This article describes a method for purging data of the effects of acquiescent response bias—a tendency to respond in the affirmative to survey items irrespective of substantive content—in the absence of a balanced scale (a scale with an equal number of positively worded and negatively worded items). The method is relevant in situations where the scale of interest is not balanced but where items similar in form from another content area are available to help identify acquiescence; it is illustrated here using data on class consciousness. Structural equation modeling techniques are used to extract a factor that captures the covariation among the items that is due to acquiescence. A series of models is presented illustrating the procedure on data from the Comparative Project on Class Structure and Class Consciousness for the United States. The model is then cross-validated on samples from Canada, Norway, and Sweden.

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The Institute's research is funded from a variety of sources including: an annual grant-in-aid from the Irish government; competitive research grants (both Irish and international); support for agreed programmes from government departments/agencies and commissioned research projects from public sector bodies. Sponsorship of the Institute's activities by Irish business and membership subscriptions provide a minor source of additional income. The Authors AcknowledgementsThis research was completed as part of a programme of research on Equality funded by the Department of Justice and Equality (DJE). We would like to express our thanks to the members of the steering group for the study, particularly Deaglán O'Broin of the DJE and Ronnie Fay of Pavee Point Traveller and Roma Centre. We also thank our ESRI colleagues Emer Smyth and Alan Barrett, who provided valuable comments. We are grateful to the Central Statistics Office for making available the micro-census data files for 2006 and 2011 and to the research team of the All Ireland Traveller Health Study, whose data we also use here. We also thank the three anonymous peer reviewers. Neither the DJE, the ESRI nor the CSO is responsible for the content of the report, which is the independent work of the authors. These Acts prohibit discrimination in the provision of goods and services (including accommodation and education), wither in the public or private sector, on any of the nine 'equality' grounds (see Employment Equality Acts, above). A further ground -being in receipt of housing assistance payment -also applies to the provision of accommodation. This report has been accepted for publication by the List of Tables Labour market participationThis involves being in employment (or self-employment) or being available for and actively seeking employment. Multivariate analysisA statistical analysis methodology used when we want to look at the impact of one factor (such as age) on another (such as health problems), after taking account of other differences (such as level of education, gender and so on).Odds ratio This is an indicator of how much more or less likely an outcome is for one group than another. An odds ratio greater than one indicates a greater likelihood, while an odds ratio less than one indicates a lower likelihood. For instance, if the odds ratio for poor health is 1.5 for Travellers compared to Non-Travellers, then Travellers have 50% higher odds of being in poor health. Overall and adjusted riskOverall risk is the actual rate of an outcome in the population (e.g. 12 per cent of Travellers have poor health compared to 9 per cent of non-Travellers). The adjusted risk is the risk we would observe if the groups were similar in respect of other characteristics taken into account in the statistical model (e.g. gender, age group, marital status, education, region, housing). For instance, the adjusted risk of poor health for married Traveller men would be 22 per cent compared to 12 per cent for non-Traveller married men. Traveller-specific accommodationThis...