Two-Group Discriminant Function Analysis: Annotated Statistical OutputGET FILE='C:\Users\Vati\Documents\_XYZZY\_Stats\SPSS\Harass90.sav'.DATASET NAME DataSet1 WINDOW=FRONT.DISCRIMINANT /GROUPS=verdict(1 2) /VARIABLES=d_excit d_calm d_indep d_sincer d_warm d_attrac d_kind d_intell d_strong d_sophis d_happy p_excit p_calm p_indep p_sincer p_warm p_attrac p_kind p_intell p_strong p_sophis p_happy /ANALYSIS ALL /SAVE=SCORES /PRIORS SIZE /STATISTICS=MEAN STDDEV UNIVF BOXM COEFF RAW TABLE /CLASSIFY=NONMISSING POOLED.

Analysis Case Processing Summary

Unweighted Cases N Percent

Valid 145 100.0

Excluded

Missing or out-of-range group codes

0 .0

At least one missing discriminating variable

0 .0

Both missing or out-of-range group codes and at least one missing discriminating variable

0 .0

Total 0 .0Total 145 100.0

Group Statistics

verdict Guilty Not Guilty

Mean Std. Deviation Mean Std. Deviation

d_excit 4.316 1.8808 6.520 1.8349

d_calm 5.463 2.0096 6.380 1.7729

d_indep 6.000 2.1586 5.860 1.9324

d_sincer 4.168 1.9659 6.840 1.4462

d_warm 4.558 2.0768 7.260 1.5238

d_attrac 4.379 3.2524 6.600 2.8927

d_kind 4.789 1.9290 6.560 1.5954

d_intell 6.463 1.7061 6.860 1.4682

d_strong 5.474 2.0774 4.740 1.5386

d_sophis 5.400 2.0021 4.280 1.6183

d_happy 5.211 1.7436 5.060 1.5120

p_excit 4.926 1.7088 4.360 2.0683

p_calm 4.284 1.8199 4.320 1.7961

p_indep 5.368 2.2076 3.960 2.0445

p_sincer 6.368 1.8222 4.740 1.6874

p_warm 6.242 1.5209 5.200 1.4769

p_attrac 5.926 3.1054 4.420 3.0369

p_kind 6.463 1.5629 4.420 1.8049

p_intell 6.147 1.5366 3.620 1.6782

p_strong 5.295 1.9509 6.520 1.5131

p_sophis 5.284 1.9056 6.380 1.8962

p_happy 3.832 2.0035 5.860 1.7600

Jurors who ruled in favor of the defendant rated him more socially desirable than did jurors who found in favor of the plaintiff, especially for

Calm (vs Nervous) Sincere (vs Insincere) Warm (vs Cold), and Kind (vs Cruel).

Jurors who ruled in favor of the plaintiff rated her more socially desirable than did jurors who found in favor of the defendant, especially for

Sincere Warm Physically Attractive, and Kind

Tests of Equality of Group Means

Wilks' Lambda F df1 df2 Sig.

d_excit .968 4.669 1 143 .032d_calm .893 17.128 1 143 .000d_indep .952 7.245 1 143 .008d_sincer .720 55.615 1 143 .000d_warm .827 29.955 1 143 .000d_attrac .951 7.317 1 143 .008d_kind .775 41.509 1 143 .000d_intell .948 7.843 1 143 .006d_strong .926 11.391 1 143 .001d_sophis .920 12.479 1 143 .001d_happy .817 32.039 1 143 .000

p_excit .998 .336 1 143 .563p_calm 1.000 .000 1 143 .989p_indep .995 .672 1 143 .414p_sincer .774 41.840 1 143 .000p_warm .728 53.365 1 143 .000p_attrac .915 13.333 1 143 .000p_kind .800 35.735 1 143 .000p_intell .924 11.681 1 143 .001p_strong .949 7.628 1 143 .007p_sophis .955 6.761 1 143 .010p_happy .997 .396 1 143 .530

ANOVAs comparing the two groups were significant for all variables except those highlighted in yellow

Box's Test of Equality of Covariance Matrices

Log Determinants

verdict Rank Log Determinant

Guilty 22 17.958Not_G 22 8.820Pooled within-groups 22 17.604

The ranks and natural logarithms of determinants printed are those of the group covariance matrices.

