IBM SPSS Web Report - Output1

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Log
DATASET ACTIVATE DataSet1.

* Table 6.2

SUMMARIZE
  /TABLES=eval age beauty students allstudents
  /FORMAT=NOLIST TOTAL
  /TITLE='Case Summaries'
  /MISSING=VARIABLE
  /CELLS=COUNT MEAN STDDEV SEMEAN.
Summarize
Case Processing SummaryCase Processing Summary, table, 3 levels of column headers and 1 levels of row headers, table with 7 columns and 9 rows
  Cases
Included Excluded Total
N Percent N Percent N Percent
evaluation score 463 100.0% 0 0.0% 463 100.0%
prof's age 463 100.0% 0 0.0% 463 100.0%
prof's beauty score 463 100.0% 0 0.0% 463 100.0%
students responded 463 100.0% 0 0.0% 463 100.0%
students enrolled in class 463 100.0% 0 0.0% 463 100.0%
Summarize
Case SummariesCase Summaries, table, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 6 rows
  evaluation score prof's age prof's beauty score students responded students enrolled in class
N 463 463 463 463 463
Mean 3.9983 48.37 .0000 36.62 55.18
Std. Deviation .55487 9.803 .78865 45.018 75.073
Std. Error of Mean .02579 .456 .03665 2.092 3.489
Log

* Figure 6.8

GGRAPH
  /GRAPHDATASET NAME="graphdataset"
    VARIABLES=eval[LEVEL=scale]
    MISSING=LISTWISE REPORTMISSING=NO
  /GRAPHSPEC SOURCE=VIZTEMPLATE(NAME="Histogram"[LOCATION=LOCAL]
    MAPPING( "x"="eval"[DATASET="graphdataset"] "Summary"="count"))
    VIZSTYLESHEET="Traditional"[LOCATION=LOCAL]
    LABEL='HISTOGRAM: eval'
    DEFAULTTEMPLATE=NO.
GGraph
evaluation score: 5.00
Count: 8 evaluation score: 5.00
Count: 8 evaluation score: 4.90
Count: 11 evaluation score: 4.90
Count: 11 evaluation score: 4.80
Count: 14 evaluation score: 4.80
Count: 14 evaluation score: 4.70
Count: 21 evaluation score: 4.70
Count: 21 evaluation score: 4.60
Count: 25 evaluation score: 4.60
Count: 25 evaluation score: 4.50
Count: 30 evaluation score: 4.50
Count: 30 evaluation score: 4.40
Count: 28 evaluation score: 4.40
Count: 28 evaluation score: 4.30
Count: 34 evaluation score: 4.30
Count: 34 evaluation score: 4.20
Count: 31 evaluation score: 4.20
Count: 31 evaluation score: 4.10
Count: 21 evaluation score: 4.10
Count: 21 evaluation score: 4.00
Count: 38 evaluation score: 4.00
Count: 38 evaluation score: 3.90
Count: 29 evaluation score: 3.90
Count: 29 evaluation score: 3.80
Count: 28 evaluation score: 3.80
Count: 28 evaluation score: 3.70
Count: 22 evaluation score: 3.70
Count: 22 evaluation score: 3.60
Count: 26 evaluation score: 3.60
Count: 26 evaluation score: 3.50
Count: 20 evaluation score: 3.50
Count: 20 evaluation score: 3.40
Count: 19 evaluation score: 3.40
Count: 19 evaluation score: 3.30
Count: 13 evaluation score: 3.30
Count: 13 evaluation score: 3.20
Count: 7 evaluation score: 3.20
Count: 7 evaluation score: 3.10
Count: 10 evaluation score: 3.10
Count: 10 evaluation score: 3.00
Count: 10 evaluation score: 3.00
Count: 10 evaluation score: 2.90
Count: 4 evaluation score: 2.90
Count: 4 evaluation score: 2.80
Count: 7 evaluation score: 2.80
Count: 7 evaluation score: 2.70
Count: 2 evaluation score: 2.70
Count: 2 evaluation score: 2.60
Count: 1 evaluation score: 2.60
Count: 1 evaluation score: 2.50
Count: 1 evaluation score: 2.50
Count: 1 evaluation score: 2.30
Count: 1 evaluation score: 2.30
Count: 1 evaluation score: 2.20
Count: 1 evaluation score: 2.20
Count: 1 evaluation score: 2.10
Count: 1 evaluation score: 2.10
Count: 1 0 10 20 30 40 40 30 20 10 0 2.00 3.00 4.00 5.00 6.00 6.00 5.00 4.00 3.00 2.00
Log

* Figure 6.9

COMPUTE norm_eval=(eval-3.998)/.554.
EXECUTE.

