Import Data dan Visualisasi Awal
# Import Data
data <- read.csv("C:/Users/HP/OneDrive/Semester 4/Analisis Multivariat/ResearchInformation3.csv", header = TRUE)
# Visualisasi Awal
options(repr.plot.width = 16, repr.plot.height = 4)
n_cols <- ncol(data)
par(mfrow = c(1, min(4, n_cols)), mar = c(4, 4, 2, 1))
for (col in colnames(data)) {
if (is.numeric(data[[col]])) {
hist(data[[col]], main = paste("Histogram of", col), xlab = col, col = "lightblue", border = "black")
} else {
counts <- table(data[[col]])
barplot(counts, main = paste("Barplot of", col), xlab = col, col = "lightgreen", border = "black", las = 2)
}
}
par(mfrow = c(1, 1))
Pre-Processing Data
Missing Value dan Duplikasi
# Cek Missing Value
sum(is.na(data))
## [1] 0
colMeans(is.na(data)) * 100
## Department Gender HSC SSC Income Hometown
## 0 0 0 0 0 0
## Computer Preparation Gaming Attendance Job English
## 0 0 0 0 0 0
## Extra Semester Last Overall
## 0 0 0 0
# Cek duplikat
duplikat <- data[duplicated(data), ]
duplikat
## [1] Department Gender HSC SSC Income Hometown
## [7] Computer Preparation Gaming Attendance Job English
## [13] Extra Semester Last Overall
## <0 rows> (or 0-length row.names)
Konversi ke Numerik
data_numeric <- data
for (col in colnames(data_numeric)) {
if (!is.numeric(data_numeric[[col]])) {
data_numeric[[col]] <- as.numeric(as.factor(data_numeric[[col]]))
}
}
Visualisasi Setelah Pre-Processing
options(repr.plot.width = 16, repr.plot.height = 4)
par(mfrow = c(1, 4), mar = c(4, 4, 2, 1))
num_plots <- 0
for (col in colnames(data_numeric)) {
if (is.numeric(data_numeric[[col]])) {
hist(data_numeric[[col]], main = paste("Histogram of", col), xlab = col, col = "lightblue", border = "black")
num_plots <- num_plots + 1
}
}
par(mfrow = c(1, 1))
Normalisasi Z-score
data_z <- as.data.frame(lapply(data_numeric, function(x) {
if (is.numeric(x)) (x - mean(x)) / sd(x) else x
}))
data_z
## Department Gender HSC SSC Income Hometown
## 1 -1.0444335 0.7085396 0.024199553 0.206996133 -0.42589497 0.8713043
## 2 -1.0444335 -1.4084909 1.396559161 0.664743679 1.37176104 -1.1453766
## 3 -1.0444335 0.7085396 1.542944186 0.178386911 0.29316743 0.8713043
## 4 -1.0444335 0.7085396 -0.286868625 -0.765717403 -1.50448858 -1.1453766
## 5 -1.0444335 -1.4084909 -3.598829813 -4.570743882 0.29316743 0.8713043
## 6 -0.2782048 0.7085396 1.085490983 -2.053132377 0.29316743 0.8713043
## 7 -0.2782048 0.7085396 0.481652756 0.664743679 -1.50448858 0.8713043
## 8 -0.2782048 0.7085396 0.628037780 0.121168468 1.37176104 -1.1453766
## 9 -0.2782048 0.7085396 -1.531141337 -0.765717403 -0.06636377 -1.1453766
## 10 -0.2782048 -1.4084909 -1.512843208 0.521697571 0.65269863 -1.1453766
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## 404 -0.2782048 0.7085396 1.542944186 0.664743679 0.29316743 0.8713043
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## 408 -0.2782048 0.7085396 -0.597936803 0.664743679 0.29316743 -1.1453766
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## 411 -0.2782048 -1.4084909 1.542944186 -0.451015965 0.29316743 0.8713043
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## 413 -0.2782048 0.7085396 0.628037780 0.350042241 -1.14495738 0.8713043
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## 415 -0.2782048 0.7085396 0.481652756 0.664743679 -1.50448858 -1.1453766
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## 417 -0.2782048 0.7085396 1.542944186 0.664743679 0.29316743 -1.1453766
## 418 -0.2782048 0.7085396 1.542944186 0.664743679 0.29316743 0.8713043
## 419 -0.2782048 -1.4084909 1.542944186 0.664743679 -0.42589497 0.8713043
## 420 -0.2782048 -1.4084909 1.542944186 0.664743679 -1.50448858 0.8713043
## 421 -0.2782048 -1.4084909 0.024199553 0.664743679 -1.50448858 -1.1453766
## 422 -0.2782048 -1.4084909 1.542944186 0.664743679 1.37176104 -1.1453766
## 423 -0.2782048 0.7085396 1.085490983 0.664743679 0.29316743 -1.1453766
## 424 -0.2782048 0.7085396 1.542944186 0.664743679 0.29316743 0.8713043
## 425 -0.2782048 -1.4084909 0.262075218 0.664743679 1.37176104 0.8713043
## 426 -0.2782048 0.7085396 -0.634533059 -1.080418841 1.37176104 0.8713043
## 427 -0.2782048 0.7085396 -0.286868625 -1.080418841 -0.78542618 0.8713043
## 428 -0.2782048 -1.4084909 0.481652756 0.664743679 -1.50448858 -1.1453766
## 429 -0.2782048 0.7085396 0.024199553 0.149777690 0.29316743 0.8713043
## 430 -0.2782048 0.7085396 -0.744321828 0.035340803 1.37176104 -1.1453766
## 431 -0.2782048 0.7085396 0.481652756 0.521697571 1.37176104 0.8713043
## 432 -0.2782048 -1.4084909 1.542944186 0.664743679 -1.50448858 0.8713043
## 433 -0.2782048 -1.4084909 -0.286868625 -0.250751413 0.29316743 -1.1453766
## 434 -0.2782048 0.7085396 0.628037780 -0.050486862 1.37176104 0.8713043
## 435 -0.2782048 0.7085396 1.542944186 0.664743679 0.29316743 -1.1453766
## 436 -0.2782048 0.7085396 0.774422805 0.664743679 0.29316743 -1.1453766
## 437 -0.2782048 0.7085396 1.542944186 0.664743679 1.37176104 -1.1453766
## 438 -0.2782048 -1.4084909 1.542944186 0.664743679 -1.50448858 -1.1453766
## 439 -0.2782048 0.7085396 1.542944186 0.664743679 -0.42589497 0.8713043
## 440 -0.2782048 0.7085396 1.542944186 0.664743679 -0.42589497 0.8713043
## 441 -0.2782048 0.7085396 1.542944186 0.664743679 -0.42589497 -1.1453766
## 442 -0.2782048 0.7085396 0.774422805 0.664743679 -1.50448858 -1.1453766
## 443 -0.2782048 -1.4084909 1.396559161 0.664743679 -1.50448858 -1.1453766
## 444 -0.2782048 0.7085396 0.316969603 0.664743679 0.29316743 0.8713043
## 445 -0.2782048 0.7085396 1.542944186 0.664743679 0.29316743 -1.1453766
## 446 -0.2782048 -1.4084909 1.542944186 0.664743679 0.29316743 0.8713043
## 447 -0.2782048 -1.4084909 1.542944186 0.664743679 0.29316743 -1.1453766
## 448 -0.2782048 -1.4084909 0.024199553 0.664743679 1.37176104 0.8713043
## 449 0.4880240 0.7085396 0.024199553 0.664743679 0.29316743 0.8713043
## 450 0.4880240 0.7085396 1.085490983 0.178386911 -0.42589497 0.8713043
## 451 0.4880240 0.7085396 1.359962905 0.664743679 -1.50448858 -1.1453766
## 452 0.4880240 0.7085396 1.542944186 0.178386911 0.29316743 0.8713043
## 453 1.2542527 0.7085396 -1.348160056 -1.252074171 0.29316743 -1.1453766
## 454 2.0204815 -1.4084909 -1.201775031 -1.623994052 1.37176104 -1.1453766
## 455 2.0204815 0.7085396 0.316969603 -1.080418841 0.29316743 0.8713043
## 456 2.0204815 -1.4084909 0.628037780 -2.196178485 1.37176104 -1.1453766
## 457 2.0204815 0.7085396 -1.055390006 0.321433020 0.29316743 0.8713043
## 458 2.0204815 -1.4084909 1.542944186 0.664743679 0.29316743 0.8713043
## 459 2.0204815 -1.4084909 1.542944186 0.664743679 -0.42589497 0.8713043
## 460 2.0204815 -1.4084909 1.542944186 0.664743679 -0.42589497 -1.1453766
## 461 2.0204815 0.7085396 0.170584578 -1.023200398 1.37176104 0.8713043
## 462 2.0204815 -1.4084909 0.170584578 0.664743679 0.29316743 0.8713043
## 463 2.0204815 -1.4084909 0.170584578 0.664743679 0.29316743 -1.1453766
## 464 2.0204815 -1.4084909 0.170584578 -0.250751413 -1.50448858 -1.1453766
## 465 2.0204815 0.7085396 1.542944186 0.664743679 0.29316743 -1.1453766
## 466 2.0204815 -1.4084909 0.774422805 0.664743679 0.29316743 0.8713043
## 467 2.0204815 0.7085396 1.542944186 0.664743679 0.29316743 0.8713043
## 468 2.7867102 0.7085396 -1.622631977 0.149777690 1.37176104 -1.1453766
## 469 2.7867102 -1.4084909 0.939105958 -0.594062073 1.37176104 -1.1453766
## 470 2.7867102 0.7085396 -0.597936803 -5.057100650 -0.42589497 0.8713043
## 471 2.7867102 0.7085396 0.481652756 -2.367833815 0.29316743 0.8713043
## 472 2.7867102 -1.4084909 0.628037780 -1.395120279 0.29316743 0.8713043
## 473 2.7867102 -1.4084909 -1.970296411 -4.112996336 0.29316743 0.8713043
## 474 2.7867102 0.7085396 0.682932165 -2.453661480 -0.42589497 0.8713043
## 475 2.7867102 -1.4084909 0.628037780 -1.223464949 -1.50448858 0.8713043
## 476 2.7867102 0.7085396 -0.286868625 -2.196178485 1.37176104 -1.1453766
## 477 2.7867102 -1.4084909 -2.391153358 -3.226110465 -0.42589497 -1.1453766
## 478 2.7867102 0.7085396 -0.286868625 -1.252074171 1.37176104 -1.1453766
## 479 2.7867102 0.7085396 -1.805613258 -4.570743882 0.29316743 -1.1453766
## 480 2.7867102 -1.4084909 0.481652756 -0.136314527 0.29316743 -1.1453766
## 481 3.5529390 -1.4084909 1.542944186 0.664743679 -0.42589497 0.8713043
## 482 3.5529390 -1.4084909 -1.055390006 0.264214576 0.29316743 0.8713043
## 483 4.3191677 -1.4084909 -1.201775031 -2.196178485 1.37176104 -1.1453766
## 484 4.3191677 0.7085396 1.542944186 0.493088350 -1.50448858 -1.1453766
## 485 4.3191677 0.7085396 1.542944186 -0.250751413 -0.42589497 -1.1453766
## 486 4.3191677 -1.4084909 0.170584578 -2.997236692 0.29316743 0.8713043
## 487 4.3191677 -1.4084909 1.542944186 0.350042241 -1.50448858 0.8713043
## 488 4.3191677 0.7085396 1.542944186 0.035340803 0.29316743 0.8713043
## 489 5.0853964 0.7085396 -1.201775031 -2.196178485 0.29316743 0.8713043
## 490 5.0853964 0.7085396 0.628037780 -2.196178485 1.37176104 -1.1453766
## 491 5.8516252 -1.4084909 0.737826549 -3.140282800 0.29316743 -1.1453766
## 492 5.8516252 0.7085396 0.133988321 -0.765717403 -0.42589497 0.8713043
## 493 5.8516252 -1.4084909 -0.597936803 0.664743679 0.29316743 0.8713043
## Computer Preparation Gaming Attendance Job English
## 1 -0.2703476 2.083050 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 2 -0.2703476 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 3 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 0.4939838
## 4 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 5 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 6 -0.2703476 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 7 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 8 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
## 9 0.5277444 -1.010154 0.7257994 2.3176632 -0.2718894 -0.6547616
## 10 -0.2703476 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 11 -0.2703476 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 12 0.5277444 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 13 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 14 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 15 -1.0684396 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 16 1.3258365 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 17 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 18 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 19 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 20 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 21 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 22 -1.0684396 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 23 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 24 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 25 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 26 0.5277444 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 27 -0.2703476 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 28 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 29 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 30 -0.2703476 -1.010154 -2.2436539 -0.4191223 -0.2718894 -1.8035071
## 31 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 32 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 33 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 34 -1.0684396 -1.010154 0.7257994 2.3176632 -0.2718894 0.4939838
## 35 0.5277444 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 36 0.5277444 -1.010154 0.7257994 -1.7875151 3.6705063 0.4939838
## 37 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 38 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 39 -1.8665317 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 40 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 41 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 42 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 43 -1.8665317 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 44 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 45 -1.0684396 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 46 -1.0684396 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 47 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 48 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 49 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 -0.6547616
## 50 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 51 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 52 -1.8665317 2.083050 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 53 -1.8665317 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 54 -1.8665317 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 55 -1.0684396 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 56 -0.2703476 -1.010154 0.7257994 -1.7875151 3.6705063 0.4939838
## 57 1.3258365 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 58 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 1.6427293
## 59 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 60 -1.0684396 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 61 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 62 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 63 -1.8665317 0.536448 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 64 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 65 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 -0.6547616
## 66 