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Investigating the Effect of Bias in Survey

Measures of Church Attendance

Philip S. Brenner*Department of Sociology, University of Massachusetts, Boston

That church attendance rates are overestimated by conventional surveys is well established. Muchof the extant literature places the locus of the error primarily on measurement; overreporting on thepart of the respondent. However, there has been relatively little research into the effect of this meas-urement error on the individual demographic predictors commonly associated with church attend-ance. In this paper, demographic subgroups are compared on their propensities to report churchattendance in conventional surveys and time diaries across 14 countries and four decades. Findingsindicate that these covariates are strongly correlated with both measures of attendance, but parame-ter estimates do not significantly or consistently differ between these modes. This finding suggeststhat, while conventional survey measures may overestimate population rates of attendance in somecountries (i.e., North America), parameter estimates for these demographic predictors are largelyunaffected by overreporting bias. Finally, limitations and future directions of research are discussed.

Key words: attendance; measurement; methodology; survey research; quantitative methods.

INTRODUCTION

That church attendance rates are overestimated by conventional surveys iswell established (see Chaves and Stephens 2003 for a review). While somehave argued that nonresponse or coverage biases are the primary cause of thiserror (Caplow 1998; Woodberry 1998; but see Brenner 2012 for evidence to

the contrary), much of the extant literature investigating this overestimationplaces its locus primarily on measurement error. Using a variety of differentsamples and methods, research has consistently found that survey respondentsoverreport whether and how often they attend, inflating estimates of attend-ance up to 100 percent. Hadaway et al. (1993) found a very high level of

*Direct correspondence to Philip S. Brenner, Department of Sociology, University ofMassachusetts, Boston, 100 Morrissey Blvd., Boston MA 02125 USA. Tel: 1 617 287 6251;

E-mail: [email protected].

# The Author 2012. Published by Oxford University Press on behalf of the Associationfor the Sociology of Religion. All rights reserved. For permissions, please e-mail:[email protected].

Sociology of Religion 2012, 73:4 361-383doi:10.1093/socrel/srs042

Advance Access Publication 10 July 2012

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overreporting among Ashtabula County (Ohio) Protestants when comparing aconventional survey estimate to an estimate based on observation and count-ing. Other research has found similar results in Oxford County, Ontario(Hadaway and Marler 1997), in a sample of American Presbyterians (Marcum

1999), among American Catholics (Chaves and Cavendish 1994), and withinan evangelical congregation (Marler and Hadaway 1999).

In light of methodological critiques (Caplow 1998; Hout and Greeley1998; Woodberry 1998), these findings have been replicated and extendedusing time use studies as a novel data source for comparison with conventionalsurvey data. Presser and Stinson (1998) found a substantial overreport whenthey compared estimates of American church attendance from conventionalsurvey data with those from time diaries. Comparing conventional survey datafrom the General Social Survey (GSS) and Gallup from 1993 and 1994 with

the 19921994 University of Maryland Time Use Study, the authors found anoverreport of nearly 50 percent.

While Presser and Stinson (1998) focused primarily on comparisons ofAmerican conventional and time diary data in the early 1990s, a new wave oftime diary studies in the early 2000s, including the American Time Use Study(ATUS), provided an excellent opportunity to extend this analysis forward intime. Moreover, recent projects aimed at the harmonization of existing datasets, like the development of the Multinational Time Use Study (MTUS)(Gershuny et al. 2000), afford an excellent opportunity to broaden compari-

sons. Using over 400 individual surveys from 14 countries in Europe and NorthAmerica reaching back over four decades and totaling nearly a millionrespondents, Brenner (2011a) found that only in North America does substan-tial overestimation occur. Estimated as the difference between conventionaland time diary estimates, both Canadian and American overreports consis-tently had substantive effect sizes equal to or exceeding 0.20 using Cohens dfor proportions.

These findings suggest that actual levels of American attendance are lowerthan conventional survey estimates suggest and have been for the last four

decades. While the Canadian pattern is less clear, it also suggests that actuallevels of church attendance are lower than survey estimates indicate in Canadaas well. In comparison, conventional survey rates in Europe appear to be rela-tively accurate. As a result, American church attendance, while still somewhathigher than some European countries, is nearly identical to Italy and moresimilar to other countries (e.g., Spain, Slovenia, and the Netherlands) thanconventional survey estimates suggest. Canadian attendance was found to morestrongly resemble that in Great Britain rather than American or Italian rates.

These comparisons rely on the assumption that time diaries avoid the

source of the bias inherent in conventional survey measurement of attendance.Conventional surveys use a direct question to measure attendance, promptingthe respondent to recall not only his or her recent behavior (Did I attendservices?) but also to reflect on his or her self-concept (Am I the sort of

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person who attends services?). Direct survey questions can therefore elicit affir-mative responses not only from individuals who actually attended but also fromthose respondents who see themselves as usual attenders. Note that thisexplanation does not assume that respondents are intentionally misrepresenting

their behavior. Rather, respondents are truthfully answering the question asthey have pragmatically interpreted its purpose to be: the measurement of theiridentity as a religious person (Hadaway et al. 1998).

