Evaluasi Lisrel 9.10 Student Edition
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Transcript of Evaluasi Lisrel 9.10 Student Edition
EVALUASI LISREL 9.10 STUDENT EDITIONOleh :
Abdullah M. Jaubah
Pendahuluan
Lisrel telah dikritik karena kekurangan-kekurangan yang terkandung di dalam
paket program tersebut. Gendro Wiyono (2011:395), dalam bukunya yang
berjudul Meracang Penelitian Bisnis dengan alat analisis SPSS 17.0 & SmartPLS
2.0, melakukan kritik dengan menyatakan bahwa :”Dibandingkan Structral
Equation Modeling (SEM) dengan pendekatan covariance based yang sudah
banyak digunakan seperti LISREL, AMOS, EQS, COSAN, dan EZPATH,
terdapat dua hal penting dari PLS yang menggunakan variance base, yaitu
memiliki kemampun menghindi dua masalah serius.
Gendro Wiyono menjelaskan kedua masalah serius itu yaitu inadmissible
solution dan factor indeterminacy. Kedua masalah ini dijelaskan sebagai berikut :
1. Inadmissible Solution
Yaitu solusi yang tidak dapat diterima, dalam hal ini, pada PLS berbasis
varians tidak akan pernah terjadi masalah matriks singulaity. Selain itu,
karena PLS bekerj pada model struktural yang bersifat rekursif, maka
masalah unidenfified, under-identified atau over-identified juga tidak akan
terjadi.
2. Factor Indeterminacy
Factor yang tidak dapat ditentukan, artinya jika terjadi adanya lebih dari
satu faktor yang terdapat dalam sekumpulan indikator sebuah variabel,
khusus indikator yang bersifat formatif tidak memerlukan adaya common
factor, sehingga selalu diperoleh variabel laten yang bersifat komposit.
Dalam hal semacam ini, variabel laten merupakan kominasi linier dari
indikator-indikatornya.
Kritik tersebut tidak disertai contoh dari data yang sama dan dijalankan dalam
PLS dan dijalankan dalam Lisrel sehingga perbedaan-perbedaan yang
terkandung dapat diungkap.
Kelemahan PLS, Visual PLS, dan Smart PLS adalah bahwa contoh-contoh yang
terkandung dalam paket program tersebut sangat terbatas sekali sehingga studi
secara mendalam tidak dimungkinkan dari contoh tersebut. Contoh-contoh yang
terkandung dalam Lisrel 9.10 Student Edition saja tercakup dalam 20 Folder dan
tiap folder mengandung banyak arsip data, arsip sintaksis proyek Lisrel, arsip
sintaksis proyek Simplis, dan arsip sintaksis Prelis. Kelemahan lain dari paket
program PLS, VisualPLS, dan SmartPLS adalah ketiadaan isi lengkap menu
Help.
Tulisan ini disusun untuk menjawab pertanyaan : Mengapakah paket program
Lisrel 9.10 Student Edition itu mengandung 20 folder dan tiap forder
mengandung banyak arsip?
LISREL 9.10 Student Edition
Banyak arsip contoh tersedia dalam Lisrel dan tidak tersedia dalam PLS,
VisualPLS, dan SmartPLS. Arsip-arsip ini disediakan mungkin dengan tujuan
meningkatkan kemampuan kognitif atas paket program Lisrel dengan cara
menjalankan arsip-arsip tersebut.
Penguasaan kemampuan kognitif atas Lisrel 9.10 mencakup kemampuan
mengetahui, kemampuan memahami, kemampuan menerapkan, kemampuan
menganalisis, kemampuan menarik kesimpulan, dan kemampuan mengevaluasi
Lisrel 9.10. Arsip-arsip contoh tersebut dapat dijalankan sehingga akan
menghasilkan informasi dan mungkin akan menghasilkan diagram jalur. Hasil-
hasil yang diperoleh ini dapat ditafsirkan sehingga kemampuan menafsirkan hasil
dan diagram jalur dapat ditingkatkan. Arsip-arsip yang tersedia mungkin
dilakukan dengan tujuan sebagai bahan studi. Hasil-hasil dari pelaksanaan
tersebut juga mungkin dapat dipakai sebagai bahan studi. Beberapa arsip
tersedia dan dapat dijalankan. Hasil-hasil dari beberapa arsip tersebut dapat
diperbandingkan sehingga hasil-hasil itu dapat dipakai sebagai studi
perbandingan.
Kemampuan Lisrel 9.10 Student Edition
Lisrel 9.10 Student Edition mengandung kemampuan di samping kemampuan
melaksanakan pemrograman persamaan struktural dan kemampuan-
kemampuan lain. Yaitu Generalized Linear Modeling, Multilevel Modeling with
Design Weights, Multilevel Structural Equation Modeling, Multilevel Nonlinear
Modeling, Multilevel Modeling, Multivariate Censored Regression, Censored
Regression, MINRES Exploratory Factor Analysis, Full Information Maximum
Likelihood (FIML) for Data with Missing Values, Multiple Imputation for Data with
Missing Values, Formal Inference-based Recursive Modeling (FIRM), Latent
Variable Scores, Robust Standard Errors and Chi-Squares, dan sebagainya.
Apakah peluang-peluang sebagaimana dikemukakan di atas terdapat juga dalam
PLS, VisualPLS, atau SmartPls?
Perbandingan Antara PLS dan Lisrel 9.10
SmartPLS merupakan salah satu metode alternatif dari Structural Equation
Modeling. SmartPLS berhubungan dengan VisualPLS. Salah satu contoh yang
disajikan dengan nama demo.vpl.
Ilmu-ilmu sosial dan perilaku sekarang mengandung permasalahan yang sangat
kompleks dan berbagai ragam faktor perlu dipertimbangkan yaitu faktor-faktor
bersifat kuantitatif atau faktor-faktor bersifat kualitatif dan faktor-faktor yang dapat
diobservasi dan diukur secara langsung dan faktor-faktor yang tidak dapat
diobservasi dan tidak dapat diukur secara langsung.
Partial Least Square, Visual Partial Least Square, Smart Partial Least Square,
atau Generalized Structrured Component Analysis merupakan perangkat-
perangkat dari Sructural Equation Modeling yang dapat dipakai untuk melakukan
analisis variabel-variabel laten, variabel-variabel indikator, dan kesalahan-
kesalahan pengukuran.
Para penganut Partial Least Square menganggap bahwa paket program Lisrel,
Amos, Eqs, Cosan, Ezpath, Ramona, Sepath mengandung masalah yaitu
masalah inadmissible solution dan masalah factor indeterminance. Model-model
dalam PLS dapat dikelompokkan ke dalam model reflektif dan model formatif.
Spesifikasi Model
Analisis hubungan antarvariabel dan indikator terdiri dari outer model, inner
model, dan weight relation. Outer model mencerminkan spesifikasi hubungan
antara variabel laten dan indikator-indikator dari variabel laten bersangkutan.
Outer model dinamakan juga outer relation atau measurement model yaitu model
yang mencerminkan karakteristik variabel laten dan variabel-variabel indikator
atau variabel-variabel manifes bersangkutan. Inner model adalah model yang
mencerminkan spesifikasi hubungan antarvariabel laten (model persamaan
struktural).Weight Relation adalah estimasi nilai dari variabel laten. Inner dan
outer mencerminkan spesifikasi yang diikuti dengan estimasi weight relation.
Pengujian Model
Pengujian model dilakukan melalui outer model dan inner model, model
pengukuran dan model struktural. Pengujian indikator refrektif dilakukan melalui
convergent validity, discriminant validity, atau average variance extracted.
Pengujian indikator formatif dilakukan melalui substantive content-nya.Pengujian
model struktural terarah pada pengujian pengaruh dari suatu variabel laten
terhadap variabel laten lain melalui persentase varians yang dijelaskan yaitu
koefisien determinasi untuk valiabel laten endogen yang mendapat pengaruh
dari variabel laten eksogen. Ukuran yang dipakai adalah stone-geusser Q square
test dan koefisien jalur.
Pengujian outer model dilakukan melalui convergent validity, discriminant validity
atau AVE, dan composite reliability. Pengujian Inner model dilakukan melalui
koefisien determinasi, koefisien parameter, dan t-statistik
Hal ini dapat diringkas dalam tabel di bawah ini :
Model Hasil Kriteria Realiasai EvaluasiModel Outer Convergent Validity Loading factor 0.50 -0.60 (Pengujian Indikator) dan Discriminant Validity Cross Loading > Korelasi variabel laten AVE >0.50 Composite Reliability >=0.70 Model Inner Koefisien Determinasi 0.19 adalah lemah (Pengujian Hipotesis) 0.33 adalah moderat 0.67 adalah kuat Koefisien Parameter Nilai estimasi adalah Statistik-T Signifikan
SmartPLS report
Model: C:\Documents and Settings\user\My Documents\employ.splsmDate: 28.07.2013
Table of contents (whole)
PLS output Goodness of fit measures
Model data
Table of contents
Iterations of the PLS-AlgorithmInner weights (structural model)Outer weights (measurement model)Outer loadings (measurement model)Scores of the latent variablesCorrelations of the latent variables
Iterations of the PLS-Algorithm
[ CSV-Version ]
iteration behave1 behave2 behave3 behave40 1 1 1 11 0.316 0.238 0.304 0.3192 0.316 0.238 0.304 0.319
iteration develop1 develop2develop
3develop
40 1 1 1 11 0.292 0.306 0.252 0.3892 0.292 0.305 0.252 0.39
iteration leader1 leader2 leader3 leader40 1 1 1 11 0.246 0.257 0.257 0.1652 0.247 0.258 0.256 0.164
iteration leader5 morale1 morale2 morale30 1 1 1 11 0.247 0.291 0.29 0.2792 0.247 0.291 0.289 0.28
iteration morale4 person1 person2 person30 1 1 1 11 0.272 0.289 0.298 0.3182 0.272 0.288 0.298 0.319
iteration person40 11 0.3552 0.355
Table of contents
Inner weights (structural model)[ CSV-Version ]
Leader Behave Develop PersonLeader 0.193Behave 0.363Develop 0.166Person Morale
MoraleLeader Behave Develop Person 0.461Morale
