Files.....................................24

. Add Cases....................................24

. Add Variables............................26

8. ) (Split Files...........................31

9. Aggregate Data.............................35

10 . Select Cases.................................37

11. Weight Cases.............................40

Data Transformation.........................42

1.Compute...............................................42

2.Random Number Seed.............................45

3.Count..................................................45

4.Recode.................................................47

.Recode in to Same variables......................47

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.Recode in to different Variable...................49

5.Categorize Variables................................50

6. Automatic recode.........................51

7.Rank Cases............................................52

8. Time Series................................56

9. Replace missing Values...............60

...........................62

41Frequencies...............................................62

42Descriptives..............................................66

Pivot Tables.................................68

51 Pivot Table...................................68

52 Edit Pivot Table.......................68

53 Book Marks.....................................72

Explore.................................74

61 Explore.................................74

Standard error..................................79

µ ................................79

Trimmed Mean........................79

.................................................80

............81

Stem-and-Leaf ..........................................81

Histogram.....................................82

Boxplot..................................................82

Normality Plots with Tests...........................82

1.Kolmogrov-Smirnov...............................82

2. Normal Q-Q Plot...................................83

3. Detrended Normal Q-Q Plot.....................84

62 Test of Homogneity of Variances....86

Spread vs. Level with Leven Test...................86

1. Levene..89

2. Spread vs. Level Plot...89

63 ............................................92

Crosstabs.........................................96

Compare Means............................103

81 Means.........................................103

82T One Sample T-Test.....................108

83T Independent Samples T-Test111

84T Paired Samples T-Test........113

Analysis of Variance............................115

91 One way ANOVA..................115

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911 Orthogonal Comparisons.................119

912 Trend Analysis...............................122

92 Way ANOVATow ...................123

93 Covariance Analysis..................129

Correlation and Regression Analysis132101Correlation.............................................132

102 Simple Linear Correlation..........132

103 Partial Correlation..........................135

104 Regression Analysis.........................138

1041 .....................................138

1042 Weighted Least Squares Method...146

1043 .....................................149

Factor Analysis................................159

111 .....................................................159

112 Principal Components Method...159

113 Factor Analysis Methods...............168

Non Parametric Tests...................171

121Chi-Square...............................................171

122 Tow Independent Samples Tests175

123K K-Related Samples Tests177

CHARTS....................................180

131 Bar Charts.....................................180

132 Chart Template........................192

13 3 Bar Line...............................193

13 4 Bar Pie................................195

135 Histogram....................................205

136 Box Plot................................................208

137 Scatterplot...............................213

1. Simple..................................214

2. Overlay..........................................218

3. Matrix..........................................220

4. 3-D.................................221

Data Exchange...................................223

141 Importing Data Files.........................223

142 Exporting Data Files.........................230

Syntax Commands..............................233

151 Syntax File.......................................233

152 Command Syntax......233

1521 Dialog Boxes................233

1522 Log ......................235

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II nn tt rr oodd uu cctt iioonn

spss

spss) statistical package for social sciences(

. )spss( MS-DOS

WINDOWS 1993 MS-DOS.

spss 10.0 27/11/1999

EXCEL LOTUS .

SPSS

SPSS .1. Data Editor:

.

2. Viewer: charts .

3. Draft viewer: ) ( .

4. Pivot Table Editor: .

5. Chart Editor: .6. Text output Editor: .7. Syntax Editor:

SPSS.8. Script Editor: .

SPSS

SPSS :

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1. Data Files: Data Editor

SAV.2

. Output Files

: SPO.3. )syntax: (

SPS. SPSS

SPSS :1. Double-click SPSS)

.(2. Start

Start Programs SPSS V.10.0

Data Editor )11.( Mouse

•click: ) .(• Double-click: ) . (

•click : short command

list Context List office .

1. Help: Menu bar

SPSS. Topics Contents Index Find. Tutorial SPSS.

2. Dialog box help button

SPSS .3. Dialog box context menu help:

Right-Click

.4. Pivot table context menu help:

Label

) (

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SPSS Viewer) SPSS ( What’s this .

5. Result Coach:

Result Coach

.

6.Tutorial: Tutorial help .

SPSS

Dialog Box SPSS Windows–

) SPSS MS- DOS( :

Sourse Variables List: .

) ( Target Variables List(s): .

Command pushbuttons: Frequencies

SourceVariable list

Target Variable list

subdialog pushbuttons

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ss pp ss ss

DD aattaa EE nn ttrryy

)1–1(

spss Data Editor Spread sheet Excel

:1.Data view : )

Variables Cases( cell .

2.Variable view : )

…..( . )11. ( )11(

Data Editor spss

Variable Name Cell Editor Active cell

Menu BarTool Bar(Standard)Case NO.Variable Name

Case NO.

Data View Tab Variable View Tab Status Bar

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)12: ( Standard Tool Bar Data Editor

Open file

Save File

Print

Dialog Recall 12

Undo

Redo

Go To Chart

Go to Case

Variables

Find

Insert Case

Insert Variable

Split File

Weight Cases

Select Cases

Value Labels

Use Sets

Data Editor

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1. Variable view )tab(variable view Data view ) ( Data view .

