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SPSS
SPSS (Statistical Package for Social Sciences) . , . , , .
SPSS 2 :
SPSS ,
SPSS for windows SPSS 19.0 for windows
SPSS Data Editor ( SPSS) ,
What would you like to do?
, SPSS
Open an existing data source
SPSS .sav , .
SPSS Data Editor 2 , , 2 :
Data View ( )
Variable View ( )
, Data View , . var ( variable), 1,2,3,. .. 1 Enter, 1 1.00 VAR00001 (, 1).
, , . , , . .. , ... Variable View (.. , ..).
SPSS DATA EDITOR
:
File: , ...
Edit: , , ...
View: ( ).
Data: .
Transform: .
Analyze: .
Graphs: .
Utilities: , .. .
Window: .
Help: .
VARIABLE VIEW
Data View, , :
VAR00001 =1 =0.
VAR00002 .. 24, 35, 27, 45,.. ...
Variable View Name 1 VAR00001, 2 VAR00002. VAR00001 Gender VAR00002 Age. Data View.
Type : (, , , ...). Numeric.
Width : , Outputs. 8.
Decimals : . 2. Type (string) SPSS Decimals ( Values Missing).
Label : . , Name Beerweek Label .
Values : . =1 =0. None . . Value 1 Label Add. 0 Value Label Add O.K.
Missing values : . SPSS . . , , 999 ( ). , , Discrete missing values 999.
Columns : Data View.
Align : Data View Right, Left, Center.
Measure : . 3 : Scale, Ordinal, Nominal.
Scale ( ).
.. ; . . ; . .
Ordinal () ( ). .. T.V 10. 1 2 3 4 5
;
20-29
30-39
40-49
Nominal . .. , ;
Nominal 2 .
..
;
SPSS Data Editor Data View . . , . . Enter . , . , Enter.
. , . PgUp PgDn . 4 .
File () Save as ( ). . Save in ( ) , .
File, Open, Data Look in ( ). , . , . File name ( ) Open. , File, New Data.
SPSS :
File Print
,
File Print Preview
. , SPSS Output. , , .
File Save as
. Outputs,
File Open Output
Output , .
File Print
1 12 :
Intelligence Age Gender
5 27 1
8 22 1
5 20 2
4 19 2
3 18 2
5 19 1
2 21 1
3 22 2
2 19 1
1 18 2
2 18 1
4 20 2
i) Data View. ii) Variable View 1=
, 2=.
iii) Data View View Value Labels. ;
iv) .
Intelligence (. ) :
Analyze Descriptive statistics ( ) Descriptive Intelligence
Intelligence
variables .
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
12 1 8 3,67 1,923
Valid N (listwise) 12
, output , , =12, minimum=1, maximum=8, Mean=3,67 Std. Deviation=1,923.
SPSS
, , :
1. ;
2. x ,
3. ..
4.
SPSS . , (values) . :
, , .
4 2, :
= 1
= 2
, = 3 = 4
= 5
:
= 1
= 2
, 999 missing value. :
= Scale
4 2 = Ordinal
= Nominal
, 2 .
, :
5. ;
5 , . , , 2 , ...
, .
, , 1 , 5 , , .. . , :
Home: 1= , 0=
Work: 1= , 0=
Outside: 1= , 0=
Fun: 1= , 0=
Else: 1= , 0=
4 2, warn_1, warn_2, warn_3, warn_4, (attitude) .
warn_1, warn_3, warn_4 1 5 . warn_2 .
, (recode) 1 5 .
4 .
:
Transform Recode into Different variables
, warn_2
Numeric Variable Output Variable
Name , warn_2_r Label change.
Old and New variables . , Old value 1 New value 5 Add.
24, 33, 42, 51.
!! 999999. :
Add Continue O.K
Variable View 999 missing value, , .
, .. . , 1 ; . .
Range. :
TransformRecode into Different variables
, Range.
, 5-15 1, 16-30 2, 31 3. , .
, Range 5 through 15, New Value Value 1 Add. , .
warn_1, warn_3, warn_4 warn_2_r , . . 1 . ; . att_scal.