Test Results

Box's M 397.149

F

Approx. 1.275

df1 253

df2 32013.563

Sig. .002

Tests null hypothesis of equal population covariance matrices.

For each population

X1 X2 X3 X4 X5 …X1 var cov cov cov covX2 cov var cov cov covX3 cov cov var cov covX4 cov cov cov var covX5 cov cov cov cov var…

The null is that the variance/covariance matrices are constant across groups. Since p > .001, we accept that null as close enough to true to continue with our analysis.

Standardized Canonical

Discriminant Function Coefficients

Function

1

d_excit .154d_calm .152d_indep .007d_sincer .301d_warm -.405d_attrac .011d_kind .382d_intell .001d_strong .078d_sophis .004d_happy .284p_excit .299p_calm .291p_indep .262p_sincer -.398p_warm -.373p_attrac -.023p_kind -.111p_intell -.243p_strong -.438p_sophis .007p_happy .056

I have removed the unstandardized coefficients from this document.

Standardized D = .154(ZD_excit) + .152((ZD_calm) + … + .056(ZP_happy). I have highlighted the larger coefficients, but am reluctant to interpret them due to the almost certain existence of effect of redundancy and suppression.

For each subject, compute standardized (or unstandardarized) D using these coefficients and the subject’s scores on the 22 variables. Then do an ANOVA comparing the two groups on D.

Summary of Canonical Discriminant Functions

Eigenvalues

Function Eigenvalue % of Variance Cumulative % Canonical Correlation

1 1.187a 100.0 100.0 .737

a. First 1 canonical discriminant functions were used in the analysis.

Wilks' Lambda

Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 .457 103.299 22 .000

For that ANOVA, SSBetween_Groups divided by SSWithin Groups = 1.187.

SSBetween Groups divided by SSTotal = the squared canonical correlation = .7372 = .543 = the ANOVA eta-squared.

Wilks’ Lambda = SSWithin Groups divided by SSTotal – the smaller lambda the greater the difference between the two groups.

Eta squared = 1 – Lambda = 1 - .457 = .543.

The difference between the two groups is significant, p < .001.

Structure Matrix(Loadings)

Function

1

d_sincer .572p_warm -.561p_sincer -.496d_kind .494p_kind -.459d_happy .434d_warm .420d_calm .318p_attrac -.280d_sophis .271p_intell -.262d_strong .259d_intell .215p_strong -.212d_attrac .208d_indep .207p_sophis -.200d_excit .166p_indep -.063p_happy -.048p_excit -.044p_calm -.001

The loadings are correlations between D and the observed variables. Use these to get a feel for what D is. The loadings here show that a high D means the juror thought the defendant sincere, kind, happy, warm, and calm, and the plaintiff cold, insincere, and cruel.

Put more simply, high D means the juror likes the defendant more than the plaintiff.

Functions at Group Centroids

verdict Function

1

Guilty -.785Not_G 1.491

Classification Statistics

Classification Processing Summary

Processed 145

Excluded

Missing or out-of-range group codes

0

At least one missing discriminating variable

0

Used in Output 145

Prior Probabilities for Groups

verdict Prior Cases Used in Analysis

Unweighted Weighted

Guilty .655 95 95.000Not_G .345 50 50.000Total 1.000 145 145.000

These are the group means on D. Jurors who ruled in favor of the defendant found him more socially desirable than did those who ruled against him

We can get better classification by using unequal priors – 95/145 = .655 of the jurors ruled against the defendant and 50/145 = .345 of them ruled against the defendant.

