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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.


This article discusses the critical and complex design decisions associated with transitioning an interviewer-administered survey to a self-administered, postal, web/paper survey. Our approach embeds adaptive, responsive, and tailored (ART) design principles and data visualization during a multi-phased data collection operation to project the outcomes of each phase in preparation for subsequent phases. This requires rapid decision making based upon experimental results using a data visualization system to monitor critical-to-quality (CTQ) metrics and facilitate projections of outcomes from the current phase of data collection to inform the design of the subsequent phase. We describe the objectives of the overall design, the features designed to address these objectives, components of the visual adaptive total design (ATD) system for monitoring quality components and relative costs in real time, and examples of the visualization elements and functionalities that were used in one case study. We also discuss subsequent initiatives to develop an interactive version of the monitoring tool and applications for other studies, including those employing adaptive, responsive, and tailored (ART) designs. Our case study is a series of pilot studies conducted for the Residential Energy Consumption Survey (RECS), sponsored by the U.S. Energy Information Administration (EIA).

developments have arisen within statistical organizations and through cross-organizational collaboration. In developing this special issue, we identified two trends – one in methodology and one in IT systems – that were influential in these developments. The methodological trend leads towards a balanced evaluation and treatment of the components of aggregate error associated with a statistical product. The result, sometimes called Total Survey Design or, more generally, Adaptive Total Design (ATD), is also well integrated with the quality perspectives on statistics