Starting SPSS - University of Floridaplaza.ufl.edu/algina/all.part1.spss.pdf · This shows how to...

1

EDF 7405 Advanced Quantitative Methods in Educational Research

DISC.SAS

The data:

Maternal Age Intelligence

15 69 16 105 17 79 18 108 19 105 20 94 21 91 22 107 23 114 24 105 25 112

Starting SPSS:

2

Entering the data:

Please note that almost all screen shots in the directions for using SPSS are from SPSS 14. The appearance of these screen shots will be slightly different than the appearance of screen shots created from earlier or later versions of SPSS. An occasional a screen shot is from SPSS16.

5

Saving the data as an SPSS SAV file

6

Listing the data: I will not list my data in every program, but you should list yours in every program and

double check for data entry errors.

9

Summarize

Case Processing Summarya

11 100.0% 0 .0% 11 100.0%

11 100.0% 0 .0% 11 100.0%

MAGE

IQ

N Percent N Percent N Percent

Included Excluded Total

Cases

Limited to first 100 cases.a.

Case Summariesa

15 69

16 105

17 79

18 108

19 105

20 94

21 91

22 107

23 114

24 105

25 112

11 11

1

2

3

4

5

6

7

8

9

10

11

NTotal

MAGE IQ

Limited to first 100 cases.a.

Analyzing the data:

10

Calculating descriptive statistics

11

Descriptives

Descriptive Statistics

11 15 25 20.00 3.32

11 69 114 99.00 14.27

11

MAGE

IQ

Valid N (listwise)

N Minimum Maximum MeanStd.

Deviation

Constructing a scatterplot

12

This screen shot is from an older version of SPSS. With SPSS 15 or 16, select legacy diaglogs.

13

Graph

Plot of Intelligence Versus Maternal Agae

MAGE

26242220181614

IQ

120

110

100

90

80

70

60

14

Calculating covariances and correlations:

16

Correlations

Correlations

1.000 .642*

. .033

110.000 304.000

11.000 30.400

11 11

.642* 1.000

.033 .

304.000 2036.000

30.400 203.600

11 11

Pearson Correlation

Sig. (2-tailed)

Sum of Squares andCross-products

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares andCross-products

Covariance

N

MAGE

IQ

MAGE IQ

Correlation is significant at the 0.05 level (2-tailed).*.

17


SIMPR.SAS

See pages 10-11 for directions to calculate descriptive statistics. Descriptives


102 14 42 23.07 5.51

102 5 149 94.44 21.14

102

MAGE

IQ

Valid N (listwise)


Deviation

See pages 11-13 for directions to construct a scatterplot. Graph

Scatterplot of IQ versus Mage

MAGE

5040302010

IQ

160

140

120

100

80

60

40

20

0

18

Conducting a regression analysis and computing predicted values and residuals:

21

Note that the predicted values (PRE_1) and residuals (RES_1) have been added to the data set:

Regression

Variables Entered/Removedb

MAGEa . EnterModel1

VariablesEntered

VariablesRemoved Method

All requested variables entered.a.

Dependent Variable: IQb.

Model Summary

.135a .018 .009 21.05Model1

R R SquareAdjustedR Square

Std. Errorof the

Estimate

Predictors: (Constant), MAGEa.

21.05 is , a quantity I

call the standard error of estimate.

Y XS

22

ANOVAb

827.941 1 827.941 1.868 .175a

44325.206 100 443.252

45153.147 101

Regression

Residual

Total

Model1

Sum ofSquares df

MeanSquare F Sig.

Predictors: (Constant), MAGEa.

Dependent Variable: IQb.

In the table above Sig. refers to the p value: Prob F

Coefficientsa

82.458 9.012 9.150 .000

.519 .380 .135 1.367 .175

(Constant)

MAGE

Model1

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: IQa.

SPSS uses beta (.135 above) to refer to the standardized slope for the sample. Beta

is not in Y X . The estimate of in this equation is b .516 . SPSS

uses Constant to refer to the intercept. The estimate of the intercept is

In the table above Sig. refers to the p value:

82.458a

Pr ob t

Drawing the regression line on a scatterplot

1. Run the program to produce a scatterplot (see pages 11-13 for directions to construct a scatterplot). 2. Click once in the center of the plot. (A box will be displayed around the plot). 3. Click two times. (The plot is displayed in the SPSS for Windows Chart Editor). 4. Click twice outside the plot area. The following is displayed:

23

The plot will be displayed, but you will be in the SPSS for Windows Chart Editor. Close

the SPSS for Windows Chart Editor to get back to the window in which the output is

displayed (SPSS for Windows Viewer).