In contrast, time diary reports lack much or all of this error, utilizing anondirective procedure to measure behavior. The chronologically based proce-dure of the diary records the actions of the respondent (and location and pres-ence of other individuals) using predetermined intervals throughout a calendarday without asking direct questions about any particular behaviors (Robinson1985, 1999; Stinson 1999). This nondirective procedure avoids the priming

effect of the conventional survey question (Bolger et al. 2003; Niemi 1993;Zuzanek and Smale 1999) yielding a self-report of normative behavior that isless susceptible to overreporting.

However, similar results could be attributable to differences in the demo-graphic compositions between countries. If different subgroups vary in theirpropensity to report attendance between modes and these groups vary in theirpopulation proportions between countries, variation in the differences betweenthe attendance estimates from these data collection methods should beexpected. For example, if overreporting is attributable to educational differen-

ces (e.g., respondents with low levels of education have a higher propensity tooverreport), variations in the distributions of educational attainment betweencountries could result in differing rates of overreporting.

Unlike the literature on the overreporting of other normative behaviors,like voting (see Belli et al. 1999; Bernstein et al. 2001), there has been rela-tively little research into the effect of these individual determinants on theoverreporting of church attendance (Ellison and Sherkat 1995, fn 1). Oneimportant exception is the work of Presser and Stinson (1996) who found nosignificant differences in rates of overreporting of church attendance in the

United States when comparing within a number of demographic categories,including sex, age, and education. The current study builds on existing work byincluding additional demographic categories commonly associated with churchattendance but not included in the analysis of Presser and Stinson (1996).Both the conventional survey and diary measures of attendance will be pre-dicted using this suite of demographic variables. Coefficients will then be com-pared between models looking specifically for any differences emerging in apattern that would even partially explain overreported attendance where andwhen it occurs. If differential responding by subgroup is a cause of the gap

between the two methods of data collection, differences between their coeffi-cients between models should rise to significance where overreporting is arguedto occur (e.g., the United States and Canada) and fail to do so where it argu-ably does not (e.g., Europe).

EFFECT OF BIAS IN SURVEY MEASURES OF CHURCH ATTENDANCE 363

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The current study also extends the current work both spatially and tempo-rally. Where existing work has focused on American overreporting in the early1990s, here multiple years of data are compared over the past four decades fromdiary and conventional surveys. Data from countries with a demonstrated gap,

the United States and Canada, are compared with 12 European countries. TheEuropean countries included here are those with adequate data for these analy-ses, but fortuitously vary on a number of important, overlapping dimensions.Countries vary on their rates of attendance, from relatively high attendancecountries, like Ireland and Italy, to low attendance countries like Norway andFinland. They also vary by their primary religious tradition. Included are pri-marily Catholic countries like France, Spain, and Austria, primarily Protestantcountries like Great Britain, and countries with large populations fromeach tradition, like West Germany1 and the Netherlands. Finally, postcom-

munist countries, like Slovenia and East Germany, are included.The current project aims to investigate the potential locus of the gap

between time diary and conventional survey estimates of attendance; an impor-tant next step in this research program for at least two reasons. First, a thor-ough understanding of measurement error is necessary if survey researchers andmethodologists are to improve the validity of measurement for normativebehaviors like church attendance. A better understanding of how measurementerror is generated and which respondents are most likely to misreport mayallow prescriptive steps to be taken to avoid the error in the first place.

Similarly, a thorough understanding of the phenomenon of overreporting ofchurch attendance may lead to better questions or improved methods to reducethis form of measurement error. Moreover, findings may be applicable to otheroverreported normative behaviors, potentially with policy implications, likevoting and physical exercise.

Second, and perhaps more importantly, measurement error is more thanjust an annoyance to be avoided. While a better measure of church attendance,and normative behavior more generally, is certainly a goal of this research, theoverreporting of normative behavior is more than just a methodological puzzle

to be solved for the sake of improving measurement. Rather, as Schuman(1982) suggested, these errors provide us an excellent opportunity to learnabout ourselves. Most forms of measurement error are not the result of randomprocesses; rather, they are the result of cognitive processes, rooted in socialinteraction (e.g., the interaction between the interviewer and respondent; therespondents use of the response options in context given his or her under-standing of normative expectations or salient comparators). Understandingthese errors, and the processes by which they occur, is an invaluable finding forunderstanding religiosity both within and between societies. By pursuing a

1Considering the recent history of Germany, West and East Lander are analyzedseparately.


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culturally situated understanding of overreporting of religious serviceattendance, we can gain a richer and more complete understanding of trendsin religious stability and change and the future of religion.

