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Process Data by Computing Scale Scores

Source: R/process_data_scale_scores.R
scale_scores.Rd

For the process_data argument. To compute scale scores from indicators and replace the indicators scores by computed scale scores.

Usage

scale_scores(data, method = c("mean", "sum"))

Arguments

data

A data frame with the indicator scores. It must has an attribute number_of_indicators. The same argument used by power4test(). This attribute is used to identify the factor names and their indicators.

method

The method to be used to compute the scale scores. Can be "mean" or "sum". The default na.rm = FALSE will be used. Therefore, data must not have missing data.

Value

It returns a data frame with the scale scores computed.

Details

This function is to be used in the process_data argument of power4test().

It retrieves the attribute "number_of_indicators", stored by power4test(), to identify factors with indicators, computes the scale scores based on method, and replace the indicators by the scale scores.

All subsequent steps, such as the test functions, will see only the scale scores, or original scores if a variable has no indicator. The model will also be fitted on the scale scores, not on the indicators.

It can be used to estimate power for analyzing the scale scores, taking into account the measurement error due to imperfect reliability.

See also

power4test()

Examples


# Specify the model

mod <-
"
m ~ x
y ~ m + x
"

# Specify the population values

mod_es <-
"
y ~ m: l
m ~ x: m
y ~ x: n
"

# Specify the numbers of indicators and reliability coefficients

k <- c(y = 3,
       m = 4,
       x = 5)
rel <- c(y = .70,
         m = .70,
         x = .70)

# Simulate the data

out <- power4test(
         nrep = 2,
         model = mod,
         pop_es = mod_es,
         n = 200,
         number_of_indicators = k,
         reliability = rel,
         process_data = list(fun = "scale_scores"),
         test_fun = test_parameters,
         test_args = list(op = "~"),
         parallel = FALSE,
         iseed = 1234)
#> Simulate the data:
#> Fit the model(s):
#> Do the test: test_parameters: CIs (op: ~) 

dat <- pool_sim_data(out)
head(dat)
#>            y           m           x
#> 1 -0.7035296 -1.75480445 -0.40209754
#> 2  0.3718087 -0.01079629 -0.25746074
#> 3 -0.4132790  0.87164340 -0.53160280
#> 4  0.3798879 -1.56624611 -0.15829899
#> 5  0.5091789  0.82256083 -0.54801654
#> 6  0.2830118  0.83206263 -0.05752692