2. Variable view Data view Data view Variable view

)( Variable view .

)12(

Gradebdategenderid

7615.7.691Ahmad

8012.4.701Khadim

831.6.682Sabah

909.5.721Mahdi

8020.9.742Zainab

785.1.671 Nabil

Data View )

( ) ( : – )id( string variable. – )gender(1 2

Numeric variable. – )bdate( date. – )Grade( .

Data View Name and Attributes Variable View Variable

View Data View )13( .

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)13( Variable View

Variables Names & Attributes

Variable Name & Attributes :1. 2. 3. 4. 5. 6. 7. 8. 9. 10..

Variable View : : Variable Name

Name Id Gender

SPSS:1 characters.2 period

( . ) @#$.3 . ( . )4 !?‘*.5

SPSS

NEWVAR

newvar

.

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: Variable Type

Type button Dialog box .:

Variable Type

: Numeric: Data View .

Comma : ),( 722667.123 722,667.123 .

Dot: ( . ) ),( 722.667,123 .

Scientific Notation: E-notation 107

1.0E+07 1234 1.2E+03.Date: .

Dollar: .Custom Currency:

Edit Options Currency.String: ) .(

Width . Decimal Places: .

id string Decimal

Places Variable Rank. gender Grade Numeric . bdate Date

:

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dd .mm .yy .

1. Numeric ) Numeric

( Data View ) ( )numeric( .

2. Numeric Comma Dot ) 16

( Data View .

3. string) ( width = 6 No " No" “NO”.

: Variable width

Width) typevariable

Variable View ( .

333,333.02 Comma 10. Gender )12( Width 1) (

grade Width 3 100 Width 8

dd.mm.yy. id Width8. id 4Width =

Data View )( 4. : Decimals

) Numeric

Comma Dot( Variable Type.

: Variable Label

256

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Date of birth bdate ) ( spss.

: Value Labels

Gender 1 Males 2

Females :Value LabelValue

m1f 2

gender :1. Value Gender Variable view .2. define Labels.3. value 1 value

label m add .4. value 2 value

label f Add ) .( Remove

change.5. OK.

1. 60 .2. 1 m 2

f .3. value label Data view

value label view .4. ) ( SPSS.

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: Missing Values

) .(

: missing variable view missing values :

• no missing values•Discrete missing values 1001012

.•Range plus one discrete missing values 7.510.3 15

: OK.

1. spss

: user- defined missing values) ( missing values.

: ) (

system – missing values string variables valid .

2.Ranges missing values Discrete missing values .

: column width

column

variable view ) .(

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: 1. Column

.2. Data view

clicking and dragging.

: Alignment

variable view

Align :Left: .

Center: . Right: .

)Right.(:Measurement

measure variable view

1.scale: ) ( ….

2.ordinal:

) (

).( 3.nominal: ) (

1 2

. ) ( nominal .

variable view :

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Data view

Active cell enter . Data view :

test AsSave File Save Data As :

Test file name Save test.sav sav spo.

Open file open. )(

Save File s+ctrl

1

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asSave File. ) ) 1 1 – – 3 3 ( ( DDaa tt aa EE dd iitt oorr

1.select) ( variable

Data view

. 2. case Data view .

3. .

shift.4.

. ctrl

.5.

cases .6. Data view

) ( :

: Data ) Data . (

insert variable . : Tool Bar

.

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short list:

insert variable .

7. case )( ) .(

8. Data view .

Edit

clear Data view . Del.

:

short list clear .

9. 8 case.10. copy :

)( . copy Edit Edit paste.

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)( ) short list( copy . )( paste

.11.

)( . cut Edit

)(. paste Edit

copy.12.

Go to case Data . Go to case.

ok. .

13. Attributes ) ( )type

width ………( : variable view . copy Edit.

. paste Edit.

: type .

copy Edit . .

paste Edit.)1–4(

Data Editor

. idgenderbdategrade

Source List

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Analyze Descriptive Statistics Frequencies Frequencies :

Source List

Variables . id grade Source

List :

Data Editor)Variable View Data View (Utilities Define sets :

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Group1 id grade : Group1 Set Name .

id grade Variables in set

. Add Set) .Remove set Change . (

: Close Group1 id gender.

) ( Frequencies

id grade :

Utilities Use Sets Use Sets

:

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Group1 Sets in Use All variables) ( New Variables)

( Sets in Use .

OK

Group1

)id

gender( .

Analyze Descriptive Statistics Frequencies frequencies id gender

:

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VV ii ee ww DD a a tt a a

)2 1( View view :

:1.status bar: ) ( .2.Font: Data Editor

Font Font:

:Font: :

ArialArabic TransparentAndalusAkhbar MT. Font Style: :

Regular

Italic Bold

Bold Italic size: .

