구조방정식 cSEM사용법 by 박중희 인지과학
2024. 2. 19. 17:36ㆍ카테고리 없음
assess(b2)
________________________________________________________________________________
Construct AVE R2 R2_adj
SAT 0.6851 0.7624 0.7585
LOY 0.5552 0.5868 0.5834
EXPE NA 0.2222 0.2190
QUAL NA 0.6963 0.6951
VAL NA 0.5474 0.5438
-------------- Common (internal consistency) reliability estimates -------------
Construct Cronbachs_alpha Joereskogs_rho Dijkstra-Henselers_rho_A
SAT 0.8940 0.8960 0.9051
LOY 0.8194 0.8237 0.8761
----------- Alternative (internal consistency) reliability estimates -----------
Construct RhoC RhoC_mm RhoC_weighted
SAT 0.8960 0.8938 0.9051
LOY 0.8237 0.8011 0.8761
Construct RhoC_weighted_mm RhoT RhoT_weighted
SAT 0.9051 0.8940 0.8869
LOY 0.8761 0.8194 0.7850
--------------------------- Distance and fit measures --------------------------
Geodesic distance = 0.6493432
Squared Euclidean distance = 2.23402
ML distance = 2.921932
Chi_square = 727.5611
Chi_square_df = 3.954137
CFI = 0.8598825
CN = 75.14588
GFI = 0.7280612
IFI = 0.8615598
NFI = 0.8229918
NNFI = 0.8240917
RMSEA = 0.108922
RMS_theta = 0.05069299
SRMR = 0.09396871
Degrees of freedom = 184
--------------------------- Model selection criteria ---------------------------
Construct AIC AICc AICu
EXPE -59.8152 192.2824 -57.8072
QUAL -294.9343 -42.8367 -292.9263
VAL -193.2127 58.9506 -190.1945
SAT -350.2874 -97.9418 -345.2368
LOY -215.9322 36.2311 -212.9141
Construct BIC FPE GM
EXPE -52.7723 0.7872 259.8087
QUAL -287.8914 0.3074 271.8568
VAL -182.6483 0.4617 312.7010
SAT -332.6801 0.2463 278.2973
LOY -205.3678 0.4216 291.0665
Construct HQ HQc Mallows_Cp
EXPE -56.9806 -56.8695 2.7658
QUAL -292.0997 -291.9886 14.8139
VAL -188.9608 -188.7516 52.1366
SAT -343.2010 -342.7088 10.6900
LOY -211.6804 -211.4711 30.5022
----------------------- Variance inflation factors (VIFs) ----------------------
Dependent construct: 'VAL'
Independent construct VIF value
EXPE 3.2928
QUAL 3.2928
Dependent construct: 'SAT'
Independent construct VIF value
EXPE 3.2985
QUAL 4.4151
IMAG 1.7280
VAL 2.6726
Dependent construct: 'LOY'
Independent construct VIF value
IMAG 1.9345
SAT 1.9345
-------------- Variance inflation factors (VIFs) for modeB weights -------------
Construct: 'IMAG'
Weight VIF value
imag1 1.7215
imag2 3.0515
imag3 2.5356
Construct: 'EXPE'
Weight VIF value
expe1 1.4949
expe2 1.6623
expe3 1.5212
Construct: 'QUAL'
Weight VIF value
qual1 1.8401
qual2 2.5005
qual3 1.7796
qual4 2.1557
qual5 2.0206
Construct: 'VAL'
Weight VIF value
val1 1.6912
val2 2.2049
val3 1.6714
-------------------------- Effect sizes (Cohen's f^2) --------------------------
Dependent construct: 'EXPE'
Independent construct f^2
IMAG 0.2856
Dependent construct: 'QUAL'
Independent construct f^2
EXPE 2.2928
Dependent construct: 'VAL'
Independent construct f^2
EXPE 0.0014
QUAL 0.3301
Dependent construct: 'SAT'
Independent construct f^2
IMAG 0.1462
EXPE 0.0004
QUAL 0.0468
VAL 0.4373
Dependent construct: 'LOY'
Independent construct f^2
IMAG 0.0414
SAT 0.4938
----------------------- Discriminant validity assessment -----------------------
Heterotrait-monotrait ratio of correlations matrix (HTMT matrix)
SAT LOY
SAT 1.0000000 0
LOY 0.7432489 1
Advanced heterotrait-monotrait ratio of correlations matrix (HTMT2 matrix)
SAT LOY
SAT 1.0000000 0
LOY 0.7140046 1
Fornell-Larcker matrix
SAT LOY
SAT 0.6851491 0.5696460
LOY 0.5696460 0.5551718
------------------------------------ Effects -----------------------------------
Estimated total effects:
========================
Total effect Estimate Std. error t-stat. p-value
EXPE ~ IMAG 0.4714 0.0618 7.6328 0.0000
QUAL ~ IMAG 0.3933 0.0577 6.8182 0.0000
QUAL ~ EXPE 0.8344 0.0230 36.2697 0.0000
VAL ~ IMAG 0.2974 0.0571 5.2092 0.0000
VAL ~ EXPE 0.6309 0.0494 12.7709 0.0000
VAL ~ QUAL 0.7013 0.0809 8.6684 0.0000
SAT ~ IMAG 0.4807 0.0654 7.3490 0.0000
SAT ~ EXPE 0.5001 0.0579 8.6328 0.0000
SAT ~ QUAL 0.5911 0.0935 6.3203 0.0000
SAT ~ VAL 0.5270 0.0848 6.2150 0.0000
LOY ~ IMAG 0.4840 0.0666 7.2622 0.0000
LOY ~ EXPE 0.3142 0.0563 5.5764 0.0000
LOY ~ QUAL 0.3714 0.0865 4.2955 0.0000
LOY ~ VAL 0.3311 0.0722 4.5831 0.0000
LOY ~ SAT 0.6283 0.0838 7.5020 0.0000
Estimated indirect effects:
===========================
Indirect effect Estimate Std. error t-stat. p-value
QUAL ~ IMAG 0.3933 0.0577 6.8182 0.0000
VAL ~ IMAG 0.2974 0.0571 5.2092 0.0000
VAL ~ EXPE 0.5852 0.0691 8.4704 0.0000
SAT ~ IMAG 0.2357 0.0470 5.0203 0.0000
SAT ~ EXPE 0.5173 0.0696 7.4290 0.0000
SAT ~ QUAL 0.3696 0.0641 5.7670 0.0000
LOY ~ IMAG 0.3020 0.0568 5.3175 0.0000
LOY ~ EXPE 0.3142 0.0563 5.5764 0.0000
LOY ~ QUAL 0.3714 0.0865 4.2955 0.0000
LOY ~ VAL 0.3311 0.0722 4.5831 0.0000
________________________________________________________________________________# Setting `.resample_method`
b1 <- csem(.data = satisfaction, .model = model, .resample_method = "bootstrap")
b1
cSEM::summarize(b1)구조방정식 cSEM은 기본적으로 구조방정식 분석에서 lavaan 신택스를 그대로 사용하기도 하는 편리한 라이브러리이다.