Table of contents
Outer weights (measurement model)[ CSV-Version ]
Leader Behave Develop Personbehave1 0.316 behave2 0.238 behave3 0.304 behave4 0.319 develop1 0.292 develop2 0.305 develop3 0.252 develop4 0.39 leader1 0.247 leader2 0.258 leader3 0.256 leader4 0.164 leader5 0.247 morale1 morale2 morale3 morale4 person1 0.288person2 0.298person3 0.319person4 0.355
Moralebehave1 behave2
behave3 behave4 develop1 develop2 develop3 develop4 leader1 leader2 leader3 leader4 leader5 morale1 0.291morale2 0.289morale3 0.28morale4 0.272person1 person2 person3 person4
Table of contents
Outer loadings (measurement model)[ CSV-Version ]
Leader Behave Develop Personbehave1 0.849 behave2 0.796 behave3 0.883 behave4 0.858 develop1 0.804 develop2 0.785 develop3 0.699 develop4 0.898 leader1 0.87 leader2 0.839 leader3 0.878 leader4 0.787 leader5 0.868 morale1 morale2 morale3 morale4 person1 0.767person2 0.766person3 0.817person4 0.816
Moralebehave1 behave2 behave3 behave4 develop1 develop2 develop3
develop4 leader1 leader2 leader3 leader4 leader5 morale1 0.91morale2 0.866morale3 0.848morale4 0.909person1 person2 person3 person4
Table of contents
Scores of the latent variables[ CSV-Version ]
Leader Behave Develop Person0 -1.725 -1.438 0.11 -0.8041 1.36 -0.591 1.569 1.5622 0.732 0 1.702 0.8573 1.36 1.081 0.513 0.2414 -1.545 -0.591 -0.777 -1.0815 -1.026 -1.133 -1.047 -0.1256 0.983 -0.242 0.274 1.2237 -1.241 -0.296 -1.288 -1.1148 1.36 1.686 2.424 0.9319 -0.398 -0.896 -0.404 -0.449
10 0.283 0.305 0.756 0.53311 1.127 0.547 2.183 0.85712 -0.166 -0.349 -2.141 -0.46413 1.126 1.148 1.54 -0.13214 0.679 0.843 0.171 0.24115 0.283 -0.846 0.142 -0.71516 -0.631 0.239 0.383 0.20817 0.283 0.547 0.383 0.53318 0.963 1.443 1.81 0.19319 1.197 -2.572 -2.205 1.1920 -1.87 1.686 -0.645 1.23821 0.446 0.547 -0.131 0.53322 0.446 -0.591 -0.536 -0.42323 0.068 0.013 -0.777 0.19324 0.678 0.839 1.026 1.56225 -1.366 -0.591 -0.917 -1.08126 0.445 1.135 0.726 0.19327 0.231 0.239 -1.047 -0.42328 1.36 1.135 0.624 1.56229 1.145 -1.192 -0.502 -0.76330 -0.793 0.547 1.299 0.24131 1.36 0.613 0.997 1.1932 -0.112 -0.283 -0.404 -0.42333 -0.166 0.239 -1.227 0.53334 0.66 0.239 1.399 0.53335 0.912 0.239 -1.047 1.19
36 -1.868 0.547 -0.133 0.53337 1.36 0.305 1.64 1.51538 -0.255 -0.538 -0.028 0.20839 0.283 0.547 -1.077 0.53340 0.283 0.251 0.11 0.19341 0.284 0.893 -0.674 -0.09942 -0.184 -0.591 -0.674 -0.78943 1.36 -0.007 0.272 0.24144 0.049 0.009 -0.534 -0.17245 0.68 -0.591 -0.163 -0.14646 -0.975 -0.85 -1.047 -0.40947 -0.741 0.547 -1.047 0.53348 -0.632 -1.73 0.11 -1.12849 -1.26 -0.833 -0.401 -0.05850 0.283 0.305 -0.129 0.24151 -0.846 -0.246 -1.016 -0.12552 -0.398 -1.126 0.039 -1.40553 1.127 0.856 1.297 -0.17254 -1.87 -2.022 -1.288 0.19355 0.049 0.305 1.267 0.53356 0.893 1.085 1.781 1.1957 -1.403 -0.299 0.11 -1.12858 -0.469 -0.833 0.11 -0.78959 0.894 -0.003 1.913 1.51560 0.893 0.547 0.653 -0.09961 1.36 1.686 1.672 0.65462 0.429 -0.591 1.058 0.53363 -0.973 0.305 -0.777 -0.78964 0.73 0.305 0.515 -0.7365 0.445 1.443 0.383 0.56666 1.127 -0.283 0.756 -0.13967 1.145 0.305 0.756 0.60668 -0.164 -2.572 -1.318 -0.06669 0.283 -0.591 0.11 -0.46470 1.36 -0.057 -0.131 1.51571 -0.864 -0.003 0.142 0.19372 0.121 0.547 1.026 0.24173 -1.637 -1.196 -0.433 0.59174 0.05 0.843 -0.775 0.24175 0.231 -0.053 0.171 -0.44976 0.283 1.135 -0.099 0.19377 -0.43 1.686 -1.114 1.85478 -0.234 -0.296 -0.232 -0.09979 0.445 -0.349 -1.932 -0.43880 0.68 -0.003 1.267 0.53381 -2.103 0.255 1.297 -0.14682 -1.333 -1.717 -1.964 -0.42383 0.499 -0.9 -0.473 -2.77484 1.36 0.305 1.026 0.20885 -2.082 -1.438 -0.47 -1.15486 0.516 0.547 0.383 -0.78987 0.516 0.239 0.653 0.19388 -0.864 -0.771 -0.674 -0.80489 0.283 -1.142 0.483 -0.46490 -1.545 0.547 0.039 -0.7391 0.283 -0.053 0.724 0.53392 1.36 1.443 1.267 1.223
93 -0.183 0.547 -0.981 1.19794 0.518 1.443 1.942 1.85495 0.283 -0.057 0.383 -0.09996 -0.776 1.135 1.399 0.60697 -0.416 0.255 -1.288 0.28298 1.127 0.547 0.383 0.94699 -2.946 -1.784 -0.433 -2.136
100 1.36 -0.538 -1.389 -0.139101 -1.096 -2.268 -2.205 -2.376102 -0.183 0.239 1.267 0.533103 0.893 -1.796 -0.401 0.241104 0.068 -0.296 -0.401 -2.063105 -1.707 -0.9 -0.129 -1.128106 0.446 0.547 0.245 -0.449107 0.051 1.686 1.267 0.533108 1.36 1.443 1.058 1.562109 0.213 1.686 1.267 0.241110 0.446 -0.003 0.441 -0.756111 0.73 0.827 -1.047 0.883112 0.213 -0.833 -0.806 -2.118113 0.213 -1.384 -0.401 -0.667114 0.913 -0.541 1.056 0.857115 -0.003 -1.138 -0.473 -2.376116 0.516 -1.425 0.11 -0.139117 0.428 0 1.672 -0.39118 0.893 -1.972 -0.374 -0.83119 0.912 -0.942 -0.433 -0.782120 -0.955 -1.129 -1.217 -2.476121 -1.26 -2.042 0.758 -2.727122 -1.096 0.013 -0.131 -0.438123 0.913 0.305 0.38 0.193124 0.661 1.151 0.653 1.271125 0.05 0.547 0.995 0.533126 -1.009 1.201 -1.114 -0.671127 0.893 1.686 0.483 1.223128 1.127 0.255 0.383 1.223129 1.126 0.839 -0.274 1.562130 -0.469 -0.016 -0.664 0.193131 -0.792 0.305 -0.131 0.533132 -0.863 0.839 -2.205 -0.161133 -1.258 -0.883 -1.964 -1.42134 -0.864 -1.434 0.11 -1.435135 -0.346 -0.9 0.11 -0.099136 -0.399 0.547 -0.131 0.241137 1.36 1.686 1.366 1.854138 0.679 0.843 1.026 0.533139 0.068 -0.604 -0.372 0.533140 0.662 1.443 1.54 0.857141 1.126 -0.883 -0.504 -0.464142 1.126 0.909 0.414 1.854143 0.283 -0.074 0.139 0.566144 -0.397 0.359 -0.502 0.533145 -1.027 0.009 0.078 -1.786146 0.051 -0.296 -0.565 -0.423147 0.05 -1.434 -0.777 0.193148 1.36 1.686 0.653 0.857149 0.517 0.305 -0.301 0.533
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213 -0.149214 -0.891215 0.888216 0.594217 -0.394218 0.843219 1.43220 1.426221 1.924222 1.181223 -0.688224 0.594225 0.301226 0.597227 -1.433228 -0.145229 1.385230 -1.389231 0.843232 -0.939233 -0.643234 1.63235 -1.474236 0.888237 1.139238 1.675239 -0.895240 0.097241 -0.889242 -0.149243 0.642244 0.888245 -0.73246 0.888247 -0.149248 0.1249 0.393250 1.385251 -0.978252 0.888253 -0.149254 -0.69255 -0.149256 -0.149257 0.349258 -0.936259 0.888260 0.346261 -0.397262 0.1263 -1.185264 0.888265 0.349266 0.097267 -1.478268 1.382269 0.888
270 -0.394271 -2.221272 0.396273 0.888274 -1.185275 -0.149276 0.393277 1.133278 0.393279 -0.441280 -0.936281 -1.43282 0.346283 -0.101284 0.642285 0.1286 -1.185287 -1.679288 0.103289 -0.936290 -1.184291 -0.149292 0.888293 -1.972294 -1.682295 0.84296 -1.975297 1.63298 1.091299 1.63300 -0.149301 1.136302 1.924303 -1.679304 -1.431305 -1.481306 -1.185307 -0.19308 -0.394309 -0.64310 -0.688311 0.349312 -0.693313 -0.939314 -0.149315 -1.433316 1.924317 -0.442318 1.924319 1.136320 -0.149321 -0.151322 0.888323 0.349324 -1.475325 -1.185326 0.888
327 1.924328 0.888329 1.091330 0.393331 0.84332 -0.149333 0.888334 -0.643335 0.888336 0.349337 1.385338 -2.221339 0.888340 -1.229341 -0.149342 1.924343 0.893344 0.888345 -1.185346 0.888347 0.594348 0.346349 1.63350 -1.185351 -0.149352 1.089353 -0.643354 0.888355 1.136356 -0.149357 -2.221358 0.888359 -0.64360 1.136361 -0.643362 1.044363 0.349364 0.346365 -0.891366 1.337367 1.133368 -0.939369 -1.729370 1.382371 0.349372 0.145373 0.594374 -0.442375 -1.478376 1.924377 -0.688378 -0.688379 0.888380 1.924381 0.594382 0.393383 -1.975
384 -0.149385 -0.394386 -0.394387 -0.643388 0.1389 1.924390 0.349391 -0.098392 1.678393 -0.445394 0.346395 1.924396 -0.391397 0.1398 -0.397399 -0.445400 0.642401 -0.149402 1.63403 0.888404 -0.149405 -0.442406 1.136407 -0.643408 -0.149409 -0.442410 0.097411 -1.682412 -0.149413 0.888414 -0.939415 0.594416 0.594417 0.393418 -0.149419 -1.185420 -1.185421 1.63422 -1.185423 0.145424 0.345425 -0.939426 0.393427 0.642428 -0.149429 0.1430 -1.185431 0.594432 -0.987433 1.678434 1.382435 0.346436 -1.433437 0.891438 -1.185439 0.888440 1.133
441 0.594442 0.642443 0.597444 -0.442445 0.349446 0.393447 0.1448 -1.232449 -0.64450 -0.442451 0.1452 -0.939453 0.1454 1.385455 -0.939456 1.136457 1.924458 -0.442459 0.349460 0.393461 0.097462 -0.693463 -0.69464 -2.064465 -0.69466 -0.397467 0.597468 -1.185469 0.097470 0.888471 -0.149472 -0.19473 -1.433474 -0.397475 -0.391476 -0.936477 1.337478 -0.64479 -1.185480 -1.727481 0.594482 -1.634483 -1.679484 0.888485 -0.149486 1.63487 -0.145488 0.346489 -1.185490 -1.137491 0.594492 1.924493 0.346494 0.642495 -1.185496 0.349497 -0.643
498 0.891499 0.888500 -0.936501 -0.394502 -1.975503 -1.975504 1.337505 -0.148506 0.642507 -0.394508 0.145509 0.642510 0.393511 0.1512 -2.221513 -0.598514 -0.149515 0.393516 0.1517 -0.685518 0.1519 -1.43520 0.594521 0.055522 -0.939523 -1.928524 0.346525 1.337526 0.888527 0.843528 1.924529 0.1530 -0.145531 -0.149532 -0.442533 0.595534 -0.397535 -0.394536 0.349537 -1.679538 -0.394539 -0.149540 0.346541 1.382542 -1.724543 -0.984544 1.385545 -0.397546 -1.43547 1.385548 0.888549 -0.101550 -1.185551 1.136552 0.396553 -1.679554 -0.442
555 0.888556 -0.394557 -0.149558 0.888559 1.63560 0.145561 -0.442
Table of contents
Correlations of the latent variables[ CSV-Version ]