0.5277444 -1.010154 0.7257994 0.9492704 3.6705063 -0.6547616
## 67 -1.8665317 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 68 1.3258365 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 69 1.3258365 0.536448 -2.2436539 0.9492704 3.6705063 1.6427293
## 70 1.3258365 -1.010154 0.7257994 -0.4191223 3.6705063 0.4939838
## 71 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 72 -1.8665317 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 73 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 74 -1.8665317 0.536448 0.7257994 -0.4191223 3.6705063 0.4939838
## 75 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 76 -1.8665317 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 77 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 78 0.5277444 2.083050 0.7257994 0.9492704 -0.2718894 0.4939838
## 79 0.5277444 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 80 -1.0684396 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 81 -1.8665317 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 82 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 83 -0.2703476 -1.010154 0.7257994 -1.7875151 -0.2718894 0.4939838
## 84 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 85 0.5277444 -1.010154 0.7257994 -0.4191223 3.6705063 0.4939838
## 86 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 87 -0.2703476 2.083050 0.7257994 0.9492704 3.6705063 -0.6547616
## 88 -0.2703476 0.536448 -0.7589272 0.9492704 3.6705063 -0.6547616
## 89 -1.8665317 -1.010154 0.7257994 0.9492704 -0.2718894 -0.6547616
## 90 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 91 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 92 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 93 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 94 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 95 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 96 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 97 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 98 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 99 -1.8665317 -1.010154 0.7257994 -0.4191223 3.6705063 0.4939838
## 100 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 101 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 102 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 103 1.3258365 -1.010154 0.7257994 -0.4191223 3.6705063 0.4939838
## 104 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 105 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 106 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 107 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 108 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 109 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 110 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 111 -1.8665317 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 112 -0.2703476 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
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## 331 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 332 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 333 1.3258365 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 334 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 0.4939838
## 335 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 336 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 0.4939838
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## 339 1.3258365 0.536448 -0.7589272 -0.4191223 -0.2718894 -0.6547616
## 340 1.3258365 0.536448 0.7257994 -0.4191223 -0.2718894 1.6427293
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## 342 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
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## 344 0.5277444 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
## 345 -0.2703476 0.536448 -0.7589272 -0.4191223 -0.2718894 -0.6547616
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## 347 1.3258365 0.536448 -0.7589272 -0.4191223 -0.2718894 -0.6547616
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## 350 -0.2703476 2.083050 -0.7589272 0.9492704 -0.2718894 -1.8035071
## 351 0.5277444 2.083050 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 352 0.5277444 0.536448 -0.7589272 0.9492704 3.6705063 0.4939838
## 353 1.3258365 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 354 1.3258365 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 355 0.5277444 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
## 356 1.3258365 0.536448 0.7257994 -0.4191223 -0.2718894 1.6427293
## 357 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 358 0.5277444 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 359 0.5277444 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 360 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
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## 362 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 363 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 364 0.5277444 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 365 0.5277444 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
## 366 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
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## 368 -1.8665317 0.536448 0.7257994 0.9492704 -0.2718894 1.6427293
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## 370 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
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## 373 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 1.6427293
## 374 1.3258365 0.536448 -0.7589272 0.9492704 3.6705063 -1.8035071
## 375 1.3258365 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 376 -1.8665317 0.536448 0.7257994 0.9492704 -0.2718894 1.6427293
## 377 -0.2703476 0.536448 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 378 0.5277444 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 379 0.5277444 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 380 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 381 0.5277444 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 382 1.3258365 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 383 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 384 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 385 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 386 0.5277444 -1.010154 -0.7589272 0.9492704 -0.2718894 0.4939838
## 387 0.5277444 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 388 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 1.6427293
## 389 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 390 1.3258365 -1.010154 -0.7589272 0.9492704 3.6705063 -1.8035071