이번에는 몇 가지 기본적인
library(tidyverse)
library(cSEM)
data("satisfaction")데이터의 구조는 다음과 같다.
satisfaction %>% str()
'data.frame': 250 obs. of 27 variables:
$ imag1: int 8 9 9 8 10 7 5 9 9 10 ...
$ imag2: int 8 9 8 9 10 8 5 9 8 10 ...
$ imag3: int 9 10 8 8 8 8 5 9 9 10 ...
$ imag4: int 5 9 8 9 10 8 5 9 8 10 ...
$ imag5: int 6 7 8 7 8 8 2 9 5 10 ...
$ expe1: int 9 9 8 10 7 8 7 10 7 10 ...
$ expe2: int 9 8 8 6 9 8 6 10 8 10 ...
$ expe3: int 5 10 8 3 8 8 5 10 7 10 ...
$ expe4: int 8 8 9 10 9 9 6 10 7 10 ...
$ expe5: int 9 9 9 10 8 10 6 10 5 10 ...
$ qual1: int 6 7 7 7 7 6 4 8 6 10 ...
$ qual2: int 8 9 9 3 9 6 4 9 8 10 ...
$ qual3: int 2 7 6 2 8 7 2 9 7 8 ...
$ qual4: int 7 8 8 10 9 7 3 9 7 10 ...
$ qual5: int 7 7 6 8 8 7 3 9 5 10 ...
$ val1 : int 7 8 8 6 9 8 5 8 9 10 ...
$ val2 : int 10 8 9 7 10 7 6 8 8 10 ...
$ val3 : int 5 7 7 6 10 6 4 8 7 10 ...
$ val4 : int 6 8 8 4 8 5 5 8 7 10 ...
$ sat1 : int 6 8 9 7 8 7 6 8 8 10 ...
$ sat2 : int 7 7 8 7 8 6 5 8 7 10 ...
$ sat3 : int 6 8 8 7 8 6 6 8 6 9 ...
$ sat4 : int 7 7 8 6 7 7 4 8 6 10 ...
$ loy1 : int 9 8 9 6 8 7 5 9 7 10 ...
$ loy2 : int 9 9 8 6 9 8 6 8 7 8 ...
$ loy3 : int 6 8 9 5 9 8 6 3 5 8 ...
$ loy4 : int 6 8 9 5 9 7 5 8 8 10 ...
모형구성
model <- "
# Structural model
EXPE ~ IMAG
QUAL ~ EXPE
VAL ~ EXPE + QUAL
SAT ~ IMAG + EXPE + QUAL + VAL
LOY ~ IMAG + SAT
# Composite model
IMAG <~ imag1 + imag2 + imag3
EXPE <~ expe1 + expe2 + expe3
QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5
VAL <~ val1 + val2 + val3
# Reflective measurement model
SAT =~ sat1 + sat2 + sat3 + sat4
LOY =~ loy1 + loy2 + loy3 + loy4
"
# Estimate using defaults
res <- csem(.data = satisfaction, .model = model)cSEM의 데이터에서 summary()함수는 간단한 요약만을 보여준다. 따라서, 각각의 저장된 데이터를 봐야 한다.