Leader Behave Develop PersonLeader 1 Behave 0.358 1 Develop 0.46 0.443 1 Person 0.4 0.506 0.416 1Morale 0.618 0.369 0.415 0.461
MoraleLeader Behave Develop Person Morale 1
Table of contents
Bootstrapping
SmartPLS report
Model: C:\Documents and Settings\user\My Documents\employee.splsmDate: 28.07.2013
Table of contents (whole)
Bootstrapping results
Table of contents
Settingsresults for inner weightsresults for outer loadingsresults for outer weightsouter weights for each
sampleouter loadings for each
sample
inner weights for each sample
Settings
[ CSV-Version ]
number of cases in original sample 562preprocessing
option no changescases per sample 50
number of samples 100
Table of contents
results for inner weights
[ CSV-Version ]
original sample
estimate
mean of subsample
sStandard deviation T-Statistic
Leader -> Person 0.193 0.218 0.135 1.43Behave -> Person 0.363 0.349 0.176 2.07Develop -> Person 0.166 0.221 0.081 2.057Person -> Morale 0.461 0.498 0.106 4.363
Table of contents
results for outer loadings[ CSV-Version ]
original sample
estimate
mean of subsample
sStandard deviation T-Statistic
Leader leader1 0.87 0.862 0.048 18.228leader2 0.839 0.852 0.07 11.942leader3 0.878 0.875 0.038 23.365leader4 0.787 0.79 0.064 12.359leader5 0.868 0.872 0.034 25.639Behave behave1 0.849 0.842 0.046 18.502behave2 0.796 0.742 0.143 5.578behave3 0.883 0.876 0.029 30.966behave4 0.858 0.859 0.029 29.255Develop develop1 0.804 0.76 0.134 6.018develop2 0.785 0.759 0.135 5.801develop3 0.699 0.727 0.118 5.918
develop4 0.898 0.884 0.059 15.311Person person1 0.767 0.788 0.083 9.292person2 0.766 0.763 0.095 8.064person3 0.817 0.805 0.073 11.24person4 0.816 0.812 0.053 15.418Morale
morale1 0.91 0.907 0.031 28.941morale2 0.866 0.851 0.061 14.229morale3 0.848 0.831 0.052 16.196morale4 0.909 0.894 0.044 20.668
Table of contents
results for outer weights[ CSV-Version ]
original sample
estimate
mean of subsample
sStandard deviation T-Statistic
Leader leader1 0.247 0.224 0.068 3.628leader2 0.258 0.252 0.068 3.807leader3 0.256 0.262 0.048 5.392leader4 0.164 0.17 0.055 2.981leader5 0.247 0.251 0.053 4.7Behave behave1 0.316 0.339 0.088 3.601behave2 0.238 0.217 0.108 2.201behave3 0.304 0.294 0.033 9.236behave4 0.319 0.326 0.038 8.481Develop develop1 0.292 0.257 0.149 1.965develop2 0.305 0.295 0.114 2.662develop3 0.252 0.316 0.117 2.145develop4 0.39 0.351 0.06 6.523Person person1 0.288 0.296 0.069 4.153person2 0.298 0.289 0.08 3.705person3 0.319 0.316 0.041 7.851person4 0.355 0.346 0.05 7.11Morale
morale1 0.291 0.298 0.035 8.3morale2 0.289 0.3 0.04 7.25morale3 0.28 0.276 0.047 6.014morale4 0.272 0.271 0.044 6.148
Table of contents
outer weights for each sample[ CSV-Version ]
behave1 behave2 behave3 behave40 0.258 0.285 0.297 0.3311 0.258 0.285 0.297 0.3312 0.329 0.218 0.351 0.2643 0.329 0.218 0.351 0.2644 0.329 0.218 0.351 0.264
5 0.329 0.218 0.351 0.2646 0.329 0.218 0.351 0.2647 0.33 0.292 0.243 0.2888 0.33 0.292 0.243 0.2889 0.33 0.292 0.243 0.288
10 0.33 0.292 0.243 0.28811 0.33 0.292 0.243 0.28812 0.396 0.234 0.282 0.30713 0.396 0.234 0.282 0.30714 0.396 0.234 0.282 0.30715 0.396 0.234 0.282 0.30716 0.396 0.234 0.282 0.30717 0.396 0.234 0.282 0.30718 0.287 0.217 0.297 0.34619 0.287 0.217 0.297 0.34620 0.287 0.217 0.297 0.34621 0.287 0.217 0.297 0.34622 0.287 0.217 0.297 0.34623 0.287 0.217 0.297 0.34624 0.265 0.314 0.336 0.29825 0.265 0.314 0.336 0.29826 0.265 0.314 0.336 0.29827 0.265 0.314 0.336 0.29828 0.265 0.314 0.336 0.29829 0.265 0.314 0.336 0.29830 0.279 0.321 0.257 0.30731 0.279 0.321 0.257 0.30732 0.279 0.321 0.257 0.30733 0.279 0.321 0.257 0.30734 0.279 0.321 0.257 0.30735 0.382 0.247 0.223 0.29236 0.382 0.247 0.223 0.29237 0.382 0.247 0.223 0.29238 0.382 0.247 0.223 0.29239 0.382 0.247 0.223 0.29240 0.382 0.247 0.223 0.29241 0.382 0.247 0.223 0.29242 0.222 0.335 0.283 0.3743 0.222 0.335 0.283 0.3744 0.222 0.335 0.283 0.3745 0.222 0.335 0.283 0.3746 0.324 0.2 0.308 0.36847 0.324 0.2 0.308 0.36848 0.324 0.2 0.308 0.36849 0.324 0.2 0.308 0.36850 0.324 0.2 0.308 0.36851 0.324 0.2 0.308 0.36852 0.262 0.247 0.327 0.29653 0.262 0.247 0.327 0.29654 0.262 0.247 0.327 0.29655 0.262 0.247 0.327 0.29656 0.262 0.247 0.327 0.29657 0.262 0.247 0.327 0.29658 0.357 0.172 0.314 0.30859 0.357 0.172 0.314 0.30860 0.357 0.172 0.314 0.30861 0.357 0.172 0.314 0.308
62 0.357 0.172 0.314 0.30863 0.357 0.172 0.314 0.30864 0.565 0.018 0.265 0.39265 0.565 0.018 0.265 0.39266 0.565 0.018 0.265 0.39267 0.565 0.018 0.265 0.39268 0.565 0.018 0.265 0.39269 0.358 0.215 0.283 0.3470 0.358 0.215 0.283 0.3471 0.358 0.215 0.283 0.3472 0.358 0.215 0.283 0.3473 0.358 0.215 0.283 0.3474 0.358 0.215 0.283 0.3475 0.358 0.215 0.283 0.3476 0.368 0.258 0.318 0.2877 0.368 0.258 0.318 0.2878 0.368 0.258 0.318 0.2879 0.368 0.258 0.318 0.2880 0.368 0.258 0.318 0.2881 0.509 -0.119 0.323 0.37682 0.509 -0.119 0.323 0.37683 0.509 -0.119 0.323 0.37684 0.509 -0.119 0.323 0.37685 0.509 -0.119 0.323 0.37686 0.509 -0.119 0.323 0.37687 0.329 0.235 0.301 0.33188 0.329 0.235 0.301 0.33189 0.329 0.235 0.301 0.33190 0.329 0.235 0.301 0.33191 0.329 0.235 0.301 0.33192 0.329 0.235 0.301 0.33193 0.329 0.235 0.301 0.33194 0.178 0.318 0.294 0.38495 0.178 0.318 0.294 0.38496 0.178 0.318 0.294 0.38497 0.178 0.318 0.294 0.38498 0.178 0.318 0.294 0.38499 0.324 0.234 0.259 0.389
develop1 develop2 develop3 develop40 0.309 0.332 0.306 0.3491 0.309 0.332 0.306 0.3492 0.198 0.412 0.305 0.2793 0.198 0.412 0.305 0.2794 0.198 0.412 0.305 0.2795 0.198 0.412 0.305 0.2796 0.198 0.412 0.305 0.2797 0.33 0.232 0.326 0.3268 0.33 0.232 0.326 0.3269 0.33 0.232 0.326 0.326
10 0.33 0.232 0.326 0.32611 0.33 0.232 0.326 0.32612 0.347 0.13 0.423 0.40913 0.347 0.13 0.423 0.40914 0.347 0.13 0.423 0.40915 0.347 0.13 0.423 0.40916 0.347 0.13 0.423 0.409
17 0.347 0.13 0.423 0.40918 0.357 0.293 0.259 0.27319 0.357 0.293 0.259 0.27320 0.357 0.293 0.259 0.27321 0.357 0.293 0.259 0.27322 0.357 0.293 0.259 0.27323 0.357 0.293 0.259 0.27324 0.524 0.273 0.067 0.32225 0.524 0.273 0.067 0.32226 0.524 0.273 0.067 0.32227 0.524 0.273 0.067 0.32228 0.524 0.273 0.067 0.32229 0.524 0.273 0.067 0.32230 0.231 0.363 0.292 0.33231 0.231 0.363 0.292 0.33232 0.231 0.363 0.292 0.33233 0.231 0.363 0.292 0.33234 0.231 0.363 0.292 0.33235 0.232 0.202 0.369 0.40836 0.232 0.202 0.369 0.40837 0.232 0.202 0.369 0.40838 0.232 0.202 0.369 0.40839 0.232 0.202 0.369 0.40840 0.232 0.202 0.369 0.40841 0.232 0.202 0.369 0.40842 -0.295 0.722 0.448 0.17943 -0.295 0.722 0.448 0.17944 -0.295 0.722 0.448 0.17945 -0.295 0.722 0.448 0.17946 0.234 0.35 0.289 0.39447 0.234 0.35 0.289 0.39448 0.234 0.35 0.289 0.39449 0.234 0.35 0.289 0.39450 0.234 0.35 0.289 0.39451 0.234 0.35 0.289 0.39452 0.219 0.256 0.435 0.32553 0.219 0.256 0.435 0.32554 0.219 0.256 0.435 0.32555 0.219 0.256 0.435 0.32556 0.219 0.256 0.435 0.32557 0.219 0.256 0.435 0.32558 0.29 0.292 0.302 0.40659 0.29 0.292 0.302 0.40660 0.29 0.292 0.302 0.40661 0.29 0.292 0.302 0.40662 0.29 0.292 0.302 0.40663 0.29 0.292 0.302 0.40664 0.14 0.277 0.46 0.3665 0.14 0.277 0.46 0.3666 0.14 0.277 0.46 0.3667 0.14 0.277 0.46 0.3668 0.14 0.277 0.46 0.3669 0.318 0.338 0.21 0.30870 0.318 0.338 0.21 0.30871 0.318 0.338 0.21 0.30872 0.318 0.338 0.21 0.30873 0.318 0.338 0.21 0.308
74 0.318 0.338 0.21 0.30875 0.318 0.338 0.21 0.30876 0.228 0.382 0.172 0.36377 0.228 0.382 0.172 0.36378 0.228 0.382 0.172 0.36379 0.228 0.382 0.172 0.36380 0.228 0.382 0.172 0.36381 0.094 0.169 0.57 0.37982 0.094 0.169 0.57 0.37983 0.094 0.169 0.57 0.37984 0.094 0.169 0.57 0.37985 0.094 0.169 0.57 0.37986 0.094 0.169 0.57 0.37987 0.373 0.204 0.23 0.44388 0.373 0.204 0.23 0.44389 0.373 0.204 0.23 0.44390 0.373 0.204 0.23 0.44391 0.373 0.204 0.23 0.44392 0.373 0.204 0.23 0.44393 0.373 0.204 0.23 0.44394 0.315 0.301 0.286 0.35295 0.315 0.301 0.286 0.35296 0.315 0.301 0.286 0.35297 0.315 0.301 0.286 0.35298 0.315 0.301 0.286 0.35299 0.154 0.362 0.24 0.461
leader1 leader2 leader3 leader40 0.351 0.222 0.227 0.1461 0.351 0.222 0.227 0.1462 0.203 0.361 0.14 0.1413 0.203 0.361 0.14 0.1414 0.203 0.361 0.14 0.1415 0.203 0.361 0.14 0.1416 0.203 0.361 0.14 0.1417 0.276 0.256 0.307 0.1828 0.276 0.256 0.307 0.1829 0.276 0.256 0.307 0.182
10 0.276 0.256 0.307 0.18211 0.276 0.256 0.307 0.18212 0.157 0.289 0.27 0.17413 0.157 0.289 0.27 0.17414 0.157 0.289 0.27 0.17415 0.157 0.289 0.27 0.17416 0.157 0.289 0.27 0.17417 0.157 0.289 0.27 0.17418 0.266 0.256 0.258 0.17319 0.266 0.256 0.258 0.17320 0.266 0.256 0.258 0.17321 0.266 0.256 0.258 0.17322 0.266 0.256 0.258 0.17323 0.266 0.256 0.258 0.17324 0.243 0.226 0.239 0.13425 0.243 0.226 0.239 0.13426 0.243 0.226 0.239 0.13427 0.243 0.226 0.239 0.13428 0.243 0.226 0.239 0.134
