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Statistical Methods and Data Analytics

Applied Regression Analysis by John Fox Chapter 8: Analysis of variance | SPSS Textbook Examples

page 160 The table in the middle of the page. 

GET FILE='D:duncan.sav'.

EXAMINE
  VARIABLES=prestige BY occ_type
  /PLOT NONE
  /STATISTICS DESCRIPTIVES.
Case Processing Summary

Cases
Valid Missing Total
N Percent N Percent N Percent
Percent of raters in NORC study rating occupation as excellent or good in presti 45 100.0% 0 .0% 45 100.0%

Descriptives

Statistic Std. Error
Percent of raters in NORC study rating occupation as excellent or good in presti Mean 47.69 4.697
95% Confidence Interval for Mean Lower Bound 38.22
Upper Bound 57.16
5% Trimmed Mean 47.46
Median 41.00
Variance 992.901
Std. Deviation 31.510
Minimum 3
Maximum 97
Range 94
Interquartile Range 65.50
Skewness .147 .354
Kurtosis -1.517 .695

Case Processing Summary

Cases
Valid Missing Total

Occupation type, Professional/Manag, White Collar, Blue Collar N Percent N Percent N Percent
Percent of raters in NORC study rating occupation as excellent or good in presti bc 21 100.0% 0 .0% 21 100.0%
prof 18 100.0% 0 .0% 18 100.0%
wc 6 100.0% 0 .0% 6 100.0%

Descriptives

Occupation type, Professional/Manag, White Collar, Blue Collar Statistic Std. Error
Percent of raters in NORC study rating occupation as excellent or good in presti bc Mean 22.76 3.940
95% Confidence Interval for Mean Lower Bound 14.54
Upper Bound 30.98
5% Trimmed Mean 21.42
Median 16.00
Variance 325.990
Std. Deviation 18.055
Minimum 3
Maximum 67
Range 64
Interquartile Range 21.00
Skewness 1.245 .501
Kurtosis .697 .972
prof Mean 80.44 3.325
95% Confidence Interval for Mean Lower Bound 73.43
Upper Bound 87.46
5% Trimmed Mean 81.49
Median 85.00
Variance 198.967
Std. Deviation 14.106
Minimum 45
Maximum 97
Range 52
Interquartile Range 14.75
Skewness -1.316 .536
Kurtosis 1.150 1.038
wc Mean 36.67 4.814
95% Confidence Interval for Mean Lower Bound 24.29
Upper Bound 49.04
5% Trimmed Mean 36.96
Median 38.50
Variance 139.067
Std. Deviation 11.793
Minimum 16
Maximum 52
Range 36
Interquartile Range 14.25
Skewness -.941 .845
Kurtosis 2.369 1.741

page 161 Figure 8.1 Parallel boxplots for occupational prestige by type of occupation. 

EXAMINE
  VARIABLES=prestige BY occ_type
  /PLOT=BOXPLOT
  /STATISTICS=NONE
  /NOTOTAL.
Case Processing Summary

Cases
Valid Missing Total

Occupation type, Professional/Manag, White Collar, Blue Collar N Percent N Percent N Percent
Percent of raters in NORC study rating occupation as excellent or good in presti bc 21 100.0% 0 .0% 21 100.0%
prof 18 100.0% 0 .0% 18 100.0%
wc 6 100.0% 0 .0% 6 100.0%

Boxplot

The table in the middle of page 161. 

ONEWAY
  prestige BY occ_type.
ANOVA
Percent of raters in NORC study rating occupation as excellent or good in presti

Sum of Squares df Mean Square F Sig.
Between Groups 33090.057 2 16545.029 65.571 .000
Within Groups 10597.587 42 252.324

Total 43687.644 44


page 167 Table 8.2 Conformity by authoritarianism and partner's status, for Moore and Krupat's (1971) experiment. Each cell shows (from top to bottom) the conformity mean, standard deviation, and cell frequency. 

GET FILE='D:moore.sav'.

