Spiekermann, Sarah: Online Information Search with Electronic Agents: Drivers, Impediments, and Privacy Issues
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204
Appendix C. DETAILS OF THE ANALYSIS
C.1 Measures employed in the Structural Equation Model on Online Information Search
|
Endogenous Constructs
|
Measurement (questions and indices)
|
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Interaction with Agent
Number of page requests <zh_int_A>
Time spent interacting <zz_int_A>
|
Standardized value of number of page requests of category survey page, questions and call for Top-10 (transition)
Standardized value of time spent on category survey page and question pages
|
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Product Inspection
Number of page requests
<zh2_dpd>
Time spent ’inspecting‘
<zz2_dpd>
|
Standardized value of number of page requests for products during orientation phase 1 and during detailed inspection phase 3, including photo enlargements and Top-10 pages (excluding transitions)
Standardized value of time spent on phase 1 and phase 3
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Perceived product risk before purchase
|
PL question, e.g. functional risk (n):How probable is it that by buying over the Internet you misjudge the functional performance of any of the 4 products, meaning that the product will not fulfill what it promises?
Compact Camera: Very improbable -15 point scale - very probable
IL question, e.g. functional risk (n): How strong would be your loss perception in case the product does not perform functionally in the way it is supposed to? Compact Camera: very low - 15 point scale - very high
|
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Exogenous Constructs (source of measurement)
|
Measurement (questions and indices)
|
205
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Purchase Involvement
|
Q1: How important is it for you to find in our online store today and for a 60% discount a compact camera that fulfils your expectations?
5 = very important -...-
1 = not at all important
Q2: How urgently do you need a compact camera?
4 = very urgently -...-
1 = not at all urgently
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Product Class Knowledge
(Q1: Srinivasan and Ratchford, 1991; Q2: Moore and Lehmann, 1980)
|
How strongly fits you the following:
Q1: In comparison to the average citizen I already know quite a lot about hifi-equipment (e.g. stereos, cameras, TVs..)
Q2: I regularly advise peers in the choice of their electronics...);
5 = very true; 4 = quite true; 3 = depends; 2 = not really; 1 = not at all
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Privacy Concern
(Ackermann et al., 1999)
|
When visiting Web sites that collect information, many people find there is some information that they generally feel comfortable providing, some information they feel comfortable providing only under certain conditions, and some information that they never or rarely feel comfortable providing. Please indicate how comfortable you would be to provide each of the following types of information to a Web site. Please check one response for each question: Q1: your first name and family name; Q2: your mail address; Q3: your e-mail address; Q4: your phone number; Q5: information on your computer, hardware and software; Q6: yours yearly income; Q7: your credit card number; Q8: information on your hobbies; Q9: information on your health or medical history; Q10: your age;
5 = I would always feel comfortable providing this information to a Web site.
4 = I would usually feel comfortable providing this information to a Web site.
3 = I would sometimes feel comfortable providing this information to a Web site.
2 =I would rarely feel comfortable providing this information to a Web site.
1 =I would never feel comfortable providing this information to a Web site.
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Time Cost
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Q1: Did you have the feeling [while being in the online store] that you had rather done something else?
Not at all - 9point scale - Yes, very much so
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206
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Benefit of Interaction
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Q1: How well did [Luci] ’hit‘ your needs with her product suggestions?
5 = very well; 4 = quite well; 3 = sufficiently well; 2 = not really well; 1 = not at all
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Flow
(Csikszentmihalyi et al., 1995)
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Please indicate what feeling corresponds best to the condition that you perceived while you were surfing in the store:
Q1: Challenge in using the shopping interface: low - 9 point scale - high
Q2: Your ability to use the shopping interface: low - 9 point scale - high
Q3: Did you have the feeling [while being in the online store] that you had rather done something else?; Not at all - 9point scale - Yes, very much so
Q4: How well could you concentrate? Not at all - 9point scale - very well
Q5: How well could you forget yourself? Not at all - 9point scale - very well
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Stage in the Buying Process
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Q1: Did you collect any information about the product you signed up for [compact cameras] before you came to us here in the laboratory (e.g. did you go to a store to look at different models?); 1 = yes, I did, 2 = no, I did not
Q2: To what extend did you already know what you wanted to buy in the store before you came...
Index...
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207
C.2 Output of Structural Equation Model on Online Information Search (M-Plus)
Mplus VERSION 2.01
MUTHEN & MUTHEN
07/26/2001 10:42 AM
INPUT INSTRUCTIONS
TITLE: MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
product inspection)
DATA:
FILE IS "E:\ANALYSEN\IWA\msc2001\letzte Modellreihe\modell_D.dat";
VARIABLE:
NAMES ARE
AVG10 AUSSAGE AUSSAGE2 KAUFWICH WUNSCH RISK_EMP F2
F10C STAGES FLOW_B H_INT_A Z_INT_A H2_DPD Z2_DPD;
USEVARIABLES ARE
AVG10 AUSSAGE AUSSAGE2 KAUFWICH WUNSCH RISK_EMP F2
F10C STAGES FLOW_B H_INT_A Z_INT_A H2_DPD Z2_DPD;
MISSING ARE ALL (-999);
ANALYSIS:
ESTIMATOR = MLM;
MODEL:
involve BY kaufwich wunsch;
pknow BY aussage aussage2;
interaA BY z_int_A h_int_A;
interaPD BY H2_DPD Z2_DPD;
risk_emp ON pknow involve stages;
interaPD ON involve pknow risk_emp f10c f2 flow_b stages;
interaA ON involve pknow risk_emp f10c flow_b f2
avg10 stages;
!h_int_a WITH h2_dpd;
z2_dpd@0;
OUTPUT:
STANDARDIZED;
modindices;
208
TECH4;
MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
product inspection)
Mplus VERSION 2.01
MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 116
Number of y-variables 9
Number of x-variables 5
Number of continuous latent variables 4
Observed variables in the analysis
AVG10 AUSSAGE AUSSAGE2 KAUFWICH WUNSCH RISK_EMP
F2 F10C STAGES FLOW_B H_INT_A Z_INT_A
H2_DPD Z2_DPD
Continuous latent variables in the analysis
INVOLVE PKNOW INTERAA INTERAPD
Estimator MLM
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Input data file(s)
E:\ANALYSEN\IWA\msc2001\letzte Modellreihe\modell_D.dat
Input data format FREE
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 51.520*
Degrees of Freedom 44
P-Value 0.2032
Scaling Correction Factor 1.446
for MLM
209
* The chi-square value for MLM, MLMV, WLSM and WLSMV cannot be used for
chi-square difference tests. MLM chi-square difference testing is
described on page 360 in the Mplus User's Guide.
Chi-Square Test of Model Fit for the Baseline Model
Value 367.485
Degrees of Freedom 81
P-Value 0.0000
CFI/TLI
CFI 0.974
TLI 0.952
Mplus VERSION 2.01
MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.038
SRMR (Standardized Root Mean Square Residual)
Value 0.053
WRMR (Weighted Root Mean Square Residual)
Value 0.796
MODEL RESULTS
Estimates S.E. Est./S.E. Std StdYX
INVOLVE BY
KAUFWICH 1.000 0.000 0.000 1.026 0.953
WUNSCH 0.492 0.088 5.605 0.505 0.656
PKNOW BY
AUSSAGE 1.000 0.000 0.000 1.041 0.989
AUSSAGE2 0.721 0.124 5.829 0.751 0.662
INTERAA BY
Z_INT_A 1.000 0.000 0.000 1.896 0.921
H_INT_A 0.886 0.186 4.775 1.680 0.675
210
INTERAPD BY
H2_DPD 1.000 0.000 0.000 4.526 0.791
Z2_DPD 0.951 0.087 10.875 4.306 1.000
INTERAPD ON
INVOLVE 1.620 0.442 3.664 0.367 0.367
PKNOW 0.024 0.458 0.052 0.005 0.005
INTERAA ON
INVOLVE 0.582 0.229 2.539 0.315 0.315
PKNOW -0.684 0.232 -2.946 -0.375 -0.375
INTERAPD ON
RISK_EMP 0.436 0.259 1.685 0.096 0.139
F10C -0.646 0.178 -3.633 -0.143 -0.299
F2 -0.094 0.473 -0.198 -0.021 -0.018
FLOW_B 0.684 0.390 1.753 0.151 0.164
STAGES -0.440 0.577 -0.762 -0.097 -0.059
INTERAA ON
RISK_EMP -0.029 0.117 -0.250 -0.015 -0.022
F10C -0.146 0.094 -1.545 -0.077 -0.161
FLOW_B 0.266 0.169 1.574 0.140 0.152
F2 -0.409 0.248 -1.650 -0.216 -0.190
AVG10 -0.782 0.283 -2.769 -0.412 -0.259
STAGES -0.241 0.292 -0.828 -0.127 -0.077
Mplus VERSION 2.01
MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
RISK_EMP ON
PKNOW -0.320 0.156 -2.057 -0.334 -0.232
INVOLVE 0.023 0.146 0.158 0.024 0.016
RISK_EMP ON
STAGES -0.193 0.207 -0.932 -0.193 -0.081
PKNOW WITH
INVOLVE 0.380 0.111 3.415 0.355 0.355
INTERAPD WITH
INTERAA 1.836 0.782 2.349 0.214 0.214
AVG10 WITH
INVOLVE 0.062 0.058 1.074 0.061 0.096
PKNOW -0.035 0.054 -0.647 -0.033 -0.053
F2 WITH
211
INVOLVE 0.006 0.074 0.085 0.006 0.007
PKNOW -0.063 0.072 -0.867 -0.060 -0.068
F10C WITH
INVOLVE 0.202 0.232 0.870 0.197 0.094
PKNOW -0.096 0.199 -0.482 -0.092 -0.044
STAGES WITH
INVOLVE 0.113 0.054 2.081 0.110 0.182
PKNOW 0.134 0.052 2.554 0.129 0.213
FLOW_B WITH
INVOLVE -0.031 0.101 -0.309 -0.031 -0.028
PKNOW 0.066 0.091 0.729 0.064 0.059
Residual Variances
AUSSAGE 0.025 0.163 0.152 0.025 0.022
AUSSAGE2 0.723 0.135 5.356 0.723 0.562
KAUFWICH 0.106 0.160 0.664 0.106 0.092
WUNSCH 0.338 0.060 5.613 0.338 0.569
RISK_EMP 1.938 0.226 8.577 1.938 0.935
H_INT_A 3.378 0.546 6.187 3.378 0.545
Z_INT_A 0.642 0.575 1.116 0.642 0.152
H2_DPD 12.255 2.175 5.634 12.255 0.374
Z2_DPD 0.000 0.000 0.000 0.000 0.000
INTERAA 2.848 0.763 3.734 0.792 0.792
INTERAPD 16.510 3.630 4.549 0.806 0.806
Variances
INVOLVE 1.053 0.221 4.760 1.000 1.000
PKNOW 1.083 0.190 5.698 1.000 1.000
Intercepts
AUSSAGE 3.647 0.095 38.581 3.647 3.465
AUSSAGE2 2.845 0.102 27.757 2.845 2.508
KAUFWICH 3.353 0.097 34.496 3.353 3.114
WUNSCH 2.362 0.071 33.186 2.362 3.067
RISK_EMP 3.525 0.385 9.149 3.525 2.448
Mplus VERSION 2.01
MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
H_INT_A 11.280 1.877 6.011 11.280 4.530
Z_INT_A 11.148 1.937 5.755 11.148 5.415
H2_DPD 7.796 3.057 2.550 7.796 1.363
Z2_DPD 7.051 2.854 2.471 7.051 1.638
R-SQUARE
Observed
212
Variable R-Square
AUSSAGE 0.978
AUSSAGE2 0.438
KAUFWICH 0.908
WUNSCH 0.431
RISK_EMP 0.065
H_INT_A 0.455
Z_INT_A 0.848
H2_DPD 0.626
Z2_DPD 1.000
Latent
Variable R-Square
INTERAA 0.208
INTERAPD 0.194
TECHNICAL 4 OUTPUT
ESTIMATES DERIVED FROM THE MODEL
ESTIMATED MEANS FOR THE LATENT VARIABLES
INVOLVE PKNOW INTERAA INTERAPD RISK_EMP
________ ________ ________ ________ ________
1 0.000 0.000 -3.757 0.419 3.207
ESTIMATED MEANS FOR THE LATENT VARIABLES
AVG10 F2 F10C STAGES FLOW_B
________ ________ ________ ________ ________
1 3.310 3.397 6.534 1.647 6.260
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
INVOLVE PKNOW INTERAA INTERAPD RISK_EMP
________ ________ ________ ________ ________
INVOLVE 1.053
PKNOW 0.380 1.083
INTERAA 0.241 -0.457 3.596
INTERAPD 1.461 0.536 2.558 20.485
RISK_EMP -0.119 -0.364 0.118 0.717 2.073
AVG10 0.062 -0.035 -0.194 0.158 0.008
F2 0.006 -0.063 -0.138 -0.205 0.031
F10C 0.202 -0.096 -0.248 -1.854 0.031
Mplus VERSION 2.01
MSC 2001 (Modell ohne letzterk, mit Effekt von stages auf
213
STAGES 0.113 0.134 -0.116 -0.052 -0.111
FLOW_B -0.031 0.066 0.100 0.065 -0.017
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
AVG10 F2 F10C STAGES FLOW_B
________ ________ ________ ________ ________
AVG10 0.395
F2 -0.162 0.774
F10C -0.231 0.590 4.387
STAGES 0.023 -0.058 0.025 0.366
FLOW_B -0.146 0.293 1.033 -0.025 1.177
ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
INVOLVE PKNOW INTERAA INTERAPD RISK_EMP
________ ________ ________ ________ ________
INVOLVE 1.000
PKNOW 0.355 1.000
INTERAA 0.124 -0.232 1.000
INTERAPD 0.315 0.114 0.298 1.000
RISK_EMP -0.081 -0.243 0.043 0.110 1.000
AVG10 0.096 -0.053 -0.163 0.055 0.009
F2 0.007 -0.068 -0.083 -0.051 0.025
F10C 0.094 -0.044 -0.062 -0.196 0.010
STAGES 0.182 0.213 -0.101 -0.019 -0.127
FLOW_B -0.028 0.059 0.049 0.013 -0.011
ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
AVG10 F2 F10C STAGES FLOW_B
________ ________ ________ ________ ________
AVG10 1.000
F2 -0.293 1.000
F10C -0.176 0.320 1.000
STAGES 0.059 -0.109 0.020 1.000
FLOW_B -0.215 0.307 0.455 -0.038 1.000
Beginning Time: 10:42:59
Ending Time: 10:43:00
Elapsed Time: 00:00:01
MUTHEN & MUTHEN
11965 Venice Blvd., Suite 407
Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
214
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998-2001 Muthen & Muthen
215
C.3 Output of Structural Equation Model on Private Consumer Information Cost (M-Plus)
C.3.1 Total sample
Mplus VERSION 1.04
MUTHEN & MUTHEN
02/05/2001 2:06 PM
INPUT INSTRUCTIONS
TITLE: Simultanes Mehrgleichungsmodell fuer die Beziehung
zwischen IC, LEG, WICH und SCHW (mit WICH --> LEG)
Stichprobe: alle Fragen, disaggregrierte Daten
DATA: FILE IS dummy_all.dat;
VARIABLE: NAMES ARE
LEG SCHW WICH IC
P1DUMMY P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
USEVARIABLES IC LEG WICH SCHW
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
ANALYSIS: TYPE IS MEANSTRUCTURE;
MODEL:
ic ON leg wich schw
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
leg ON wich
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
216
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
[ic leg];
OUTPUT:
Tech4;
Tech3;
217
Mplus VERSION 1.04 PAGE 2
INPUT READING TERMINATED NORMALLY
Simultanes Mehrgleichungsmodell fuer die Beziehung
zwischen IC, LEG, WICH und SCHW (mit WICH --> LEG)
Stichprobe: alle Fragen, disaggregrierte Daten
SUMMARY OF ANALYSIS
Mplus VERSION 1.04 PAGE 3
Simultanes Mehrgleichungsmodell fuer die Beziehung
Number of groups 1
Number of observations 4256
Number of y-variables 2
Number of x-variables 39
Number of continuous latent variables 0
Observed variables in the analysis
IC LEG WICH SCHW P2DUMMY P3DUMMY
P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY
P17DUMMY P18DUMMY P19DUMMY P20DUMMY P21DUMMY P22DUMMY
P23DUMMY P24DUMMY P25DUMMY P26DUMMY P27DUMMY P28DUMMY
P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY
Estimator ML
Maximum number of iterations 1000
Convergence criterion .500D-04
Input data file(s)
dummy_all.dat
Input data format FREE
THE MODEL ESTIMATION TERMINATED NORMALLY
218
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 1.864
Degrees of Freedom 1
P-Value .1722
Loglikelihood
H0 Value 32004.760
H1 Value 32005.692
Information Criteria
Number of Free Parameters 82
Akaike (AIC) -63845.520
Bayesian (BIC) -63324.321
Sample-Size Adjusted BIC -63584.883
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate .014
Mplus VERSION 1.04 PAGE 4
Simultanes Mehrgleichungsmodell fuer die Beziehung
90 Percent C.I. .000 .046
Probability RMSEA <= .05 .971
MODEL RESULTS
Estimates S.E. Est./S.E.