29 0.243 0.226 0.239 0.13430 0.263 0.238 0.231 0.1531 0.263 0.238 0.231 0.1532 0.263 0.238 0.231 0.1533 0.263 0.238 0.231 0.1534 0.263 0.238 0.231 0.1535 0.257 0.289 0.357 0.12436 0.257 0.289 0.357 0.12437 0.257 0.289 0.357 0.12438 0.257 0.289 0.357 0.12439 0.257 0.289 0.357 0.12440 0.257 0.289 0.357 0.12441 0.257 0.289 0.357 0.12442 0.222 0.313 0.211 0.18143 0.222 0.313 0.211 0.18144 0.222 0.313 0.211 0.18145 0.222 0.313 0.211 0.18146 0.128 0.326 0.302 0.16647 0.128 0.326 0.302 0.16648 0.128 0.326 0.302 0.16649 0.128 0.326 0.302 0.16650 0.128 0.326 0.302 0.16651 0.128 0.326 0.302 0.16652 0.244 0.245 0.271 0.14553 0.244 0.245 0.271 0.14554 0.244 0.245 0.271 0.14555 0.244 0.245 0.271 0.14556 0.244 0.245 0.271 0.14557 0.244 0.245 0.271 0.14558 0.061 0.345 0.29 0.10359 0.061 0.345 0.29 0.10360 0.061 0.345 0.29 0.10361 0.061 0.345 0.29 0.10362 0.061 0.345 0.29 0.10363 0.061 0.345 0.29 0.10364 0.278 0.188 0.267 0.27565 0.278 0.188 0.267 0.27566 0.278 0.188 0.267 0.27567 0.278 0.188 0.267 0.27568 0.278 0.188 0.267 0.27569 0.223 0.235 0.215 0.21970 0.223 0.235 0.215 0.21971 0.223 0.235 0.215 0.21972 0.223 0.235 0.215 0.21973 0.223 0.235 0.215 0.21974 0.223 0.235 0.215 0.21975 0.223 0.235 0.215 0.21976 0.267 0.063 0.32 0.18977 0.267 0.063 0.32 0.18978 0.267 0.063 0.32 0.18979 0.267 0.063 0.32 0.18980 0.267 0.063 0.32 0.18981 0.122 0.26 0.231 0.31482 0.122 0.26 0.231 0.31483 0.122 0.26 0.231 0.31484 0.122 0.26 0.231 0.31485 0.122 0.26 0.231 0.314
86 0.122 0.26 0.231 0.31487 0.296 0.244 0.246 0.09288 0.296 0.244 0.246 0.09289 0.296 0.244 0.246 0.09290 0.296 0.244 0.246 0.09291 0.296 0.244 0.246 0.09292 0.296 0.244 0.246 0.09293 0.296 0.244 0.246 0.09294 0.285 0.131 0.282 0.16695 0.285 0.131 0.282 0.16696 0.285 0.131 0.282 0.16697 0.285 0.131 0.282 0.16698 0.285 0.131 0.282 0.16699 0.228 0.248 0.237 0.186
leader5 morale1 morale2 morale30 0.204 0.253 0.316 0.2441 0.204 0.253 0.316 0.2442 0.288 0.257 0.228 0.3523 0.288 0.257 0.228 0.3524 0.288 0.257 0.228 0.3525 0.288 0.257 0.228 0.3526 0.288 0.257 0.228 0.3527 0.171 0.298 0.303 0.2658 0.171 0.298 0.303 0.2659 0.171 0.298 0.303 0.265
10 0.171 0.298 0.303 0.26511 0.171 0.298 0.303 0.26512 0.286 0.312 0.269 0.3213 0.286 0.312 0.269 0.3214 0.286 0.312 0.269 0.3215 0.286 0.312 0.269 0.3216 0.286 0.312 0.269 0.3217 0.286 0.312 0.269 0.3218 0.262 0.312 0.359 0.23419 0.262 0.312 0.359 0.23420 0.262 0.312 0.359 0.23421 0.262 0.312 0.359 0.23422 0.262 0.312 0.359 0.23423 0.262 0.312 0.359 0.23424 0.331 0.284 0.252 0.28325 0.331 0.284 0.252 0.28326 0.331 0.284 0.252 0.28327 0.331 0.284 0.252 0.28328 0.331 0.284 0.252 0.28329 0.331 0.284 0.252 0.28330 0.249 0.357 0.326 0.15331 0.249 0.357 0.326 0.15332 0.249 0.357 0.326 0.15333 0.249 0.357 0.326 0.15334 0.249 0.357 0.326 0.15335 0.128 0.305 0.295 0.26236 0.128 0.305 0.295 0.26237 0.128 0.305 0.295 0.26238 0.128 0.305 0.295 0.26239 0.128 0.305 0.295 0.26240 0.128 0.305 0.295 0.262
41 0.128 0.305 0.295 0.26242 0.264 0.301 0.321 0.23443 0.264 0.301 0.321 0.23444 0.264 0.301 0.321 0.23445 0.264 0.301 0.321 0.23446 0.192 0.208 0.31 0.37847 0.192 0.208 0.31 0.37848 0.192 0.208 0.31 0.37849 0.192 0.208 0.31 0.37850 0.192 0.208 0.31 0.37851 0.192 0.208 0.31 0.37852 0.254 0.334 0.222 0.27853 0.254 0.334 0.222 0.27854 0.254 0.334 0.222 0.27855 0.254 0.334 0.222 0.27856 0.254 0.334 0.222 0.27857 0.254 0.334 0.222 0.27858 0.34 0.287 0.284 0.2659 0.34 0.287 0.284 0.2660 0.34 0.287 0.284 0.2661 0.34 0.287 0.284 0.2662 0.34 0.287 0.284 0.2663 0.34 0.287 0.284 0.2664 0.24 0.285 0.32 0.29665 0.24 0.285 0.32 0.29666 0.24 0.285 0.32 0.29667 0.24 0.285 0.32 0.29668 0.24 0.285 0.32 0.29669 0.237 0.35 0.298 0.29970 0.237 0.35 0.298 0.29971 0.237 0.35 0.298 0.29972 0.237 0.35 0.298 0.29973 0.237 0.35 0.298 0.29974 0.237 0.35 0.298 0.29975 0.237 0.35 0.298 0.29976 0.282 0.335 0.386 0.25177 0.282 0.335 0.386 0.25178 0.282 0.335 0.386 0.25179 0.282 0.335 0.386 0.25180 0.282 0.335 0.386 0.25181 0.255 0.289 0.318 0.27982 0.255 0.289 0.318 0.27983 0.255 0.289 0.318 0.27984 0.255 0.289 0.318 0.27985 0.255 0.289 0.318 0.27986 0.255 0.289 0.318 0.27987 0.259 0.275 0.329 0.28388 0.259 0.275 0.329 0.28389 0.259 0.275 0.329 0.28390 0.259 0.275 0.329 0.28391 0.259 0.275 0.329 0.28392 0.259 0.275 0.329 0.28393 0.259 0.275 0.329 0.28394 0.28 0.282 0.301 0.24595 0.28 0.282 0.301 0.24596 0.28 0.282 0.301 0.24597 0.28 0.282 0.301 0.245
98 0.28 0.282 0.301 0.24599 0.231 0.317 0.259 0.291
morale4 person1 person2 person30 0.269 0.374 0.245 0.2761 0.269 0.374 0.245 0.2762 0.283 0.326 0.267 0.3253 0.283 0.326 0.267 0.3254 0.283 0.326 0.267 0.3255 0.283 0.326 0.267 0.3256 0.283 0.326 0.267 0.3257 0.251 0.132 0.466 0.3588 0.251 0.132 0.466 0.3589 0.251 0.132 0.466 0.358
10 0.251 0.132 0.466 0.35811 0.251 0.132 0.466 0.35812 0.3 0.234 0.331 0.35913 0.3 0.234 0.331 0.35914 0.3 0.234 0.331 0.35915 0.3 0.234 0.331 0.35916 0.3 0.234 0.331 0.35917 0.3 0.234 0.331 0.35918 0.21 0.301 0.248 0.31819 0.21 0.301 0.248 0.31820 0.21 0.301 0.248 0.31821 0.21 0.301 0.248 0.31822 0.21 0.301 0.248 0.31823 0.21 0.301 0.248 0.31824 0.277 0.41 0.16 0.37725 0.277 0.41 0.16 0.37726 0.277 0.41 0.16 0.37727 0.277 0.41 0.16 0.37728 0.277 0.41 0.16 0.37729 0.277 0.41 0.16 0.37730 0.313 0.338 0.318 0.31731 0.313 0.338 0.318 0.31732 0.313 0.338 0.318 0.31733 0.313 0.338 0.318 0.31734 0.313 0.338 0.318 0.31735 0.316 0.255 0.321 0.26736 0.316 0.255 0.321 0.26737 0.316 0.255 0.321 0.26738 0.316 0.255 0.321 0.26739 0.316 0.255 0.321 0.26740 0.316 0.255 0.321 0.26741 0.316 0.255 0.321 0.26742 0.281 0.208 0.311 0.31743 0.281 0.208 0.311 0.31744 0.281 0.208 0.311 0.31745 0.281 0.208 0.311 0.31746 0.267 0.3 0.314 0.32447 0.267 0.3 0.314 0.32448 0.267 0.3 0.314 0.32449 0.267 0.3 0.314 0.32450 0.267 0.3 0.314 0.32451 0.267 0.3 0.314 0.32452 0.382 0.262 0.254 0.319
53 0.382 0.262 0.254 0.31954 0.382 0.262 0.254 0.31955 0.382 0.262 0.254 0.31956 0.382 0.262 0.254 0.31957 0.382 0.262 0.254 0.31958 0.26 0.338 0.207 0.3759 0.26 0.338 0.207 0.3760 0.26 0.338 0.207 0.3761 0.26 0.338 0.207 0.3762 0.26 0.338 0.207 0.3763 0.26 0.338 0.207 0.3764 0.235 0.385 0.172 0.37865 0.235 0.385 0.172 0.37866 0.235 0.385 0.172 0.37867 0.235 0.385 0.172 0.37868 0.235 0.385 0.172 0.37869 0.225 0.308 0.362 0.25970 0.225 0.308 0.362 0.25971 0.225 0.308 0.362 0.25972 0.225 0.308 0.362 0.25973 0.225 0.308 0.362 0.25974 0.225 0.308 0.362 0.25975 0.225 0.308 0.362 0.25976 0.196 0.17 0.46 0.29677 0.196 0.17 0.46 0.29678 0.196 0.17 0.46 0.29679 0.196 0.17 0.46 0.29680 0.196 0.17 0.46 0.29681 0.223 0.3 0.205 0.382 0.223 0.3 0.205 0.383 0.223 0.3 0.205 0.384 0.223 0.3 0.205 0.385 0.223 0.3 0.205 0.386 0.223 0.3 0.205 0.387 0.3 0.332 0.275 0.2988 0.3 0.332 0.275 0.2989 0.3 0.332 0.275 0.2990 0.3 0.332 0.275 0.2991 0.3 0.332 0.275 0.2992 0.3 0.332 0.275 0.2993 0.3 0.332 0.275 0.2994 0.273 0.375 0.277 0.23495 0.273 0.375 0.277 0.23496 0.273 0.375 0.277 0.23497 0.273 0.375 0.277 0.23498 0.273 0.375 0.277 0.23499 0.274 0.294 0.33 0.318
person40 0.3821 0.3822 0.313 0.314 0.315 0.316 0.317 0.333
8 0.3339 0.333
10 0.33311 0.33312 0.34213 0.34214 0.34215 0.34216 0.34217 0.34218 0.3419 0.3420 0.3421 0.3422 0.3423 0.3424 0.33325 0.33326 0.33327 0.33328 0.33329 0.33330 0.37731 0.37732 0.37733 0.37734 0.37735 0.30936 0.30937 0.30938 0.30939 0.30940 0.30941 0.30942 0.38743 0.38744 0.38745 0.38746 0.28747 0.28748 0.28749 0.28750 0.28751 0.28752 0.3953 0.3954 0.3955 0.3956 0.3957 0.3958 0.38659 0.38660 0.38661 0.38662 0.38663 0.38664 0.341
65 0.34166 0.34167 0.34168 0.34169 0.29470 0.29471 0.29472 0.29473 0.29474 0.29475 0.29476 0.43477 0.43478 0.43479 0.43480 0.43481 0.39882 0.39883 0.39884 0.39885 0.39886 0.39887 0.2688 0.2689 0.2690 0.2691 0.2692 0.2693 0.2694 0.43795 0.43796 0.43797 0.43798 0.43799 0.256
Table of contents
outer loadings for each sample[ CSV-Version ]
behave1 behave2 behave3 behave40 0.843 0.789 0.878 0.8961 0.843 0.789 0.878 0.8962 0.893 0.829 0.881 0.8223 0.893 0.829 0.881 0.8224 0.893 0.829 0.881 0.8225 0.893 0.829 0.881 0.8226 0.893 0.829 0.881 0.8227 0.89 0.843 0.893 0.8468 0.89 0.843 0.893 0.8469 0.89 0.843 0.893 0.846
10 0.89 0.843 0.893 0.84611 0.89 0.843 0.893 0.84612 0.802 0.799 0.856 0.82513 0.802 0.799 0.856 0.82514 0.802 0.799 0.856 0.825
15 0.802 0.799 0.856 0.82516 0.802 0.799 0.856 0.82517 0.802 0.799 0.856 0.82518 0.856 0.804 0.881 0.91919 0.856 0.804 0.881 0.91920 0.856 0.804 0.881 0.91921 0.856 0.804 0.881 0.91922 0.856 0.804 0.881 0.91923 0.856 0.804 0.881 0.91924 0.788 0.812 0.875 0.81725 0.788 0.812 0.875 0.81726 0.788 0.812 0.875 0.81727 0.788 0.812 0.875 0.81728 0.788 0.812 0.875 0.81729 0.788 0.812 0.875 0.81730 0.819 0.865 0.874 0.87731 0.819 0.865 0.874 0.87732 0.819 0.865 0.874 0.87733 0.819 0.865 0.874 0.87734 0.819 0.865 0.874 0.87735 0.919 0.738 0.922 0.89236 0.919 0.738 0.922 0.89237 0.919 0.738 0.922 0.89238 0.919 0.738 0.922 0.89239 0.919 0.738 0.922 0.89240 0.919 0.738 0.922 0.89241 0.919 0.738 0.922 0.89242 0.785 0.826 0.846 0.83543 0.785 0.826 0.846 0.83544 0.785 0.826 0.846 0.83545 0.785 0.826 0.846 0.83546 0.807 0.707 0.891 0.87647 0.807 0.707 0.891 0.87648 0.807 0.707 0.891 0.87649 0.807 0.707 0.891 0.87650 0.807 0.707 0.891 0.87651 0.807 0.707 0.891 0.87652 0.891 0.815 0.93 0.88353 0.891 0.815 0.93 0.88354 0.891 0.815 0.93 0.88355 0.891 0.815 0.93 0.88356 0.891 0.815 0.93 0.88357 0.891 0.815 0.93 0.88358 0.899 0.755 0.894 0.87159 0.899 0.755 0.894 0.87160 0.899 0.755 0.894 0.87161 0.899 0.755 0.894 0.87162 0.899 0.755 0.894 0.87163 0.899 0.755 0.894 0.87164 0.809 0.43 0.818 0.81265 0.809 0.43 0.818 0.81266 0.809 0.43 0.818 0.81267 0.809 0.43 0.818 0.81268 0.809 0.43 0.818 0.81269 0.833 0.783 0.845 0.86870 0.833 0.783 0.845 0.86871 0.833 0.783 0.845 0.868