summary(res)
Length Class Mode
Estimates 14 -none- list
Information 7 -none- list
## Get a summary
cSEM::summarize(res)
cSEM::summarize(res)
________________________________________________________________________________
----------------------------------- Overview -----------------------------------
General information:
------------------------
Estimation status = Ok
Number of observations = 250
Weight estimator = PLS-PM
Inner weighting scheme = "path"
Type of indicator correlation = Pearson
Path model estimator = OLS
Second-order approach = NA
Type of path model = Linear
Disattenuated = Yes (PLSc)
Construct details:
------------------
Name Modeled as Order Mode
IMAG Composite First order "modeB"
EXPE Composite First order "modeB"
QUAL Composite First order "modeB"
VAL Composite First order "modeB"
SAT Common factor First order "modeA"
LOY Common factor First order "modeA"
----------------------------------- Estimates ----------------------------------
Estimated path coefficients:
============================
Path Estimate Std. error t-stat. p-value
EXPE ~ IMAG 0.4714 NA NA NA
QUAL ~ EXPE 0.8344 NA NA NA
VAL ~ EXPE 0.0457 NA NA NA
VAL ~ QUAL 0.7013 NA NA NA
SAT ~ IMAG 0.2450 NA NA NA
SAT ~ EXPE -0.0172 NA NA NA
SAT ~ QUAL 0.2215 NA NA NA
SAT ~ VAL 0.5270 NA NA NA
LOY ~ IMAG 0.1819 NA NA NA
LOY ~ SAT 0.6283 NA NA NA
Estimated loadings:
===================
Loading Estimate Std. error t-stat. p-value
IMAG =~ imag1 0.6306 NA NA NA
IMAG =~ imag2 0.9246 NA NA NA
IMAG =~ imag3 0.9577 NA NA NA
EXPE =~ expe1 0.7525 NA NA NA
EXPE =~ expe2 0.9348 NA NA NA
EXPE =~ expe3 0.7295 NA NA NA
QUAL =~ qual1 0.7861 NA NA NA
QUAL =~ qual2 0.9244 NA NA NA
QUAL =~ qual3 0.7560 NA NA NA
QUAL =~ qual4 0.7632 NA NA NA
QUAL =~ qual5 0.7834 NA NA NA
VAL =~ val1 0.9518 NA NA NA
VAL =~ val2 0.8056 NA NA NA
VAL =~ val3 0.6763 NA NA NA
SAT =~ sat1 0.9243 NA NA NA
SAT =~ sat2 0.8813 NA NA NA
SAT =~ sat3 0.7127 NA NA NA
SAT =~ sat4 0.7756 NA NA NA
LOY =~ loy1 0.9097 NA NA NA
LOY =~ loy2 0.5775 NA NA NA
LOY =~ loy3 0.9043 NA NA NA
LOY =~ loy4 0.4917 NA NA NA
Estimated weights:
==================
Weight Estimate Std. error t-stat. p-value
IMAG <~ imag1 0.0156 NA NA NA
IMAG <~ imag2 0.4473 NA NA NA
IMAG <~ imag3 0.6020 NA NA NA
EXPE <~ expe1 0.2946 NA NA NA
EXPE <~ expe2 0.6473 NA NA NA
EXPE <~ expe3 0.2374 NA NA NA
QUAL <~ qual1 0.2370 NA NA NA
QUAL <~ qual2 0.4712 NA NA NA
QUAL <~ qual3 0.1831 NA NA NA
QUAL <~ qual4 0.1037 NA NA NA
QUAL <~ qual5 0.2049 NA NA NA
VAL <~ val1 0.7163 NA NA NA
VAL <~ val2 0.2202 NA NA NA
VAL <~ val3 0.2082 NA NA NA
SAT <~ sat1 0.3209 NA NA NA
SAT <~ sat2 0.3059 NA NA NA
SAT <~ sat3 0.2474 NA NA NA
SAT <~ sat4 0.2692 NA NA NA
LOY <~ loy1 0.3834 NA NA NA
LOY <~ loy2 0.2434 NA NA NA
LOY <~ loy3 0.3812 NA NA NA
LOY <~ loy4 0.2073 NA NA NA
Estimated indicator correlations:
=================================
Correlation Estimate Std. error t-stat. p-value
imag1 ~~ imag2 0.6437 NA NA NA
imag1 ~~ imag3 0.5433 NA NA NA
imag2 ~~ imag3 0.7761 NA NA NA
expe1 ~~ expe2 0.5353 NA NA NA
expe1 ~~ expe3 0.4694 NA NA NA
expe2 ~~ expe3 0.5467 NA NA NA
qual1 ~~ qual2 0.6053 NA NA NA
qual1 ~~ qual3 0.5406 NA NA NA
qual1 ~~ qual4 0.5662 NA NA NA
qual1 ~~ qual5 0.5180 NA NA NA
qual2 ~~ qual3 0.6187 NA NA NA
NA NA NA LOY ~ VAL 0.3311 NA NA NA LOY ~ SAT 0.6283 NA NA NA
Estimated indirect effects:
===========================
Indirect effect Estimate Std. error t-stat. p-value
QUAL ~ IMAG 0.3933 NA NA NA
VAL ~ IMAG 0.2974 NA NA NA
VAL ~ EXPE 0.5852 NA NA NA
SAT ~ IMAG 0.2357 NA NA NA
SAT ~ EXPE 0.5173 NA NA NA
SAT ~ QUAL 0.3696 NA NA NA
LOY ~ IMAG 0.3020 NA NA NA
LOY ~ EXPE 0.3142 NA NA NA
LOY ~ QUAL 0.3714 NA NA NA
LOY ~ VAL 0.3311 NA NA NA
________________________________________________________________________________verify(res)
verify(res)
________________________________________________________________________________
Verify admissibility:
admissible
Details:
Code Status Description
1 ok Convergence achieved
2 ok All absolute standardized loading estimates <= 1
3 ok Construct VCV is positive semi-definite
4 ok All reliability estimates <= 1
5 ok Model-implied indicator VCV is positive semi-definite
________________________________________________________________________________모델핏에 대한 테스트
## Test overall model fit
testOMF(res)
testOMF(res)
________________________________________________________________________________
--------- Test for overall model fit based on Beran & Srivastava (1985) --------
Null hypothesis:
┌──────────────────────────────────────────────────────────────────┐
│ │
│ H0: The model-implied indicator covariance matrix equals the │
│ population indicator covariance matrix. │
│ │
└──────────────────────────────────────────────────────────────────┘
Test statistic and critical value:
Critical value
Distance measure Test statistic 95%
dG 0.6493 0.3164
SRMR 0.0940 0.0522
dL 2.2340 0.6888
dML 2.9219 1.5728
Decision:
Significance level
Distance measure 95%
dG reject
SRMR reject
dL reject
dML reject
Additional information:
Out of 499 bootstrap replications 477 are admissible.