Classification Function Coefficients

verdict

Guilty Not_G

d_excit .735 .923d_calm .346 .526d_indep .557 .565d_sincer .049 .428d_warm .810 .327d_attrac -.741 -.733d_kind -.470 .008d_intell 2.142 2.143d_strong -.014 .079d_sophis -.091 -.086d_happy 1.116 1.503p_excit .936 1.305p_calm .758 1.124p_indep .647 .924p_sincer .659 .149p_warm 1.610 1.046p_attrac -.198 -.215p_kind .511 .358p_intell .767 .419p_strong .718 .168p_sophis -.452 -.443p_happy -.495 -.429(Constant) -29.338 -30.930

Fisher's linear discriminant functions

These are Fisher’s Classification Coefficients. For each subject, compute G1 = .735(d_Excit) + .346(d_Calm) + … - .495(p_Happy) – 29.338. Then compute G0 using the Not Guilty column of coefficients. If G1 > G0 then classify the subject as voting guilty If G0 > G1 then classify the subject as voting not guilty.

Classification using this methods produces results identical to classification using Bayes Theorem

Classification Resultsa

verdict Predicted Group Membership Total

Guilty Not_G

Original

CountGuilty 85 10 95

Not_G 7 43 50

%Guilty 89.5 10.5 100.0

Not_G 14.0 86.0 100.0

a. 88.3% of original grouped cases correctly classified.

Among those jurors who actually voted guilty, we correctly classified 89.5% of them.Among those jurors who actually voted not guilty, we correctly classified 86% of them.

LOGISTIC REGRESSION VARIABLES verdict /METHOD=ENTER d_excit d_calm d_indep d_sincer d_warm d_attrac d_kind d_intell d_strong d_sophis d_happy p_excit p_calm p_indep p_sincer p_warm p_attrac p_kind p_intell p_strong p_sophis p_happy /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

Block 0: Beginning Block

Classification Tablea,b

Observed Predicted

verdict Percentage CorrectGuilty Not_G

Step 0verdict

Guilty 95 0 100.0

Not_G 50 0 .0

Overall Percentage 65.5

a. Constant is included in the model.

We can correctly classify 65.5 percent of the cases by simply classifying every case as having voted guilty.

b. The cut value is .500

Variables in the Equation

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

Step 0 Constant -.642 .175 13.496 1 .000 .526

Variables not in the Equation

Score df Sig.

Step 0 Variables d_excit 4.585 1 .032

d_calm 15.510 1 .000

d_indep 6.992 1 .008

d_sincer 40.602 1 .000

d_warm 25.113 1 .000

d_attrac 7.058 1 .008

d_kind 32.621 1 .000

d_intell 7.539 1 .006

d_strong 10.698 1 .001

d_sophis 11.638 1 .001

d_happy 26.541 1 .000

p_excit .340 1 .560

p_calm .000 1 .989

p_indep .678 1 .410

p_sincer 32.822 1 .000

p_warm 39.406 1 .000

p_attrac 12.367 1 .000

p_kind 28.990 1 .000

p_intell 10.950 1 .001

p_strong 7.343 1 .007

p_sophis 6.546 1 .011

The p values here are identical to those we got for the univariate ANOVAs in the discriminant function analysis.

p_happy .401 1 .527

Overall Statistics 78.702 22 .000

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 119.275 22 .000

Block 119.275 22 .000

Model 119.275 22 .000

Model Summary

Step -2 Log likelihood Cox & Snell R Square

Nagelkerke R Square

1 67.539a .561 .774

a. Estimation terminated at iteration number 7 because parameter estimates changed by less than .001.

Classification Tablea

Observed Predicted

verdict Percentage CorrectGuilty Not_G

Step 1verdict

Guilty 87 8 91.6

Not_G 8 42 84.0

Overall Percentage 89.0

a. The cut value is .500

Using the 22 variables in a binary logistic regression, we classify significantly better than just by classifying every case in the group that has the highest number of cases.