IC ON
LEG -.559 .017 -33.334
WICH -.010 .017 -.589
SCHW .138 .014 9.931
P2DUMMY -1.253 .279 -4.492
P3DUMMY 1.864 .280 6.651
P4DUMMY .512 .283 1.813
P5DUMMY -3.194 .282 -11.337
P6DUMMY .424 .281 1.508
P8DUMMY .041 .279 .148
P9DUMMY 1.175 .280 4.199
P10DUMMY -1.137 .279 -4.067
P11DUMMY -.713 .286 -2.492
P12DUMMY .610 .280 2.178
219
P13DUMMY 1.531 .279 5.494
P14DUMMY -.739 .278 -2.661
P15DUMMY -1.789 .278 -6.426
P16DUMMY -1.500 .279 -5.375
P17DUMMY .516 .282 1.825
P18DUMMY -2.910 .279 -10.416
P19DUMMY -3.861 .282 -13.704
P20DUMMY .541 .280 1.929
P21DUMMY -.118 .279 -.422
P22DUMMY -.019 .280 -.067
P23DUMMY -1.390 .282 -4.923
P24DUMMY -.128 .279 -.460
P25DUMMY 1.381 .278 4.959
P26DUMMY -1.209 .279 -4.337
P27DUMMY .684 .278 2.457
P28DUMMY -.809 .279 -2.902
P29DUMMY .482 .281 1.715
P30DUMMY -4.318 .282 -15.289
P31DUMMY .248 .282 .878
P32DUMMY -1.572 .279 -5.635
P33DUMMY -.893 .279 -3.198
P34DUMMY -1.284 .278 -4.620
P35DUMMY -.711 .280 -2.544
P36DUMMY .429 .282 1.521
P37DUMMY 1.737 .282 6.149
P38DUMMY 1.342 .279 4.809
P39DUMMY .821 .279 2.947
LEG ON
WICH .875 .009 101.396
P2DUMMY -.113 .254 -.444
P3DUMMY .119 .256 .465
P4DUMMY .110 .258 .427
P5DUMMY 1.271 .256 4.959
220
Mplus VERSION 1.04 PAGE 5
Simultanes Mehrgleichungsmodell fuer die Beziehung
P6DUMMY 2.477 .254 9.738
P8DUMMY .373 .254 1.468
P9DUMMY -.530 .254 -2.086
P10DUMMY .871 .255 3.416
P11DUMMY 3.740 .255 14.668
P12DUMMY 1.298 .255 5.087
P13DUMMY -.929 .254 -3.660
P14DUMMY .328 .254 1.294
P15DUMMY -.396 .254 -1.559
P16DUMMY .920 .255 3.613
P17DUMMY -.453 .257 -1.763
P18DUMMY .376 .255 1.475
P19DUMMY .102 .256 .398
P20DUMMY -.211 .255 -.826
P21DUMMY -.408 .255 -1.600
P22DUMMY .006 .256 .025
P23DUMMY 1.415 .256 5.535
P24DUMMY .759 .254 2.985
P25DUMMY .472 .254 1.854
P26DUMMY .074 .255 .291
P27DUMMY .214 .254 .841
P28DUMMY -.859 .254 -3.377
P29DUMMY 1.578 .256 6.165
P30DUMMY 1.212 .257 4.715
P31DUMMY .365 .256 1.426
P32DUMMY -.138 .255 -.542
P33DUMMY .368 .255 1.442
P34DUMMY .091 .254 .358
P35DUMMY .312 .255 1.222
P36DUMMY 1.058 .257 4.112
P37DUMMY -.787 .255 -3.082
P38DUMMY .436 .255 1.709
P39DUMMY .225 .254 .884
Residual Variances
IC 4.314 .094 46.130
LEG 3.605 .078 46.130
Intercepts
IC 6.649 .216 30.848
LEG .876 .193 4.549
TECHNICAL 3 OUTPUT
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
1 2 3 4 5
221
________ ________ ________ ________ ________
1 .046
2 .000 .037
3 .000 .000 .000
4 -.001 .000 .000 .000
5 -.001 .000 .000 .000 .000
6 -.041 .000 .000 .000 .000
7 -.042 .000 .000 .000 .000
8 -.043 .000 .000 .001 .000
9 -.042 .000 .000 .001 .000
10 -.040 .000 -.001 .001 .000
11 -.039 .000 .000 .000 .000
12 -.039 .000 .000 .000 .000
13 -.040 .000 .000 .000 .000
14 -.039 .000 -.001 .001 .000
15 -.041 .000 .000 .001 .000
16 -.040 .000 .000 .000 .000
17 -.040 .000 .000 .000 .000
18 -.040 .000 .000 .000 .000
19 -.040 .000 .000 .000 .000
20 -.041 .000 .000 .000 .000
21 -.041 .000 .000 .000 .000
22 -.043 .000 .000 .000 .000
23 -.040 .000 .000 .000 .000
24 -.041 .000 .000 .000 .000
25 -.041 .000 .000 .000 .000
26 -.042 .000 .000 .001 .000
27 -.039 .000 .000 .000 .000
28 -.040 .000 .000 .000 .000
29 -.041 .000 .000 .000 .000
30 -.040 .000 .000 .000 .000
31 -.040 .000 .000 .000 .000
32 -.041 .000 .000 .001 .000
33 -.041 .000 .000 .001 .000
34 -.040 .000 .000 .000 .000
35 -.040 .000 .000 .000 .000
36 -.040 .000 .000 .000 .000
37 -.039 .000 .000 .000 .000
38 -.040 .000 .000 .000 .000
39 -.042 .000 .000 .001 .000
40 -.039 .000 .000 .000 -.001
41 -.041 .000 .000 .000 .000
42 -.040 .000 .000 .000 .000
43 .000 -.001 .000 .000 .000
44 .000 -.033 .000 .000 .000
45 .000 -.035 .000 .000 .000
46 .000 -.035 .000 .000 .000
47 .000 -.035 .000 .000 .000
48 .000 -.033 .000 .000 .000
49 .000 -.034 .000 .000 .000
50 .000 -.033 .000 .000 .000
51 .000 -.034 .000 .000 .000
222
52 .000 -.034 .000 .000 .000
53 .000 -.034 .000 .000 .000
54 .000 -.033 .000 .000 .000
55 .000 -.033 .000 .000 .000
56 .000 -.033 .000 .000 .000
57 .000 -.034 .000 .000 .000
58 .000 -.035 .000 .000 .000
59 .000 -.034 .000 .000 .000
60 .000 -.035 .000 .000 .000
61 .000 -.034 .000 .000 .000
62 .000 -.034 .000 .000 .000
63 .000 -.035 .000 .000 .000
64 .000 -.034 .000 .000 .000
65 .000 -.033 .000 .000 .000
66 .000 -.034 .000 .000 .000
67 .000 -.034 .000 .000 .000
68 .000 -.033 .000 .000 .000
69 .000 -.033 .000 .000 .000
70 .000 -.035 .000 .000 .000
71 .000 -.035 .000 .000 .000
72 .000 -.035 .000 .000 .000
73 .000 -.034 .000 .000 .000
74 .000 -.034 .000 .000 .000
75 .000 -.033 .000 .000 .000
76 .000 -.034 .000 .000 .000
77 .000 -.035 .000 .000 .000
78 .000 -.034 .000 .000 .000
79 .000 -.034 .000 .000 .000
80 .000 -.034 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
6 7 8 9 10
________ ________ ________ ________ ________
6 .078
7 .039 .079
8 .040 .041 .080
9 .040 .040 .041 .079
10 .039 .039 .040 .040 .079
11 .039 .039 .039 .039 .039
12 .038 .039 .039 .038 .038
13 .039 .039 .040 .040 .040
14 .039 .040 .040 .041 .042
15 .039 .040 .040 .040 .040
16 .039 .039 .039 .039 .038
17 .039 .039 .039 .039 .039
18 .039 .039 .040 .039 .039
19 .039 .039 .040 .040 .040
20 .039 .040 .040 .040 .039
21 .039 .040 .040 .040 .039
223
22 .040 .040 .041 .041 .040
23 .039 .039 .040 .039 .039
24 .039 .040 .040 .040 .039
25 .039 .040 .040 .040 .039
26 .040 .040 .041 .041 .040
27 .039 .039 .039 .039 .039
28 .039 .039 .040 .039 .039
29 .039 .040 .040 .040 .039
30 .039 .039 .039 .039 .039
31 .039 .039 .039 .039 .038
32 .039 .040 .040 .040 .040
33 .039 .040 .041 .040 .040
34 .039 .040 .040 .040 .039
35 .039 .039 .040 .039 .039
36 .039 .039 .040 .039 .039
37 .038 .039 .039 .039 .039
38 .039 .039 .040 .039 .039
39 .039 .040 .041 .041 .040
40 .038 .039 .039 .039 .038
41 .039 .040 .040 .040 .039
42 .039 .039 .039 .039 .039
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
224
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
11 12 13 14 15
________ ________ ________ ________ ________
11 .078
12 .039 .078
13 .039 .039 .078
14 .040 .039 .040 .082
15 .039 .039 .040 .041 .078
16 .038 .038 .038 .038 .039
17 .039 .038 .039 .039 .039
18 .039 .039 .039 .038 .039
19 .039 .039 .039 .040 .039
20 .040 .040 .040 .040 .039
21 .039 .038 .039 .039 .040
22 .039 .038 .039 .039 .040
23 .039 .040 .040 .039 .039
24 .039 .039 .039 .039 .039
25 .039 .039 .040 .040 .040
26 .039 .038 .039 .041 .040
27 .039 .039 .039 .040 .039
28 .039 .039 .039 .040 .039
29 .039 .039 .039 .039 .039
30 .039 .039 .039 .039 .039
31 .039 .039 .039 .038 .039
32 .040 .039 .040 .041 .040
33 .040 .040 .040 .041 .040
34 .040 .040 .040 .040 .040
35 .039 .039 .039 .039 .039
36 .039 .039 .039 .040 .039
37 .039 .039 .039 .039 .039
38 .039 .040 .040 .040 .039
39 .040 .039 .040 .041 .040
40 .040 .040 .040 .039 .039
41 .039 .039 .039 .040 .040
42 .039 .039 .039 .039 .039
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
225
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
16 17 18 19 20
________ ________ ________ ________ ________
16 .078
17 .039 .077
18 .039 .039 .078
19 .038 .039 .039 .078
20 .039 .039 .039 .040 .080
21 .039 .039 .039 .039 .039
22 .039 .039 .040 .039 .039
23 .039 .039 .039 .039 .041
24 .039 .039 .039 .039 .040
25 .039 .039 .039 .039 .040
26 .039 .039 .039 .039 .039
27 .038 .039 .039 .039 .039
28 .039 .039 .039 .039 .039
226
29 .039 .039 .039 .039 .039
30 .039 .039 .039 .039 .040
31 .039 .039 .039 .039 .040
32 .038 .039 .039 .040 .040
33 .038 .039 .039 .040 .041
34 .038 .039 .039 .040 .041
35 .039 .039 .039 .039 .040
36 .039 .039 .039 .039 .040
37 .038 .039 .039 .039 .039
38 .038 .039 .039 .039 .040
39 .039 .039 .039 .040 .040
40 .038 .038 .039 .039 .041
41 .039 .039 .039 .039 .039
42 .039 .039 .039 .039 .040
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
227
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
21 22 23 24 25
________ ________ ________ ________ ________
21 .078
22 .040 .079
23 .039 .039 .079
24 .039 .040 .039 .078
25 .039 .040 .040 .040 .078
26 .040 .041 .039 .039 .040
27 .039 .039 .039 .039 .039
28 .039 .039 .039 .039 .039
29 .039 .040 .039 .039 .039
30 .039 .039 .039 .039 .039
31 .039 .039 .039 .039 .039
32 .039 .040 .040 .039 .040
33 .039 .040 .040 .040 .040
34 .039 .039 .040 .040 .040
35 .039 .039 .040 .039 .040
36 .039 .039 .040 .039 .040
37 .038 .038 .039 .039 .039
38 .039 .039 .040 .039 .040
39 .040 .041 .040 .040 .040
40 .038 .039 .040 .040 .040
41 .039 .040 .039 .039 .039
42 .039 .039 .039 .039 .039
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
228
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
26 27 28 29 30
________ ________ ________ ________ ________
26 .080
27 .039 .078
28 .039 .039 .078
29 .040 .039 .039 .078
30 .039 .039 .039 .039 .078
31 .039 .039 .039 .039 .039
32 .040 .040 .039 .039 .039
33 .040 .040 .040 .040 .040
34 .039 .040 .039 .039 .040
35 .039 .039 .039 .039 .039
36 .039 .039 .039 .039 .039
37 .038 .039 .039 .039 .039
38 .039 .039 .039 .039 .039
39 .041 .039 .040 .040 .040
40 .038 .039 .039 .039 .039
41 .040 .039 .039 .039 .039
42 .039 .039 .039 .039 .039
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
229
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
31 32 33 34 35
________ ________ ________ ________ ________
31 .078
32 .039 .079
33 .039 .041 .080
34 .039 .040 .041 .080
35 .039 .040 .040 .040 .078
36 .039 .040 .040 .040 .039
37 .039 .039 .039 .039 .039
38 .039 .040 .040 .040 .039
39 .039 .041 .041 .040 .040
40 .040 .040 .040 .041 .040
41 .039 .040 .040 .039 .039
42 .039 .039 .040 .040 .039
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
230
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
36 37 38 39 40
________ ________ ________ ________ ________
36 .078
37 .039 .077
38 .040 .039 .078
39 .040 .039 .040 .080
40 .040 .039 .040 .040 .080
41 .039 .039 .039 .040 .039
42 .039 .039 .039 .040 .039
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
231
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
41 42 43 44 45
________ ________ ________ ________ ________
41 .078
42 .039 .078
43 .000 .000 .000
44 .000 .000 .000 .065
45 .000 .000 .000 .033 .066
46 .000 .000 .000 .033 .034
47 .000 .000 .000 .033 .033
48 .000 .000 .000 .033 .033
49 .000 .000 .000 .033 .033
50 .000 .000 .000 .032 .033
51 .000 .000 .000 .033 .033
52 .000 .000 .000 .033 .033
53 .000 .000 .000 .033 .033
54 .000 .000 .000 .032 .033
55 .000 .000 .000 .032 .033
232
56 .000 .000 .000 .032 .033
57 .000 .000 .000 .033 .033
58 .000 .000 .000 .033 .034
59 .000 .000 .000 .033 .033
60 .000 .000 .000 .033 .033
61 .000 .000 .000 .033 .033
62 .000 .000 .000 .033 .033
63 .000 .000 .000 .033 .033
64 .000 .000 .000 .033 .033
65 .000 .000 .000 .032 .033
66 .000 .000 .000 .033 .033
67 .000 .000 .000 .033 .033
68 .000 .000 .000 .033 .033
69 .000 .000 .000 .032 .033
70 .000 .000 .000 .033 .033
71 .000 .000 .000 .033 .034
72 .000 .000 .000 .033 .033
73 .000 .000 .000 .033 .033
74 .000 .000 .000 .033 .033
75 .000 .000 .000 .032 .032
76 .000 .000 .000 .033 .033
77 .000 .000 .000 .033 .034
78 .000 .000 .000 .033 .033
79 .000 .000 .000 .033 .033
80 .000 .000 .000 .033 .033
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
46 47 48 49 50
________ ________ ________ ________ ________
46 .067
47 .034 .066
48 .033 .033 .065
49 .033 .033 .033 .065
50 .033 .033 .032 .033 .065
51 .033 .033 .033 .033 .033
52 .033 .033 .033 .033 .033
53 .033 .033 .033 .033 .033
54 .033 .033 .032 .032 .032
55 .033 .033 .032 .032 .032
56 .033 .033 .033 .033 .032
57 .033 .033 .033 .033 .033
58 .034 .034 .033 .033 .033
59 .033 .033 .033 .033 .033
60 .034 .033 .033 .033 .033
61 .033 .033 .033 .033 .033
62 .033 .033 .033 .033 .033
63 .034 .033 .033 .033 .033
64 .034 .033 .033 .033 .033
65 .033 .033 .032 .032 .032
233
66 .033 .033 .033 .033 .032
67 .033 .033 .033 .033 .033
68 .033 .033 .033 .033 .032
69 .033 .033 .032 .033 .032
70 .034 .033 .033 .033 .033
71 .034 .034 .033 .033 .033
72 .034 .033 .033 .033 .033
73 .033 .033 .033 .033 .033
74 .033 .033 .033 .033 .033
75 .033 .032 .032 .032 .032
76 .033 .033 .033 .033 .033
77 .034 .034 .033 .033 .033
78 .034 .033 .033 .033 .033
79 .033 .033 .033 .033 .033
80 .033 .033 .033 .033 .033
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
51 52 53 54 55
________ ________ ________ ________ ________
51 .065
52 .033 .065
53 .033 .033 .065