See ?verify() for what constitutes an inadmissible result.
The seed used was: -612123717
________________________________________________________________________________
중요한 것은 cSEM에는 bootstrap을 해서 유의성 결과를 얻어야 한다.
# Setting `.resample_method`
b1 <- csem(.data = satisfaction, .model = model, .resample_method = "bootstrap")
b1
cSEM::summarize(b1)b1
________________________________________________________________________________
----------------------------------- Overview -----------------------------------
Estimation was successful.
The result is a list of class cSEMResults with list elements:
- Estimates
- Information
To get an overview or help type:
- ?cSEMResults
- str(<object-name>)
- listviewer::jsondedit(<object-name>, mode = 'view')
If you wish to access the list elements directly type e.g.
- <object-name>$Estimates
Available postestimation commands:
- assess(<object-name>)
- infer(<object-name)
- predict(<object-name>)
- summarize(<object-name>)
- verify(<object-name>)
________________________________________________________________________________전체 결과 요약정리
cSEM::summarize(b1)
________________________________________________________________________________
----------------------------------- Overview -----------------------------------
General information:
------------------------
Estimation status = Ok
Number of observations = 250
Weight estimator = PLS-PM
Inner weighting scheme = "path"
Type of indicator correlation = Pearson
Path model estimator = OLS
Second-order approach = NA
Type of path model = Linear
Disattenuated = Yes (PLSc)
Resample information:
---------------------
Resample method = "bootstrap"
Number of resamples = 499
Number of admissible results = 487
Approach to handle inadmissibles = "drop"
Sign change option = "none"
Random seed = -2118513441
Construct details:
------------------
Name Modeled as Order Mode
IMAG Composite First order "modeB"
EXPE Composite First order "modeB"
QUAL Composite First order "modeB"
VAL Composite First order "modeB"
SAT Common factor First order "modeA"
LOY Common factor First order "modeA"
----------------------------------- Estimates ----------------------------------
Estimated path coefficients:
============================
CI_percentile
Path Estimate Std. error t-stat. p-value 95%
EXPE ~ IMAG 0.4714 0.0665 7.0927 0.0000 [ 0.3391; 0.5869 ]
QUAL ~ EXPE 0.8344 0.0240 34.7047 0.0000 [ 0.7802; 0.8747 ]
VAL ~ EXPE 0.0457 0.0876 0.5218 0.6018 [-0.1188; 0.2260 ]
VAL ~ QUAL 0.7013 0.0838 8.3650 0.0000 [ 0.5227; 0.8526 ]
SAT ~ IMAG 0.2450 0.0529 4.6282 0.0000 [ 0.1451; 0.3435 ]
SAT ~ EXPE -0.0172 0.0749 -0.2301 0.8180 [-0.1681; 0.1311 ]
SAT ~ QUAL 0.2215 0.1038 2.1348 0.0328 [ 0.0520; 0.4472 ]
SAT ~ VAL 0.5270 0.0843 6.2478 0.0000 [ 0.3597; 0.6648 ]
LOY ~ IMAG 0.1819 0.0754 2.4115 0.0159 [ 0.0372; 0.3295 ]
LOY ~ SAT 0.6283 0.0792 7.9338 0.0000 [ 0.4730; 0.7894 ]
Estimated loadings:
===================
CI_percentile
Loading Estimate Std. error t-stat. p-value 95%
IMAG =~ imag1 0.6306 0.0955 6.6047 0.0000 [ 0.4255; 0.8058 ]
IMAG =~ imag2 0.9246 0.0406 22.7559 0.0000 [ 0.8244; 0.9759 ]
IMAG =~ imag3 0.9577 0.0287 33.3615 0.0000 [ 0.8815; 0.9910 ]
EXPE =~ expe1 0.7525 0.0762 9.8692 0.0000 [ 0.5806; 0.8752 ]
EXPE =~ expe2 0.9348 0.0288 32.4265 0.0000 [ 0.8569; 0.9716 ]
EXPE =~ expe3 0.7295 0.0693 10.5274 0.0000 [ 0.5670; 0.8348 ]
QUAL =~ qual1 0.7861 0.0631 12.4486 0.0000 [ 0.6440; 0.8867 ]
QUAL =~ qual2 0.9244 0.0232 39.8112 0.0000 [ 0.8718; 0.9580 ]
QUAL =~ qual3 0.7560 0.0580 13.0446 0.0000 [ 0.6194; 0.8479 ]
