This open access book demonstrates how data quality issues affect all surveys and proposes
methods that can be utilised to deal with the observable components of survey error in a
statistically sound manner. This book begins by profiling the post-Apartheid period in South
Africa's history when the sampling frame and survey methodology for household surveys was
undergoing periodic changes due to the changing geopolitical landscape in the country. This
book profiles how different components of error had disproportionate magnitudes in different
survey years including coverage error sampling error nonresponse error measurement error
processing error and adjustment error. The parameters of interest concern the earnings
distribution but despite this outcome of interest the discussion is generalizable to any
question in a random sample survey of households or firms. This book then investigates
questionnaire design and item nonresponse by building a response propensity model for the
employee income question in two South African labour market surveys: the October Household
Survey (OHS 1997-1999) and the Labour Force Survey (LFS 2000-2003). This time period isolates
a period of changing questionnaire design for the income question. Finally this book is
concerned with how to employee income data with a mixture of continuous data bounded response
data and nonresponse. A variable with this mixture of data types is called coarse data. Because
the income question consists of two parts -- an initial exact income question and a bounded
income follow-up question -- the resulting statistical distribution of employee income is both
continuous and discrete. The book shows researchers how to appropriately deal with coarse
income data using multiple imputation. The take-home message from this book is that researchers
have a responsibility to treat data quality concerns in a statistically sound manner rather
than making adjustments to public-use data in arbitrary ways often underpinned by undefensible
assumptions about an implicit unobservable loss function in the data. The demonstration of how
this can be done provides a replicable concept map with applicable methods that can be utilised
in any sample survey.
