The term "digital divide" has been used for almost a decade and typically relates to sociodemographic differences in the use of information and communication technology. However, the corresponding measurement is still relatively imprecise. Very often it is simply reduced to comparisons of Internet penetration rates. This article extends the measurements above the usual bivariate comparisons. Within this context, three essential approaches are presented and critically evaluated. First, loglinear modeling is used to address the interactions among the factors affecting the digital divide. Second, compound measures (i.e., the Digital Divide Index) that integrate a number of variables into a single indicator are discussed. Third, time-distance methodology is applied to analyze changes in the digital divide. The article argues that these approaches often yield entirely different conclusions compared to simple bivariate analysis. The examples are presented as a general warning against an oversimplified methodological approach to digital divide studies.

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An extended conceptual and analytical framework is elaborated where proximity in time as one dimension of a multidimensional concept of disparity is used as a tool to integrate space and time in comparative analysis. Time distance measures the difference in time (number of years) when two compared units achieve a given level of the indicator. Time distance emphasises a novel perspective of disparity between the compared units, and time is a universal unit of measurement comparable between countries, levels and units of comparison. It complements rather than substitutes for conventional measures, at the conceptual level the overall degree of disparity is looked upon as a weighted combination of static and dynamic dimensions of disparity.The comparative analysis of socio-economic data sometimes employs comparison across time within a country, e.g. monitoring trends in productivity, per capita income or unemployment. Alternatively, comparison is made across space between countries, e.g. comparing the level of productivity or unemployment in different countries at a given point in time. In each case, the analysis seeks to identify disparities. The purpose of this article is to demonstrate the advantage of analysing disparities, referred to often as inequalities or differences, by intergrating comparisons across both time and space. Empirical examples from the regions (republics and autonomous provinces) of the former Yugoslavia are used to provide empirical illustrations of this expanded analytical framework

S-time-distance, Economic and social indicators, Well-being, Societal progress, USA, EU,

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A novel generic statistical measure S-time-distance complementing existing methods of analysis of time series data is briefly presented. The application to indicator analysis shows that the gap between compared units may be very different when compared with commonly used static measures and with time distance measure, leading to a special typology of indicators. Analysis of indicators like PC per 100 inhabitants and Internet users per capita will show the empirical results for EU countries.

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