72 0.833 0.783 0.845 0.86873 0.833 0.783 0.845 0.86874 0.833 0.783 0.845 0.86875 0.833 0.783 0.845 0.86876 0.82 0.755 0.855 0.82777 0.82 0.755 0.855 0.82778 0.82 0.755 0.855 0.82779 0.82 0.755 0.855 0.82780 0.82 0.755 0.855 0.82781 0.875 0.304 0.837 0.85282 0.875 0.304 0.837 0.85283 0.875 0.304 0.837 0.85284 0.875 0.304 0.837 0.85285 0.875 0.304 0.837 0.85286 0.875 0.304 0.837 0.85287 0.848 0.716 0.893 0.85888 0.848 0.716 0.893 0.85889 0.848 0.716 0.893 0.85890 0.848 0.716 0.893 0.85891 0.848 0.716 0.893 0.85892 0.848 0.716 0.893 0.85893 0.848 0.716 0.893 0.85894 0.762 0.856 0.863 0.87995 0.762 0.856 0.863 0.87996 0.762 0.856 0.863 0.87997 0.762 0.856 0.863 0.87998 0.762 0.856 0.863 0.87999 0.736 0.81 0.891 0.878
develop1 develop2 develop3 develop40 0.833 0.705 0.689 0.8541 0.833 0.705 0.689 0.8542 0.771 0.877 0.84 0.8233 0.771 0.877 0.84 0.8234 0.771 0.877 0.84 0.8235 0.771 0.877 0.84 0.8236 0.771 0.877 0.84 0.8237 0.844 0.74 0.795 0.898 0.844 0.74 0.795 0.899 0.844 0.74 0.795 0.89
10 0.844 0.74 0.795 0.8911 0.844 0.74 0.795 0.8912 0.783 0.299 0.755 0.90413 0.783 0.299 0.755 0.90414 0.783 0.299 0.755 0.90415 0.783 0.299 0.755 0.90416 0.783 0.299 0.755 0.90417 0.783 0.299 0.755 0.90418 0.898 0.82 0.752 0.89619 0.898 0.82 0.752 0.89620 0.898 0.82 0.752 0.89621 0.898 0.82 0.752 0.89622 0.898 0.82 0.752 0.89623 0.898 0.82 0.752 0.89624 0.922 0.724 0.387 0.91225 0.922 0.724 0.387 0.91226 0.922 0.724 0.387 0.912
27 0.922 0.724 0.387 0.91228 0.922 0.724 0.387 0.91229 0.922 0.724 0.387 0.91230 0.731 0.797 0.823 0.90831 0.731 0.797 0.823 0.90832 0.731 0.797 0.823 0.90833 0.731 0.797 0.823 0.90834 0.731 0.797 0.823 0.90835 0.786 0.712 0.789 0.93736 0.786 0.712 0.789 0.93737 0.786 0.712 0.789 0.93738 0.786 0.712 0.789 0.93739 0.786 0.712 0.789 0.93740 0.786 0.712 0.789 0.93741 0.786 0.712 0.789 0.93742 0.281 0.914 0.681 0.65343 0.281 0.914 0.681 0.65344 0.281 0.914 0.681 0.65345 0.281 0.914 0.681 0.65346 0.698 0.861 0.664 0.87247 0.698 0.861 0.664 0.87248 0.698 0.861 0.664 0.87249 0.698 0.861 0.664 0.87250 0.698 0.861 0.664 0.87251 0.698 0.861 0.664 0.87252 0.674 0.759 0.817 0.9353 0.674 0.759 0.817 0.9354 0.674 0.759 0.817 0.9355 0.674 0.759 0.817 0.9356 0.674 0.759 0.817 0.9357 0.674 0.759 0.817 0.9358 0.687 0.723 0.766 0.88259 0.687 0.723 0.766 0.88260 0.687 0.723 0.766 0.88261 0.687 0.723 0.766 0.88262 0.687 0.723 0.766 0.88263 0.687 0.723 0.766 0.88264 0.687 0.781 0.828 0.8565 0.687 0.781 0.828 0.8566 0.687 0.781 0.828 0.8567 0.687 0.781 0.828 0.8568 0.687 0.781 0.828 0.8569 0.87 0.882 0.65 0.93770 0.87 0.882 0.65 0.93771 0.87 0.882 0.65 0.93772 0.87 0.882 0.65 0.93773 0.87 0.882 0.65 0.93774 0.87 0.882 0.65 0.93775 0.87 0.882 0.65 0.93776 0.841 0.885 0.734 0.94977 0.841 0.885 0.734 0.94978 0.841 0.885 0.734 0.94979 0.841 0.885 0.734 0.94980 0.841 0.885 0.734 0.94981 0.579 0.675 0.908 0.82982 0.579 0.675 0.908 0.82983 0.579 0.675 0.908 0.829
84 0.579 0.675 0.908 0.82985 0.579 0.675 0.908 0.82986 0.579 0.675 0.908 0.82987 0.855 0.732 0.605 0.88888 0.855 0.732 0.605 0.88889 0.855 0.732 0.605 0.88890 0.855 0.732 0.605 0.88891 0.855 0.732 0.605 0.88892 0.855 0.732 0.605 0.88893 0.855 0.732 0.605 0.88894 0.834 0.82 0.64 0.87395 0.834 0.82 0.64 0.87396 0.834 0.82 0.64 0.87397 0.834 0.82 0.64 0.87398 0.834 0.82 0.64 0.87399 0.674 0.882 0.645 0.915
leader1 leader2 leader3 leader40 0.912 0.82 0.919 0.7731 0.912 0.82 0.919 0.7732 0.908 0.92 0.794 0.7693 0.908 0.92 0.794 0.7694 0.908 0.92 0.794 0.7695 0.908 0.92 0.794 0.7696 0.908 0.92 0.794 0.7697 0.83 0.744 0.879 0.8938 0.83 0.744 0.879 0.8939 0.83 0.744 0.879 0.893
10 0.83 0.744 0.879 0.89311 0.83 0.744 0.879 0.89312 0.843 0.922 0.857 0.78113 0.843 0.922 0.857 0.78114 0.843 0.922 0.857 0.78115 0.843 0.922 0.857 0.78116 0.843 0.922 0.857 0.78117 0.843 0.922 0.857 0.78118 0.831 0.833 0.857 0.7519 0.831 0.833 0.857 0.7520 0.831 0.833 0.857 0.7521 0.831 0.833 0.857 0.7522 0.831 0.833 0.857 0.7523 0.831 0.833 0.857 0.7524 0.835 0.836 0.857 0.76625 0.835 0.836 0.857 0.76626 0.835 0.836 0.857 0.76627 0.835 0.836 0.857 0.76628 0.835 0.836 0.857 0.76629 0.835 0.836 0.857 0.76630 0.901 0.891 0.856 0.84631 0.901 0.891 0.856 0.84632 0.901 0.891 0.856 0.84633 0.901 0.891 0.856 0.84634 0.901 0.891 0.856 0.84635 0.841 0.9 0.904 0.7736 0.841 0.9 0.904 0.7737 0.841 0.9 0.904 0.7738 0.841 0.9 0.904 0.77
39 0.841 0.9 0.904 0.7740 0.841 0.9 0.904 0.7741 0.841 0.9 0.904 0.7742 0.845 0.87 0.849 0.70843 0.845 0.87 0.849 0.70844 0.845 0.87 0.849 0.70845 0.845 0.87 0.849 0.70846 0.797 0.917 0.944 0.87647 0.797 0.917 0.944 0.87648 0.797 0.917 0.944 0.87649 0.797 0.917 0.944 0.87650 0.797 0.917 0.944 0.87651 0.797 0.917 0.944 0.87652 0.832 0.826 0.896 0.85453 0.832 0.826 0.896 0.85454 0.832 0.826 0.896 0.85455 0.832 0.826 0.896 0.85456 0.832 0.826 0.896 0.85457 0.832 0.826 0.896 0.85458 0.768 0.912 0.911 0.69759 0.768 0.912 0.911 0.69760 0.768 0.912 0.911 0.69761 0.768 0.912 0.911 0.69762 0.768 0.912 0.911 0.69763 0.768 0.912 0.911 0.69764 0.871 0.723 0.834 0.7465 0.871 0.723 0.834 0.7466 0.871 0.723 0.834 0.7467 0.871 0.723 0.834 0.7468 0.871 0.723 0.834 0.7469 0.918 0.892 0.883 0.86870 0.918 0.892 0.883 0.86871 0.918 0.892 0.883 0.86872 0.918 0.892 0.883 0.86873 0.918 0.892 0.883 0.86874 0.918 0.892 0.883 0.86875 0.918 0.892 0.883 0.86876 0.927 0.666 0.939 0.82577 0.927 0.666 0.939 0.82578 0.927 0.666 0.939 0.82579 0.927 0.666 0.939 0.82580 0.927 0.666 0.939 0.82581 0.838 0.883 0.817 0.8382 0.838 0.883 0.817 0.8383 0.838 0.883 0.817 0.8384 0.838 0.883 0.817 0.8385 0.838 0.883 0.817 0.8386 0.838 0.883 0.817 0.8387 0.928 0.884 0.87 0.67288 0.928 0.884 0.87 0.67289 0.928 0.884 0.87 0.67290 0.928 0.884 0.87 0.67291 0.928 0.884 0.87 0.67292 0.928 0.884 0.87 0.67293 0.928 0.884 0.87 0.67294 0.923 0.791 0.887 0.7995 0.923 0.791 0.887 0.79
96 0.923 0.791 0.887 0.7997 0.923 0.791 0.887 0.7998 0.923 0.791 0.887 0.7999 0.92 0.903 0.887 0.834
leader5 morale1 morale2 morale30 0.863 0.954 0.903 0.8881 0.863 0.954 0.903 0.8882 0.918 0.909 0.845 0.8953 0.918 0.909 0.845 0.8954 0.918 0.909 0.845 0.8955 0.918 0.909 0.845 0.8956 0.918 0.909 0.845 0.8957 0.87 0.922 0.878 0.8558 0.87 0.922 0.878 0.8559 0.87 0.922 0.878 0.855
10 0.87 0.922 0.878 0.85511 0.87 0.922 0.878 0.85512 0.816 0.881 0.775 0.77913 0.816 0.881 0.775 0.77914 0.816 0.881 0.775 0.77915 0.816 0.881 0.775 0.77916 0.816 0.881 0.775 0.77917 0.816 0.881 0.775 0.77918 0.821 0.938 0.895 0.85119 0.821 0.938 0.895 0.85120 0.821 0.938 0.895 0.85121 0.821 0.938 0.895 0.85122 0.821 0.938 0.895 0.85123 0.821 0.938 0.895 0.85124 0.905 0.948 0.897 0.86125 0.905 0.948 0.897 0.86126 0.905 0.948 0.897 0.86127 0.905 0.948 0.897 0.86128 0.905 0.948 0.897 0.86129 0.905 0.948 0.897 0.86130 0.907 0.916 0.84 0.73831 0.907 0.916 0.84 0.73832 0.907 0.916 0.84 0.73833 0.907 0.916 0.84 0.73834 0.907 0.916 0.84 0.73835 0.82 0.889 0.776 0.80736 0.82 0.889 0.776 0.80737 0.82 0.889 0.776 0.80738 0.82 0.889 0.776 0.80739 0.82 0.889 0.776 0.80740 0.82 0.889 0.776 0.80741 0.82 0.889 0.776 0.80742 0.883 0.933 0.882 0.74943 0.883 0.933 0.882 0.74944 0.883 0.933 0.882 0.74945 0.883 0.933 0.882 0.74946 0.88 0.831 0.846 0.85747 0.88 0.831 0.846 0.85748 0.88 0.831 0.846 0.85749 0.88 0.831 0.846 0.85750 0.88 0.831 0.846 0.857
51 0.88 0.831 0.846 0.85752 0.898 0.883 0.726 0.75553 0.898 0.883 0.726 0.75554 0.898 0.883 0.726 0.75555 0.898 0.883 0.726 0.75556 0.898 0.883 0.726 0.75557 0.898 0.883 0.726 0.75558 0.889 0.936 0.929 0.8859 0.889 0.936 0.929 0.8860 0.889 0.936 0.929 0.8861 0.889 0.936 0.929 0.8862 0.889 0.936 0.929 0.8863 0.889 0.936 0.929 0.8864 0.816 0.947 0.815 0.8965 0.816 0.947 0.815 0.8966 0.816 0.947 0.815 0.8967 0.816 0.947 0.815 0.8968 0.816 0.947 0.815 0.8969 0.869 0.917 0.862 0.83670 0.869 0.917 0.862 0.83671 0.869 0.917 0.862 0.83672 0.869 0.917 0.862 0.83673 0.869 0.917 0.862 0.83674 0.869 0.917 0.862 0.83675 0.869 0.917 0.862 0.83676 0.904 0.885 0.905 0.74477 0.904 0.885 0.905 0.74478 0.904 0.885 0.905 0.74479 0.904 0.885 0.905 0.74480 0.904 0.885 0.905 0.74481 0.86 0.902 0.919 0.90282 0.86 0.902 0.919 0.90283 0.86 0.902 0.919 0.90284 0.86 0.902 0.919 0.90285 0.86 0.902 0.919 0.90286 0.86 0.902 0.919 0.90287 0.903 0.866 0.77 0.85988 0.903 0.866 0.77 0.85989 0.903 0.866 0.77 0.85990 0.903 0.866 0.77 0.85991 0.903 0.866 0.77 0.85992 0.903 0.866 0.77 0.85993 0.903 0.866 0.77 0.85994 0.897 0.924 0.928 0.81795 0.897 0.924 0.928 0.81796 0.897 0.924 0.928 0.81797 0.897 0.924 0.928 0.81798 0.897 0.924 0.928 0.81799 0.872 0.922 0.885 0.818