54 .032 .032 .032 .064
55 .032 .032 .032 .032 .064
56 .033 .033 .033 .032 .032
57 .033 .033 .033 .032 .032
58 .033 .033 .033 .033 .033
59 .033 .033 .033 .032 .032
60 .033 .033 .033 .033 .033
61 .033 .033 .033 .032 .032
62 .033 .033 .033 .032 .032
63 .033 .033 .033 .033 .033
64 .033 .033 .033 .033 .032
65 .033 .033 .033 .032 .032
66 .033 .033 .033 .032 .032
67 .033 .033 .033 .032 .032
68 .033 .033 .033 .032 .032
69 .033 .033 .033 .032 .032
70 .033 .033 .033 .033 .033
71 .033 .033 .033 .033 .033
72 .033 .033 .033 .033 .033
73 .033 .033 .033 .032 .032
74 .033 .033 .033 .032 .032
75 .032 .032 .032 .032 .032
76 .033 .033 .033 .032 .032
77 .033 .033 .033 .033 .033
78 .033 .033 .033 .032 .032
79 .033 .033 .033 .032 .032
80 .033 .033 .033 .032 .032
234
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
56 57 58 59 60
________ ________ ________ ________ ________
56 .065
57 .033 .065
58 .033 .033 .066
59 .033 .033 .033 .065
60 .033 .033 .034 .033 .066
61 .033 .033 .033 .033 .033
62 .033 .033 .033 .033 .033
63 .033 .033 .034 .033 .033
64 .033 .033 .033 .033 .033
65 .032 .033 .033 .033 .033
66 .033 .033 .033 .033 .033
67 .033 .033 .033 .033 .033
68 .033 .033 .033 .033 .033
69 .032 .033 .033 .033 .033
70 .033 .033 .034 .033 .033
71 .033 .033 .034 .033 .034
72 .033 .033 .034 .033 .033
73 .033 .033 .033 .033 .033
74 .033 .033 .033 .033 .033
75 .032 .032 .032 .032 .032
76 .033 .033 .033 .033 .033
77 .033 .033 .034 .033 .034
78 .033 .033 .033 .033 .033
79 .033 .033 .033 .033 .033
80 .033 .033 .033 .033 .033
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
61 62 63 64 65
________ ________ ________ ________ ________
61 .065
62 .033 .065
63 .033 .033 .066
64 .033 .033 .033 .065
65 .033 .033 .033 .033 .065
66 .033 .033 .033 .033 .032
67 .033 .033 .033 .033 .033
68 .033 .033 .033 .033 .032
69 .033 .033 .033 .033 .032
70 .033 .033 .033 .033 .033
71 .033 .033 .034 .033 .033
72 .033 .033 .033 .033 .033
73 .033 .033 .033 .033 .033
235
74 .033 .033 .033 .033 .033
75 .032 .032 .032 .032 .032
76 .033 .033 .033 .033 .033
77 .033 .033 .034 .034 .033
78 .033 .033 .033 .033 .033
79 .033 .033 .033 .033 .033
80 .033 .033 .033 .033 .032
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
66 67 68 69 70
________ ________ ________ ________ ________
66 .065
67 .033 .065
68 .033 .033 .065
69 .032 .033 .032 .065
70 .033 .033 .033 .033 .066
71 .033 .033 .033 .033 .034
72 .033 .033 .033 .033 .033
73 .033 .033 .033 .033 .033
74 .033 .033 .033 .033 .033
75 .032 .032 .032 .032 .032
76 .033 .033 .033 .033 .033
77 .033 .033 .033 .033 .034
78 .033 .033 .033 .033 .033
79 .033 .033 .033 .033 .033
80 .033 .033 .033 .033 .033
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
71 72 73 74 75
________ ________ ________ ________ ________
71 .066
72 .034 .066
73 .033 .033 .065
74 .033 .033 .033 .065
75 .033 .032 .032 .032 .064
76 .033 .033 .033 .033 .032
77 .034 .034 .033 .033 .033
78 .033 .033 .033 .033 .032
79 .033 .033 .033 .033 .032
80 .033 .033 .033 .033 .032
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
76 77 78 79 80
236
________ ________ ________ ________ ________
76 .065
77 .033 .066
78 .033 .033 .065
79 .033 .033 .033 .065
80 .033 .033 .033 .033 .065
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED COV. MATRIX FOR PARAMETER ESTIMATES
81 82
________ ________
81 .009
82 .000 .006
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
1 2 3 4 5
________ ________ ________ ________ ________
1 1.000
2 .000 1.000
3 -.068 .000 1.000
4 -.149 .000 -.839 1.000
5 -.202 .000 .000 .075 1.000
6 -.679 .000 .007 .036 .067
7 -.693 .000 -.007 .081 .045
8 -.707 .000 -.007 .106 .062
9 -.692 .000 -.076 .143 .066
10 -.654 .000 -.148 .164 .016
11 -.656 .000 -.022 .056 -.050
12 -.642 .000 .032 .000 -.107
13 -.661 .000 -.052 .092 -.045
14 -.635 .000 -.219 .233 -.045
15 -.676 .000 -.078 .125 .032
16 -.670 .000 .056 -.021 .053
17 -.660 .000 -.020 .039 .026
18 -.675 .000 .024 .019 .037
19 -.660 .000 -.055 .090 -.033
20 -.669 .000 .027 .053 -.100
21 -.683 .000 -.023 .070 .074
22 -.704 .000 -.006 .088 .098
23 -.660 .000 .013 .041 -.085
24 -.680 .000 .025 .038 .002
25 -.681 .000 .000 .070 -.017
26 -.692 .000 -.084 .144 .107
27 -.653 .000 -.046 .069 -.038
28 -.666 .000 -.028 .063 -.003
29 -.676 .000 -.004 .053 .018
30 -.660 .000 -.013 .048 -.033
31 -.662 .000 .052 -.011 -.029
32 -.671 .000 -.094 .148 -.021
237
33 -.673 .000 -.072 .143 -.056
34 -.658 .000 -.022 .085 -.114
35 -.673 .000 .008 .046 -.021
36 -.663 .000 -.022 .066 -.047
37 -.643 .000 -.005 .017 -.048
38 -.659 .000 -.019 .063 -.067
39 -.691 .000 -.063 .144 .015
40 -.647 .000 .047 .010 -.141
41 -.681 .000 -.026 .077 .042
42 -.660 .000 -.014 .049 -.034
43 .000 -.363 .000 .000 .000
44 .000 -.682 .000 .000 .000
45 .000 -.701 .000 .000 .000
46 .000 -.713 .000 .000 .000
47 .000 -.703 .000 .000 .000
48 .000 -.684 .000 .000 .000
49 .000 -.685 .000 .000 .000
50 .000 -.681 .000 .000 .000
51 .000 -.691 .000 .000 .000
52 .000 -.692 .000 .000 .000
53 .000 -.694 .000 .000 .000
54 .000 -.673 .000 .000 .000
55 .000 -.672 .000 .000 .000
56 .000 -.682 .000 .000 .000
57 .000 -.688 .000 .000 .000
58 .000 -.707 .000 .000 .000
59 .000 -.688 .000 .000 .000
60 .000 -.704 .000 .000 .000
61 .000 -.694 .000 .000 .000
62 .000 -.694 .000 .000 .000
63 .000 -.701 .000 .000 .000
64 .000 -.698 .000 .000 .000
65 .000 -.680 .000 .000 .000
66 .000 -.684 .000 .000 .000
67 .000 -.689 .000 .000 .000
68 .000 -.684 .000 .000 .000
69 .000 -.681 .000 .000 .000
70 .000 -.701 .000 .000 .000
71 .000 -.709 .000 .000 .000
72 .000 -.703 .000 .000 .000
73 .000 -.692 .000 .000 .000
74 .000 -.690 .000 .000 .000
75 .000 -.669 .000 .000 .000
76 .000 -.691 .000 .000 .000
77 .000 -.710 .000 .000 .000
78 .000 -.695 .000 .000 .000
79 .000 -.691 .000 .000 .000
80 .000 -.684 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
238
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
6 7 8 9 10
________ ________ ________ ________ ________
6 1.000
7 .505 1.000
8 .505 .513 1.000
9 .504 .510 .513 1.000
10 .496 .500 .499 .508 1.000
11 .497 .501 .499 .499 .499
12 .491 .494 .492 .488 .487
13 .497 .503 .502 .503 .504
14 .485 .493 .492 .505 .517
15 .502 .506 .508 .511 .508
16 .502 .500 .499 .496 .487
17 .501 .501 .499 .500 .499
18 .503 .504 .504 .501 .493
19 .498 .502 .502 .504 .504
20 .493 .502 .504 .497 .490
21 .505 .507 .508 .508 .500
22 .506 .511 .515 .512 .498
23 .495 .501 .500 .496 .493
24 .502 .506 .507 .503 .494
25 .501 .507 .509 .505 .498
26 .504 .508 .512 .515 .508
27 .497 .500 .498 .500 .502
28 .501 .503 .503 .503 .501
29 .503 .506 .506 .505 .498
30 .499 .502 .501 .500 .498
31 .499 .500 .498 .494 .488
32 .498 .506 .507 .510 .510
33 .496 .506 .508 .508 .506
34 .491 .500 .501 .498 .496
35 .501 .505 .505 .502 .496
36 .498 .503 .502 .501 .500
37 .496 .496 .493 .493 .495
38 .496 .502 .501 .500 .498
39 .502 .510 .514 .514 .507
40 .487 .494 .493 .486 .483
41 .504 .507 .508 .508 .502
42 .499 .502 .501 .500 .498
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
239
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
11 12 13 14 15
________ ________ ________ ________ ________
11 1.000
12 .503 1.000
13 .505 .502 1.000
14 .497 .485 .504 1.000
15 .501 .492 .505 .506 1.000
16 .495 .493 .493 .472 .495
17 .499 .494 .499 .491 .501
18 .499 .495 .498 .483 .500
19 .504 .500 .506 .504 .505
20 .505 .509 .506 .490 .498
21 .498 .489 .499 .492 .505
22 .496 .487 .499 .488 .507
23 .505 .508 .505 .492 .498
24 .502 .500 .502 .487 .502
25 .504 .502 .506 .494 .504
26 .495 .481 .499 .502 .510
27 .504 .501 .505 .501 .502
28 .503 .498 .504 .497 .504
29 .502 .497 .502 .492 .504
240
30 .504 .502 .504 .495 .502
31 .501 .503 .499 .479 .495
32 .504 .497 .508 .513 .509
33 .506 .501 .510 .511 .508
34 .506 .508 .508 .500 .500
35 .504 .502 .504 .491 .502
36 .505 .503 .506 .498 .503
37 .502 .502 .501 .490 .497
38 .505 .505 .506 .498 .502
39 .503 .495 .507 .506 .510
40 .504 .511 .502 .483 .490
41 .501 .494 .502 .495 .506
42 .504 .502 .504 .495 .502
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
241
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
16 17 18 19 20
________ ________ ________ ________ ________
16 1.000
17 .499 1.000
18 .502 .501 1.000
19 .494 .500 .499 1.000
20 .492 .493 .497 .503 1.000
21 .501 .502 .503 .500 .493
22 .501 .500 .504 .499 .496
23 .494 .496 .498 .504 .512
24 .501 .500 .503 .502 .505
25 .498 .500 .502 .505 .509
26 .496 .500 .500 .500 .489
27 .494 .500 .498 .504 .502
28 .498 .501 .501 .504 .501
29 .500 .501 .503 .502 .501
30 .497 .500 .500 .504 .504
31 .500 .498 .501 .499 .504
32 .490 .499 .498 .508 .505
33 .489 .497 .497 .508 .511
34 .488 .494 .495 .506 .515
35 .499 .500 .502 .503 .507
36 .495 .499 .500 .505 .507
37 .496 .499 .498 .501 .500
38 .494 .498 .498 .505 .509
39 .494 .499 .501 .506 .506
40 .489 .490 .493 .500 .515
41 .500 .502 .503 .502 .498
42 .497 .500 .500 .504 .504
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
242
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
21 22 23 24 25
________ ________ ________ ________ ________
21 1.000
22 .509 1.000
23 .495 .495 1.000
24 .502 .505 .504 1.000
25 .502 .505 .506 .507 1.000
26 .509 .512 .490 .500 .501
27 .498 .496 .503 .501 .503
28 .502 .501 .502 .503 .504
29 .504 .505 .501 .504 .505
30 .499 .498 .504 .503 .504
31 .497 .497 .503 .503 .503
32 .502 .503 .503 .502 .507
33 .499 .502 .508 .504 .509
34 .492 .493 .511 .503 .508
35 .501 .502 .505 .505 .507
36 .499 .498 .506 .504 .506
37 .495 .491 .502 .499 .500
38 .497 .496 .508 .503 .506
39 .505 .510 .503 .505 .509
40 .486 .485 .511 .501 .504
41 .506 .507 .499 .504 .505
42 .499 .498 .504 .503 .504
43 .000 .000 .000 .000 .000
243
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
26 27 28 29 30
________ ________ ________ ________ ________
26 1.000
27 .497 1.000
28 .501 .503 1.000
29 .503 .501 .503 1.000
30 .496 .503 .503 .502 1.000
31 .490 .499 .500 .501 .501
32 .507 .505 .504 .503 .503
33 .503 .505 .504 .503 .504
244
34 .490 .504 .501 .500 .504
35 .498 .502 .503 .504 .504
36 .497 .504 .503 .503 .504
37 .490 .501 .500 .499 .501
38 .494 .504 .503 .502 .504
39 .511 .503 .504 .505 .503
40 .477 .500 .497 .496 .502
41 .507 .501 .503 .504 .501
42 .497 .503 .503 .502 .503
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
245
31 32 33 34 35
________ ________ ________ ________ ________
31 1.000
32 .496 1.000
33 .498 .514 1.000
34 .501 .508 .514 1.000
35 .503 .504 .506 .505 1.000
36 .501 .506 .508 .508 .505
37 .500 .498 .498 .501 .500
38 .502 .506 .509 .510 .505
39 .497 .513 .514 .507 .505
40 .503 .498 .505 .514 .503
41 .499 .505 .503 .498 .503
42 .501 .503 .505 .505 .504
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
246
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
36 37 38 39 40
________ ________ ________ ________ ________
36 1.000
37 .501 1.000
38 .506 .502 1.000
39 .505 .496 .505 1.000
40 .505 .500 .507 .497 1.000
41 .502 .497 .501 .507 .492
42 .504 .501 .505 .503 .502
43 .000 .000 .000 .000 .000
44 .000 .000 .000 .000 .000
45 .000 .000 .000 .000 .000
46 .000 .000 .000 .000 .000
47 .000 .000 .000 .000 .000
48 .000 .000 .000 .000 .000
49 .000 .000 .000 .000 .000
50 .000 .000 .000 .000 .000
51 .000 .000 .000 .000 .000
52 .000 .000 .000 .000 .000
53 .000 .000 .000 .000 .000
54 .000 .000 .000 .000 .000
55 .000 .000 .000 .000 .000
56 .000 .000 .000 .000 .000
57 .000 .000 .000 .000 .000
58 .000 .000 .000 .000 .000
59 .000 .000 .000 .000 .000
60 .000 .000 .000 .000 .000
61 .000 .000 .000 .000 .000
62 .000 .000 .000 .000 .000
63 .000 .000 .000 .000 .000
64 .000 .000 .000 .000 .000
65 .000 .000 .000 .000 .000
66 .000 .000 .000 .000 .000
67 .000 .000 .000 .000 .000
68 .000 .000 .000 .000 .000
69 .000 .000 .000 .000 .000
70 .000 .000 .000 .000 .000
71 .000 .000 .000 .000 .000
72 .000 .000 .000 .000 .000
73 .000 .000 .000 .000 .000
74 .000 .000 .000 .000 .000
75 .000 .000 .000 .000 .000
76 .000 .000 .000 .000 .000
77 .000 .000 .000 .000 .000
78 .000 .000 .000 .000 .000
79 .000 .000 .000 .000 .000
80 .000 .000 .000 .000 .000
247
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
41 42 43 44 45
________ ________ ________ ________ ________
41 1.000
42 .501 1.000
43 .000 .000 1.000
44 .000 .000 .068 1.000
45 .000 .000 .132 .503 1.000
46 .000 .000 .179 .503 .511
