John L. Eltinge, Paul P. Biemer and Anders Holmberg
This article outlines a framework for formal description, justification and evaluation in development of architectures for large-scale statistical production systems. Following an introduction of the main components of the framework, we consider four related issues: (1) Use of some simple schematic models for survey quality, cost, risk, and stakeholder utility to outline several groups of questions that may inform decisions on system design and architecture. (2) Integration of system architecture with models for total survey quality (TSQ) and adaptive total design (ATD). (3) Possible use of concepts from the Generic Statistical Business Process Model (GSBPM) and the Generic Statistical Information Model (GSIM). (4) The role of governance processes in the practical implementation of these ideas.
Giles Reid, Felipa Zabala and Anders Holmberg
Many national statistics offices acknowledge that making better use of existing administrative data can reduce the cost of meeting ongoing statistical needs. Stats NZ has developed a framework to help facilitate this reuse. The framework is an adapted Total Survey Error (TSE) paradigm for understanding how the strengths and limitations of different data sets flow through a statistical design to affect final output quality. Our framework includes three phases: 1) a single source assessment, 2) an integrated data set assessment, and 3) an estimation and output assessment. We developed a process and guidelines for applying this conceptual framework to practical decisions about statistical design, and used these in recent redevelopment projects. We discuss how we used the framework with data sources that have a non-statistical primary purpose, and how it has helped us spread total survey error ideas to non-methodologists.