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American Religious Identification Survey, 2001




Kosmin, B. A., Mayer, E., & Keysar, A. (2020, August 27). American Religious Identification Survey, 2001.


The American Religious Identification Survey (ARIS) 2001 is a 10-year follow-up study of religious identification among American adults, undertaken for the first time in 1990. Carried out under the auspices of The Graduate Center of the City University of New York, the 1990 National Survey of Religious Identification (NSRI) was the most extensive survey of religious identification in the later half of 20th-century America. That study, like the current follow-up, was undertaken because the U.S. Census does not produce a religious profile of the American population. Yet, the religious categories into which a population sorts itself is surely no less important than some of the other social-demographic categories that are enumerated by the decennial census. This survey represents the first large-scale national survey of religious identification conducted among Americans in the 21st century. The primary question of the interview was: What is your religion, if any? The religion of the spouse/partner also was asked. If the initial answer was 'Protestant' or 'Christian,' further questions were asked to probe which particular denomination.

The ARDA has added five additional variables to the original data set to enhance the users' experience on our site.

Data File

Cases: 50281
Variables: 109
Weight Variable: WGT_POP, WGT_HH

Data Collection

Feb. 2, 2001 - June 7, 2001

Original Survey (Instrument)

American Religious Identification Survey 2001

Funded By

Posen Foundation

Collection Procedures

The American Religious Identification Survey (ARIS) 2001 was based on a random digit-dialed telephone survey of 50,281 American residential households in the continental U.S.A (48 states). The methodology largely replicates the widely reported and pioneering 1990 National Survey of Religious Identification (NSRI) carried out at the Graduate Center of the City University of New York. The data were collected over a 17-week period, from February to June 2001 at the rate of about 3,000 completed interviews a week by ICR/CENTRIS Survey Research Group of Media, PA as part of their national telephone omnibus market research (EXCEL/ACCESS) surveys.

Sampling Procedures

This study was conducted as part of established, ongoing national telephone omnibus programs. Omnibus surveys provide a means of reaching and interviewing extremely large household samples in relatively short periods of time while taking advantage of the shared nature of the high costs of survey research. The survey and data collection incorporated three phases corresponding to the gathering of information for distinct sub-samples and questionnaire segments. 34,295 interviews were conducted in the EXCEL omnibus and 15,987 were conducted through ACCESS. The two additional segments involve the Comparative Belief/Secularity (CB/S) battery, however they are not archived at present. Both of the telephone omnibus programs utilize National RDD samples. They were both designed by the same research group and are operated and overseen by the ICR Survey Research Group of Media, Pennsylvania. Moreover, EXCEL was the vehicle used in the 1990 NSRI.

EXCEL is the research industry's largest telephone omnibus service and has been in continuous operation for more than 15 years. EXCEL surveys are fielded at least twice each week, with each survey having a minimum of 1,000 interviews. Approximately one-half of these are male and one-half female. The sample employs basic geographic stratification at the Census Division level, with target sample sizes allocated proportionately. Respondents are randomly designated using the Last Birthday Selection Method. The RDD sample utilized is provided by GENESYS Sampling Systems.

ACCESS was designed as primarily an omnibus vehicle focusing on residential telecommunications, entertainment and technology issues. ACCESS is an ongoing survey as opposed to periodic. The RDD sample was supplied by GENESYS Sampling Systems.

Principal Investigators

Barry A. Kosmin
Egon Mayer
Ariela Keysar

Notes on Weighted Data

An initial household weight for each respondent was computed based on the number of voice lines serving the household. This weight actually is the inverse of the number of phone lines as it adjusts for the greater probability of selecting that household with two, three or more phone lines, relative to a household with just one line. A second weight, corresponding to the selection of the adult member is then computed: a household with one adult has weight of 1.0; two adults, 2.0 and so on. With these initial weights computed, the interviews were segregated into the 201 post-strata and a sample balancing (i.e., raking) routine was conducted within each stratum. This is an iterative process that utilizes the marginal distributions of each of the target demographic variables and the corresponding weighted sample variable categories to compute a series of adjustment factors, which successively bring the sample and population demographic distributions into close alignment. The final step in this process is the calculation of simple expansion factors to bring the weighted sample totals within each of the 201 strata to the Total Household and Population 18+ estimates. Following this process, each respondent record contains two weights: one for household estimates, the other for estimates of the adult population.

A few guidelines regarding the correct application of household and population weights:

The Population weights produce estimates of people -- specifically, people over 18 years of age. Using the Population Weight will produce an estimate of the number of people married for any given length of time. However, this is not the same as the number of couples, which would be produced by using the Household Weight.

Similarly, the number of adults with a specific religious identification can be computed by applying the Population Weight. However, there are theoretical problems with using the Household Weight with religious identification because that is a respondent variable. Using the Household Weight in this case would be the equivalent of classifying a household based solely on the gender of the respondent and ignoring the fact that households can be mixed religions as most contain both males and females.

Demographics present similar difficulties. Income is a household level variable and intuitively one would use the Household Weight to produce a distribution of household incomes. But one could use the Population Weight to show the distribution of adults with certain household incomes. These will not be the same because household income is not perfectly correlated with household size.

In summary, it is critical to explore the relationship between the context of the variable, or variable being used, and the resultant base produced by a given weight.

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