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: ) ( start :

Start Settings Control panel Regional setting

Number Number style )context , Hindi ,Arabic.(

3.Gridlines: Data Editor.4.value labels: ) . (

.5.variables: variable view Data view .6.toolbars:

. standard toolbar

. . ) . ( toolbars view Show Toolbars:

:Document Type: ) Data Editor. (

:1.all .2.Data Editor .3.Viewer .4.Draft Viewer .5.chart .6.syntax .7.script Editor .

) ( .

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Toolbars: Data

Editor Data Editor Check box

Data Editor

. Show ToolTips: Toolbar

).( .Large Buttons: checkbox

. 1: Copy Data Editor :

view toolbar show toolbars

. customize show toolbars customize

toolbar. Copy Edit " "

:

OK Copy.

: ) Copy( toolbar) Data Editor( Toolbars Show Toolbars Reset OK.

2:) New Toolbar( Linear Regression Factor

Data Editor Data Editor Toolbar : view toolbar Show Toolbars.

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New Tool Toolbar :properties. statistics Toolbar Name

Data Editor :

customize customize : Toolbar

Linear Regression Factor )statistics( :

OK statistic Data Editor Data Editor statistics

:

Standard Toolbar

New Toolbar(Statistics)

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)22( Data: Data :1. Define Date: date

Variables

Create Time series

Transform

. revenue ) /2000 /2002( Data editor 1:

revenue . :

Data Define Dates :

Years,quarters,months cases are :

years: . years,quarters: . years,months: .

Not dated: .Custom: ) Cases are( Syntax.

1 Data Editor Revenue Data Editor

Revenue 20 .

revenue117120130145150190220250243257260340360

362380340350420389400

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First Case is : 2000.

2) . (

6

. 3

2 . Periodicity at higher level

4 2. OK Define Dates Data Editor

: revenue year_ quarter_ month_ date_

117 2000 2 6 JUN 2000120 2000 3 7 JUL 2000130 2000 3 8 AUG 2000145 2000 3 9 SEP 2000150 2000 4 10 OCT 2000190 2000 4 11 NOV 2000220 2000 4 12 DEC 2000250 2001 1 1 JAN 2001243 2001 1 2 FEB 2001257 2001 1 3 MAR 2001260 2001 2 4 APR 2001

340 2001 2 5 MAY 2001360 2001 2 6 JUN 2001362 2001 3 7 JUL 2001380 2001 3 8 AUG 2001340 2001 3 9 SEP 2001350 2001 4 10 OCT 2001420 2001 4 11 NOV 2001389 2001 4 12 DEC 2001400 2002 1 1 JAN 2002

2.Insert Variable: Data Editor ) . (

3.Insert Case: Case Data Editor

) . (4.Go to Case: Case Number

.5.CasesSort: ) ( Sorting Variable.

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) Sort: (salary degree

salary Data Editor :

: . :

Salary : Data Sort Cases

Sort Cases :

ok Salary :

.: salary Salary degree :

Data Sort Cases Sort Cases :

Salary name degree salary

Ahmad 3 40Samer 3 35Loay 3 50Mahmood 1 80Ayad 1 70Yassin 2 66Satar 1 85Razak 1 77Kamal 2 59Abas 3 45Mahdi 1 90Salim 2 62Sabah 2 57Falah 2 55Imad 1 82

salary

name degree salarySamer 3 35Ahmad 3 40Abas 3 45

Loay 3 50Falah 2 55Sabah 2 57Kamal 2 59Salim 2 62Yassin 2 66Ayad 1 70Razak 1 77Mahmood 1 80

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OK :

6

.Transpose

: Variables

Cases . :

x1,x2,x3 y

Data Editor :

x y

. :

Data Transpose

Transpose :

OK Data Editor :case_lbl y1 y2 y3X1 3 4 5X2 6 7 8X3 9 10 11

y1,y2,y3 case_lbl ) . (

7. Merge files

SPSS :

salary

Ayad 1 70Razak 1 77Mahmood 1 80Imad 1 82

Satar 1 85Mahdi 1 90Falah 2 55Sabah 2 57Kamal 2 59Salim 2 62Yassin 2 66

Samer 3 35Ahmad 3 40Abas 3 45Loay 3 50

x1 x2 x3 y3 6 9 y14 7 10 y2

5 8 11 y3

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. Add Cases . Add Variables

. Add Cases:

). ( :Group1 ) ( )variables( Group2 ) (

Data Editor:Group1

name math chem physc music Samir 100 90 95 87Lubna 95 87 90 85

Group2 name math chem physc paintYousif 85 90 77 88Ammar 95 83 82 90Sinan 90 92 86 95

) ( : ) open( Group1

working data file. Data Merge Files Add Cases

Add Cases : Read File

Group2

External Data File music paint .

open Add Cases : Read File :

:Variables in New Working Data File:

) ( .

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Unpaired Variables: merged File.

Working Data File

External Data File + Unpaired Variables :

• .•

.• Unequal Width .

music Group1 paint Group2 .

1: ) ( Rename .

