What is the Strength of the Link Between Objective and Subjective Indicators of Urban Quality of Life?

Urban quality of life is usually measured by either subjective indicators using surveys of residents' perceptions, evaluations and satisfaction with urban living or by objective indicators using secondary data and relative weights for objective indicators of the urban environment. However, rarely are subjective and objective indicators of urban quality of life related to each other. In this paper, these two types of indicators were linked using Geographical Information Systems (GIS) to both locate respondents to the “2003 Survey of Quality of Life in South East Queensland” and also to gather objective indicators about their urban environment within the region with regard to services, facilities and overcrowding. Using Structural Equation Modelling (SEM), the strength of the relationships between these objective indicators and subjective indicators was examined. The results show that relationships between objective and subjective indicators of urban QOL can be weak, and suggests care should be taken when making inferences about improvements in subjective urban QOL based on improvements in objective urban QOL. However, further research is needed into the links between objective and subjective indicators of urban QOL including examining other aspects of the urban environment, non-linear relationships, and moderating effects for individual differences.     

Should “multiple imputations” be treated as “multiple indicators”?

Rubin's multiple imputation approach to missing data creates synthetic data sets, in which each missing variable is replaced by a draw from its predictive distribution, conditional on the observed data. By construction, analyses of such filled-in data sets as if the imputations were true values have the correct expectations for population parameters. In a recent paper, Mislevy showed how this approach can be applied to estimate the distributions of latent variables from complex samples. Multiple imputations for a latent variable bear a surface similarity to classical multiple indicators of a latent variable, as might be addressed in structural equation modelling or hierarchical modelling of successive stages of random sampling. This note demonstrates with a simple example why analyzing multiple imputations as if they were multiple indicators does not generally yield correct results; they must instead be analyzed by means concordant with their construction.I am grateful to Frank Jenkins, John Mazzeo, Kentaro Yamamoto, and Rebecca Zwick for comments on earlier versions of this paper.     

Can Life Be Evaluated? The Jewish Halachic Approach vs. The Quality of Life Approach in Medical Ethics: A Critical View

In recent years there has been an increase in the number of requests formercy killings by patients and their relatives. Under certain conditions,the patient may prefer death to a life devoid of quality. In contrast to thosewho uphold this quality of life approach, those who hold the sanctity oflife approach claim that life has intrinsic value and must be preservedregardless of its quality. This essay describes these two approaches,examines their flaws, and offers a golden path between the two extremepositions.We discuss the halachic and the secular views, arguing for a balancebetween the sanctity of life and the quality of life. We argue that, indeed,such a balance exists in practice, and that life is important, but it is not sacred. Life can be evaluated, but quality of life is not the solecriterion.     

Global Progress II: Evolutionary Mechanisms and their Side-effects

This paper attempts to update the 18th century concept of progress by an evolutionary theoretical framework, while replying to some of the contemporary critiques. Progress, understood as increase in fitness (or its proxy, quality of life) necessarily accompanies evolution by natural selection. In socio-cultural evolution, this mechanism is reinforced by growth of knowledge and virtuous cycles, but can be accompanied by negative side-effects such as overshooting and parasitism. The most pressing of the contemporary side-effects, such as pollution and the increased pace of life, are discussed, but it is concluded that they can be tackled without really endangering global progress. The anxiety that they engender is unfortunately amplified by a bad news bias in the media, leading to an inappropriately pessimistic view of the situation by the public.     

Design and analysis of semi-controlled studies

Semi-controlled studies provide a hybrid approach in between controlled experiments and naturalistic driving studies. As in controlled experiments, the researcher can assign participants to groups, select the route and define the tasks, but the participants are given more freedom when it comes to if, when, where and how to perform the tasks. Increased flexibility makes it possible to investigate how drivers use tactical behaviour to accommodate task execution. The disadvantage is decreased control and more complicated analyses. The main objective of this paper is to discuss how to analyse data obtained in semi-controlled studies.The analysis of data from a semi-controlled study include three types of variables: (i) variables that describe the experimental design, (ii) variables that describe the tactical choices of the participants and (iii), operational variables such as speed, lateral position or glance behaviour. To analyse the three types of variables a two-step procedure is suggested. First, the tactical indicators are analysed with regard to the experimental design. Second, the operational indicators are analysed and the tactical indicators are used to divide participants into sub-populations. The semi-controlled design does not need any new statistical procedures to be developed. It is more important that the analysis conditions on the initial properties and not on structures that happen to occur during the experiment, like where the participant chose to do a certain task.We recommend to use the semi-controlled study method when investigating questions involving adaptive and compensatory behaviour on the tactical level. It is especially useful if causal relationships are of interest, if the data collection should be accelerated in comparison to naturalistic studies, and if certain geographical locations definitely should be included.     

Social indicators as indexes of neuroticism and extraversion

Lynn and Hampson (1975) and recently Lester (2000) suggested that national level of extraversion (E) and neuroticism (N) could be assessed by using certain national indicators like suicide and divorce rates. In this study, Lynn and Hampson’s (1975) and Lester’s (2000) models were assessed by using 1990s data. Although Lynn and Hampson’s original N and E factors correlated with EPQ N (r=0.48) and E (r=0.60) scores as hypothesised, the factor structure did not recur in the 1990s data. Factor analysis supported Lester’s (2000) model of N and E. However, the factor loadings for EPQ N and E were rather modest (0.49 for N and 0.36 for E). It was concluded that more research with larger datasets and new social indicators are needed before social indicators could replace the EPQ in comparisons of national characteristics of nations.     

Scenario Planning: The Basics

This article covers the basics of scenario planning: Why scenarios? What are scenarios? How do you develop scenarios? After covering the preliminaries—the constitution of the scenario team; interviews; research; the identification of a focal issue; set and setting for a scenario workshop; staffing; the trajectory of a scenario planning project—the article moves on to describe several methods for identifying a finite set of diverse scenario logics. After a set of scenarios has been developed, there are several different routes from scenarios to strategy. Early indicators can help identify which of several scenarios is in fact unfolding.