Exploring homeless people’s use of outreach services: applying a social psychological perspective

A number of critiques have been published drawing attention to the gaps in research methods applied to issues surrounding homelessness and service utilisation in Britain. This paper discusses the use of social identity, a theory drawn from the field of applied social psychology, and synthesises it with the pathways model, thereby providing a framework to further explore service utilisation. The synthesised framework was used to predict the uptake of outreach services in a prospective study of 121 homeless people in a major UK city. In general, homeless people's use of intervention services was affected by the extent to which they identified with the support services themselves. The study demonstrates the central role of social identity in understanding service utilisation patterns, and shows the importance of applying fresh techniques to fine-tune our understanding of uptake in the long term.

Type-1 error inflation in the traditional by-participant analysis to metamemory accuracy: a generalized mixed-effects model perspective

In order to examine metacognitive accuracy (i.e., the relationship between metacognitive judgment and memory performance), researchers often rely on by-participant analysis, where metacognitive accuracy (e.g., resolution, as measured by the gamma coefficient or signal detection measures) is computed for each participant and the computed values are entered into group-level statistical tests such as the t-test. In the current work, we argue that the by-participant analysis, regardless of the accuracy measurements used, would produce a substantial inflation of Type-1 error rates, when a random item effect is present. A mixed-effects model is proposed as a way to effectively address the issue, and our simulation studies examining Type-1 error rates indeed showed superior performance of mixed-effects model analysis as compared to the conventional by-participant analysis. We also present real data applications to illustrate further strengths of mixed-effects model analysis. Our findings imply that caution is needed when using the by-participant analysis, and recommend the mixed-effects model analysis.