2015
DOI: 10.1016/j.socscimed.2015.06.025
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Where there's smoke: Cigarette use, social acceptability, and spatial approaches to multilevel modeling

Abstract: I contribute to understandings of how context is related to individual outcomes by assessing the added value of combining multilevel and spatial modeling techniques. This methodological approach leads to substantive contributions to the smoking literature, including improved clarity on the central contextual factors and the examination of one manifestation of the social acceptability hypothesis. For this analysis I use restricted-use natality data from the Vital Statistics, and county-level data from the 2005–… Show more

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Cited by 8 publications
(4 citation statements)
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“… 9. We also tested whether unaccounted for spatial processes, such as contagion or diffusion, influenced our results. Following O’Connell (2015), we conducted our analysis using multilevel modeling and tested for spatial autocorrelation of Level 2 residuals. The Moran’s I statistic revealed no significant correlation, which gives us confidence that the analysis does not suffer from bias due to the omission of spatially clustered variables or an association between neighboring PUMAs’ outcomes. …”
mentioning
confidence: 99%
“… 9. We also tested whether unaccounted for spatial processes, such as contagion or diffusion, influenced our results. Following O’Connell (2015), we conducted our analysis using multilevel modeling and tested for spatial autocorrelation of Level 2 residuals. The Moran’s I statistic revealed no significant correlation, which gives us confidence that the analysis does not suffer from bias due to the omission of spatially clustered variables or an association between neighboring PUMAs’ outcomes. …”
mentioning
confidence: 99%
“…Multilevel analyses with binary dependent variables typically employ multilevel logit models, but given issues comparing coefficients across logit models or groups in logit models (see Karlson et al, 2012; Mood, 2010), we employ linear probability models (LPMs) for our multilevel analyses for more straightforward interpretation and comparison of coefficients (Mood, 2010). Our use of contiguous geographic units as our level‐2 unit of analysis gives us reason to be concerned about spatial autocorrelation in our level‐2 residuals (see O'Connell, 2015). Unfortunately, we were unable to formally test for this form of autocorrelation among our residuals due to limitations of the remote server where the LIS data are housed.…”
Section: Methodsmentioning
confidence: 99%
“…Given the nested structure of the data, weekly allocations per ZIP code, we used this approach to model the cumulative change over time in the number of vaccine doses per ZIP code. However, because the data were still organized by physically adjacent spatial units of analysis at Level 2, we used Savitz and Raudenbush’s (2009) routine to account for spatial dependency using HLM 8.1, which is an approach used in similar health research (O’Connell 2015). These results can be found in Table 3.…”
Section: Methodsmentioning
confidence: 99%