Indicate Case source variable: Source01 0 1 . ok Add Cases From

Data editor ) Save As: (

Merged File

name math chem physcSamir 100 90 95Lubna 95 87 90Yousif 85 90 77Ammar 95 83 82Sinan 90 92 86

2: Unpaired Variables in New

Working Data File + music paint Unpaired Add

Cases from : 1. ) CTRL . (2. Pair music & paint Add Cases

from :

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3. OK :Merged File

name math chem physc musicSamir 100 90 95 87Lubna 95 87 90 85Yousif 85 90 77 88Ammar 95 83 82 90Sinan 90 92 86 95

music . 3: music paint Variables in New

Working Data File ) ( Add Cases From OK) (

:

Merged File name math chem physc music paintSamir 100 90 95 87 .Lubna 95 87 90 85 .

Yousif 85 90 77 . 88Ammar 95 83 82 . 90Sinan 90 92 86 . 95

. Add Variables

)( .

1

Group1 Sub2 :

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Group1name math chem physc music

Samir 100 90 95 87Lubna 95 87 90 85

sub2name arabic english

Samir 80 98Lubna 85 95

) ( : Group1 open) . (

VariablesData Merge Files Add Add Variables : Read File Sub2 )

. ( open Add Variables : Read File Add

Variables From :

:New Working data File

: .Excluded Variables:

sub2 Group1

name Sub2) (+ .Key Variables :

. Key Variables.

1. Key Variable .

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2. Sorting Ascending Key Variable. Key Variable.

OK Add Variables From :

Merged File

name math chem physc music arabic englishSamir 100 90 95 87 80 98Lubna 95 87 90 85 85 95

2) : Key variable(

Group1 )1( Sub3 :

Sub3name arabic englishYousif 90 85Ammar 87 92Sinan 85 91Samir 80 98Lubna 85 95

Key Variable :name math chem physc music arabic englishSamir 100 90 95 87 90 85Lubna 95 87 90 85 87 92

. . . . 85 91

. . . . 80 98

. . . . 85 95

Sub3 Group1 Key Variable : Group1 sub3 Sort Ascending name

Data Sort Cases SGruop1 SSub3 :

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1SGroup

name math chem physc musicLubna 95 87 90 85Samir 100 90 95 87

SSub3name arabic englishAmmar 87 92Lubna 85 95Samir 80 98Sinan 85 91Yousif 90 85

name) ( .

SGroup1. Data Merge Files Add Variables

Add Variables From :• Match Cases on Key Variable in Sorted files Both Files

Provide cases.• Exclude Variables name

Key Variables Add Variables from :

ok :

Merged File name math chem physc music arabic englishAmmar . . . . 87 92Lubna 95 87 90 85 85 95Samir 100 90 95 87 80 98

Sinan . . . . 85 91Yousif . . . . 90 85

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Table Look up fileKeyed Table: Cases ) (

.

3) ( household )name Age

Edu housno( :

householdname Age Edu housnoAhmad 20 Sec 10Zeki 35 Bsc 10Sabah 30 sec 10Zainab 15 Prim 10Ibrahim 17 Sec 12Samir 40 Ma 12Selma 36 Bsc 12

house Size Location housno :

house Size Location housno4 Baghdad 103 Baghdad 12

housno key Variable Data sort Cases .

) house( household :

household Working File

Data Merge Files Add Variables house Add Variables : Read File External File

open Add Variables : Read File Add Variables From housno Excluded Variables

:• Match Cases on Key Variables in Sorted Files External File

is Keyed Table Table Look Up file.

10

12

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• Excluded Variables housno Key Variables

Add Variables From :

OK :

Merged Filename Age Edu housno Size LocationAhmad 20 Sec 10 4 BaghdadZeki 35 Bsc 10 4 BaghdadSabah 30 sec 10 4 BaghdadZainab 15 Prim 10 4 BaghdadIbrahim 17 Sec 12 3 BaghdadSamir 40 Ma 12 3 BaghdadSelma 36 Bsc 12 3 Baghdad

8. ) (Split Files

) ( . 1

:

wage gender60 m30 f 70 m35 f 65 m40 f

m f :

Data Split File

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Split File :

:

: Analyse All Cases,do not Creat Groups .Compare Groups: )

Groups Based on( .

Organize Output by Groups: Compare Groups

) gender. ( Compare Groups

Frequencies.Sort The File by Grouping Variable: ) (

.File is Already Sorted :

. Sort )Sort The File by Grouping Variable. (

OK Split File m

f Sort gender :wage gender30 f 35 f 40 f 60 m70 m

65 m

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Compare groups wage

Analyze Descriptive Statistics Frequencies)

Frequencies

( : 1. Organize Output by GroupsFrequenciesGENDER = f

Statistics a

WAGE30

35.00

ValidMissing

N

Mean

GENDER = f a.

GENDER = mStatistics a

WAGE30

65.00

ValidMissing

N

Mean

GENDER = ma.

2. compare groups:Frequencies

Statistics

WAGE30

35.0030

65.00

ValidMissing

N

MeanValidMissing

N

Mean

f

m

2: 2000 2001 ) (

prod year region 800 2000 North 600 2000 South 1400 2001 North 900 2000 North

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1180 2001 South1000 2001 South1150 2001 South

frequencies prod Organize Output by Groups : FrequenciesYEAR = 2000, REIGON = North

Statistics a

PROD40

906.25

ValidMissing

N

Mean

YEAR = 2000, REIGON = Northa.

