04.05.2026_10.ders
SC
2026-05-07
04.05.2026_10.ders
Doğrulayıcı Faktör Analizi uygulamaları yapacağınız 3 farklı problem durumu veri setleri ile birlikte sunulmuştur. DFA gerçekleştirme sürecindeki tüm adımları uygulayarak gerekli açıklamaları ve yorumları yapmanız beklenmektedir.
❔Soru 1
Aşağıda künyesi verilen makalede 339 çocuğa 21 tane yetenek sorusu içeren bir zeka ölçeği uygulanmıştır. Bu ölçek mantıksal, dilsel, müziksel, bedensel, uzamsal, ve doğal olarak adlandırılan 6 farklı zeka boyutunu ölçmek için tasarlanmıştır.
- Makalede Tablo 2’de korelasyon matrisi, ortalama ve standart sapma değerlerine yer verilmiştir. Makalede kullanılan veri seti Şen’deki DFA alıştırmalarında da kullanılmaktadır. Standart sapma değerleri ve korelasyon matrisi “castrohen.txt” dosyasında yer almaktadır.
- Korelasyon matrisindeki sıralama doğal boyutunda 6 madde, bedensel boyutunda 4 madde, uzamsal boyutunda 3 madde, müziksel boyutunda 2 madde, mantıksal boyutunda 3 madde, dilsel boyutunda ise 3 madde şeklindedir.
Makaledeki Tablo 2 verisini kullanarak; 1. Altı faktörlü modelin uyumunu, 2. İkinci dereceli altı faktörlü modelin uyumunu, 3. Mantıksal, dilsel, uzamsal ve doğal boyutlarının “bilişsel”; müziksel ve bedensel boyutlarının ise “bilişsel olmayan” bir genel faktöre yüklendiği, birbiriyle ilişkili iki ikinci dereceden genel faktöre sahip modelin uyumunu, 4. İkili faktörlü (bi-factor) modelin uyumunu değerlendiriniz.
(Kaynak: Castejon, J. L., Perez, A. M., & Gilar, R. (2010). Confirmatory factor analysis of Project Spectrum activities. A second-order g factor or multiple intelligences?. Intelligence, 38(5), 481-496.)
🤔Cevap 1
Txt ile başa çıkamayınca excele çevirip ilerleyeyim dedim hocam :)
1.Altı faktörlü modelin uyumu
library(readxl)## Warning: package 'readxl' was built under R version 4.5.3df <- read_excel("veri_9.xlsx", col_names = FALSE)## New names:
## • `` -> `...1`
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
## • `` -> `...20`
## • `` -> `...21`
## • `` -> `...22`class(df)## [1] "tbl_df" "tbl" "data.frame"library(dplyr)## Warning: package 'dplyr' was built under R version 4.5.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
df_clean <- df %>%
mutate(across(everything(), ~as.numeric(as.character(.x))))
df_clean[is.na(df_clean)] <- 0
df_matrix <- as.matrix(df_clean)
diag(df_matrix) <- 1.0df_matrix_1 <- df_matrix[1:21, 1:21]
colnames(df_matrix_1) <- paste0("v", 1:21)
rownames(df_matrix_1) <- paste0("v", 1:21)df_matrix_1 <- as.matrix(df_matrix_1)
df_matrix_1[] <- as.numeric(as.character(df_matrix_1))
diag(df_matrix_1)[is.na(diag(df_matrix_1))] <- 1.0
df_matrix_1[is.na(df_matrix_1)] <- 0
library(lavaan)## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.model1 <- '
dogal =~ v1 + v2 + v3 + v4 + v5 + v6
bedensel =~ v7 + v8 + v9 + v10
uzamsal =~ v11 + v12 + v13
muziksel =~ v14 + v15
mantiksal=~ v16 + v17 + v18
dilsel =~ v19 + v20 + v21
'
fit1 <- cfa(
model = model1,
sample.cov = df_matrix_1,
sample.nobs = 339
)## Warning: lavaan->lav_object_post_check():
## some estimated ov variances are negativesummary(fit1, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 663.506
## Degrees of freedom 174
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3345.560
## Degrees of freedom 210
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.844
## Tucker-Lewis Index (TLI) 0.812
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8749.881
## Loglikelihood unrestricted model (H1) -8418.128
##
## Akaike (AIC) 17613.762
## Bayesian (BIC) 17831.844
## Sample-size adjusted Bayesian (SABIC) 17651.030
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.099
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.994
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.106
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dogal =~
## v1 1.000 0.642 0.643
## v2 0.905 0.094 9.576 0.000 0.580 0.581
## v3 1.091 0.097 11.212 0.000 0.700 0.701
## v4 1.222 0.100 12.276 0.000 0.784 0.785
## v5 1.368 0.103 13.337 0.000 0.878 0.879
## v6 1.405 0.104 13.563 0.000 0.902 0.903
## bedensel =~
## v7 1.000 0.692 0.694
## v8 1.019 0.114 8.936 0.000 0.706 0.707
## v9 0.583 0.096 6.067 0.000 0.403 0.404
## v10 0.682 0.098 6.953 0.000 0.472 0.473
## uzamsal =~
## v11 1.000 0.878 0.879
## v12 0.969 0.050 19.440 0.000 0.851 0.852
## v13 0.931 0.051 18.391 0.000 0.817 0.818
## muziksel =~
## v14 1.000 0.495 0.495
## v15 2.320 0.614 3.776 0.000 1.147 1.149
## mantiksal =~
## v16 1.000 0.725 0.726
## v17 0.451 0.084 5.404 0.000 0.327 0.328
## v18 0.898 0.086 10.439 0.000 0.651 0.652
## dilsel =~
## v19 1.000 0.170 0.170
## v20 4.773 1.712 2.789 0.005 0.811 0.812
## v21 4.864 1.750 2.780 0.005 0.826 0.827
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dogal ~~
