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Difference of Means T-test and the Eurobarometer (63.1, Jan–Feb 2005): Science Literacy and Gender

Dataset
By: Nick Allum Published: 2015 | Product: SAGE Research Methods Datasets
Methods: T-test
Data Type: Survey
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Abstract

This dataset example introduces difference of means t-tests. This method allows researchers to compare the means of a single variable for two subsets of the data to evaluate whether the means for those two subsets are statistically significantly different from each other or not. This technique builds on the single mean t-test.

This example describes the difference of means t-test, discusses the assumptions underlying it, and shows how to compute and interpret it. There are many variants of difference of means testing – this example focuses on the independent samples t-test. We illustrate the difference of means t-test using a subset of data from the 2005 Eurobarometer: Europeans, Science and Technology (EB63.1). Specifically, we test whether knowledge about science varies between men and women. This is ...

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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

03.01.2005 – 17.02.2005

Time frame of analysis

03.01.2005 – 17.02.2005

Unit of analysis

Individual

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

v5

ID SERIAL NUMBER

v6

NATION - ALL SAMPLES

kstot

age

male

toomuchscience

solveprob