Graph

Scatterplot of IQ versus Mage

MAGE

5040302010

IQ

160

140

120

100

80

60

40

20

0

25


This shows how to use SPSS to do a basic logistic regression. After importing the

data into the SPSS Data Editor, click Analyze, Regression (see page 18). However click

Binary Logistic in place of Linear. For my the data the result is

Move PROMATH into the dependent slot because it is the 0-1 indicating failing and

passing the 8th grade test. Move MATMATH4 into the Covariates slot. Here covariate is

being used as a synonym for independent variable. The results are

Logistic Regression

Case Processing Summary

399 100.0

0 .0

399 100.0

0 .0

399 100.0

Unweighted Casesa

Included in Analysis

Missing Cases

Total

Selected Cases

Unselected Cases

Total

N Percent

If weight is in effect, see classification table for the totalnumber of cases.

a.

26

Dependent Variable Encoding

0

1

Original Value0

1

Internal Value

Block 0: Beginning Block

Classification Tablea,b

229 0 100.0

170 0 .0

57.4

Observed0

1

PROMATH

Overall Percentage

Step 00 1

PROMATH PercentageCorrect

Predicted

Constant is included in the model.a.

The cut value is .500b.

Variables in the Equation

-.298 .101 8.660 1 .003 .742ConstantStep 0B S.E. Wald df Sig. Exp(B)

Variables not in the Equation

119.299 1 .000

119.299 1 .000

MATMATH4Variables

Overall Statistics

Step 0Score df Sig.

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

137.446 1 .000

137.446 1 .000

137.446 1 .000

Step

Block

Model

Step 1Chi-square df Sig.

Model Summary

406.929a .291 .391Step1

-2 Loglikelihood

Cox & SnellR Square

NagelkerkeR Square

Estimation terminated at iteration number 5 becauseparameter estimates changed by less than .001.

a.

27

Classification Tablea

188 41 82.1

58 112 65.9

75.2

Observed0

1

PROMATH

Overall Percentage

Step 10 1

PROMATH PercentageCorrect

Predicted

The cut value is .500a.

Variables in the Equation

.062 .007 90.218 1 .000 1.064

-4.205 .441 90.964 1 .000 .015

MATMATH4

Constant

Step1

a

B S.E. Wald df Sig. Exp(B)

Variable(s) entered on step 1: MATMATH4.a.

29


HETERO.SAS

The variables in this example are the number of workers supervised 27 industrial

companies and the number of supervisors in the same companies. In the analysis number

of supervisors is the dependent variable. The new feature of this program is a residual

plot. Residual plots are used to detect violations of assumptions,

The Data Workers Supervisors

294 30 247 32 267 37 358 44 423 47 311 49 450 56 534 62 438 68 697 78 688 80 630 84 709 88 627 97 615 100 999 109 1022 114 1015 117 700 106 850 128 980 130 1025 160 1021 97 1200 180 1250 112 1500 210 1650 135

30

See pages 10-11 for directions to calculate descriptive statistics. Descriptives


27 247 1650 759.26 376.27

27 30 210 94.44 45.01

27

WORKERS

SUPERS

Valid N (listwise)


Deviation

See pages 11-13 for directions to construct a scatterplot. Graph

Plot of # of Supervisors vs. # of Workers

WORKERS

18001600140012001000800600400200

SU

PE

RS

300

200

100

0

31

Conducting the regression analysis and the Saving Studentized residuals

We want to save the Studentized residuals. A studentized residual is a residual

( ) divided by its standard error (denoted as ). Follow the steps to produce a

regression analysis (see pages 18-19) until you produce the following screen:

ˆe Y Y eS

32

The following shows that the Studentized residuals have been added to the data set.

These are now available for plotting. If you want to, these can be saved for future work.

33

Regression


WORKERSa

. Enter

Model1

VariablesEntered



Dependent Variable: SUPERSb.

Model Summaryb

.881a .776 .767 21.73Model1


Std. Errorof the

Estimate

Predictors: (Constant), WORKERSa.


ANOVAb

40862.603 1 40862.603 86.544 .000a

11804.064 25 472.163

52666.667 26

Regression

Residual

Total

Model1

Sum ofSquares df

MeanSquare F Sig.



Coefficientsa

14.448 9.562 1.511 .143

.105 .011 .881 9.303 .000

(Constant)

WORKERS

Model1

B Std. Error


Beta

Standardized

Coefficients

t Sig.

Dependent Variable: SUPERSa.

34

Residuals Statisticsa

40.47 188.29 94.44 39.64 27

-53.29 39.12 -2.11E-15 21.31 27

-1.361 2.367 .000 1.000 27

-2.453 1.800 .000 .981 27

Predicted Value

Residual

Std. Predicted Value

Std. Residual

Minimum Maximum MeanStd.