morale4 person1 person2 person30 0.95 0.825 0.641 0.6991 0.95 0.825 0.641 0.6992 0.918 0.8 0.715 0.8763 0.918 0.8 0.715 0.8764 0.918 0.8 0.715 0.8765 0.918 0.8 0.715 0.876
6 0.918 0.8 0.715 0.8767 0.927 0.577 0.845 0.8288 0.927 0.577 0.845 0.8289 0.927 0.577 0.845 0.828
10 0.927 0.577 0.845 0.82811 0.927 0.577 0.845 0.82812 0.891 0.689 0.852 0.81913 0.891 0.689 0.852 0.81914 0.891 0.689 0.852 0.81915 0.891 0.689 0.852 0.81916 0.891 0.689 0.852 0.81917 0.891 0.689 0.852 0.81918 0.889 0.858 0.784 0.83319 0.889 0.858 0.784 0.83320 0.889 0.858 0.784 0.83321 0.889 0.858 0.784 0.83322 0.889 0.858 0.784 0.83323 0.889 0.858 0.784 0.83324 0.942 0.812 0.561 0.85425 0.942 0.812 0.561 0.85426 0.942 0.812 0.561 0.85427 0.942 0.812 0.561 0.85428 0.942 0.812 0.561 0.85429 0.942 0.812 0.561 0.85430 0.915 0.773 0.727 0.67831 0.915 0.773 0.727 0.67832 0.915 0.773 0.727 0.67833 0.915 0.773 0.727 0.67834 0.915 0.773 0.727 0.67835 0.915 0.811 0.878 0.84736 0.915 0.811 0.878 0.84737 0.915 0.811 0.878 0.84738 0.915 0.811 0.878 0.84739 0.915 0.811 0.878 0.84740 0.915 0.811 0.878 0.84741 0.915 0.811 0.878 0.84742 0.928 0.787 0.853 0.82743 0.928 0.787 0.853 0.82744 0.928 0.787 0.853 0.82745 0.928 0.787 0.853 0.82746 0.899 0.811 0.778 0.87847 0.899 0.811 0.778 0.87848 0.899 0.811 0.778 0.87849 0.899 0.811 0.778 0.87850 0.899 0.811 0.778 0.87851 0.899 0.811 0.778 0.87852 0.876 0.819 0.784 0.80153 0.876 0.819 0.784 0.80154 0.876 0.819 0.784 0.80155 0.876 0.819 0.784 0.80156 0.876 0.819 0.784 0.80157 0.876 0.819 0.784 0.80158 0.916 0.71 0.681 0.8559 0.916 0.71 0.681 0.8560 0.916 0.71 0.681 0.8561 0.916 0.71 0.681 0.8562 0.916 0.71 0.681 0.85
63 0.916 0.71 0.681 0.8564 0.875 0.821 0.623 0.8265 0.875 0.821 0.623 0.8266 0.875 0.821 0.623 0.8267 0.875 0.821 0.623 0.8268 0.875 0.821 0.623 0.8269 0.762 0.835 0.876 0.77670 0.762 0.835 0.876 0.77671 0.762 0.835 0.876 0.77672 0.762 0.835 0.876 0.77673 0.762 0.835 0.876 0.77674 0.762 0.835 0.876 0.77675 0.762 0.835 0.876 0.77676 0.853 0.614 0.751 0.69777 0.853 0.614 0.751 0.69778 0.853 0.614 0.751 0.69779 0.853 0.614 0.751 0.69780 0.853 0.614 0.751 0.69781 0.878 0.864 0.656 0.84982 0.878 0.864 0.656 0.84983 0.878 0.864 0.656 0.84984 0.878 0.864 0.656 0.84985 0.878 0.864 0.656 0.84986 0.878 0.864 0.656 0.84987 0.886 0.911 0.866 0.83188 0.886 0.911 0.866 0.83189 0.886 0.911 0.866 0.83190 0.886 0.911 0.866 0.83191 0.886 0.911 0.866 0.83192 0.886 0.911 0.866 0.83193 0.886 0.911 0.866 0.83194 0.951 0.79 0.708 0.58995 0.951 0.79 0.708 0.58996 0.951 0.79 0.708 0.58997 0.951 0.79 0.708 0.58998 0.951 0.79 0.708 0.58999 0.875 0.801 0.872 0.859
person40 0.8931 0.8932 0.8493 0.8494 0.8495 0.8496 0.8497 0.7028 0.7029 0.702
10 0.70211 0.70212 0.76613 0.76614 0.76615 0.76616 0.76617 0.766
18 0.82919 0.82920 0.82921 0.82922 0.82923 0.82924 0.76725 0.76726 0.76727 0.76728 0.76729 0.76730 0.77631 0.77632 0.77633 0.77634 0.77635 0.92336 0.92337 0.92338 0.92339 0.92340 0.92341 0.92342 0.79843 0.79844 0.79845 0.79846 0.79447 0.79448 0.79449 0.79450 0.79451 0.79452 0.84853 0.84854 0.84855 0.84856 0.84857 0.84858 0.79159 0.79160 0.79161 0.79162 0.79163 0.79164 0.7865 0.7866 0.7867 0.7868 0.7869 0.7670 0.7671 0.7672 0.7673 0.7674 0.76
75 0.7676 0.79377 0.79378 0.79379 0.79380 0.79381 0.88382 0.88383 0.88384 0.88385 0.88386 0.88387 0.8488 0.8489 0.8490 0.8491 0.8492 0.8493 0.8494 0.84595 0.84596 0.84597 0.84598 0.84599 0.797
Table of contents
inner weights for each sample[ CSV-Version ]
Leader ->
PersonBehave ->
PersonDevelop -> Person
Person -> Morale
0 0.084 0.683 0.163 0.5171 0.084 0.683 0.163 0.5172 0.181 0.279 0.162 0.5183 0.181 0.279 0.162 0.5184 0.181 0.279 0.162 0.5185 0.181 0.279 0.162 0.5186 0.181 0.279 0.162 0.5187 0.169 0.503 0.16 0.2588 0.169 0.503 0.16 0.2589 0.169 0.503 0.16 0.258
10 0.169 0.503 0.16 0.25811 0.169 0.503 0.16 0.25812 0.217 0.247 0.176 0.55613 0.217 0.247 0.176 0.55614 0.217 0.247 0.176 0.55615 0.217 0.247 0.176 0.55616 0.217 0.247 0.176 0.55617 0.217 0.247 0.176 0.55618 0.252 0.432 0.179 0.45219 0.252 0.432 0.179 0.45220 0.252 0.432 0.179 0.45221 0.252 0.432 0.179 0.45222 0.252 0.432 0.179 0.45223 0.252 0.432 0.179 0.452
24 0.304 0.247 0.154 0.59225 0.304 0.247 0.154 0.59226 0.304 0.247 0.154 0.59227 0.304 0.247 0.154 0.59228 0.304 0.247 0.154 0.59229 0.304 0.247 0.154 0.59230 0.18 0.282 0.37 0.47131 0.18 0.282 0.37 0.47132 0.18 0.282 0.37 0.47133 0.18 0.282 0.37 0.47134 0.18 0.282 0.37 0.47135 0.048 0.427 0.297 0.49736 0.048 0.427 0.297 0.49737 0.048 0.427 0.297 0.49738 0.048 0.427 0.297 0.49739 0.048 0.427 0.297 0.49740 0.048 0.427 0.297 0.49741 0.048 0.427 0.297 0.49742 0.304 0.338 0.115 0.48943 0.304 0.338 0.115 0.48944 0.304 0.338 0.115 0.48945 0.304 0.338 0.115 0.48946 0.117 0.67 0.219 0.33647 0.117 0.67 0.219 0.33648 0.117 0.67 0.219 0.33649 0.117 0.67 0.219 0.33650 0.117 0.67 0.219 0.33651 0.117 0.67 0.219 0.33652 0.494 0.01 0.187 0.48753 0.494 0.01 0.187 0.48754 0.494 0.01 0.187 0.48755 0.494 0.01 0.187 0.48756 0.494 0.01 0.187 0.48757 0.494 0.01 0.187 0.48758 0.017 0.549 0.132 0.55459 0.017 0.549 0.132 0.55460 0.017 0.549 0.132 0.55461 0.017 0.549 0.132 0.55462 0.017 0.549 0.132 0.55463 0.017 0.549 0.132 0.55464 0.565 0.109 0.202 0.64865 0.565 0.109 0.202 0.64866 0.565 0.109 0.202 0.64867 0.565 0.109 0.202 0.64868 0.565 0.109 0.202 0.64869 0.239 0.437 0.396 0.66870 0.239 0.437 0.396 0.66871 0.239 0.437 0.396 0.66872 0.239 0.437 0.396 0.66873 0.239 0.437 0.396 0.66874 0.239 0.437 0.396 0.66875 0.239 0.437 0.396 0.66876 0.231 0.25 0.191 0.28877 0.231 0.25 0.191 0.28878 0.231 0.25 0.191 0.28879 0.231 0.25 0.191 0.28880 0.231 0.25 0.191 0.288
81 0.189 0.144 0.246 0.52582 0.189 0.144 0.246 0.52583 0.189 0.144 0.246 0.52584 0.189 0.144 0.246 0.52585 0.189 0.144 0.246 0.52586 0.189 0.144 0.246 0.52587 0.129 0.528 0.196 0.54988 0.129 0.528 0.196 0.54989 0.129 0.528 0.196 0.54990 0.129 0.528 0.196 0.54991 0.129 0.528 0.196 0.54992 0.129 0.528 0.196 0.54993 0.129 0.528 0.196 0.54994 0.188 0.249 0.344 0.47895 0.188 0.249 0.344 0.47896 0.188 0.249 0.344 0.47897 0.188 0.249 0.344 0.47898 0.188 0.249 0.344 0.47899 0.432 0.247 0.144 0.514
Table of contents
Lisrel 8.30
Lisrel 8.30 dipakai karena Lisrel 9.10 Student Edition tidak dapat mengolah
jumlah indikator sebanyak 21. Hal ini tidak menghalangi usaha perbandingan
antara Lisrel dan SmartPLS. Hasil pengolahan data Employee.sav adalah
sebagai berikut:
TI FRONTLINE EMPLOYEE FULL MODEL Observed VariablesMORALE1 MORALE2 MORALE3 MORALE4 PERSON1 PERSON2 PERSON3 PERSON4 LEADER1 LEADER2 LEADER3 LEADER4 LEADER5 BEHAVE1 BEHAVE2 BEHAVE3 BEHAVE4 DEVELOP1 DEVELOP2 DEVELOP3 DEVELOP4
Covariance Matrix 1.31 0.82 0.96 0.90 0.69 1.26 1.03 0.78 0.86 1.20 0.31 0.27 0.30 0.27 0.76 0.34 0.29 0.33 0.32 0.43 0.92 0.36 0.31 0.32 0.34 0.41 0.47 0.75 0.43 0.35 0.44 0.40 0.43 0.51 0.48 1.00 0.68 0.49 0.56 0.64 0.28 0.34 0.29 0.36 1.22 0.84 0.61 0.66 0.75 0.30 0.33 0.32 0.39 0.88 1.20 0.59 0.43 0.52 0.54 0.27 0.35 0.28 0.35 0.90 0.80 1.22 0.51 0.32 0.46 0.44 0.22 0.18 0.18 0.22 0.74 0.68 0.73 1.00 0.61 0.42 0.50 0.54 0.24 0.33 0.27 0.33 0.83 0.77 0.80 0.64 1.06 0.32 0.28 0.39 0.30 0.28 0.35 0.32 0.39 0.29 0.31 0.30 0.24 0.26 0.98 0.28 0.21 0.26 0.26 0.22 0.29 0.22 0.28 0.30 0.28 0.29 0.25 0.27 0.59 0.98 0.31 0.26 0.36 0.30 0.32 0.35 0.33 0.38 0.32 0.33 0.30 0.25 0.28 0.72 0.66 1.11 0.33 0.31 0.41 0.33 0.34 0.38 0.36 0.44 0.39 0.36 0.37 0.31 0.30 0.70 0.63 0.79 1.19 0.32 0.32 0.36 0.36 0.20 0.27 0.18 0.31 0.41 0.36 0.39 0.29 0.34 0.36 0.28 0.32 0.41 1.12 0.30 0.32 0.40 0.33 0.25 0.30 0.23 0.30 0.45 0.40 0.40 0.35 0.36 0.35 0.27 0.34 0.49 0.61 1.24 0.24 0.24 0.24 0.23 0.17 0.23 0.16 0.25 0.34 0.32 0.29 0.22 0.27 0.25 0.21 0.21 0.29 0.45 0.45 1.04 0.42 0.36 0.44 0.43 0.26 0.37 0.24 0.41 0.56 0.46 0.48 0.34 0.42 0.40 0.29 0.36 0.41 0.73 0.70 0.55 1.08 Means 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sample Size = 538Latent Variables MORALE PERSON LEADER BEHAVE DEVELOP RelationshipsMORALE1 = MORALE MORALE2 = MORALE MORALE3 = MORALE MORALE4 = MORALE PERSON1 = PERSON PERSON2 = PERSON PERSON3 = PERSON PERSON4 = PERSON LEADER1 = LEADER LEADER2 = LEADER LEADER3 = LEADER LEADER4 = LEADER LEADER5 = LEADER BEHAVE1 = BEHAVE BEHAVE2 = BEHAVE BEHAVE3 = BEHAVE BEHAVE4 = BEHAVE DEVELOP1 = DEVELOP DEVELOP2 = DEVELOP DEVELOP3 = DEVELOP DEVELOP4 = DEVELOP MORALE = PERSON PERSON = LEADER BEHAVE DEVELOP Set the Variance of LEADER to 1.00Set the Variance of BEHAVE to 1.00Set the Variance of DEVELOP to 1.00Set the Error Variance of MORALE to 0.64Set the Error Variance of PERSON to 0.50Path Diagram
Print ResidualsMethod of Estimation: Maximum LikelihoodEnd of Problem
DATE: 7/28/2013 TIME: 5:41
L I S R E L 8.30
BY
Karl G. Jöreskog & Dag Sörbom