## bedensel 0.136 0.033 4.100 0.000 0.306 0.306
## uzamsal 0.202 0.038 5.279 0.000 0.359 0.359
## muziksel 0.050 0.021 2.322 0.020 0.157 0.157
## mantiksal 0.203 0.038 5.399 0.000 0.436 0.436
## dilsel 0.029 0.013 2.326 0.020 0.268 0.268
## bedensel ~~
## uzamsal 0.275 0.048 5.734 0.000 0.452 0.452
## muziksel 0.108 0.037 2.880 0.004 0.314 0.314
## mantiksal 0.279 0.047 5.906 0.000 0.556 0.556
## dilsel 0.034 0.015 2.296 0.022 0.292 0.292
## uzamsal ~~
## muziksel 0.057 0.027 2.104 0.035 0.132 0.132
## mantiksal 0.522 0.059 8.896 0.000 0.820 0.820
## dilsel 0.035 0.016 2.239 0.025 0.236 0.236
## muziksel ~~
## mantiksal 0.121 0.041 2.938 0.003 0.338 0.338
## dilsel 0.013 0.007 1.743 0.081 0.149 0.149
## mantiksal ~~
## dilsel 0.034 0.015 2.236 0.025 0.274 0.274
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .v1 0.585 0.048 12.235 0.000 0.585 0.587
## .v2 0.660 0.053 12.453 0.000 0.660 0.662
## .v3 0.507 0.042 11.937 0.000 0.507 0.509
## .v4 0.382 0.034 11.188 0.000 0.382 0.383
## .v5 0.226 0.025 8.989 0.000 0.226 0.227
## .v6 0.184 0.023 7.879 0.000 0.184 0.184
## .v7 0.518 0.061 8.528 0.000 0.518 0.519
## .v8 0.499 0.061 8.206 0.000 0.499 0.501
## .v9 0.834 0.069 12.125 0.000 0.834 0.837
## .v10 0.774 0.066 11.688 0.000 0.774 0.776
## .v11 0.227 0.029 7.763 0.000 0.227 0.228
## .v12 0.273 0.031 8.878 0.000 0.273 0.274
## .v13 0.329 0.033 9.902 0.000 0.329 0.330
## .v14 0.752 0.083 9.057 0.000 0.752 0.755
## .v15 -0.319 0.322 -0.990 0.322 -0.319 -0.320
## .v16 0.472 0.053 8.855 0.000 0.472 0.473
## .v17 0.890 0.070 12.660 0.000 0.890 0.893
## .v18 0.573 0.055 10.499 0.000 0.573 0.575
## .v19 0.968 0.075 12.928 0.000 0.968 0.971
## .v20 0.340 0.093 3.659 0.000 0.340 0.341
## .v21 0.315 0.096 3.288 0.001 0.315 0.316
## dogal 0.412 0.064 6.423 0.000 1.000 1.000
## bedensel 0.480 0.080 6.000 0.000 1.000 1.000
## uzamsal 0.770 0.078 9.851 0.000 1.000 1.000
## muziksel 0.245 0.078 3.135 0.002 1.000 1.000
## mantiksal 0.525 0.078 6.740 0.000 1.000 1.000
## dilsel 0.029 0.020 1.419 0.156 1.000 1.0002. İkinci dereceli altı faktörlü modelin uyumu
model1_id <- "
dogal =~ v1 + v2 + v3 + v4 + v5 + v6
bedensel =~ v7 + v8 + v9 + v10
uzamsal =~ v11 + v12 + v13
muziksel =~ a*v14 + a*v15
mantiksal=~ v16 + v17 + v18
dilsel =~ v19 + v20 + v21
# ikinci duzey model
zeka_ol =~ dogal + bedensel + uzamsal + muziksel + mantiksal + dilsel
mantiksal ~~ 0.01*mantiksal
"fit_model1_id <- cfa(
model = model1_id,
sample.cov = df_matrix_1,
sample.nobs = 339
)
summary(fit_model1_id, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 46
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 708.946
## Degrees of freedom 185
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3345.560
## Degrees of freedom 210
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.833
## Tucker-Lewis Index (TLI) 0.810
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8772.601
## Loglikelihood unrestricted model (H1) -8418.128
##
## Akaike (AIC) 17637.203
## Bayesian (BIC) 17813.199
## Sample-size adjusted Bayesian (SABIC) 17667.279
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.099
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.996
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.111
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dogal =~
## v1 1.000 0.641 0.642
## v2 0.905 0.095 9.564 0.000 0.580 0.581
## v3 1.089 0.097 11.185 0.000 0.699 0.700
## v4 1.223 0.100 12.259 0.000 0.784 0.785
## v5 1.371 0.103 13.331 0.000 0.879 0.880
## v6 1.407 0.104 13.548 0.000 0.902 0.903
## bedensel =~
## v7 1.000 0.682 0.683
## v8 1.047 0.121 8.646 0.000 0.714 0.715
## v9 0.591 0.099 5.981 0.000 0.403 0.403
## v10 0.699 0.101 6.900 0.000 0.477 0.477
## uzamsal =~
## v11 1.000 0.876 0.878
## v12 0.970 0.050 19.311 0.000 0.851 0.852
## v13 0.934 0.051 18.351 0.000 0.819 0.820
## muziksel =~
## v14 (a) 1.000 0.755 0.741
## v15 (a) 1.000 0.755 0.770
## mantiksal =~
## v16 1.000 0.713 0.714
## v17 0.487 0.087 5.627 0.000 0.347 0.348
## v18 0.940 0.092 10.245 0.000 0.670 0.671
## dilsel =~
## v19 1.000 0.179 0.179
## v20 4.784 1.657 2.887 0.004 0.855 0.856
## v21 4.376 1.484 2.949 0.003 0.782 0.783
## zeka_ol =~
## dogal 1.000 0.459 0.459
## bedensel 1.363 0.260 5.241 0.000 0.588 0.588
## uzamsal 2.365 0.371 6.368 0.000 0.794 0.794
## muziksel 0.898 0.217 4.138 0.000 0.350 0.350
## mantiksal 2.400 0.377 6.373 0.000 0.990 0.990
## dilsel 0.202 0.083 2.436 0.015 0.333 0.333
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mantiksal 0.010 0.020 0.020