QUAL =~ qual4 0.7632 0.0539 14.1715 0.0000 [ 0.6388; 0.8556 ]
QUAL =~ qual5 0.7834 0.0454 17.2667 0.0000 [ 0.6862; 0.8572 ]
VAL =~ val1 0.9518 0.0226 42.1357 0.0000 [ 0.8995; 0.9827 ]
VAL =~ val2 0.8056 0.0608 13.2497 0.0000 [ 0.6606; 0.9055 ]
VAL =~ val3 0.6763 0.0725 9.3255 0.0000 [ 0.5232; 0.8189 ]
SAT =~ sat1 0.9243 0.0237 38.9743 0.0000 [ 0.8678; 0.9617 ]
SAT =~ sat2 0.8813 0.0295 29.8441 0.0000 [ 0.8165; 0.9328 ]
SAT =~ sat3 0.7127 0.0516 13.8022 0.0000 [ 0.6128; 0.8028 ]
SAT =~ sat4 0.7756 0.0496 15.6344 0.0000 [ 0.6739; 0.8666 ]
LOY =~ loy1 0.9097 0.0505 17.9990 0.0000 [ 0.7980; 0.9867 ]
LOY =~ loy2 0.5775 0.0840 6.8745 0.0000 [ 0.3857; 0.7155 ]
LOY =~ loy3 0.9043 0.0411 22.0016 0.0000 [ 0.8086; 0.9716 ]
LOY =~ loy4 0.4917 0.1012 4.8580 0.0000 [ 0.2887; 0.6755 ]
Estimated weights:
==================
CI_percentile
Weight Estimate Std. error t-stat. p-value 95%
IMAG <~ imag1 0.0156 0.1094 0.1430 0.8863 [-0.1923; 0.2252 ]
IMAG <~ imag2 0.4473 0.1510 2.9619 0.0031 [ 0.1568; 0.7311 ]
IMAG <~ imag3 0.6020 0.1407 4.2795 0.0000 [ 0.3083; 0.8548 ]
EXPE <~ expe1 0.2946 0.1124 2.6218 0.0087 [ 0.0849; 0.5221 ]
EXPE <~ expe2 0.6473 0.0845 7.6576 0.0000 [ 0.4684; 0.7906 ]
EXPE <~ expe3 0.2374 0.0900 2.6369 0.0084 [ 0.0503; 0.3868 ]
QUAL <~ qual1 0.2370 0.0873 2.7148 0.0066 [ 0.0855; 0.4180 ]
QUAL <~ qual2 0.4712 0.0754 6.2475 0.0000 [ 0.3140; 0.6219 ]
QUAL <~ qual3 0.1831 0.0777 2.3546 0.0185 [ 0.0127; 0.3269 ]
QUAL <~ qual4 0.1037 0.0605 1.7138 0.0866 [-0.0076; 0.2203 ]
QUAL <~ qual5 0.2049 0.0639 3.2084 0.0013 [ 0.0768; 0.3307 ]
VAL <~ val1 0.7163 0.0912 7.8573 0.0000 [ 0.5205; 0.8657 ]
VAL <~ val2 0.2202 0.0896 2.4584 0.0140 [ 0.0644; 0.4103 ]
VAL <~ val3 0.2082 0.0623 3.3399 0.0008 [ 0.0830; 0.3336 ]
SAT <~ sat1 0.3209 0.0152 21.1303 0.0000 [ 0.2943; 0.3545 ]
SAT <~ sat2 0.3059 0.0128 23.8497 0.0000 [ 0.2860; 0.3347 ]
SAT <~ sat3 0.2474 0.0115 21.5064 0.0000 [ 0.2229; 0.2703 ]
SAT <~ sat4 0.2692 0.0133 20.2356 0.0000 [ 0.2439; 0.2961 ]
LOY <~ loy1 0.3834 0.0265 14.4769 0.0000 [ 0.3346; 0.4332 ]
LOY <~ loy2 0.2434 0.0300 8.1070 0.0000 [ 0.1792; 0.2933 ]
LOY <~ loy3 0.3812 0.0269 14.1562 0.0000 [ 0.3312; 0.4342 ]
LOY <~ loy4 0.2073 0.0373 5.5603 0.0000 [ 0.1339; 0.2761 ]
Estimated indicator correlations:
=================================
CI_percentile
Correlation Estimate Std. error t-stat. p-value 95%
imag1 ~~ imag2 0.6437 0.0622 10.3439 0.0000 [ 0.5088; 0.7493 ]
imag1 ~~ imag3 0.5433 0.0672 8.0853 0.0000 [ 0.4067; 0.6691 ]
imag2 ~~ imag3 0.7761 0.0393 19.7487 0.0000 [ 0.6865; 0.8403 ]
expe1 ~~ expe2 0.5353 0.0625 8.5613 0.0000 [ 0.3952; 0.6362 ]
expe1 ~~ expe3 0.4694 0.0638 7.3586 0.0000 [ 0.3406; 0.5949 ]
expe2 ~~ expe3 0.5467 0.0594 9.2026 0.0000 [ 0.4137; 0.6534 ]
qual1 ~~ qual2 0.6053 0.0548 11.0443 0.0000 [ 0.4908; 0.7045 ]
qual1 ~~ qual3 0.5406 0.0593 9.1169 0.0000 [ 0.4272; 0.6549 ]
qual1 ~~ qual4 0.5662 0.0652 8.6818 0.0000 [ 0.4138; 0.6805 ]
qual1 ~~ qual5 0.5180 0.0678 7.6396 0.0000 [ 0.3819; 0.6455 ]
qual2 ~~ qual3 0.6187 0.0553 11.1933 0.0000 [ 0.5004; 0.7101 ]
qual2 ~~ qual4 0.6517 0.0599 10.8783 0.0000 [ 0.5216; 0.7550 ]
qual2 ~~ qual5 0.6291 0.0590 10.6628 0.0000 [ 0.5151; 0.7318 ]
qual3 ~~ qual4 0.4752 0.0635 7.4791 0.0000 [ 0.3268; 0.5881 ]
qual3 ~~ qual5 0.5074 0.0616 8.2412 0.0000 [ 0.3842; 0.6136 ]
qual4 ~~ qual5 0.6402 0.0558 11.4721 0.0000 [ 0.5164; 0.7411 ]
val1 ~~ val2 0.6344 0.0522 12.1569 0.0000 [ 0.5273; 0.7313 ]
val1 ~~ val3 0.4602 0.0694 6.6320 0.0000 [ 0.3385; 0.6031 ]
