River Sampling - a Fishing Expedition: A Non-Probability Case Study
| Autor: | Murray-Watters, Alexander, Zins, Stefan, Silber, Henning, Gummer, Tobias, Lechner, Clemens |
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| Rok vydání: | 2023 |
| Předmět: |
Sozialwissenschaften
Soziologie Social sciences sociology anthropology River Sample Non-probability Sample BIG-5 Non-linear Dimension reduction Web Survey Research Erhebungstechniken und Analysetechniken der Sozialwissenschaften Methods and Techniques of Data Collection and Data Analysis Statistical Methods Computer Methods Datengewinnung Internet Stichprobe Online-Befragung Umfrageforschung data capture sample online survey survey research |
| Zdroj: | Methods, data, analyses : a journal for quantitative methods and survey methodology (mda), 17, 1, 3-27 |
| Druh dokumentu: | Zeitschriftenartikel<br />journal article |
| ISSN: | 2190-4936 |
| DOI: | 10.12758/mda.2022.05 |
| Popis: | The ease with which large amounts of data can be collected via the Internet has led to a renewed interest in the use of non-probability samples. To that end, this paper performs a case study, comparing two non-probability datasets - one based on a river-sampling approach, one drawn from an online-access panel - to a reference probability sample. Of particular interest is the single-question river-sampling approach, as the data collected for this study presents an attempt to field a multi-item scale with such a sampling method. Each dataset consists of the same psychometric measures for two of the Big-5 personality traits, which are expected to perform independently of sample composition. To assess the similarity of the three datasets we compare their correlation matrices, apply linear and non-linear dimension reduction techniques, and analyze the distance between the datasets. Our results show that there are important limitations when implementing a multi-item scale via a single-question river sample. We find that, while the correlation between our data sets is similar, the samples are composed of persons with different personality traits. |
| Databáze: | SSOAR – Social Science Open Access Repository |
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