## .v1 0.586 0.048 12.237 0.000 0.586 0.588
## .v2 0.661 0.053 12.454 0.000 0.661 0.663
## .v3 0.509 0.043 11.945 0.000 0.509 0.511
## .v4 0.382 0.034 11.192 0.000 0.382 0.384
## .v5 0.225 0.025 8.944 0.000 0.225 0.225
## .v6 0.184 0.023 7.865 0.000 0.184 0.184
## .v7 0.532 0.062 8.566 0.000 0.532 0.534
## .v8 0.487 0.063 7.761 0.000 0.487 0.489
## .v9 0.835 0.069 12.094 0.000 0.835 0.837
## .v10 0.770 0.066 11.600 0.000 0.770 0.772
## .v11 0.229 0.030 7.733 0.000 0.229 0.230
## .v12 0.274 0.031 8.811 0.000 0.274 0.275
## .v13 0.326 0.033 9.809 0.000 0.326 0.327
## .v14 0.468 0.056 8.349 0.000 0.468 0.450
## .v15 0.392 0.052 7.469 0.000 0.392 0.407
## .v16 0.489 0.052 9.445 0.000 0.489 0.490
## .v17 0.876 0.070 12.559 0.000 0.876 0.879
## .v18 0.548 0.054 10.193 0.000 0.548 0.550
## .v19 0.965 0.075 12.920 0.000 0.965 0.968
## .v20 0.266 0.114 2.345 0.019 0.266 0.267
## .v21 0.386 0.098 3.933 0.000 0.386 0.387
## .dogal 0.325 0.052 6.253 0.000 0.789 0.789
## .bedensel 0.304 0.059 5.185 0.000 0.654 0.654
## .uzamsal 0.284 0.047 6.032 0.000 0.370 0.370
## .muziksel 0.501 0.059 8.530 0.000 0.878 0.878
## .dilsel 0.028 0.019 1.498 0.134 0.889 0.889
## zeka_ol 0.087 0.025 3.405 0.001 1.000 1.0003. Mantıksal, dilsel, uzamsal ve doğal boyutlarının “bilişsel”; müziksel ve bedensel boyutlarının ise “bilişsel olmayan” bir genel faktöre yüklendiği, birbiriyle ilişkili iki ikinci dereceden genel faktöre sahip modelin uyumu
model2_id <- "
dogal =~ v1+v2+v3+v4+v5+v6
bedensel =~ v7+v8+v9+v10
uzamsal =~ v11+v12+v13
muziksel =~ a*v14+a*v15
mantiksal =~ v16+v17+v18
dilsel =~ v19+v20+v21
mantiksal ~~ 0.01*mantiksal
bilissel =~ mantiksal + dilsel + uzamsal + dogal
bilissel_olmayan =~ muziksel + bedensel
bilissel ~~ bilissel_olmayan
"
fit2 <- cfa(model2_id, sample.cov = df_matrix_1, sample.nobs = 339)
summary(fit2, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 72 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 704.380
## Degrees of freedom 184
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3345.560
## Degrees of freedom 210
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.834
## Tucker-Lewis Index (TLI) 0.811
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8770.318
## Loglikelihood unrestricted model (H1) -8418.128
##
## Akaike (AIC) 17634.636
## Bayesian (BIC) 17814.458
## Sample-size adjusted Bayesian (SABIC) 17665.366
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.099
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.995
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.112
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dogal =~
## v1 1.000 0.641 0.642
## v2 0.904 0.095 9.562 0.000 0.580 0.581
## v3 1.089 0.097 11.184 0.000 0.698 0.699
## v4 1.223 0.100 12.259 0.000 0.784 0.785
## v5 1.371 0.103 13.332 0.000 0.879 0.880
## v6 1.407 0.104 13.548 0.000 0.902 0.903
## bedensel =~
## v7 1.000 0.680 0.681
## v8 1.053 0.121 8.723 0.000 0.716 0.717
## v9 0.602 0.099 6.090 0.000 0.410 0.410
## v10 0.693 0.101 6.858 0.000 0.471 0.472
## uzamsal =~
## v11 1.000 0.877 0.878
## v12 0.970 0.050 19.334 0.000 0.851 0.852
## v13 0.934 0.051 18.356 0.000 0.819 0.820
## muziksel =~
## v14 (a) 1.000 0.757 0.738
## v15 (a) 1.000 0.757 0.776
## mantiksal =~
## v16 1.000 0.714 0.715
## v17 0.482 0.086 5.581 0.000 0.344 0.345
## v18 0.938 0.092 10.245 0.000 0.670 0.671
## dilsel =~
## v19 1.000 0.178 0.178
## v20 4.815 1.676 2.872 0.004 0.857 0.858
## v21 4.387 1.493 2.938 0.003 0.780 0.782
## bilissel =~
## mantiksal 1.000 0.990 0.990
## dilsel 0.083 0.033 2.542 0.011 0.329 0.329
## uzamsal 0.994 0.095 10.479 0.000 0.802 0.802
## dogal 0.415 0.065 6.361 0.000 0.458 0.458
## bilissel_olmayan =~
## muziksel 1.000 0.438 0.438
## bedensel 1.564 0.366 4.271 0.000 0.762 0.762
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## bilissel ~~
## bilissel_olmyn 0.177 0.041 4.312 0.000 0.753 0.753
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mantiksal 0.010 0.020 0.020
## .v1 0.586 0.048 12.237 0.000 0.586 0.588
## .v2 0.661 0.053 12.454 0.000 0.661 0.663
## .v3 0.509 0.043 11.946 0.000 0.509 0.511
## .v4 0.382 0.034 11.192 0.000 0.382 0.384
## .v5 0.224 0.025 8.939 0.000 0.224 0.225
## .v6 0.184 0.023 7.865 0.000 0.184 0.184
## .v7 0.534 0.062 8.657 0.000 0.534 0.536
## .v8 0.484 0.062 7.766 0.000 0.484 0.485
## .v9 0.829 0.069 12.065 0.000 0.829 0.832
## .v10 0.775 0.066 11.659 0.000 0.775 0.777
## .v11 0.229 0.030 7.742 0.000 0.229 0.229
## .v12 0.273 0.031 8.819 0.000 0.273 0.274
## .v13 0.327 0.033 9.825 0.000 0.327 0.328
## .v14 0.480 0.056 8.509 0.000 0.480 0.456
## .v15 0.379 0.052 7.339 0.000 0.379 0.398
## .v16 0.487 0.052 9.400 0.000 0.487 0.489
## .v17 0.878 0.070 12.567 0.000 0.878 0.881
## .v18 0.548 0.054 10.178 0.000 0.548 0.550
## .v19 0.965 0.075 12.922 0.000 0.965 0.968
## .v20 0.263 0.115 2.288 0.022 0.263 0.264
## .v21 0.388 0.099 3.930 0.000 0.388 0.389
## .dogal 0.325 0.052 6.254 0.000 0.790 0.790
## .bedensel 0.194 0.074 2.604 0.009 0.419 0.419
## .uzamsal 0.275 0.047 5.847 0.000 0.358 0.358
## .muziksel 0.463 0.060 7.683 0.000 0.808 0.808
## .dilsel 0.028 0.019 1.492 0.136 0.892 0.892
## bilissel 0.500 0.076 6.615 0.000 1.000 1.000
## bilissel_olmyn 0.110 0.040 2.749 0.006 1.000 1.0004. İkili faktörlü (bi-factor) modelin uyumunu değerlendiriniz. (Bunu çok denedim fakat yapamadım:( derste size soracağım hocam)
model3 <- "