## 391 1.3258365 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 392 -1.0684396 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 393 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 394 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 395 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 396 0.5277444 -1.010154 0.7257994 0.9492704 3.6705063 -0.6547616
## 397 1.3258365 -1.010154 0.7257994 0.9492704 -0.2718894 0.4939838
## 398 1.3258365 -1.010154 -0.7589272 0.9492704 -0.2718894 0.4939838
## 399 1.3258365 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 400 -0.2703476 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 401 0.5277444 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 402 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 403 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 404 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 405 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 406 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 407 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 408 0.5277444 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 409 -0.2703476 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 410 -0.2703476 0.536448 -2.2436539 0.9492704 -0.2718894 0.4939838
## 411 -1.8665317 0.536448 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 412 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 413 -0.2703476 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 414 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 415 -0.2703476 0.536448 -2.2436539 0.9492704 3.6705063 1.6427293
## 416 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 417 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 418 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 419 -1.8665317 2.083050 -0.7589272 0.9492704 -0.2718894 -2.9522525
## 420 -0.2703476 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 421 0.5277444 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 422 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 423 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 424 1.3258365 0.536448 -2.2436539 0.9492704 3.6705063 1.6427293
## 425 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 426 -0.2703476 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 427 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 428 -0.2703476 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 429 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 430 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 431 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
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## 433 1.3258365 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 434 -1.8665317 2.083050 -2.2436539 0.9492704 -0.2718894 0.4939838
## 435 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 436 1.3258365 -1.010154 0.7257994 0.9492704 -0.2718894 -0.6547616
## 437 1.3258365 -1.010154 0.7257994 -0.4191223 -0.2718894 0.4939838
## 438 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 0.4939838
## 439 0.5277444 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 440 1.3258365 -1.010154 0.7257994 0.9492704 -0.2718894 -0.6547616
## 441 -0.2703476 0.536448 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 442 1.3258365 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 443 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 444 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 445 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 446 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 447 1.3258365 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 448 0.5277444 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 449 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 -0.6547616
## 450 -1.0684396 -1.010154 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 451 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 452 -1.0684396 -1.010154 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 453 0.5277444 0.536448 -0.7589272 0.9492704 3.6705063 -1.8035071
## 454 -1.0684396 2.083050 0.7257994 -0.4191223 -0.2718894 -1.8035071
## 455 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 456 -1.0684396 0.536448 -2.2436539 -0.4191223 -0.2718894 -0.6547616
## 457 -0.2703476 -1.010154 -0.7589272 -0.4191223 -0.2718894 0.4939838
## 458 -1.8665317 2.083050 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 459 -1.8665317 -1.010154 0.7257994 0.9492704 -0.2718894 1.6427293
## 460 -1.8665317 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 461 0.5277444 0.536448 -2.2436539 0.9492704 -0.2718894 0.4939838
## 462 -0.2703476 -1.010154 -0.7589272 0.9492704 -0.2718894 0.4939838
## 463 0.5277444 0.536448 -0.7589272 0.9492704 -0.2718894 0.4939838
## 464 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 465 -1.0684396 -1.010154 -2.2436539 0.9492704 -0.2718894 0.4939838
## 466 1.3258365 0.536448 0.7257994 0.9492704 -0.2718894 1.6427293
## 467 -1.0684396 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 468 -1.0684396 -1.010154 -0.7589272 -1.7875151 -0.2718894 -0.6547616
## 469 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 0.4939838
## 470 -1.0684396 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 471 1.3258365 0.536448 0.7257994 0.9492704 -0.2718894 1.6427293
## 472 -1.0684396 0.536448 -2.2436539 0.9492704 -0.2718894 -1.8035071
## 473 -0.2703476 0.536448 0.7257994 -0.4191223 -0.2718894 -0.6547616
## 474 1.3258365 -1.010154 -2.2436539 0.9492704 -0.2718894 -1.8035071
## 475 -0.2703476 2.083050 -2.2436539 0.9492704 -0.2718894 1.6427293
## 476 -0.2703476 0.536448 0.7257994 0.9492704 -0.2718894 -0.6547616
## 477 0.5277444 0.536448 0.7257994 0.9492704 3.6705063 -0.6547616
## 478 -0.2703476 0.536448 -2.2436539 0.9492704 -0.2718894 -0.6547616
## 479 -0.2703476 -1.010154 0.7257994 0.9492704 -0.2718894 -0.6547616
## 480 0.5277444 -1.010154 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 481 -1.8665317 2.083050 -0.7589272 0.9492704 -0.2718894 0.4939838
## 482 -1.8665317 -1.010154 -0.7589272 -0.4191223 -0.2718894 -2.9522525
## 483 -1.0684396 2.083050 0.7257994 0.9492704 3.6705063 1.6427293
## 484 1.3258365 -1.010154 -0.7589272 2.3176632 -0.2718894 0.4939838
## 485 1.3258365 2.083050 0.7257994 0.9492704 -0.2718894 1.6427293
## 486 -1.8665317 -1.010154 0.7257994 0.9492704 3.6705063 -2.9522525
## 487 -1.0684396 0.536448 -0.7589272 0.9492704 -0.2718894 -1.8035071
## 488 -1.8665317 0.536448 -2.2436539 0.9492704 -0.2718894 -2.9522525
## 489 -1.0684396 2.083050 -0.7589272 -1.7875151 3.6705063 -1.8035071
## 490 0.5277444 0.536448 -0.7589272 -0.4191223 -0.2718894 -0.6547616
## 491 -0.2703476 -1.010154 -0.7589272 -1.7875151 -0.2718894 -1.8035071
## 492 -0.2703476 2.083050 -0.7589272 0.9492704 -0.2718894 -0.6547616
## 493 -0.2703476 2.083050 -0.7589272 0.9492704 -0.2718894 0.4939838
## Extra Semester Last Overall
## 1 1.1840728 0.8391351 0.087472777 0.273092796
## 2 1.1840728 1.1730283 0.472523115 0.470794335
## 3 1.1840728 -0.1625443 1.303421213 0.898302792
## 4 1.1840728 0.1713488 0.991639563 0.948995495
## 5 1.1840728 0.1713488 1.209886718 1.270049277
## 6 -0.8428296 -0.4964375 -3.373303542 -3.697835560
## 7 -0.8428296 -0.4964375 -3.279769046 -3.596450155
## 8 1.1840728 -1.1642238 -0.333432451 -3.275396373
## 9 1.1840728 0.5052420 -2.718562076 -2.954342591
## 10 -0.8428296 -0.4964375 -2.593849416 -2.852957186
## 11 -0.8428296 -0.4964375 -2.500314921 -2.751571781
## 12 -0.8428296 1.8408145 -2.484725838 -2.734674214
## 13 1.1840728 1.5069214 -2.484725838 -2.734674214
## 14 -0.8428296 1.5069214 -3.373303542 -2.683981512
## 15 -0.8428296 -0.4964375 -2.406780426 -2.650186377
## 16 -0.8428296 -0.4964375 -2.297656848 -2.531903404
## 17 1.1840728 1.5069214 -2.079409693 -2.498108269
## 18 -0.8428296 -0.4964375 -2.297656848 -2.481210702
## 19 -0.8428296 0.5052420 -2.858863819 -2.430517999
## 20 1.1840728 -0.4964375 -2.204122353 -2.430517999
## 21 -0.8428296 -0.4964375 -2.983576479 -2.430517999
## 22 1.1840728 -0.1625443 -2.141766023 -2.346030162
## 23 -0.8428296 -0.4964375 -2.391191343 -2.244644757
## 24 1.1840728 -0.4964375 -2.001464280 -2.210849622
## 25 -0.8428296 0.5052420 -1.985875198 -2.193952055
## 26 -0.8428296 1.8408145 -2.671794828 -2.177054487
## 27 -0.8428296 0.5052420 -2.858863819 -2.177054487
## 28 -0.8428296 -0.4964375 -1.939107950 -2.143259352
## 29 -0.8428296 -0.4964375 -1.720860795 -2.058771515
## 30 -0.8428296 -1.1642238 -1.299955567 -2.008078813
## 31 1.1840728 1.5069214 -1.564969970 -2.008078813
## 32 -0.8428296 0.5052420 -2.593849416 -1.974283678
## 33 -0.8428296 1.5069214 -2.375602261 -1.839103138
## 34 -0.8428296 0.5052420 -2.858863819 -1.839103138
## 35 -0.8428296 -0.1625443 -1.034941164 -1.839103138
## 36 -0.8428296 -1.4981169 -1.455846392 -1.720820166
## 37 -0.8428296 1.5069214 -1.642915382 -1.720820166
## 38 -0.8428296 -0.1625443 -1.549380887 -1.720820166
## 39 -0.8428296 -0.1625443 -1.689682630 -1.720820166
## 40 1.1840728 0.5052420 -1.907929785 -1.670127463
## 41 -0.8428296 1.8408145 -1.814395290 -1.636332328
## 42 -0.8428296 1.8408145 -1.424668227 -1.585639626
## 43 1.1840728 1.1730283 -1.424668227 -1.585639626
## 44 -0.8428296 -0.4964375 -1.424668227 -1.585639626
## 45 -0.8428296 -0.4964375 -1.424668227 -1.585639626
## 46 1.1840728 -0.4964375 -1.331133732 -1.534946924
## 47 -0.8428296 1.8408145 -1.346722815 -1.501151789
## 48 1.1840728 1.1730283 -1.424668227 -1.501151789
## 49 -0.8428296 -0.4964375 -1.331133732 -1.484254221
## 50 -0.8428296 -0.4964375 -1.331133732 -1.484254221
## 51 -0.8428296 -0.4964375 -1.331133732 -1.484254221
## 52 -0.8428296 0.5052420 -1.128475659 -1.450459086
## 53 -0.8428296 0.5052420 -1.299955567 -1.450459086
## 54 -0.8428296 -0.1625443 -1.299955567 -1.450459086
## 55 -0.8428296 -0.1625443 -1.299955567 -1.450459086
## 56 -0.8428296 1.8408145 -1.206421072 -1.416663951
## 57 1.1840728 1.5069214 -1.268777402 -1.416663951
## 58 1.1840728 0.8391351 -1.268777402 -1.416663951
## 59 -0.8428296 -0.4964375 -1.222010154 -1.365971249
## 60 -0.8428296 -0.4964375 -1.222010154 -1.365971249
## 61 -0.8428296 -0.4964375 -1.222010154 -1.365971249
## 62 -0.8428296 -0.4964375 -1.222010154 -1.365971249
## 63 -0.8428296 -1.4981169 -1.175242907 -1.315278546
## 64 -0.8428296 -0.4964375 -1.128475659 -1.264585844
## 65 -0.8428296 -0.4964375 -1.128475659 -1.264585844
## 66 -0.8428296 -1.1642238 -1.268777402 -1.247688277
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## 362 -0.8428296 -0.4964375 0.835748738 0.864507657
## 363 -0.8428296 -0.4964375 0.851337820 0.881405225
## 364 1.1840728 -1.4981169 0.882515985 0.881405225
## 365 -0.8428296 1.8408145 0.882515985 0.881405225
## 366 -0.8428296 1.1730283 0.898105068 0.881405225
## 367 -0.8428296 -1.4981169 0.804570572 0.932097927
## 368 1.1840728 -1.4981169 0.804570572 0.932097927
## 369 -0.8428296 -1.4981169 1.069584975 0.932097927
## 370 -0.8428296 -1.4981169 1.069584975 0.932097927
## 371 -0.8428296 1.8408145 0.773392407 0.932097927
## 372 1.1840728 1.8408145 0.835748738 0.932097927
## 373 1.1840728 1.5069214 0.788981490 0.932097927
## 374 -0.8428296 1.5069214 1.100763140 0.932097927
## 375 1.1840728 0.8391351 0.773392407 0.932097927
## 376 1.1840728 -0.1625443 0.788981490 0.932097927
## 377 -0.8428296 -1.1642238 1.147530388 0.948995495
## 378 -0.8428296 -1.1642238 0.695446995 0.948995495
## 379 -0.8428296 -1.4981169 1.303421213 0.948995495
## 380 1.1840728 1.8408145 1.303421213 0.948995495
## 381 1.1840728 1.8408145 1.303421213 0.948995495
## 382 1.1840728 0.5052420 1.303421213 0.948995495
## 383 1.1840728 -0.1625443 1.303421213 0.948995495
## 384 -0.8428296 -0.4964375 0.913694150 0.948995495
## 385 -0.8428296 -0.4964375 0.913694150 0.948995495
## 386 -0.8428296 -0.4964375 0.913694150 0.948995495
## 387 -0.8428296 -0.4964375 0.913694150 0.948995495
## 388 1.1840728 -0.4964375 0.913694150 0.948995495
## 389 -0.8428296 -0.4964375 0.913694150 0.948995495
## 390 -0.8428296 -0.4964375 0.913694150 0.948995495
## 391 -0.8428296 -0.4964375 0.913694150 0.948995495
## 392 -0.8428296 0.1713488 0.773392407 0.965893062
## 393 -0.8428296 -1.4981169 1.131941305 0.999688197
## 394 1.1840728 -1.4981169 1.116352223 0.999688197
## 395 1.1840728 0.5052420 1.116352223 0.999688197
## 396 1.1840728 -1.4981169 0.913694150 1.033483332
## 397 -0.8428296 0.1713488 1.147530388 1.033483332
## 398 -0.8428296 -1.4981169 1.147530388 1.033483332
## 399 1.1840728 0.8391351 1.194297635 1.033483332
## 400 1.1840728 -0.1625443 1.178708553 1.033483332
## 401 1.1840728 -0.4964375 1.007228645 1.050380899
## 402 1.1840728 -0.4964375 1.007228645 1.050380899
## 403 1.1840728 -0.4964375 1.007228645 1.050380899
## 404 1.1840728 -0.4964375 1.007228645 1.050380899
## 405 1.1840728 -0.4964375 1.007228645 1.050380899
## 406 1.1840728 -0.4964375 1.007228645 1.050380899
## 407 1.1840728 -0.4964375 1.007228645 1.050380899
## 408 1.1840728 -0.4964375 1.007228645 1.050380899
## 409 1.1840728 -0.4964375 1.053995893 1.101073602
## 410 -0.8428296 0.5052420 0.788981490 1.151766304
## 411 -0.8428296 -0.4964375 1.116352223 1.168663872
## 412 1.1840728 -0.4964375 1.116352223 1.168663872
## 413 1.1840728 -0.4964375 1.116352223 1.168663872
## 414 1.1840728 -0.4964375 1.116352223 1.168663872
## 415 -0.8428296 -0.4964375 