Reshape repeated measurements for racked or stacked analysis

Repeated measurements (the same persons and items at two or more time points) enter a Rasch analysis in one of two designs (Andrich & Marais 2019, ch. 26). Racking keeps one row per person and duplicates the items per time point (columns item@time), so change over time shows in the item estimates. Stacking keeps one column per item and duplicates the persons per time point (rows person@time), so change shows in the person estimates and DIF of items over time can be examined with time as a person factor.

Usage

rack_data(data, person, time, items)

stack_data(data, person, time, items)

Arguments

data: A long data frame with one measurement per row.
person, time: Names of the person and time-point columns.
items: Character vector naming the item columns.

Value

rack_data: a wide data frame with one row per person and length(items) * n_times item columns. stack_data: a data frame with one row per person-time (id column), the original item columns, and time as a factor column for DIF analysis.

Examples

d <- data.frame(pid = rep(1:100, 2), t = rep(1:2, each = 100),
                Q1 = rbinom(200, 1, 0.6), Q2 = rbinom(200, 1, 0.5))
racked <- rack_data(d, person = "pid", time = "t", items = c("Q1", "Q2"))
names(racked)
#> [1] "id"   "Q1@1" "Q2@1" "Q1@2" "Q2@2"
stacked <- stack_data(d, person = "pid", time = "t", items = c("Q1", "Q2"))
head(stacked)
#>    id time Q1 Q2
#> 1 1@1    1  0  1
#> 2 2@1    1  0  0
#> 3 3@1    1  1  0
#> 4 4@1    1  0  1
#> 5 5@1    1  1  0
#> 6 6@1    1  1  1