YEAR = 2000, REIGON = South

Statistics a

PROD30

683.33

ValidMissing

N

Mean

YEAR = 2000, REIGON = Southa.

YEAR = 2001, REIGON = North

Statistics a

PROD40

1337.50

ValidMissing

N

Mean

YEAR = 2001, REIGON = Northa.

YEAR = 2001, REIGON = South

Statistics a

PROD40

1105.00

ValidMissing

N

Mean

YEAR = 2001, REIGON = Southa.

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9. Aggregate Data

cases .

) (

.

salary degree salary Data Editor :

salaryname degree salaryAhmad 3 40Samer 3 35Loay 3 50Mahmood 1 80Ayad 1 70Yassin 2 66Satar 1 85Razak 1 77Kamal 2 59Abas 3 45Mahdi 1 90Salim 2 62Sabah 2 57Falah 2 55Imad 1 82

) ( degree. :

Data Aggregate Aggregate

Data :

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:Break : ) ( breakdown Variable(s) ) (

degree .Aggregate Variable(s)

: )( . salary Aggregate Variable(s)

salary_1 :• Name & Label

Aggregate Variables.• ) Mean( Function

Aggregate Variables Standard DeviationSum

of Cases No. of Cases….Save Number of cases in break Group as variable:

N_Break 6 5 4.

Creat new Data File: Aggr salary .

File.

Replace Working Data File: salary . Ok AGGR

File Open Data :

AGGR degree salary

1 80.672 59.80

3 42.50 salary Aggregate Variable(s) salary_1salary_2… salary_1

salary_2 … .

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10. Select Cases: . .

1990–2002.

19972002 : Data Select Cases Select Cases

:

All Cases

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Deleted :

)19972002( Based on

Time or Case Range

Range )813. (

:

1. filter variable 1 0

)X ( :

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Select Cases :

if condition is satisfied

2.Random Number of Cases ) 5% ( .

3. All Cases Select Cases.11. Weight Cases: Cases

. : Data Editor SPSS.

degreeweight

7010

6030

7510

5550

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Data Transformation

Row Data .

Transform :

1.Compute: 70 ) . (

: x1 x2 Data Editor :

x1 x2

60 9087 8870 4390 8057 5573 4795 9066 5040 5555 8085 75

88 8635 70

Mean) ( x1 x2 502,1 ≥ X X . :

Transform compute compute variable :

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• include all cases• include if case satisfies condition

),1 x502 ≥ x

( if Cases :

continue If Cases OK Compute Variable x3

) x1 x2(Data Editor :x1 x2 x360 90 7587 88 8870 43 .90 80 8557 55 5673 47 .95 90 9366 50 5840 55 .55 80 6885 75 8088 86 8735 70 .

: SPSS

) t…( CDF parameters

:

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25 228 57 7

y3 1 6 20

: Transform Count

Count Occurrence of values within cases :

: . Target variable y3

).( . y1 y2 ) ) ( Numeric variable.

. Define values Values to count 1 6 20

:• 1 value 1 Add

value to Count .• 6 value 6 Add

Values to count .• 20 Range) (

Range 20 :

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Add 20 Values to Count . values to Count :

Change Remove Values to count.

Continue count occurrence of values within

cases OK y1 y2 y31 8 1

15 3 014 7 09 1 11 1 24 10 06 7 1

11 6 115 9 09 1 16 6 2

20 9 116 3 025 22 28 5 07 7 0

4.Recode: code) ( :

.Recode into same variables: .

: salary salary : 20 16 95 88 65 53 35 46 90 22 30 28 51 60 85

Data Editor. Code :

24

1

25-492

y3Data Editor :

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60

5074 3

75 4

:

Transform Recode into same variables

Recode into same variables :

salary ) ) .(

Old and New Values Old and New Values :

old value: ) .(

new value: .• 24 old value range

:

• New Value value )24 (

1

. • Add new old. old and new

values :

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:Change Old Value

New Value.Remove

Old New

. Continue salary Data Editor :

salary : 1 1 4 4 3 3 2 2 4 1 2 2 3 3 4

Salary Data Editor Codes .

.Recede into different variable:

. : Salary

. salary

Transform Recede into different variables

Recode in to different variables : Salary Numeric Variable Output

Name salcat Change .

• Old and New Values Recode into different variables

:

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: ) ( All other values old value copy old values new value.

Continue Recode into

different variables salcat Data

Editor :5.categorize variables) (

)(

categories

4

salary

1 ) 25.( %

2 ) 25%50%.(

3 ) 50%75.( % 4 )75% .(

)salary( : Transform Categorize variables

categorize Variables :

salary salcat20 116 195 488 465 353 335 246 290 422 130 228 251 360 385 4

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OK nsalaryData Editor : nsalary

114433224122

334

. : Transform Automatic Recode

Automatic Recode salary name Variable New Name ) (

rsalary rname• ) (

New Name New Name.

valueRecode starting from Lowest

Recode starting from Highest value

.