Overall, the classification was a little bit better (89%) than it was with discriminant function analysis (88.3%).

Variables in the Equation

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

Step 1a

d_excit .288 .291 .980 1 .322 1.334

d_calm .099 .209 .224 1 .636 1.104

d_indep -.029 .212 .019 1 .890 .971

d_sincer .808 .346 5.436 1 .020 2.243

d_warm -.749 .404 3.447 1 .063 .473

d_attrac -.130 .184 .497 1 .481 .878

d_kind .697 .382 3.323 1 .068 2.007

d_intell .002 .352 .000 1 .996 1.002

d_strong .056 .409 .019 1 .892 1.057

d_sophis .153 .393 .153 1 .696 1.166

d_happy .592 .334 3.142 1 .076 1.807

p_excit .448 .348 1.655 1 .198 1.565

p_calm .545 .282 3.728 1 .054 1.724

p_indep .179 .223 .645 1 .422 1.196

p_sincer -.092 .306 .091 1 .763 .912

p_warm -.991 .355 7.815 1 .005 .371

p_attrac -.049 .224 .048 1 .827 .952

p_kind -.496 .350 2.005 1 .157 .609

p_intell -.458 .330 1.926 1 .165 .632

p_strong -.729 .297 6.031 1 .014 .482

p_sophis -.252 .284 .788 1 .375 .777

p_happy .313 .304 1.062 1 .303 1.368

Constant -1.644 3.194 .265 1 .607 .193

a. Variable(s) entered on step 1: d_excit, d_calm, d_indep, d_sincer, d_warm, d_attrac, d_kind, d_intell, d_strong, d_sophis, d_happy, p_excit, p_calm, p_indep, p_sincer, p_warm, p_attrac, p_kind, p_intell, p_strong, p_sophis, p_happy.

Here we see which predictors have significant unique effects in the regression. The Exp(B) contains odds ratios.Each one point increase in rated sincerity of the defendant more than doubles the odds that the finding we be in favor of the defendant.

Each one point increase in rated warmth of the plaintiff almost triples (1/.371 = 2.7) the odds that the juror will rule in her favor.

Here is the Discriminant Function Analysis Done With SAS

DATA seckel; INFILE 'C:\Users\Vati\Documents\_XYZZY\_Stats\StatData\harass90.dat'; INPUT VERDICT $ 6 D_excit 16 D_calm 17 D_indep 18 D_sincer 19 D_warm 20 D_attrac 21 D_kind 22 D_intell 23 D_strong 24 D_sophis 25 D_happy 26 P_excit 28 P_calm 29 P_indep 30 P_sincer 31 P_warm 32 P_attrac 33 P_kind 34 P_intell 35 P_strong 36 P_sophis 37 P_happy 38;If verdict = 'Y' then verdic = 1; else verdic = 2;PROC DISCRIM simple anova canonical listerr;CLASS verdict; VAR D_excit -- P_happy; PRIORS proportional;PROC REG; MODEL verdic = D_excit -- P_happy / stb; run;