G =~ v1+v2+v3+v4+v5+v6+v7+v8+v9+v10+v11+v12+v13+v14+v15+v16+v17+v18+v19+v20+v21
f1 =~ v1+v2+v3+v4+v5+v6
f2 =~ v7+v8+v9+v10
f3 =~ v11+v12+v13
f4 =~ a*v14+a*v15
f5 =~ v16+v17+v18
f6 =~ v19+v20+v21
f1 ~~ f2
f1 ~~ f3
f1 ~~ f4
f1 ~~ f5
f1 ~~ f6
f2 ~~ f3
f2 ~~ f4
f2 ~~ f5
f2 ~~ f6
f3 ~~ f4
f3 ~~ f5
f3 ~~ f6
f4 ~~ f5
f4 ~~ f6
f5 ~~ f6
f5 ~~ 0.01*f5
f6 ~~ 0.01*f6
"
fit3 <- cfa(model3, sample.cov = df_matrix_1, sample.nobs = 339)## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.summary(fit3, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 2137 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 81
##
## Number of observations 339
##
## Model Test User Model:
##
## Test statistic 308.221
## Degrees of freedom 150
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3345.560
## Degrees of freedom 210
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.950
## Tucker-Lewis Index (TLI) 0.929
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8572.239
## Loglikelihood unrestricted model (H1) -8418.128
##
## Akaike (AIC) 17306.477
## Bayesian (BIC) 17616.383
## Sample-size adjusted Bayesian (SABIC) 17359.438
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.065
## P-value H_0: RMSEA <= 0.050 0.138
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## G =~
## v1 1.000 2.052 2.055
## v2 0.926 NA 1.901 1.904
## v3 1.118 NA 2.294 2.297
## v4 1.234 NA 2.531 2.535
## v5 1.367 NA 2.806 2.810
## v6 1.415 NA 2.903 2.907
## v7 0.153 NA 0.315 0.315
## v8 0.162 NA 0.333 0.333
## v9 0.064 NA 0.131 0.131
## v10 0.191 NA 0.392 0.393
## v11 0.358 NA 0.734 0.735
## v12 0.328 NA 0.672 0.673
## v13 0.306 NA 0.627 0.628
## v14 0.042 NA 0.087 0.085
## v15 0.106 NA 0.218 0.222
## v16 -5.650 NA -11.591 -11.717
## v17 0.240 NA 0.493 0.494
## v18 0.409 NA 0.840 0.841
## v19 0.422 NA 0.867 0.868
## v20 2.324 NA 4.769 4.776
## v21 3.307 NA 6.785 6.795
## f1 =~
## v1 1.000 1.886 1.888
## v2 1.011 NA 1.907 1.910
## v3 1.217 NA 2.294 2.297
## v4 1.270 NA 2.394 2.398
## v5 1.341 NA 2.529 2.533
## v6 1.464 NA 2.761 2.765
## f2 =~
## v7 1.000 0.651 0.652
## v8 1.033 NA 0.672 0.673
## v9 0.655 NA 0.427 0.427
## v10 0.489 NA 0.319 0.319
## f3 =~
## v11 1.000 0.639 0.640
## v12 1.090 NA 0.696 0.697
## v13 1.090 NA 0.696 0.697
## f4 =~
## v14 (a) 1.000 0.735 0.719
## v15 (a) 1.000 0.735 0.749
## f5 =~
## v16 1.000 0.100 0.101
## v17 0.002 NA 0.000 0.000
## v18 0.001 NA 0.000 0.000
## f6 =~
## v19 1.000 0.100 0.100
## v20 46.519 NA 4.652 4.659
## v21 67.496 NA 6.750 6.760
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f1 ~~
## f2 0.168 NA 0.137 0.137
## f3 0.315 NA 0.262 0.262
## f4 -0.053 NA -0.038 -0.038
## f5 -21.938 NA -116.340 -116.340
## f6 0.179 NA 0.951 0.951
## f2 ~~
## f3 0.110 NA 0.263 0.263
## f4 0.119 NA 0.248 0.248
## f5 -0.622 NA -9.553 -9.553
## f6 0.008 NA 0.117 0.117
## f3 ~~
## f4 -0.016 NA -0.035 -0.035
## f5 -1.546 NA -24.206 -24.206
## f6 0.014 NA 0.227 0.227
## f4 ~~
## f5 0.705 NA 9.594 9.594
## f6 -0.003 NA -0.043 -0.043
## f5 ~~
## f6 -1.213 NA -121.291 -121.291
## G ~~
## f1 -3.677 NA -0.951 -0.951
## f2 -0.119 NA -0.089 -0.089
## f3 -0.278 NA -0.212 -0.212
## f4 0.103 NA 0.068 0.068
## f5 25.170 NA 122.679 122.679
## f6 -0.203 NA -0.989 -0.989
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f5 0.010 1.000 1.000
## f6 0.010 1.000 1.000
## .v1 0.586 NA 0.586 0.588
## .v2 0.638 NA 0.638 0.640
## .v3 0.476 NA 0.476 0.478
## .v4 0.379 NA 0.379 0.380
## .v5 0.218 NA 0.218 0.219
## .v6 0.183 NA 0.183 0.184
## .v7 0.511 NA 0.511 0.512
## .v8 0.474 NA 0.474 0.475
## .v9 0.808 NA 0.808 0.810
## .v10 0.764 NA 0.764 0.766
## .v11 0.249 NA 0.249 0.250
## .v12 0.259 NA 0.259 0.260
## .v13 0.305 NA 0.305 0.306
## .v14 0.489 NA 0.489 0.468
## .v15 0.353 NA 0.353 0.367
## .v16 151.012 NA 151.012 154.290
## .v17 0.734 NA 0.734 0.736
## .v18 0.278 NA 0.278 0.279
## .v19 0.407 NA 0.407 0.409
## .v20 0.517 NA 0.517 0.519
## .v21 0.033 NA 0.033 0.033
## G 4.209 NA 1.000 1.000
## f1 3.556 NA 1.000 1.000
## f2 0.424 NA 1.000 1.000
## f3 0.408 NA 1.000 1.000
## f4 0.540 NA 1.000 1.000❔Soru 2
Motivasyon ölçeğinde 17 madde bulunmaktadır. Maddeler 1-0 olarak puanlanmıştır. “mot.Rds” verisini kullanarak motivasyonun iki faktörlü yapısını tetrakorik korelasyon matrisi kullanarak değerlendiriniz. Kullandığınız kestirim yönteminin kategorik veriye uygun olmasına dikkat ediniz. *Dışsal motivasyon (ext1, ext2, ext3, ext4, ext5, ext6, ext7, ext8, ext9, ext10, ext11, ext12) *İçsel motivasyon (int1, int2, int3, int4, int5) 1. Elde ettiğiniz çıktıda yapmanız gereken modifikasyonları belirleyip, yeni belirlediğiniz modeli tekrar test ediniz. 2. İki model uyumunu karşılaştırıp değerlendiriniz. (Not: İki faktörlü modellerde ikinci düzey DFA yapılamaz.)
