ILSA's proficiency levels

Estimates the proficiency levels for all countries within a cycle of an ILSA. Arguments method, and reps, are extracted from autoILSA and can be overridden by the user.

Usage

proflevels(
  df,
  study = NULL,
  year,
  subject = NULL,
  method = NULL,
  reps = NULL,
  type = c("long", "wide1", "wide2"),
  separateSE = TRUE,
  fixN = TRUE,
  accumulated = FALSE
)

Arguments

df

a data frame.

study

an optional character vector indicating the ILSA name, for a list of available ILSA, check autoILSA. If NULL, the ILSA name will be determined by the column names in the data frame.

year

a numeric vector indicating the ILSA name, for a list of available cycles, check autoILSA.

subject

an optional character vector indicating the subject for a list of available ILSA, check autoILSA.

method

a string indicating the name of the replication method. Available options are: "JK2-full", "JK2-half", "FAY-0.5", and "JK2-half-1PV".

Additionally, ILSA names can be used, defaulting into:

• "TIMSS", "PIRLS", or "LANA" for "JK2-full";

• "ICILS", "ICCS", or "CIVED" for "JK2-half";

• "PISA" or "TALIS" for "FAY-0.5";

• and "oldTIMSS", "oldPIRLS", or "RLII" for "JK2-half-1PV".

Note that "oldTIMSS" and "oldPIRLS" refer to the method used for TIMSS and PIRLS before 2015, where within imputation variance is estimated using only 1 plausible value.

reps

an integer indicating the number of replications to be created. If NULL the maximum number of zones will be used.

type

a character value indicating the type of table to produce. Options include: "long", for a long table with a column with the proportions and another one for the standard error; "wide1" for a wide table where groups are distributed in lines; "wide2" for a wide table where groups are distributed in columns.

separateSE

a logical value indicating if standard errors should be separated from proportions, each as an element from a list. Only works for wide tables. Default is TRUE.

fixN

a logical value indicating if data should be "fixed" to meet official criteria. For example, reducing the sample for certain countries in TIMSS 1995. Default is TRUE.

accumulated

a logical value indicating if proficiency levels should be accumulated.

Value

a data frame or a list.

Examples

data(timss99)

proflevels(timss99,year = 1999,type = "long",subject = "math")
#>     group category                  level    prop      se
#> 1   Chile        0    Below Low Benchmark 0.53764 0.02425
#> 2   Japan        0    Below Low Benchmark 0.01731 0.00536
#> 3  Taiwan        0    Below Low Benchmark 0.04854 0.01000
#> 4   Chile        1          Low Benchmark 0.30316 0.01657
#> 5   Japan        1          Low Benchmark 0.08603 0.01251
#> 6  Taiwan        1          Low Benchmark 0.08282 0.01075
#> 7   Chile        2 Intermediate Benchmark 0.12673 0.02006
#> 8   Japan        2 Intermediate Benchmark 0.23168 0.01508
#> 9  Taiwan        2 Intermediate Benchmark 0.17295 0.01455
#> 10  Chile        3         High Benchmark 0.02846 0.00878
#> 11  Japan        3         High Benchmark 0.38279 0.01848
#> 12 Taiwan        3         High Benchmark 0.31132 0.01833
#> 13  Chile        4     Advanced Benchmark 0.00400 0.00356
#> 14  Japan        4     Advanced Benchmark 0.28218 0.01601
#> 15 Taiwan        4     Advanced Benchmark 0.38437 0.02309