In certain studies, variable names should be masked to prevent researcher bias. Examples can include exploratory factor analysis, network analysis, etc. The vazul package provides means to mask variable names in a dataset, ensuring that analyses can be conducted without preconceived notions about the variables.
In this example, we will use the williams dataset from the vazul package.
data("williams", package = "vazul")
head(williams)
#> # A tibble: 6 × 25
#> subject SexUnres_1 SexUnres_2 SexUnres_3 SexUnres_4_r SexUnres_5_r Impuls_1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A30MP4LXV… 5 3 2 3 2 3
#> 2 A16X5FB3H… 7 7 7 4 2 6
#> 3 A1E9D1OT9… 2 4 6 3 3 3
#> 4 A16FPOYD7… 5 4 5 3 3 4
#> 5 A11NOTVHW… 5 5 6 3 3 5
#> 6 A3TDR6MXS… 5 6 6 3 2 5
#> # ℹ 18 more variables: Impuls_2_r <dbl>, Impul_3_r <dbl>, Opport_1 <dbl>,
#> # Opport_2 <dbl>, Opport_3 <dbl>, Opport_4 <dbl>, Opport_5 <dbl>,
#> # Opport_6_r <dbl>, InvEdu_1_r <dbl>, InvEdu_2_r <dbl>, InvChild_1 <dbl>,
#> # InvChild_2_r <dbl>, age <dbl>, gender <dbl>, ecology <chr>,
#> # duration_in_seconds <dbl>, attention_1 <dbl>, attention_2 <dbl>
glimpse(williams)
#> Rows: 112
#> Columns: 25
#> $ subject <chr> "A30MP4LXV4MIFD", "A16X5FB3HAFCKN", "A1E9D1OT9VJYD…
#> $ SexUnres_1 <dbl> 5, 7, 2, 5, 5, 5, 5, 4, 6, 4, 5, 3, 5, 6, 3, 6, 2,…
#> $ SexUnres_2 <dbl> 3, 7, 4, 4, 5, 6, 6, 6, 5, 4, 5, 2, 5, 3, 2, 3, 1,…
#> $ SexUnres_3 <dbl> 2, 7, 6, 5, 6, 6, 5, 5, 6, 4, 7, 3, 5, 3, 3, 6, 3,…
#> $ SexUnres_4_r <dbl> 3, 4, 3, 3, 3, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 3, 4,…
#> $ SexUnres_5_r <dbl> 2, 2, 3, 3, 3, 2, 4, 3, 2, 1, 2, 2, 4, 6, 3, 1, 3,…
#> $ Impuls_1 <dbl> 3, 6, 3, 4, 5, 5, 5, 6, 6, 1, 6, 2, 4, 7, 5, 4, 5,…
#> $ Impuls_2_r <dbl> 3, 2, 3, 4, 2, 3, 3, 2, 3, 1, 3, 2, 3, 6, 3, 3, 7,…
#> $ Impul_3_r <dbl> 2, 1, 3, 4, 4, 3, 2, 3, 2, 1, 4, 1, 4, 5, 3, 3, 5,…
#> $ Opport_1 <dbl> 1, 5, 3, 4, 4, 4, 5, 5, 5, 1, 7, 3, 5, 7, 5, 2, 6,…
#> $ Opport_2 <dbl> 2, 7, 5, 4, 6, 3, 5, 4, 4, 4, 6, 4, 6, 5, 2, 3, 4,…
#> $ Opport_3 <dbl> 2, 7, 5, 4, 4, 4, 6, 5, 5, 1, 7, 3, 5, 5, 5, 3, 1,…
#> $ Opport_4 <dbl> 3, 6, 3, 5, 4, 6, 5, 5, 4, 4, 6, 3, 6, 4, 4, 3, 3,…
#> $ Opport_5 <dbl> 1, 6, 3, 4, 5, 4, 6, 6, 4, 1, 6, 3, 6, 5, 5, 4, 6,…
#> $ Opport_6_r <dbl> 3, 2, 3, 4, 3, 3, 2, 2, 3, 2, 1, 3, 1, 6, 4, 3, 4,…
#> $ InvEdu_1_r <dbl> 2, 3, 2, 3, 4, 3, 3, 3, 3, 2, 3, 4, 4, 4, 3, 1, 6,…
#> $ InvEdu_2_r <dbl> 3, 2, 3, 4, 3, 4, 2, 3, 4, 1, 2, 2, 3, 7, 4, 1, 3,…
#> $ InvChild_1 <dbl> 2, 5, 6, 5, 5, 5, 6, 5, 6, 1, 5, 2, 4, 4, 5, 2, 6,…
#> $ InvChild_2_r <dbl> 3, 2, 3, 4, 3, 4, 2, 4, 3, 2, 4, 2, 3, 7, 3, 2, 6,…
#> $ age <dbl> 34, 30, 40, 35, 26, 33, 33, 30, 48, 33, 40, 39, 25…
#> $ gender <dbl> 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1,…
#> $ ecology <chr> "Hopeful", "Desperate", "Desperate", "Hopeful", "D…
#> $ duration_in_seconds <dbl> 164, 100, 47, 31, 40, 32, 98, 34, 32, 87, 71, 103,…
#> $ attention_1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,…
#> $ attention_2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,…We will apply masking to the variables related to life history strategy, which are prefixed with SexUnres, Impuls, Opport, InvEdu, and InvChild. We’ll mask each variable group separately with randomized letter prefixes (e.g., C_01, A_01, E_01, etc.). This way, variables within the same original scale keep a common prefix for the analysis, but analysts won’t know which prefix corresponds to which original scale due to the randomization.