1.116352223 1.168663872
## 416 1.1840728 -0.4964375 1.116352223 1.168663872
## 417 1.1840728 -0.4964375 1.116352223 1.168663872
## 418 1.1840728 -0.4964375 1.131941305 1.185561439
## 419 -0.8428296 -0.4964375 1.163119470 1.219356574
## 420 1.1840728 -0.4964375 1.194297635 1.253151709
## 421 1.1840728 -1.4981169 1.209886718 1.270049277
## 422 1.1840728 -1.4981169 1.256653965 1.270049277
## 423 1.1840728 -1.4981169 1.209886718 1.270049277
## 424 -0.8428296 0.8391351 1.272243048 1.270049277
## 425 1.1840728 0.8391351 1.209886718 1.270049277
## 426 1.1840728 -0.4964375 1.209886718 1.270049277
## 427 1.1840728 -0.4964375 1.209886718 1.270049277
## 428 1.1840728 -0.4964375 1.209886718 1.270049277
## 429 1.1840728 -0.4964375 1.209886718 1.270049277
## 430 1.1840728 -0.4964375 1.209886718 1.270049277
## 431 1.1840728 -0.4964375 1.209886718 1.270049277
## 432 1.1840728 -0.4964375 1.209886718 1.270049277
## 433 1.1840728 -0.4964375 1.209886718 1.270049277
## 434 -0.8428296 1.5069214 1.303421213 1.286946844
## 435 1.1840728 0.1713488 1.303421213 1.303844412
## 436 -0.8428296 -1.1642238 1.303421213 1.337639546
## 437 1.1840728 0.1713488 1.303421213 1.371434681
## 438 -0.8428296 -0.1625443 1.303421213 1.371434681
## 439 -0.8428296 -0.4964375 1.303421213 1.371434681
## 440 -0.8428296 -0.4964375 1.303421213 1.371434681
## 441 -0.8428296 -0.4964375 1.303421213 1.371434681
## 442 1.1840728 -0.4964375 1.303421213 1.371434681
## 443 1.1840728 -0.4964375 1.303421213 1.371434681
## 444 1.1840728 -0.4964375 1.303421213 1.371434681
## 445 1.1840728 -0.4964375 1.303421213 1.371434681
## 446 1.1840728 -0.4964375 1.303421213 1.371434681
## 447 1.1840728 -0.4964375 1.303421213 1.371434681
## 448 1.1840728 -0.4964375 1.303421213 1.371434681
## 449 1.1840728 -1.1642238 -0.037239883 0.864507657
## 450 1.1840728 0.1713488 0.773392407 0.932097927
## 451 1.1840728 -1.4981169 0.991639563 0.948995495
## 452 1.1840728 -0.4964375 1.303421213 1.371434681
## 453 1.1840728 1.1730283 1.038406810 -0.369014768
## 454 1.1840728 0.8391351 0.134240024 -0.064858554
## 455 -0.8428296 0.1713488 0.134240024 0.222400093
## 456 1.1840728 1.1730283 0.258952684 0.239297661
## 457 1.1840728 -1.1642238 0.290130849 0.442068471
## 458 1.1840728 0.5052420 0.414843510 0.475863605
## 459 -0.8428296 -0.1625443 0.601912500 0.475863605
## 460 -0.8428296 -0.1625443 0.648679747 0.627941713
## 461 1.1840728 -0.8303306 0.773392407 0.695531983
## 462 1.1840728 -1.4981169 0.913694150 0.830712522
## 463 -0.8428296 -1.4981169 1.069584975 0.932097927
## 464 1.1840728 -0.1625443 0.851337820 0.948995495
## 465 1.1840728 0.1713488 1.194297635 1.033483332
## 466 1.1840728 -0.1625443 1.038406810 1.084176034
## 467 1.1840728 0.1713488 1.303421213 1.253151709
## 468 1.1840728 -0.4964375 -1.424668227 -1.585639626
## 469 -0.8428296 -0.4964375 -1.034941164 -1.163200439
## 470 1.1840728 -0.1625443 0.009527364 -0.318322066
## 471 -0.8428296 -0.4964375 -0.115185296 -0.166243958
## 472 1.1840728 -0.4964375 0.009527364 -0.031063419
## 473 -0.8428296 -0.1625443 -1.549380887 0.526556308
## 474 1.1840728 -0.1625443 1.038406810 0.526556308
## 475 1.1840728 -0.4964375 0.523967087 0.526556308
## 476 1.1840728 -0.8303306 0.617501582 0.695531983
## 477 1.1840728 0.8391351 0.523967087 0.813814955
## 478 1.1840728 -1.1642238 1.116352223 0.999688197
## 479 1.1840728 -0.1625443 1.038406810 1.033483332
## 480 1.1840728 -0.8303306 1.303421213 1.371434681
## 481 1.1840728 -0.4964375 0.820159655 0.847610090
## 482 1.1840728 0.1713488 1.147530388 1.016585765
## 483 -0.8428296 -1.4981169 -0.021650801 -0.571785578
## 484 1.1840728 -0.4964375 0.227774519 0.205502526
## 485 1.1840728 0.5052420 0.617501582 0.627941713
## 486 1.1840728 -0.4964375 0.804570572 0.830712522
## 487 1.1840728 -0.1625443 1.116352223 1.168663872
## 488 1.1840728 -0.4964375 1.209886718 1.270049277
## 489 1.1840728 1.1730283 0.134240024 -0.149346391
## 490 1.1840728 1.8408145 0.523967087 0.813814955
## 491 -0.8428296 -0.4964375 0.617501582 0.627941713
## 492 1.1840728 -1.4981169 0.695446995 0.948995495
## 493 1.1840728 -0.8303306 1.303421213 1.134868737
Statistika Deskriptif
# Sebelum Pre-Processing
summary(data)
## Department Gender HSC SSC
## Length:493 Length:493 Min. :2.170 Min. :3.000
## Class :character Class :character 1st Qu.:3.830 1st Qu.:4.680
## Mode :character Mode :character Median :4.170 Median :4.940
## Mean :4.157 Mean :4.768
## 3rd Qu.:4.500 3rd Qu.:5.000
## Max. :5.000 Max. :5.000
## Income Hometown Computer Preparation
## Length:493 Length:493 Min. :1.000 Length:493
## Class :character Class :character 1st Qu.:3.000 Class :character
## Mode :character Mode :character Median :3.000 Mode :character
## Mean :3.339
## 3rd Qu.:4.000
## Max. :5.000
## Gaming Attendance Job English
## Length:493 Length:493 Length:493 Min. :1.00
## Class :character Class :character Class :character 1st Qu.:3.00
## Mode :character Mode :character Mode :character Median :4.00
## Mean :3.57
## 3rd Qu.:4.00
## Max. :5.00
## Extra Semester Last Overall
## Length:493 Length:493 Min. :1.000 Min. :1.000
## Class :character Class :character 1st Qu.:2.810 1st Qu.:2.880
## Mode :character Mode :character Median :3.250 Median :3.270
## Mean :3.164 Mean :3.188
## 3rd Qu.:3.670 3rd Qu.:3.680
## Max. :4.000 Max. :4.000
# Sesudah Pre-Processing
summary(data_numeric)
## Department Gender HSC SSC
## Min. : 1.000 Min. :1.000 Min. :2.170 Min. :3.000
## 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.:3.830 1st Qu.:4.680
## Median : 2.000 Median :2.000 Median :4.170 Median :4.940
## Mean : 2.363 Mean :1.665 Mean :4.157 Mean :4.768
## 3rd Qu.: 2.000 3rd Qu.:2.000 3rd Qu.:4.500 3rd Qu.:5.000
## Max. :10.000 Max. :2.000 Max. :5.000 Max. :5.000
## Income Hometown Computer Preparation
## Min. : 1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:1.000
## Median : 6.000 Median :2.000 Median :3.000 Median :2.000
## Mean : 5.185 Mean :1.568 Mean :3.339 Mean :1.653
## 3rd Qu.: 6.000 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:2.000
## Max. :10.000 Max. :2.000 Max. :5.000 Max. :3.000
## Gaming Attendance Job English Extra
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:3.00 1st Qu.:1.000
## Median :3.000 Median :2.000 Median :1.000 Median :4.00 Median :1.000
## Mean :2.511 Mean :2.306 Mean :1.069 Mean :3.57 Mean :1.416
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:4.00 3rd Qu.:2.000
## Max. :3.000 Max. :4.000 Max. :2.000 Max. :5.00 Max. :2.000
## Semester Last Overall
## Min. : 1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 4.000 1st Qu.:2.810 1st Qu.:2.880
## Median : 4.000 Median :3.250 Median :3.270
## Mean : 5.487 Mean :3.164 Mean :3.188
## 3rd Qu.: 8.000 3rd Qu.:3.670 3rd Qu.:3.680
## Max. :11.000 Max. :4.000 Max. :4.000
library(psych)
