pacman::p_load(readxl, rmarkdown, irr, psych, tidyverse)
dat <- read_excel("~/Desktop/karina.inter.xlsx")
paged_table(dat)
Jtech.Flx
Jtech.flex <- dat %>%
select(Jtech.Flx1, Jtech.Flx3)
paged_table(Jtech.flex)
Descriptive
describe(Jtech.flex)
## vars n mean sd median trimmed mad min max range skew kurtosis
## Jtech.Flx1 1 10 183.4 74.99 163.5 175.25 53.37 93 339 246 0.81 -0.61
## Jtech.Flx3 2 10 183.4 74.80 153.0 176.75 42.25 99 321 222 0.64 -1.28
## se
## Jtech.Flx1 23.72
## Jtech.Flx3 23.65
Reliability
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 10
## Raters = 2
## ICC(A,1) = 0.857
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,9) = 11.8 , p = 0.000546
##
## 95%-Confidence Interval for ICC Population Values:
## 0.518 < ICC < 0.963
Jtech.ext
Jtech.ext <- dat %>%
select(Jtech.Ext1, Jtech.Ext3)
paged_table(Jtech.ext)
Descriptive
describe(Jtech.ext)
## vars n mean sd median trimmed mad min max range skew kurtosis
## Jtech.Ext1 1 10 161.3 60.14 168.5 157.62 43.74 73 279 206 0.23 -0.74
## Jtech.Ext3 2 10 170.2 52.83 155.5 169.62 44.48 90 255 165 0.33 -1.21
## se
## Jtech.Ext1 19.02
## Jtech.Ext3 16.71
ICC
icc(Jtech.ext, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 10
## Raters = 2
## ICC(A,1) = 0.848
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,9.93) = 12 , p = 0.000304
##
## 95%-Confidence Interval for ICC Population Values:
## 0.524 < ICC < 0.96
Jtech.abd
Jtech.abd <- dat %>%
select(Jtech.Abd1, Jtech.Abd3)
paged_table(Jtech.abd)
Descriptive
describe(Jtech.abd)
## vars n mean sd median trimmed mad min max range skew kurtosis
## Jtech.Abd1 1 10 153.9 57.45 137.5 149.25 45.96 88 257 169 0.61 -1.21
## Jtech.Abd3 2 10 153.4 58.98 130.0 147.88 31.13 89 262 173 0.69 -1.27
## se
## Jtech.Abd1 18.17
## Jtech.Abd3 18.65
ICC
icc(Jtech.abd, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 10
## Raters = 2
## ICC(A,1) = 0.919
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,9.01) = 21.5 , p = 4.77e-05
##
## 95%-Confidence Interval for ICC Population Values:
## 0.707 < ICC < 0.979
Jtech.add
Jtech.add <- dat %>%
select(Jtech.Add1, Jtech.Add3)
paged_table(Jtech.add)
Descriptive
describe(Jtech.add)
## vars n mean sd median trimmed mad min max range skew
## Jtech.Add1 1 10 163.0 37.82 180.5 167.75 17.05 96 192 96 -0.92
## Jtech.Add3 2 10 160.2 51.75 171.5 162.25 48.18 63 241 178 -0.32
## kurtosis se
## Jtech.Add1 -0.96 11.96
## Jtech.Add3 -0.95 16.36
ICC
icc(Jtech.add, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 10
## Raters = 2
## ICC(A,1) = 0.76
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,9.12) = 6.74 , p = 0.0043
##
## 95%-Confidence Interval for ICC Population Values:
## 0.275 < ICC < 0.935
JT_GRIP
JT.GRIP <- dat %>%
select(JT_GRIP1, JT_GRIP3)
paged_table(JT.GRIP)
Descriptive
describe(JT.GRIP)
## vars n mean sd median trimmed mad min max range skew kurtosis
## JT_GRIP1 1 10 393.5 131.12 350.5 380.5 94.15 248 643 395 0.69 -1.15
## JT_GRIP3 2 10 410.1 119.03 382.5 401.0 110.45 275 618 343 0.52 -1.42
## se
## JT_GRIP1 41.46
## JT_GRIP3 37.64
ICC
icc(JT.GRIP, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 10
## Raters = 2
## ICC(A,1) = 0.949
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(9,9.51) = 41.6 , p = 1.62e-06
##
## 95%-Confidence Interval for ICC Population Values:
## 0.817 < ICC < 0.987