: .

6. Automatic Recode

) ( )

( : salary) ( name) ( :name salary

Ahmad 40Samer 35Loay 50Mahmood 80Ayad 70Yassin 66Satar 85

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OK Data Editor : name salary rname rsalaryAhmad 40 1 2Samer 35 5 1Loay 50 3 3Mahmood 80 4 6Ayad 70 2 5Yassin 66 7 4Satar 85 6 7

7.:Rank Cases

. .

: salary gender region Data Editor:

region gender salary1 2 301 1 701 1 1001 1 501 2 451 2 361 1 701 2 251 2 221 1 422 2 152 1 1002 1 1102 1 882 1 922 2 552 2 322 1 472 2 20

Salary Variables Gender

region Grouping Variables By Assign Rank 1 to Smallest Value .

salary gender

region. :

CasesTransform

Rank Rank Cases :

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Rank Types Rank cases Rank Cases:Types Rank

continue OK ) ( rsalary

Data Editor :region gender salary rsalary

1 2 30 31 1 70 41 1 100 51 1 50 21 2 45 51 2 36 4

1 1 70 41 2 25 21 2 22 11 1 42 12 2 15 12 1 100 42 1 110 52 1 88 22 1 92 32 2 55 42 2 32 3

2 1 47 12 2 20 2

salary Gender) ( Region

)1 2. ( :

1. .

2. Grouping

Variables Salary

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gender region By Rank Cases .

3. )

( Ties

Rank cases

:

:sequentialHighLow MeanValue

111110

242315

242315

242315

355516

466620

4. Rank Cases : Types

Rank: ) . (Savage Scores: .

Fractional Rank: ) .(

Fractional Rank as %: 100.sum of cases weights:

) . ( Ntiles: )

( 4Ntiles) ( 1 25% 2 25%50% 3 50%75%

4 75% .proportion estimates: :

Blom: )41()83( +− wr

w r

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67

Tukey: )31()31( +− wr

RanKit: wr

)21(−

w r Vander waerden:

)1( +wr

w r ) (x

X : Variable rx : Rank(simple)sx : Savage Score

nx : Ntilesrfr001 : Fractional Rank

) ( 6/7= 0.8571per001 : Fractional Rank as %

0.8571*100= 85.71n001 : Sum of case Weightspx : Proportion Estimate (Blom)

(6-3/8)/(7+1/4) =0.7759Pro001 : Proportion Estimate (Tukey)

(6-1/3)/(7+1/3) =0.7727

Pro002 : Proportion Estimate (Rankit)

(6-1/2)/7 = 0.7857Pro003 : Proportion Estimate (Vander Waeden)

6/(7+1) = 0.7500

Normal Scores: Normal Scores Z Scores Estimated Cumulative

Proportions )BloomTuky…( Normal Scores :

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Normal Scores Blom Proportion

Estimates Blom)px( Cumulative probabilities

Z ) ( Transform Compute IDF

IDF.NORMAL(px,0,1) nx .

8. Create Time Series: Time Series

. Data Define Dates

:Differences Moving Averages running Medians laglead function.

11:: ttvv 1717 )) DDa a tta a DDeef f iinnee DDa a ttee((

DDiif f f f eerreenncceess ..year_ month_ date_ tv

2002 1 JAN 2002 2742002 2 FEB 2002 207

2002 3 MAR 2002 2552002 4 APR 2002 3502002 5 MAY 2002 3822002 6 JUN 2002 3832002 7 JUL 2002 3512002 8 AUG 2002 2682002 9 SEP 2002 3802002 10OCT 2002 4092002 11NOV 2002 4452002 12DEC 2002 4552003 1 JAN 2003 4602003 2 FEB 2003 4822003 3 MAR 2003 449

Blom Tukey Rankit Vander Waeden

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2003 4 APR 2003 3892003 5 MAY 2003 398

: Create time series Transform

Create time series tv New variables Difference Order ) . (

tv )underscore( ) tv_1.(

OK tv_ 1Data Editor :year_ month_ date_ tv tv_12002 1 JAN 2002 274 .2002 2 FEB 2002 207 -67

2002 3 MAR 2002 255 482002 4 APR 2002 350 952002 5 MAY 2002 382 322002 6 JUN 2002 383 12002 7 JUL 2002 351 -322002 8 AUG 2002 268 -832002 9 SEP 2002 380 1122002 10OCT 2002 409 292002 11NOV 2002 445 362002 12DEC 2002 455 102003 1 JAN 2003 460 5

2003 2 FEB 2003 482 222003 3 MAR 2003 449 -33

order tv tv_1

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2003 4 APR 2003 389 -602003 5 MAY 2003 398 9

1.