2 GROUP DFA, Harass90 Study

The DISCRIM Procedure

Total Sample Size 145 DF Total 144

Variables 22 DF Within Classes 143

Classes 2 DF Between Classes 1

Number of Observations Read 145

Number of Observations Used 145

Class Level Information

VERDICT VariableName

Frequency Weight Proportion PriorProbability

N N 50 50.0000 0.344828 0.344828

Y Y 95 95.0000 0.655172 0.655172

The DISCRIM ProcedureSimple Statistics

Total-Sample

Variable N Sum Mean Variance StandardDeviation

D_excit 145 661.00000 4.55862

3.56772 1.8888

Total-Sample

Variable N Sum Mean Variance StandardDeviation

D_calm 145 862.00000 5.94483

4.14971 2.0371

D_indep 145 919.00000 6.33793

4.53084 2.1286

D_sincer 145 722.00000 4.97931

4.49262 2.1196

D_warm 145 752.00000 5.18621

4.36092 2.0883

D_attrac 145 709.00000 4.8896610.25163 3.2018

D_kind 145 797.00000 5.49655

4.25172 2.0620

D_intell 145 977.00000 6.73793

2.77807 1.6668

D_strong

145 850.00000 5.86207

3.91140 1.9777

D_sophis 145 841.00000 5.80000

3.81389 1.9529

D_happy 145 838.00000 5.77931

3.38151 1.8389

P_excit 145 705.00000 4.86207

3.36973 1.8357

P_calm 145 621.00000 4.28276

3.25977 1.8055

P_indep 145 763.00000 5.26207

4.62529 2.1506

P_sincer 145 823.00000 5.67586

4.05393 2.0134

Total-Sample

Variable N Sum Mean Variance StandardDeviation

P_warm 145 809.00000 5.57931

3.09262 1.7586

P_attrac 145 761.00000 5.2482810.31293 3.2114

P_kind 145 851.00000 5.86897

3.37854 1.8381

P_intell 145 844.00000 5.82069

2.70374 1.6443

P_strong 145 724.00000 4.99310

3.43745 1.8540

P_sophis 145 723.00000 4.98621

3.76370 1.9400

P_happy 145 545.00000 3.75862

3.68439 1.9195

 VERDICT = N

Variable N Sum Mean Variance StandardDeviation

D_excit 50 251.000005.02000 3.36694 1.8349

D_calm 50 343.000006.86000 3.14327 1.7729

D_indep 50 349.000006.98000 3.73429 1.9324

D_sincer 50 326.000006.52000 2.09143 1.4462

D_warm 50 319.000006.38000 2.32204 1.5238

 VERDICT = N

Variable N Sum Mean Variance StandardDeviation

D_attrac 50 293.000005.86000 8.36776 2.8927

D_kind 50 342.000006.84000 2.54531 1.5954

D_intell 50 363.000007.26000 2.15551 1.4682

D_strong 50 330.000006.60000 2.36735 1.5386

D_sophis 50 328.000006.56000 2.61878 1.6183

D_happy 50 343.000006.86000 2.28612 1.5120

P_excit 50 237.000004.74000 4.27796 2.0683

P_calm 50 214.000004.28000 3.22612 1.7961

P_indep 50 253.000005.06000 4.18000 2.0445

P_sincer 50 218.000004.36000 2.84735 1.6874

P_warm 50 216.000004.32000 2.18122 1.4769

P_attrac 50 198.000003.96000 9.22286 3.0369

P_kind 50 237.000004.74000 3.25755 1.8049

P_intell 50 260.000005.20000 2.81633 1.6782

 VERDICT = N

Variable N Sum Mean Variance StandardDeviation

P_strong 50 221.000004.42000 2.28939 1.5131

P_sophis 50 221.000004.42000 3.59551 1.8962

P_happy 50 181.000003.62000 3.09755 1.7600

 