TransformCompute
Target Variable att_scal Numeric Expression
warn_1 + warn_2_r + warn_3 + warn_4
., Editor View
att_scal ( 4 x 20).
!! missing values . missing values (999). , Variable View missing values .
(SELECTED CASES)
. , 5-15 (. ). ,
DataSelected CasesIf condition is satisfied
if , , . .
, smokers
smokers = 1. Data View . . , :
DataSelected CasesAll Cases
(SPLIT FILES)
. , 2 . , 2 :
DataSplit FileOrganize output by groups
Groups Based on .. Data View 1 2. , 2 .
output 2 .
:
DataSplit FileAnalyze all cases, do not create groupsOK
(SORT CASES)
(Ascending) (Descending) , :
DataSort Cases
, , Sort by ciga_num, Ascending .
(RANK CASES)
, (. , ..), :
TransformRank Cases
, att_scale Variable.
Data View () .
&
, :
.
() ( , ) .
, , (.. ).
, , (.. , ) :
.
.
.
().
, ( ) () .
Scale
, , , , , ... , ,
2
( .) 1997 2004.
( .)
1997 2024
1998 4911
1999 4938
2000 5691
2001 5139
2002 5260
2003 5320
2004 5541
:
.
.
.
, :
AnalyzeDescriptive StatisticsDescriptives
Variable(s) , .. Passengers.
Options :
(Mean)
(Std. Deviation)
(Range)
(Sum)
(Minimum)
(Maximum)
Output :
Descriptive Statistics
N Range Minimum Maximum Sum Mean
Std.
Deviation
8 3667 2024 5691 38824 4853,00 1174,427
Valid N (listwise) 8
=8 . Missing Values (Valid N).
Range = Maximum-Minimum = 3667 . .
2024 . .
5691 . .
8 38824 . .
4853 . .
, 1174,427 . .
, :
GraphsLegacy DialogsLineDefineOther statistic
Passengers Variable Year Category Axis.
, Bar Line, :
GraphsLegacy DialogsBarDefineOther statistic
, Area Line, :
GraphsLegacy DialogsAreaDefineOther statistic
Line Area , .
Ordinal Nominal
Ordinal Nominal , , , , (.. 1= , 2 = , 3 = , 4 = , 2.5 ).
.
3
15 , :
, :
AnalyzeDescriptive StatisticsFrequencies
2 Variables Charts Bar Charts.
:
Graphs Legacy DialogsBarDefine
N of cases % of Cases. (, ) Category Axis.
(Cross Tabs)
. , 2 .
, 3 :
AnalyzeDescriptivesCrosstabs
Gender Rows Brand Columns.
Cells Total.
Scale
, , , ,
Frequencies. .
4
(min) 15 24.
12
15
30
40
20
8
10
25
11
35
17
19
18
24
6
:
AnalyzeDescriptives StatisticsFrequencies
Min Variable.
: Display frequency tables .
Charts Histograms .
Statistics Mean, Median, Mode, Std. deviation, Minimum, Maximum.
GraphsHistogram
Variable Display normal curve.
Scale Nominal Ordinal
2 nominal ordinal ( scale), 3 . 2 scale Nominal Ordinal.
5
(min) 15 24.
12
15
30
40
20
8
10
25
11
35
17
19
18
24
6
:
AnalyzeCompare MeansMeans
scale Min Dependent List nominal Gender Independent List.
.
Scale Nominal Ordinal 3
6
(min) 15 24.
.
12 /
15 /
30 /
40 /
20 /
8 /
10 .
25 /
11 /
35 /
17 .
19 /
18 /
24 /
6 .
3 .
3 :
AnalyzeCompare MeansMeans
scale Min Dependent List nominal Gender Independent List. Next Independent , ordinal Edlevel.
Explore
Explore Means 5, Boxplots ().
:
AnalyzeDescriptives StatisticsExplore
Min Dependent Gender Factor.
Plots Boxplots.
Boxplots 2 . . 4 (outlier). , .