🤔Cevap 2
df_mot <- readRDS("mot.Rds")
model_2 <-
"
dissal =~ ext1 + ext2 + ext3 + ext4 + ext5 + ext6 + ext7 + ext8 + ext9 + ext10 + ext11 + ext12
icsel =~ int1 + int2 + int3 + int4 + int5
"
fit1 <- cfa(model_2,
data = df_mot,
ordered = names(df_mot),
estimator = "WLSMV")
summary(fit1, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 42 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 35
##
## Used Total
## Number of observations 794 852
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 492.422 464.935
## Degrees of freedom 118 118
## P-value (Unknown) NA 0.000
## Scaling correction factor 1.136
## Shift parameter 31.654
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 4421.778 3011.080
## Degrees of freedom 136 136
## P-value NA 0.000
## Scaling correction factor 1.491
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.913 0.879
## Tucker-Lewis Index (TLI) 0.899 0.861
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063 0.061
## 90 Percent confidence interval - lower 0.058 0.055
## 90 Percent confidence interval - upper 0.069 0.067
## P-value H_0: RMSEA <= 0.050 0.000 0.001
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.119 0.119
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dissal =~
## ext1 1.000 0.391 0.391
## ext2 1.018 0.173 5.881 0.000 0.398 0.398
## ext3 0.529 0.148 3.562 0.000 0.207 0.207
## ext4 1.494 0.263 5.684 0.000 0.585 0.585
## ext5 0.181 0.175 1.034 0.301 0.071 0.071
## ext6 0.419 0.173 2.425 0.015 0.164 0.164
## ext7 1.422 0.208 6.846 0.000 0.557 0.557
## ext8 2.045 0.274 7.470 0.000 0.801 0.801
## ext9 1.893 0.273 6.930 0.000 0.741 0.741
## ext10 1.577 0.229 6.899 0.000 0.617 0.617
## ext11 2.030 0.276 7.363 0.000 0.794 0.794
## ext12 1.835 0.250 7.330 0.000 0.718 0.718
## icsel =~
## int1 1.000 0.821 0.821
## int2 0.924 0.066 14.092 0.000 0.758 0.758
## int3 0.997 0.050 19.793 0.000 0.818 0.818
## int4 0.906 0.057 15.980 0.000 0.744 0.744
## int5 1.100 0.050 21.863 0.000 0.903 0.903
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dissal ~~
## icsel 0.047 0.017 2.744 0.006 0.148 0.148
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ext1|t1 0.041 0.045 0.922 0.356 0.041 0.041
## ext2|t1 0.584 0.047 12.327 0.000 0.584 0.584
## ext3|t1 0.493 0.047 10.591 0.000 0.493 0.493
## ext4|t1 1.541 0.070 21.954 0.000 1.541 1.541
## ext5|t1 -1.128 0.056 -19.964 0.000 -1.128 -1.128
## ext6|t1 -1.059 0.055 -19.296 0.000 -1.059 -1.059
## ext7|t1 0.242 0.045 5.386 0.000 0.242 0.242
## ext8|t1 1.042 0.055 19.122 0.000 1.042 1.042
## ext9|t1 1.481 0.068 21.880 0.000 1.481 1.481
## ext10|t1 0.622 0.048 13.016 0.000 0.622 0.622
## ext11|t1 1.048 0.055 19.180 0.000 1.048 1.048
## ext12|t1 0.013 0.045 0.284 0.777 0.013 0.013
## int1|t1 -0.657 0.048 -13.632 0.000 -0.657 -0.657
## int2|t1 -1.242 0.060 -20.865 0.000 -1.242 -1.242
## int3|t1 0.194 0.045 4.324 0.000 0.194 0.194
## int4|t1 -0.873 0.051 -17.028 0.000 -0.873 -0.873
## int5|t1 -0.272 0.045 -6.022 0.000 -0.272 -0.272
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ext1 0.847 0.847 0.847
## .ext2 0.841 0.841 0.841
## .ext3 0.957 0.957 0.957
## .ext4 0.658 0.658 0.658
## .ext5 0.995 0.995 0.995
## .ext6 0.973 0.973 0.973
## .ext7 0.690 0.690 0.690
## .ext8 0.359 0.359 0.359
## .ext9 0.451 0.451 0.451
## .ext10 0.619 0.619 0.619
## .ext11 0.369 0.369 0.369
## .ext12 0.484 0.484 0.484
## .int1 0.327 0.327 0.327
## .int2 0.425 0.425 0.425
## .int3 0.331 0.331 0.331
## .int4 0.447 0.447 0.447
## .int5 0.185 0.185 0.185
## dissal 0.153 0.038 3.984 0.000 1.000 1.000
## icsel 0.673 0.051 13.305 0.000 1.000 1.000model_3_fit <- cfa(model_2, data = df_mot)
summary(model_3_fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 82 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Used Total
## Number of observations 794 852
##
## Model Test User Model:
##
## Test statistic 604.693
## Degrees of freedom 118
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2090.130
## Degrees of freedom 136
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.751
## Tucker-Lewis Index (TLI) 0.713
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5829.536
## Loglikelihood unrestricted model (H1) -5527.189
##
## Akaike (AIC) 11729.071
## Bayesian (BIC) 11892.769
## Sample-size adjusted Bayesian (SABIC) 11781.625
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.072
