COLOR(green){SIZE(20){α係数,信頼性係数の算出}} #contents ---- * [[クロンバックのα信頼性係数:http://aoki2.si.gunma-u.ac.jp/R/alpha.html]] [#lb3dd771] 青木先生のページ [[解説:http://aoki2.si.gunma-u.ac.jp/lecture/misc/alpha.html]] もあり ---- * alpha() 関数 [#a3ce24c0] ** 含まれているパッケージ: psych [#qf290f8a] ** 使用例 [#e6157cc8] > library(psych) > example(alpha) alpha> r4 <- sim.congeneric() alpha> alpha(r4) Reliability analysis Call: alpha(x = r4) raw_alpha std.alpha G6(smc) average_r 0.74 0.74 0.69 0.42 Reliability if an item is dropped: raw_alpha std.alpha G6(smc) average_r V1 0.62 0.62 0.53 0.36 V2 0.66 0.66 0.57 0.39 V3 0.70 0.70 0.62 0.44 V4 0.74 0.74 0.66 0.49 Item statistics r r.cor V1 0.81 0.74 V2 0.78 0.67 V3 0.73 0.59 V4 0.68 0.50 alpha> r9 <- sim.hierarchical() alpha> alpha(r9) Reliability analysis Call: alpha(x = r9) raw_alpha std.alpha G6(smc) average_r 0.76 0.76 0.76 0.26 Reliability if an item is dropped: raw_alpha std.alpha G6(smc) average_r V1 0.71 0.71 0.70 0.24 V2 0.72 0.72 0.71 0.25 V3 0.74 0.74 0.73 0.26 V4 0.73 0.73 0.72 0.25 V5 0.74 0.74 0.73 0.26 V6 0.75 0.75 0.74 0.27 V7 0.75 0.75 0.74 0.27 V8 0.76 0.76 0.75 0.28 V9 0.77 0.77 0.76 0.29 Item statistics r r.cor V1 0.72 0.71 V2 0.67 0.63 V3 0.61 0.55 V4 0.65 0.59 V5 0.59 0.52 V6 0.53 0.43 V7 0.56 0.46 V8 0.50 0.39 V9 0.45 0.32 alpha> #an example of two independent factors that produce reasonable alphas alpha> #this is a case where alpha is a poor indicator of unidimensionality alpha> two.f <- sim.item(8) alpha> alpha(two.f,keys=c(rep(1,4),rep(-1,4))) Reliability analysis Call: alpha(x = two.f, keys = c(rep(1, 4), rep(-1, 4))) raw_alpha std.alpha G6(smc) average_r mean sd 0.59 0.59 0.63 0.15 0.011 0.3 Reliability if an item is dropped: raw_alpha std.alpha G6(smc) average_r V1 0.54 0.54 0.57 0.14 V2 0.55 0.55 0.58 0.15 V3 0.57 0.57 0.60 0.16 V4 0.55 0.55 0.59 0.15 V5 0.57 0.57 0.61 0.16 V6 0.55 0.55 0.59 0.15 V7 0.56 0.56 0.60 0.15 V8 0.54 0.54 0.58 0.14 Item statistics n r r.cor r.drop mean sd V1 500 0.55 0.47 0.34 0.0717 1.07 V2 500 0.52 0.42 0.30 0.0423 1.01 V3 500 0.47 0.35 0.25 -0.0055 0.98 V4 500 0.52 0.42 0.30 0.0556 0.97 V5 500 0.46 0.33 0.24 -0.0433 1.00 V6 500 0.50 0.40 0.29 0.0100 0.99 V7 500 0.49 0.37 0.26 -0.0203 0.98 V8 500 0.55 0.46 0.33 -0.0186 1.05 alpha> #an example with discrete item responses -- show the frequencies alpha> items <- sim.congeneric(N=500,short=FALSE,low=-2,high=2,categorical=TRUE) #500 responses to 4 discrete items alpha> a4 <- alpha(items$observed) #item response analysis of congeneric measures alpha> a4 Reliability analysis Call: alpha(x = items$observed) raw_alpha std.alpha G6(smc) average_r mean sd 0.7 0.7 0.66 0.37 0.0045 0.74 Reliability if an item is dropped: raw_alpha std.alpha G6(smc) average_r V1 0.55 0.55 0.46 0.29 V2 0.62 0.62 0.55 0.36 V3 0.65 0.65 0.57 0.38 V4 0.71 0.72 0.63 0.46 Item statistics n r r.cor r.drop mean sd V1 500 0.81 0.74 0.62 0.004 0.99 V2 500 0.74 0.61 0.51 0.000 1.01 V3 500 0.71 0.57 0.47 0.062 1.07 V4 500 0.64 0.43 