set.seed(84)
# Sample 5 random letters for the 5 variable groups
random_prefixes <- paste0(sample(LETTERS, 5), "_")
masked_williams <-
williams |>
mask_names(starts_with("SexUnres"), prefix = random_prefixes[1]) |>
mask_names(starts_with("Impul"), prefix = random_prefixes[2]) |>
mask_names(starts_with("Opport"), prefix = random_prefixes[3]) |>
mask_names(starts_with("InvEdu"), prefix = random_prefixes[4]) |>
mask_names(starts_with("InvChild"), prefix = random_prefixes[5])
# Show the randomized prefixes used (but not which corresponds to which)
sort(unique(sub("_.*", "_", grep("^[A-Z]_", names(masked_williams), value = TRUE))))
#> [1] "L_" "P_" "S_" "V_" "Y_"We can now perform an exploratory factor analysis (EFA) on the masked variables. Since the variable names are masked with randomized prefixes, we won’t know which original variables correspond to which factor, thus preventing bias in interpreting the results.
set.seed(123)
efa_blind <-
masked_williams |>
select(matches("^[A-Z]_")) |>
factanal(factors = 5, rotation = "varimax")
# Get the loadings of the EFA on the masked data
efa_blind |>
loadings() |>
print(cutoff = 0.3, sort = TRUE)
#>
#> Loadings:
#> Factor1 Factor2 Factor3 Factor4 Factor5
#> S_05 0.666 0.472
#> S_03 0.701
#> S_01 0.749 0.331
#> V_01 0.762
#> P_03 0.893
#> P_06 0.834
#> P_01 0.870
#> P_04 0.758
#> P_05 0.853
#> L_01 0.766
#> S_04 0.735 0.335
#> S_02 0.567 0.425
#> V_02 0.867
#> V_03 0.746
#> P_02 0.715
#> Y_02 0.699
#> Y_01 0.839
#> L_02 0.852
#>
#> Factor1 Factor2 Factor3 Factor4 Factor5
#> SS loadings 6.398 4.815 0.717 0.345 0.284
#> Proportion Var 0.355 0.267 0.040 0.019 0.016
#> Cumulative Var 0.355 0.623 0.663 0.682 0.698The loading table shows that factors are not necessarily loading to their original category. By using masking, researchers may be able to make decisions without being biased on the variable names.
Applying the same analysis on the original dataset reveals the names of the variables. Please note that the loadings may differ slightly due to the randomness in the factor analysis process.
set.seed(123)
efa_orig <-
williams |>
select(SexUnres_1:InvChild_2_r) |>
factanal(factors = 5, rotation = "varimax")
# Get the loadings of the EFA on the original data
efa_orig |>
loadings() |>
print(cutoff = 0.3, sort = TRUE)
#>
#> Loadings:
#> Factor1 Factor2 Factor3 Factor4 Factor5
#> SexUnres_1 0.666 0.472
#> SexUnres_2 0.701
#> SexUnres_3 0.749 0.331
#> Impuls_1 0.762
#> Opport_1 0.893
#> Opport_2 0.834
#> Opport_3 0.870
#> Opport_4 0.758
#> Opport_5 0.853
#> InvChild_1 0.766
#> SexUnres_4_r 0.735 0.335
#> SexUnres_5_r 0.567 0.425
#> Impuls_2_r 0.867
#> Impul_3_r 0.746
#> Opport_6_r 0.715
#> InvEdu_1_r 0.699
#> InvEdu_2_r 0.839
#> InvChild_2_r 0.852
#>
#> Factor1 Factor2 Factor3 Factor4 Factor5
#> SS loadings 6.398 4.815 0.717 0.345 0.284
#> Proportion Var 0.355 0.267 0.040 0.019 0.016
#> Cumulative Var 0.355 0.623 0.663 0.682 0.698