# Ada skew dan kurtosis
describe(data_numeric)
## vars n mean sd median trimmed mad min max range skew
## Department 1 493 2.36 1.31 2.00 2.00 0.00 1.00 10 9.00 3.69
## Gender 2 493 1.67 0.47 2.00 1.71 0.00 1.00 2 1.00 -0.70
## HSC 3 493 4.16 0.55 4.17 4.18 0.50 2.17 5 2.83 -0.40
## SSC 4 493 4.77 0.35 4.94 4.84 0.09 3.00 5 2.00 -2.14
## Income 5 493 5.18 2.78 6.00 5.23 2.97 1.00 10 9.00 -0.16
## Hometown 6 493 1.57 0.50 2.00 1.58 0.00 1.00 2 1.00 -0.27
## Computer 7 493 3.34 1.25 3.00 3.42 1.48 1.00 5 4.00 -0.30
## Preparation 8 493 1.65 0.65 2.00 1.57 1.48 1.00 3 2.00 0.48
## Gaming 9 493 2.51 0.67 3.00 2.64 0.00 1.00 3 2.00 -1.04
## Attendance 10 493 2.31 0.73 2.00 2.35 1.48 1.00 4 3.00 -0.20
## Job 11 493 1.07 0.25 1.00 1.00 0.00 1.00 2 1.00 3.39
## English 12 493 3.57 0.87 4.00 3.60 1.48 1.00 5 4.00 -0.34
## Extra 13 493 1.42 0.49 1.00 1.39 0.00 1.00 2 1.00 0.34
## Semester 14 493 5.49 2.99 4.00 5.41 2.97 1.00 11 10.00 0.37
## Last 15 493 3.16 0.64 3.25 3.23 0.64 1.00 4 3.00 -0.89
## Overall 16 493 3.19 0.59 3.27 3.25 0.59 1.00 4 3.00 -0.94
## kurtosis se
## Department 13.82 0.06
## Gender -1.52 0.02
## HSC 0.12 0.02
## SSC 5.28 0.02
## Income -1.11 0.13
## Hometown -1.93 0.02
## Computer -0.72 0.06
## Preparation -0.71 0.03
## Gaming -0.17 0.03
## Attendance -0.61 0.03
## Job 9.52 0.01
## English 0.08 0.04
## Extra -1.89 0.02
## Semester -0.92 0.13
## Last 0.47 0.03
## Overall 0.61 0.03
Uji Asumsi
Uji Normalitas Multivariat
library(psych)
mardia(data_z)
## Call: mardia(x = data_z)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 493 num.vars = 16
## b1p = 61.13 skew = 5022.89 with probability <= 0
## small sample skew = 5057.06 with probability <= 0
## b2p = 353.94 kurtosis = 30.5 with probability <= 0
Uji Multikolinearitas
# Determinan Matriks Kovarians
data_manifest <- data_z[, c("Income", "Hometown", "English", "Computer", "Extra",
"Job", "Gaming", "Preparation", "Attendance", "Last",
"Overall", "Semester", "HSC", "SSC")]
data_manifest[] <- lapply(data_manifest, function(x) as.numeric(as.character(x)))
cov_matrix <- cov(data_manifest, use = "complete.obs")
det(cov_matrix)
## [1] 0.03145631
library(car)
## Warning: package 'car' was built under R version 4.5.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.5.3
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
# Pake VIF
data_manifest_clean <- na.omit(data_manifest)
model_vif <- lm(Overall ~ Income + Hometown + English + Computer + Extra +
Job + Gaming + Preparation + Attendance + Last + Semester + HSC + SSC,
data = data_manifest_clean)
vif(model_vif)
## Income Hometown English Computer Extra Job
## 1.019885 1.114107 1.243650 1.177522 1.207527 1.049524
## Gaming Preparation Attendance Last Semester HSC
## 1.497352 1.232204 1.379779 1.658416 1.067148 1.316999
## SSC
## 1.219241
Uji Kecukupan Sampel
# KMO
r <- cor(data_z)
KMO(r)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.68
## MSA for each item =
## Department Gender HSC SSC Income Hometown
## 0.60 0.65 0.67 0.51 0.59 0.49
## Computer Preparation Gaming Attendance Job English
## 0.57 0.88 0.76 0.83 0.50 0.48
## Extra Semester Last Overall
## 0.82 0.69 0.66 0.66
Confirmatory Factor Analysis (CFA)
library(lavaan)
## Warning: package 'lavaan' was built under R version 4.5.3
## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
model_cfa_x1 <- 'SosialEkonomi =~ Hometown + English + Computer + Income + Job'
model_cfa_x2 <- 'AktivitasNonAkademik =~ Extra + Gaming + Preparation + Attendance'
model_cfa_y <- 'PrestasiAkademik =~ Semester + Overall + HSC + Last'
fit_x1 <- cfa(model_cfa_x1, data = data_z, std.lv = TRUE)
## Warning: lavaan->lav_object_post_check():
## some estimated ov variances are negative
fit_x2 <- cfa(model_cfa_x2, data = data_z, std.lv = TRUE)
fit_y <- cfa(model_cfa_y, data = data_z, std.lv = TRUE)
summary(fit_x1, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 493
##
## Model Test User Model:
##
## Test statistic 10.198
## Degrees of freedom 5
## P-value (Chi-square) 0.070
##
## Model Test Baseline Model:
##
## Test statistic 63.400
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.903
## Tucker-Lewis Index (TLI) 0.805
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3468.580
## Loglikelihood unrestricted model (H1) -3463.481
##
## Akaike (AIC) 6957.160
## Bayesian (BIC) 6999.165
## Sample-size adjusted Bayesian (SABIC) 6967.425
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.086
## P-value H_0: RMSEA <= 0.050 0.504
## P-value H_0: RMSEA >= 0.080 0.089
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.037
##
## 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
## SosialEkonomi =~
## Hometown 0.253 0.139 1.828 0.068 0.253 0.254
## English -0.145 0.087 -1.663 0.096 -0.145 -0.146
## Computer -1.080 0.561 -1.926 0.054 -1.080 -1.081
## Income 0.042 0.047 0.886 0.376 0.042 0.042
## Job -0.048 0.049 -0.980 0.327 -0.048 -0.048
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Hometown 0.934 0.089 10.463 0.000 0.934 0.936
## .English 0.977 0.066 14.814 0.000 0.977 0.979
## .Computer -0.167 1.209 -0.139 0.890 -0.167 -0.168
## .Income 0.996 0.063 15.698 0.000 0.996 0.998
## .Job 0.996 0.063 15.694 0.000 0.996 0.998
## SosialEkonomi 1.000 1.000 1.000
## Visualisasi CFA Konstruk Sosial Ekonomi
library(semPlot)
## Warning: package 'semPlot' was built under R version 4.5.3
## Registered S3 method overwritten by 'lme4':
## method from
## na.action.merMod car
semPaths(
object = fit_x1,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(lat = "lightblue", man = "lightgreen")
)
summary(fit_x2, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Number of observations 493
##
## Model Test User Model:
##
## Test statistic 5.698
## Degrees of freedom 2
## P-value (Chi-square) 0.058
##
## Model Test Baseline Model:
##
## Test statistic 150.940
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.974
## Tucker-Lewis Index (TLI) 0.923
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2723.524
## Loglikelihood unrestricted model (H1) -2720.675
##
## Akaike (AIC) 5463.048
## Bayesian (BIC) 5496.652
## Sample-size adjusted Bayesian (SABIC) 5471.260
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.061
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.123
## P-value H_0: RMSEA <= 0.050 0.292
## P-value H_0: RMSEA >= 0.080 0.369
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.026
