Programme for the International Assessment of Adult Competencies (PIAAC)

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The Programme for the International Assessment of Adult Competencies (PIAAC) offers cross-national comparisons for the serious study of advanced-nation labor markets.

Simplified Download and Importation

The R lodown package easily downloads and imports all available PIAAC microdata by simply specifying "piaac" with an output_dir = parameter in the lodown() function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

library(lodown)
lodown( "piaac" , output_dir = file.path( path.expand( "~" ) , "PIAAC" ) )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the PIAAC catalog, you could pass a subsetted catalog through the lodown() function in order to download and import specific extracts (rather than all available extracts).

library(lodown)
# examine all available PIAAC microdata files
piaac_cat <-
    get_catalog( "piaac" ,
        output_dir = file.path( path.expand( "~" ) , "PIAAC" ) )

# download the microdata to your local computer
piaac_cat <- lodown( "piaac" , piaac_cat )

Analysis Examples with the survey library  

Construct a multiply-imputed, complex sample survey design:

library(survey)
library(mitools)

piaac_design <- readRDS( file.path( path.expand( "~" ) , "PIAAC" , "prgusap1 design.rds" ) )

Variable Recoding

Add new columns to the data set:

piaac_design <-
    update(
        piaac_design ,
        
        one = 1 ,
        
        sex = factor( gender_r , labels = c( "male" , "female" ) ) ,

        age_categories = factor( ageg10lfs , levels = 1:5 , labels = c( "24 or less" , "25-34" , "35-44" , "45-54" , "55 plus" ) ) ,
        
        working_at_paid_job_last_week = as.numeric( c_q01a == 1 )
        
    )

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

MIcombine( with( piaac_design , svyby( ~ one , ~ one , unwtd.count ) ) )

MIcombine( with( piaac_design , svyby( ~ one , ~ age_categories , unwtd.count ) ) )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

MIcombine( with( piaac_design , svytotal( ~ one ) ) )

MIcombine( with( piaac_design ,
    svyby( ~ one , ~ age_categories , svytotal )
) )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

MIcombine( with( piaac_design , svymean( ~ pvnum , na.rm = TRUE ) ) )

MIcombine( with( piaac_design ,
    svyby( ~ pvnum , ~ age_categories , svymean , na.rm = TRUE )
) )

Calculate the distribution of a categorical variable, overall and by groups:

MIcombine( with( piaac_design , svymean( ~ sex ) ) )

MIcombine( with( piaac_design ,
    svyby( ~ sex , ~ age_categories , svymean )
) )

Calculate the sum of a linear variable, overall and by groups:

MIcombine( with( piaac_design , svytotal( ~ pvnum , na.rm = TRUE ) ) )

MIcombine( with( piaac_design ,
    svyby( ~ pvnum , ~ age_categories , svytotal , na.rm = TRUE )
) )

Calculate the weighted sum of a categorical variable, overall and by groups:

MIcombine( with( piaac_design , svytotal( ~ sex ) ) )

MIcombine( with( piaac_design ,
    svyby( ~ sex , ~ age_categories , svytotal )
) )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

MIcombine( with( piaac_design , svyquantile( ~ pvnum , 0.5 , se = TRUE , na.rm = TRUE ) ) )

MIcombine( with( piaac_design ,
    svyby( 
        ~ pvnum , ~ age_categories , svyquantile , 0.5 ,
        se = TRUE , keep.var = TRUE , ci = TRUE , na.rm = TRUE
) ) )

Estimate a ratio:

MIcombine( with( piaac_design ,
    svyratio( numerator = ~ pvnum , denominator = ~ pvlit , na.rm = TRUE )
) )

Subsetting

Restrict the survey design to self-reported fair or poor health:

sub_piaac_design <- subset( piaac_design , i_q08 %in% 4:5 )

Calculate the mean (average) of this subset:

MIcombine( with( sub_piaac_design , svymean( ~ pvnum , na.rm = TRUE ) ) )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <-
    MIcombine( with( piaac_design ,
        svymean( ~ pvnum , na.rm = TRUE )
    ) )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    MIcombine( with( piaac_design ,
        svyby( ~ pvnum , ~ age_categories , svymean , na.rm = TRUE )
    ) )

coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( piaac_design$designs[[1]] )

Calculate the complex sample survey-adjusted variance of any statistic:

MIcombine( with( piaac_design , svyvar( ~ pvnum , na.rm = TRUE ) ) )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
MIcombine( with( piaac_design ,
    svymean( ~ pvnum , na.rm = TRUE , deff = TRUE )
) )

# SRS with replacement
MIcombine( with( piaac_design ,
    svymean( ~ pvnum , na.rm = TRUE , deff = "replace" )
) )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

MIsvyciprop( ~ working_at_paid_job_last_week , piaac_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

MIsvyttest( pvnum ~ working_at_paid_job_last_week , piaac_design )

Perform a chi-squared test of association for survey data:

MIsvychisq( ~ working_at_paid_job_last_week + sex , piaac_design )

Perform a survey-weighted generalized linear model:

glm_result <- 
    MIcombine( with( piaac_design ,
        svyglm( pvnum ~ working_at_paid_job_last_week + sex )
    ) )
    
summary( glm_result )

Replication Example