Did you create aggregates of items like this
scale <- scale_1 + scale_2R + scale_3R?
If you run this function on a dataset, it will detect these
relationships and set the appropriate attributes. Once they are set,
the codebook package can perform reliability computations for you.
Did you create aggregates of items like this
scale <- scale_1 + scale_2R + scale_3R?
If you run this function on a dataset, it will detect these
relationships and set the appropriate attributes. Once they are set,
the codebook package can perform reliability computations for you.
Examples
bfi <- data.frame(matrix(data = rnorm(300), ncol = 3))
names(bfi) <- c("bfi_e1", "bfi_e2R", "bfi_e3")
bfi$bfi_e <- rowMeans(bfi[, c("bfi_e1", "bfi_e2R", "bfi_e3")])
bfi <- detect_scales(bfi)
#> 3 bfi_e items connected to scale
bfi$bfi_e
#> [1] 0.68249929 0.77928889 -1.03398789 0.24238137 0.41108163 0.10804157
#> [7] -0.56779166 0.09721003 -1.66922283 -0.45429517 0.17750418 -0.87430768
#> [13] -0.17472868 0.23680844 -0.47623162 0.45740618 0.46387713 0.89456606
#> [19] -0.73573798 0.58412309 -0.76562949 0.83910280 0.83225778 0.62808009
#> [25] 0.30561358 -0.09151848 0.74031423 -0.66943123 0.21972537 0.22164853
#> [31] 1.35203360 0.34120713 -0.32233926 0.73158080 0.31688055 0.28018593
#> [37] -0.27610186 -0.64574898 -0.90461595 0.24135359 0.10752722 -0.82755238
#> [43] 0.25932466 -0.31279482 0.09607748 -1.59163543 -0.12815941 0.62063074
#> [49] 0.74520853 0.04094594 0.59952548 -0.08642481 0.81854511 0.77670992
#> [55] -0.06537362 -0.37510175 0.67992387 0.52871967 -1.03568875 -1.30363811
#> [61] 0.19828320 0.42895503 -0.13062254 -0.32051806 0.43242926 -0.06552491
#> [67] -0.85728745 -0.59051293 1.28965980 -0.51938666 -1.32629468 0.34616546
#> [73] -0.07559116 0.58369035 -0.91976184 -0.13099517 -0.67531725 0.39364522
#> [79] -0.04407500 -0.64801309 -0.15780638 -0.24514966 -1.12287812 0.46228456
#> [85] -1.16745698 -0.18866174 1.34459574 0.30778443 0.73996007 0.72889848
#> [91] -0.43522305 -0.02962567 0.12905313 -0.76422008 0.43919957 -0.23828486
#> [97] -0.45097870 0.32985639 1.30993934 -1.31037685
#> attr(,"scale_item_names")
#> [1] "bfi_e1" "bfi_e2R" "bfi_e3"
#> attr(,"label")
#> [1] "aggregate of 3 bfi_e items"
bfi <- data.frame(matrix(data = rnorm(300), ncol = 3))
names(bfi) <- c("bfi_e1", "bfi_e2R", "bfi_e3")
bfi$bfi_e <- rowMeans(bfi[, c("bfi_e1", "bfi_e2R", "bfi_e3")])
bfi <- detect_scales(bfi)
#> 3 bfi_e items connected to scale
bfi$bfi_e
#> [1] -0.502172218 -0.431879929 -0.495985363 -1.120832852 0.049686067
#> [6] -0.563059761 1.105095369 0.371067763 -0.607880164 -0.301060799
#> [11] 0.369429090 -0.138117669 0.541330714 0.256596110 0.661218543
#> [16] -0.664322719 0.434891800 -0.031779575 0.365696596 -1.306240212
#> [21] -0.623121950 -0.612711377 -1.106205100 0.614808891 -0.075827088
#> [26] 0.182669487 -0.451533900 -0.454538313 0.910818570 -0.405420594
#> [31] 0.233886875 -0.937625452 -0.193737168 -0.228149409 0.687654432
#> [36] 0.395421746 0.599151407 1.174867760 -0.155523117 1.098220036
#> [41] -0.456091906 -0.336469935 1.512587955 0.399805250 -0.396619665
#> [46] -0.269658597 -0.214700479 -0.378173689 0.443365702 0.908733475
#> [51] 0.052400284 0.242154948 0.149008884 0.209647490 0.356641492
#> [56] 0.614080058 -0.274160930 -0.084715522 -0.531040879 -0.422708294
#> [61] -0.470475155 -0.814858208 -0.588315100 1.091467536 0.320599935
#> [66] 0.996773978 -0.200175736 -0.446062843 0.543623739 -0.167868130
#> [71] -0.007257087 0.656378028 1.005426062 -0.560318383 0.133006507
#> [76] -0.188995550 -1.334626037 1.470283416 -0.527778950 0.287272734
#> [81] -0.306084078 -0.312422337 -0.129541534 -0.525578583 -0.759719946
#> [86] -1.071909603 0.601158262 -0.297178907 -0.165597041 0.777162680
#> [91] 1.383476210 -0.256776664 0.041581469 -0.822791525 0.586172987
#> [96] -0.522463683 0.723380995 -0.679383729 0.379866122 -0.806330625
#> attr(,"scale_item_names")
#> [1] "bfi_e1" "bfi_e2R" "bfi_e3"
#> attr(,"label")
#> [1] "aggregate of 3 bfi_e items"