Create scale by calculating item mean and returns descriptives for srvyr objects

This function creates a scale by calculating the mean of a set of items, and prints and returns descriptives that allow to assess internal consistency and spread. It is primarily based on the psych::alpha function, with more parsimonious output and some added functionality.

svy_make_scale(
  data,
  scale_items,
  scale_name,
  print_hist = TRUE,
  print_desc = TRUE,
  scale_title = scale_name,
  reversed = NULL,
  r_key = NULL
)

Arguments

data

A srvyr survey object

scale_items

A characters vector containing the items for that scale (variables in data)

scale_name

Character. The name of the variable the scale should be saved as

print_hist

Logical. Should histograms of the scale and its items be printed.

print_desc

Logical. Should descriptive statistics for the scale be printed.

scale_title

Character. Name of scale for printing. Defaults to scale_name

reversed

(optional) A characters vector containing the items that should be reverse-coded ( subset of scale_items)

r_key

(optional) Numeric. Set to the possible maximum value of the scale if the whole scale should be reversed, or to -1 to reverse the scale based on the observed maximum

Value

The survey object with the scale added as an additional variable.

Examples

library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#> 
#> Attaching package: ‘survey’
#> The following object is masked from ‘package:graphics’:
#> 
#>     dotchart
data(api)

svy_data <- svydesign(id = ~1, strata = ~stype, weights = ~pw, 
                   data = apistrat, fpc = ~fpc)
scale_items <- c("ell", "meals", "mobility", "col.grad", "full")
scale_reversed <- c("col.grad", "full")                    
svy_make_scale(svy_data, scale_items, "SES", reversed = scale_reversed)    
#>     
#> Descriptives for SES scale:
#> Mean: 41.113  SD: 13.525
#> Cronbach's alpha: .74
#> 
#> The following items were reverse coded (with min and max values):                        
#> col.grad (0, 100)
#> full (24, 100)

#> Stratified Independent Sampling design
#> update(data, SES = rowMeans(data[, scale_items_num]$variables, 
#>     na.rm = T))