##
## 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
## AktivitasNonAkademik =~
## Extra 0.455 0.060 7.619 0.000 0.455 0.456
## Gaming -0.428 0.059 -7.224 0.000 -0.428 -0.428
## Preparation 0.430 0.059 7.256 0.000 0.430 0.431
## Attendance 0.649 0.067 9.673 0.000 0.649 0.649
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Extra 0.791 0.063 12.516 0.000 0.791 0.792
## .Gaming 0.815 0.063 13.017 0.000 0.815 0.816
## .Preparation 0.813 0.063 12.981 0.000 0.813 0.815
## .Attendance 0.577 0.079 7.315 0.000 0.577 0.579
## AktivtsNnAkdmk 1.000 1.000 1.000
## Visualisasi CFA Konstruk Aktivitas Non-Akademik
library(semPlot)
semPaths(
object = fit_x2,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(lat = "lightcoral", man = "lightyellow")
)
summary(fit_y, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Number of observations 493
##
## Model Test User Model:
##
## Test statistic 1.043
## Degrees of freedom 2
## P-value (Chi-square) 0.594
##
## Model Test Baseline Model:
##
## Test statistic 998.885
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.003
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2297.224
## Loglikelihood unrestricted model (H1) -2296.702
##
## Akaike (AIC) 4610.448
## Bayesian (BIC) 4644.052
## Sample-size adjusted Bayesian (SABIC) 4618.660
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.074
## P-value H_0: RMSEA <= 0.050 0.843
## P-value H_0: RMSEA >= 0.080 0.034
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.011
##
## 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
## PrestasiAkademik =~
## Semester 0.148 0.046 3.254 0.001 0.148 0.149
## Overall -0.966 0.044 -22.186 0.000 -0.966 -0.967
## HSC -0.258 0.045 -5.724 0.000 -0.258 -0.258
## Last -0.956 0.044 -21.928 0.000 -0.956 -0.957
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Semester 0.976 0.062 15.686 0.000 0.976 0.978
## .Overall 0.065 0.055 1.165 0.244 0.065 0.065
## .HSC 0.931 0.059 15.655 0.000 0.931 0.933
## .Last 0.085 0.054 1.561 0.119 0.085 0.085
## PrestasiAkadmk 1.000 1.000 1.000
## Visualisasi CFA Konstruk Prestasi Akademik
library(semPlot)
semPaths(
object = fit_y,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(lat = "lightpink", man = "lightcyan")
)
Ringkasan Fit Indeks CFA
cfa_fits <- data.frame(
Konstruk = c("SosialEkonomi", "AktivitasNonAkademik", "PrestasiAkademik"),
CFI = c(fitMeasures(fit_x1, "cfi"),
fitMeasures(fit_x2, "cfi"),
fitMeasures(fit_y, "cfi")),
RMSEA = c(fitMeasures(fit_x1, "rmsea"),
fitMeasures(fit_x2, "rmsea"),
fitMeasures(fit_y, "rmsea")),
SRMR = c(fitMeasures(fit_x1, "srmr"),
fitMeasures(fit_x2, "srmr"),
fitMeasures(fit_y, "srmr")),
TLI = c(fitMeasures(fit_x1, "tli"),
fitMeasures(fit_x2, "tli"),
fitMeasures(fit_y, "tli"))
)
print(cfa_fits)
## Konstruk CFI RMSEA SRMR TLI
## 1 SosialEkonomi 0.9026604 0.04592034 0.03677934 0.8053209
## 2 AktivitasNonAkademik 0.9744853 0.06124214 0.02605335 0.9234560
## 3 PrestasiAkademik 1.0000000 0.00000000 0.01097337 1.0028923
Perhitungan Composite Reliability (CR)
# Fungsi untuk menghitung CR dari fit CFA
hitung_CR <- function(fit) {
std <- standardizedSolution(fit)
lambda <- std$est[std$op == "=~"]
theta <- 1 - lambda^2
CR <- sum(lambda)^2 / (sum(lambda)^2 + sum(theta))
return(CR)
}
CR_x1 <- hitung_CR(fit_x1)
CR_x2 <- hitung_CR(fit_x2)
CR_y <- hitung_CR(fit_y)
cr_df <- data.frame(
Konstruk = c("SosialEkonomi", "AktivitasNonAkademik", "PrestasiAkademik"),
CR = c(CR_x1, CR_x2, CR_y)
)
print(cr_df)
## Konstruk CR
## 1 SosialEkonomi 0.2037489
## 2 AktivitasNonAkademik 0.2898675
## 3 PrestasiAkademik 0.6673638
Structural Equation Modeling (SEM)
model_sem <- '
SosialEkonomi =~ Income + Hometown + English + Computer + Job
AktivitasNonAkademik =~ Extra + Gaming + Preparation + Attendance
PrestasiAkademik =~ Last + Semester + Overall + HSC
PrestasiAkademik ~ SosialEkonomi + AktivitasNonAkademik
'
fit_sem <- sem(model_sem, data = data_z, std.lv = TRUE)
summary(fit_sem, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
##
## Number of observations 493
##
## Model Test User Model:
##
## Test statistic 197.603
## Degrees of freedom 62
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1607.493
## Degrees of freedom 78
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.911
## Tucker-Lewis Index (TLI) 0.888
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8382.525
## Loglikelihood unrestricted model (H1) -8283.724
##
## Akaike (AIC) 16823.051
## Bayesian (BIC) 16944.866
## Sample-size adjusted Bayesian (SABIC) 16852.819
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.077
## P-value H_0: RMSEA <= 0.050 0.005
## P-value H_0: RMSEA >= 0.080 0.018
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.058
##
## 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
## SosialEkonomi =~
## Income 0.056 0.055 1.017 0.309 0.056 0.056
## Hometown 0.334 0.076 4.380 0.000 0.334 0.335
## English -0.194 0.061 -3.164 0.002 -0.194 -0.194
## Computer -0.809 0.153 -5.298 0.000 -0.809 -0.810
## Job -0.064 0.055 -1.153 0.249 -0.064 -0.064
## AktivitasNonAkademik =~
## Extra 0.414 0.051 8.170 0.000 0.414 0.414
## Gaming -0.511 0.050 -10.236 0.000 -0.511 -0.512
## Preparation 0.483 0.050 9.639 0.000 0.483 0.484
## Attendance 0.544 0.050 10.925 0.000 0.544 0.545
## PrestasiAkademik =~
## Last 0.573 0.050 11.512 0.000 0.969 0.970
## Semester -0.090 0.028 -3.226 0.001 -0.152 -0.152
## Overall 0.563 0.049 11.609 0.000 0.952 0.953
## HSC 0.154 0.029 5.263 0.000 0.260 0.260
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PrestasiAkademik ~
## SosialEkonomi -0.326 0.108 -3.018 0.003 -0.193 -0.193
## AktivtsNnAkdmk 1.311 0.176 7.457 0.000 0.775 0.775
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SosialEkonomi ~~
## AktivtsNnAkdmk -0.039 0.073 -0.537 0.592 -0.039 -0.039
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Income 0.995 0.063 15.676 0.000 0.995 0.997
## .Hometown 0.886 0.070 12.613 0.000 0.886 0.888
## .English 0.960 0.063 15.164 0.000 0.960 0.962
## .Computer 0.344 0.241 1.429 0.153 0.344 0.345
## .Job 0.994 0.063 15.668 0.000 0.994 0.996
## .Extra 0.827 0.057 14.476 0.000 0.827 0.828
## .Gaming 0.736 0.055 13.509 0.000 0.736 0.738
## .Preparation 0.764 0.055 13.843 0.000 0.764 0.766
## .Attendance 0.702 0.054 13.044 0.000 0.702 0.703
## .Last 0.059 0.021 2.793 0.005 0.059 0.059
## .Semester 0.975 0.062 15.686 0.000 0.975 0.977
## .Overall 0.091 0.021 4.352 0.000 0.091 0.091
## .HSC 0.930 0.059 15.657 0.000 0.930 0.932
## SosialEkonomi 1.000 1.000 1.000
## AktivtsNnAkdmk 1.000 1.000 1.000
## .PrestasiAkadmk 1.000 0.350 0.350
Visualisasi SEM
library(semPlot)
semPaths(
object = fit_sem,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 7,
sizeLat = 7,
color = "lightgray",
edge.label.cex = 1.2,
label.cex = 1.3
)