val2 ~~ val3 0.6288 0.0661 9.5089 0.0000 [ 0.4971; 0.7577 ]
------------------------------------ Effects -----------------------------------
Estimated total effects:
========================
CI_percentile
Total effect Estimate Std. error t-stat. p-value 95%
EXPE ~ IMAG 0.4714 0.0665 7.0927 0.0000 [ 0.3391; 0.5869 ]
QUAL ~ IMAG 0.3933 0.0612 6.4218 0.0000 [ 0.2750; 0.5033 ]
QUAL ~ EXPE 0.8344 0.0240 34.7047 0.0000 [ 0.7802; 0.8747 ]
VAL ~ IMAG 0.2974 0.0611 4.8641 0.0000 [ 0.1778; 0.4207 ]
VAL ~ EXPE 0.6309 0.0516 12.2371 0.0000 [ 0.5193; 0.7167 ]
VAL ~ QUAL 0.7013 0.0838 8.3650 0.0000 [ 0.5227; 0.8526 ]
SAT ~ IMAG 0.4807 0.0649 7.4036 0.0000 [ 0.3540; 0.6050 ]
SAT ~ EXPE 0.5001 0.0567 8.8127 0.0000 [ 0.3882; 0.6035 ]
SAT ~ QUAL 0.5911 0.0966 6.1181 0.0000 [ 0.4159; 0.7823 ]
SAT ~ VAL 0.5270 0.0843 6.2478 0.0000 [ 0.3597; 0.6648 ]
LOY ~ IMAG 0.4840 0.0651 7.4298 0.0000 [ 0.3594; 0.6093 ]
LOY ~ EXPE 0.3142 0.0544 5.7759 0.0000 [ 0.2135; 0.4231 ]
LOY ~ QUAL 0.3714 0.0809 4.5899 0.0000 [ 0.2336; 0.5499 ]
LOY ~ VAL 0.3311 0.0750 4.4151 0.0000 [ 0.1953; 0.4792 ]
LOY ~ SAT 0.6283 0.0792 7.9338 0.0000 [ 0.4730; 0.7894 ]
Estimated indirect effects:
===========================
CI_percentile
Indirect effect Estimate Std. error t-stat. p-value 95%
QUAL ~ IMAG 0.3933 0.0612 6.4218 0.0000 [ 0.2750; 0.5033 ]
VAL ~ IMAG 0.2974 0.0611 4.8641 0.0000 [ 0.1778; 0.4207 ]
VAL ~ EXPE 0.5852 0.0728 8.0366 0.0000 [ 0.4209; 0.7287 ]
SAT ~ IMAG 0.2357 0.0489 4.8245 0.0000 [ 0.1449; 0.3346 ]
SAT ~ EXPE 0.5173 0.0683 7.5795 0.0000 [ 0.3838; 0.6544 ]
SAT ~ QUAL 0.3696 0.0621 5.9513 0.0000 [ 0.2438; 0.4888 ]
LOY ~ IMAG 0.3020 0.0549 5.4973 0.0000 [ 0.2072; 0.4204 ]
LOY ~ EXPE 0.3142 0.0544 5.7759 0.0000 [ 0.2135; 0.4231 ]
LOY ~ QUAL 0.3714 0.0809 4.5899 0.0000 [ 0.2336; 0.5499 ]
LOY ~ VAL 0.3311 0.0750 4.4151 0.0000 [ 0.1953; 0.4792 ]
________________________________________________________________________________
># Using resamplecSEMResults()
b2 <- resamplecSEMResults(res)
b
b2
________________________________________________________________________________
----------------------------------- Overview -----------------------------------
Estimation was successful.
The result is a list of class cSEMResults with list elements:
- Estimates
- Information
To get an overview or help type:
- ?cSEMResults
- str(<object-name>)
- listviewer::jsondedit(<object-name>, mode = 'view')
If you wish to access the list elements directly type e.g.
- <object-name>$Estimates
Available postestimation commands:
- assess(<object-name>)
- infer(<object-name)
- predict(<object-name>)
- summarize(<object-name>)
- verify(<object-name>)
________________________________________________________________________________
> cSEM::summarize(b2)
________________________________________________________________________________
----------------------------------- Overview -----------------------------------
General information:
------------------------
Estimation status = Ok
Number of observations = 250
Weight estimator = PLS-PM
Inner weighting scheme = "path"
Type of indicator correlation = Pearson
Path model estimator = OLS
Second-order approach = NA
Type of path model = Linear
Disattenuated = Yes (PLSc)
Resample information:
---------------------
Resample method = "bootstrap"
Number of resamples = 499
Number of admissible results = 488
Approach to handle inadmissibles = "drop"
Sign change option = "none"
Random seed = 2134958388
Construct details:
------------------
Name Modeled as Order Mode
IMAG Composite First order "modeB"
EXPE Composite First order "modeB"
QUAL Composite First order "modeB"
VAL Composite First order "modeB"
SAT Common factor First order "modeA"
LOY Common factor First order "modeA"
----------------------------------- Estimates ----------------------------------
Estimated path coefficients:
============================
CI_percentile
Path Estimate Std. error t-stat. p-value 95%
EXPE ~ IMAG 0.4714 0.0618 7.6328 0.0000 [ 0.3499; 0.5965 ]
QUAL ~ EXPE 0.8344 0.0230 36.2697 0.0000 [ 0.7807; 0.8704 ]
VAL ~ EXPE 0.0457 0.0825 0.5542 0.5795 [-0.1051; 0.2011 ]
VAL ~ QUAL 0.7013 0.0809 8.6684 0.0000 [ 0.5254; 0.8507 ]
SAT ~ IMAG 0.2450 0.0546 4.4834 0.0000 [ 0.1392; 0.3572 ]
SAT ~ EXPE -0.0172 0.0712 -0.2419 0.8089 [-0.1532; 0.1098 ]
SAT ~ QUAL 0.2215 0.1013 2.1864 0.0288 [ 0.0302; 0.4255 ]
SAT ~ VAL 0.5270 0.0848 6.2150 0.0000 [ 0.3473; 0.6748 ]
LOY ~ IMAG 0.1819 0.0796 2.2854 0.0223 [ 0.0219; 0.3504 ]
LOY ~ SAT 0.6283 0.0838 7.5020 0.0000 [ 0.4651; 0.8067 ]
Estimated loadings:
===================
CI_percentile
Loading Estimate Std. error t-stat. p-value 95%
IMAG =~ imag1 0.6306 0.0944 6.6795 0.0000 [ 0.4277; 0.8014 ]
IMAG =~ imag2 0.9246 0.0418 22.1197 0.0000 [ 0.8125; 0.9766 ]
IMAG =~ imag3 0.9577 0.0287 33.4218 0.0000 [ 0.8848; 0.9928 ]
EXPE =~ expe1 0.7525 0.0739 10.1787 0.0000 [ 0.5875; 0.8720 ]
EXPE =~ expe2 0.9348 0.0282 33.1043 0.0000 [ 0.8631; 0.9722 ]
EXPE =~ expe3 0.7295 0.0707 10.3163 0.0000 [ 0.5740; 0.8460 ]
QUAL =~ qual1 0.7861 0.0673 11.6854 0.0000 [ 0.6351; 0.8910 ]
QUAL =~ qual2 0.9244 0.0231 39.9599 0.0000 [ 0.8667; 0.9558 ]
QUAL =~ qual3 0.7560 0.0604 12.5179 0.0000 [ 0.6266; 0.8564 ]
QUAL =~ qual4 0.7632 0.0557 13.6929 0.0000 [ 0.6362; 0.8511 ]
QUAL =~ qual5 0.7834 0.0434 18.0410 0.0000 [ 0.6819; 0.8493 ]
VAL =~ val1 0.9518 0.0240 39.5935 0.0000 [ 0.8941; 0.9840 ]
VAL =~ val2 0.8056 0.0632 12.7398 0.0000 [ 0.6642; 0.9024 ]
VAL =~ val3 0.6763 0.0740 9.1333 0.0000 [ 0.5236; 0.8084 ]
SAT =~ sat1 0.9243 0.0221 41.7425 0.0000 [ 0.8763; 0.9606 ]
SAT =~ sat2 0.8813 0.0262 33.6867 0.0000 [ 0.8246; 0.9234 ]
SAT =~ sat3 0.7127 0.0515 13.8309 0.0000 [ 0.6073; 0.8047 ]
SAT =~ sat4 0.7756 0.0524 14.7935 0.0000 [ 0.6680; 0.8617 ]
LOY =~ loy1 0.9097 0.0454 20.0311 0.0000 [ 0.8152; 0.9854 ]
LOY =~ loy2 0.5775 0.0875 6.5991 0.0000 [ 0.4008; 0.7271 ]
LOY =~ loy3 0.9043 0.0422 21.4215 0.0000 [ 0.8034; 0.9735 ]
LOY =~ loy4 0.4917 0.0963 5.1063 0.0000 [ 0.3044; 0.6800 ]
Estimated weights:
==================
CI_percentile
Weight Estimate Std. error t-stat. p-value 95%
IMAG <~ imag1 0.0156 0.1131 0.1383 0.8900 [-0.2008; 0.2454 ]
IMAG <~ imag2 0.4473 0.1511 2.9607 0.0031 [ 0.1461; 0.7243 ]
IMAG <~ imag3 0.6020 0.1422 4.2345 0.0000 [ 0.3137; 0.8680 ]
EXPE <~ expe1 0.2946 0.1050 2.8054 0.0050 [ 0.0967; 0.5036 ]
EXPE <~ expe2 0.6473 0.0856 7.5658 0.0000 [ 0.4445; 0.7863 ]
EXPE <~ expe3 0.2374 0.0918 2.5864 0.0097 [ 0.0446; 0.3982 ]
QUAL <~ qual1 0.2370 0.0863 2.7466 0.0060 [ 0.0953; 0.4197 ]
QUAL <~ qual2 0.4712 0.0785 5.9999 0.0000 [ 0.3055; 0.6117 ]
QUAL <~ qual3 0.1831 0.0833 2.1983 0.0279 [ 0.0083; 0.3329 ]
QUAL <~ qual4 0.1037 0.0642 1.6147 0.1064 [-0.0228; 0.2283 ]
QUAL <~ qual5 0.2049 0.0586 3.4951 0.0005 [ 0.0841; 0.3211 ]
VAL <~ val1 0.7163 0.0960 7.4644 0.0000 [ 0.5327; 0.8835 ]
VAL <~ val2 0.2202 0.0916 2.4031 0.0163 [ 0.0605; 0.4140 ]
VAL <~ val3 0.2082 0.0603 3.4494 0.0006 [ 0.0838; 0.3131 ]
SAT <~ sat1 0.3209 0.0154 20.8047 0.0000 [ 0.2935; 0.3509 ]
SAT <~ sat2 0.3059 0.0133 22.9457 0.0000 [ 0.2826; 0.3336 ]
SAT <~ sat3 0.2474 0.0115 21.4575 0.0000 [ 0.2254; 0.2710 ]
