Estimates of Sampling Error
The ISS survey estimates are derived from complex sampling procedures and are subject to various sources of error. These estimates may differ from the true population parameters due to unavoidable survey errors. Such errors, inherent in the survey process, arise from both sampling and non-sampling sources.
The key measures used to assess sampling error is the Coefficient of Variation (CV). The Coefficient of Variation is a standardized measure of dispersion that expresses the extent of variability in relation to the mean of the population. It is calculated as the ratio of the standard deviation to the mean and is usually expressed as a percentage.
Non-sampling errors are all other types of errors that may occur during the survey process, excluding those related to sampling. These can include errors in data collection, respondent misunderstandings, interviewer bias, data entry mistakes, non-response and processing errors.
Reducing non-sampling errors requires rigorous quality control measures at every stage of the survey - from questionnaire design and interviewer training to data validation and processing.
Sampling and non-sampling errors were computed for the following variables;- Informal Sector Operator, Informal Sector, Youth informal Sector Operators Aged 15-35 years, Informal Businesses under Individual Ownership, Operators in Agriculture, Forestry and Fishing, Informal Businesses Operating without Permanent Premises, Proportion of Main Customers and Principal Suppliers, Businesses reporting lack of or inadequate capital as an operating challenge, Businesses reporting difficulty getting markets/customers, Informal Sector Intermediate Consumption, Businesses indicating access to loans as assistance needed, Informal Sector Growth Output, Informal Sector Intermediate Consumption and Informal Sector Value Added.
Data Appraisal
In addition to estimating sampling and non-sampling errors, the Total Survey Error (TSE) was computed to assess the overall difference between a population parameter (such as a mean or total) and its corresponding estimate derived from the sample survey. Understanding and minimizing TSE is essential for ensuring the quality, accuracy and reliability of survey results.
Furthermore, enumerators play a critical role in ensuring the accuracy and reliability of data collected at the household level. The quality of their work directly influences the production of reliable labour statistics, which are essential for evidence-based policy formulation, planning and decision-making on employment, unemployment and broader labour market dynamics.