Caucasus Barometer, 2010

Data Archive > International Surveys and Data > Multiple Nation Surveys > Summary


The Caucasus Barometer is an annual nationwide survey conducted by the Caucasus Research Resource Centers (CRRC) in Georgia, Armenia, and Azerbaijan. The target population for the 2010 Caucasus Barometer was all non-foreign adults residing in Armenia, Azerbaijan, and Georgia outside of occupied territories (Abkhazia, South Ossetia and Nagorno Karabagh) and the Nakhchivan Autonomous Republic of Azerbaijan during the period of November-December 2010.The Caucasus Barometer was designed in 2003 in order to collect reliable representative data on a wide range of social, political, and economic attitudes of the population of the South Caucasus, as well as information on household composition and household economic behavior. From the very beginning, the data collected by CRRC was meant to be open to all interested researchers and/or policymakers both from the region and from other parts of the world. For more information, visit the CRRC website.

Religion variables include religious preference, religious salience, views of clergy and religious institutions, frequency of fasting, and frequency of attendance at religious services.

Data File
Cases: 6,977
Variables: 426
Weight Variable: WTHH, WTIND
Data Collection
Date Collected: November-December 2010
Funded By
CRRC is a program of the Eurasia Partnership Foundation and is a network of resource, research, and training centers established in 2003 in the South Caucasus with the goal of strengthening social science research and public policy analysis in the region. The program is supported by Carnegie Corporation of New York.
Collection Procedures
Face-to-face interviews
Sampling Procedures
The target population for the 2010 Caucasus Barometer was all non-foreign adults residing in Armenia, Azerbaijan, and Georgia outside of occupied territories (Abkhazia, South Ossetia and Nagorno Karabagh) and the Nakhchivan Autonomous Republic of Azerbaijan during the period of November-December 2010.

Primary sampling units (PSUs) were electoral precincts. The sampling frame was divided into three “macro-strata” by settlement type: capital, urban and rural. Necessary sample size calculations were made using data from the 2009 Caucasus Barometer, which had the same sample design as the 2010 Caucasus Barometer.
Principal Investigators
Dr. Tinatin Zurabishvili, Caucasus Barometer Project Coordinator
Citing the Data
Please use the following citation when citing data from the Caucasus Barometer:

Caucasus Research Resource Centers. (dataset year) "Caucasus Barometer". [dataset] Retrieved from http://www.crrccenters.org/caucasusbarometer/ on {date of accessing the database}.

CRRC requests that those who cite CRRC data to notify them by completing this form.
Weighting
Interview Disposition Codes

The proportion of applicable questions that the respondent answered was calculated for all fully and partially completed interviews. Disposition codes were assigned according to AAPOR standards so that interviews wherein the respondent answered fewer than 50 percent of the applicable questions were assigned a disposition code of zero. Those wherein the respondent answered at least 50 percent but fewer than 80 percent of the applicable questions were assigned a disposition code of 0.5, and those wherein the respondent answered at least 80 percent of the questions applicable to him or her were assigned a disposition code of 1. Interviews with a disposition code of zero were classified as non-response and were not assigned sampling weights; interviews with a disposition code of greater than zero were classified as response and assigned sampling weights.

Household weights

Population weights for households were calculated as the inverse of selection probability so that each household’s weight is equivalent to the number of households that it represents in the entire population of households in the country. Although precincts were selected without replacement, selection probabilities were calculated as though they had been selected with replacement. The resulting selection probabilities are not different in any meaningful way since each precinct comprises such a small proportion of the total population of each stratum. Additionally, the process of computing weights is much more computationally efficient.