GGRAPH
  /GRAPHDATASET NAME="graphdataset"
    VARIABLES=norm_eval[LEVEL=scale]
    MISSING=LISTWISE REPORTMISSING=NO
  /GRAPHSPEC SOURCE=VIZTEMPLATE(NAME="Histogram"[LOCATION=LOCAL]
    MAPPING( "x"="norm_eval"[DATASET="graphdataset"] "Summary"="count"))
    VIZSTYLESHEET="Traditional"[LOCATION=LOCAL]
    LABEL='HISTOGRAM: norm_eval'
    DEFAULTTEMPLATE=NO.
GGraph
x: 1.81
Count: 8 x: 1.81
Count: 8 x: 1.63
Count: 11 x: 1.63
Count: 11 x: 1.45
Count: 14 x: 1.45
Count: 14 x: 1.27
Count: 21 x: 1.27
Count: 21 x: 1.09
Count: 25 x: 1.09
Count: 25 x: .91
Count: 30 x: .91
Count: 30 x: .73
Count: 28 x: .73
Count: 28 x: .55
Count: 34 x: .55
Count: 34 x: .36
Count: 31 x: .36
Count: 31 x: .18
Count: 21 x: .18
Count: 21 x: .00
Count: 38 x: .00
Count: 38 x: -.18
Count: 29 x: -.18
Count: 29 x: -.36
Count: 28 x: -.36
Count: 28 x: -.54
Count: 22 x: -.54
Count: 22 x: -.72
Count: 26 x: -.72
Count: 26 x: -.90
Count: 20 x: -.90
Count: 20 x: -1.08
Count: 19 x: -1.08
Count: 19 x: -1.26
Count: 13 x: -1.26
Count: 13 x: -1.44
Count: 7 x: -1.44
Count: 7 x: -1.62
Count: 10 x: -1.62
Count: 10 x: -1.80
Count: 10 x: -1.80
Count: 10 x: -1.98
Count: 4 x: -1.98
Count: 4 x: -2.16
Count: 7 x: -2.16
Count: 7 x: -2.34
Count: 2 x: -2.34
Count: 2 x: -2.52
Count: 1 x: -2.52
Count: 1 x: -2.70
Count: 1 x: -2.70
Count: 1 x: -3.06
Count: 1 x: -3.06
Count: 1 x: -3.25
Count: 1 x: -3.25
Count: 1 x: -3.43
Count: 1 x: -3.43
Count: 1 0 10 20 30 40 40 30 20 10 0 -4.00 -3.00 -2.00 -1.00 .00 1.00 2.00 2.00 1.00 .00 -1.00 -2.00 -3.00 -4.00
Log


* Table 6.5

SUMMARIZE
  /TABLES=eval BY gender
  /FORMAT=NOLIST TOTAL
  /TITLE='Case Summaries'
  /MISSING=VARIABLE
  /CELLS=MEAN STDDEV.
Summarize
Case Processing SummaryCase Processing Summary, table, 3 levels of column headers and 1 levels of row headers, table with 7 columns and 5 rows
  Cases
Included Excluded Total
N Percent N Percent N Percent
evaluation score * gender 463 100.0% 0 0.0% 463 100.0%
Summarize
Case SummariesCase Summaries, table, evaluation score, 1 layers, 1 levels of column headers and 1 levels of row headers, table with 3 columns and 6 rows
evaluation score evaluation score
gender Mean Std. Deviation
female 3.9010 .53880
male 4.0690 .55665
Total 3.9983 .55487
Log

* Figure 6.26 Fig. 6.30, and Fig. 6.35

T-TEST GROUPS=gen2(1 2)
  /MISSING=ANALYSIS
  /VARIABLES=eval
  /CRITERIA=CI(.95).
T-Test
Group StatisticsGroup Statistics, table, 1 levels of column headers and 2 levels of row headers, table with 6 columns and 4 rows
  instructor's gender N Mean Std. Deviation Std. Error Mean
evaluation score female 195 3.9010 .53880 .03858
male 268 4.0690 .55665 .03400
T-Test
Independent Samples TestIndependent Samples Test, table, 3 levels of column headers and 2 levels of row headers, table with 11 columns and 6 rows
  Levene's Test for Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference
Lower Upper
evaluation score Equal variances assumed .190 .663 -3.250 461 .001 -.16800 .05169 -.26959 -.06642
Equal variances not assumed     -3.267 425.756 .001 -.16800 .05143 -.26909 -.06692
Log

