cosme

The cosme package allows you to calculate several estimations of the quality of your survey questions and also adjust your estimations for measurement error.

Assuming you have a data frame with the reliability, validity and quality of your question, you can correct for the quality and common method variance easily.

Load the package as:

# devtools::install_github("sociometricresearch/cosme")
library(cosme)

A simple example

cosme introduces the concept of a measurement error design, such as the survey package has a survey design object. You can define this measurement error design with three objects: your model design, your measurement error data and the data of the analysis. For a simple case, let’s use the data from the European Social Survey already loaded with the package. The variables trstplt, trstprl and trstprt were asked with the same type of the question. In other words, they share a common method (for example, a likert type scale question). Sharing a common method allows us to correct for their common method variance. We could define the measurement error design object as this:

# Data
data(ess7es)
ess_subset <- ess7es[1:3]

# This is the model definition
model_definition <- "
  # Correct for measurement error
  ~~ trstplt + trstprl + trstprt

  # Correct for common method variance
  ~ trstplt + trstprl + trstprt
"

# The measurement error data
me_data <-
  data.frame(
    question = c("trstprl", "trstplt", "trstprt"),
    reliability = c(0.812, 0.852, 0.858),
    validity = c(0.959, 0.965, 0.956),
    quality = c(0.779, 0.822, 0.821)
  )

# Define your measurement error design
me_obj <- medesign(model_definition, ess_subset, me_data)
#> Correcting for measurement error in trstplt, trstprl, trstprt. If you want to correct other variables, make sure they are both in `me_data` and `.data` and you specify their names in the model syntax (`~~`).
me_obj
#> <Measurement error design>
#> Parsed model:
#>    ~~ trstprl + trstplt + trstprt
#>    ~ trstplt + trstprl + trstprt

This object describes your measurement error design. With this, we simply pass it to me_cmv_cor to adjust the correlation of trstplt, trstprl and trstprt for common method variance as well as their measurement error:

me_cmv_cor(me_obj)
#> # A tibble: 3 x 4
#>   rowname trstprl trstplt trstprt
#>   <chr>     <dbl>   <dbl>   <dbl>
#> 1 trstprl   1       0.771   0.704
#> 2 trstplt   0.771   1       0.984
#> 3 trstprt   0.704   0.984   1

Another simple example

The previous example corrected only variables trstplt, trstprl and trstprt. What if we have more groups of variables that share a common method? Just keep adding them to the model syntax! For example, stfedu and stfhlth also share a common method, let’s add them to the model syntax and make sure we have data on me_data for them:

# Data
data(ess7es)
ess_subset <- ess7es[1:5]

# This is the model definition
model_definition <- "
   # Correct for measurement error on all variables (.)
   ~~ .;

   # Correct for common method variance on these group of variables
   ~ trstplt + trstprl + trstprt;
   ~ stfedu + stfhlth
"

# The measurement error data
me_data <-
  data.frame(
    question = c("trstprl", "trstplt", "trstprt", "stfedu", "stfhlth"),
    reliability = c(0.812, 0.852, 0.858, 0.870, 0.871),
    validity = c(0.959, 0.965, 0.956, 0.915, 0.893),
    quality = c(0.779, 0.822, 0.821, 0.796, 0.779)
  )

# Define your measurement error design
me_obj <- medesign(model_definition, ess_subset, me_data)
#> Correcting for measurement error in trstprl, trstplt, trstprt, stfedu, stfhlth. If you want to correct other variables, make sure they are both in `me_data` and `.data` and you specify their names in the model syntax (`~~`).
me_obj
#> <Measurement error design>
#> Parsed model:
#>    ~~ trstprl + trstplt + trstprt + stfedu + stfhlth
#>    ~ trstplt + trstprl + trstprt
#>    ~ stfedu + stfhlth

Once you have your measurement error design, we simply pass it to me_cmv_cor to adjust the correlation for common method variance as well as their quality:

me_cmv_cor(me_obj)
#> # A tibble: 5 x 6
#>   rowname trstprl trstplt trstprt stfedu stfhlth
#>   <chr>     <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
#> 1 trstprl   1       0.774   0.704  0.442   0.430
#> 2 trstplt   0.774   1       0.981  0.436   0.462
#> 3 trstprt   0.704   0.981   1      0.390   0.371
#> 4 stfedu    0.442   0.436   0.390  1       0.704
#> 5 stfhlth   0.430   0.462   0.371  0.704   1

Alternatively, you can use me_cmv_cov to adjust a covariance matrix for common method variance as well as quality.