library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#library(semPLS)
library(umx)
## Cargando paquete requerido: OpenMx
## For an overview type '?umx'
##
## Adjuntando el paquete: 'umx'
##
## The following object is masked from 'package:stats':
##
## loadings
JudgesT1 <- read_csv("Judges_T1.csv")
## Rows: 16 Columns: 13
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## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT2 <- read_csv("Judges_T2.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT3 <- read_csv("Judges_T3.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT4 <- read_csv("Judges_T4.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT5 <- read_csv("Judges_T5.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT6 <- read_csv("Judges_T6.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT7 <- read_csv("Judges_T7.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesT8 <- read_csv("Judges_T8.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesCT120 <- read_csv("Judges_CT120.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesCT144 <- read_csv("Judges_CT144.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesCT168 <- read_csv("Judges_CT168.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
JudgesCT192 <- read_csv("Judges_CT192.csv")
## Rows: 16 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (13): J1, J2, J3, J4, J5, J6, J7, J8, J9, J10, J11, J12, J13
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
T1
df_judges <- JudgesT1 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9809
## Standardized alpha = 0.9812
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9785 0.9788 0.9285
## J2 0.9786 0.9789 0.9279
## J3 0.9794 0.9796 0.8863
## J4 0.9790 0.9792 0.9047
## J5 0.9789 0.9792 0.9096
## J6 0.9814 0.9819 0.7707
## J7 0.9797 0.9800 0.8687
## J8 0.9783 0.9787 0.9405
## J9 0.9789 0.9790 0.9237
## J10 0.9813 0.9817 0.7762
## J11 0.9800 0.9804 0.8517
## J12 0.9793 0.9796 0.8906
## J13 0.9782 0.9785 0.9444
T2
df_judges <- JudgesT2 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9739
## Standardized alpha = 0.9745
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9722 0.9729 0.8306
## J2 0.9746 0.9755 0.7041
## J3 0.9711 0.9714 0.9012
## J4 0.9705 0.9713 0.9077
## J5 0.9710 0.9713 0.9061
## J6 0.9715 0.9721 0.8691
## J7 0.9746 0.9756 0.7029
## J8 0.9706 0.9713 0.9088
## J9 0.9701 0.9709 0.9291
## J10 0.9720 0.9727 0.8402
## J11 0.9714 0.9719 0.8857
## J12 0.9724 0.9733 0.8215
## J13 0.9710 0.9717 0.8878
T3
df_judges <- JudgesT3 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9485
## Standardized alpha = 0.9508
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9426 0.9451 0.8090
## J2 0.9455 0.9484 0.7148
## J3 0.9396 0.9428 0.9018
## J4 0.9488 0.9509 0.5960
## J5 0.9446 0.9476 0.7395
## J6 0.9460 0.9482 0.6873
## J7 0.9472 0.9496 0.6418
## J8 0.9392 0.9424 0.9092
## J9 0.9420 0.9455 0.8255
## J10 0.9484 0.9507 0.5944
## J11 0.9475 0.9480 0.7259
## J12 0.9429 0.9448 0.8204
## J13 0.9426 0.9450 0.8263
T4
df_judges <- JudgesT4 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9634
## Standardized alpha = 0.9638
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9575 0.9582 0.9192
## J2 0.9625 0.9630 0.7144
## J3 0.9592 0.9599 0.8555
## J4 0.9599 0.9604 0.8267
## J5 0.9573 0.9580 0.9271
## J6 0.9631 0.9637 0.6843
## J7 0.9605 0.9609 0.8048
## J8 0.9592 0.9593 0.8697
## J9 0.9595 0.9600 0.8407
## J10 0.9628 0.9635 0.6966
## J11 0.9619 0.9620 0.7599
## J12 0.9630 0.9634 0.6949
## J13 0.9588 0.9593 0.8721
T5
df_judges <- JudgesT5 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.964
## Standardized alpha = 0.966
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9603 0.9621 0.8588
## J2 0.9632 0.9657 0.7119
## J3 0.9604 0.9626 0.8358
## J4 0.9592 0.9616 0.8817
## J5 0.9602 0.9626 0.8395
## J6 0.9659 0.9676 0.6162
## J7 0.9612 0.9620 0.8657
## J8 0.9610 0.9633 0.8096
## J9 0.9584 0.9610 0.9089
## J10 0.9632 0.9656 0.7083
## J11 0.9603 0.9627 0.8348
## J12 0.9594 0.9618 0.8772
## J13 0.9609 0.9630 0.8232
T6
df_judges <- JudgesT6 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9616
## Standardized alpha = 0.9605
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9555 0.9545 0.9075
## J2 0.9602 0.9592 0.7205
## J3 0.9555 0.9543 0.9068
## J4 0.9573 0.9562 0.8417
## J5 0.9616 0.9605 0.6608
## J6 0.9600 0.9592 0.7294
## J7 0.9632 0.9622 0.5853
## J8 0.9585 0.9574 0.7951
## J9 0.9547 0.9536 0.9377
## J10 0.9566 0.9553 0.8699
## J11 0.9561 0.9549 0.8855
## J12 0.9594 0.9580 0.7599
## J13 0.9606 0.9595 0.7036
T7
df_judges <- JudgesT7 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9427
## Standardized alpha = 0.9422
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9401 0.9393 0.6644
## J2 0.9380 0.9370 0.7375
## J3 0.9334 0.9331 0.8733
## J4 0.9417 0.9412 0.6013
## J5 0.9385 0.9378 0.7160
## J6 0.9432 0.9431 0.5417
## J7 0.9389 0.9387 0.7018
## J8 0.9363 0.9358 0.7849
## J9 0.9334 0.9330 0.8713
## J10 0.9402 0.9396 0.6613
## J11 0.9370 0.9365 0.7632
## J12 0.9424 0.9420 0.5848
## J13 0.9322 0.9310 0.9338
T8
df_judges <- JudgesT8 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.967
## Standardized alpha = 0.9669
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9635 0.9634 0.8562
## J2 0.9652 0.9652 0.7802
## J3 0.9623 0.9623 0.9040
## J4 0.9645 0.9642 0.8145
## J5 0.9656 0.9655 0.7612
## J6 0.9687 0.9689 0.6078
## J7 0.9647 0.9641 0.8229
## J8 0.9620 0.9620 0.9168
## J9 0.9628 0.9627 0.8835
## J10 0.9670 0.9669 0.7034
## J11 0.9636 0.9636 0.8518
## J12 0.9620 0.9619 0.9174
## J13 0.9644 0.9641 0.8193
CT120
df_judges <- JudgesCT120 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9642
## Standardized alpha = 0.9652
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9605 0.9617 0.8409
## J2 0.9607 0.9620 0.8328
## J3 0.9589 0.9602 0.9028
## J4 0.9652 0.9661 0.6445
## J5 0.9592 0.9605 0.8928
## J6 0.9657 0.9664 0.6381
## J7 0.9610 0.9622 0.8248
## J8 0.9595 0.9605 0.8834
## J9 0.9582 0.9597 0.9279
## J10 0.9684 0.9703 0.4649
## J11 0.9593 0.9607 0.8882
## J12 0.9603 0.9594 0.9341
## J13 0.9596 0.9607 0.8812
CT144
df_judges <- JudgesCT144 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9634
## Standardized alpha = 0.9644
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9602 0.9608 0.8334
## J2 0.9590 0.9601 0.8645
## J3 0.9577 0.9590 0.9116
## J4 0.9644 0.9659 0.6222
## J5 0.9603 0.9613 0.8191
## J6 0.9600 0.9608 0.8394
## J7 0.9619 0.9630 0.7431
## J8 0.9589 0.9598 0.8764
## J9 0.9574 0.9588 0.9215
## J10 0.9617 0.9627 0.7580
## J11 0.9590 0.9600 0.8727
## J12 0.9623 0.9637 0.7233
## J13 0.9627 0.9638 0.7129
CT168
df_judges <- JudgesCT168 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9793
## Standardized alpha = 0.9808
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9769 0.9787 0.9157
## J2 0.9777 0.9790 0.8952
## J3 0.9783 0.9797 0.8581
## J4 0.9783 0.9794 0.8723
## J5 0.9765 0.9783 0.9287
## J6 0.9780 0.9796 0.8550
## J7 0.9785 0.9803 0.8265
## J8 0.9772 0.9789 0.8940
## J9 0.9767 0.9785 0.9209
## J10 0.9781 0.9800 0.8495
## J11 0.9791 0.9806 0.8074
## J12 0.9760 0.9777 0.9625
## J13 0.9772 0.9789 0.8985
CT192
df_judges <- JudgesCT192 %>%
mutate(across(everything(), ~ as.numeric(as.character(.)), .names = "num_{col}")) %>%
select(starts_with("num_")) %>%
setNames(gsub("num_", "", names(.)))
reliability(cov(df_judges))
## Alpha reliability = 0.9561
## Standardized alpha = 0.9575
##
## Reliability deleting each item in turn:
## Alpha Std.Alpha r(item, total)
## J1 0.9504 0.9521 0.8536
## J2 0.9569 0.9575 0.6437
## J3 0.9500 0.9517 0.8654
## J4 0.9514 0.9516 0.8634
## J5 0.9541 0.9559 0.7143
## J6 0.9504 0.9519 0.8584
## J7 0.9558 0.9574 0.6400
## J8 0.9499 0.9515 0.8774
## J9 0.9519 0.9536 0.7983
## J10 0.9551 0.9566 0.6737
## J11 0.9509 0.9528 0.8365
## J12 0.9545 0.9560 0.6981
## J13 0.9522 0.9537 0.7950