MEANS
  TABLES=conform  BY status by fcat
  /CELLS MEAN COUNT STDDEV.
Case Processing Summary

Cases
Included Excluded Total
N Percent N Percent N Percent
Amount of conformity * Status of partner * F-scale categorized 45 100.0% 0 .0% 45 100.0%

Report
Amount of conformity
Status of partner F-scale categorized Mean N Std. Deviation
high high 11.86 7 3.934
low 17.40 5 4.506
medium 14.27 11 3.952
Total 14.22 23 4.369
low high 12.63 8 7.347
low 8.90 10 2.644
medium 7.25 4 3.948
Total 9.95 22 5.278
Total high 12.27 15 5.812
low 11.73 15 5.244
medium 12.40 15 4.983
Total 12.13 45 5.242

page 169 Figure 8.5 Cell means for the Moore and Krupat conformity experiment. 

compute fcat1 = fcat.
recode fcat1 (1 = 3) (2 = 1) (3 = 2).
execute.

UNIANOVA
  conform  BY status fcat1
  /PLOT = PROFILE( fcat1*status )
  /DESIGN = status fcat1 status*fcat1.
Between-Subjects Factors

Value Label N
Status of partner 1 high 23
2 low 22
FCAT1 1.00
15
2.00
15
3.00
15

Tests of Between-Subjects Effects
Dependent Variable: Amount of conformity
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 391.436(a) 5 78.287 3.734 .007
Intercept 5752.848 1 5752.848 274.359 .000
STATUS 239.562 1 239.562 11.425 .002
FCAT1 36.019 2 18.009 .859 .431
STATUS * FCAT1 175.489 2 87.744 4.185 .023
Error 817.764 39 20.968

Total 7834.000 45


Corrected Total 1209.200 44


a R Squared = .324 (Adjusted R Squared = .237)

page 170 Figure 8.6 Data from Moore and Krupat's experiment on conformity and authoritarianism. The horizontal coordinates of the points have been jittered to separate overlapping points. The means are plotted as X's, with the profiles connected by broken lines. Note the two unusual points, for observations 16 and 19, in the high-authoritarian, low-status-partner group. 

GET FILE='D:moore.sav'.

recode fcat (1=3) (2=1) (3=2)  into fcat1.
value labels fcat1 1 "Low" 2 "Medium" 3 "High".
sort cases by status fcat1.
aggregate outfile = * mode=addvariables
/break fcat1 status
/mean_fcat1 = mean(conform).
exe.

sort cases by status.
split file by status.

GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= conform fcat1 mean_fcat1
  /GRAPHSPEC SOURCE=INLINE .
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: conform=col( source(s), name( "conform" ) )
DATA: fcat1=col( source(s), name( "fcat1" ), unit.category() )
DATA: mean_fcat1=col( source(s), name( "mean_fcat1" ) )
GUIDE: axis( dim( 1 ), label( "Authoritiarianism" ) )
GUIDE: axis( dim( 2 ), label( "Conformity" ), start(5), delta(10) )
SCALE: linear( dim( 2 ), min(5), max(25) )
ELEMENT: point.jitter( position( fcat1 * conform ) )
ELEMENT: point( position( fcat1 * mean_fcat1 ), shape(shape.square) )
ELEMENT: line(position(fcat1 * mean_fcat1), shape(shape.dash))
END GPL.

split file off.

page 177 The sums of squares listed in the middle of the page. 

compute c1 = fcat.
recode c1 (1 = 1) (2 = 0) (3 = -1).
compute c2 = fcat.
recode c2 (1 = 0) (2 = 1) (3 = -1).
compute r = status.
recode r (2 = -1).
compute rc1 = r*c1.
compute rc2 = r*c2.
execute.

