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Reliability analysis (Cronbach's \(\alpha\) and McDonald's \(\omega\)).

Source: R/bruceR-stats_2_scale.R
Alpha.Rd

An extension of psych::alpha() and psych::omega(), reporting (1) scale statistics (Cronbach's \(\alpha\) and McDonald's \(\omega\)) and (2) item statistics (item-rest correlation [i.e., corrected item-total correlation] and Cronbach's \(\alpha\) if item deleted).

Three options to specify variables:

  1. var + items: common and unique parts of variable names (suggested).

  2. vars: a character vector of variable names (suggested).

  3. varrange: starting and stopping positions of variables (NOT suggested).

Usage

Alpha(data, var, items, vars = NULL, varrange = NULL, rev = NULL, digits = 3)

Arguments

data

Data frame.

var

[Option 1] Common part across variables: e.g., "RSES", "XX.{i}.pre" (if var string has any placeholder in braces {...}, then items will be pasted into the braces, see examples)

items

[Option 1] Unique part across variables: e.g., 1:10, c("a", "b", "c")

vars

[Option 2] Character vector specifying variables: e.g., c("X1", "X2", "X3", "X4", "X5")

varrange

[Option 3] Character string specifying positions ("start:stop") of variables: e.g., "A1:E5"

rev

[Optional] Variables that need to be reversed. It can be (1) a character vector specifying the reverse-scoring variables (recommended), or (2) a numeric vector specifying the item number of reverse-scoring variables (not recommended).

digits

Number of decimal places of output. Defaults to 3.

Value

A list of results obtained from psych::alpha() and psych::omega().