Deviation N


Constructing a residual plot Follow the steps for constructing a scatterplot (see pages 11-13 for directions to construct a scatterplot). The Studentized residuals will be available for plotting:

35

Graph

Residual Plot: Studentized Residuals vs. Worke

WORKERS

18001600140012001000800600400200

Stu

dent

ized

Res

idua

l

2

1

0

-1

-2

-3

37


HETERO1.SAS

This program uses the worker data to illustrate weighted least squares analysis, a

procedure used when the homogeneity of variance assumption is violated. This is

accomplished by including a weight in the data. Typically one starts with

2

1

X

where X is the independent variable. In our case this is workers. Calculating the weight

40

Conducting the weighted least squares regression analysis

Follow the steps to produce a regression analysis (see page 18) until you get to the

following screen:

42

Regression

Variables Entered/Removedb,c

WORKERSa

. Enter

Model1

VariablesEntered




Weighted Least Squares Regression -Weighted by WEIGHT

c.

Model Summaryb,c

.937a .879 .874 2.27E-02Model1


Std. Errorof the

Estimate



Weighted Least Squares Regression - Weighted byWEIGHT

c.

ANOVAb,c

9.286E-02 1 9.286E-02 180.779 .000a

1.284E-02 25 5.137E-04

.106 26

Regression

Residual

Total

Model1

Sum ofSquares df

MeanSquare F Sig.



Weighted Least Squares Regression - Weighted by WEIGHTc.

Coefficientsa,b

3.803 4.570 .832 .413

.121 .009 .937 13.445 .000

(Constant)

WORKERS

Model1

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Weighted Least Squares Regression - Weighted by WEIGHTb.

43

Residuals Statisticsb,c

33.69 203.44 95.67 45.52 27

. . . . 0

2.22 11.07 4.30 2.38 27

34.27 210.02 95.89 46.05 27

-68.44 32.18 -1.22 22.10 27

. . . . 0

-1.916 1.593 -.005 1.018 27

-75.02 34.23 -1.44 23.82 27

-2.032 1.647 -.006 1.039 27

.001 2.612 .963 .724 27

.000 .208 .039 .052 27

.000 .100 .037 .028 27

Predicted Value

Std. Predicted Valuea

Standard Error ofPredicted Value

Adjusted Predicted Value

Residual

Std. Residuala

Stud. Residual

Deleted Residual

Stud. Deleted Residual

Mahal. Distance

Cook's Distance

Centered Leverage Value


Deviation N

Not computed for Weighted Least Squares regression.a.


Weighted Least Squares Regression - Weighted by WEIGHTc.

See pages 31-35 for directions to construct this scatterplot. Graph

Resid. Plot from Weight. Least Squares

WORKERS

18001600140012001000800600400200

Stu

dent

ized

Res

idua

l

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

45


HETERO2.SAS

This program uses the workers data to illustrate the use of transformations in

regression analysis. Here a logarithmic transformation is used in an attempt to remove

heteroscedasticity of variance. In this approach the logarithm of the dependent variable is

used as the dependent variable. In our example Number of supervisors is the dependent

variable.

Calculating the logarithm

47

Constructing the scatterplot Follow the usual steps to construct a scatterplot (see pages 11-13 for directions to

construct a scatterplot), but use logs as the dependent variable.

48

Graph

WORKERS

18001600140012001000800600400200

LOG

S5.5

5.0

4.5

4.0

3.5

3.0

Conducting the regression analysis Follow the usual steps to conduct the regression analysis but use logs as the dependent

variable. As usual save the Studentized residuals.

49

Regression


WORKERSa

. Enter

Model1

VariablesEntered



Dependent Variable: LOGSb.

Model Summaryb

.878a .770 .761 .2524Model1


Std. Errorof the

Estimate



50

ANOVAb

5.337 1 5.337 83.774 .000a

1.593 25 6.370E-02

6.929 26

Regression

Residual

Total

Model1

Sum ofSquares df

MeanSquare F Sig.



Coefficientsa

3.515 .111 31.648 .000

1.204E-03 .000 .878 9.153 .000

(Constant)

WORKERS

Model1

B Std. Error


Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LOGSa.

Residuals Statisticsa

3.8124 5.5018 4.4292 .4531 27

-1.361 2.367 .000 1.000 27

4.902E-02 .1268 6.621E-02 1.867E-02 27

3.8545 5.7033 4.4388 .4669 27

-.5965 .3496 8.224E-17 .2475 27

-2.363 1.385 .000 .981 27

-2.734 1.416 -.018 1.045 27

-.7980 .3652 -9.57E-03 .2821 27

-3.199 1.446 -.038 1.108 27

.018 5.604 .963 1.259 27

.000 1.263 .077 .241 27

.001 .216 .037 .048 27

Predicted Value

Std. Predicted Value

Standard Error ofPredicted Value

Adjusted Predicted Value

Residual

Std. Residual

Stud. Residual

Deleted Residual

Stud. Deleted Residual

Mahal. Distance

Cook's Distance

Centered Leverage Value


Deviation N

Dependent Variable: LOGSa.

51

Graph

WORKERS

18001600140012001000800600400200

Stu

dent

ized

Res

idua

l2

1

0

-1

-2

-3

Starting SPSS - University of Floridaplaza.ufl.edu/algina/all.part1.spss.pdf · This shows how to...

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