. 20 ( ). , :
House (): 1= , 2=
m2: 1= 80m2 , 2=80m2-120m2 , 3= 80m2
7
House m2
1 2
1 3
2 2
1 3
1 2
2 3
1 2
1 1
1 3
1 3
2 1
2 2
1 3
2 1
1 2
2 2
1 2
1 3
1 3
2 1
Bar Chart :
GraphsBarClustered
House Category Axis m2 Define Clusters by.
120 m2. 3 80 m2, 3 80-120 m2 120 m2.
SPSS (editing) . , , , ..
7 Pie Chart m2 :
GraphsPieSummaries for groups of cases
m2 Define slices by.
double click . double click . Properties Fill & Border.
Pie Chart Depth & Angle 3-D
2 . () .
, .. :
(, ) () (, ) .
, , -1 +1. -1 0 , 0 +1 .
: , .
PEARSON
.. Pearson 2 . :
scale
( )
( scatter diagram - )
Pearson nominal. , 1=, 2=, 3=, 4= , . ;
: 0 , .
8
supermarket , (. ) 10 , . .
/ (%)
1 16 4
2 29 6
3 36 9
4 43 10
5 20 5
6 32 7
7 20 5
8 3 1
9 4 2
10 6 3
Scatterplot
Graphs Legacy Dialogs Scatter/Dot Simple Scatter Define
unplanned discount .
: , . 2 .
.. Pearson
Analyze Correlate Bivariate
2 Variables. .. Pearson Two-tailed significance. Options , Means and standard deviations
20,9%
5,2. .. Pearson 0,982 . . , , .
Pearson 3
9
8 ( = price)
/ (%)
1 16 4 30
2 29 6 27
3 36 9 20
4 43 10 18
5 20 5 25
6 32 7 22
7 20 5 35
8 3 1 60
9 4 2 50
10 6 3 40
, . output 2 , 6 2 (3 , 2).
Pearson: discount unplanned 0,982 (. ).
Pearson: discount price -0,9 . , , .
Pearson: unplanned price -0,906 . , , .
(Partial Correlation Coefficient)
, .. Pearson .
. , 2 . :
Analyze Correlate Partial
discount unplanned Variables price Controlling for.
... 2 , , .
Kendalls Tau b
2 ordinal (.. 5
).
. Kendall tau . -1 +1.
10
10 1 10 (1= , 10= ).
1 2
1 1
2 3
3 2
4 4
5 6
6 8
7 5
8 7
9 10
10 9
, :
Analyze Correlate Bivariate
2 spec_1 spec_2 Variables. .. Kendalls tau-b Two-tailed significance.
output (0,778).
Phi
(Phi Coefficient)
Phi () 2 ( ).
( ) 2 .
11
2 ( ) 2 ( ).
Phi :
Analyze Descriptive Statistics Crosstabs
shop Rows milk Columns. Statistics Phi and Cramers V. 2 , Cells Total.
Output 8 (40%) 3 11 (15%). 2 9 (10%) 7 (35%).
Phi. .
(dependent) response (independent) explanatory.
ordinal scale. ordinal scale.
:
Y = + X
Y =
X=
=
=
(.. 1 ). 0 (. 0 ).
13
( ) 8 . .
clothes age
45 34
60 25
100 19
40 39
30 40
80 22
55 29
50 30
:
( ) = + ()
:
Analyze RegressionLinear
clothes Dependent age Independent.
Statistics : Estimates, Model fit, Descriptives.
Descriptive Statistics
Mean Std. Deviation N
clothes 57,50 22,678 8
age 29,75 7,630 8
2
.
Pearson:
Correlations
clothes age
Pearson Correlation clothes 1,000 -,941
age -,941 1,000
Sig. (1-tailed) clothes . ,000
age ,000 .
N clothes 8 8
age 8 8
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 ,941a ,886 ,867 8,274
a. Predictors: (Constant), age
R2 = 0.886
. Adjusted R
Square ( R)
. ,
86,7%
clothes age.
,
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 140,727 12,540 11,222 ,000
age -2,798 ,410 -,941 -6,825 ,000
a. Dependent Variable: clothes
= 140,727 = -2,798
.
( ) =140,727 2,798 ()
1 ,
2,798 .
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