Turn a psych::alpha() object into HTML tables.
Examples
example("alpha", "psych")
#>
#> Attaching package: ‘psych’
#> The following object is masked _by_ ‘.GlobalEnv’:
#>
#> bfi
#> The following object is masked from ‘package:codebook’:
#>
#> bfi
#>
#> alpha> set.seed(42) #keep the same starting values
#>
#> alpha> #four congeneric measures
#> alpha> r4 <- sim.congeneric()
#>
#> alpha> alpha(r4)
#>
#> Reliability analysis
#> Call: alpha(x = r4)
#>
#> raw_alpha std.alpha G6(smc) average_r S/N median_r
#> 0.74 0.74 0.69 0.42 2.9 0.41
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt -0.31 0.74 0.98
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N var.r med.r
#> V1 0.62 0.62 0.53 0.36 1.7 0.0036 0.35
#> V2 0.66 0.66 0.57 0.39 1.9 0.0081 0.40
#> V3 0.70 0.70 0.62 0.44 2.3 0.0120 0.40
#> V4 0.74 0.74 0.66 0.49 2.8 0.0049 0.48
#>
#> Item statistics
#> r r.cor r.drop
#> V1 0.81 0.74 0.64
#> V2 0.78 0.67 0.57
#> V3 0.73 0.59 0.51
#> V4 0.68 0.50 0.43
#>
#> alpha> #nine hierarchical measures -- should actually use omega
#> alpha> r9 <- sim.hierarchical()
#>
#> alpha> alpha(r9)
#>
#> Reliability analysis
#> Call: alpha(x = r9)
#>
#> raw_alpha std.alpha G6(smc) average_r S/N median_r
#> 0.76 0.76 0.76 0.26 3.2 0.25
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt 0.43 0.76 0.94
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N var.r med.r
#> V1 0.71 0.71 0.70 0.24 2.5 0.0067 0.22
#> V2 0.72 0.72 0.71 0.25 2.6 0.0085 0.23
#> V3 0.74 0.74 0.73 0.26 2.8 0.0101 0.25
#> V4 0.73 0.73 0.72 0.25 2.7 0.0106 0.23
#> V5 0.74 0.74 0.73 0.26 2.9 0.0112 0.24
#> V6 0.75 0.75 0.74 0.27 3.0 0.0113 0.25
#> V7 0.75 0.75 0.74 0.27 3.0 0.0129 0.25
#> V8 0.76 0.76 0.75 0.28 3.1 0.0118 0.26
#> V9 0.77 0.77 0.76 0.29 3.3 0.0099 0.28
#>
#> Item statistics
#> r r.cor r.drop
#> V1 0.72 0.71 0.61
#> V2 0.67 0.63 0.54
#> V3 0.61 0.55 0.47
#> V4 0.65 0.59 0.51
#> V5 0.59 0.52 0.45
#> V6 0.53 0.43 0.38
#> V7 0.56 0.46 0.40
#> V8 0.50 0.39 0.34
#> V9 0.45 0.32 0.28
#>
#> alpha> # examples of two independent factors that produce reasonable alphas
#> alpha> #this is a case where alpha is a poor indicator of unidimensionality
#> alpha>
#> alpha> two.f <- sim.item(8)
#>
#> alpha> #specify which items to reverse key by name
#> alpha> alpha(two.f,keys=c("V3","V4","V5","V6"))
#> Number of categories should be increased in order to count frequencies.