1 1−−= t tvt tvt tv)tv tv

t ( . 2

.2. 1 _ tv Name

Change. Function Function

Change Order. 2) : ( Moving Averages

Span. Product 19902000

year_ date_ product1990 1990 50.01991 1991 36.51992 1992 43.0

1993 1993 44.51994 1994 38.91995 1995 38.11996 1996 32.61997 1997 38.71998 1998 41.71999 1999 41.12000 2000 33.8

Centered Moving Averages Span=5. :

Transform Create Time series Create

Time series :

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OK Produc_1Data Editor product :

year_ date_ product produc_11990 1990 50.0 .

1991 1991 36.5 .1992 1992 43.0 42.61993 1993 44.5 40.21994 1994 38.9 39.41995 1995 38.1 38.61996 1996 32.6 38.01997 1997 38.7 38.41998 1998 41.7 37.61999 1999 41.1 .2000 2000 33.8 .

n/2)n Span( . ) =5(

:6.42

5

9.212

5

9.385.44435.36501 ==

++++= M

2.405

1.38509.212

5

201

5

1.389.385.44435.362 =

+−==

++++= M

span is even

Uncentered Means ) Span = 4. (

product 4 product_1

Span=4

50.0 .36.5 .43.0 43.500 42.11344.5 40.725 40.92538.9 41.125 39.82538.1 38.525 37.80032.6 37.075 37.42538.7 37.775 38.15041.7 38.525 38.67541.1 . 38.82533.8 .

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9. Replace Missing Values

SPSS .

: income : . :

Transform Replace Missing Values

Replace Missing Values :

)underscore(

Name Change. Method :

1.Series Mean: .2.Mean of Nearby Points:

Span .3.Median of nearby points: .4.linear Interpolation: .5.linear trend at point: Predicted Values

) () ( 1n.

Mean of nearby points .Span of nearby points: :

income95

10011

120100140

.

145147150166170190210199

.215217230

Method

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1. Number: .2. All:

.

Span =2

. OK Replace Missing Values income_1 Data Editor :

income income_1

95 95.0100 100.011 11.0

120 120.0100 100.0

140 140.0. 133.0145 145.0147 147.0150 150.0166 166.0170 170.0190 190.0210 210.0199 199.0

. 210.3215 215.0217 217.0230 230.0

7 :1334/)147145140100( =+++

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74

DD eessccrr iipp tt iivvee SStt aa tt iiss tt iiccss

)41( Frequencies

.

1:Tall 80

Frequencies :

Analyze Descriptive Statistics Frequencies

Frequencies Tall Variables

) (

:Display frequency table:

.Statistics:

Statistics :

:

Tall

Tall 8084

71

7235

93

9174

606379

80

70

68

90

9280

70

6376

4890

92

85

8376

61

99

83

8874

70

6551

73

7172

95

8270

33

37

32

41

4449

47

5059

55

5356

52

6460

66

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Percentile Values Quartiles percentiles) Boxplots

)6

1

( . ( . Percentiles Add 1 Percentiles

Change Remove . Cut points for Equal Groups

.Dispersion: .

Central Tendency: .Distribution: .

: Values are group midpoints SPSS Median Percentiles Values

.chart : BarPie…

format: :

:Ordered by: values

Counts .

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Multiple Variables: variables Frequencies :

Compare variables: .Organize output by variables: . .

with more than categoriessupress tables: .

OK Frequencies :

Frequencies

Statistics

TALL56

068.16

2.2970.00

70

17.17-.314.319

-.639.628

3299

381732.0034.7055.25

70.0081.5091.30

ValidMissing

N

MeanStd. Error of MeanMedianModeStd. DeviationSkewnessStd. Error of SkewnessKurtosisStd. Error of KurtosisMinimumMaximumSum

1525

507590

Percentiles

:Valid: ) (

Missing: .

Quartiles(25,50,75)Percentiles(1,5,50,90)

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77

TALL

1 1.8 1.8 1.8

1 1.8 1.8 3.61 1.8 1.8 5.41 1.8 1.8 7.11 1.8 1.8 8.91 1.8 1.8 10.71 1.8 1.8 12.51 1.8 1.8 14.31 1.8 1.8 16.11 1.8 1.8 17.91 1.8 1.8 19.61 1.8 1.8 21.4

1 1.8 1.8 23.21 1.8 1.8 25.01 1.8 1.8 26.81 1.8 1.8 28.62 3.6 3.6 32.11 1.8 1.8 33.92 3.6 3.6 37.51 1.8 1.8 39.31 1.8 1.8 41.11 1.8 1.8 42.91 1.8 1.8 44.6

4 7.1 7.1 51.82 3.6 3.6 55.42 3.6 3.6 58.91 1.8 1.8 60.72 3.6 3.6 64.32 3.6 3.6 67.91 1.8 1.8 69.63 5.4 5.4 75.01 1.8 1.8 76.82 3.6 3.6 80.41 1.8 1.8 82.1

1 1.8 1.8 83.91 1.8 1.8 85.72 3.6 3.6 89.31 1.8 1.8 91.12 3.6 3.6 94.61 1.8 1.8 96.41 1.8 1.8 98.21 1.8 1.8 100.0

56 100.0 100.0

323335374144474849505152

5355565960616364656668

7071727374767980828384

8588909192939599Total

ValidFrequency Percent Valid Percent

CumulativePercent

tall.

