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Internet use and religion, part three

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This article reports preliminary results from an exploration of the relationship between religion and Internet use in Europe, using data from the European Social Survey (ESS). I describe the data processing pipeline and models in this previous article.  All the code for this article is in this IPython notebook. Data inventory The dependent variables I use in the models are rlgblg : Do you consider yourself as belonging to any particular religion or denomination? rlgdgr : Regardless of whether you belong to a particular religion, how religious would you say you are?  Scale from 0 = Not at all religious to 10 = Very religious. The explanatory variables are yrbrn : And in what year were you born? hincrank : Household income, rank from 0-1 indicating where this respondent falls relative to respondents from the same country, same round of interviews. edurank : Years of education,  rank from 0-1 indicating where this respondent falls relative to responden...
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Internet use and religion, part four

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[If you are jumping into the middle of this series, you might want to start with this article, which explains the methodological approach I am taking.] In the previous article, I presented preliminary results from a study of relationships between Internet use and religion.  Using data from the European Social Survey, I ran regressions to estimate the effect of media consumption (television, radio, newspapers, and Internet) on religious affiliation and degree of religiosity. As control variables, I include year born, education and income (expressed as relative ranks within each country) and the year the data were gathered (between 2002 and 2010). Some of the findings so far: In almost every country, younger people are less religious. In most countries, people with more education are less religious. In about half of the 34 countries, people with lower income are less religious.  In the rest, the effect (if any) is too small to be distinguished from noise wit...
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Internet use and religion, part five

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[If you are jumping into the middle of this series, you might want to start with this article, which explains the methodological approach I am taking.] In the previous article, I show results from two regression models that predict religious affiliation and degree of religiosity.  I use the models to compare hypothetical respondents who are at their national means for all explanatory factors; then I vary one factor at a time, comparing someone at the 25th percentile with someone at the 75th percentile.  I compute the different in the predicted probability of religious affiliation and the predicted level of religiosity: 1) In almost every country, a hypothetical respondent with high Internet use is less likely to report a religious affiliation.  The median effect size across countries is 3.5 percentage points. 2) In almost every country, a hypothetical respondent with high Internet use reports a lower degree of religiosity.  The median effect size is 0.36 p...
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Internet use and religion, part six

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In my work on Internet use and religion, one of the recurring questions is whether the analysis I am doing, primarily regression models using observational data, provides evidence that Internet use causes decreased religiosity, or only shows a statistical association between them. I discuss this question in the next section, which is probably too long.  If you get bored, you can skip to the following section, which presents a different method for estimating effect size, a "propensity score matching estimator", and compares the results to the regression models. What does "evidence" mean? In the previous article I presented results from two regression models and made the following argument: Internet use predicts religiosity fairly strongly: the effect size is stronger than education, income, and use of other media (but not as strong as age). Controlling for the same variables, religiosity predicts Internet use only weakly: the effect is weaker than age, date...
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Internet use and religion, part two

In the previous article, I posted a preliminary exploration of the relationship between Internet use and religious affiliation in Europe.  In this article I clean up some data issues and present results broken by country. Cleaning and resampling Here are the steps of the data cleaning pipeline: I replace sentinel values with NaNs. I recode some of the explanatory variables, and shift "year born" and "interview year" so their mean is 0. Within each data collection round and each country, I resample the respondents using their post stratification weights ( pspwght ).  In Rounds 1 and 2, there are a few countries that were not asked about Internet use.  I remove these countries from those rounds. For the variables eduyrs and hinctnta , I replace each value with its rank (from 0-1) among respondents in the same round and country. I replace missing values with random samples from the same round and country. Finally, I merge rounds 1-5 into a single DataFrame...
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