#>
#> Reliability analysis
#> Call: alpha(x = two.f, keys = c("V3", "V4", "V5", "V6"))
#>
#> raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
#> 0.58 0.58 0.62 0.15 1.4 0.029 0.072 0.51 0.051
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt 0.52 0.58 0.63
#> Duhachek 0.53 0.58 0.64
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
#> V1 0.55 0.55 0.58 0.15 1.2 0.031 0.032 0.056
#> V2 0.53 0.53 0.57 0.14 1.1 0.033 0.034 0.029
#> V3- 0.55 0.55 0.58 0.15 1.2 0.031 0.031 0.056
#> V4- 0.55 0.54 0.58 0.15 1.2 0.032 0.032 0.056
#> V5- 0.56 0.56 0.59 0.15 1.3 0.031 0.030 0.056
#> V6- 0.56 0.56 0.59 0.15 1.3 0.031 0.030 0.056
#> V7 0.53 0.53 0.56 0.14 1.1 0.033 0.031 0.041
#> V8 0.56 0.57 0.59 0.16 1.3 0.030 0.030 0.047
#>
#> Item statistics
#> n raw.r std.r r.cor r.drop mean sd
#> V1 500 0.50 0.50 0.38 0.28 0.0117 1.01
#> V2 500 0.55 0.55 0.46 0.34 -0.0018 1.00
#> V3- 500 0.50 0.50 0.39 0.27 0.1443 1.05
#> V4- 500 0.51 0.51 0.41 0.30 0.1502 0.99
#> V5- 500 0.48 0.48 0.36 0.26 0.1030 1.01
#> V6- 500 0.48 0.48 0.36 0.26 0.1128 1.00
#> V7 500 0.56 0.56 0.48 0.35 0.0222 1.00
#> V8 500 0.45 0.46 0.33 0.23 0.0320 0.96
#>
#> alpha> cov.two <- cov(two.f)
#>
#> alpha> alpha(cov.two,check.keys=TRUE)
#> Warning: Some items were negatively correlated with the first principal component and were automatically reversed.
#> This is indicated by a negative sign for the variable name.
#>
#> Reliability analysis
#> Call: alpha(x = cov.two, check.keys = TRUE)
#>
#> raw_alpha std.alpha G6(smc) average_r S/N median_r
#> 0.58 0.58 0.62 0.15 1.4 0.051
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt -0.07 0.58 0.9
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N var.r med.r
#> V1 0.55 0.55 0.58 0.15 1.2 0.032 0.056
#> V2 0.53 0.53 0.57 0.14 1.1 0.034 0.029
#> V3- 0.55 0.55 0.58 0.15 1.2 0.031 0.056
#> V4- 0.55 0.54 0.58 0.15 1.2 0.032 0.056
#> V5- 0.56 0.56 0.59 0.15 1.3 0.030 0.056
#> V6- 0.56 0.56 0.59 0.15 1.3 0.030 0.056
#> V7 0.53 0.53 0.56 0.14 1.1 0.031 0.041
#> V8 0.56 0.57 0.59 0.16 1.3 0.030 0.047
#>
#> Item statistics
#> r r.cor r.drop
#> V1 0.50 0.38 0.28
#> V2 0.55 0.46 0.34
#> V3- 0.50 0.39 0.27
#> V4- 0.51 0.41 0.30
#> V5- 0.48 0.36 0.26
#> V6- 0.48 0.36 0.26
#> V7 0.56 0.48 0.35
#> V8 0.46 0.33 0.23
#>
#> alpha> #automatic reversal base upon first component
#> alpha> alpha(two.f,check.keys=TRUE) #note that the median is much less than the average R
#> Number of categories should be increased in order to count frequencies.
#> Warning: Some items were negatively correlated with the first principal component and were automatically reversed.
#> This is indicated by a negative sign for the variable name.
#>
#> Reliability analysis
#> Call: alpha(x = two.f, check.keys = TRUE)
#>
#> raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
#> 0.58 0.58 0.62 0.15 1.4 0.029 0.072 0.51 0.051
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt 0.52 0.58 0.63
#> Duhachek 0.53 0.58 0.64
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
#> V1 0.55 0.55 0.58 0.15 1.2 0.031 0.032 0.056
#> V2 0.53 0.53 0.57 0.14 1.1 0.033 0.034 0.029
#> V3- 0.55 0.55 0.58 0.15 1.2 0.031 0.031 0.056
#> V4- 0.55 0.54 0.58 0.15 1.2 0.032 0.032 0.056
#> V5- 0.56 0.56 0.59 0.15 1.3 0.031 0.030 0.056