DATA AND METHODS

Conventional Survey Estimates of Church Attendance

The conventional survey questions vary slightly by data set but all follow asimilar format. These questions directly ask for the respondents frequency ofattendance, usually over the past year. They use a closed format, typically offeringbetween four and seven response categories that range from more than once aweek to never. Regular and frequent attendance is operationalized as attending

nearly every week (about two to three times a month) or more frequently. (SeeAppendix B in the Supplementary material for more information on responsecategories and coding.) To minimize the idiosyncratic measurement errors intro-duced by any single study, multiple studies are included for each country. Theycome from four series of cross-cultural surveys, including the World ValuesSurveys (WVS) 19812008 (World Values Survey Association 2008, 2009;World Values Survey Association and European Values Study Foundation 2006),the Eurobarometer (EB) from 1970 to 2005 (Papacostas 2005 2006, 2006;Schmitt and Scholz 2005), the International Social Survey Program (ISSP)

(International Social Survey Program 1988, 1989, 1990, 1991, 1992, 1993, 1994,1996a, 1996b, 1997, 1998, 1999, 2000, 2001, 2002, 2004) including theAmerican GSS (Davis et al. 2009), and the European Social Survey (ESS)(Jowell and the Central Coordinating Team 2003, 2005, 2007, 2008). A lack ofFinnish data necessitates the use of an additional study in the GallupEcclesiastica Study 1999 (Gallup Ecclesiastica 2002). Other comparable data areused for non-European countries to fill in temporal gaps, including the AmericanNational Election Study (ANES) (Sapiro et al. 2004) and the Canadian GeneralSocial Survey (CGSS) Cycle 18 (Statistics Canada 2005). See Appendix A in

the Supplementary material for more information on these studies.

Time Use Measures

The majority of time use studies used in the following analyses are compo-nent surveys of the MTUS (Multinational Time Use Study 2007, 2010). Notincluded in the MTUS at the time of writing but included here are theGerman Time Use Study 2001 2002 (Statistisches Bundesamt 2005), IrishNational Time Use Survey 2005 (Irish Department of Justice Equality and LawReform 2008), the Italian Time Use Study 20022003 (Istituto Nazionale di

Statistica 2005), the Spanish Time Use Survey 20022003 (Instituto Nacionalde Estadstica 2005), the United Kingdom Time Use Study 2005 (Office forNational Statistics 2007), the CGSS Cycle 19Time Use 2005 (StatisticsCanada 2008), and the ATUS 20032008 (Abraham et al. 2008). For the


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current purposes, these studies are straightforwardly harmonizable with theMTUS. Attendance is estimated as that reported by those respondents ran-domly assigned to report on a Sunday. More information on each of thesestudies, including sample sizes, response rates, and a brief summary of sample

characteristics, is available in Appendix C, which can be found in theSupplementary material.

Predictors

Within the collection of commonly used indicators of socioeconomicstatus, education has the most consistently and strongly negative associationwith religious practice (Hunsberger 1985; Johnson 1997; Roof and McKinney1987; Sherkat 1998; Wilson and Sherkat 1994). However, the relationship

between education and attendance may vary over time and place. For example,education may have a positive relationship with self-reported church attend-ance in Great Britain (Berger et al. 2008). A second indicator of SES, house-hold income, is also likely to have a negative relationship with attendance,although it may not necessarily be strong (Azzi and Ehrenberg 1975; Lipfordand Tollison 2003). Education is in three categories (less than secondary, com-pleted secondary, completed nonvocational postsecondary) as is income,broken into quartiles (lower 25 percent, middle 50 percent, upper 25 percent).

While it might be simply stated that age and attendance have a positive

relationship in both the United States (Argue et al. 1999; Gallup and Lindsay1999) and Europe (Berger et al. 2008; Halman and Draulans 2006), this rela-tionship may arguably conceal a more complex relationship better described asthe effect of life-stage (Chaves 1991). After being baptized in, named by, orintroduced to the religious community as an infant, the young individualattends regularly until s/he leaves the parental home. The stage of prodigality,characterized by a lack of attendance, ends after the young adult marries2 andreturns to a pattern of regular attendance, perhaps hastened by the arrival of achild (Wilson and Sherkat 1994). Attendance remains relatively stable until

fluctuations in midlife, after the departure of adult children. While this patternis certainly not an accurate picture of the behavior of all Americans, much lessall Westerners, living or dead (Bahr 1970; Chaves 1989, 1990, 1991), it doespresent a normative attendance pattern over the different stages of an ideal typelife course (see Wingrove and Alston 1974; Wuthnow 1976). Age is included insix categories (2024, 2534, 3544, 4554, 5564, 65 and older), althoughin a few models, small cell sizes require combining adjacent categories.