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.078
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.011
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dissal =~
## ext1 1.000 0.142 0.285
## ext2 0.893 0.182 4.904 0.000 0.127 0.283
## ext3 0.414 0.149 2.778 0.005 0.059 0.127
## ext4 0.568 0.107 5.333 0.000 0.081 0.336
## ext5 0.019 0.100 0.189 0.850 0.003 0.008
## ext6 0.195 0.108 1.812 0.070 0.028 0.079
## ext7 1.444 0.248 5.813 0.000 0.205 0.419
## ext8 1.403 0.223 6.296 0.000 0.200 0.561
## ext9 0.736 0.127 5.785 0.000 0.105 0.413
## ext10 1.568 0.255 6.143 0.000 0.223 0.504
## ext11 1.415 0.224 6.311 0.000 0.201 0.568
## ext12 1.835 0.296 6.197 0.000 0.261 0.522
## icsel =~
## int1 1.000 0.291 0.668
## int2 0.511 0.046 11.125 0.000 0.149 0.482
## int3 1.037 0.077 13.495 0.000 0.302 0.612
## int4 0.741 0.060 12.403 0.000 0.216 0.549
## int5 1.252 0.083 15.006 0.000 0.365 0.747
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dissal ~~
## icsel 0.005 0.002 2.190 0.029 0.112 0.112
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ext1 0.229 0.012 19.191 0.000 0.229 0.919
## .ext2 0.185 0.010 19.200 0.000 0.185 0.920
## .ext3 0.211 0.011 19.789 0.000 0.211 0.984
## .ext4 0.051 0.003 18.865 0.000 0.051 0.887
## .ext5 0.113 0.006 19.924 0.000 0.113 1.000
## .ext6 0.123 0.006 19.873 0.000 0.123 0.994
## .ext7 0.199 0.011 18.151 0.000 0.199 0.825
## .ext8 0.087 0.005 16.057 0.000 0.087 0.685
## .ext9 0.053 0.003 18.211 0.000 0.053 0.830
## .ext10 0.146 0.009 17.062 0.000 0.146 0.746
## .ext11 0.085 0.005 15.918 0.000 0.085 0.677
## .ext12 0.182 0.011 16.773 0.000 0.182 0.727
## .int1 0.105 0.007 14.935 0.000 0.105 0.554
## .int2 0.073 0.004 18.124 0.000 0.073 0.768
## .int3 0.153 0.009 16.281 0.000 0.153 0.626
## .int4 0.108 0.006 17.333 0.000 0.108 0.699
## .int5 0.105 0.009 12.196 0.000 0.105 0.442
## dissal 0.020 0.006 3.434 0.001 1.000 1.000
## icsel 0.085 0.009 9.205 0.000 1.000 1.000Yeni kurulan modifikasyonlu model
model_2_y <- '
dissal =~ ext1 + ext2 + ext4 + ext7 + ext8 + ext9 + ext10 + ext11 + ext12
icsel =~ int1 + int2 + int3 + int4 + int5
'
fit5 <- cfa(model_2_y,
data = df_mot,
ordered = names(df_mot),
estimator = "WLSMV")
summary(fit5, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 39 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 29
##
## Used Total
## Number of observations 796 852
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 246.192 251.341
## Degrees of freedom 76 76
## P-value (Unknown) NA 0.000
## Scaling correction factor 1.055
## Shift parameter 17.925
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 4117.723 2850.807
## Degrees of freedom 91 91
## P-value NA 0.000
## Scaling correction factor 1.459
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958 0.936
## Tucker-Lewis Index (TLI) 0.949 0.924
##
## Robust Comparative Fit Index (CFI) 0.726
## Robust Tucker-Lewis Index (TLI) 0.672
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.054
## 90 Percent confidence interval - lower 0.046 0.047
## 90 Percent confidence interval - upper 0.061 0.061
## P-value H_0: RMSEA <= 0.050 0.238 0.186
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.172
## 90 Percent confidence interval - lower 0.149
## 90 Percent confidence interval - upper 0.196
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.105 0.105
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dissal =~
## ext1 1.000 0.377 0.377
## ext2 1.052 0.182 5.772 0.000 0.397 0.397
## ext4 1.591 0.282 5.644 0.000 0.600 0.600
## ext7 1.474 0.225 6.563 0.000 0.556 0.556
## ext8 2.161 0.302 7.149 0.000 0.814 0.814
## ext9 1.946 0.291 6.684 0.000 0.734 0.734
## ext10 1.669 0.251 6.651 0.000 0.629 0.629
## ext11 2.101 0.300 7.011 0.000 0.792 0.792
## ext12 1.898 0.270 7.029 0.000 0.715 0.715
## icsel =~
## int1 1.000 0.820 0.820
## int2 0.926 0.066 14.099 0.000 0.758 0.758
## int3 1.000 0.051 19.772 0.000 0.820 0.820
## int4 0.906 0.057 15.938 0.000 0.743 0.743
## int5 1.100 0.050 21.880 0.000 0.901 0.901
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## dissal ~~
## icsel 0.040 0.017 2.410 0.016 0.130 0.130
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ext1|t1 0.041 0.044 0.921 0.357 0.041 0.041