47 .000 .000 .139 .503 .509
48 .000 .000 .072 .502 .504
49 .000 .000 .076 .503 .504
50 .000 .000 .065 .502 .503
51 .000 .000 .096 .503 .506
52 .000 .000 .100 .503 .506
53 .000 .000 .106 .503 .507
54 .000 .000 .040 .501 .501
55 .000 .000 .038 .501 .500
56 .000 .000 .067 .502 .503
57 .000 .000 .085 .503 .505
58 .000 .000 .156 .503 .510
59 .000 .000 .085 .503 .505
60 .000 .000 .141 .503 .509
61 .000 .000 .108 .503 .507
62 .000 .000 .109 .503 .507
63 .000 .000 .131 .503 .509
64 .000 .000 .122 .503 .508
65 .000 .000 .062 .502 .503
66 .000 .000 .074 .502 .504
67 .000 .000 .090 .503 .505
68 .000 .000 .074 .502 .504
69 .000 .000 .064 .502 .503
70 .000 .000 .131 .503 .509
71 .000 .000 .162 .503 .510
72 .000 .000 .140 .503 .509
73 .000 .000 .101 .503 .506
74 .000 .000 .095 .503 .506
75 .000 .000 .030 .501 .499
76 .000 .000 .098 .503 .506
77 .000 .000 .167 .503 .511
78 .000 .000 .112 .503 .507
79 .000 .000 .096 .503 .506
80 .000 .000 .075 .503 .504
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
248
46 47 48 49 50
________ ________ ________ ________ ________
46 1.000
47 .512 1.000
48 .504 .504 1.000
49 .504 .504 .503 1.000
50 .503 .503 .502 .502 1.000
51 .507 .506 .503 .504 .503
52 .507 .507 .503 .504 .503
53 .508 .507 .504 .504 .503
54 .499 .500 .501 .501 .501
55 .498 .500 .501 .501 .501
56 .503 .503 .502 .503 .502
57 .505 .505 .503 .503 .503
58 .514 .511 .504 .504 .503
59 .505 .505 .503 .503 .503
60 .512 .510 .504 .504 .503
61 .508 .507 .504 .504 .503
62 .508 .507 .504 .504 .503
63 .511 .509 .504 .504 .503
64 .510 .508 .504 .504 .503
65 .502 .503 .502 .502 .502
66 .504 .504 .503 .503 .502
67 .506 .506 .503 .503 .503
68 .504 .504 .503 .503 .502
69 .502 .503 .502 .502 .502
70 .511 .509 .504 .504 .503
71 .514 .511 .504 .504 .503
72 .512 .510 .504 .504 .503
73 .507 .507 .503 .504 .503
74 .507 .506 .503 .504 .503
75 .497 .499 .501 .501 .501
76 .507 .506 .503 .504 .503
77 .515 .511 .504 .504 .503
78 .509 .508 .504 .504 .503
79 .507 .506 .503 .504 .503
80 .504 .504 .503 .503 .502
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
51 52 53 54 55
________ ________ ________ ________ ________
51 1.000
52 .505 1.000
53 .505 .505 1.000
54 .501 .501 .501 1.000
55 .501 .501 .501 .501 1.000
56 .503 .503 .503 .501 .501
57 .504 .504 .504 .501 .501
58 .507 .507 .508 .500 .499
249
59 .504 .504 .504 .501 .501
60 .506 .507 .507 .500 .500
61 .505 .505 .506 .501 .501
62 .505 .505 .506 .501 .501
63 .506 .506 .507 .501 .500
64 .506 .506 .506 .501 .501
65 .503 .503 .503 .501 .501
66 .503 .504 .504 .501 .501
67 .504 .504 .505 .501 .501
68 .503 .504 .504 .501 .501
69 .503 .503 .503 .501 .501
70 .506 .506 .507 .501 .500
71 .507 .507 .508 .500 .499
72 .506 .507 .507 .500 .500
73 .505 .505 .505 .501 .501
74 .505 .505 .505 .501 .501
75 .500 .500 .500 .501 .501
76 .505 .505 .505 .501 .501
77 .507 .507 .508 .499 .499
78 .505 .506 .506 .501 .501
79 .505 .505 .505 .501 .501
80 .504 .504 .504 .501 .501
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
56 57 58 59 60
________ ________ ________ ________ ________
56 1.000
57 .503 1.000
58 .503 .505 1.000
59 .503 .504 .505 1.000
60 .503 .505 .511 .505 1.000
61 .503 .504 .508 .504 .507
62 .503 .504 .508 .504 .507
63 .503 .505 .510 .505 .509
64 .503 .505 .509 .505 .509
65 .502 .502 .503 .502 .503
66 .502 .503 .504 .503 .504
67 .503 .504 .506 .504 .506
68 .502 .503 .504 .503 .504
69 .502 .503 .503 .503 .503
70 .503 .505 .510 .505 .509
71 .503 .505 .513 .505 .511
72 .503 .505 .511 .505 .510
73 .503 .504 .507 .504 .507
74 .503 .504 .506 .504 .506
75 .501 .500 .498 .500 .499
76 .503 .504 .507 .504 .506
77 .503 .505 .513 .505 .512
78 .503 .505 .508 .505 .508
250
79 .503 .504 .507 .504 .506
80 .503 .503 .504 .503 .504
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
61 62 63 64 65
________ ________ ________ ________ ________
61 1.000
62 .506 1.000
63 .507 .507 1.000
64 .507 .507 .508 1.000
65 .503 .503 .503 .503 1.000
66 .504 .504 .504 .504 .502
67 .505 .505 .505 .505 .503
68 .504 .504 .504 .504 .502
69 .503 .503 .503 .503 .502
70 .507 .507 .509 .508 .503
71 .508 .508 .510 .509 .502
72 .507 .507 .509 .508 .503
73 .505 .505 .506 .506 .503
74 .505 .505 .506 .506 .503
75 .500 .500 .499 .500 .501
76 .505 .505 .506 .506 .503
77 .508 .508 .511 .510 .502
78 .506 .506 .507 .507 .503
79 .505 .505 .506 .506 .503
80 .504 .504 .504 .504 .502
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
66 67 68 69 70
________ ________ ________ ________ ________
66 1.000
67 .503 1.000
68 .503 .503 1.000
69 .502 .503 .502 1.000
70 .504 .505 .504 .503 1.000
71 .504 .506 .504 .503 .510
72 .504 .506 .504 .503 .509
73 .504 .504 .504 .503 .506
74 .503 .504 .503 .503 .506
75 .501 .500 .501 .501 .499
76 .503 .504 .503 .503 .506
77 .504 .506 .504 .503 .511
78 .504 .505 .504 .503 .507
79 .503 .504 .503 .503 .506
80 .503 .503 .503 .502 .504
81 .000 .000 .000 .000 .000
251
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
71 72 73 74 75
________ ________ ________ ________ ________
71 1.000
72 .511 1.000
73 .507 .507 1.000
74 .507 .506 .505 1.000
75 .498 .499 .500 .500 1.000
76 .507 .506 .505 .505 .500
77 .513 .512 .507 .507 .498
78 .508 .508 .506 .505 .500
79 .507 .506 .505 .505 .500
80 .504 .504 .504 .503 .501
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
76 77 78 79 80
________ ________ ________ ________ ________
76 1.000
77 .507 1.000
78 .505 .509 1.000
79 .505 .507 .505 1.000
80 .504 .504 .504 .503 1.000
81 .000 .000 .000 .000 .000
82 .000 .000 .000 .000 .000
ESTIMATED CORR. MATRIX FOR PARAMETER ESTIMATES
81 82
________ ________
81 1.000
82 .000 1.000
TECHNICAL 4 OUTPUT
ESTIMATES DERIVED FROM THE MODEL
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
IC LEG WICH SCHW P2DUMMY
________ ________ ________ ________ ________
IC 11.415
Mplus VERSION 1.04 PAGE 34
Simultanes Mehrgleichungsmodell fuer die Beziehung
LEG -8.044 13.812
252
WICH -6.186 10.948 12.744
SCHW 2.100 -1.153 -1.179 6.805
P2DUMMY -.032 .006 .023 -.043 .026
P3DUMMY .074 -.032 -.028 -.026 -.001
P4DUMMY .056 -.065 -.065 -.032 -.001
P5DUMMY -.075 -.007 -.033 -.038 -.001
P6DUMMY -.021 .071 .019 -.016 -.001
P8DUMMY .007 .013 .016 .020 -.001
P9DUMMY .050 -.003 .025 .049 -.001
P10DUMMY -.024 .013 .001 .018 -.001
P11DUMMY -.053 .086 -.002 .020 -.001
P12DUMMY .014 .017 -.007 -.021 -.001
P13DUMMY .043 .004 .045 -.038 -.001
P14DUMMY -.034 .038 .046 -.023 -.001
P15DUMMY -.040 -.001 .023 -.027 -.001
P16DUMMY -.040 .021 .009 .011 -.001
P17DUMMY .067 -.063 -.046 .053 -.001
P18DUMMY -.076 .007 .009 -.046 -.001
P19DUMMY -.077 -.039 -.035 -.054 -.001
P20DUMMY .044 -.024 -.009 .041 -.001
P21DUMMY .023 -.030 -.009 -.006 -.001
P22DUMMY .030 -.034 -.027 .006 -.001
P23DUMMY -.039 .009 -.020 -.061 -.001
P24DUMMY -.010 .033 .028 .012 -.001
P25DUMMY .036 .017 .018 -.006 -.001
P26DUMMY -.021 -.004 .006 -.015 -.001
P27DUMMY .024 .011 .018 .010 -.001
P28DUMMY -.004 -.011 .026 .008 -.001
P29DUMMY .021 .007 -.027 .009 -.001
P30DUMMY -.085 -.024 -.051 .030 -.001
P31DUMMY .043 -.031 -.034 .059 -.001
P32DUMMY -.020 -.017 -.003 .006 -.001
P33DUMMY -.011 .001 .002 .020 -.001
P34DUMMY -.043 .037 .053 .015 -.001
P35DUMMY -.002 -.003 .000 .030 -.001
P36DUMMY .039 -.031 -.055 -.008 -.001
P37DUMMY .090 -.042 -.012 .071 -.001
P38DUMMY .041 .002 .001 -.027 -.001
P39DUMMY .028 .010 .017 .011 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY
________ ________ ________ ________ ________
P3DUMMY .026
P4DUMMY -.001 .026
P5DUMMY -.001 -.001 .026
P6DUMMY -.001 -.001 -.001 .026
P8DUMMY -.001 -.001 -.001 -.001 .026
P9DUMMY -.001 -.001 -.001 -.001 -.001
P10DUMMY -.001 -.001 -.001 -.001 -.001
P11DUMMY -.001 -.001 -.001 -.001 -.001
253
P12DUMMY -.001 -.001 -.001 -.001 -.001
P13DUMMY -.001 -.001 -.001 -.001 -.001
P14DUMMY -.001 -.001 -.001 -.001 -.001
P15DUMMY -.001 -.001 -.001 -.001 -.001
P16DUMMY -.001 -.001 -.001 -.001 -.001
P17DUMMY -.001 -.001 -.001 -.001 -.001
P18DUMMY -.001 -.001 -.001 -.001 -.001
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P9DUMMY P10DUMMY P11DUMMY P12DUMMY P13DUMMY
________ ________ ________ ________ ________
P9DUMMY .026
P10DUMMY -.001 .026
P11DUMMY -.001 -.001 .026
P12DUMMY -.001 -.001 -.001 .026
P13DUMMY -.001 -.001 -.001 -.001 .026
P14DUMMY -.001 -.001 -.001 -.001 -.001
P15DUMMY -.001 -.001 -.001 -.001 -.001
P16DUMMY -.001 -.001 -.001 -.001 -.001
P17DUMMY -.001 -.001 -.001 -.001 -.001
P18DUMMY -.001 -.001 -.001 -.001 -.001
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
254
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
________ ________ ________ ________ ________
P14DUMMY .026
P15DUMMY -.001 .026
P16DUMMY -.001 -.001 .026
P17DUMMY -.001 -.001 -.001 .026
P18DUMMY -.001 -.001 -.001 -.001 .026
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY
________ ________ ________ ________ ________
P19DUMMY .026
P20DUMMY -.001 .026
P21DUMMY -.001 -.001 .026
P22DUMMY -.001 -.001 -.001 .026
255
P23DUMMY -.001 -.001 -.001 -.001 .026
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P24DUMMY P25DUMMY P26DUMMY P27DUMMY P28DUMMY
________ ________ ________ ________ ________
P24DUMMY .026
P25DUMMY -.001 .026
P26DUMMY -.001 -.001 .026
P27DUMMY -.001 -.001 -.001 .026
P28DUMMY -.001 -.001 -.001 -.001 .026
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY
________ ________ ________ ________ ________
P29DUMMY .026
P30DUMMY -.001 .026
P31DUMMY -.001 -.001 .026
P32DUMMY -.001 -.001 -.001 .026
P33DUMMY -.001 -.001 -.001 -.001 .026
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
256
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P34DUMMY P35DUMMY P36DUMMY P37DUMMY P38DUMMY
________ ________ ________ ________ ________
P34DUMMY .026
P35DUMMY -.001 .026
P36DUMMY -.001 -.001 .026
P37DUMMY -.001 -.001 -.001 .026
P38DUMMY -.001 -.001 -.001 -.001 .026
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P39DUMMY
________
P39DUMMY .026
Mplus VERSION 1.04 PAGE 38
Simultanes Mehrgleichungsmodell fuer die Beziehung
Beginning Time: 14:06:08
Ending Time: 14:06:14
Elapsed Time: 00:00:06
MUTHEN & MUTHEN
11965 Venice Blvd., Suite 407
Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998 Muthen & Muthen
257
C.3.2 Group A (peip & u questions)
Mplus VERSION 1.04
MUTHEN & MUTHEN
02/02/2001 3:45 PM
INPUT INSTRUCTIONS
TITLE: Simultanes Mehrgleichungsmodell fuer die Beziehung
zwischen IC, LEG, WICH und SCHW (mit WICH --> LEG)
Stichprobe: Fragen U + PEIP, disaggregrierte Daten
DATA: FILE IS dummy_pers.dat;
VARIABLE: NAMES ARE
LEG SCHW WICH IC
P1DUMMY P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
USEVARIABLES IC LEG WICH SCHW
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
ANALYSIS: TYPE IS MEANSTRUCTURE;
MODEL:
ic ON leg wich schw
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
leg ON wich
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
[ic leg];
OUTPUT:
258
Tech4;
259
Mplus VERSION 1.04 PAGE 2
INPUT READING TERMINATED NORMALLY
Simultanes Mehrgleichungsmodell fuer die Beziehung
zwischen IC, LEG, WICH und SCHW (mit WICH --> LEG)
Stichprobe: Fragen U + PEIP, disaggregrierte Daten
Mplus VERSION 1.04 PAGE 3
Simultanes Mehrgleichungsmodell fuer die Beziehung
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1786
Number of y-variables 2
Number of x-variables 39
Number of continuous latent variables 0
Observed variables in the analysis
IC LEG WICH SCHW P2DUMMY P3DUMMY
P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY
P17DUMMY P18DUMMY P19DUMMY P20DUMMY P21DUMMY P22DUMMY
P23DUMMY P24DUMMY P25DUMMY P26DUMMY P27DUMMY P28DUMMY
P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY
Estimator ML
Maximum number of iterations 1000
Convergence criterion .500D-04
Input data file(s)
dummy_pers.dat
Input data format FREE
THE MODEL ESTIMATION TERMINATED NORMALLY
260
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 9.735
Degrees of Freedom 1
P-Value .0018
Loglikelihood
H0 Value 13874.508
H1 Value 13879.375
Information Criteria
Number of Free Parameters 82
Akaike (AIC) -27585.015
Bayesian (BIC) -27135.021
Sample-Size Adjusted BIC -27395.530
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Mplus VERSION 1.04 PAGE 4
Simultanes Mehrgleichungsmodell fuer die Beziehung
Estimate .070
90 Percent C.I. .035 .113
Probability RMSEA <= .05 .157
MODEL RESULTS
Estimates S.E. Est./S.E.
