The advent of the Internet has increased access to information and impacted many aspects of life, including politics. The present study utilized Pew Internet & American Life survey data from the November 2008 presidential election time period to investigate the degree to which political blog reading predicted online political discussion, online political participation, whether or not a person voted, and voting choice, over and above the predication that could be explained by demographic measures of age, education level, gender, income, marital status, race/ethnicity, and region. Ordinary least squares hierarchical regression revealed that political blog reading was positively and statistically significantly related to online political discussion and online political participation. Hierarchical logistic regression analysis indicated that the odds of a political blog reader voting were 1.98 the odds of a nonreader voting, but vote choice was not predicted by reading political blogs. These results are interpreted within the uses and gratifications framework and the understanding that blogs add an interpersonal communication aspect to a mass medium. As more people use blogs and the nature of the blog-reading audience shifts, continuing to track and describe the blog audience with valid measures will be important for researchers and practitioners alike. Subsequent potential effects …

Covariate quality has been primarily theory driven in propensity score matching with a general adversity to the interpretation of group prediction. However, effect sizes are well supported in the literature and may help to inform the method. Specifically, I index can be used as a measure of effect size in logistic regression to evaluate group prediction. As such, simulation was used to create 35 conditions of I, initial bias and sample size to examine statistical differences in (a) post-matching bias reduction and (b) treatment effect sensitivity. The results of this study suggest these conditions do not explain statistical differences in percent bias reduction of treatment likelihood after matching. However, I and sample size do explain statistical differences in treatment effect sensitivity. Treatment effect sensitivity was lower when sample sizes and I increased. However, this relationship was mitigated within smaller sample sizes as I increased above I = .50.