Sums of squares (alpha, beta, gamma) and TTS. 

regression 
 /dep=conform 
 /method=enter r c1 c2 rc1 rc2.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 RC2, R, C1, RC1, C2(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .569(a) .324 .237 4.579
a Predictors: (Constant), RC2, R, C1, RC1, C2

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 391.436 5 78.287 3.734 .007(a)
Residual 817.764 39 20.968

Total 1209.200 44


a Predictors: (Constant), RC2, R, C1, RC1, C2
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.051 .728
16.564 .000
R 2.459 .728 .474 3.380 .002
C1 .190 .999 .030 .191 .850
C2 1.099 1.026 .173 1.071 .291
RC1 -2.843 .999 -.437 -2.847 .007
RC2 1.791 1.026 .267 1.745 .089
a Dependent Variable: Amount of conformity

Sums of squares (alpha, beta) 

regression 
 /dep=conform 
 /method=enter r c1 c2.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 C2, R, C1(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .423(a) .179 .118 4.922
a Predictors: (Constant), C2, R, C1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 215.947 3 71.982 2.971 .043(a)
Residual 993.253 41 24.226

Total 1209.200 44


a Predictors: (Constant), C2, R, C1
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.082 .734
16.462 .000
R 2.303 .778 .444 2.960 .005
C1 .338 1.040 .053 .325 .747
C2 .419 1.074 .066 .390 .698
a Dependent Variable: Amount of conformity

Sums of squares (alpha, gamma) 

regression 
 /dep=conform 
 /method=enter r c1 c2.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 C2, R, C1(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .423(a) .179 .118 4.922
a Predictors: (Constant), C2, R, C1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 215.947 3 71.982 2.971 .043(a)
Residual 993.253 41 24.226

Total 1209.200 44


a Predictors: (Constant), C2, R, C1
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.082 .734
16.462 .000
R 2.303 .778 .444 2.960 .005
C1 .338 1.040 .053 .325 .747
C2 .419 1.074 .066 .390 .698
a Dependent Variable: Amount of conformity

Sums of squares (beta, gamma) 

regression 
 /dep=conform 
 /method=enter c1 c2 rc1 rc2.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 RC2, C2, RC1, C1(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .354(a) .126 .038 5.141
a Predictors: (Constant), RC2, C2, RC1, C1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 151.874 4 37.968 1.436 .240(a)
Residual 1057.326 40 26.433

Total 1209.200 44


a Predictors: (Constant), RC2, C2, RC1, C1
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.162 .816
14.904 .000
C1 -6.629E-02 1.118 -.010 -.059 .953
C2 .176 1.111 .028 .158 .875
RC1 -2.558 1.117 -.393 -2.290 .027
RC2 1.815 1.152 .270 1.575 .123
a Dependent Variable: Amount of conformity

Sums of squares (alpha) 

regression 
 /dep=conform 
 /method=enter r.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 R(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .411(a) .169 .150 4.834
a Predictors: (Constant), R

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 204.332 1 204.332 8.744 .005(a)
Residual 1004.868 43 23.369

Total 1209.200 44


a Predictors: (Constant), R
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.086 .721
16.767 .000
R 2.131 .721 .411 2.957 .005
a Dependent Variable: Amount of conformity

Sums of squares (beta) 

regression 
 /dep=conform 
 /method=enter c1 c2.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 C2, C1(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .056(a) .003 -.044 5.357
a Predictors: (Constant), C2, C1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 3.733 2 1.867 .065 .937(a)
Residual 1205.467 42 28.702

Total 1209.200 44


a Predictors: (Constant), C2, C1
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.133 .799
15.193 .000
C1 .133 1.129 .021 .118 .907
C2 -.400 1.129 -.063 -.354 .725
a Dependent Variable: Amount of conformity

page 178 Table 8.6 Analysis of variance table for Moore and Krupat's conformity experiment. Alternative tests are shown for the Partner's status and authoritarianism main effects.

NOTE: This yields the values for residuals (called "error" in the SPSS output) and the total (called "corrected total" in the SPSS output).

Partner's status: 

regression 
 /dep conform 
 /method=enter c1 c2 rc1 rc2 
 /method=test(r).