## ext2|t1 0.582 0.047 12.312 0.000 0.582 0.582
## ext4|t1 1.542 0.070 21.982 0.000 1.542 1.542
## ext7|t1 0.242 0.045 5.379 0.000 0.242 0.242
## ext8|t1 1.044 0.054 19.163 0.000 1.044 1.044
## ext9|t1 1.483 0.068 21.910 0.000 1.483 1.483
## ext10|t1 0.624 0.048 13.069 0.000 0.624 0.624
## ext11|t1 1.049 0.055 19.221 0.000 1.049 1.049
## ext12|t1 0.009 0.044 0.213 0.832 0.009 0.009
## int1|t1 -0.655 0.048 -13.616 0.000 -0.655 -0.655
## int2|t1 -1.244 0.060 -20.901 0.000 -1.244 -1.244
## int3|t1 0.193 0.045 4.319 0.000 0.193 0.193
## int4|t1 -0.874 0.051 -17.074 0.000 -0.874 -0.874
## int5|t1 -0.274 0.045 -6.085 0.000 -0.274 -0.274
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ext1 0.858 0.858 0.858
## .ext2 0.843 0.843 0.843
## .ext4 0.640 0.640 0.640
## .ext7 0.691 0.691 0.691
## .ext8 0.337 0.337 0.337
## .ext9 0.462 0.462 0.462
## .ext10 0.604 0.604 0.604
## .ext11 0.373 0.373 0.373
## .ext12 0.488 0.488 0.488
## .int1 0.328 0.328 0.328
## .int2 0.425 0.425 0.425
## .int3 0.328 0.328 0.328
## .int4 0.448 0.448 0.448
## .int5 0.188 0.188 0.188
## dissal 0.142 0.038 3.785 0.000 1.000 1.000
## icsel 0.672 0.051 13.286 0.000 1.000 1.000Model uyumunun karşılaştırılması
fitMeasures(model_3_fit, data=df_mot)## Warning: lavaan->fitMeasures():
## Unknown argument 'data' for 'fitMeasures'## npar fmin chisq
## 35.000 0.381 604.693
## df pvalue baseline.chisq
## 118.000 0.000 2090.130
## baseline.df baseline.pvalue cfi
## 136.000 0.000 0.751
## tli nnfi rfi
## 0.713 0.713 0.667
## nfi pnfi ifi
## 0.711 0.617 0.753
## rni logl unrestricted.logl
## 0.751 -5829.536 -5527.189
## aic bic ntotal
## 11729.071 11892.769 794.000
## bic2 rmsea rmsea.ci.lower
## 11781.625 0.072 0.066
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.078 0.900 0.000
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.011 0.080
## rmr rmr_nomean srmr
## 0.010 0.010 0.064
## srmr_bentler srmr_bentler_nomean crmr
## 0.064 0.064 0.068
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.068 0.064 0.064
## cn_05 cn_01 gfi
## 190.545 206.689 0.918
## agfi pgfi mfi
## 0.894 0.708 0.736
## ecvi
## 0.850fitMeasures(fit5, data=df_mot)## Warning: lavaan->fitMeasures():
## Unknown argument 'data' for 'fitMeasures'## npar fmin
## 29.000 0.155
## chisq df
## 246.192 76.000
## pvalue chisq.scaled
## NA 251.341
## df.scaled pvalue.scaled
## 76.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.055 4117.723
## baseline.df baseline.pvalue
## 91.000 NA
## baseline.chisq.scaled baseline.df.scaled
## 2850.807 91.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.459
## cfi tli
## 0.958 0.949
## cfi.scaled tli.scaled
## 0.936 0.924
## cfi.robust tli.robust
## 0.726 0.672
## nnfi rfi
## 0.949 0.928
## nfi pnfi
## 0.940 0.785
## ifi rni
## 0.958 0.958
## nnfi.scaled rfi.scaled
## 0.924 0.894
## nfi.scaled pnfi.scaled
## 0.912 0.762
## ifi.scaled rni.scaled
## 0.937 0.936
## nnfi.robust rni.robust
## 0.672 0.726
## rmsea rmsea.ci.lower
## 0.053 0.046
## rmsea.ci.upper rmsea.ci.level
## 0.061 0.900
## rmsea.pvalue rmsea.close.h0
## 0.238 0.050
## rmsea.notclose.pvalue rmsea.notclose.h0
## 0.000 0.080
## rmsea.scaled rmsea.ci.lower.scaled
## 0.054 0.047
## rmsea.ci.upper.scaled rmsea.pvalue.scaled
## 0.061 0.186
## rmsea.notclose.pvalue.scaled rmsea.robust
## 0.000 0.172
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.149 0.196
## rmsea.pvalue.robust rmsea.notclose.pvalue.robust
## 0.000 1.000
## rmr rmr_nomean
## 0.098 0.105
## srmr srmr_bentler
## 0.105 0.098
## srmr_bentler_nomean crmr
## 0.105 0.105
## crmr_nomean srmr_mplus
## 0.112 NA
## srmr_mplus_nomean cn_05
## NA 315.364
## cn_01 gfi
## 348.404 0.966
## agfi pgfi
## 0.952 0.699
## mfi wrmr
## 0.898 1.531
## attr(,"scaled.test")
## [1] "scaled.shifted"❔Soru 3
Aidiyet ölçeğinde 12 madde bulunmaktadır. Maddeler 1-0 olarak puanlanmıştır. “aidiyet.Rds” verisini kullanarak aidiyetin üç faktörlü yapısını tetrakorik korelasyon matrisi kullanarak doğrulayınız. Kullandığınız kestirim yönteminin kategorik veriye uygun olmasına dikkat ediniz. * Kurumsal (kurumsal1, kurumsal2, kurumsal3, kurumsal4) * Katılımsal (katilimsal1, katilimsal2, katilimsal3, katilimsal4, katilimsal5) * Bireysel (bireysel1, bireysel2, bireysel3, bireysel4) 1. Üç faktörlü modelin uyumunu, 2. İkinci dereceli üç faktörlü modelin uyumunu değerlendiriniz. Kolaylıklar dilerim.