See also

MEAN, EFA, CFA

Examples

# ?psych::bfi
data = psych::bfi
Alpha(data, "E", 1:5)   # "E1" & "E2" should be reversed
#> 
#> Reliability Analysis
#> 
#> Summary:
#> Total Items: 5
#> Scale Range: 1 ~ 6
#> Total Cases: 2800
#> Valid Cases: 2713 (96.9%)
#> 
#> Scale Statistics:
#> Mean = 3.791
#> S.D. = 0.542
#> Cronbach’s α = -0.624
#> McDonald’s ω = 0.150
#> 
#> Warning: Scale reliability is low. You may check item codings.
#> Items E1, E2 correlate negatively with the scale and may be reversed.
#> You can specify this argument: rev=c("E1", "E2")
#> 
#> Item Statistics (Cronbach’s α If Item Deleted):
#> ─────────────────────────────────────────────
#>      Mean    S.D. Item-Rest Cor. Cronbach’s α
#> ─────────────────────────────────────────────
#> E1  2.972 (1.632)         -0.263       -0.270
#> E2  3.144 (1.607)         -0.355       -0.074
#> E3  4.000 (1.352)         -0.002       -0.881
#> E4  4.421 (1.461)         -0.202       -0.423
#> E5  4.418 (1.337)         -0.047       -0.765
#> ─────────────────────────────────────────────
#> Item-Rest Cor. = Corrected Item-Total Correlation
#> 
Alpha(data, "E", 1:5, rev=1:2)            # correct
#> 
#> Reliability Analysis
#> 
#> Summary:
#> Total Items: 5
#> Scale Range: 1 ~ 6
#> Total Cases: 2800
#> Valid Cases: 2713 (96.9%)
#> 
#> Scale Statistics:
#> Mean = 4.145
#> S.D. = 1.060
#> Cronbach’s α = 0.761
#> McDonald’s ω = 0.763
#> 
#> Item Statistics (Cronbach’s α If Item Deleted):
#> ───────────────────────────────────────────────────
#>            Mean    S.D. Item-Rest Cor. Cronbach’s α
#> ───────────────────────────────────────────────────
#> E1 (rev)  4.028 (1.632)          0.513        0.725
#> E2 (rev)  3.856 (1.607)          0.606        0.688
#> E3        4.000 (1.352)          0.501        0.728
#> E4        4.421 (1.461)          0.578        0.701
#> E5        4.418 (1.337)          0.455        0.742
#> ───────────────────────────────────────────────────
#> Item-Rest Cor. = Corrected Item-Total Correlation
#> 
Alpha(data, "E", 1:5, rev=cc("E1, E2"))   # also correct
#> 
#> Reliability Analysis
#> 
#> Summary:
#> Total Items: 5
#> Scale Range: 1 ~ 6
#> Total Cases: 2800
#> Valid Cases: 2713 (96.9%)
#> 
#> Scale Statistics:
#> Mean = 4.145
#> S.D. = 1.060
#> Cronbach’s α = 0.761
#> McDonald’s ω = 0.763
#> 
#> Item Statistics (Cronbach’s α If Item Deleted):
#> ───────────────────────────────────────────────────
#>            Mean    S.D. Item-Rest Cor. Cronbach’s α
#> ───────────────────────────────────────────────────
#> E1 (rev)  4.028 (1.632)          0.513        0.725
#> E2 (rev)  3.856 (1.607)          0.606        0.688
#> E3        4.000 (1.352)          0.501        0.728
#> E4        4.421 (1.461)          0.578        0.701
#> E5        4.418 (1.337)          0.455        0.742
#> ───────────────────────────────────────────────────
#> Item-Rest Cor. = Corrected Item-Total Correlation
#> 
Alpha(data, vars=cc("E1, E2, E3, E4, E5"), rev=cc("E1, E2"))
#> 
#> Reliability Analysis
#> 
#> Summary:
#> Total Items: 5
#> Scale Range: 1 ~ 6
#> Total Cases: 2800
#> Valid Cases: 2713 (96.9%)
#> 
#> Scale Statistics:
#> Mean = 4.145
#> S.D. = 1.060
#> Cronbach’s α = 0.761
#> McDonald’s ω = 0.763
#> 
#> Item Statistics (Cronbach’s α If Item Deleted):
#> ───────────────────────────────────────────────────
#>            Mean    S.D. Item-Rest Cor. Cronbach’s α
#> ───────────────────────────────────────────────────
#> E1 (rev)  4.028 (1.632)          0.513        0.725
#> E2 (rev)  3.856 (1.607)          0.606        0.688
#> E3        4.000 (1.352)          0.501        0.728
#> E4        4.421 (1.461)          0.578        0.701
#> E5        4.418 (1.337)          0.455        0.742
#> ───────────────────────────────────────────────────
#> Item-Rest Cor. = Corrected Item-Total Correlation
#> 
Alpha(data, varrange="E1:E5", rev=cc("E1, E2"))
#> 
#> Reliability Analysis
#> 
#> Summary:
#> Total Items: 5
#> Scale Range: 1 ~ 6
#> Total Cases: 2800
#> Valid Cases: 2713 (96.9%)
#> 
#> Scale Statistics:
#> Mean = 4.145
#> S.D. = 1.060
#> Cronbach’s α = 0.761
#> McDonald’s ω = 0.763
#> 
#> Item Statistics (Cronbach’s α If Item Deleted):
#> ───────────────────────────────────────────────────
#>            Mean    S.D. Item-Rest Cor. Cronbach’s α
#> ───────────────────────────────────────────────────
#> E1 (rev)  4.028 (1.632)          0.513        0.725
#> E2 (rev)  3.856 (1.607)          0.606        0.688
#> E3        4.000 (1.352)          0.501        0.728
#> E4        4.421 (1.461)          0.578        0.701
#> E5        4.418 (1.337)          0.455        0.742
#> ───────────────────────────────────────────────────
#> Item-Rest Cor. = Corrected Item-Total Correlation
#> 

# using dplyr::select()
data %>% select(E1, E2, E3, E4, E5) %>%
  Alpha(vars=names(.), rev=cc("E1, E2"))
#> 
#> Reliability Analysis
#> 
#> Summary:
#> Total Items: 5
#> Scale Range: 1 ~ 6
#> Total Cases: 2800
#> Valid Cases: 2713 (96.9%)
#> 
#> Scale Statistics:
#> Mean = 4.145
#> S.D. = 1.060
#> Cronbach’s α = 0.761
#> McDonald’s ω = 0.763
#> 
#> Item Statistics (Cronbach’s α If Item Deleted):
#> ───────────────────────────────────────────────────
#>            Mean    S.D. Item-Rest Cor. Cronbach’s α
#> ───────────────────────────────────────────────────
#> E1 (rev)  4.028 (1.632)          0.513        0.725
#> E2 (rev)  3.856 (1.607)          0.606        0.688
#> E3        4.000 (1.352)          0.501        0.728
#> E4        4.421 (1.461)          0.578        0.701
#> E5        4.418 (1.337)          0.455        0.742
#> ───────────────────────────────────────────────────
#> Item-Rest Cor. = Corrected Item-Total Correlation
#>