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Measuring Religion in Britain: Richness versus Parsimony. Siobhan McAndrew British Religion in Numbers University of Manchester. Need for efficient measures. Survey costs Right to privacy Typical official questions: Which, if any, is your religion?

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measuring religion in britain richness versus parsimony

Measuring Religion in Britain: Richness versus Parsimony

Siobhan McAndrew

British Religion in Numbers

University of Manchester

need for efficient measures
Need for efficient measures
  • Survey costs
  • Right to privacy
  • Typical official questions:
    • Which, if any, is your religion?
    • Do you currently practise this religion?
survey data issues
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?
measuring religiosity
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
example of mixture distribution1
Example of mixture distribution

‘Unreligious’

‘Religious’

‘Fuzzy’ = mixture of two distinct types??

“We observe these with error”

latent classes finding ideal types
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
slide9
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.
structure of religiosity may relate to theory
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
choice of manifest variables
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
slide17
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?
exploratory cluster analysis
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

however
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.

exploratory factor analysis
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
factor 2 afterlife supernatural
Factor 2: Afterlife/supernatural

(Weakly but significantly correlated with factor 1.)

predictors of religiosity
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.
latent class analysis
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
belief practice patterns
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%)

predictors of class membership
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.

uncovering religiosity
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.
parsimony vs richness
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.