CONTINUOUS NUMBERS - COUNTS
calc_stats(d_hadi, d_cody, 1:10, 6, continuous = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## number_of_student_first_names_in_post
## 1 0
## 2 0
## 3 0
## 4 1
## 5 0
## 6 0
## 7 0
## 8 1
## 9 1
## 10 0
## number_of_student_first_names_in_post.1
## 1 0
## 2 0
## 3 0
## 4 1
## 5 0
## 6 0
## 7 0
## 8 1
## 9 1
## 10 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 10
## Raters = 2
## ICC(1) = 1
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,10) = Inf , p = 0
##
## 95%-Confidence Interval for ICC Population Values:
## NaN < ICC < NaN
## mean_diff
## 1 0
calc_stats(d_hadi, d_cody, 1:10, 7, continuous = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## number_of_student_last_names_in_post number_of_student_last_names_in_post.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 1 1
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 1 1
## 10 0 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 10
## Raters = 2
## ICC(1) = 1
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,10) = Inf , p = 0
##
## 95%-Confidence Interval for ICC Population Values:
## NaN < ICC < NaN
## mean_diff
## 1 0
calc_stats(d_hadi, d_cody, 1:10, 8, continuous = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## number_of_student_tagged_accounts number_of_student_tagged_accounts.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
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 10
## Raters = 2
## ICC(1) = NaN
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,10) = NaN , p = NaN
##
## 95%-Confidence Interval for ICC Population Values:
## NaN < ICC < NaN
## mean_diff
## 1 0
calc_stats(d_hadi, d_cody, 1:10, 9, continuous = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## number_of_student_faces_in_image number_of_student_faces_in_image.1
## 1 21 21
## 2 17 17
## 3 7 7
## 4 7 7
## 5 24 24
## 6 4 4
## 7 0 0
## 8 1 1
## 9 1 1
## 10 16 16
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 10
## Raters = 2
## ICC(1) = 1
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,10) = Inf , p = 0
##
## 95%-Confidence Interval for ICC Population Values:
## NaN < ICC < NaN
## mean_diff
## 1 0
calc_stats(d_hadi, d_cody, 1:10, 10, continuous = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## how_many_names_faces_connected how_many_names_faces_connected.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 1 1
## 5 0 0
## 6 0 0
## 7 0 0
## 8 1 1
## 9 1 1
## 10 0 0
## Single Score Intraclass Correlation
##
## Model: oneway
## Type : consistency
##
## Subjects = 10
## Raters = 2
## ICC(1) = 1
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,10) = Inf , p = 0
##
## 95%-Confidence Interval for ICC Population Values:
## NaN < ICC < NaN
## mean_diff
## 1 0
CATEGORICAL VARIABLES - 0,1,2 or 0,1
calc_stats(d_hadi, d_cody, 1:10, 11, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## is_there_an_identifiable_face_in_this_post
## 1 0
## 2 0
## 3 0
## 4 1
## 5 0
## 6 0
## 7 0
## 8 1
## 9 1
## 10 0
## is_there_an_identifiable_face_in_this_post.1
## 1 0
## 2 0
## 3 0
## 4 1
## 5 0
## 6 0
## 7 0
## 8 1
## 9 1
## 10 0
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = 1
##
## z = 3.16
## p-value = 0.00157
calc_stats(d_hadi, d_cody, 1:10, 12, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## 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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN
calc_stats(d_hadi, d_cody, 1:10, 13, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## 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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN
calc_stats(d_hadi, d_cody, 1:10, 14, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## date_of_birth date_of_birth.1
## 1 1 1
## 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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = 1
##
## z = 3.16
## p-value = 0.00157
calc_stats(d_hadi, d_cody, 1:10, 15, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## 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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN
calc_stats(d_hadi, d_cody, 1:10, 16, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## gender_identity gender_identity.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 1 1
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = 1
##
## z = 3.16
## p-value = 0.00157
calc_stats(d_hadi, d_cody, 1:10, 17, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## retweet retweet.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 1 1
## 8 0 0
## 9 0 0
## 10 1 1
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = 1
##
## z = 3.16
## p-value = 0.00157
calc_stats(d_hadi, d_cody, 1:10, 18, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## external_link external_link.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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN
calc_stats(d_hadi, d_cody, 1:10, 19, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## 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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN
calc_stats(d_hadi, d_cody, 1:10, 20, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## 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
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = NaN
##
## z = NaN
## p-value = NaN
calc_stats(d_hadi, d_cody, 1:10, 21, categorical = TRUE)
## Percentage agreement (Tolerance=0)
##
## Subjects = 10
## Raters = 2
## %-agree = 100
## location location.1
## 1 0 0
## 2 0 0
## 3 0 0
## 4 1 1
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 1 1
## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 10
## Raters = 2
## Kappa = 1
##
## z = 3.16
## p-value = 0.00157