calc_stats(d_hadi, d_cody, 32:57, 5, continuous = TRUE) # 5th column is for first names## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 96.2
## number_of_student_first_names_in_post
## 1 0
## 2 0
## 3 1
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 0
## 21 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## number_of_student_first_names_in_post.1
## 1 0
## 2 0
## 3 1
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 2
## 17 0
## 18 0
## 19 0
## 20 0
## 21 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 26
## Raters = 2
## ICC(1) = 0.315
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(25,26) = 1.92 , p = 0.0523
##
## 95%-Confidence Interval for ICC Population Values:
## -0.069 < ICC < 0.62## mean_diff
## 1 -0.07692308calc_stats(d_hadi, d_cody, 32:57, 6, continuous = TRUE) # 5th column is for first names## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 96.2
## number_of_student_last_names_in_post number_of_student_last_names_in_post.1
## 1 0 0
## 2 0 0
## 3 1 1
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 1
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 26
## Raters = 2
## ICC(1) = 0.658
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(25,26) = 4.84 , p = 7.73e-05
##
## 95%-Confidence Interval for ICC Population Values:
## 0.374 < ICC < 0.83## mean_diff
## 1 -0.03846154calc_stats(d_hadi, d_cody, 32:57, 7, continuous = TRUE) # 5th column is for first names## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## number_of_student_tagged_accounts number_of_student_tagged_accounts.1
## 1 0 0
## 2 0 0
## 3 1 1
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 26
## Raters = 2
## ICC(1) = 1
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(25,26) = Inf , p = 0
##
## 95%-Confidence Interval for ICC Population Values:
## NaN < ICC < NaN## mean_diff
## 1 0calc_stats(d_hadi, d_cody, 32:57, 8, continuous = TRUE) # 5th column is for first names## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 84.6
## number_of_student_faces_in_image number_of_student_faces_in_image.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 3 3
## 6 0 2
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 1 1
## 13 0 0
## 14 0 0
## 15 0 0
## 16 2 2
## 17 30 33
## 18 8 9
## 19 2 2
## 20 0 0
## 21 1 1
## 22 0 0
## 23 0 0
## 24 3 2
## 25 0 0
## 26 0 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 26
## Raters = 2
## ICC(1) = 0.993
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(25,26) = 273 , p = 1.17e-25
##
## 95%-Confidence Interval for ICC Population Values:
## 0.984 < ICC < 0.997## mean_diff
## 1 -0.1923077calc_stats(d_hadi, d_cody, 32:57, 9, continuous = TRUE) # 5th column is for first names## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 96.2
## how_many_names_faces_connected how_many_names_faces_connected.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 1
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 26
## Raters = 2
## ICC(1) = 0
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(25,26) = 1 , p = 0.499
##
## 95%-Confidence Interval for ICC Population Values:
## -0.376 < ICC < 0.379## mean_diff
## 1 -0.03846154calc_stats(d_hadi, d_cody, 32:57, 10, categorical = TRUE) # 5th column is for first names## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 96.2
## is_there_an_identifiable_face_in_this_post
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 0
## 21 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## is_there_an_identifiable_face_in_this_post.1
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 1
## 17 0
## 18 0
## 19 0
## 20 0
## 21 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = 0
##
## z = 0
## p-value = 1calc_stats(d_hadi, d_cody, 32:57, 11, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## ethnic_group ethnic_group.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaNcalc_stats(d_hadi, d_cody, 32:57, 12, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## first_generation_americans first_generation_americans.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaNcalc_stats(d_hadi, d_cody, 32:57, 13, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## date_of_birth date_of_birth.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaNcalc_stats(d_hadi, d_cody, 32:57, 14, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## religion religion.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaNcalc_stats(d_hadi, d_cody, 32:57, 15, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## gender_identity gender_identity.1
## 1 0 0
## 2 0 0
## 3 2 2
## 4 0 0
## 5 2 2
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = 1
##
## z = 5.1
## p-value = 3.41e-07calc_stats(d_hadi, d_cody, 32:57, 16, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## retweet retweet.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 1 1
## 6 0 0
## 7 0 0
## 8 1 1
## 9 0 0
## 10 0 0
## 11 0 0
## 12 1 1
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 1 1
## 20 0 0
## 21 0 0
## 22 1 1
## 23 0 0
## 24 0 0
## 25 0 0
## 26 1 1
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = 1
##
## z = 5.1
## p-value = 3.41e-07calc_stats(d_hadi, d_cody, 32:57, 17, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 76.9
## external_link external_link.1
## 1 No Link School Website
## 2 Facebook Facebook
## 3 Other No Link
## 4 No Link No Link
## 5 No Link No Link
## 6 No Link No Link
## 7 School Website School Website
## 8 No Link No Link
## 9 No Link No Link
## 10 Other Other
## 11 No Link No Link
## 12 No Link No Link
## 13 Facebook Facebook
## 14 Facebook Facebook
## 15 Facebook Facebook
## 16 No Link No Link
## 17 No Link Other
## 18 No Link No Link
## 19 No Link No Link
## 20 School Website No Link
## 21 No Link No Link
## 22 No Link No Link
## 23 No Link No Link
## 24 No Link No Link
## 25 School Website No Link
## 26 School Website No Link
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = 0.567
##
## z = 4.48
## p-value = 7.43e-06calc_stats(d_hadi, d_cody, 32:57, 18, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## phone_number phone_number.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaNcalc_stats(d_hadi, d_cody, 32:57, 19, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## email email.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaNcalc_stats(d_hadi, d_cody, 32:57, 20, categorical = TRUE)## Percentage agreement (Tolerance=0)
##
## Subjects = 26
## Raters = 2
## %-agree = 100
## location location.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 26
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN