## Thursday, November 19, 2015

### Internet use and religion, part three

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).

### 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 respondents from the same country, same round of interviews.

tvtot: On an average weekday, how much time, in total, do you spend watching television?  Scale from 0 = No time at all to 7 = More than 3 hours.

rdtot: On an average weekday, how much time, in total, do you spend listening to the radio? Scale from 0 = No time at all to 7 = More than 3 hours.

nwsptot: On an average weekday, how much time, in total, do you spend reading the newspapers? Scale from 0 = No time at all to 7 = More than 3 hours.

netuse: Now, using this card, how often do you use the internet, the World Wide Web or e-mail - whether at home or at work - for your personal use?  Scale from 0 = No access at home or work, 1 = Never use, 6 = Several times a week, 7 = Every day.

### Model 1: Affiliated or not?

In the first model, the dependent variable is rlgblg, which indicates whether the respondent is affiliated with a religion.

The following figures shows estimated parameters from logistic regression, for each of the explanatory variables.  The parameters are log odds ratios: negative values indicate that the variable decreases the likelihood of affiliation; positive values indicate that it increases the likelihood.

The horizontal lines show the 95% confidence interval for the parameters, which includes the effects of random sampling and filling missing values.  Confidence intervals that cross the zero line indicate that the parameter is not statistically significant at the p<0.05 level.
In most countries, interview year has no apparent effect.  I will probably drop it from the next iteration of the model.
Year born has a consistent negative effect, indicating that younger people are less likely to be affiliated.  Possible exceptions are Israel, Turkey and Cyprus.
In most countries, people with more education are less likely to be affiliated.  Possible exceptions: Latvia, Sweden, and the UK.
In a few countries, income might have an effect, positive or negative.  But it most countries it is not statistically significant.
It looks like television might have a positive or negative effect in several countries.
In most countries the effect of radio is not statistically significant.  Possible exceptions are Portugal, Greece, Bulgaria, the Netherlands, Estonia, the UK, Belgium, and Germany.
In most countries the effect of newspapers is not statistically significant.  Possible exceptions are Turkey, Greece, Italy, Spain, Croatia, Estonia, Portugal and Norway.
In the majority of countries, Internet use (which includes email and web) is associated with religious disaffiliation.  The estimated parameter is only positive in 4 countries, and not statistically significant in any of them.  The effect of Internet use appears strongest in Poland, Portugal, Israel, and Austria.

The following scatterplot shows the estimated parameter for Internet use on the x-axis, and the fraction of people who report religious affiliation on the y-axis.  There is a weak negative correlation between then (rho = -0.38), indicating that the effect of Internet use is stronger in countries with higher rates of affiliation.

### Model 2: Degree of religiosity

In the first model, the dependent variable is rlgdgr, a self-reported degree of religiosity on a 0-10 scale (where 0 = not at all religious and 10 = very religious).

The following figures shows estimated parameters from linear regression, for each of the explanatory variables.  Negative values indicate that the variable decreases the likelihood of affiliation; positive values indicate that it increases the likelihood.

Again, the horizontal lines show the 95% confidence interval for the parameters; intervals that cross the zero line are not statistically significant at the p<0.05 level.
As in Model 1, interview year is almost never statistically significant.
Younger people are less religious in every country except Israel.
In most countries, people with more education are less religious, with possible exceptions Estonia, the UK, and Latvia.
In about half of the countries, people with higher income are less religious.  One possible exception: Germany.
In most countries, people who watch more television are less religious.  Possible exceptions: Greece and Italy.
In several countries, people who listen to the radio are less religious.  Possible exceptions: Slovenia, Austria, Polans, Israel, Croatia, Lithuania.
In some countries, people who read newspapers more are less religious, but in some other countries they are more religious.
In almost every country, people who use the Internet more are less religious.  The estimated parameter is only positive in three countries, and none of them are statistically significant.  The effect of Internet use appears to be particularly strong in Israel and Luxembourg.

The following scatterplot shows the estimated parameter for Internet use on the x-axis and the national average degree of religiosity on the y-axis.  Using all data points, the coefficient of correlation is -0.16, but if we exclude the outliers, it is -0.36, indicating that the effect of Internet use is stronger in countries with higher degrees of religiosity.

### Next steps

I am working on a second round of visualizations that show the size of the Internet effect in each country, expressed in terms of differences between people at the 25th, 50th, and 75th percentiles of Internet use.

I am also open to suggestions for further explorations.  And if anyone has insight into some of the countries that show up as exceptions to the common patterns, I would be interested to hear it.

1. Why not run a multilevel model using country instead of running models on subsets of the data?

1. Interesting idea: I've been thinking about it all day. The reasons I haven't done it yet are:

1) Partly for simplicity: I find it hard enough to explain single-level models.

2) Partly because I don't really have a theory about what explanatory factors to include at the higher level.

3) And partly because the subset models answer the question I am interested in (whether the association between Internet use and religiosity is consistent across countries).

4) A multilevel model might answer different questions, like what national factors influence the size of the Internet effect. But with the data I have, I'm not confident I would find anything reliable.

If you look at the IPython notebook for part four, I've got some scatterplots of (x) effect size of Internet use and (y) a few national factors. There are some weak correlations, but nothing that seems super meaningful.

Thanks for the suggestion!