VERDICT = Y

Variable N Sum Mean Variance StandardDeviation

D_excit 95 410.000004.31579 3.53751 1.8808

D_calm 95 519.000005.46316 4.03852 2.0096

D_indep 95 570.000006.00000 4.65957 2.1586

D_sincer 95 396.000004.16842 3.86495 1.9659

D_warm 95 433.000004.55789 4.31310 2.0768

D_attrac 95 416.000004.37895 10.57828 3.2524

D_kind 95 455.000004.78947 3.72116 1.9290

D_intell 95 614.000006.46316 2.91086 1.7061

D_strong 95 520.000005.47368 4.31579 2.0774

VERDICT = Y

Variable N Sum Mean Variance StandardDeviation

D_sophis 95 513.000005.40000 4.00851 2.0021

D_happy 95 495.000005.21053 3.04031 1.7436

P_excit 95 468.000004.92632 2.92004 1.7088

P_calm 95 407.000004.28421 3.31198 1.8199

P_indep 95 510.000005.36842 4.87346 2.2076

P_sincer 95 605.000006.36842 3.32027 1.8222

P_warm 95 593.000006.24211 2.31310 1.5209

P_attrac 95 563.000005.92632 9.64345 3.1054

P_kind 95 614.000006.46316 2.44278 1.5629

P_intell 95 584.000006.14737 2.36103 1.5366

P_strong 95 503.000005.29474 3.80582 1.9509

P_sophis 95 502.000005.28421 3.63113 1.9056

P_happy 95 364.000003.83158 4.01389 2.0035

The DISCRIM Procedure

Univariate Test Statistics

F Statistics, Num DF=1, Den DF=143

Variable TotalStandardDeviatio

n

PooledStandardDeviation

BetweenStandardDeviation

R-Square R-Square

/ (1-RSq)

F Value Pr > F

D_excit 1.8888 1.8652 0.4734 0.0316 0.0327 4.67 0.0324

D_calm 2.0371 1.9318 0.9389 0.1070 0.1198 17.13 <.0001

D_indep 2.1286 2.0839 0.6587 0.0482 0.0507 7.24 0.0080

D_sincer 2.1196 1.8048 1.5807 0.2800 0.3889 55.62 <.0001

D_warm 2.0883 1.9055 1.2248 0.1732 0.2095 29.95 <.0001

D_attrac 3.2018 3.1338 0.9956 0.0487 0.0512 7.32 0.0077

D_kind 2.0620 1.8216 1.3783 0.2250 0.2903 41.51 <.0001

D_intell 1.6668 1.6285 0.5356 0.0520 0.0548 7.84 0.0058

D_strong 1.9777 1.9100 0.7571 0.0738 0.0797 11.39 0.0010

D_sophis 1.9529 1.8794 0.7797 0.0803 0.0873 12.48 0.0006

D_happy 1.8389 1.6679 1.1088 0.1830 0.2240 32.04 <.0001

P_excit 1.8357 1.8399 0.1252 0.0023 0.0023 0.34 0.5631

P_calm 1.8055 1.8118 0.002830 0.0000 0.0000 0.00 0.9894

P_indep 2.1506 2.1531 0.2073 0.0047 0.0047 0.67 0.4137

P_sincer 2.0134 1.7771 1.3500 0.2264 0.2926 41.84 <.0001

P_warm 1.7586 1.5060 1.2920 0.2718 0.3732 53.36 <.0001

P_attrac 3.2114 3.0821 1.3217 0.0853 0.0932 13.33 0.0004

P_kind 1.8381 1.6498 1.1583 0.1999 0.2499 35.73 <.0001

P_intell 1.6443 1.5865 0.6368 0.0755 0.0817 11.68 0.0008

P_strong 1.8540 1.8128 0.5880 0.0506 0.0533 7.63 0.0065

P_sophis 1.9400 1.9023 0.5809 0.0451 0.0473 6.76 0.0103

Univariate Test Statistics

F Statistics, Num DF=1, Den DF=143

Variable TotalStandardDeviatio

n

PooledStandardDeviation

BetweenStandardDeviation

R-Square R-Square

/ (1-RSq)

F Value Pr > F

P_happy 1.9195 1.9235 0.1422 0.0028 0.0028 0.40 0.5300

Average R-Square

Unweighted 0.1030531

Weighted by Variance

0.0998517

The DISCRIM ProcedureCanonical Discriminant Analysis

Canonical

Correlation

Adjusted

Canonical

Correlation

Approximate

StandardError

Squared

Canonical

Correlation

Eigenvalues of Inv(E)*H= CanRsq/(1-CanRsq)

Test of H0: The canonical correlations in the current row and all that follow are zero

Eigenvalue

Difference

Proportion

Cumulative

LikelihoodRatio

ApproximateF Value

Num DF Den DF Pr > F

1 0.7367290.69264

50.038103 0.54277

01.1871 1.0000 1.0000 0.45723020 6.58 22 122 <.0001

Note: The F statistic is exact.