0.36 -0.048 0.98 Non missing response frequency for each item -2 -1 0 1 2 miss V1 0.05 0.27 0.35 0.27 0.06 0 V2 0.06 0.25 0.37 0.25 0.07 0 V3 0.06 0.25 0.34 0.25 0.10 0 V4 0.07 0.24 0.42 0.22 0.05 0 alpha> #summary just gives Alpha alpha> summary(a4) Reliability analysis raw_alpha std.alpha G6(smc) average_r mean sd 0.7 0.7 0.66 0.37 0.0045 0.74 ---- * cronbach.alpha() 関数 [#q34d8060] ** 含まれているパッケージ: ltm [#se424624] ** 使用例 [#ieff8373] > library(ltm) > example(cronbach.alpha) crnbc.> # Cronbach's alpha for the LSAT data-set crnbc.> # with a Bootstrap 95% CI crnbc.> cronbach.alpha(LSAT, CI = TRUE, B = 500) Cronbach's alpha for the 'LSAT' data-set Items: 5 Sample units: 1000 alpha: 0.295 Bootstrap 95% CI based on 500 samples 2.5% 97.5% 0.210 0.356 ---- * cronbach() 関数 [#af5b0dbc] ** 含まれているパッケージ: psy [#zb0f59b5] ** 使用例 [#m6b6bc86] > library(psy) > example(cronbach) crnbch> data(expsy) crnbch> cronbach(expsy[,1:10]) ## not good because item 2 is reversed (1 is high and 4 is low) $sample.size [1] 27 $number.of.items [1] 10 $alpha [1] 0.1762655 crnbch> cronbach(cbind(expsy[,c(1,3:10)],-1*expsy[,2])) ## better $sample.size [1] 27 $number.of.items [1] 10 $alpha [1] 0.3752657 crnbch> datafile <- cbind(expsy[,c(1,3:10)],-1*expsy[,2]) crnbch> library(boot) Attaching package: 'boot' The following object(s) are masked _by_ '.GlobalEnv': simplex crnbch> cronbach.boot <- function(data,x) {cronbach(data[x,])[[3]]} crnbch> res <- boot(datafile,cronbach.boot,1000) crnbch> quantile(res$t,c(0.025,0.975)) ## two-sided bootstrapped confidence interval of Cronbach's alpha 2.5% 97.5% -0.2710921 0.6082468 crnbch> boot.ci(res,type="bca") ## adjusted bootstrap percentile (BCa) confidence interval (better) BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 1000 bootstrap replicates CALL : boot.ci(boot.out = res, type = "bca") Intervals : Level BCa 95% (-0.1680, 0.6404 ) Calculations and Intervals on Original Scale ---- * cronbach() 関数 [#l286cf92] ** 含まれているパッケージ: multilevel [#q16b24a5] ** 使用例 [#s58ee2ca] > library(multilevel) > example(cronbach) crnbch> data(bhr2000) crnbch> cronbach(bhr2000[,2:11]) $Alpha [1] 0.8798196 $N [1] 5400 ---- * reliablity() 関数 [#cabcb21b] ** 含まれているパッケージ: Rcmdr [#dd6eb4cf] CRAN のパッケージ置場にあるので install.packages( 'Rcmdr' ) で入手できる. ** 使用方法 [#e630ec52] reliability 関数を用いて Cronbach's alpha を求められる. > scale01 <- as.data.frame( cbind( Q1, Q2, Q3, Q4, Q5 ) ) > reliability( cov( scale01 ) ) *** 問題点 [#weca9616] reliability 関数の含まれる Rcmdr パッケージは R Commander とよばれるウィンドウを開いて操作するので,library() 関数や require() 関数で呼び出すとウィンドウが開いてしまう.単に reliability() を使いたいだけである場合非常に欝陶しい. 対策:R Commander を終了させ,save(reliability, file="reliability.R") とでもして保存し,次回以降は使いたいときに load("reliability.R") とすればよいでしょう。 対策:R Commander を終了させ,save(reliability, file="reliability.R") とでもして保存し,次回以降は使いたいときに load("reliability.R") とすればよいでしょう。(←Rcmdr::reliability(x)でよいのでは?) ---- * その他 [#qec2402f] http://home.earthlink.net/~bmagill/MyMisc.html 個人の作成した関数集に含まれているが,出力はあまり丁寧ではない.