Reliability Analysis

First, it will determine whether the data is uni-dimensional or multi-dimensional using parameters::n_factors(). If the data is uni-dimensional, then it will print a summary consists of alpha, G6, single-factor CFA, and descriptive statistics result. If it is multi-dimensional, it will print a summary consist of alpha, G6, omega result. You can bypass this by specifying the dimensionality argument.

reliability_summary(
  data,
  cols,
  dimensionality = NULL,
  digits = 3,
  descriptive_table = TRUE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

data

data.frame

cols

items for reliability analysis. Support dplyr::select() syntax.

dimensionality

Specify the dimensionality. Either uni (uni-dimensionality) or multi (multi-dimensionality). Default is NULL that determines the dimensionality using EFA.

digits

number of digits to round to

descriptive_table

Get descriptive statistics. Default is TRUE

quite

suppress printing output

streamline

print streamlined output

return_result

If it is set to TRUE (default is FALSE), it will return psych::alpha for uni-dimensional scale, and psych::omega for multidimensional scale.

Value

a psych::alpha object for unidimensional scale, and a psych::omega object for multidimensional scale.

Examples

fit <- reliability_summary(data = lavaan::HolzingerSwineford1939, cols = x1:x3)
#> Model Summary
#> Model Type = Reliability Analysis
#> Dimensionality = uni-dimensionality
#> 
#> Composite Reliability Measures
#> ────────────────────────────
#>   Alpha  Alpha.Std  G6 (smc)
#> ────────────────────────────
#>   0.626      0.627     0.535
#> ────────────────────────────
#> 
#> Item Reliability (item dropped)
#> ─────────────────────────────────
#>   Var  Alpha  Alpha.Std  G6 (smc)
#> ─────────────────────────────────
#>    x1  0.507      0.507     0.340
#>    x2  0.612      0.612     0.441
#>    x3  0.458      0.458     0.297
#> ─────────────────────────────────
#> 
#> CFA Model:
#> Fit measure is not printed due to factor <= 3 
#> Factor Loadings
#> ───────────────────────────────────────────────────────────────────────────────
#>   Latent.Factor  Observed.Var  Std.Est     SE      Z          P          95% CI
#> ───────────────────────────────────────────────────────────────────────────────
#>             DV1            x1    0.621  0.067  9.223  0.000 ***  [0.489, 0.753]
#>                            x2    0.479  0.063  7.645  0.000 ***  [0.356, 0.602]
#>                            x3    0.710  0.071  9.936  0.000 ***  [0.570, 0.850]
#> ───────────────────────────────────────────────────────────────────────────────
#> *** p < 0.001, ** p < 0.01, * p < 0.05, . p < 0.1
#> 
#> Descriptive Statistics Table:
#> 
#> ─────────────────────────────────────────
#>   Var   mean     sd         x1         x2
#> ─────────────────────────────────────────
#>    x1  4.936  1.167                      
#>    x2  6.088  1.177  0.297 ***           
#>    x3  2.250  1.131  0.441 ***  0.340 ***
#> ─────────────────────────────────────────
#> 
fit <- reliability_summary(data = lavaan::HolzingerSwineford1939, cols = x1:x9)

#> Model Summary
#> Model Type = Reliability Analysis
#> Dimensionality = multi-dimensionality
#> 
#> Composite Reliability Measures
#> ──────────────────────────────────────────────────────────
#>   Alpha  Alpha.Std    G.6  Omega.Hierarchical  Omega.Total
#> ──────────────────────────────────────────────────────────
#>    0.76       0.76  0.808               0.449        0.851
#> ──────────────────────────────────────────────────────────
#> 
#> Item Reliability (item dropped)
#> ─────────────────────────────────
#>   Var  Alpha  Alpha.Std  G6 (smc)
#> ─────────────────────────────────
#>    x1  0.725      0.725     0.780
#>    x2  0.764      0.763     0.811
#>    x3  0.749      0.748     0.796
#>    x4  0.715      0.719     0.761
#>    x5  0.724      0.726     0.764
#>    x6  0.714      0.717     0.764
#>    x7  0.766      0.765     0.800
#>    x8  0.748      0.747     0.789
#>    x9  0.731      0.728     0.782
#> ─────────────────────────────────
#> 
#> Descriptive Statistics Table:
#> 
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>   Var   mean     sd          x1          x2          x3          x4          x5          x6          x7          x8
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>    x1  4.936  1.167                                                                                                
#>    x2  6.088  1.177   0.297 ***                                                                                    
#>    x3  2.250  1.131   0.441 ***   0.340 ***                                                                        
#>    x4  3.061  1.164   0.373 ***   0.153  **   0.159  **                                                            
#>    x5  4.341  1.290   0.293 ***   0.139   *   0.077       0.733 ***                                                
#>    x6  2.186  1.096   0.357 ***   0.193 ***   0.198 ***   0.704 ***   0.720 ***                                    
#>    x7  4.186  1.090   0.067      -0.076       0.072       0.174  **   0.102       0.121   *                        
#>    x8  5.527  1.013   0.224 ***   0.092       0.186  **   0.107       0.139   *   0.150  **   0.487 ***            
#>    x9  5.374  1.009   0.390 ***   0.206 ***   0.329 ***   0.208 ***   0.227 ***   0.214 ***   0.341 ***   0.449 ***
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>