Inclusion of mobile telephone numbers into an ongoing population health survey in New South Wales, Australia, using an overlapping dual-frame design: impact on the time series

Background: Since 1997, the NSW Population Health Survey (NSWPHS) had selected the sample using random digit dialing of landline telephone numbers. When the survey began coverage of the population by landline phone frames was high (96%). As landline coverage in Australia has declined and continues to do so, in 2012, a sample of mobile telephone numbers was added to the survey using an overlapping dual-frame design. Details of the methodology are published elsewhere. This paper discusses the impacts of the sampling frame change on the time series, and provides possible approaches to handling these impacts. Methods. Prevalence estimates were calculated for type of phone-use, and a range of health indicators. Prevalence ratios (PR) for each of the health indicators were also calculated using Poisson regression analysis with robust variance estimation by type of phone-use. Health estimates for 2012 were compared to 2011. The full time series was examined for selected health indicators. Results: It was estimated from the 2012 NSWPHS that 20.0% of the NSW population were mobile-only phone users. Looking at the full time series for overweight or obese and current smoking if the NSWPHS had continued to be undertaken only using a landline frame, overweight or obese would have been shown to continue to increase and current smoking would have been shown to continue to decrease. However, with the introduction of the overlapping dual-frame design in 2012, overweight or obese increased until 2011 and then decreased in 2012, and current smoking decreased until 2011, and then increased in 2012. Our examination of these time series showed that the changes were a consequence of the sampling frame change and were not real changes. Both the backcasting method and the minimal coverage method could adequately adjust for the design change and allow for the continuation of the time series. Conclusions: The inclusion of the mobile telephone numbers, through an overlapping dual-frame design, did impact on the time series for some of the health indicators collected through the NSWPHS, but only in that it corrected the estimates that were being calculated from a sample frame that was progressively covering less of the population

Interpersonal Chemistry through Negativity: Bonding by Sharing Negative Attitudes about Others

We propose that sharing a negative—as compared to a positive—attitude about a third party is particularly effective in promoting closeness between people. Findings from two survey studies and an experiment support this idea. In Studies 1 and 2, participants’ open‐ended responses revealed a tendency to recall sharing with their closest friends more negative than positive attitudes about other people. Study 3 established that discovering a shared negative attitude about a target person predicted liking for a stranger more strongly than discovering a shared positive attitude (but only when attitudes were weak). Presumably, sharing negative attitudes is alluring because it establishes in‐group/out‐group boundaries, boosts self‐esteem, and conveys highly diagnostic information about attitude holders. Despite the apparent ubiquity of this effect, participants seemed unaware of it. Instead, they asserted that sharing positive attitudes about others would be particularly effective in promoting closeness