Dunn's test is the appropriate nonparametric pairwise multiplecomparison procedure when a Kruskal-Wallis test is rejected, and it is now implemented for Stata in the dunntest command. dunntest produces multiple comparisons following a Kruskal-Wallis k-way test by using Stata's built-in kwallis command. It includes options to control the familywise error rate by using Dunn's proposed Bonferroni adjustment, theŠidák adjustment, the Holm stepwise adjustment, or the Holm-Šidák stepwise adjustment. There is also an option to control the false discovery rate using the Benjamini-Hochberg stepwise adjustment.

Horn’s parallel analysis (PA) is the method of consensus in the literature on empirical methods for deciding how many components/factors to retain. Different authors have proposed various implementations of PA. Horn’s seminal 1965 article, a 1996 article by Thompson and Daniel, and a 2004 article by Hayton et al., all make assertions about the requisite distributional forms of the random data generated for use in PA. Readily available software is used to test whether the results of PA are sensitive to several distributional prescriptions in the literature regarding the rank, normality, mean, variance, and range of simulated data on a portion of the National Comorbidity Survey Replication (Pennell et al., 2004) by varying the distributions in each PA. The results of PA were found not to vary by distributional assumption. The conclusion is that PA may be reliably performed with the computationally simplest distributional assumptions about the simulated data.

I present paran, an implementation of Horn's parallel analysis criteria for factor or component retention in common factor analysis or principal component analysis in Stata. The command permits classical parallel analysis and more recent extensions to it for the pca and factor commands. paran provides a needed extension to Stata's built-in factor-and component-retention criteria.

The online version of the original article can be found at http://dx.doi.

BackgroundLarge state tobacco control programs have been shown to reduce smoking and would be expected to affect health care costs. We investigate the effect of California's large-scale tobacco control program on aggregate personal health care expenditures in the state.Methods and FindingsCointegrating regressions were used to predict (1) the difference in per capita cigarette consumption between California and 38 control states as a function of the difference in cumulative expenditures of the California and control state tobacco control programs, and (2) the relationship between the difference in cigarette consumption and the difference in per capita personal health expenditures between the control states and California between 1980 and 2004. Between 1989 (when it started) and 2004, the California program was associated with $86 billion (2004 US dollars) (95% confidence interval [CI] $28 billion to $151 billion) lower health care expenditures than would have been expected without the program. This reduction grew over time, reaching 7.3% (95% CI 2.7%–12.1%) of total health care expenditures in 2004.ConclusionsA strong tobacco control program is not only associated with reduced smoking, but also with reductions in health care expenditures.

Antiviolence public health programs should identify young and Black or Latina transfeminine women as an especially vulnerable population.

This study models independent associations of state or local strong clean indoor air laws and cigarette prices with current smoker status and consumption in a multilevel framework, including interactions with educational attainment, household income and race/ethnicity and the relationships of these policies to vulnerabilities in smoking behavior. Cross sectional survey data are employed from the February 2002 panel of the Tobacco Use Supplement of the Current Population Survey (54,024 individuals representing the US population aged 15 to 80). Nonlinear relationships between both outcome variables and the predictors were modeled. Independent associations of strong clean indoor air laws were found for current smoker status and consumption among current smokers. Cigarette price was found to have independent associations with both outcomes, an effect that saturated at higher prices. The odds ratio for smoking for the highest versus lowest price over the range where there was a price effect was 0.83. Average consumption declined over the range of effect of price on consumption. Neither policy varied in its effect by educational attainment, or household income. The association of cigarette price with reduced smoking participation and consumption was not found to vary with race/ethnicity. Population vulnerability in consumption appears to be structured by nonwhite race categories, but not at the state and county levels at which the policies we studied were enacted. Clean indoor air laws and price increases appear to benefit all socio-economic and race/ ethnic groups in our study equally in terms of reducing smoking participation and consumption.

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