α係数，信頼性係数の算出
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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....
青木先生のページ
[[解説:http://aoki2.si.gunma-u.ac.jp/lecture/misc/alpha.h...
----
* 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 produ...
alpha> #this is a case where alpha is a poor indicator o...
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...
alpha> items <- sim.congeneric(N=500,short=FALSE,low=-2,...
alpha> a4 <- alpha(items$observed) #item response analy...
alpha> a4
Reliability analysis
Call: alpha(x = items$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...
$sample.size
[1] 27
$number.of.items
[1] 10
$alpha
[1] 0.1762655
crnbch> cronbach(cbind(expsy[,c(1,3:10)],-1*expsy[,2])) ...
$sample.size
[1] 27
$number.of.items
[1] 10
$alpha
[1] 0.3752657
crnbch> datafile <- cbind(expsy[,c(1,3:10)],-1*expsy[,2])
crnbch> library(boot)
Attaching package: 'boot'
The following object(s) are masked _by_ '.GlobalEnv':
simplex
crnbch> cronbach.boot <- function(data,x) {cronbach(data...
crnbch> res <- boot(datafile,cronbach.boot,1000)
crnbch> quantile(res$t,c(0.025,0.975)) ## two-sided boo...
2.5% 97.5%
-0.2710921 0.6082468
crnbch> boot.ci(res,type="bca") ## adjusted boot...
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...
対策：R Commander を終了させ，save(reliability, file="rel...
----
* 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...
終了行:
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....
青木先生のページ
[[解説:http://aoki2.si.gunma-u.ac.jp/lecture/misc/alpha.h...
----
* 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 produ...
alpha> #this is a case where alpha is a poor indicator o...
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...
alpha> items <- sim.congeneric(N=500,short=FALSE,low=-2,...
alpha> a4 <- alpha(items$observed) #item response analy...
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...
$sample.size
[1] 27
$number.of.items
[1] 10
$alpha
[1] 0.1762655
crnbch> cronbach(cbind(expsy[,c(1,3:10)],-1*expsy[,2])) ...
$sample.size
[1] 27
$number.of.items
[1] 10
$alpha
[1] 0.3752657
crnbch> datafile <- cbind(expsy[,c(1,3:10)],-1*expsy[,2])
crnbch> library(boot)
Attaching package: 'boot'
The following object(s) are masked _by_ '.GlobalEnv':
simplex
crnbch> cronbach.boot <- function(data,x) {cronbach(data...
crnbch> res <- boot(datafile,cronbach.boot,1000)
crnbch> quantile(res$t,c(0.025,0.975)) ## two-sided boo...
2.5% 97.5%
-0.2710921 0.6082468
crnbch> boot.ci(res,type="bca") ## adjusted boot...
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...
対策：R Commander を終了させ，save(reliability, file="rel...
----
* 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...
ページ名:
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