As the previous discussion suggests, the individuals life stage and familialrelationships provide some of the most powerful determinants of religious

2The church returned to is typically that of ones parents or ones spouses parents,although the two have traditionally been the same, given the normative expectation of reli-gious homogamy (Landis 1949; Putnam and Campbell 2010; Thomas 1951).


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practice (Chaves 1991). Marriage is associated with increased likelihood ofattendance among young American adults, although this effect may be at leastpartially due to the effect of children (see Stoltzenberg et al. 1995). Parentswith children living in the household are more likely to attend regularly

(Wilcox 2002). Marital status (married or unmarried) and the presence of chil-dren in the household are both included as dichotomous variables.

Ideal-type life-course trajectories (Bahr 1970) described above may not fitmen and women equally well (see Wilson and Sherkat 1994). To capture theseimportant differences, one of the most central variables to include in anyexamination of religious practice is gender. Women have been characterized asmore devoutly religious (Berger et al. 2008) and register higher levels of attend-ance on surveys in both the United States (Gallup and Lindsay 1999) andEurope (Halman and Draulans 2006; Stark 2002).

Analysis Plan

Separate logistic regression models are estimated to predict attendance foreach of the two dependent variables and compared by mode for country/yearpairings. In years when time diary data are available but conventional surveydata are not, the closest available years are used. Whenever possible, time usedata for a given year are compared with conventional survey data from multipledata sets, given the availability of dependent and independent variables.

Mismatches may also occur in terms of missing independent variables.Estimation will proceed using as many covariates as possible given their avail-ability by country/year. Covariates will then be compared between modelslooking for differences in their values when predicting the two measures ofattendance in the same country and year.

However, because coefficients in logistic regression models are inherentlyconfounded with unobserved heterogeneity, comparison across groups withvarying levels of residual variation can lead to spurious findings of differencebetween groups (Allison 1999).3 Allison suggests a way to test the assumption

3Allison explains that unlike comparisons of unstandardized coefficients betweenmodels estimated using ordinary least squares, coefficients in logistic regression models areinherently standardized and confounded with residual variation (unobserved heterogene-ity). Differences in the degree of residual variation across groups can produce apparent dif-ferences in coefficients that are not indicative of true differences in causal effects (Allison1999:18687). While Allison is concerned with comparisons between models run for sepa-rate groups in the same sample (e.g., whites and nonwhites; men and women), his concernmay be applicable to comparisons between these two measures of church attendance aswell. Comparing two very different measures of religious service attendance known to differsystematically, even across two random samples from the same target population, might

encounter this problem. If respondents answer the stylized and time diary questionsdifferentlyand there is good reason to believe that they may in some countries/yearsunobserved heterogeneity could cause problems in across-measure comparisons of the esti-mated effects. For further information on this problem, readers should refer to Allison andthe other literature cited here.


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that the residual variation is equal across groups by rewriting the underlyingmodel to allow residual variation to differ between groups (Hoetker 2007a).This equation,

yi b0 b1G1

Xj.1

bjxij siei

where

Gi 0 for group 01 for group 1

and

si 11 dGi

; d. 1;

can be rewritten, in logit form, as,

lnpi

1 pi

b0 b1G1

Xj.1

bjxij siei

!1 dGi:

The null hypothesis that the values of the underlying coefficients are the same

across groups while accounting for residual variation can then be tested. If thenull (d 0) is not rejected, the conventional approach may be used to directlycompare effects of the predictors between groups with a Wald test (Allison1999; Hoetker 2007a; Liao 2004). However, if the null hypothesis is rejected,proceeding with a Wald test could yield misleading results. In this situation,Allison proposed an omnibus test of the equality of the coefficients acrossgroups. In the situation that neither the equality of residual variances norunderlying coefficients can be assumed, Hoetker proposed indirect procedureswhich abandon direct comparisons altogether, focusing instead on patterns of

sign and significance within each of the groups. Allisons procedure is appliedhere using Hoetkers Stata program complogit (2007b).

RESULTS

The assumption of equal residual variation is tested twice for each modelcomparison, using a Wald test and a likelihood ratio test (table 1). Given thelarge number of preplanned tests, a Bonferroni correction is used to adjust indi-

vidual test a values to achieve a familywise level of 5 percent (based on 100nonindependent pairs of tests). This adjustment yields an a value of 5.0 1024 for each test (x2. 12.12). This restriction is quite conservative, yieldingonly three country/year models where the null hypothesis of equal residual


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TABLE 1 Testing the Equality of Residual Variation Assumption and Equality of Underlying Coefficients