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 RC2, C2, RC1, C1(a) . Enter
2 R . Test
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .354(a) .126 .038 5.141
2 .569(b) .324 .237 4.579
a Predictors: (Constant), RC2, C2, RC1, C1
b Predictors: (Constant), RC2, C2, RC1, C1, R

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 151.874 4 37.968 1.436 .240(a)
Residual 1057.326 40 26.433


Total 1209.200 44



2 Subset Tests R 239.562 1 239.562 11.425 .002(b) .198
Regression 391.436 5 78.287 3.734 .007(c)
Residual 817.764 39 20.968


Total 1209.200 44



a Predictors: (Constant), RC2, C2, RC1, C1
b Tested against the full model.
c Predictors in the Full Model: (Constant), RC2, C2, RC1, C1, R.
d Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.162 .816
14.904 .000
C1 -6.629E-02 1.118 -.010 -.059 .953
C2 .176 1.111 .028 .158 .875
RC1 -2.558 1.117 -.393 -2.290 .027
RC2 1.815 1.152 .270 1.575 .123
2 (Constant) 12.051 .728
16.564 .000
C1 .190 .999 .030 .191 .850
C2 1.099 1.026 .173 1.071 .291
RC1 -2.843 .999 -.437 -2.847 .007
RC2 1.791 1.026 .267 1.745 .089
R 2.459 .728 .474 3.380 .002
a Dependent Variable: Amount of conformity





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 R .474(a) 3.380 .002 .476 .881
a Predictors in the Model: (Constant), RC2, C2, RC1, C1
b Dependent Variable: Amount of conformity 
regression 
 /dep conform 
 /method=enter c1 c2 
 /method=test(r).

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 C2, C1(a) . Enter
2 R . Test
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .056(a) .003 -.044 5.357
2 .423(b) .179 .118 4.922
a Predictors: (Constant), C2, C1
b Predictors: (Constant), C2, C1, R

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 3.733 2 1.867 .065 .937(a)
Residual 1205.467 42 28.702


Total 1209.200 44



2 Subset Tests R 212.214 1 212.214 8.760 .005(b) .175
Regression 215.947 3 71.982 2.971 .043(c)
Residual 993.253 41 24.226


Total 1209.200 44



a Predictors: (Constant), C2, C1
b Tested against the full model.
c Predictors in the Full Model: (Constant), C2, C1, R.
d Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.133 .799
15.193 .000
C1 .133 1.129 .021 .118 .907
C2 -.400 1.129 -.063 -.354 .725
2 (Constant) 12.082 .734
16.462 .000
C1 .338 1.040 .053 .325 .747
C2 .419 1.074 .066 .390 .698
R 2.303 .778 .444 2.960 .005
a Dependent Variable: Amount of conformity





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 R .444(a) 2.960 .005 .420 .889
a Predictors in the Model: (Constant), C2, C1
b Dependent Variable: Amount of conformity

Authoritarianism: 

regression 
 /dep conform 
 /method=enter r 
 /method=test(c1 c2).

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 R(a) . Enter
2 C1, C2 . Test
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .411(a) .169 .150 4.834
2 .423(b) .179 .118 4.922
a Predictors: (Constant), R
b Predictors: (Constant), R, C1, C2

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 204.332 1 204.332 8.744 .005(a)
Residual 1004.868 43 23.369


Total 1209.200 44



2 Subset Tests C1, C2 11.615 2 5.807 .240 .788(b) .010
Regression 215.947 3 71.982 2.971 .043(c)
Residual 993.253 41 24.226


Total 1209.200 44



a Predictors: (Constant), R
b Tested against the full model.
c Predictors in the Full Model: (Constant), R, C1, C2.
d Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.086 .721
16.767 .000
R 2.131 .721 .411 2.957 .005
2 (Constant) 12.082 .734
16.462 .000
R 2.303 .778 .444 2.960 .005
C1 .338 1.040 .053 .325 .747
C2 .419 1.074 .066 .390 .698
a Dependent Variable: Amount of conformity





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 C1 .083(a) .578 .566 .089 .953
C2 .092(a) .618 .540 .095 .893
a Predictors in the Model: (Constant), R
b Dependent Variable: Amount of conformity 
regression 
 /dep conform 
 /method=enter r rc1 rc2 
 /method=test(c1 c2).

