In this paper the examination of the modifiable areal unit problem is extended into multivariate statistical analysis. In an investigation of the parameter estimates from a multiple linear regression model and a multiple logit regression model, conclusions are drawn about the sensitivity of such estimates to variations in scale and zoning systems. The modifiable areal unit problem is shown to be essentially unpredictable in its intensity and effects in multivariate statistical analysis and is therefore a much greater problem than in univariate or bivariate analysis. The results of this analysis are rather depressing in that they provide strong evidence of the unreliability of any multivariate analysis undertaken with data from areal units. Given that such analyses can only be expected to increase with the imminent availability of new census data both in the United Kingdom and in the USA, and the current proliferation of GIS (geographical information system) technology which permits even more access to aggregated data, this paper serves as a topical warning.

The spatial scale effect, or the large umbrella of the modifiable areal unit problem (MAUP), has been a persistent issue in spatial analysis and geographical research (Fate and Atkinson 2001). The inconsistency of analytical results derived from data gathered at different scale levels and/or gathered from different spatial partitioning systems are found in almost all types of analysis involving spatial data. Measuring levels of segregation is no exception. In general, using data of higher spatial resolution or smaller enumeration units will yeld a higher level of segregation reflected by measures such as the dissimilarity index D (Wong 1997).Different approaches have been suggested to deal with the scale effect, including a call for multiscale analyses to obtain a more comprehensive understanding of the geographical issues involved (Fotheringham 1989). Using Geographic Information Systems (GIs), spatial analysis can be performed repeatedly with multiple scale data to assess the scale effect. Another appealing approach is to develop scale-insensitive spatial analytical techniques such that analyses conducted at different scale levels are This research is partially supported by