Country Time diary Conventional Equality of residual variation Equality of underlying

coefficients

L-ratio Wald L-ratio

Year Study Year X2 (1) p0 X2 (1) p0 X2 (df) p

Austria 1992 WVS 1990 1.27 1.09

Austria 1992 ISSP 1992 0.24 0.26

Canada 1981 WVS 1982 0.02 0.01

Canada 1992 WVS 1990 0.06 0.07

Canada 1992 ISSP 1992 1.49 1.87

Canada 1999 ISSP 1998 0.68 0.80

Canada 1999 WVS 2000 0.38 0.43

Canada 2005 CGSS 2004 0.81 0.97

Canada 2005 WVS 2006 0.11 0.10Finland 1999 EB 1998 0.47 0.42

Finland 1999 Gallup 1999 1.07 0.85

Finland 1999 ESS 2002 0.85 0.74

Finland 1999 ISSP 2002 1.77 1.43

France 1998 1999 EB 1998 0.31 0.29

France 1998 1999 ISSP 1998 4.91 3.60

France 1998 1999 WVS 1999 1.26 1.49

Germany, East 2001 2002 ISSP 1998 0.95 0.35

Germany, East 2001 2002 WVS 1999 2.41 0.21

Germany, East 2001 2002 ESS 2002 0.00 0.00

Germany, West 2001 2002 ISSP 1998 0.06 0.06

Germany, West 2001 2002 WVS 1999 4.92 3.61Germany, West 2001 2002 ESS 2002 1.57 1.95

Great Britain 1974 1975 EB 1975 3.82 1.23

Great Britain 1983 1984 WVS 1981 0.13 0.14

Great Britain 1983 1984 EB 1985 0.30 0.27

Great Britain 1987 EB 1985 0.01 0.01

Great Britain 1987 WVS 1990 0.25 0.29

Great Britain 1987 EB 1988 4.57 7.22 11.46 (10)

Great Britain 1987 ISSP 1988 0.02 0.02

Great Britain 2000 2001 WVS 1998 0.11 0.10

Great Britain 2000 2001 ISSP 2000 1.17 0.98

Great Britain 2000 2001 ESS 2002 1.30 1.09

Great Britain 2000 2001 ISSP 2002 2.84 2.09Great Britain 2005 ESS 2004 2.89 7.16 8.73 (9)

Great Britain 2005 EB 2005 0.06 0.07

Great Britain 2005 ESS 2006 3.29 6.89 15.92 (11)

Great Britain 2005 WVS 2006 8.64 26.47 *** 12.60 (11)

Ireland 2005 EB 2005 7.87 4.20

Ireland 2005 ESS 2006 4.66 2.80

Italy 1988 1989 EB 1988 12.32 * 6.75 23.64 (9) **

Italy 1988 1989 EB 1989 15.03 ** 5.83 47.14 (9) ***

Italy 1988 1989 WVS 1990 8.86 4.95 20.67 (6) **

Italy 2003 ESS 2002 0.25 0.23

Italy 2003 ESS 2004 9.27 5.61 10.73 (9)

Netherlands 1975 EB 1975 1.86 0.94

Netherlands 1980 EB 1980 0.01 0.01

Continued


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TABLE 1 Continued

Country Time diary Conventional Equality of residual variation Equality of underlying

coefficients


Year Study Year X2 (1) p0 X2 (1) p0 X2 (df) p

Netherlands 1985 EB 1985 7.28 4.69 14.33 (11)

Netherlands 1990 EB 1990 4.65 3.24

Netherlands 1990 WVS 1990 7.48 5.18 9.02 (8)

Netherlands 1995 EB 1994 0.97 0.82

Netherlands 1995 EB 1995 0.50 0.45

Netherlands 1995 ISSP 1995 2.71 2.04

Netherlands 1995 EB 1996 4.20 5.33

Netherlands 2000 WVS 1999 0.04 0.04

Netherlands 2000 ISSP 2000 0.01 0.01

Netherlands 2005 EB 2005 0.06 0.07Norway 1980 1981 WVS 1982 5.61 0.32

Norway 1990 1991 EB 1990 0.63 0.89

Norway 1990 1991 ISSP 1990 1.62 3.80

Norway 1990 1991 WVS 1990 2.70 6.83 3.34 (8)

Norway 1990 1991 EB 1991 0.93 0.62

Norway 1990 1991 ISSP 1991 2.90 6.89 6.56 (10)

Norway 2000 2001 ISSP 2000 0.40 0.54

Norway 2000 2001 ESS 2002 0.00 0.00

Norway 2000 2001 ISSP 2002 0.15 0.13

Slovenia 2000 WVS 1999 4.16 3.24

Slovenia 2000 ESS 2002 1.63 1.93

Slovenia 2000 ISSP 2000 0.11 0.10

Slovenia 2000 ISSP 2002 0.01 0.01

Spain 20022003 ESS 2002 8.89 7.42 17.46 (10)

Spain 20022003 ISSP 2002 6.24 5.46

Spain 20022003 ESS 2004 0.01 0.01

United States 1975 1976 ANES 1974 0.51 0.57

United States 19751976 GSS 1975 5.17 7.90 9.97 (11)

United States 19751976 GSS 1976 4.16 6.16


United States 1985 ANES 1984 2.56 1.48


United States 1993 GSS 1991 2.50 3.66United States 1993 ANES 1992 0.07 0.08




United States 19981999 WVS 1999 1.11 2.13







United States 2005 ANES 2004 1.48 1.22United States 2005 GSS 2004 4.00 2.68


Continued


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variation should be rejected. Two of these cases are those pairing stylized EBdata with the 19881989 Italian time use data where the likelihood ratio testindicates a violation of the assumption. The final case is for the 2005 UK timeuse data paired with the 2006 WVS. In this case, the Wald test indicates aviolation of the assumption of equal residual variation, but the likelihood ratiodoes not.