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 RC2, R, RC1(a) . Enter
2 C1, C2 . Test
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .542(a) .294 .242 4.563
2 .569(b) .324 .237 4.579
a Predictors: (Constant), RC2, R, RC1
b Predictors: (Constant), RC2, R, RC1, C1, C2

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 355.417 3 118.472 5.689 .002(a)
Residual 853.783 41 20.824


Total 1209.200 44



2 Subset Tests C1, C2 36.019 2 18.009 .859 .431(b) .030
Regression 391.436 5 78.287 3.734 .007(c)
Residual 817.764 39 20.968


Total 1209.200 44



a Predictors: (Constant), RC2, R, RC1
b Tested against the full model.
c Predictors in the Full Model: (Constant), RC2, R, RC1, C1, C2.
d Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.110 .722
16.784 .000
R 2.131 .680 .411 3.132 .003
RC1 -2.524 .964 -.388 -2.617 .012
RC2 1.772 .996 .264 1.780 .082
2 (Constant) 12.051 .728
16.564 .000
R 2.459 .728 .474 3.380 .002
RC1 -2.843 .999 -.437 -2.847 .007
RC2 1.791 1.026 .267 1.745 .089
C1 .190 .999 .030 .191 .850
C2 1.099 1.026 .173 1.071 .291
a Dependent Variable: Amount of conformity





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 C1 .106(a) .754 .455 .118 .880
C2 .187(a) 1.313 .197 .203 .833
a Predictors in the Model: (Constant), RC2, R, RC1
b Dependent Variable: Amount of conformity

Partner's status and authoritarianism: 

regression 
 /dep conform 
 /method=enter r c1 c2 
 /method=test(rc1 rc2).

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 C2, R, C1(a) . Enter
2 RC2, RC1 . Test
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .423(a) .179 .118 4.922
2 .569(b) .324 .237 4.579
a Predictors: (Constant), C2, R, C1
b Predictors: (Constant), C2, R, C1, RC2, RC1

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 215.947 3 71.982 2.971 .043(a)
Residual 993.253 41 24.226


Total 1209.200 44



2 Subset Tests RC1, RC2 175.489 2 87.744 4.185 .023(b) .145
Regression 391.436 5 78.287 3.734 .007(c)
Residual 817.764 39 20.968


Total 1209.200 44



a Predictors: (Constant), C2, R, C1
b Tested against the full model.
c Predictors in the Full Model: (Constant), C2, R, C1, RC2, RC1.
d Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 12.082 .734
16.462 .000
R 2.303 .778 .444 2.960 .005
C1 .338 1.040 .053 .325 .747
C2 .419 1.074 .066 .390 .698
2 (Constant) 12.051 .728
16.564 .000
R 2.459 .728 .474 3.380 .002
C1 .190 .999 .030 .191 .850
C2 1.099 1.026 .173 1.071 .291
RC1 -2.843 .999 -.437 -2.847 .007
RC2 1.791 1.026 .267 1.745 .089
a Dependent Variable: Amount of conformity





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 RC1 -.316(a) -2.251 .030 -.335 .924
RC2 .070(a) .474 .638 .075 .933
a Predictors in the Model: (Constant), C2, R, C1
b Dependent Variable: Amount of conformity

page 192 The R-squared in the middle of the page. 

compute d = 0.
if status = 2 d = 1.
compute intfd = fscore*d.
execute.


REGRESSION
  /DEPENDENT conform
  /METHOD=ENTER fscore d intfd.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 INTFD, F-scale score, D(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .542(a) .294 .243 4.562
a Predictors: (Constant), INTFD, F-scale score, D

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 355.783 3 118.594 5.698 .002(a)
Residual 853.417 41 20.815

Total 1209.200 44


a Predictors: (Constant), INTFD, F-scale score, D
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 20.793 3.263
6.373 .000
F-scale score -.151 .072 -.411 -2.107 .041
D -15.534 4.400 -1.498 -3.530 .001
INTFD .261 .097 1.196 2.692 .010
a Dependent Variable: Amount of conformity

page 194 The R-squared in the middle of the page. 

compute s = status.
if status = 1 s = -1.
compute intfs = fscore*s.
execute.


