### Abstract

Based on 2000-2010 data from the General Social Survey (GSS), I present results of a logistic regression that measures the relationship between Internet use and religious affiliation, controlling for religious upbringing, income and socioeconomic index, year born (age), and education.

I find that moderate Internet use reduces the chance of religious affiliation by 2 percentage points (odds ratio 0.8); heavier Internet use reduces affiliation by an additional 5 percentage points (odds ratio 0.7). Four years of college reduces affiliation by an additional 2 percentage points (odds ratio 0.8).

All reported effects are statistically significant with N=8960 respondents.

Results of logistic regression can be difficult to interpret; it might help to imagine the following progression:

- Start with a hypothetical baseline person raised in any religion, with moderate or high household income ($25,000 per year or more), born in 1960, with high school education but no college, and low Internet use (less than 2 hours per week): in the GSS survey, 91% of people in this category have a religious affiliation. Now we change one variable at a time.
- If this person were born 10 years later (in 1970) the fraction would drop to 89%.
- If this person went to college, the fraction would drop to 87%
- If this person used the Internet 2 or more hours per week, the fraction would drop to 85%.
- If this person used the Internet 8 or more hours per week, the fraction would drop to 80%.

### Introduction

From 1990 to 2010 the fraction of Protestants in the U.S. population dropped from 62% to 51%; at the same time the fraction of people with no religious preference increased from 8% to 18%. The following graph shows these trends:In a previous article I presented evidence that something happened in the 1990s, continuing through the 2000s, that is causing disaffiliation from religion across all generations, with the largest effect on the youngest generations in the survey, people born in the 1960s and 1970s.

There are many possible explanations, but for me, the Internet pops to the top of this list. First, the timing is at least approximately right. Here is data from the World Bank, showing number of Internet users per hundred people in the U.S.

Internet use increased rapidly from 1995 to 2010, which is the interval of steepest change in religious affiliation.

### Regressions

To identify factors that contribute to disaffiliation, I ran logistic regressions with the following dependent variable:

**has_relig**: 1 if the respondent reported any religious affiliation when interviewed as an adult, or 0 if the respondent reported "None" (based on the GSS variable RELIG)

And these explanatory variables:

**had_relig**: 1 if the respondent reported being raised in a religion, 0 otherwise (based on RELIG16)

**born_from_1960**: year the respondent was born minus 1960 (based on AGE and survey year). Subtracting 1960 makes it easier to interpret the results of the regression.

**educ_from_12**: number of years of school completed, minus 12 (based on EDUC).

**somewww**: 1 if the respondent reported using the Internet 2 of more hours per week, 0 otherwise (based on WWWHR, with the threshold chosen near the median)

**heavywww**: 1 if the respondent uses the Internet more than 8 hours per week, 0 otherwise (threshold chosen near the 75th percentile)

**SEI**: Socioeconomic index (a measure of occupational prestige developed by the GSS).

**high_income**: 1 if the respondent reports annual household income of $25,000 or more, which includes 62% of respondents who answered the question.

I used data from GSS survey years 2000, 2002, 2004, 2006, and 2010 (the relevant questions were not asked in 2008). I excluded respondents who were not asked or did not answer one or more of the questions I used in my analysis.

It turns out that SEI does not make a contribution that is either statistically or practically significant, so I omit it from the model.

Here are the results of the model as reported by R:

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -0.164434 0.094978 -1.731 0.0834 .

had_relig 2.318141 0.087372 26.532 < 2e-16 ***

high_income 0.166673 0.072345 2.304 0.0212 *

born_from_1960 -0.020161 0.002128 -9.474 < 2e-16 ***

educ_from_12 -0.051850 0.012228 -4.240 2.23e-05 ***

somewww -0.178409 0.078490 -2.273 0.0230 *

heavywww -0.336658 0.080546 -4.180 2.92e-05 ***

---

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 7860.3 on 8959 degrees of freedom

Residual deviance: 6872.5 on 8953 degrees of freedom

AIC: 6886.5

Number of Fisher Scoring iterations: 5

All explanatory variables are statistically significant:

**high_income**and**somewww**are borderline, both at p=0.02.
The odds ratios and cumulative probabilities are:

odds cumulative

ratio probability

(Intercept) 0.85 46

had_relig 10.16 90

high_income 1.18 91

born_from_1960 0.82 89

educ_from_12 0.81 87

somewww 0.84 85

heavywww 0.71 80

These results are summarized and interpreted in the Abstract, above.