IC ON
LEG -.457 .027 -16.740
WICH -.055 .029 -1.915
SCHW .159 .020 7.771
P2DUMMY .227 .425 .534
P3DUMMY 4.017 .431 9.329
P4DUMMY 2.510 .438 5.732
P5DUMMY -4.084 .436 -9.369
P6DUMMY 1.143 .433 2.640
P8DUMMY 1.183 .430 2.748
P9DUMMY .288 .431 .669
P10DUMMY -.838 .428 -1.958
P11DUMMY -.924 .444 -2.080
261
P12DUMMY 2.123 .435 4.878
P13DUMMY 2.980 .426 6.988
P14DUMMY -1.800 .422 -4.262
P15DUMMY -2.024 .423 -4.785
P16DUMMY -1.369 .428 -3.196
P17DUMMY 2.383 .435 5.480
P18DUMMY -3.933 .429 -9.160
P19DUMMY -3.760 .431 -8.725
P20DUMMY 2.033 .430 4.723
P21DUMMY .132 .429 .308
P22DUMMY 2.578 .435 5.920
P23DUMMY -.725 .437 -1.657
P24DUMMY .862 .425 2.030
P25DUMMY 2.059 .428 4.815
P26DUMMY -2.206 .431 -5.122
P27DUMMY 2.925 .424 6.897
P28DUMMY .173 .426 .407
P29DUMMY 2.928 .434 6.750
P30DUMMY -5.087 .431 -11.801
P31DUMMY -.143 .438 -.327
P32DUMMY -.659 .424 -1.555
P33DUMMY -.027 .436 -.061
P34DUMMY -1.218 .423 -2.878
P35DUMMY .166 .428 .389
P36DUMMY 2.028 .435 4.659
P37DUMMY 2.833 .439 6.457
P38DUMMY 3.094 .431 7.173
P39DUMMY 1.460 .425 3.435
LEG ON
WICH .839 .015 56.354
P2DUMMY -.157 .367 -.428
P3DUMMY -.973 .372 -2.616
P4DUMMY .142 .378 .375
P5DUMMY .959 .377 2.546
P6DUMMY 3.470 .366 9.474
P8DUMMY .237 .371 .639
P9DUMMY -.739 .369 -2.002
P10DUMMY .841 .370 2.275
P11DUMMY 4.806 .368 13.051
P12DUMMY 1.429 .375 3.808
P13DUMMY -1.544 .368 -4.200
P14DUMMY .498 .366 1.360
P15DUMMY .244 .367 .666
P16DUMMY 1.705 .369 4.618
P17DUMMY -.247 .374 -.659
P18DUMMY -.025 .371 -.067
P19DUMMY .245 .372 .661
P20DUMMY -.700 .373 -1.878
P21DUMMY -.683 .372 -1.837
P22DUMMY -.252 .378 -.668
P23DUMMY 1.574 .375 4.195
262
P24DUMMY 1.069 .367 2.911
P25DUMMY .860 .370 2.322
P26DUMMY .305 .373 .817
P27DUMMY -1.146 .367 -3.124
P28DUMMY -.985 .368 -2.675
P29DUMMY .217 .376 .578
P30DUMMY .599 .373 1.606
P31DUMMY .714 .374 1.909
P32DUMMY .187 .367 .508
P33DUMMY .197 .375 .527
P34DUMMY -.106 .367 -.288
P35DUMMY -.089 .369 -.242
P36DUMMY .516 .377 1.370
P37DUMMY -.608 .377 -1.616
P38DUMMY .096 .374 .258
P39DUMMY .356 .369 .966
Residual Variances
IC 4.150 .139 29.883
LEG 3.122 .104 29.883
Intercepts
IC 5.974 .328 18.185
LEG .373 .280 1.330
TECHNICAL 4 OUTPUT
ESTIMATES DERIVED FROM THE MODEL
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
IC LEG WICH SCHW P2DUMMY
________ ________ ________ ________ ________
IC 13.528
LEG -7.066 11.795
WICH -5.069 8.635 10.179
SCHW 2.027 -.633 -.487 7.972
P2DUMMY -.018 .016 .035 -.046 .026
P3DUMMY .114 -.047 -.015 -.037 -.001
P4DUMMY .077 -.055 -.059 -.060 -.001
P5DUMMY -.107 -.026 -.050 -.039 -.001
P6DUMMY -.043 .127 .054 -.026 -.001
P8DUMMY .035 -.007 -.005 .054 -.001
P9DUMMY .017 -.015 .016 .075 -.001
P10DUMMY -.034 .019 .007 .030 -.001
P11DUMMY -.096 .139 .025 .008 -.001
P12DUMMY .046 -.005 -.040 -.040 -.001
P13DUMMY .072 -.021 .034 -.037 -.001
P14DUMMY -.087 .053 .058 -.026 -.001
P15DUMMY -.086 .038 .048 -.028 -.001
P16DUMMY -.065 .046 .012 .002 -.001
263
P17DUMMY .086 -.042 -.032 .066 -.001
P18DUMMY -.112 -.015 -.006 -.051 -.001
P19DUMMY -.111 -.011 -.011 -.065 -.001
P20DUMMY .070 -.043 -.019 .019 -.001
P21DUMMY .015 -.038 -.013 .007 -.001
P22DUMMY .092 -.063 -.057 .002 -.001
P23DUMMY -.035 -.001 -.040 -.066 -.001
P24DUMMY -.009 .053 .040 .014 -.001
P25DUMMY .040 .016 .003 .006 -.001
P26DUMMY -.057 -.022 -.025 -.016 -.001
P27DUMMY .068 .000 .047 .009 -.001
P28DUMMY .004 -.014 .025 .014 -.001
P29DUMMY .090 -.042 -.046 -.008 -.001
P30DUMMY -.131 -.012 -.022 .026 -.001
P31DUMMY .012 -.016 -.031 .090 -.001
P32DUMMY -.040 .026 .035 -.005 -.001
P33DUMMY .020 -.034 -.036 .072 -.001
P34DUMMY -.052 .024 .043 .009 -.001
P35DUMMY .003 .001 .015 .048 -.001
P36DUMMY .062 -.040 -.053 -.028 -.001
P37DUMMY .112 -.066 -.049 .075 -.001
P38DUMMY .084 -.030 -.028 -.033 -.001
P39DUMMY .021 .018 .021 -.002 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY
________ ________ ________ ________ ________
P3DUMMY .026
P4DUMMY -.001 .026
P5DUMMY -.001 -.001 .026
P6DUMMY -.001 -.001 -.001 .026
P8DUMMY -.001 -.001 -.001 -.001 .026
P9DUMMY -.001 -.001 -.001 -.001 -.001
P10DUMMY -.001 -.001 -.001 -.001 -.001
P11DUMMY -.001 -.001 -.001 -.001 -.001
P12DUMMY -.001 -.001 -.001 -.001 -.001
P13DUMMY -.001 -.001 -.001 -.001 -.001
P14DUMMY -.001 -.001 -.001 -.001 -.001
P15DUMMY -.001 -.001 -.001 -.001 -.001
P16DUMMY -.001 -.001 -.001 -.001 -.001
P17DUMMY -.001 -.001 -.001 -.001 -.001
P18DUMMY -.001 -.001 -.001 -.001 -.001
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
264
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P9DUMMY P10DUMMY P11DUMMY P12DUMMY P13DUMMY
________ ________ ________ ________ ________
P9DUMMY .026
P10DUMMY -.001 .026
P11DUMMY -.001 -.001 .026
P12DUMMY -.001 -.001 -.001 .026
P13DUMMY -.001 -.001 -.001 -.001 .026
P14DUMMY -.001 -.001 -.001 -.001 -.001
P15DUMMY -.001 -.001 -.001 -.001 -.001
P16DUMMY -.001 -.001 -.001 -.001 -.001
P17DUMMY -.001 -.001 -.001 -.001 -.001
P18DUMMY -.001 -.001 -.001 -.001 -.001
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
265
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
________ ________ ________ ________ ________
P14DUMMY .026
P15DUMMY -.001 .026
P16DUMMY -.001 -.001 .026
P17DUMMY -.001 -.001 -.001 .026
P18DUMMY -.001 -.001 -.001 -.001 .026
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY
________ ________ ________ ________ ________
P19DUMMY .026
P20DUMMY -.001 .026
P21DUMMY -.001 -.001 .026
P22DUMMY -.001 -.001 -.001 .026
P23DUMMY -.001 -.001 -.001 -.001 .026
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
266
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
Mplus VERSION 1.04 PAGE 9
Simultanes Mehrgleichungsmodell fuer die Beziehung
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P24DUMMY P25DUMMY P26DUMMY P27DUMMY P28DUMMY
________ ________ ________ ________ ________
P24DUMMY .026
P25DUMMY -.001 .026
P26DUMMY -.001 -.001 .026
P27DUMMY -.001 -.001 -.001 .026
P28DUMMY -.001 -.001 -.001 -.001 .026
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY
________ ________ ________ ________ ________
P29DUMMY .026
P30DUMMY -.001 .026
P31DUMMY -.001 -.001 .026
P32DUMMY -.001 -.001 -.001 .026
P33DUMMY -.001 -.001 -.001 -.001 .026
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P34DUMMY P35DUMMY P36DUMMY P37DUMMY P38DUMMY
________ ________ ________ ________ ________
P34DUMMY .026
P35DUMMY -.001 .026
P36DUMMY -.001 -.001 .026
P37DUMMY -.001 -.001 -.001 .026
267
P38DUMMY -.001 -.001 -.001 -.001 .026
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P39DUMMY
________
P39DUMMY .026
Beginning Time: 15:45:47
Ending Time: 15:45:53
Mplus VERSION 1.04 PAGE 10
Simultanes Mehrgleichungsmodell fuer die Beziehung
Elapsed Time: 00:00:06
MUTHEN & MUTHEN
11965 Venice Blvd., Suite 407
Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998 Muthen & Muthen
268
C.3.3 Group B (pepr & pd questions)
Mplus VERSION 1.04
MUTHEN & MUTHEN
02/02/2001 3:51 PM
INPUT INSTRUCTIONS
TITLE: Simultanes Mehrgleichungsmodell fuer die Beziehung
zwischen IC, LEG, WICH und SCHW (mit WICH --> LEG)
Stichprobe: Fragen PDD, PDI + PEPR, disaggregrierte Daten
DATA: FILE IS dummy_prod.dat;
VARIABLE: NAMES ARE
LEG SCHW WICH IC
P1DUMMY P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
USEVARIABLES IC LEG WICH SCHW
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
ANALYSIS: TYPE IS MEANSTRUCTURE;
MODEL:
ic ON leg wich schw
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
leg ON wich
P2DUMMY P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY P24DUMMY P25DUMMY P26DUMMY
P27DUMMY P28DUMMY P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY;
[ic leg];
OUTPUT:
269
Tech4;
270
Mplus VERSION 1.04 PAGE 2
INPUT READING TERMINATED NORMALLY
Simultanes Mehrgleichungsmodell fuer die Beziehung
zwischen IC, LEG, WICH und SCHW (mit WICH --> LEG)
Stichprobe: Fragen PDD, PDI + PEPR, disaggregrierte Daten
SUMMARY OF ANALYSIS
Mplus VERSION 1.04 PAGE 3
Simultanes Mehrgleichungsmodell fuer die Beziehung
Number of groups 1
Number of observations 2470
Number of y-variables 2
Number of x-variables 39
Number of continuous latent variables 0
Observed variables in the analysis
IC LEG WICH SCHW P2DUMMY P3DUMMY
P4DUMMY P5DUMMY P6DUMMY P8DUMMY P9DUMMY P10DUMMY
P11DUMMY P12DUMMY P13DUMMY P14DUMMY P15DUMMY P16DUMMY
P17DUMMY P18DUMMY P19DUMMY P20DUMMY P21DUMMY P22DUMMY
P23DUMMY P24DUMMY P25DUMMY P26DUMMY P27DUMMY P28DUMMY
P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY P34DUMMY
P35DUMMY P36DUMMY P37DUMMY P38DUMMY P39DUMMY
Estimator ML
Maximum number of iterations 1000
Convergence criterion .500D-04
Input data file(s)
dummy_prod.dat
Input data format FREE
THE MODEL ESTIMATION TERMINATED NORMALLY
271
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 4.340
Degrees of Freedom 1
P-Value .0372
Loglikelihood
H0 Value 20369.999
H1 Value 20372.169
Information Criteria
Number of Free Parameters 82
Akaike (AIC) -40575.998
Bayesian (BIC) -40099.416
Sample-Size Adjusted BIC -40359.950
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate .037
90 Percent C.I. .007 .075
Mplus VERSION 1.04 PAGE 4
Simultanes Mehrgleichungsmodell fuer die Beziehung
Probability RMSEA <= .05 .656
MODEL RESULTS
Estimates S.E. Est./S.E.