Using a Bonferroni correction makes the null hypothesisa strict assump-tion of equality of varianceseasier to fail to reject. In light of this, some loos-

ening of the restriction may be instructive. Loosening this restriction to 0.001(x2 . 10.83) does not change the result. Loosening this restriction by an orderof magnitude to 0.01 (x2. 6.64) adds a dozen cases to this list, although inonly one of these cases do the likelihood ratio and Wald tests agree that resid-ual variation could differ: Spain ESS 2003.

In these cases in which the assumption of equal residual variance may beunwarranted, Allisons omnibus test of the equality of the underlying coeffi-cients is used. For each 19881989 Italian data comparison, the null hypothe-sis was rejected at the a 0.01 level, indicating that the effects of covariates

may differ between models predicting the two attendance measures. However,indirect comparisons suggest that few effects vary consistently enough betweenthe models to draw strong conclusions. In every other case with possible viola-tions of residual variance equality, the null hypothesis of equality between

TABLE 1 Continued

Country Time

diary

Conventional Equality of residual

variation

Equality of

underlying

coefficients


Year Study Year X2

(1)

p0 X2

(1)

p0 X2 (df) p





United States 2007 WVS 2006 0.09 0.09United States 2007 GSS 2008 2.28 2.90


Notes: *p .05, **p .01, ***p .001, two-tailed; p0 is the Bonferroni corrected sig-nificance. See online Supplementary material appendices for more information on thestudies included here. ANES American National Election Study; CGSS CanadianGeneral Social Survey; CNES Canadian National Election Study; EB Eurobarometer;ESS European Social Survey; GSS American General Social Survey;ISSP International Social Survey Program; WVS World Values Survey.


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coefficients is not rejected. In these cases, along with those that did not violatethe equality of residual variance assumption, logits will be compared betweenthe models using diary and stylized estimates of the dependent variable usingWald tests (Liao 2004).

Comparing Coefficients between Models Predicting the Two Attendance

Measures

A Bonferroni adjustment for 1,072 planned comparisons yields anextremely conservative a value of 4.7 1025 for a familywise a equaling 0.05.At this rather strict level, none of the differences between coefficients reachstatistical significance (x2. 16.58; see Supplementary Material Table S2).While this verifies the hypothesized outcome that none of these covariatesadequately explains the difference in reporting between measures of attendance

over the countries considered, some adjustment to the Bonferroni correctionmay be enlightening.

Loosening the restriction to a still-strict a 0.0001 (x2. 15.14) contin-ues to result in no comparisons reaching statistical significance. Only when therestriction is further eased by an order of magnitude to a 0.001 (x2. 10.83)do any significant differences emergebut only three in over a thousand com-parisons. This rate of significant tests is about what should be expected due tochance alone. When the Bonferroni correction is further eased by an addi-tional order of magnitude to an a level of 0.01 (x2 . 6.64), 22 additional

cases, about 2 percent of the total number of comparisons, yield significantresults. These cases are primarily in the age, marriage, and education variables,some likely the result of the error that comes with the chunking of age andeducation into categories as well as subtle differences in the categorizations ofmarital status between countries and surveys. In summary, virtually no differen-ces emerge in the comparison of coefficients between the models predictingthe conventional survey attendance measure and that from the time diaries.Moreover, the few differences that do emerge appear in numbers that areapproximately what would be expected due to chance alone and do not express

a consistent or predictive pattern.

DISCUSSION

Do demographic categories explain when and where differences emergebetween diary and conventional survey estimates of attendance? In order toanswer this question, logistic regression models were estimated predicting bothdiary and conventional survey measures of attendance using a set of key demo-

graphic variables: sex, age, marital status, presence of children in the house-hold, educational attainment, and household income. The effects of thesevariables were compared across models for each of the dependent variables. Ifany of these covariates, or some combination of them, was a potential cause of


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the gap between attendance estimates, the difference in the coefficientsbetween the models predicting time diary and conventional survey measures ofattendance should be consistently statistically significant where the gapemerges (i.e., the United States and Canada) but not where it does not (i.e.,

Europe). However, with very few exceptions, the predictive effects of these var-iables failed to differ between attendance measures. In short, no subgroup (e.g.,women, parents, college graduates, older adults) in any country was consis-tently more likely to report attendance on the conventional survey than onthe time diary.

