Measuring Religion in Britain: Richness versus Parsimony

1. Measuring Religion in Britain: Richness versus Parsimony Siobhan McAndrew British Religion in Numbers University of Manchester

2. Need for efficient measures • Survey costs • Right to privacy • Typical official questions: • Which, if any, is your religion? • Do you currently practise this religion?

3. Survey data issues • Survey data are of the following types: • Continuous • Count • Ordinal • Nominal (e.g. red, blue, yellow; yes, no). • Rich array of measures in 2008 ISSP Religion III – mostly ordinal or nominal But: • Is religiosity a continuous trait (we are more or less religious)? • If yes, are there different dimensions of religiosity? • Or are there qualitatively different religious classes?

4. Measuring religiosity • Typology approach: categorise respondents as • Religious: believe, affiliate, and attend.................28% • Fuzzily-faithful: believe, affiliate, or attend...........39% • Unreligious: do not believe, affiliate or attend.....33% • Religiosity scale based on 14 items

6. Example of mixture distribution

7. Example of mixture distribution ‘Unreligious’ ‘Religious’ ‘Fuzzy’ = mixture of two distinct types?? “We observe these with error”

8. Latent classes: finding ‘ideal types’ • Social class • Cultural classes: omnivores and univores • Consumer types Religion: • Latent classes of Muslim women: religious, traditional, secular, fundamentalist (Blaydes & Linzer 2008) • Orthodox, non-orthodox, non-religious - Hagenaars & Halman 1989 • 11 belief systems (e.g. Strong believers, Secular, God + Sin, Strong but no hell/devil, etc.) - Owen & Videras 2006

9. Or... • Is religiosity not a class but a continuous trait: • we are more or less religious • may be different types of religiosity (doctrine, practice, belief) • may be skewed, twin-peaked, fat-tailed... • If there are a small number of religious classes, continuous measures imply we are observing them with error (hence twin-peaked graph) • Or, if religiosity is a continuous trait, we may be observing that religiosity is distributed in interesting ways • Conceptually different.

10. Structure of religiosity may relate to theory • Secularisation theory: moving from one class to another • can label classes as more or less religious • Postsecularist or rational choice approach: religiosity is rich and complex phenomenon. Distribution of religiosity may be changing in line with the particular dynamics of religious change • different dimensions of religiosity • factor scores are standardised

14. Mean ‘religiosity’

15. Mean ‘religiosity’

16. Choice of ‘manifest variables’ • Belief in Nirvana • Belief in miracles • Belief in reincarnation • Religion important in upbringing • Confidence in churches • Inner peace • Sciencevs religion • Truths in many religions • R is adherent • Church attendance • Church activities • Prayer • R is spiritual • Belief in God • Belief in heaven • Belief in hell • Belief in afterlife

17. Why? • To cover variety of potential religiosities: • Belonging/background • Activity • Vicarious religion • Orthodox/heterodox beliefs • Broadly applicable to most religious groups • Too much on belief? • Too Christian?

18. Exploratory cluster analysis • Two-step cluster procedure in SPSS • Determines number of clusters from the data Secular: 26% Fuzzy: 44% Religious: 30% Secular: heaven, afterlife, miracles, hell Fuzzy: heaven, afterlife, hell, miracles Religious: heaven, spiritual, prayer, hell

19. However... • SPSS throws away information • Missings (1683 of 2250!!) • treats ordered responses as not ordered • Doesn’t incorporate predictors such as age, education, sex • This makes it more likely that the clusters found aren’t ‘real’ - e.g. grouping together the definitelys, probablys and definitely nots.

20. Exploratory factor analysis • CFA assumes both the trait and the responses are continuous & normally distributed (hmm....) • 2 main factors, 49% of variance: • Traditional religiosity (attends church, prayer, church activities, belief in God) – 28% of variance • Afterlife/supernatural (afterlife, reincarnation, Nirvana, afterlife, heaven, hell) – 22% of variance

21. Factor 1: “Traditional religiosity”: negative skew

22. Factor 2: Afterlife/supernatural (Weakly but significantly correlated with factor 1.)

23. Predictors of religiosity • Traditional religiosity: being older, being female were significantly associated with this factor. Marital status, education, socio-economic status and ethnicity were not. • Afterlife/supernatural dimension: being younger, being female was significantly associated with this factor. Being non-white (p = 0.06) and not holding a professional post (p = 0.09) on borderline. Other controls not significant.

24. Latent class analysis • Incorporates information from co-variates (e.g. sex, education, socio-economic status) to uncover latent class structure • poLCA also uses information even where the respondent did not answer all the questions – fewer cases missing. • Four latent classes found

25. Belief/practice patterns Class I: belief in God, adherence, religion good for inner peace, heaven........................................................religious moderates(31%) Class II: no or never to belief in God, hell, heaven, Nirvana, reincarnation, church activity, prayer, spirituality, belief in the afterlife, church attendance, truth in any religion.............................................................strong secularity(24%) Class III: belief in God, truth in one religion only, confidence in churches, too much science, religion good for inner peace, belief in the afterlife, church attendance.....................strongly religious (17%) Class IV: no or never to Nirvana, miracles, church attendance, prayer, church activity, heaven..........................................secular/fuzzy (28%)

26. Predictors of class membership Compared with Class I (religious moderates): The strongly secular: significantly more likely to be male, younger, non-ethnic background. Education, income NS. The strongly religious: significantly more likely to have an ethnic background. Not having A-levels/some college on border of significance. Age, gender, income NS. The moderately secular: significantly more likely to be younger and not earn a top-quartile income. Male, non-ethnic background on border of significance. Education NS.

27. Uncovering religiosity? • Need similar exercise to examine religiosity as a trait (latent variable analysis) • Intuition there is also a small number of classes which are qualitatively different, which structure religiosity: but religiosity is a continuous trait • Testing requires combined latent class and latent trait model • Danger that I am searching for method that gets the results I want.

28. Parsimony vs richness? • KISS. Latent variable analysis still tricksy • Problems with cluster analysis: FA sensitive to choice of variables, treatment of missing data • Three-fold typology approach is cheap and simple to understand • May miss complexity of the broad middle or different dimensions of religiosity • Missing responses to religious questions a chronic problem • Surveys are expensive.