IC ON
LEG -.397 .022 -18.224
WICH .003 .019 .142
SCHW .182 .016 11.248
P2DUMMY -1.986 .303 -6.560
P3DUMMY .859 .305 2.820
P4DUMMY -.082 .309 -.265
P5DUMMY -2.250 .304 -7.393
P6DUMMY -.300 .303 -.988
P8DUMMY -.737 .301 -2.444
P9DUMMY 1.973 .303 6.512
P10DUMMY -1.239 .303 -4.095
P11DUMMY -1.008 .307 -3.283
P12DUMMY -.452 .302 -1.497
P13DUMMY .758 .302 2.509
P14DUMMY .061 .301 .203
272
P15DUMMY -1.188 .304 -3.907
P16DUMMY -1.523 .302 -5.036
P17DUMMY -.119 .310 -.385
P18DUMMY -1.944 .302 -6.429
P19DUMMY -3.172 .308 -10.285
P20DUMMY -.243 .305 -.797
P21DUMMY .201 .303 .663
P22DUMMY -1.483 .303 -4.895
P23DUMMY -1.638 .304 -5.380
P24DUMMY -.853 .302 -2.827
P25DUMMY .984 .302 3.265
P26DUMMY -.252 .302 -.834
P27DUMMY -.774 .303 -2.558
P28DUMMY -1.178 .303 -3.894
P29DUMMY -1.272 .305 -4.165
P30DUMMY -3.355 .308 -10.899
P31DUMMY .935 .306 3.056
P32DUMMY -1.732 .305 -5.669
P33DUMMY -1.406 .302 -4.664
P34DUMMY -1.457 .302 -4.820
P35DUMMY -1.105 .303 -3.647
P36DUMMY -.255 .306 -.833
P37DUMMY 1.289 .307 4.196
P38DUMMY .264 .302 .876
P39DUMMY .512 .302 1.692
LEG ON
WICH .591 .013 46.850
P2DUMMY -.592 .279 -2.124
P3DUMMY -.201 .281 -.717
P4DUMMY -1.420 .284 -5.005
P5DUMMY .521 .280 1.861
P6DUMMY 1.053 .279 3.770
P8DUMMY .110 .278 .394
P9DUMMY -.721 .278 -2.590
P10DUMMY .161 .279 .578
P11DUMMY 2.056 .280 7.340
P12DUMMY .646 .279 2.318
P13DUMMY -.590 .278 -2.122
P14DUMMY -.033 .278 -.118
P15DUMMY -1.454 .279 -5.212
P16DUMMY -.259 .279 -.930
P17DUMMY -1.947 .283 -6.892
P18DUMMY .179 .279 .644
P19DUMMY -1.278 .282 -4.529
P20DUMMY -.592 .279 -2.119
P21DUMMY -.990 .279 -3.545
P22DUMMY -.618 .279 -2.211
P23DUMMY .505 .279 1.810
P24DUMMY .077 .279 .278
P25DUMMY -.190 .278 -.681
P26DUMMY -.510 .278 -1.831
273
P27DUMMY .519 .279 1.858
P28DUMMY -1.157 .278 -4.155
P29DUMMY 1.678 .280 6.000
P30DUMMY .158 .284 .556
P31DUMMY -1.004 .281 -3.574
P32DUMMY -1.368 .281 -4.876
P33DUMMY .072 .278 .259
P34DUMMY .215 .278 .773
P35DUMMY -.204 .280 -.731
P36DUMMY .083 .283 .295
P37DUMMY -1.496 .279 -5.369
P38DUMMY .196 .278 .702
P39DUMMY -.398 .279 -1.429
Residual Variances
IC 2.951 .084 35.143
LEG 2.517 .072 35.143
Intercepts
IC 5.235 .265 19.754
LEG 3.970 .225 17.671
TECHNICAL 4 OUTPUT
ESTIMATES DERIVED FROM THE MODEL
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
IC LEG WICH SCHW P2DUMMY
________ ________ ________ ________ ________
IC 5.681
LEG -2.555 6.198
WICH -1.736 4.869 7.977
SCHW 1.771 -.862 -1.286 5.938
P2DUMMY -.042 -.001 .014 -.040 .026
P3DUMMY .045 -.021 -.037 -.019 -.001
P4DUMMY .042 -.072 -.069 -.012 -.001
P5DUMMY -.052 .007 -.021 -.037 -.001
P6DUMMY -.004 .031 -.005 -.009 -.001
P8DUMMY -.014 .028 .032 -.005 -.001
P9DUMMY .073 .006 .032 .031 -.001
P10DUMMY -.017 .008 -.003 .010 -.001
P11DUMMY -.023 .047 -.022 .028 -.001
P12DUMMY -.009 .033 .017 -.008 -.001
P13DUMMY .022 .021 .052 -.038 -.001
P14DUMMY .004 .027 .037 -.021 -.001
P15DUMMY -.007 -.029 .005 -.026 -.001
P16DUMMY -.021 .003 .007 .018 -.001
P17DUMMY .053 -.079 -.057 .043 -.001
P18DUMMY -.050 .023 .020 -.041 -.001
P19DUMMY -.051 -.059 -.052 -.047 -.001
274
P20DUMMY .025 -.010 -.001 .056 -.001
P21DUMMY .029 -.024 -.006 -.015 -.001
P22DUMMY -.015 -.013 -.005 .009 -.001
P23DUMMY -.043 .016 -.005 -.057 -.001
P24DUMMY -.011 .019 .019 .011 -.001
P25DUMMY .034 .018 .029 -.014 -.001
P26DUMMY .004 .010 .029 -.015 -.001
P27DUMMY -.008 .018 -.002 .011 -.001
P28DUMMY -.009 -.009 .027 .004 -.001
P29DUMMY -.029 .043 -.013 .021 -.001
P30DUMMY -.052 -.032 -.072 .032 -.001
P31DUMMY .065 -.042 -.036 .036 -.001
P32DUMMY -.007 -.048 -.031 .014 -.001
P33DUMMY -.033 .025 .029 -.018 -.001
P34DUMMY -.036 .047 .059 .020 -.001
P35DUMMY -.006 -.006 -.011 .017 -.001
P36DUMMY .022 -.025 -.057 .006 -.001
P37DUMMY .074 -.024 .016 .069 -.001
P38DUMMY .010 .025 .022 -.023 -.001
P39DUMMY .033 .004 .014 .020 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P3DUMMY P4DUMMY P5DUMMY P6DUMMY P8DUMMY
________ ________ ________ ________ ________
P3DUMMY .026
P4DUMMY -.001 .026
P5DUMMY -.001 -.001 .026
P6DUMMY -.001 -.001 -.001 .026
P8DUMMY -.001 -.001 -.001 -.001 .026
P9DUMMY -.001 -.001 -.001 -.001 -.001
P10DUMMY -.001 -.001 -.001 -.001 -.001
P11DUMMY -.001 -.001 -.001 -.001 -.001
P12DUMMY -.001 -.001 -.001 -.001 -.001
P13DUMMY -.001 -.001 -.001 -.001 -.001
P14DUMMY -.001 -.001 -.001 -.001 -.001
P15DUMMY -.001 -.001 -.001 -.001 -.001
P16DUMMY -.001 -.001 -.001 -.001 -.001
P17DUMMY -.001 -.001 -.001 -.001 -.001
P18DUMMY -.001 -.001 -.001 -.001 -.001
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
275
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P9DUMMY P10DUMMY P11DUMMY P12DUMMY P13DUMMY
________ ________ ________ ________ ________
P9DUMMY .026
P10DUMMY -.001 .026
P11DUMMY -.001 -.001 .026
P12DUMMY -.001 -.001 -.001 .026
P13DUMMY -.001 -.001 -.001 -.001 .026
P14DUMMY -.001 -.001 -.001 -.001 -.001
P15DUMMY -.001 -.001 -.001 -.001 -.001
P16DUMMY -.001 -.001 -.001 -.001 -.001
P17DUMMY -.001 -.001 -.001 -.001 -.001
P18DUMMY -.001 -.001 -.001 -.001 -.001
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
276
Mplus VERSION 1.04 PAGE 8
Simultanes Mehrgleichungsmodell fuer die Beziehung
P14DUMMY P15DUMMY P16DUMMY P17DUMMY P18DUMMY
________ ________ ________ ________ ________
P14DUMMY .026
P15DUMMY -.001 .026
P16DUMMY -.001 -.001 .026
P17DUMMY -.001 -.001 -.001 .026
P18DUMMY -.001 -.001 -.001 -.001 .026
P19DUMMY -.001 -.001 -.001 -.001 -.001
P20DUMMY -.001 -.001 -.001 -.001 -.001
P21DUMMY -.001 -.001 -.001 -.001 -.001
P22DUMMY -.001 -.001 -.001 -.001 -.001
P23DUMMY -.001 -.001 -.001 -.001 -.001
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P19DUMMY P20DUMMY P21DUMMY P22DUMMY P23DUMMY
________ ________ ________ ________ ________
P19DUMMY .026
P20DUMMY -.001 .026
P21DUMMY -.001 -.001 .026
P22DUMMY -.001 -.001 -.001 .026
P23DUMMY -.001 -.001 -.001 -.001 .026
P24DUMMY -.001 -.001 -.001 -.001 -.001
P25DUMMY -.001 -.001 -.001 -.001 -.001
P26DUMMY -.001 -.001 -.001 -.001 -.001
P27DUMMY -.001 -.001 -.001 -.001 -.001
P28DUMMY -.001 -.001 -.001 -.001 -.001
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
277
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
Mplus VERSION 1.04 PAGE 9
Simultanes Mehrgleichungsmodell fuer die Beziehung
P24DUMMY P25DUMMY P26DUMMY P27DUMMY P28DUMMY
________ ________ ________ ________ ________
P24DUMMY .026
P25DUMMY -.001 .026
P26DUMMY -.001 -.001 .026
P27DUMMY -.001 -.001 -.001 .026
P28DUMMY -.001 -.001 -.001 -.001 .026
P29DUMMY -.001 -.001 -.001 -.001 -.001
P30DUMMY -.001 -.001 -.001 -.001 -.001
P31DUMMY -.001 -.001 -.001 -.001 -.001
P32DUMMY -.001 -.001 -.001 -.001 -.001
P33DUMMY -.001 -.001 -.001 -.001 -.001
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P29DUMMY P30DUMMY P31DUMMY P32DUMMY P33DUMMY
________ ________ ________ ________ ________
P29DUMMY .026
P30DUMMY -.001 .026
P31DUMMY -.001 -.001 .026
P32DUMMY -.001 -.001 -.001 .026
P33DUMMY -.001 -.001 -.001 -.001 .026
P34DUMMY -.001 -.001 -.001 -.001 -.001
P35DUMMY -.001 -.001 -.001 -.001 -.001
P36DUMMY -.001 -.001 -.001 -.001 -.001
P37DUMMY -.001 -.001 -.001 -.001 -.001
P38DUMMY -.001 -.001 -.001 -.001 -.001
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P34DUMMY P35DUMMY P36DUMMY P37DUMMY P38DUMMY
________ ________ ________ ________ ________
P34DUMMY .026
P35DUMMY -.001 .026
278
P36DUMMY -.001 -.001 .026
P37DUMMY -.001 -.001 -.001 .026
P38DUMMY -.001 -.001 -.001 -.001 .026
P39DUMMY -.001 -.001 -.001 -.001 -.001
ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
P39DUMMY
________
P39DUMMY .026
Beginning Time: 15:51:15
Ending Time: 15:51:21
Elapsed Time: 00:00:06
Mplus VERSION 1.04 PAGE 10
Simultanes Mehrgleichungsmodell fuer die Beziehung
MUTHEN & MUTHEN
11965 Venice Blvd., Suite 407
Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998 Muthen & Muthen
279
C.4 Questions employed to derive Privacy Attitudes
|
1. (CONCERN ON PRIVACY)
Wie starke Sorgen machen Sie sich über die Gefahr einer Einbuße Ihrer Privatheit durch die Nutzung des Internets?
 sorge mich sehr
sorge mich ein bisschen
sorge mich nicht so sehr
sorge mich gar nicht
|
|
2. (INDEX SCENARIO 1)
Szenario 1: Stellen Sie sich vor, Sie gingen auf die WWW-Seite Ihrer Hausbank und entdeckten ein elektronisches Formular, welches Sie ausfüllen können, um daraufhin auf Sie persönlich zugeschnittene Anlageempfehlungen zu bekommen. Auf dem Formular werden Sie gebeten, Angaben zu Ihrem Einkommen, Ihren gegenwärtigen Anlagen und Sparzielen zu machen. Gleichzeitig werden keine Angaben zu Ihrer Person, Ihrem Namen oder andere Informationen abgefragt, von denen auf Ihre Person geschlossen werden könnte. Ausgehend von den Informationen auf der Website sieht es so aus, als könnten Sie durch das Ausfüllen des Formulars nützliche Informationen bekommen.
Würden Sie das Formular ausfüllen?
Auf gar keinen Fall
Wahrscheinlich nicht
Ich bin nicht sicher
Wahrscheinlich schon
Ganz bestimmt
Wie würden Sie sich in Szenario 1 verhalten, angenommen das Formular würde doch nach Ihrem Namen und Ihrer Adresse fragen, so dass Ihnen die Bank einen Anlageführer zuschicken kann? Nehmen Sie an, daß Sie davon ausgehen, dass dieser Anlageführer für Sie nützlich sein könnte.
Würden Sie die Angaben (Namen und Adresse) machen?
Auf gar keinen Fall
Wahrscheinlich nicht
Ich bin nicht sicher
Wahrscheinlich schon
Ganz bestimmt
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280
|
2. (INDEX SCENARIO 3)
Szenario 3: Während Sie online Informationen zu einem Ihrer Lieblingshobbies suchen, landen Sie auf einer Website, die ein paar wirklich interessante Informationen enthält. Die Site wird gesponsert von einer Firma, deren Name Sie noch nie gehört haben, aber die Leute scheinen sich auszukennen. Sie finden ein Formular auf der Seite, welches Sie ausfüllen können, um eine kostenlose Broschüre und einige Zusatzinformationen zu erhalten sowie Coupons auf einige Produkte der Firma. Das Formular verlangt Ihren Namen und Ihre Postanschrift. Wie würden Sie voraussichtlich reagieren?
Ich würde die verlangte Information eingeben
Ich würde versuchen, die Firma anzurufen, um so die Broschüre und die Coupons zu bekommen
Ich würde wahrscheinlich auf die Möglichkeit verzichten, die Broschüre und die Coupons zu
bekommen
Wie würden Sie Ihr Verhalten in Szenario 3 ändern, enthielte die Website eine Police zum Umgang mit Ihren Daten (privacy policy). In der Police steht, daß die Firma Ihren Namen und Ihre Adresse ausschließlich nutzen wird, um Ihnen die angeforderte Broschüre und die Coupons zuzuschicken.
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Wie würde sich Ihr Verhalten in Szenario 3 ändern, enthielte die Website nicht nur eine Privacy Police, sondern außerdem noch das Gütesiegel einer anerkannten Organisation, wie z.B. dem TÜV, die für die Vertrauenswürdigkeit der Website garantiert?
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Wie würden Sie Ihr Verhalten in Szenario 3 ändern, gäbe es ein Gesetz, welches dem Betreiber der WWW Seite verbietet, Ihren Namen und Ihre Adresse für einen anderen Zweck als Ihre Anfrage einzusetzen.