Pooled Within Canonical Structure

Variable Can1

D_excit 0.165854

D_calm 0.317647

D_indep 0.206590

D_sincer 0.572386

D_warm 0.420072

Pooled Within Canonical Structure

Variable Can1

D_attrac 0.207611

D_kind 0.494499

D_intell 0.214950

D_strong 0.259047

D_sophis 0.271133

D_happy 0.434440

P_excit -0.044484

P_calm -0.001021

P_indep -0.062927

P_sincer -0.496464

P_warm -0.560684

P_attrac -0.280260

P_kind -0.458816

P_intell -0.262318

P_strong -0.211975

P_sophis -0.199565

P_happy -0.048321

Pooled Within-Class StandardizedCanonical Coefficients

Variable Can1

D_excit 0.1541140637

D_calm 0.1523704773

D_indep 0.0073134520

D_sincer 0.3010566728

Pooled Within-Class StandardizedCanonical Coefficients

Variable Can1

D_warm -.4050223910

D_attrac 0.0109195313

D_kind 0.3821464501

D_intell 0.0007561941

D_strong 0.0779772017

D_sophis 0.0037975433

D_happy 0.2836650494

P_excit 0.2989623192

P_calm 0.2909494239

P_indep 0.2616602872

P_sincer -.3978884508

P_warm -.3731094653

P_attrac -.0228988069

P_kind -.1111228607

P_intell -.2431145298

P_strong -.4379553438

P_sophis 0.0074865572

P_happy 0.0560626065

Raw Canonical Coefficients

Variable Can1

D_excit 0.0826249085

D_calm 0.0788758824

D_indep 0.0035095516

Raw Canonical Coefficients

Variable Can1

D_sincer 0.1668105078

D_warm -.2125567335

D_attrac 0.0034844162

D_kind 0.2097854697

D_intell 0.0004643480

D_strong 0.0408255288

D_sophis 0.0020205680

D_happy 0.1700733582

P_excit 0.1624856615

P_calm 0.1605872482

P_indep 0.1215272156

P_sincer -.2238928571

P_warm -.2477553595

P_attrac -.0074296159

P_kind -.0673538015

P_intell -.1532377558

P_strong -.2415919611

P_sophis 0.0039354313

P_happy 0.0291459672

Class Means on CanonicalVariables

VERDICT Can1

N 1.491424258

Y -0.784960136

The DISCRIM Procedure

Linear Discriminant Function forVERDICT

Variable N Y

Constant -30.92984 -29.33807

D_excit 0.92262 0.73453

D_calm 0.52585 0.34630

D_indep 0.56494 0.55695

D_sincer 0.42824 0.04852

D_warm 0.32660 0.81046

D_attrac -0.73290 -0.74084

D_kind 0.00781 -0.46974

D_intell 2.14284 2.14179

D_strong 0.07928 -0.01365

D_sophis -0.08639 -0.09099

D_happy 1.50288 1.11572

P_excit 1.30538 0.93550

P_calm 1.12401 0.75845

P_indep 0.92383 0.64719

P_sincer 0.14921 0.65888

P_warm 1.04596 1.60995

P_attrac -0.21529 -0.19837

P_kind 0.35755 0.51087

P_intell 0.41856 0.76739

P_strong 0.16787 0.71783

P_sophis -0.44340 -0.45236

P_happy -0.42887 -0.49522

The DISCRIM ProcedureClassification Results for Calibration Data: WORK.SECKELResubstitution Results using Linear Discriminant Function