This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Chicago, IL 60646-1704, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-99 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com
The following lines were read from file C:\LISREL83\LS8EX\EMPLOYEE.SPJ:
TI FRONTLINE EMPLOYEE FULL MODEL Observed Variables MORALE1 MORALE2 MORALE3 MORALE4 PERSON1 PERSON2 PERSON3 PERSON4 LEADER1 LEADER2 LEADER3 LEADER4 LEADER5 BEHAVE1 BEHAVE2 BEHAVE3 BEHAVE4 DEVELOP1 DEVELOP2 DEVELOP3 DEVELOP4 Covariance Matrix 1.31 0.82 0.96 0.90 0.69 1.26 1.03 0.78 0.86 1.20 0.31 0.27 0.30 0.27 0.76 0.34 0.29 0.33 0.32 0.43 0.92 0.36 0.31 0.32 0.34 0.41 0.47 0.75 0.43 0.35 0.44 0.40 0.43 0.51 0.48 1.00 0.68 0.49 0.56 0.64 0.28 0.34 0.29 0.36 1.22 0.84 0.61 0.66 0.75 0.30 0.33 0.32 0.39 0.88 1.20 0.59 0.43 0.52 0.54 0.27 0.35 0.28 0.35 0.90 0.80 1.22 0.51 0.32 0.46 0.44 0.22 0.18 0.18 0.22 0.74 0.68 0.73 1.00 0.61 0.42 0.50 0.54 0.24 0.33 0.27 0.33 0.83 0.77 0.80 0.64 1.06 0.32 0.28 0.39 0.30 0.28 0.35 0.32 0.39 0.29 0.31 0.30 0.24 0.26 0.98 0.28 0.21 0.26 0.26 0.22 0.29 0.22 0.28 0.30 0.28 0.29 0.25 0.27 0.59 0.98 0.31 0.26 0.36 0.30 0.32 0.35 0.33 0.38 0.32 0.33 0.30 0.25 0.28 0.72 0.66 1.11 0.33 0.31 0.41 0.33 0.34 0.38 0.36 0.44 0.39 0.36 0.37 0.31 0.30 0.70 0.63 0.79 1.19 0.32 0.32 0.36 0.36 0.20 0.27 0.18 0.31 0.41 0.36 0.39 0.29 0.34 0.36 0.28 0.32 0.41 1.12 0.30 0.32 0.40 0.33 0.25 0.30 0.23 0.30 0.45 0.40 0.40 0.35 0.36 0.35 0.27 0.34 0.49 0.61 1.24 0.24 0.24 0.24 0.23 0.17 0.23 0.16 0.25 0.34 0.32 0.29 0.22 0.27 0.25 0.21 0.21 0.29 0.45 0.45 1.04 0.42 0.36 0.44 0.43 0.26 0.37 0.24 0.41 0.56 0.46 0.48 0.34 0.42 0.40 0.29 0.36 0.41 0.73 0.70 0.55 1.08 Means 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sample Size = 538 Latent Variables MORALE PERSON LEADER BEHAVE DEVELOP Relationships MORALE1 = MORALE MORALE2 = MORALE MORALE3 = MORALE MORALE4 = MORALE PERSON1 = PERSON PERSON2 = PERSON PERSON3 = PERSON PERSON4 = PERSON LEADER1 = LEADER LEADER2 = LEADER LEADER3 = LEADER LEADER4 = LEADER LEADER5 = LEADER BEHAVE1 = BEHAVE BEHAVE2 = BEHAVE BEHAVE3 = BEHAVE BEHAVE4 = BEHAVE DEVELOP1 = DEVELOP
DEVELOP2 = DEVELOP DEVELOP3 = DEVELOP DEVELOP4 = DEVELOP MORALE = PERSON PERSON = LEADER BEHAVE DEVELOP Set the Variance of LEADER to 1.00 Set the Variance of BEHAVE to 1.00 Set the Variance of DEVELOP to 1.00 Set the Error Variance of MORALE to 0.64 Set the Error Variance of PERSON to 0.50 Path Diagram Print Residuals Method of Estimation: Maximum Likelihood End of Problem
Sample Size = 538
TI FRONTLINE EMPLOYEE FULL MODEL
Covariance Matrix to be Analyzed
MORALE1 MORALE2 MORALE3 MORALE4 PERSON1 PERSON2 -------- -------- -------- -------- -------- -------- MORALE1 1.31 MORALE2 0.82 0.96 MORALE3 0.90 0.69 1.26 MORALE4 1.03 0.78 0.86 1.20 PERSON1 0.31 0.27 0.30 0.27 0.76 PERSON2 0.34 0.29 0.33 0.32 0.43 0.92 PERSON3 0.36 0.31 0.32 0.34 0.41 0.47 PERSON4 0.43 0.35 0.44 0.40 0.43 0.51 LEADER1 0.68 0.49 0.56 0.64 0.28 0.34 LEADER2 0.84 0.61 0.66 0.75 0.30 0.33 LEADER3 0.59 0.43 0.52 0.54 0.27 0.35 LEADER4 0.51 0.32 0.46 0.44 0.22 0.18 LEADER5 0.61 0.42 0.50 0.54 0.24 0.33 BEHAVE1 0.32 0.28 0.39 0.30 0.28 0.35 BEHAVE2 0.28 0.21 0.26 0.26 0.22 0.29 BEHAVE3 0.31 0.26 0.36 0.30 0.32 0.35 BEHAVE4 0.33 0.31 0.41 0.33 0.34 0.38 DEVELOP1 0.32 0.32 0.36 0.36 0.20 0.27 DEVELOP2 0.30 0.32 0.40 0.33 0.25 0.30 DEVELOP3 0.24 0.24 0.24 0.23 0.17 0.23 DEVELOP4 0.42 0.36 0.44 0.43 0.26 0.37
Covariance Matrix to be Analyzed
PERSON3 PERSON4 LEADER1 LEADER2 LEADER3 LEADER4 -------- -------- -------- -------- -------- -------- PERSON3 0.75 PERSON4 0.48 1.00 LEADER1 0.29 0.36 1.22 LEADER2 0.32 0.39 0.88 1.20 LEADER3 0.28 0.35 0.90 0.80 1.22 LEADER4 0.18 0.22 0.74 0.68 0.73 1.00 LEADER5 0.27 0.33 0.83 0.77 0.80 0.64 BEHAVE1 0.32 0.39 0.29 0.31 0.30 0.24 BEHAVE2 0.22 0.28 0.30 0.28 0.29 0.25 BEHAVE3 0.33 0.38 0.32 0.33 0.30 0.25 BEHAVE4 0.36 0.44 0.39 0.36 0.37 0.31 DEVELOP1 0.18 0.31 0.41 0.36 0.39 0.29 DEVELOP2 0.23 0.30 0.45 0.40 0.40 0.35 DEVELOP3 0.16 0.25 0.34 0.32 0.29 0.22 DEVELOP4 0.24 0.41 0.56 0.46 0.48 0.34
Covariance Matrix to be Analyzed
LEADER5 BEHAVE1 BEHAVE2 BEHAVE3 BEHAVE4 DEVELOP1 -------- -------- -------- -------- -------- -------- LEADER5 1.06 BEHAVE1 0.26 0.98 BEHAVE2 0.27 0.59 0.98 BEHAVE3 0.28 0.72 0.66 1.11
BEHAVE4 0.30 0.70 0.63 0.79 1.19 DEVELOP1 0.34 0.36 0.28 0.32 0.41 1.12 DEVELOP2 0.36 0.35 0.27 0.34 0.49 0.61 DEVELOP3 0.27 0.25 0.21 0.21 0.29 0.45 DEVELOP4 0.42 0.40 0.29 0.36 0.41 0.73
Covariance Matrix to be Analyzed
DEVELOP2 DEVELOP3 DEVELOP4 -------- -------- -------- DEVELOP2 1.24 DEVELOP3 0.45 1.04 DEVELOP4 0.70 0.55 1.08
TI FRONTLINE EMPLOYEE FULL MODEL
Number of Iterations = 11
LISREL Estimates (Maximum Likelihood) MORALE1 = 1.04*MORALE, Errorvar.= 0.23 , R² = 0.82 (0.043) (0.023) 23.97 9.98 MORALE2 = 0.80*MORALE, Errorvar.= 0.33 , R² = 0.65 (0.039) (0.024) 20.66 13.86 MORALE3 = 0.88*MORALE, Errorvar.= 0.50 , R² = 0.61 (0.045) (0.035) 19.67 14.35 MORALE4 = 0.99*MORALE, Errorvar.= 0.23 , R² = 0.81 (0.042) (0.022) 23.79 10.36 PERSON1 = 0.59*PERSON, Errorvar.= 0.41 , R² = 0.46 (0.039) (0.029) 15.26 13.88 PERSON2 = 0.69*PERSON, Errorvar.= 0.44 , R² = 0.52 (0.043) (0.033) 16.24 13.21 PERSON3 = 0.64*PERSON, Errorvar.= 0.33 , R² = 0.55 (0.038) (0.026) 16.77 12.75 PERSON4 = 0.73*PERSON, Errorvar.= 0.46 , R² = 0.54 (0.044) (0.036) 16.51 12.98 LEADER1 = 0.98*LEADER, Errorvar.= 0.26 , R² = 0.78 (0.038) (0.023) 25.62 11.34 LEADER2 = 0.90*LEADER, Errorvar.= 0.40 , R² = 0.67 (0.040) (0.029) 22.59 13.59 LEADER3 = 0.92*LEADER, Errorvar.= 0.37 , R² = 0.70 (0.039) (0.028) 23.42 13.13 LEADER4 = 0.76*LEADER, Errorvar.= 0.43 , R² = 0.57 (0.038) (0.029) 20.19 14.53 LEADER5 = 0.85*LEADER, Errorvar.= 0.33 , R² = 0.69 (0.037) (0.025)
23.09 13.32 BEHAVE1 = 0.81*BEHAVE, Errorvar.= 0.33 , R² = 0.67 (0.037) (0.027) 22.09 12.17 BEHAVE2 = 0.72*BEHAVE, Errorvar.= 0.46 , R² = 0.53 (0.038) (0.033) 18.83 14.02 BEHAVE3 = 0.89*BEHAVE, Errorvar.= 0.32 , R² = 0.71 (0.038) (0.029) 23.25 11.11 BEHAVE4 = 0.88*BEHAVE, Errorvar.= 0.41 , R² = 0.66 (0.040) (0.033) 21.84 12.37 DEVELOP1 = 0.78*DEVELOP, Errorvar.= 0.50 , R² = 0.55 (0.042) (0.038) 18.89 13.15 DEVELOP2 = 0.77*DEVELOP, Errorvar.= 0.65 , R² = 0.48 (0.045) (0.046) 17.20 14.06 DEVELOP3 = 0.59*DEVELOP, Errorvar.= 0.69 , R² = 0.33 (0.043) (0.046) 13.77 15.16 DEVELOP4 = 0.92*DEVELOP, Errorvar.= 0.23 , R² = 0.79 (0.038) (0.031) 24.26 7.24 MORALE = 0.59*PERSON, Errorvar.= 0.64, R² = 0.35 (0.055) 10.83 PERSON = 0.31*LEADER + 0.40*BEHAVE + 0.16*DEVELOP, Errorvar.= 0.50, R² = 0.50 (0.052) (0.054) (0.056) 5.99 7.44 2.82
Correlation Matrix of Independent Variables
LEADER BEHAVE DEVELOP -------- -------- -------- LEADER 1.00 BEHAVE 0.41 1.00 (0.04) 9.92 DEVELOP 0.56 0.50 1.00 (0.04) (0.04) 15.83 12.94
Covariance Matrix of Latent Variables
MORALE PERSON LEADER BEHAVE DEVELOP -------- -------- -------- -------- -------- MORALE 0.99 PERSON 0.59 1.00 LEADER 0.33 0.56 1.00 BEHAVE 0.36 0.60 0.41 1.00 DEVELOP 0.31 0.53 0.56 0.50 1.00
Goodness of Fit Statistics
Degrees of Freedom = 182 Minimum Fit Function Chi-Square = 476.59 (P = 0.0) Normal Theory Weighted Least Squares Chi-Square = 443.60 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 261.60 90 Percent Confidence Interval for NCP = (203.63 ; 327.26) Minimum Fit Function Value = 0.89 Population Discrepancy Function Value (F0) = 0.49 90 Percent Confidence Interval for F0 = (0.38 ; 0.61) Root Mean Square Error of Approximation (RMSEA) = 0.052 90 Percent Confidence Interval for RMSEA = (0.046 ; 0.058) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.31 Expected Cross-Validation Index (ECVI) = 1.01 90 Percent Confidence Interval for ECVI = (0.90 ; 1.13) ECVI for Saturated Model = 0.86 ECVI for Independence Model = 13.13 Chi-Square for Independence Model with 210 Degrees of Freedom = 7010.32 Independence AIC = 7052.32 Model AIC = 541.60 Saturated AIC = 462.00 Independence CAIC = 7163.36 Model CAIC = 800.70 Saturated CAIC = 1683.50 Root Mean Square Residual (RMR) = 0.099 Standardized RMR = 0.085 Goodness of Fit Index (GFI) = 0.93 Adjusted Goodness of Fit Index (AGFI) = 0.91 Parsimony Goodness of Fit Index (PGFI) = 0.73 Normed Fit Index (NFI) = 0.93 Non-Normed Fit Index (NNFI) = 0.95 Parsimony Normed Fit Index (PNFI) = 0.81 Comparative Fit Index (CFI) = 0.96 Incremental Fit Index (IFI) = 0.96 Relative Fit Index (RFI) = 0.92 Critical N (CN) = 259.37
TI FRONTLINE EMPLOYEE FULL MODEL
Fitted Covariance Matrix