Looking specifically at the American and Canadian cases where strongevidence for overreporting has been found, we might expect to find that somesubgroupswomen and older adults, for instanceare more likely to reportattendance on conventional surveys than on time diaries. But this is not the

case. Instead, no patterns of consistent significant differences were discoveredto explain the presence of overreporting. While these covariates are associatedwith both self-reported and actual attendance in the United States, they donot vary consistently or significantly between models predicting the two differ-ent attendance measures. Moreover, similar findings appear in the Europeancountries included here. From relatively high attendance Ireland and Italy tolow attendance Norway and Finland, no predictable pattern of significant find-ings emerges. Taken altogether, no pattern emerges that would explain why anoverreport is found in North America but not in Europe.

These results support and extend those of existing work in two ways.Where Presser and Stinson (1996) attempted to predict overreporting in theUnited States in the early 1990s, the current project expands the scope ofinvestigation to include Canada and 12 European countries from the 1970suntil the 2000s. Secondly, this work includes additional demographic variablesrelated to family status associated with increased probability of attendance butunexamined by Presser and Stinson (1996). In these ways, the current projectsupports and extends their finding: the demographic factors commonly associ-ated with church attendance do not appear to be related to the gap between

time diary and conventional survey estimates of attendance. In short, the biasin survey measures of church attendance appears to be orthogonal to the demo-graphic covariates.

This null finding adds credence to the residual identity-based hypothesisproposed by Hadaway et al. (1998) and expanded on by Brenner (2011b,2011c, 2012). This approach takes overreporting seriously as an opportunity tounderstand human behavior (Schuman 1982) rather than treating it as just abothersome survey artifact adding error to measurements of religious behaviors.As such, it enables sociologists and other social scientists to better understand

religiosity in the societies where overreporting occurs and in the societies inwhich it does not. In short, Americans and Canadians differ from Europeans intheir rates of overreporting not because of varying population distributions


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(e.g., lower mean levels of education or higher fertility) but rather becausethey view religiosity as a more central part of their identities.

Moreover, the finding that these usual suspects demographic variablesappear to be largely unbiased by overreporting may also be applicable to other

overreported, normative behaviors. For example, a great deal of research on theoverreporting of voting participation has focused on a similar set of covariates,attempting to identify those individuals who have a higher propensity to misre-port based on their demographic profile. This research program has been gener-ally inconclusive as studies disagree on which demographic categories showsignificant effects (Cassell 2004). In light of the findings of current project, itmay be more fruitful for researchers in these areas to focus on more distalcauses, like the importance of these normative identities (Brenner 2011b),rather than more proximal associations with a set of demographic categories.

Limitations and Future Directions

The breadth and quality of data used in this study are strengths, but a lackof data still limits the findings in a few important ways and suggests a numberof excellent opportunities for future research. Nearly 40 percent of the conven-tional survey and diary models paired by country and year could not be esti-mated with all the covariatesan unfortunate necessity due to limitations ofthe available data. While these missing variables may contribute some error tothe estimated coefficients, variables are only omitted pairwise across models. A

variable missing in one data set (e.g., income in the Netherlands EB 1975) ina given year for a given country is omitted in the accompanying data set (e.g.,the Netherlands Time Use Survey 1975). As such, paired models are kept ascomparable as possible.

Controls have been operationalized in a manner to make them as compara-ble as possible between countries and over time. This standardization effortcan, however, yield relatively rough categorizations, leading to some relativelyheterogeneous groupings in the covariates (e.g., educational operationalizationinto three categories: incomplete secondary education and less, completed sec-

ondary, postsecondary). However, that the coefficients for these covariates arelarge and significant when predicting each type of attendance for every countryunder consideration suggests that these operationalizations retain much of theirexplanatory power.

Within many of the countries under consideration, there exist racial,ethnic, or linguistic subgroups or communities with differing patterns of reli-gious attendance compared with the general pattern of the majority ethnic/racial group (see Gallup and Lindsay 1999, for the United States). Work byPresser and Stinson (1996, 1998) points to the possibility that these may have

some purchase in explaining overreporting. However, race/ethnicity has notbeen included in these analyses as the concern of the current project has beenon cross-national comparisons. Analyzing separately the minority ethnic popu-lations of every country would exponentially lengthen the current study,


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making it untenable. Moreover, many of these subpopulations do not appear insufficient numbers in national surveys to allow for reliable estimates. Futurework should address this limitation. For example, comparisons of Anglo- andFrancophone Canadians or primarily English- and Spanish-speaking Americans

would further elucidate intracountry variation in overreporting.These two examples raise an additional concern, as both are highly corre-

lated with variation in religious tradition. Perhaps the greatest limitation of thecurrent study is its inability to consider religious affiliation. Adherents of differ-ent religious traditions or denominations may attend at different rates.Moreover, it is possible that adherents of different religious denominations andtraditions vary in their rates of overreporting (Presser and Stinson 1996, 1998)as their religions differ in their normative expectations for adherents attend-ance at services. Unfortunately, as many of the studies used here do not

contain measures of religious affiliation, these comparisons are not possible.This limitation also suggests a potential area for future work in comparing var-iation in the overreporting between religious groups within the countries inves-tigated here.