Posterior Probability of Membershipin VERDICT

Obs From VERDICT

Classified intoVERDICT

N Y

1 Y N * 0.7291 0.2709

9 Y N * 0.6584 0.3416

10 N Y * 0.3733 0.6267

17 Y N * 0.7670 0.2330

20 N Y * 0.0760 0.9240

23 N Y * 0.1512 0.8488

77 Y N * 0.5424 0.4576

79 Y N * 0.9103 0.0897

86 Y N * 0.7346 0.2654

87 Y N * 0.5668 0.4332

109 Y N * 0.5151 0.4849

112 Y N * 0.8826 0.1174

114 N Y * 0.3420 0.6580

128 N Y * 0.4784 0.5216

130 Y N * 0.6275 0.3725

133 N Y * 0.2261 0.7739

137 N Y * 0.2865 0.7135

* Misclassified observation

The DISCRIM ProcedureClassification Summary for Calibration Data: WORK.SECKELResubstitution Summary using Linear Discriminant Function

Number of Observations and PercentClassified into VERDICT

From VERDICT N Y Total

N 43

86.00

7

14.00

50

100.00

Y 10

10.53

85

89.47

95

100.00

Total 53

36.55

92

63.45

145

100.00

Priors 0.34483 0.65517

Error Count Estimates for VERDICT

N Y Total

Rate 0.1400 0.1053 0.1172

Priors 0.3448 0.6552

2 GROUP DFA, Harass90 Study

The REG ProcedureModel: MODEL1

Dependent Variable: verdic

Number of Observations Read 145

Number of Observations Used 145

Analysis of Variance

Source DF Sum ofSquares

MeanSquare

F Value Pr > F

Model 22 17.78039 0.80820 6.58 <.0001

Error 122 14.97823 0.12277

Analysis of Variance

Source DF Sum ofSquares

MeanSquare

F Value Pr > F

Corrected Total 144 32.75862

Root MSE 0.35039 R-Square 0.5428

Dependent Mean 1.34483 Adj R-Sq 0.4603

Coeff Var 26.05456

Parameter Estimates

Variable DF ParameterEstimate

Standard

Error

t Value Pr > |t| StandardizedEstimate

Intercept

1 1.32955 0.22372 5.94 <.0001 0

D_excit 1 0.01970 0.02500 0.79 0.4322 0.07802

D_calm 1 0.01881 0.01983 0.95 0.3447 0.08032

D_indep 1 0.00083680 0.01701 0.05 0.9608 0.00373

D_sincer 1 0.03977 0.02705 1.47 0.1441 0.17675

D_warm 1 -0.05068 0.02855 -1.77 0.0784 -0.22190

D_attrac 1 0.00083081 0.01594 0.05 0.9585 0.00558

D_kind 1 0.05002 0.02818 1.77 0.0784 0.21625

D_intell 1 0.00011072 0.02636 0.00 0.9967 0.00038690

D_strong

1 0.00973 0.02665 0.37 0.7156 0.04036

D_sophis 1 0.00048177 0.02692 0.02 0.9857 0.00197

D_happy 1 0.04055 0.02405 1.69 0.0944 0.15634

P_excit 1 0.03874 0.02391 1.62 0.1078 0.14911

Parameter Estimates

Variable DF ParameterEstimate

Standard

Error

t Value Pr > |t| StandardizedEstimate

P_calm 1 0.03829 0.02034 1.88 0.0621 0.14494

P_indep 1 0.02898 0.01699 1.71 0.0907 0.13066

P_sincer 1 -0.05338 0.02234 -2.39 0.0184 -0.22535

P_warm 1 -0.05907 0.02616 -2.26 0.0257 -0.21781

P_attrac 1 -0.00177 0.01521 -0.12 0.9075 -0.01193

P_kind 1 -0.01606 0.02789 -0.58 0.5658 -0.06189

P_intell 1 -0.03654 0.02575 -1.42 0.1585 -0.12596

P_strong 1 -0.05760 0.02093 -2.75 0.0068 -0.22392

P_sophis 1 0.00093834 0.02194 0.04 0.9659 0.00382

P_happy 1 0.00695 0.02018 0.34 0.7312 0.02797

The REG ProcedureModel: MODEL1

Dependent Variable: verdic