### Discussion

As always, statistical association does not prove causation, but in this case I think there are reasons to believe that Internet use causes disaffiliation from religion:

- It is easy to imagine how Internet use could allow a person in a homogeneous community to find information about people of other religions (and none), and to interact with them personally. And there is anecdotal evidence that those interactions contribute to religious disaffiliation (for example, numerous personal reports on reddit.com/r/atheism).
- Conversely it is harder to imagine plausible reasons why disaffiliation might cause increased Internet use (except possibly on Sunday mornings).
- Although it is possible that a third factor causes both disaffiliation and Internet use, that factor would also have to be new, coincidentally rising in prevalence, like the Internet, during the 1990s and 2000s.
- Whatever causes disaffiliation has the strongest effect on the youngest generations, which is consistent with the hypothesis that Internet use during adolescence and young adulthood has the strongest effect on religious affiliation.

So with appropriate caution, I think there is a strong case here for causation, and not just statistical association.

Furthermore, the magnitude of the effect is large enough to explain a substantial part of the observed changes in religious affiliation. In my next article I will incorporate this regression model into the generational model I presented in Part Six, in order to estimate the effect of Internet use on these trends.

In Part Two I used data from the 1988 General Social Survey (GSS) to model transmission of religion from parent to child, and found that the model failed to predict the decrease in Protestants and the increase in Nones that occurred between 1988 and 2010.

In Part Three I looked at changes, between 1988 and 2008, in the spouse tables (which describe the tendencies of people to marry within their religions), the environment table (which describes parents' decisions about their children's religious upbringing), and the transmission table (which describes the likely outcomes for children raised within each religion). I found that the transmission table has changed substantially since 1988, and accounts for a large part of the observed increase in Nones, but not the decrease in Protestants.

In Part Four I looked at changes in religiosity over the lifetime of respondents. I tentatively concluded that the differences between generations were larger than changes in affiliation, within generations, over time.

But in Part Five I looked more closely and saw that all generations were becoming more religious, or staying the same, prior to 1990, and that all generations began to disaffiliate during the 1990s, continuing into the 2000s.

Furthermore, the magnitude of the effect is large enough to explain a substantial part of the observed changes in religious affiliation. In my next article I will incorporate this regression model into the generational model I presented in Part Six, in order to estimate the effect of Internet use on these trends.

### Summary of previous reports

In Part One I described trends in market share of major religions in the U.S.: since 1988, the fraction of Protestants dropped from 60% to 51%, and the fraction of people with no religious affiliation increased from 8% to 18%.In Part Two I used data from the 1988 General Social Survey (GSS) to model transmission of religion from parent to child, and found that the model failed to predict the decrease in Protestants and the increase in Nones that occurred between 1988 and 2010.

In Part Three I looked at changes, between 1988 and 2008, in the spouse tables (which describe the tendencies of people to marry within their religions), the environment table (which describes parents' decisions about their children's religious upbringing), and the transmission table (which describes the likely outcomes for children raised within each religion). I found that the transmission table has changed substantially since 1988, and accounts for a large part of the observed increase in Nones, but not the decrease in Protestants.

In Part Four I looked at changes in religiosity over the lifetime of respondents. I tentatively concluded that the differences between generations were larger than changes in affiliation, within generations, over time.

But in Part Five I looked more closely and saw that all generations were becoming more religious, or staying the same, prior to 1990, and that all generations began to disaffiliate during the 1990s, continuing into the 2000s.

In Part Six I presented a generational model that retroactively "predicts" the changes we have seen since 1988, and used it to predict how those changes are likely to continue in the next 30 years. I expect the fraction of Protestants to continue to decrease, and the fraction of Nones to increase and overtake Catholic as the second-largest affiliation by 2030.