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.
detect_scales(data, quiet = FALSE) detect_scales(data, quiet = FALSE)
| data | the data frame |
|---|---|
| quiet | defaults to false. Suppresses messages about found items. |
bfi <- data.frame(matrix(data = rnorm(500), ncol = 5)) names(bfi) <- c("bfi_e1", "bfi_e2R", "bfi_e3", "bfi_n1", "bfi_n2") bfi$bfi_e <- rowMeans(bfi[, c("bfi_e1", "bfi_e2R", "bfi_e3")]) bfi <- detect_scales(bfi)#>#> Warning: bfi_n items found, but no aggregatebfi$bfi_e#> [1] 0.8015485778 0.1242008715 1.4111601036 -1.2359843828 0.8755174016 #> [6] 0.9704337425 0.1256702396 0.4579800147 0.1481186619 -0.2613590844 #> [11] -0.0454207511 1.2847818751 0.0969500761 -0.4646285832 0.9872776221 #> [16] 1.0607406147 0.8092427361 -0.3909060260 -0.1631451792 -0.0168259197 #> [21] 0.0379601509 -1.3348544542 -0.3418188349 -0.3469655384 0.2681050820 #> [26] 0.0160450021 0.6795888007 -0.6340680673 -0.9698365999 0.5346686176 #> [31] -1.1772199366 0.5013617944 0.7480301550 0.7210123466 -0.5085987698 #> [36] -0.7530780276 0.7376071929 0.7836556475 0.2392642892 0.9698502499 #> [41] 0.2076311038 0.4118653252 -0.0735040359 0.4506413326 0.2195415811 #> [46] 0.1802557582 -0.2274196804 0.0477936993 -0.8153949023 0.6790446071 #> [51] -1.0123051762 -0.1929429826 0.3603824703 -0.9452471654 -0.2475681063 #> [56] 0.7194436012 -0.2554316310 -0.4332763827 0.8590040320 0.9552586648 #> [61] 0.1924536606 -0.5312016165 1.4697668632 -0.3988861741 -0.0001894151 #> [66] -0.3956289014 0.8160424138 -0.3266707117 -0.1756387935 -0.6017318640 #> [71] 0.1412798858 1.3190123866 0.4557577536 -0.0185777490 0.3302301687 #> [76] -0.1244923341 0.4662560852 -0.6980709833 0.1884465481 0.1645677940 #> [81] 0.6181046675 -0.9340002428 -0.6359859871 -0.3176546090 -0.4788309317 #> [86] 0.2373336437 0.1946028667 0.0456007471 -1.2833583570 0.0641209995 #> [91] 0.0325788440 0.1460103547 1.3553405723 0.6966568876 -0.3455142832 #> [96] 0.1093705621 1.0434134739 0.1010924876 -0.8042213045 -0.4034993055 #> 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(500), ncol = 5)) names(bfi) <- c("bfi_e1", "bfi_e2R", "bfi_e3", "bfi_n1", "bfi_n2") bfi$bfi_e <- rowMeans(bfi[, c("bfi_e1", "bfi_e2R", "bfi_e3")]) bfi <- detect_scales(bfi)#>#> Warning: bfi_n items found, but no aggregatebfi$bfi_e#> [1] -0.194491951 0.599303261 1.361161754 1.210202575 -0.714562458 #> [6] 0.074074287 0.039096102 0.703875680 0.001766981 0.420126472 #> [11] 0.097186704 -0.299350416 -0.110115099 -0.138134406 0.243545579 #> [16] 0.891731006 -0.793210394 0.888058876 -0.054600266 0.167755563 #> [21] 0.940822540 -0.230862032 -0.859446408 -0.601098601 -0.320550995 #> [26] 0.204980824 0.237595906 0.469456438 -0.236321642 -0.686148562 #> [31] -0.600329415 -0.101751952 0.379019532 -0.649607631 -0.713580039 #> [36] 0.139134092 -1.595526920 1.584097982 -0.498378003 0.969178199 #> [41] 0.567448748 -0.584465676 -0.569081556 0.480772528 0.540286912 #> [46] -0.925905068 0.513920167 0.163976300 -0.391761062 -0.676471023 #> [51] -0.150523719 0.807791932 -0.044178093 -0.209198969 -0.642383154 #> [56] 0.279816237 -0.155907350 -0.059505201 -0.109381977 -0.502682568 #> [61] -0.356437722 0.125351971 0.211673668 -0.211059997 -0.257598539 #> [66] 0.047509227 -0.275738223 -0.110972537 -0.183728282 0.314123494 #> [71] -0.443647377 0.160335079 -0.084228446 -0.535079859 -0.195346962 #> [76] -0.057451475 0.158969490 -0.302475088 0.579821667 -0.216988427 #> [81] -0.389228199 -0.643880452 -0.151623621 -0.204034908 0.035256290 #> [86] 0.390260787 1.185438234 -1.081983377 -0.025601498 -0.196204865 #> [91] -0.193417478 -0.295844292 0.390382634 -0.476297982 0.225940071 #> [96] -0.439571536 -0.437470518 -0.286314983 -0.771266013 -0.314513363 #> attr(,"scale_item_names") #> [1] "bfi_e1" "bfi_e2R" "bfi_e3" #> attr(,"label") #> [1] "aggregate of 3 bfi_e items"