🤔Cevap 3
library(lavaan)
library(semPlot)
library(FCO)## Warning: package 'FCO' was built under R version 4.5.3aidiyet <- readRDS("aidiyet.Rds")model_1 <-
"
kurumsal =~ kurumsal1 + kurumsal2 + kurumsal3
katilimsal =~ katilimsal1 + katilimsal2 + katilimsal3
bireysel =~ bireysel1 + bireysel2 + bireysel3 + bireysel4
"model_1_fit <- cfa(model_1, data = aidiyet)
summary(model_1_fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 73 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Number of observations 794
##
## Model Test User Model:
##
## Test statistic 161.242
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 934.260
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.855
## Tucker-Lewis Index (TLI) 0.796
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4487.646
## Loglikelihood unrestricted model (H1) -4407.025
##
## Akaike (AIC) 9021.293
## Bayesian (BIC) 9128.866
## Sample-size adjusted Bayesian (SABIC) 9055.828
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.082
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.102
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.059
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## kurumsal =~
## kurumsal1 1.000 0.168 0.337
## kurumsal2 0.580 0.201 2.886 0.004 0.098 0.218
## kurumsal3 1.015 0.289 3.516 0.000 0.171 0.369
## katilimsal =~
## katilimsal1 1.000 0.321 0.737
## katilimsal2 0.489 0.059 8.348 0.000 0.157 0.512
## katilimsal3 0.763 0.092 8.274 0.000 0.245 0.495
## bireysel =~
## bireysel1 1.000 0.120 0.282
## bireysel2 1.497 0.267 5.610 0.000 0.179 0.358
## bireysel3 3.037 0.460 6.601 0.000 0.363 0.762
## bireysel4 2.824 0.426 6.635 0.000 0.338 0.730
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## kurumsal ~~
## katilimsal 0.020 0.006 3.451 0.001 0.374 0.374
## bireysel 0.009 0.003 3.474 0.001 0.437 0.437
## katilimsal ~~
## bireysel 0.013 0.003 4.736 0.000 0.344 0.344
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .kurumsal1 0.221 0.015 15.083 0.000 0.221 0.886
## .kurumsal2 0.191 0.011 18.122 0.000 0.191 0.952
## .kurumsal3 0.186 0.013 13.852 0.000 0.186 0.864
## .katilimsal1 0.086 0.012 7.186 0.000 0.086 0.456
## .katilimsal2 0.069 0.004 15.437 0.000 0.069 0.737
## .katilimsal3 0.185 0.012 15.926 0.000 0.185 0.755
## .bireysel1 0.165 0.009 19.316 0.000 0.165 0.920
## .bireysel2 0.218 0.012 18.881 0.000 0.218 0.872
## .bireysel3 0.096 0.012 8.232 0.000 0.096 0.420
## .bireysel4 0.100 0.010 9.582 0.000 0.100 0.468
## kurumsal 0.028 0.011 2.530 0.011 1.000 1.000
## katilimsal 0.103 0.014 7.332 0.000 1.000 1.000
## bireysel 0.014 0.004 3.490 0.000 1.000 1.000#library(semoutput)
#sem_sig(model_1_fit)fitmeasures(model_1_fit,fit.measures = c("chisq" ,"df" , "pvalue","cfi","tli","rmsea","rmsea.ci.lower",
"rmsea.ci.upper","srmr"))## chisq df pvalue cfi tli
## 161.242 32.000 0.000 0.855 0.796
## rmsea rmsea.ci.lower rmsea.ci.upper srmr
## 0.071 0.061 0.082 0.059library(semPlot)
semPaths(model_1_fit, what="par",
style="lisrel",layout="tree",residuals = TRUE,rotation = 2 )
model_2order <- "
kurumsal =~ kurumsal1 + kurumsal2 + kurumsal3
katilimsal =~ katilimsal1 + katilimsal2 + katilimsal3
bireysel =~ bireysel1 + bireysel2 + bireysel3 + bireysel4
# ikinci duzey model
aidiyet =~ kurumsal + katilimsal + bireysel
"fit_model_2order <- cfa(model_2order, aidiyet)
summary(fit_model_2order, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 74 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Number of observations 794
##
## Model Test User Model:
##
## Test statistic 161.242
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 934.260
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.855
## Tucker-Lewis Index (TLI) 0.796
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4487.646
## Loglikelihood unrestricted model (H1) -4407.025
##
## Akaike (AIC) 9021.293
## Bayesian (BIC) 9128.866
## Sample-size adjusted Bayesian (SABIC) 9055.828
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.082
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.102
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.059
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## kurumsal =~
## kurumsal1 1.000 0.168 0.337
## kurumsal2 0.580 0.201 2.886 0.004 0.098 0.218
## kurumsal3 1.015 0.289 3.516 0.000 0.171 0.369
## katilimsal =~
## katilimsal1 1.000 0.321 0.737
## katilimsal2 0.489 0.059 8.348 0.000 0.157 0.512
## katilimsal3 0.763 0.092 8.274 0.000 0.245 0.495
## bireysel =~
## bireysel1 1.000 0.120 0.282
## bireysel2 1.497 0.267 5.610 0.000 0.179 0.358
## bireysel3 3.037 0.460 6.601 0.000 0.363 0.762
## bireysel4 2.824 0.426 6.635 0.000 0.338 0.730
## aidiyet =~
## kurumsal 1.000 0.690 0.690
## katilimsal 1.497 0.416 3.598 0.000 0.542 0.542
## bireysel 0.653 0.216 3.016 0.003 0.634 0.634
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .kurumsal1 0.221 0.015 15.083 0.000 0.221 0.886
## .kurumsal2 0.191 0.011 18.122 0.000 0.191 0.952
## .kurumsal3 0.186 0.013 13.852 0.000 0.186 0.864
## .katilimsal1 0.086 0.012 7.186 0.000 0.086 0.456
## .katilimsal2 0.069 0.004 15.437 0.000 0.069 0.737
## .katilimsal3 0.185 0.012 15.926 0.000 0.185 0.755
## .bireysel1 0.165 0.009 19.316 0.000 0.165 0.920
## .bireysel2 0.218 0.012 18.881 0.000 0.218 0.872
## .bireysel3 0.096 0.012 8.232 0.000 0.096 0.420
## .bireysel4 0.100 0.010 9.582 0.000 0.100 0.468
## .kurumsal 0.015 0.008 1.750 0.080 0.524 0.524
## .katilimsal 0.073 0.014 5.180 0.000 0.706 0.706
## .bireysel 0.009 0.003 2.923 0.003 0.598 0.598
## aidiyet 0.013 0.006 2.138 0.033 1.000 1.000🥺Öğrenme Günlüğü
R ile DFA analizi yapmak gerçekten pratik. Sadece biraz daha örneklerle üzerine çalışmam gerektiğini düşünüyorum. Txt dosyası ile intercorrelations’lar verilince veri seti üzerinde nasıl işlem yapacağım konusunda kafam karıştı. Makalelerde yapılan analizler üzerinden ödev vermeniz de bizim açımızdan R’ı yazacağımız makalelerde nasıl kullanabileceğimize dair bir örnek oluşturuyor. Karakter, nesne vb. durumlara göre fonksiyonların kullanım durumlarının değiştirmesi R’da benim kafamı karıştıran bir nokta oluyor :( 1.soruda veri seti ile çalışırken o sebeple çok takıldım. Bifaktör modeli yapamadım. Kendime de diyorum ah Selin ah kaçıncı derstesin artık….