* Figure 6.36

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT eval
  /METHOD=ENTER gen2.
Regression
Variables Entered/RemovedaVariables Entered/Removed, table, 1 levels of column headers and 1 levels of row headers, table with 4 columns and 5 rows
Model Variables Entered Variables Removed Method
1 instructor's genderb . Enter
a. Dependent Variable: evaluation score
b. All requested variables entered.
Regression
Model SummaryModel Summary, table, 1 levels of column headers and 1 levels of row headers, table with 5 columns and 4 rows
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .150a .022 .020 .54921
a. Predictors: (Constant), instructor's gender
Regression
ANOVAaANOVA, table, 1 levels of column headers and 2 levels of row headers, table with 7 columns and 7 rows
Model Sum of Squares df Mean Square F Sig.
1 Regression 3.186 1 3.186 10.562 .001b
Residual 139.053 461 .302    
Total 142.239 462      
a. Dependent Variable: evaluation score
b. Predictors: (Constant), instructor's gender
Regression
CoefficientsaCoefficients, table, 2 levels of column headers and 2 levels of row headers, table with 7 columns and 6 rows
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.733 .086   43.653 .000
instructor's gender .168 .052 .150 3.250 .001
a. Dependent Variable: evaluation score
Log

* Figure 6.37

SPSSINC CREATE DUMMIES VARIABLE=age_cat
ROOTNAME1=newage
/OPTIONS ORDER=A USEVALUELABELS=YES USEML=YES OMITFIRST=NO.
Create dummy variables
Variable CreationVariable Creation, table, 1 levels of column headers and 1 levels of row headers, table with 2 columns and 5 rows
  Label
newage_1 age_cat=0-
newage_2 age_cat=44-
newage_3 age_cat=59-
Log

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT eval
  /METHOD=ENTER newage_2 newage_3.
Regression
Variables Entered/RemovedaVariables Entered/Removed, table, 1 levels of column headers and 1 levels of row headers, table with 4 columns and 5 rows
Model Variables Entered Variables Removed Method
1 age_cat=59-, age_cat=44-b . Enter
a. Dependent Variable: evaluation score
b. All requested variables entered.
Regression
Model SummaryModel Summary, table, 1 levels of column headers and 1 levels of row headers, table with 5 columns and 4 rows
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .100a .010 .006 .55329
a. Predictors: (Constant), age_cat=59-, age_cat=44-
Regression
ANOVAaANOVA, table, 1 levels of column headers and 2 levels of row headers, table with 7 columns and 7 rows
Model Sum of Squares df Mean Square F Sig.
1 Regression 1.418 2 .709 2.316 .100b
Residual 140.821 460 .306    
Total 142.239 462      
a. Dependent Variable: evaluation score
b. Predictors: (Constant), age_cat=59-, age_cat=44-
Regression
CoefficientsaCoefficients, table, 2 levels of column headers and 2 levels of row headers, table with 7 columns and 7 rows
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 4.004 .044   91.831 .000
age_cat=44- .032 .057 .029 .553 .580
age_cat=59- -.123 .076 -.084 -1.626 .105
a. Dependent Variable: evaluation score
Log

* Figure 6.38

ONEWAY eval BY age_cat
  /MISSING ANALYSIS.
Oneway
ANOVAANOVA, table, evaluation score, 1 layers, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 6 rows
evaluation score evaluation score
  Sum of Squares df Mean Square F Sig.
Between Groups 1.418 2 .709 2.316 .100
Within Groups 140.821 460 .306    
Total 142.239 462      
Log

* Figure 6.39

ONEWAY eval BY beauty_cat
  /MISSING ANALYSIS.
Oneway
ANOVAANOVA, table, evaluation score, 1 layers, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 6 rows
evaluation score evaluation score
  Sum of Squares df Mean Square F Sig.
Between Groups 2.203 2 1.101 3.618 .028
Within Groups 140.036 460 .304    
Total 142.239 462      
Log

* Figure 6.40

CROSSTABS
  /TABLES=gender BY tenure
  /FORMAT=AVALUE TABLES
  /STATISTICS=CHISQ
  /CELLS=COUNT ROW
  /COUNT ROUND CELL.
Crosstabs
Case Processing SummaryCase Processing Summary, table, 3 levels of column headers and 1 levels of row headers, table with 7 columns and 5 rows
  Cases
Valid Missing Total
N Percent N Percent N Percent
gender * tenured 463 100.0% 0 0.0% 463 100.0%
Crosstabs
gender * tenured Crosstabulationgender * tenured Crosstabulation, table, 2 levels of column headers and 3 levels of row headers, table with 6 columns and 9 rows
  tenured Total
no yes
gender female Count 50 145 195
% within gender 25.6% 74.4% 100.0%
male Count 52 216 268
% within gender 19.4% 80.6% 100.0%
Total Count 102 361 463
% within gender 22.0% 78.0% 100.0%
Crosstabs
Chi-Square TestsChi-Square Tests, table, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 9 rows
  Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)
Pearson Chi-Square 2.557a 1 .110    
Continuity Correctionb 2.207 1 .137    
Likelihood Ratio 2.537 1 .111    
Fisher's Exact Test       .113 .069
N of Valid Cases 463        
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 42.96.
b. Computed only for a 2x2 table
IBM SPSS Web Report
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