#> V6- 0.56 0.56 0.59 0.15 1.3 0.031 0.030 0.056
#> V7 0.53 0.53 0.56 0.14 1.1 0.033 0.031 0.041
#> V8 0.56 0.57 0.59 0.16 1.3 0.030 0.030 0.047
#>
#> Item statistics
#> n raw.r std.r r.cor r.drop mean sd
#> V1 500 0.50 0.50 0.38 0.28 0.0117 1.01
#> V2 500 0.55 0.55 0.46 0.34 -0.0018 1.00
#> V3- 500 0.50 0.50 0.39 0.27 0.1443 1.05
#> V4- 500 0.51 0.51 0.41 0.30 0.1502 0.99
#> V5- 500 0.48 0.48 0.36 0.26 0.1030 1.01
#> V6- 500 0.48 0.48 0.36 0.26 0.1128 1.00
#> V7 500 0.56 0.56 0.48 0.35 0.0222 1.00
#> V8 500 0.45 0.46 0.33 0.23 0.0320 0.96
#>
#> alpha> #this suggests (correctly) that the 1 factor model is probably wrong
#> alpha> #an example with discrete item responses -- show the frequencies
#> alpha> items <- sim.congeneric(N=500,short=FALSE,low=-2,high=2,
#> alpha+ categorical=TRUE) #500 responses to 4 discrete items with 5 categories
#>
#> 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 S/N ase mean sd median_r
#> 0.73 0.73 0.68 0.4 2.7 0.02 -0.013 0.76 0.4
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt 0.69 0.73 0.76
#> Duhachek 0.69 0.73 0.77
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
#> V1 0.61 0.61 0.52 0.34 1.6 0.031 0.0063 0.32
#> V2 0.64 0.64 0.55 0.37 1.8 0.028 0.0097 0.37
#> V3 0.68 0.68 0.60 0.41 2.1 0.025 0.0134 0.37
#> V4 0.73 0.73 0.65 0.48 2.8 0.021 0.0036 0.47
#>
#> Item statistics
#> n raw.r std.r r.cor r.drop mean sd
#> V1 500 0.80 0.80 0.73 0.62 0.050 1.00
#> V2 500 0.77 0.77 0.67 0.57 -0.022 1.03
#> V3 500 0.72 0.73 0.58 0.50 -0.028 0.99
#> V4 500 0.67 0.66 0.46 0.40 -0.050 1.05
#>
#> Non missing response frequency for each item
#> -2 -1 0 1 2 miss
#> V1 0.06 0.24 0.38 0.25 0.07 0
#> V2 0.07 0.26 0.35 0.25 0.07 0
#> V3 0.05 0.27 0.38 0.22 0.07 0
#> V4 0.10 0.22 0.39 0.22 0.07 0
#>
#> alpha> #summary just gives Alpha
#> alpha> summary(a4)
#>
#> Reliability analysis
#> raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
#> 0.73 0.73 0.68 0.4 2.7 0.02 -0.013 0.76 0.4
#>
#> alpha> alpha2r(alpha = .74,n.var=4)
#> [1] 0.4157303
#>
#> alpha> #because alpha.ci returns an invisible object, you need to print it
#> alpha> print(alpha.ci(.74, 100,p.val=.05,n.var=4))
#>
#> 95% confidence boundaries (Feldt)
#> lower alpha upper
#> 0.65 0.74 0.81
knitr::knit_print(a4)
#>
#> Reliability analysis
#> Call: alpha(x = items$observed)
#>
#> raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
#> 0.73 0.73 0.68 0.4 2.7 0.02 -0.013 0.76 0.4
#>
#> 95% confidence boundaries
#> lower alpha upper
#> Feldt 0.69 0.73 0.76
#> Duhachek 0.69 0.73 0.77
#>
#> Reliability if an item is dropped:
#> raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
#> V1 0.61 0.61 0.52 0.34 1.6 0.031 0.0063 0.32
#> V2 0.64 0.64 0.55 0.37 1.8 0.028 0.0097 0.37
#> V3 0.68 0.68 0.60 0.41 2.1 0.025 0.0134 0.37
#> V4 0.73 0.73 0.65 0.48 2.8 0.021 0.0036 0.47
#>
#> Item statistics
#> n raw.r std.r r.cor r.drop mean sd
#> V1 500 0.80 0.80 0.73 0.62 0.050 1.00
#> V2 500 0.77 0.77 0.67 0.57 -0.022 1.03
#> V3 500 0.72 0.73 0.58 0.50 -0.028 0.99
#> V4 500 0.67 0.66 0.46 0.40 -0.050 1.05
#>
#> Non missing response frequency for each item
#> -2 -1 0 1 2 miss
#> V1 0.06 0.24 0.38 0.25 0.07 0
#> V2 0.07 0.26 0.35 0.25 0.07 0
#> V3 0.05 0.27 0.38 0.22 0.07 0
#> V4 0.10 0.22 0.39 0.22 0.07 0