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Multiple Regression and the Eurobarometer (63.1, Jan–Feb 2005): Knowledge and Attitudes About Science

By: Nick Allum Published: 2015 | Product: SAGE Research Methods Datasets
Data Type: Survey
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This dataset example introduces readers to multiple regression. This technique allows researchers to evaluate whether a continuous dependent variable is a linear function of two or more independent variables. The multiple linear regression model is most commonly estimated via ordinary least squares (OLS), and is sometimes called OLS regression.

This example describes multiple regression, discusses the assumptions underlying it, and shows how to estimate and interpret multiple regression models. We illustrate multiple regression using a subset of data from the 2005 Eurobarometer: Europeans, Science and Technology (EB63.1). Specifically, we test whether attitudes to science and faith are related to knowledge about science and to age. This is useful if we want to understand the bases of public support for research on science amongst different segments of ...

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About This Dataset
Data Source Citation

European Commission (2012): Eurobarometer 63.1 (Jan-Feb 2005). TNS OPINION & SOCIAL, Brussels [Producer]. GESIS Data Archive, Cologne. ZA4233 Data file Version 1.1.0, doi:10.4232/1.10965

Full title of originating dataset

Eurobarometer 63.1 (Jan-Feb 2005): Science and Technology, Social Values, and Services of General Interest

Data author(s) and affiliations

European Commission, Brussels; DG Communication Public Opinion Analysis Sector

First publication date

June 2005

Data Universe

All respondents were residents in the respective country and aged 15 and over.

Sample/sampling procedures

A multi-stage, random (probability) sampling design was used for this Eurobarometer. In the first stage, primary sampling units (PSU) were selected from each of the administrative regionals units in every country (Statistical Office of the European Community, EUROSTAT NUTS 2 or equivalent). PSU selection was systematic with probability proportional to population size, from sampling frames stratified by the degree of urbanization. In the next stage, a cluster of starting addresses was selected from each sampled PSU, at random. Further addresses were chosen systematically using standard random route procedures as every Nth address from the initial address. In each household, a respondent was drawn, at random, following the closest birthday rule. No more than one interview was conducted in each household. They were supposed to have sufficient command of one of the respective national language(s) to answer the questionnaire.

Separate samples were drawn for Northern Ireland and East Germany.

Data collection dates


Time frame of analysis


Unit of analysis


Location covered by data

Austria (AT); Belgium (BE); Bulgaria (BG); Croatia (HR); Cyprus (CY); Czech Republic (CZ); Denmark (DK); Estonia (EE); Finland (FI); France (FR); Germany (DE); Greece (GR); Hungary (HU); Iceland (IS); Ireland (IE); Italy (IT); Latvia (LV); Lithuania (LT); Luxembourg (LU); Malta (MT); Netherlands (NL); Norway (NO); Poland (PL); Portugal (PT); Romania (RO); Slovakia (SK); Slovenia (SI); Spain (ES); Sweden (SE); Switzerland (CH); Turkey (TR); United Kingdom (GB)

Links to SRM content
List of variables










Methods Map
Multiple regression