Relatedly, differential rates in the percentage of non-Sunday-attendingChristians between countries could generate differences in the diary and con-ventional survey estimates. However, the percentage of non-Sunday attendersis small. For example, Smith (1998) estimates that 8.3% of weekly(American) church attenders attend only on a non-Sunday (132). While the

Canadian rate is likely similar, the rate may be different in the European coun-tries under investigation here. This may cause some small amount of error inthe estimation of and cross-national differences in the gap. Adjusting theAmerican time diary attendance rate for non-Sunday attendance would onlyyield an increase of about 2 percent points. Such a small adjustment is unlikelyto contribute a great deal to the gap between modes.

Finally, variation in the methods used to collect diary information may bean alternative cause of observed differences between modes. Many of theEuropean time use studies used self-administered tomorrow diaries (a self-

completed diary delivered in advance of the diary day) and most of those inNorth America used interviewer administered yesterday diaries (the respond-ent retrospectively reported on activity that occurred the previous day).However, this dichotomy is somewhat misleading. Some self-administereddiaries are completed in the presence or with the assistance of the interviewer;others are respondent-completed but reviewed jointly by the interviewer andrespondent. In short, self-administration of diaries is not necessarily an entirelyprivate procedure. Moreover, it is unlikely that Hawthorne type effect has ledEuropeans to change their behavior, leading to higher levels of normative

behavior and smaller gaps between diary and conventional survey estimatesthan those found in North America. Prior work on time diary methods hasfound only minimal differences between estimates from these types of diarydata collection (Bianchi et al. 2006; Robinson and Godbey 1997). Moreover,


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Kalfs and Saris (1998) found no difference between time spent on religiousactivity when data are collected using paper-and-pencil diaries, computerizedself-administered diaries, or interviewer-administered diaries. In short, thesetypes of methodological differences are unlikely to cause the pattern seen here,

although further investigation of the effect of interviewer- versus self-administration of time diaries on estimates of church attendance may be fruit-ful avenue for research. Related differences in the response process involved inreporting activity on anticipatory tomorrow and yesterday recalled diaries couldalso be further explored.

CONCLUSION

Can membership in demographic subgroups explain the overestimationof attendance in conventional survey estimates when and where it occurs?In order to answer this question, models were estimated predicting attend-ance from time diaries and conventional surveys using a suite of demo-graphic covariates often associated with church attendance. A comparison ofthese coefficients found that none differed between the two sets of models.Moreover, no pattern in the differences emerged that would explain whereoverreporting occurs (the United States and Canada) and where it does not

(Europe). In sum, bias in the attendance measure does not appear to affectthese predictors. While conventional survey measures may overestimate pop-ulation rates of attendance in some countries (i.e., North America), parame-ter estimates for these demographic predictors are largely unaffected byoverreporting bias.

That the usual suspects demographic variables fail to explain overreport-ing where and when it occurs suggests that the gap between the conventionaland diary estimates may be generated by inherent differences in the measure-ment procedures. In short, overreporting appears to be less about demographic

subgroups and more about the characteristics of the data collection method.Direct questions, like conventional survey items, promote the respondentsreflection on his or her self-concept. In essence, the question about attendanceis transformed from one about the specific religious behavior into one aboutreligious identity: Are you the sort of person who attends church regularly?Conversely, nondirective measures, like the chronological procedure used intime diaries, avoid priming the respondent on a given topic of interest, therebysidestepping the biasing effect of identity. From this perspective, overreportingis more than an annoyance that biases the measurement of religious service

attendance and other normative behaviors. Rather, it is a survey artifact, asSchuman (1982) suggests, that provides an excellent opportunity to betterunderstand culturally situated human behavior. Overreporting, in this light, isnot about who we are, but rather, about who we think we are.


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SUPPLEMENTARY MATERIAL

A supplementary section is located with the electronic version of thisarticle at Sociology of Religion online (http://www.socrel.oxfordjournals.org).

ACKNOWLEDGMENTS

The author would like to thank John DeLamater, Bob Hauser, JanePiliavin, Felix Elwert, Byron Shafer, Fred Conrad, and three anonymousreviewers for comments on earlier versions of this manuscript.

FUNDING

This work was supported by a Doctoral Dissertation Improvement Grantfrom the National Science Foundation (SES-0824759) and a ResearchCollaborative Fellowship from the Center for German and European Studies atthe University of Wisconsin, Madison.

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Sociology of Religion 2012 Brenner 361 83