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Wie würde sich Ihr Verhalten in Szenario 3 ändern, enthielte die Website eine Privacy Police, die Ihnen erklärt, daß die Firma Ihren Namen und Ihre Adresse nicht nur dafür nutzen möchte, Ihnen die angeforderte Broschüre und die Coupons zuzuschicken, sondern auch, um Ihnen in Zukunft regelmäßig Neuigkeiten zu ihren Produkten zukommen zu lassen. Ferner plant die Firma Ihre Daten auch anderen Unternehmen zur Verfügung zu stellen, die Produkte verkaufen, für die Sie sich eventuell auch interessieren könnten.
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Vor dem Hintergrund von Frage 15: Wären Sie eher bereit, die Information einzugeben, wenn die Website Ihnen die Möglichkeit geben würde, auf Wunsch von ihrer Mailinglist jederzeit wieder entfernt zu werden?
ja
nein
|
281
282
|
2. (INDEX SCENARIO 4)
Szenario 4: Sie besuchen eine Website, die Nachrichten, Wetter und Sportergebnisse bereitstellt. Die Seite sieht so aus, als würden Sie sie gerne häufiger besuchen. Die Website fordert Sie auf, Ihre Postleitzahl anzugeben sowie einige Fragen zu Ihren Interessen zu beantworten, damit die Interaktion mit der Website in Zukunft auf Sie persönlich zugeschnitten werden kann. Die Privacy Police der Website erklärt, daß alle Informationen, die Sie angeben sowie Ihr Suchverhalten auf der Website registriert werden. Beides wird genutzt, um die Seiten auf Sie ‚zuzuschneiden‘ und um die Seite insgesamt zu erhalten und zu verbessern. Gewährleistet ist, daß Ihr Name nie mit diesen Informationen assoziiert wird. Wie würden Sie voraussichtlich reagieren?
Ich würde die verlangte Information eingeben
Ich würde die verlangte Information nicht eingeben
Ich würde die verlangte Information vielleicht eingeben, aber wahrscheinlich würde ich lügen
Wie würdest Du in Szenario 4 reagieren, wenn die Website Sie außerdem nach einigen Informationen über Ihren Computer fragt, damit die Seite besser auf Sie zugeschnitten werden kann. Die Fragen könnten Informationen zu dem von Ihnen genutzten Betriebssystem, dem Browser, dem Monitor oder Modem enthalten.
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Ich würde die verlangte Information vielleicht eingeben, aber wahrscheinlich würde ich lügen
Wie würden Sie in Szenario 4 reagieren, wenn die Website von Ihnen demographische oder soziographische Informationen abfragt, eingeschlossen Ihr Alter, Ihr Geschlecht und Ihr Familieneinkommen?
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Ich würde die verlangte Information vielleicht eingeben, aber wahrscheinlich würde ich lügen
Wie würden Sie in Szenario 4 reagieren, wenn die Website Ihren Namen abfragt?
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Ich würde die verlangte Information vielleicht eingeben, aber wahrscheinlich würde ich lügen
Wie würden Sie in Szenario 4 reagieren, wenn die Website Ihren Namen wissen möchte, ihre Privacy Police jedoch aussagt, daß wenn Sie die Website über 3 Monate nicht besuchen, Ihr Name und alle Informationen gelöscht werden, die man über Sie gesammelt hat.
An meiner Reaktion würde sich nichts ändern
Ich würde die verlangte Information eher eingeben
Ich würde weniger gewillt sein, die verlangte Information einzugeben
Ich würde die verlangte Information vielleicht eingeben, aber wahrscheinlich würde ich lügen
|
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4. NAME
Beim Besuch von Websites, die Informationen über User sammeln, besteht bei vielen Leuten die Haltung, daß sie einige Informationen grundsätzlich bedenkenlos herausgeben, während sie andere Informationen nur unter besonderen Umständen von sich preisgeben. Wieder andere Informationen würden sie nur sehr ungern oder nie auf einer Website hinterlassen. Bitte sagen Sie uns, wie wohl Sie sich dabei fühlen, die folgenden Informationen auf einer Website anzugeben.
Ihren Vor- und Nachnamen
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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5. ADDRESS
Ihre Postanschrift
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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6. EMAIL ADDRESS
Ihre e-mail Adresse
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
_Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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7. PHONE NUMBER
Ihre Telefonnummer
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
|
283
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8. COMPUTER
Informationen über Ihren Computer, Hardware und Software
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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9. SALARY
Ihr jährliches Haushaltseinkommen
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.+
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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10. CREDIT CARD NUMBER
Ihre Kreditkartennummer
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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11. HOBBY AND INTEREST
Informationen über Ihre Hobbies
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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12. HEALTNEW
Informationen über Ihre Gesundheit und Krankheitsgeschichte
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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284
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13. AGENEW
Ihr Alter
Ich würde mich immer wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich normalerweise wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich manchmal wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich selten wohl fühlen, diese Information auf einer Website anzugeben.
Ich würde mich nie wohl fühlen, diese Information auf einer Website anzugeben.
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285
C.5 Agglomorative Clustering Table
Average Linkage Hierarchical Clustering (cameras and jackets)
Case Processing Summary
|
Cases
|
|
|
|
|
|
|
Valid
|
|
Missing
|
|
Total
|
|
|
N
|
Percent
|
N
|
Percent
|
N
|
Percent
|
|
171
|
97.7
|
4
|
2.3
|
175
|
100.0
|
a Squared Euclidean Distance used
b Average Linkage (Between Groups)
Agglomeration schedule
|
|
Cluster Combined
|
|
Coefficients
|
Stage Cluster First Appears
|
|
Next Stage
|
|
Stage
|
Cluster 1
|
Cluster 2
|
|
Cluster 1
|
Cluster 2
|
|
|
1
|
94
|
206
|
1.979
|
0
|
0
|
37
|
|
2
|
66
|
185
|
2.633
|
0
|
0
|
23
|
|
3
|
6
|
111
|
2.654
|
0
|
0
|
19
|
|
4
|
71
|
196
|
3.029
|
0
|
0
|
45
|
|
5
|
102
|
134
|
3.240
|
0
|
0
|
29
|
|
6
|
184
|
226
|
3.672
|
0
|
0
|
8
|
|
7
|
46
|
79
|
3.717
|
0
|
0
|
48
|
|
8
|
184
|
186
|
4.213
|
6
|
0
|
39
|
|
9
|
193
|
234
|
4.251
|
0
|
0
|
45
|
|
10
|
15
|
40
|
4.433
|
0
|
0
|
121
|
|
11
|
76
|
131
|
4.461
|
0
|
0
|
71
|
|
12
|
51
|
117
|
4.564
|
0
|
0
|
48
|
|
13
|
92
|
221
|
4.620
|
0
|
0
|
49
|
|
14
|
212
|
222
|
4.697
|
0
|
0
|
58
|
|
15
|
121
|
127
|
4.795
|
0
|
0
|
80
|
|
16
|
50
|
231
|
4.829
|
0
|
0
|
103
|
|
17
|
85
|
138
|
4.883
|
0
|
0
|
96
|
|
18
|
14
|
189
|
4.892
|
0
|
0
|
22
|
|
19
|
6
|
59
|
5.191
|
3
|
0
|
30
|
|
20
|
9
|
194
|
5.306
|
0
|
0
|
39
|
|
21
|
84
|
135
|
5.402
|
0
|
0
|
84
|
|
22
|
14
|
235
|
5.450
|
18
|
0
|
55
|
|
23
|
65
|
66
|
5.589
|
0
|
2
|
29
|
|
24
|
47
|
195
|
5.709
|
0
|
0
|
47
|
286
|
25
|
32
|
62
|
5.751
|
0
|
0
|
116
|
|
26
|
44
|
211
|
5.756
|
0
|
0
|
86
|
|
27
|
36
|
133
|
5.805
|
0
|
0
|
42
|
|
28
|
42
|
199
|
5.908
|
0
|
0
|
62
|
|
29
|
65
|
102
|
6.188
|
23
|
5
|
55
|
|
30
|
6
|
240
|
6.283
|
19
|
0
|
56
|
|
31
|
54
|
208
|
6.294
|
0
|
0
|
88
|
|
32
|
29
|
77
|
6.362
|
0
|
0
|
91
|
|
33
|
89
|
118
|
6.413
|
0
|
0
|
157
|
|
34
|
113
|
223
|
6.420
|
0
|
0
|
85
|
|
35
|
10
|
41
|
6.463
|
0
|
0
|
85
|
|
36
|
128
|
220
|
6.465
|
0
|
0
|
78
|
|
37
|
94
|
110
|
6.477
|
1
|
0
|
104
|
|
38
|
4
|
61
|
6.479
|
0
|
0
|
110
|
|
39
|
9
|
184
|
6.527
|
20
|
8
|
70
|
|
40
|
35
|
104
|
6.534
|
0
|
0
|
94
|
|
41
|
11
|
237
|
6.549
|
0
|
0
|
113
|
|
42
|
36
|
108
|
6.641
|
27
|
0
|
97
|
|
43
|
2
|
87
|
6.647
|
0
|
0
|
57
|
|
44
|
8
|
90
|
6.746
|
0
|
0
|
69
|
|
45
|
71
|
193
|
6.862
|
4
|
9
|
93
|
|
46
|
22
|
101
|
6.881
|
0
|
0
|
112
|
|
47
|
47
|
188
|
6.885
|
24
|
0
|
82
|
|
48
|
46
|
51
|
6.913
|
7
|
12
|
87
|
|
49
|
7
|
92
|
7.034
|
0
|
13
|
81
|
|
50
|
20
|
55
|
7.290
|
0
|
0
|
129
|
|
51
|
27
|
232
|
7.374
|
0
|
0
|
86
|
|
52
|
18
|
45
|
7.395
|
0
|
0
|
114
|
|
53
|
43
|
99
|
7.426
|
0
|
0
|
76
|
|
54
|
100
|
136
|
7.475
|
0
|
0
|
77
|
|
55
|
14
|
65
|
7.709
|
22
|
29
|
88
|
|
56
|
6
|
216
|
7.756
|
30
|
0
|
109
|
|
57
|
2
|
239
|
7.764
|
43
|
0
|
70
|
|
58
|
33
|
212
|
7.849
|
0
|
14
|
89
|
|
59
|
34
|
58
|
7.867
|
0
|
0
|
90
|
|
60
|
52
|
233
|
7.883
|
0
|
0
|
111
|
|
61
|
129
|
207
|
7.935
|
0
|
0
|
120
|
|
62
|
23
|
42
|
8.069
|
0
|
28
|
119
|
|
63
|
17
|
139
|
8.122
|
0
|
0
|
109
|
|
64
|
38
|
236
|
8.123
|
0
|
0
|
112
|
|
65
|
203
|
218
|
8.267
|
0
|
0
|
92
|
|
66
|
183
|
197
|
8.292
|
0
|
0
|
125
|
|
67
|
1
|
192
|
8.296
|
0
|
0
|
101
|
|
68
|
97
|
137
|
8.466
|
0
|
0
|
127
|
|
69
|
8
|
53
|
8.477
|
44
|
0
|
75
|
|
70
|
2
|
9
|
8.564
|
57
|
39
|
89
|
287
|
71
|
76
|
88
|
8.600
|
11
|
0
|
98
|
|
72
|
26
|
132
|
8.625
|
0
|
0
|
142
|
|
73
|
16
|
28
|
8.660
|
0
|
0
|
106
|
|
74
|
73
|
215
|
8.668
|
0
|
0
|
95
|
|
75
|
8
|
83
|
8.862
|
69
|
0
|
120
|
|
76
|
43
|
122
|
8.964
|
53
|
0
|
139
|
|
77
|
100
|
225
|
9.085
|
54
|
0
|
102
|
|
78
|
106
|
128
|
9.099
|
0
|
36
|
105
|
|
79
|
48
|
120
|
9.224
|
0
|
0
|
155
|
|
80
|
31
|
121
|
9.278
|
0
|
15
|
100
|
|
81
|
7
|
72
|
9.304
|
49
|
0
|
108
|
|
82
|
47
|
91
|
9.365
|
47
|
0
|
115
|
|
83
|
12
|
200
|
9.468
|
0
|
0
|
137
|
|
84
|
84
|
123
|
9.564
|
21
|
0
|
152
|
|
85
|
10
|
113
|
9.631
|
35
|
34
|
103
|
|
86
|
27
|
44
|
9.643
|
51
|
26
|
117
|
|
87
|
46
|
103
|
9.690
|
48
|
0
|
99
|
|
88
|
14
|
54
|
9.738
|
55
|
31
|
107
|
|
89
|
2
|
33
|
9.823
|
70
|
58
|
93
|
|
90
|
34
|
98
|
10.246
|
59
|
0
|
107
|
|
91
|
29
|
63
|
10.252
|
32
|
0
|
101
|
|
92
|
203
|
205
|
10.336
|
65
|
0
|
124
|
|
93
|
2
|
71
|
10.481
|
89
|
45
|
116
|
|
94
|
21
|
35
|
10.493
|
