Making the Most of Statistical Analyses: Improving Interpretation and Presentation

King, Gary; Tomz, Michael; Wittenberg, Jason

doi:10.3886/icpsr01255.v1

Cited by 318 publications

(370 citation statements)

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“…The estimated results are visually presented in Figure 5. 27 Figure 5 reinforces the regression findings presented in Table 5. Lines presenting the before period (blue dashed lines in Fig.…”

Section: Data and Variablessupporting

confidence: 87%

“…Statistical software packages including Clarify 2.0 and Stata 8.0 are used to estimate the expected values and their uncertainty. 27 For the case of a typical core urban county and the other case of a typical suburban county, expected value algorithm is repeated to approximate 90% confidence interval around mortality rates. We then plot the estimated expected values and the range of uncertainty in Figure 3, which illustrates the regression findings in Table 3 quite sharply.…”

Section: Resultsmentioning

confidence: 99%

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Is Sprawl Associated with a Widening Urban–Suburban Mortality Gap?

Fan

Song

2009

J Urban Health

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This paper examines whether sprawl, featured by low development density, segregated land uses, lack of significant centers, and poor street connectivity, contributes to a widening mortality gap between urban and suburban residents. We employ two mortality datasets, including a national cross-sectional dataset examining the impact of metropolitanlevel sprawl on urban-suburban mortality gaps and a longitudinal dataset from Portland examining changes in urban-suburban mortality gaps over time. The national and Portland studies provide the only evidence to date that (1) across metropolitan areas, the size of urban-suburban mortality gaps varies by the extent of sprawl: in sprawling metropolitan areas, urban residents have significant excess mortality risks than suburban residents, while in compact metropolitan areas, urbanicity-related excess mortality becomes insignificant; (2) the Portland metropolitan area not only experienced net decreases in mortality rates but also a narrowing urban-suburban mortality gap since its adoption of smart growth regime in the past decade; and (3) the existence of excess mortality among urban residents in US sprawling metropolitan areas, as well as the net mortality decreases and narrowing urban-suburban mortality gap in the Portland metropolitan area, is not attributable to sociodemographic variations. These findings suggest that health threats imposed by sprawl affect urban residents disproportionately compared to suburban residents and that efforts curbing sprawl may mitigate urban-suburban health disparities.

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Section: Data and Variablessupporting

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Is Sprawl Associated with a Widening Urban–Suburban Mortality Gap?

Fan

Song

2009

J Urban Health

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“…The substantive effects based on simulations (King, Tomz, and Wittenberg, 2000) As expected, population has a positive effect on both CDM projects and PoA. An increase of one standard deviation above the mean increases the number of CDM projects and PoAs by factors of 2.06 and 2.84, respectively.…”

Section: Main Findingssupporting

confidence: 63%

Small and beautiful? The Programme of Activities and the least developed countries

Bayer

Pinkerton

Urpelainen

2014

Climate and Development

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Most carbon abatement projects under the Kyoto Protocol's Clean Development Mechanism (CDM) have been implemented in rapidly industrializing countries, notably China and India. To support small carbon abatement projects and to promote decarbonization in the least developed countries, the Programme of Activities (PoA) modality was introduced. But are the determinants of project implementation different under the PoA from those for conventional CDM projects? To answer this question, we conduct a statistical analysis of the global distribution of CDM projects and PoAs during the years 2007-2012. In regard to country size, large countries clearly dominate both the CDM and PoA, suggesting that the PoA may do only little to facilitate project implementation in small countries. However, the number of PoAs has a strong negative association with the proportion of a country's corruption level, while the importance of corruption for the CDM is much smaller. Moreover, per capita income has no effect on PoA implementation, while high wealth levels have a weak positive effect on CDM projects. Thus, the PoA modality seems to promote sustainable development in poor countries that have exceeded a certain threshold of good governance. In this regard, PoAs are directing carbon credits to new areas, as many had initially hoped.

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“…Uncertainty intervals provide explicit characterisations of the precision around estimates derived from limited information sources. 36,37 Uncertainty ranges for HALE estimates were estimated using Monte Carlo simulation techniques to quantify the uncertainty in life expectancy projections, in GBD 2000 estimates for prevalence and disability severity, and in the survey-based prevalence estimates. 38 …”

Section: Calculation Of Halementioning

confidence: 99%