MORALE1 MORALE2 MORALE3 MORALE4 PERSON1 PERSON2 -------- -------- -------- -------- -------- -------- MORALE1 1.31 MORALE2 0.82 0.96 MORALE3 0.91 0.69 1.26 MORALE4 1.02 0.78 0.86 1.20 PERSON1 0.37 0.28 0.31 0.35 0.76 PERSON2 0.43 0.33 0.36 0.41 0.41 0.92 PERSON3 0.40 0.30 0.34 0.38 0.38 0.45 PERSON4 0.45 0.35 0.38 0.43 0.43 0.51 LEADER1 0.34 0.26 0.29 0.32 0.33 0.38 LEADER2 0.31 0.24 0.26 0.30 0.30 0.35 LEADER3 0.32 0.25 0.27 0.31 0.31 0.36 LEADER4 0.26 0.20 0.22 0.25 0.25 0.30 LEADER5 0.30 0.23 0.25 0.28 0.29 0.33 BEHAVE1 0.30 0.23 0.25 0.29 0.29 0.34 BEHAVE2 0.27 0.21 0.23 0.26 0.26 0.30 BEHAVE3 0.33 0.25 0.28 0.32 0.32 0.37 BEHAVE4 0.33 0.25 0.28 0.31 0.32 0.37 DEVELOP1 0.26 0.20 0.22 0.25 0.25 0.29 DEVELOP2 0.25 0.19 0.21 0.24 0.24 0.28 DEVELOP3 0.19 0.15 0.16 0.18 0.19 0.22 DEVELOP4 0.30 0.23 0.26 0.29 0.29 0.34
Fitted Covariance Matrix
PERSON3 PERSON4 LEADER1 LEADER2 LEADER3 LEADER4 -------- -------- -------- -------- -------- -------- PERSON3 0.75 PERSON4 0.47 1.00 LEADER1 0.35 0.40 1.22 LEADER2 0.32 0.37 0.88 1.20 LEADER3 0.34 0.38 0.90 0.83 1.22 LEADER4 0.27 0.31 0.74 0.68 0.70 1.00 LEADER5 0.31 0.35 0.83 0.76 0.79 0.65 BEHAVE1 0.31 0.36 0.32 0.29 0.30 0.25 BEHAVE2 0.28 0.32 0.29 0.26 0.27 0.22 BEHAVE3 0.35 0.39 0.35 0.32 0.33 0.27 BEHAVE4 0.34 0.39 0.35 0.32 0.33 0.27 DEVELOP1 0.27 0.31 0.43 0.39 0.40 0.33 DEVELOP2 0.26 0.30 0.42 0.38 0.40 0.32 DEVELOP3 0.20 0.23 0.32 0.29 0.30 0.25 DEVELOP4 0.32 0.36 0.50 0.46 0.48 0.39
Fitted Covariance Matrix
LEADER5 BEHAVE1 BEHAVE2 BEHAVE3 BEHAVE4 DEVELOP1 -------- -------- -------- -------- -------- -------- LEADER5 1.06 BEHAVE1 0.28 0.98 BEHAVE2 0.25 0.58 0.98 BEHAVE3 0.31 0.72 0.64 1.11 BEHAVE4 0.31 0.71 0.64 0.79 1.19 DEVELOP1 0.37 0.32 0.28 0.35 0.35 1.12 DEVELOP2 0.37 0.31 0.28 0.34 0.34 0.60 DEVELOP3 0.28 0.24 0.21 0.26 0.26 0.46 DEVELOP4 0.44 0.37 0.33 0.41 0.41 0.72
Fitted Covariance Matrix
DEVELOP2 DEVELOP3 DEVELOP4 -------- -------- -------- DEVELOP2 1.24 DEVELOP3 0.45 1.04 DEVELOP4 0.71 0.54 1.08
Fitted Residuals
MORALE1 MORALE2 MORALE3 MORALE4 PERSON1 PERSON2 -------- -------- -------- -------- -------- -------- MORALE1 0.00 MORALE2 0.00 0.00 MORALE3 -0.01 0.00 0.00 MORALE4 0.01 0.00 0.00 0.00 PERSON1 -0.06 -0.01 -0.01 -0.08 0.00 PERSON2 -0.09 -0.04 -0.03 -0.09 0.02 0.00 PERSON3 -0.04 0.01 -0.02 -0.04 0.03 0.02 PERSON4 -0.02 0.00 0.06 -0.03 0.00 0.00 LEADER1 0.34 0.23 0.27 0.32 -0.05 -0.04 LEADER2 0.53 0.37 0.40 0.45 0.00 -0.02 LEADER3 0.27 0.18 0.25 0.23 -0.04 -0.01 LEADER4 0.25 0.12 0.24 0.19 -0.03 -0.12 LEADER5 0.31 0.19 0.25 0.26 -0.05 0.00 BEHAVE1 0.02 0.05 0.14 0.01 -0.01 0.01 BEHAVE2 0.01 0.00 0.03 0.00 -0.04 -0.01 BEHAVE3 -0.02 0.01 0.08 -0.02 0.00 -0.02 BEHAVE4 0.00 0.06 0.13 0.02 0.02 0.01 DEVELOP1 0.06 0.12 0.14 0.11 -0.05 -0.02 DEVELOP2 0.05 0.13 0.19 0.09 0.01 0.02 DEVELOP3 0.05 0.09 0.08 0.05 -0.02 0.01 DEVELOP4 0.12 0.13 0.18 0.14 -0.03 0.03
Fitted Residuals
PERSON3 PERSON4 LEADER1 LEADER2 LEADER3 LEADER4
-------- -------- -------- -------- -------- -------- PERSON3 0.00 PERSON4 0.01 0.00 LEADER1 -0.06 -0.04 0.00 LEADER2 0.00 0.02 0.00 0.00 LEADER3 -0.06 -0.03 0.00 -0.03 0.00 LEADER4 -0.09 -0.09 0.00 0.00 0.03 0.00 LEADER5 -0.04 -0.02 0.00 0.01 0.01 -0.01 BEHAVE1 0.01 0.03 -0.03 0.02 0.00 -0.01 BEHAVE2 -0.06 -0.04 0.01 0.02 0.02 0.03 BEHAVE3 -0.02 -0.01 -0.03 0.01 -0.03 -0.02 BEHAVE4 0.02 0.05 0.04 0.04 0.04 0.04 DEVELOP1 -0.09 0.00 -0.02 -0.03 -0.01 -0.04 DEVELOP2 -0.03 0.00 0.03 0.02 0.00 0.03 DEVELOP3 -0.04 0.02 0.02 0.03 -0.01 -0.03 DEVELOP4 -0.08 0.05 0.06 0.00 0.00 -0.05
Fitted Residuals
LEADER5 BEHAVE1 BEHAVE2 BEHAVE3 BEHAVE4 DEVELOP1 -------- -------- -------- -------- -------- -------- LEADER5 0.00 BEHAVE1 -0.02 0.00 BEHAVE2 0.02 0.01 0.00 BEHAVE3 -0.03 0.00 0.02 0.00 BEHAVE4 -0.01 -0.01 -0.01 0.00 0.00 DEVELOP1 -0.03 0.04 0.00 -0.03 0.06 0.00 DEVELOP2 -0.01 0.04 -0.01 0.00 0.15 0.01 DEVELOP3 -0.01 0.01 0.00 -0.05 0.03 -0.01 DEVELOP4 -0.02 0.03 -0.04 -0.05 0.00 0.01
Fitted Residuals
DEVELOP2 DEVELOP3 DEVELOP4 -------- -------- -------- DEVELOP2 0.00 DEVELOP3 0.00 0.00 DEVELOP4 -0.01 0.01 0.00
Summary Statistics for Fitted Residuals
Smallest Fitted Residual = -0.12 Median Fitted Residual = 0.00 Largest Fitted Residual = 0.53
Stemleaf Plot
- 1|2 - 0|99999886666555555 - 0|444444444444333333333333333222222222222221111111111111111111000000000000+41 0|11111111111111111111222222222222222233333333333444444 0|555555666668899 1|1222333444 1|588999 2|334 2|555677 3|124 3|7 4|0 4|5 5|3
Standardized Residuals
MORALE1 MORALE2 MORALE3 MORALE4 PERSON1 PERSON2 -------- -------- -------- -------- -------- -------- MORALE1 - - MORALE2 -0.30 - - MORALE3 -0.72 -0.16 - - MORALE4 1.75 -0.28 -0.25 - - PERSON1 -2.29 -0.40 -0.29 -3.31 - -
PERSON2 -3.47 -1.44 -0.99 -3.55 1.39 - - PERSON3 -1.74 0.28 -0.57 -1.83 2.34 2.04 PERSON4 -0.88 0.17 1.90 -1.23 -0.34 0.18 LEADER1 8.73 6.42 6.53 8.45 -1.86 -1.59 LEADER2 12.97 10.05 9.23 11.59 0.04 -0.67 LEADER3 6.63 5.00 5.80 6.02 -1.45 -0.37 LEADER4 6.37 3.41 5.89 5.10 -1.25 -4.14 LEADER5 8.25 5.59 6.21 7.06 -1.80 -0.10 BEHAVE1 0.52 1.52 3.57 0.38 -0.42 0.49 BEHAVE2 0.27 0.12 0.83 0.10 -1.52 -0.45 BEHAVE3 -0.60 0.19 2.01 -0.45 0.04 -0.91 BEHAVE4 0.01 1.61 3.14 0.43 0.90 0.38 DEVELOP1 1.51 3.31 3.31 2.89 -1.70 -0.64 DEVELOP2 1.06 3.18 4.04 2.09 0.25 0.52 DEVELOP3 1.08 2.42 1.75 1.11 -0.51 0.40 DEVELOP4 3.18 3.79 4.67 4.01 -1.35 1.28
Standardized Residuals
PERSON3 PERSON4 LEADER1 LEADER2 LEADER3 LEADER4 -------- -------- -------- -------- -------- -------- PERSON3 - - PERSON4 0.63 - - LEADER1 -2.90 -1.65 - - LEADER2 -0.20 0.70 0.48 - - LEADER3 -2.28 -1.11 -0.45 -2.29 - - LEADER4 -3.87 -3.23 -0.07 0.10 2.18 - - LEADER5 -1.73 -0.83 -0.59 0.45 0.98 -0.53 BEHAVE1 0.24 1.28 -1.17 0.57 -0.10 -0.28 BEHAVE2 -2.57 -1.44 0.45 0.54 0.60 0.89 BEHAVE3 -0.78 -0.57 -1.28 0.23 -1.17 -0.79 BEHAVE4 0.67 1.75 1.38 1.22 1.23 1.21 DEVELOP1 -3.42 0.13 -0.64 -1.04 -0.49 -1.37 DEVELOP2 -1.16 0.01 0.96 0.46 0.10 0.74 DEVELOP3 -1.43 0.60 0.56 0.73 -0.39 -0.86 DEVELOP4 -3.78 2.08 2.96 -0.07 0.16 -2.04
Standardized Residuals
LEADER5 BEHAVE1 BEHAVE2 BEHAVE3 BEHAVE4 DEVELOP1 -------- -------- -------- -------- -------- -------- LEADER5 - - BEHAVE1 -0.74 - - BEHAVE2 0.66 0.46 - - BEHAVE3 -1.04 -0.01 1.49 - - BEHAVE4 -0.19 -1.56 -0.67 0.33 - - DEVELOP1 -1.18 1.47 -0.15 -1.06 1.96 - - DEVELOP2 -0.19 1.19 -0.25 -0.11 4.20 0.37 DEVELOP3 -0.32 0.34 -0.10 -1.57 0.80 -0.58 DEVELOP4 -0.93 1.20 -1.75 -2.57 0.02 0.94
Standardized Residuals
DEVELOP2 DEVELOP3 DEVELOP4 -------- -------- -------- DEVELOP2 - - DEVELOP3 -0.11 - - DEVELOP4 -1.34 0.64 - -
Summary Statistics for Standardized Residuals
Smallest Standardized Residual = -4.14 Median Standardized Residual = 0.00 Largest Standardized Residual = 12.97
Stemleaf Plot
- 4|1 - 3|9855432 - 2|9663330 - 1|9887777766654444443332222211000
- 0|999988877776666666555554444433333332222211111111000000000000000000000000000 0|1111122222223333444444555555556666667777788999 1|00111222223344555567789 2|000112349 3|012233468 4|0027 5|01689 6|024456 7|1 8|257 9|2 10|1 11|6 12| 13|0 Largest Negative Standardized Residuals Residual for PERSON1 and MORALE4 -3.31 Residual for PERSON2 and MORALE1 -3.47 Residual for PERSON2 and MORALE4 -3.55 Residual for LEADER1 and PERSON3 -2.90 Residual for LEADER4 and PERSON2 -4.14 Residual for LEADER4 and PERSON3 -3.87 Residual for LEADER4 and PERSON4 -3.23 Residual for DEVELOP1 and PERSON3 -3.42 Residual for DEVELOP4 and PERSON3 -3.78 Largest Positive Standardized Residuals Residual for LEADER1 and MORALE1 8.73 Residual for LEADER1 and MORALE2 6.42 Residual for LEADER1 and MORALE3 6.53 Residual for LEADER1 and MORALE4 8.45 Residual for LEADER2 and MORALE1 12.97 Residual for LEADER2 and MORALE2 10.05 Residual for LEADER2 and MORALE3 9.23 Residual for LEADER2 and MORALE4 11.59 Residual for LEADER3 and MORALE1 6.63 Residual for LEADER3 and MORALE2 5.00 Residual for LEADER3 and MORALE3 5.80 Residual for LEADER3 and MORALE4 6.02 Residual for LEADER4 and MORALE1 6.37 Residual for LEADER4 and MORALE2 3.41 Residual for LEADER4 and MORALE3 5.89 Residual for LEADER4 and MORALE4 5.10 Residual for LEADER5 and MORALE1 8.25 Residual for LEADER5 and MORALE2 5.59 Residual for LEADER5 and MORALE3 6.21 Residual for LEADER5 and MORALE4 7.06 Residual for BEHAVE1 and MORALE3 3.57 Residual for BEHAVE4 and MORALE3 3.14 Residual for DEVELOP1 and MORALE2 3.31 Residual for DEVELOP1 and MORALE3 3.31 Residual for DEVELOP1 and MORALE4 2.89 Residual for DEVELOP2 and MORALE2 3.18 Residual for DEVELOP2 and MORALE3 4.04 Residual for DEVELOP2 and BEHAVE4 4.20 Residual for DEVELOP4 and MORALE1 3.18 Residual for DEVELOP4 and MORALE2 3.79 Residual for DEVELOP4 and MORALE3 4.67 Residual for DEVELOP4 and MORALE4 4.01 Residual for DEVELOP4 and LEADER1 2.96
The Modification Indices Suggest to Add the Path to from Decrease in Chi-Square New Estimate PERSON2 MORALE 13.1 -0.17 PERSON MORALE 76.8 -0.63 MORALE LEADER 141.5 0.62 MORALE DEVELOP 28.9 0.28
The Modification Indices Suggest to Add an Error Covariance Between and Decrease in Chi-Square New Estimate PERSON MORALE 76.8 -0.40 LEADER2 MORALE1 13.5 0.06
LEADER4 PERSON2 9.8 -0.07 DEVELOP2 BEHAVE4 18.2 0.11 DEVELOP4 LEADER1 11.9 0.06
The Problem used 62832 Bytes (= 0.1% of Available Workspace)
Time used: 0.141 Seconds