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)42( Descriptives

zscores.

2

x1,x2,x3 Data Editor SPSSx1 x2 x3

90 50 1270 52 1556 55 1965 60 2285 65 206069575075625185

Descriptives : Analyze Descriptive statistics Descriptives

Descriptives :

:Save standardized values as variables:

s

x x −Data

Editor ) ( .Options: :

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Display Order :

Variable List: variables Descriptives.Alphabetic: .

Ascending means: .Descending means: .

OK Descriptives :Descriptives

Descriptive Statistics

5 12.00 22.00 17.6000 4.03735 50 65 56.40 6.11

13 50.00 90.00 67.3077 13.21865

X3X2X1Valid N (listwise)

N Minimum Maximum Mean Std. Deviation

variables

Descriptives Data Editor :x1 x2 x3 zx3 zx2 zx190 50 12 -1.387 -1.048 1.71770 52 15 -.644 -.720 .20456 55 19 .347 -.229 -.85565 60 22 1.090 .589 -.17585 65 20 .594 1.408 1.33860 . . . . -.55369 . . . . .12857 . . . . -.78050 . . . . -1.30975 . . . . .58262 . . . . -.40251 . . . . -1.234

85 . . . . 1.338

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P P i i vv oo t t T T aa bb l l ee s s

)5–1( Pivot Table

SPSS SPSS

Viewer :1.Rows.2.Columns.3.Layers.

.

SPSS )(

) (

Excel SPSS . )5–2( Edit Pivot Tables

SPSS Viewer Pivot Tables Editor

SPSS Pivot Table Object Edit .

1:

Analyze Descriptives Crosstabs) ( SPSS Viewer.

TREAT * RECOVER Crosstabulation

Count

8 2 10

3 9 12

11 11 22

a

b

TREAT

Total

a1 b1

RECOVER

Total

:

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. Pivot Pivoting Trays

Pivoting Trays :

:

1. )( Treat .2. )( Recover .3. Layers )(

Observed Expected

) 5

1

(. ) ( .

: Pivoting

trays Recover .

) : ( : Pivoting Trays

. : Pivot Transpose Rows & Columns

:TREAT * RECOVER Crosstabulation

Count

8 3 11

2 9 11

10 12 22

a1

b1

RECOVER

Total

a b

TREAT

Total

Column icon

Column tray

Row icon Row tray

Layer icon

Layer tray

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)Treat( Pivoting Trays

:TREAT * RECOVER Crosstabulation

Count8

3

11

2

9

11

10

12

22

a

b

TREAT

Total

a

b

TREAT

Total

a

b

TREAT

Total

a1

b1

RECOVER

Total

: ) ( Reset Pivots to defaultsPivot SPSS Viewer. 2: CrosstabsDescriptivesAnalyze)

( SPSS Viewer.TREAT * RECOVER * GENDER Crosstabulation

Count

2 1 3

1 4 5

3 5 8

6 1 7

2 5 7

8 6 14

a

b

TREAT

Total

a

b

TREAT

Total

GENDERf

m

a1 b1

RECOVER

Total

Pivoting Trays :

)Recover(

)Gender(

)Treat(

)Statistics(

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Gender Treat. m f Gender

Statistics

Count

Gender

) ( :

f m :

: f Gender m. :

Gender ) (

m) . ( : SPSS Viewer) (Pivot Goto Layer Go to Layer Category

)Gender ( )( f )( m.

:

: ) : ( a

. Ctrl+Alt+Click .

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View Hide. a. Del .

:

). ( b

Treat.

View Show All categories in Treat.

View Show All)5–3( Book Marks

.

3: x1,x2,x3

Analyze descriptive statistics Frequencies

Descriptive Statistics

13 50.00 90.00 67.3077 13.2186

5 50 65 56.40 6.11

5 12.00 22.00 17.6000 4.0373

X1

X2

X3

N Minimum Maximum Mean Std. Deviation

) : (

Bookmarks Mean :

SPSS Viewer . Mean :

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SPSS Viewer Pivot Bookmarks Bookmarks )mean ( Add

mean bookmarks :

Mean mean. .

)( )mean( : SPSS Viewer .

Pivot Bookmarks Bookmarks. mean .

GO TO. mean.

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EExx p plloorree

)61( plore Ex

Explore .

Screening

Transformation .

1:

Tall) )41( ( Explore :

Analyze Descriptive Statistics Explore

Explore :

:dependent List : )( .

Factor List: Break down variable ) . (

.

Tall . .

Label Cases by :

) ( Box plot.

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Statistics : :

:Confidence Interval for Mean: 95%99%…

Descriptives: MeanStandard DeviationSkewness

Kurtosis….M -estimators: Robust maximum Likelihood

Estimators . Tukey ,Hample,Andrew,Huber.

Outliers: Extreme

Values SPSS.Percentiles : 5 10 25 50 75 90 95

5th Percentile 5% 95% .

Check Box.Plots : :

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