0
|
40
|
113
|
|
95
|
73
|
229
|
10.634
|
74
|
0
|
123
|
|
96
|
67
|
85
|
10.662
|
0
|
17
|
144
|
|
97
|
36
|
109
|
10.704
|
42
|
0
|
151
|
|
98
|
76
|
82
|
10.903
|
71
|
0
|
122
|
|
99
|
46
|
68
|
11.180
|
87
|
0
|
108
|
|
100
|
31
|
96
|
11.209
|
80
|
0
|
154
|
|
101
|
1
|
29
|
11.541
|
67
|
91
|
114
|
|
102
|
100
|
202
|
11.752
|
77
|
0
|
130
|
|
103
|
10
|
50
|
12.005
|
85
|
16
|
140
|
|
104
|
78
|
94
|
12.016
|
0
|
37
|
124
|
|
105
|
5
|
106
|
12.030
|
0
|
78
|
126
|
|
106
|
16
|
126
|
12.034
|
73
|
0
|
121
|
|
107
|
14
|
34
|
12.036
|
88
|
90
|
125
|
|
108
|
7
|
46
|
12.058
|
81
|
99
|
128
|
|
109
|
6
|
17
|
12.178
|
56
|
63
|
118
|
|
110
|
4
|
116
|
12.360
|
38
|
0
|
129
|
|
111
|
52
|
125
|
12.522
|
60
|
0
|
150
|
|
112
|
22
|
38
|
12.552
|
46
|
64
|
145
|
|
113
|
11
|
21
|
12.666
|
41
|
94
|
133
|
|
114
|
1
|
18
|
12.679
|
101
|
52
|
148
|
|
115
|
47
|
60
|
12.940
|
82
|
0
|
118
|
|
116
|
2
|
32
|
13.016
|
93
|
25
|
131
|
288
|
117
|
27
|
228
|
13.411
|
86
|
0
|
133
|
|
118
|
6
|
47
|
13.572
|
109
|
115
|
136
|
|
119
|
23
|
264
|
13.579
|
62
|
0
|
128
|
|
120
|
8
|
129
|
13.598
|
75
|
61
|
137
|
|
121
|
15
|
16
|
13.976
|
10
|
106
|
130
|
|
122
|
76
|
140
|
14.011
|
98
|
0
|
138
|
|
123
|
73
|
219
|
14.104
|
95
|
0
|
131
|
|
124
|
78
|
203
|
14.202
|
104
|
92
|
135
|
|
125
|
14
|
183
|
14.510
|
107
|
66
|
135
|
|
126
|
3
|
5
|
14.545
|
0
|
105
|
136
|
|
127
|
97
|
217
|
14.551
|
68
|
0
|
146
|
|
128
|
7
|
23
|
14.686
|
108
|
119
|
143
|
|
129
|
4
|
20
|
14.800
|
110
|
50
|
149
|
|
130
|
15
|
100
|
14.954
|
121
|
102
|
140
|
|
131
|
2
|
73
|
15.201
|
116
|
123
|
144
|
|
132
|
49
|
115
|
15.232
|
0
|
0
|
165
|
|
133
|
11
|
27
|
15.302
|
113
|
117
|
141
|
|
134
|
210
|
214
|
15.479
|
0
|
0
|
163
|
|
135
|
14
|
78
|
15.886
|
125
|
124
|
141
|
|
136
|
3
|
6
|
16.302
|
126
|
118
|
143
|
|
137
|
8
|
12
|
16.651
|
120
|
83
|
146
|
|
138
|
39
|
76
|
17.185
|
0
|
122
|
145
|
|
139
|
43
|
190
|
17.238
|
76
|
0
|
155
|
|
140
|
10
|
15
|
17.280
|
103
|
130
|
154
|
|
141
|
11
|
14
|
17.324
|
133
|
135
|
153
|
|
142
|
26
|
254
|
17.642
|
72
|
0
|
149
|
|
143
|
3
|
7
|
17.843
|
136
|
128
|
153
|
|
144
|
2
|
67
|
17.979
|
131
|
96
|
150
|
|
145
|
22
|
39
|
18.133
|
112
|
138
|
151
|
|
146
|
8
|
97
|
18.915
|
137
|
127
|
159
|
|
147
|
130
|
209
|
18.919
|
0
|
0
|
157
|
|
148
|
1
|
30
|
18.975
|
114
|
0
|
162
|
|
149
|
4
|
26
|
19.040
|
129
|
142
|
161
|
|
150
|
2
|
52
|
19.359
|
144
|
111
|
152
|
|
151
|
22
|
36
|
19.398
|
145
|
97
|
160
|
|
152
|
2
|
84
|
19.728
|
150
|
84
|
156
|
|
153
|
3
|
11
|
19.971
|
143
|
141
|
158
|
|
154
|
10
|
31
|
20.025
|
140
|
100
|
156
|
|
155
|
43
|
48
|
21.035
|
139
|
79
|
158
|
|
156
|
2
|
10
|
21.271
|
152
|
154
|
159
|
|
157
|
89
|
130
|
21.331
|
33
|
147
|
162
|
|
158
|
3
|
43
|
22.019
|
153
|
155
|
161
|
|
159
|
2
|
8
|
22.838
|
156
|
146
|
166
|
|
160
|
22
|
224
|
23.981
|
151
|
0
|
164
|
|
161
|
3
|
4
|
24.039
|
158
|
149
|
163
|
|
162
|
1
|
89
|
24.489
|
148
|
157
|
166
|
289
|
163
|
3
|
210
|
26.181
|
161
|
134
|
164
|
|
164
|
3
|
22
|
27.735
|
163
|
160
|
165
|
|
165
|
3
|
49
|
28.229
|
164
|
132
|
167
|
|
166
|
1
|
2
|
28.497
|
162
|
159
|
167
|
|
167
|
1
|
3
|
31.844
|
166
|
165
|
169
|
|
168
|
37
|
56
|
43.079
|
0
|
0
|
169
|
|
169
|
1
|
37
|
57.659
|
167
|
168
|
170
|
|
170
|
1
|
112
|
59.228
|
169
|
0
|
0
|
290
C.6 Cluster tables, K-means analysis, Camera shoppers
Initial Cluster Centres
|
|
Cluster
|
|
|
|
|
|
1
|
2
|
3
|
4
|
|
Z-Wert(INDEX 1)
|
-.7530
|
-.4230
|
.0822
|
.5618
|
|
Z-Wert(INDEX 3)
|
-.7870
|
.1864
|
.1447
|
.2676
|
|
Z-Wert(INDEX 4)
|
-.2496
|
-.2971
|
-.3902
|
.5284
|
|
Z-Wert(CONCERN ON PRIVACY)
|
-.1315
|
-.1793
|
-.1473
|
.2258
|
|
Z-Wert(NAME)
|
-1.1447
|
-.4315
|
.4126
|
.5417
|
|
Z-Wert(ADDRESS)
|
-1.1217
|
-.4069
|
.3580
|
.5743
|
|
Z-Wert(EMAIL USAGE)
|
-.7703
|
-.4992
|
.0120
|
.6639
|
|
Z-Wert(PHONE NUMBER)
|
-1.1542
|
-.1811
|
.3522
|
.5112
|
|
Z-Wert(COMPUTER)
|
-.5974
|
.0233
|
-.5213
|
.6218
|
|
Z-Wert(MONEYNEW)
|
-1.0439
|
.4240
|
-.3432
|
.5935
|
|
Z-Wert(CREDIT CARD NUMBER)
|
.1117
|
-1.0819
|
.2094
|
.2868
|
|
Z-Wert(HOBBY AND INTERESTS)
|
-.7066
|
.0246
|
-.6891
|
.8149
|
|
Z-Wert(HEALTH)
|
-.8764
|
.5939
|
-.4967
|
.5193
|
|
Z-Wert(AGE)
|
-.7420
|
-.0198
|
-.5064
|
.7205
|
Iteration History
|
|
Change in Cluster Centers
|
|
|
|
|
Iteration
|
1
|
2
|
3
|
4
|
|
1
|
.281
|
.503
|
.422
|
.206
|
|
2
|
.000
|
.000
|
.000
|
.000
|
Final Cluster Centres
|
|
Cluster
|
|
|
|
|
|
1
|
2
|
3
|
4
|
|
Z-Wert(INDEX 1)
|
-.6470
|
-.7472
|
.1850
|
.6132
|
|
Z-Wert(INDEX 3)
|
-.8163
|
.1962
|
.1735
|
.2007
|
|
Z-Wert(INDEX 4)
|
-.3343
|
-.2759
|
-.6846
|
.5269
|
|
Z-Wert(CONCERN ON PRIVACY)
|
-.2124
|
-.3106
|
-.0101
|
.1517
|
|
Z-Wert(NAME)
|
-1.0424
|
-.5599
|
.3563
|
.4757
|
|
Z-Wert(ADDRESS)
|
-1.0488
|
-.6046
|
.4411
|
.4654
|
|
Z-Wert(EMAIL USAGE)
|
-.8038
|
-.4687
|
.0674
|
.6202
|
|
Z-Wert(PHONE NUMBER)
|
-1.2049
|
-.1855
|
.2831
|
.4606
|
|
Z-Wert(COMPUTER)
|
-.7552
|
.0447
|
-.5905
|
.6549
|
|
Z-Wert(MONEYNEW)
|
-1.0210
|
.3327
|
-.5319
|
.6411
|
|
Z-Wert(CREDIT CARD NUMBER)
|
.1999
|
-.8702
|
.2439
|
.2549
|
|
Z-Wert(HOBBY AND INTERESTS)
|
-.6917
|
-.0607
|
-.7215
|
.8267
|
|
Z-Wert(HEALTH)
|
-.8612
|
.5978
|
-.4953
|
.4536
|
|
Z-Wert(AGE)
|
-.7509
|
-.1307
|
-.5374
|
.7302
|
291
Number of Cases in each Cluster
|
Cluster
|
1
|
30.000
|
|
|
2
|
21.000
|
|
|
3
|
30.000
|
|
|
4
|
48.000
|
|
Valid
|
|
129.000
|
|
Missing
|
|
42.000
|
292
C.7 Cluster tables, K-means analysis, Jacket shoppers
Initial Cluster Centers
|
|
Cluster
|
|
|
|
|
|
1
|
2
|
3
|
4
|
|
Z-Wert(INDEX 1)
|
-.7530
|
-.4230
|
.0822
|
.5618
|
|
Z-Wert(INDEX 3)
|
-.7870
|
.1864
|
.1447
|
.2676
|
|
Z-Wert(INDEX 4)
|
-.2496
|
-.2971
|
-.3902
|
.5284
|
|
Z-Wert(CONCERN ON PRIVACY)
|
-.1315
|
-.1793
|
-.1473
|
.2258
|
|
Z-Wert(NAME)
|
-1.1447
|
-.4315
|
.4126
|
.5417
|
|
Z-Wert(ADDRESS)
|
-1.1217
|
-.4069
|
.3580
|
.5743
|
|
Z-Wert(EMAIL USAGE)
|
-.7703
|
-.4992
|
.0120
|
.6639
|
|
Z-Wert(PHONE NUMBER)
|
-1.1542
|
-.1811
|
.3522
|
.5112
|
|
Z-Wert(COMPUTER)
|
-.5974
|
.0233
|
-.5213
|
.6218
|
|
Z-Wert(MONEYNEW)
|
-1.0439
|
.4240
|
-.3432
|
.5935
|
|
Z-Wert(CREDIT CARD NUMBER)
|
.1117
|
-1.0819
|
.2094
|
.2868
|
|
Z-Wert(HOBBY AND INTERESTS)
|
-.7066
|
.0246
|
-.6891
|
.8149
|
|
Z-Wert(HEALTH)
|
-.8764
|
.5939
|
-.4967
|
.5193
|
|
Z-Wert(AGE)
|
-.7420
|
-.0198
|
-.5064
|
.7205
|
Iteration History
|
|
Change in Cluster Centers
|
|
|
|
|
Iteration
|
1
|
2
|
3
|
4
|
|
1
|
1.159
|
1.801
|
.898
|
.598
|
|
2
|
.000
|
.000
|
.300
|
.197
|
|
3
|
.000
|
.000
|
.000
|
.000
|
Final Cluster Centers
|
|
Cluster
|
|
|
|
|
|
1
|
2
|
3
|
4
|
|
Z-Wert(INDEX 1)
|
-.5822
|
-.6905
|
-.3464
|
.3918
|
|
Z-Wert(INDEX 3)
|
-.4516
|
-.1761
|
-.3342
|
.2500
|
|
Z-Wert(INDEX 4)
|
-.6986
|
.3446
|
-.0664
|
.5159
|
|
Z-Wert(CONCERN ON PRIVACY)
|
-.0075
|
-.8214
|
-.2705
|
.2723
|
|
Z-Wert(NAME)
|
-.7426
|
-.5180
|
.3715
|
.2669
|
|
Z-Wert(ADDRESS)
|
-.8040
|
-.4317
|
.2849
|
.2307
|
|
Z-Wert(EMAIL USAGE)
|
-.6629
|
-.9254
|
.1141
|
.6712
|
|
Z-Wert(PHONE NUMBER)
|
-1.0823
|
.0000
|
.6494
|
.5730
|
|
Z-Wert(COMPUTER)
|
-.4845
|
-.9104
|
-.4623
|
.8748
|
|
Z-Wert(MONEYNEW)
|
-.5343
|
.2950
|
.1030
|
.4363
|
|
Z-Wert(CREDIT CARD NUMBER)
|
-.4300
|
-1.2150
|
.1134
|
.4650
|
|
Z-Wert(HOBBY AND INTERESTS)
|
-.6798
|
-.3210
|
-.5183
|
.9560
|
|
Z-Wert(HEALTH)
|
-.6750
|
.6312
|
-.1204
|
.7482
|
|
Z-Wert(AGE)
|
-.4283
|
-1.0032
|
-.3708
|
.7948
|
293
Number of Cases in each Cluster
|
Cluster
|
1
|
11.000
|
|
|
2
|
4.000
|
|
|
3
|
10.000
|
|
|
4
|
17.000
|
|
Valid
|
|
42.000
|
|
Missing
|
|
129.000
|
294
C.8 Cluster tables of K-means analysis all products
Table C8: Cluster tables, K-means analysis, Camera & Jacket shoppers
Initial Cluster Centers
|
Variables
|
Cluster
|
|
|
|
|
|
1
|
2
|
3
|
4
|
|
Z-Wert(INDEX 1)
|
-.7530
|
-.4230
|
.0822
|
.5618
|
|
Z-Wert(INDEX 3)
|
-.7870
|
.1864
|
.1447
|
.2676
|
|
Z-Wert(INDEX 4)
|
-.2496
|
-.2971
|
-.3902
|
.5284
|
|
Z-Wert(CONCERN ON PRIVACY)
|
-.1315
|
-.1793
|
-.1473
|
.2258
|
|
Z-Wert(NAME)
|
-1.1447
|
-.4315
|
.4126
|
.5417
|
|
Z-Wert(ADDRESS)
|
-1.1217
|
-.4069
|
.3580
|
.5743
|
|
Z-Wert(EMAIL USAGE)
|
-.7703
|
-.4992
|
.0120
|
.6639
|
|
Z-Wert(PHONE NUMBER)
|
-1.1542
|
-.1811
|
.3522
|
.5112
|
|
Z-Wert(COMPUTER)
|
-.5974
|
.0233
|
-.5213
|
.6218
|
|
Z-Wert(MONEYNEW)
|
-1.0439
|
.4240
|
-.3432
|
.5935
|
|
Z-Wert(CREDIT CARD NUMBER)
|
.1117
|
-1.0819
|
.2094
|
.2868
|
|
Z-Wert(HOBBY AND INTERESTS)
|
-.7066
|
.0246
|
-.6891
|
.8149
|
|
Z-Wert(HEALTH)
|
-.8764
|
.5939
|
-.4967
|
.5193
|
|
Z-Wert(AGE)
|
-.7420
|
-.0198
|
-.5064
|
.7205
|
Iteration History
|
|
Change in Cluster Centers
|
|
|
|
|
Iteration
|
1
|
2
|
3
|
4
|
|
1
|
.402
|
.534
|
.227
|
.212
|
|
2
|
6.937E-02
|
.165
|
.000
|
5.399E-02
|
|
3
|
.000
|
.000
|
.000
|
.000
|
295
Final Cluster Centers
|
|
Cluster
|
|
|
|
|
|
1
|
2
|
3
|
4
|
|
Z-Wert(INDEX 1)
|
-.6358
|
-.7402
|
.0476
|
.5986
|
|
Z-Wert(INDEX 3)
|
-.7125
|
.1060
|
.0867
|
.2380
|
|
Z-Wert(INDEX 4)
|
-.4303
|
-.1145
|
-.4898
|
.5259
|
|
Z-Wert(CONCERN ON PRIVACY)
|
-.1580
|
-.3589
|
-.0661
|
.2062
|
|
Z-Wert(NAME)
|
-.9566
|
-.5794
|
.3534
|
.4806
|
|
Z-Wert(ADDRESS)
|
-.9865
|
-.6028
|
.4109
|
.4517
|
|
Z-Wert(EMAIL USAGE)
|
-.7649
|
-.3957
|
.0742
|
.6417
|
|
Z-Wert(PHONE NUMBER)
|
-1.1736
|
-.1784
|
.3906
|
.5204
|
|
Z-Wert(COMPUTER)
|
-.6835
|
-.0031
|
-.5431
|
.7023
|
|
Z-Wert(MONEYNEW)
|
-.8993
|
.2859
|
-.3773
|
.6227
|
|
Z-Wert(CREDIT CARD NUMBER)
|
.0353
|
-.7898
|
.2290
|
.2967
|
|
Z-Wert(HOBBY AND INTERESTS)
|
-.6763
|
.0095
|
-.6540
|
.8871
|
|
Z-Wert(HEALTH)
|
-.8081
|
.6265
|
-.3880
|
.5355
|
|
Z-Wert(AGE)
|
-.6535
|
-.2286
|
-.4468
|
.7775
|
Number of Cases in each Cluster
|
Cluster
|
1
|
42.000
|
|
|
2
|
45.000
|
|
|
3
|
34.000
|
|
|
4
|
50.000
|
|
Valid
|
|
171.000
|
|
Missing
|
|
4.000
|
© Die inhaltliche Zusammenstellung und Aufmachung dieser Publikation sowie die elektronische
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vom Urheberrechtsgesetz zugelassen ist, bedarf der vorherigen Zustimmung. Das gilt insbesondere für
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