SAT <~ sat4 0.2692 0.0122 22.0146 0.0000 [ 0.2460; 0.2916 ]
LOY <~ loy1 0.3834 0.0252 15.1963 0.0000 [ 0.3352; 0.4323 ]
LOY <~ loy2 0.2434 0.0301 8.0757 0.0000 [ 0.1814; 0.2969 ]
LOY <~ loy3 0.3812 0.0276 13.8057 0.0000 [ 0.3303; 0.4380 ]
LOY <~ loy4 0.2073 0.0361 5.7362 0.0000 [ 0.1364; 0.2759 ]
Estimated indicator correlations:
=================================
CI_percentile
Correlation Estimate Std. error t-stat. p-value 95%
imag1 ~~ imag2 0.6437 0.0607 10.6018 0.0000 [ 0.5184; 0.7480 ]
imag1 ~~ imag3 0.5433 0.0620 8.7559 0.0000 [ 0.4190; 0.6506 ]
imag2 ~~ imag3 0.7761 0.0377 20.5656 0.0000 [ 0.6964; 0.8422 ]
expe1 ~~ expe2 0.5353 0.0587 9.1207 0.0000 [ 0.4098; 0.6347 ]
expe1 ~~ expe3 0.4694 0.0638 7.3628 0.0000 [ 0.3433; 0.5866 ]
expe2 ~~ expe3 0.5467 0.0602 9.0828 0.0000 [ 0.4242; 0.6521 ]
qual1 ~~ qual2 0.6053 0.0605 10.0013 0.0000 [ 0.4724; 0.7083 ]
qual1 ~~ qual3 0.5406 0.0622 8.6984 0.0000 [ 0.4150; 0.6495 ]
qual1 ~~ qual4 0.5662 0.0692 8.1867 0.0000 [ 0.4257; 0.6854 ]
qual1 ~~ qual5 0.5180 0.0685 7.5664 0.0000 [ 0.3667; 0.6354 ]
qual2 ~~ qual3 0.6187 0.0523 11.8227 0.0000 [ 0.5143; 0.7106 ]
qual2 ~~ qual4 0.6517 0.0624 10.4472 0.0000 [ 0.5141; 0.7589 ]
qual2 ~~ qual5 0.6291 0.0558 11.2732 0.0000 [ 0.5099; 0.7344 ]
qual3 ~~ qual4 0.4752 0.0612 7.7650 0.0000 [ 0.3507; 0.5879 ]
qual3 ~~ qual5 0.5074 0.0594 8.5419 0.0000 [ 0.3894; 0.6226 ]
qual4 ~~ qual5 0.6402 0.0545 11.7533 0.0000 [ 0.5280; 0.7328 ]
val1 ~~ val2 0.6344 0.0523 12.1350 0.0000 [ 0.5293; 0.7334 ]
val1 ~~ val3 0.4602 0.0705 6.5299 0.0000 [ 0.3153; 0.5960 ]
val2 ~~ val3 0.6288 0.0645 9.7537 0.0000 [ 0.5052; 0.7520 ]
------------------------------------ Effects -----------------------------------
Estimated total effects:
========================
CI_percentile
Total effect Estimate Std. error t-stat. p-value 95%
EXPE ~ IMAG 0.4714 0.0618 7.6328 0.0000 [ 0.3499; 0.5965 ]
QUAL ~ IMAG 0.3933 0.0577 6.8182 0.0000 [ 0.2840; 0.5083 ]
QUAL ~ EXPE 0.8344 0.0230 36.2697 0.0000 [ 0.7807; 0.8704 ]
VAL ~ IMAG 0.2974 0.0571 5.2092 0.0000 [ 0.2040; 0.4125 ]
VAL ~ EXPE 0.6309 0.0494 12.7709 0.0000 [ 0.5404; 0.7225 ]
VAL ~ QUAL 0.7013 0.0809 8.6684 0.0000 [ 0.5254; 0.8507 ]
SAT ~ IMAG 0.4807 0.0654 7.3490 0.0000 [ 0.3519; 0.6080 ]
SAT ~ EXPE 0.5001 0.0579 8.6328 0.0000 [ 0.3866; 0.5987 ]
SAT ~ QUAL 0.5911 0.0935 6.3203 0.0000 [ 0.3979; 0.7733 ]
SAT ~ VAL 0.5270 0.0848 6.2150 0.0000 [ 0.3473; 0.6748 ]
LOY ~ IMAG 0.4840 0.0666 7.2622 0.0000 [ 0.3499; 0.6197 ]
LOY ~ EXPE 0.3142 0.0563 5.5764 0.0000 [ 0.2059; 0.4249 ]
LOY ~ QUAL 0.3714 0.0865 4.2955 0.0000 [ 0.2157; 0.5645 ]
LOY ~ VAL 0.3311 0.0722 4.5831 0.0000 [ 0.1901; 0.4708 ]
LOY ~ SAT 0.6283 0.0838 7.5020 0.0000 [ 0.4651; 0.8067 ]
Estimated indirect effects:
===========================
CI_percentile
Indirect effect Estimate Std. error t-stat. p-value 95%
QUAL ~ IMAG 0.3933 0.0577 6.8182 0.0000 [ 0.2840; 0.5083 ]
VAL ~ IMAG 0.2974 0.0571 5.2092 0.0000 [ 0.2040; 0.4125 ]
VAL ~ EXPE 0.5852 0.0691 8.4704 0.0000 [ 0.4373; 0.7168 ]
SAT ~ IMAG 0.2357 0.0470 5.0203 0.0000 [ 0.1577; 0.3301 ]
SAT ~ EXPE 0.5173 0.0696 7.4290 0.0000 [ 0.3820; 0.6574 ]
SAT ~ QUAL 0.3696 0.0641 5.7670 0.0000 [ 0.2398; 0.4879 ]
LOY ~ IMAG 0.3020 0.0568 5.3175 0.0000 [ 0.2057; 0.4248 ]
LOY ~ EXPE 0.3142 0.0563 5.5764 0.0000 [ 0.2059; 0.4249 ]
LOY ~ QUAL 0.3714 0.0865 4.2955 0.0000 [ 0.2157; 0.5645 ]
LOY ~ VAL 0.3311 0.0722 4.5831 0.0000 [ 0.1901; 0.4708 ]
________________________________________________________________________________assess(b2)