REGRESSION
  /DEPENDENT conform
  /METHOD=ENTER fscore s intfs.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 INTFS, F-scale score, S(a) . Enter
a All requested variables entered.
b Dependent Variable: Amount of conformity

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .542(a) .294 .243 4.562
a Predictors: (Constant), INTFS, F-scale score, S

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 355.783 3 118.594 5.698 .002(a)
Residual 853.417 41 20.815

Total 1209.200 44


a Predictors: (Constant), INTFS, F-scale score, S
b Dependent Variable: Amount of conformity



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 15.615 2.387
6.541 .000
F-scale score -6.406E-02 .053 -.174 -1.220 .230
S -5.178 1.467 -1.498 -3.530 .001
INTFS 8.703E-02 .032 1.148 2.692 .010
a Dependent Variable: Amount of conformity

page 197 Means and standard deviations in the middle of the page. 

GET FILE='D:friendly.sav'.


MEANS
  TABLES=correct  BY cond
  /CELLS MEAN COUNT STDDEV.

Case Processing Summary

Cases
Included Excluded Total
N Percent N Percent N Percent
Number correct (of 40) * Experimental Condition 30 100.0% 0 .0% 30 100.0%

Report
Number correct (of 40)
Experimental Condition Mean N Std. Deviation
Before 36.60 10 5.337
Meshed 36.60 10 3.026
SFR 30.30 10 7.334
Total 34.50 30 6.124

page 198 Figure 8.8 Number of words recalled (out of 40) by experimental condition, from Friendly and Franklin's (1980) memory experiment. The horizontal coordinates of the points are jittered to separate overlapping points. The mean of each group is plotted as an "X" and the means are connected by a broken line. 

if cond = "SFR" g1 = 1.
if cond = "Before"  g1 = 2.
if cond = "Meshed" g1 = 3.
execute.


UNIANOVA
  correct BY g1
  /PLOT = PROFILE(g1)
  /DESIGN = g1.

Between-Subjects Factors

N
G1 1.00 10
2.00 10
3.00 10

Tests of Between-Subjects Effects
Dependent Variable: Number correct (of 40)
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 264.600(a) 2 132.300 4.341 .023
Intercept 35707.500 1 35707.500 1171.591 .000
G1 264.600 2 132.300 4.341 .023
Error 822.900 27 30.478

Total 36795.000 30


Corrected Total 1087.500 29


a R Squared = .243 (Adjusted R Squared = .187)

G1

page 199 The ANOVA table in the middle of the page. 

if cond = "SFR" c1 = 1.
if cond = "SFR" c2 = 0.
if cond = "Before"  c1 = .5.
if cond = "Before" c2 = 1.
if cond = "Meshed" c1 = -.5.
if cond = "Meshed" c2 = -1.
execute.


regression 
 /dep correct
 /method=enter c2
 /test(c1).

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 C2(a) . Enter
2 C1 . Test
a All requested variables entered.
b Dependent Variable: Number correct (of 40)

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .000(a) .000 -.036 6.232
2 .493(b) .243 .187 5.521
a Predictors: (Constant), C2
b Predictors: (Constant), C2, C1

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression .000 1 .000 . .(a)
Residual 1087.500 28 38.839


Total 1087.500 29



2 Subset Tests C1 264.600 1 264.600 8.682 .007(b) .243
Regression 264.600 2 132.300 4.341 .023(c)
Residual 822.900 27 30.478


Total 1087.500 29



a Predictors: (Constant), C2
b Tested against the full model.
c Predictors in the Full Model: (Constant), C2, C1.
d Dependent Variable: Number correct (of 40)



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 34.500 1.138
30.321 .000
C2 .000 1.394 .000 .000 1.000
2 (Constant) 36.600 1.234
29.649 .000
C2 3.150 1.633 .427 1.929 .064
C1 -6.300 2.138 -.653 -2.946 .007
a Dependent Variable: Number correct (of 40)





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 C1 -.653(a) -2.946 .007 -.493 .571
a Predictors in the Model: (Constant), C2
b Dependent Variable: Number correct (of 40)

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