Recent trends in pay equity: Beyond the aggregate statistics

The earnings gap between men and women has remained comparatively stable at an aggregate level over the 1990s in Australia. From one perspective, this is a reminder of the considerable difficulty of addressing wage differentials once the most overt forms of wage discrimination have been removed, and of the limited impact of most policy initiatives. From another, it may be seen as evidence that dire predictions about the effects of decentralisation on the earnings gap have failed to materialise. In this paper, I use Australian Bureau of Statistics data to show that a number of different trends are evident underneath the relatively static picture shown by the aggregate statistics, particularly as wage dispersion has increased. The data suggest not only that the prospects for pay equity are far from benign, but also that in the current labour market the issue of gender pay inequality cannot be effectively addressed separately from wage inequality more generally.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.

Data quality initiatives - striving for continuous improvements

Although managers consider accurate, timely, and relevant information as critical to the quality of their decisions, evidence of large variations in data quality abounds. Over a period of twelve months, the action research project reported herein attempted to investigate and track data quality initiatives undertaken by the participating organisation. The investigation focused on two types of errors: transaction input errors and processing errors. Whenever the action research initiative identified non-trivial errors, the participating organisation introduced actions to correct the errors and prevent similar errors in the future. Data quality metrics were taken quarterly to measure improvements resulting from the activities undertaken during the action research project. The action research project results indicated that for a mission-critical database to ensure and maintain data quality, commitment to continuous data quality improvement is necessary. Also, communication among all stakeholders is required to ensure common understanding of data quality improvement goals. The action research project found that to further substantially improve data quality, structural changes within the organisation and to the information systems are sometimes necessary. The major goal of the action research study is to increase the level of data quality awareness within all organisations and to motivate them to examine the importance of achieving and maintaining high-quality data.

Dissecting query performance in logical data models: Parsimony vs. greater ontological clarity

Even when data repositories exhibit near perfect data quality, users may formulate queries that do not correspond to the information requested. Users’ poor information retrieval performance may arise from either problems understanding of the data models that represent the real world systems, or their query skills. This research focuses on users’ understanding of the data structures, i.e., their ability to map the information request and the data model. The Bunge-Wand-Weber ontology was used to formulate three sets of hypotheses. Two laboratory experiments (one using a small data model and one using a larger data model) tested the effect of ontological clarity on users’ performance when undertaking component, record, and aggregate level tasks. The results indicate for the hypotheses associated with different representations but equivalent semantics that parsimonious data model participants performed better for component level tasks but that ontologically clearer data model participants performed better for record and aggregate level tasks.

Cohort trends in youth suicide in Australia 1964-1997

Objective: This paper examines trends in the rate of suicide among young Australians aged 15-24 years from 1964 to 1997 and presents an age-period-cohort analysis of these trends. Method: Study design consisted of an age-period-cohort analysis of suicide mortality in Australian youth aged between 15 and 24 for the years 1964-1997 inclusive. Data sources were Australian Bureau of Statistics data on: numbers of deaths due to suicide by gender and age at death; and population at risk in each of eight birth cohorts (1940-1944, 1945-1949, 1950-1954, 1955-1959, 1960-1964, 1965-1969, 1970-1974, and 1975-1979). Main outcome measures were population rates of deaths among males and females in each birth cohort attributed to suicide in each year 1964-1997. Results: The rate of suicide deaths among Australian males aged 15-24 years increased from 8.7 per 100 000 in 1964 to 30.9 per 100 000 in 1997, with the rate among females changing little over the period, from 5.2 per 100 000 in 1964 to 7.1 per 100 000 in 1997. While the rate of deaths attributed to suicide increased over the birth cohorts, analyses revealed that these increases were largely due to period effects, with suicide twice as likely among those aged 15-24 years in 1985-1997 than between 1964 and 1969. Conclusions: The rate of youth suicide in Australia has increased since 1964, particularly among males. This increase can largely be attributed to period effects rather than to a cohort effect and has been paralleled by an increased rate of youth suicides internationally and by an increase in other psychosocial problems including psychiatric illness, criminal offending and substance use disorders.