Information Technology in Construction – Domain Definition and Research Issues

This article discusses the scope of research on the application of information technology in construction (ITC). A model of the information and material activities which together constitute the construction process is presented, using the IDEF0 activity modelling methodology. Information technology is defined to include all kinds of technology used for the storage, transfer and manipulation of information, thus also including devices such as copying machines, faxes and mobile phones. Using the model the domain of ITC research is defined as the use of information technology to facilitate and re-engineer the information process component of construction. Developments during the last decades in IT use in construction is discussed against a background of a simplified model of generic information processing tasks. The scope of ITC is compared with the scopes of research in related areas such as design methodology, construction management and facilities management. Health care is proposed as an interesting alternative (to the often used car manufacturing industry), as an IT application domain to compare with. Some of the key areas of ITC research in recent years; expert systems, company IT strategies, and product modelling are shortly discussed. The article finishes with a short discussion of the problems of applying standard scientific methodology in ITC research, in particular in product model research.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.