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P 1.3: Postersession
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Presentations | ||
Long Term Attrition and Sample Composition Over Time: 11 Years of the German Internet Panel University of Mannheim, Germany Relevance & Research Question For longitudinal research and panel practitioners, it is important to understand how much attrition to expect over time and which groups of respondents are especially at risk. These insights aid in guiding researchers in determining how many respondents to recruit, when to refresh the sample and which respondents should be especially targeted with strategies for improving recruitment rates or reducing attrition. SampcompR: A new R-Package for Sample Comparisons and Bias Analyses GESIS - Leibnitz Institute for Social Sciences, Germany Relevance & Research Question The steady trend in declining response rates and the rise of non-probability surveys makes it increasingly important to conduct nonresponse and selection bias analyses for social science surveys or conduct robustness checks to evaluate if the results are robust across population subgroups. Although this is important for any research project, it can be very time-consuming. The new R-Package SampcompR was created to provide easy-to-apply functions for those analyses and make it easier for any researcher to compare their survey against benchmark data for bias estimation on a univariate, bivariate, and multivariate level. Methods & Data To illustrate the functions of the package, we compare three web surveys conducted in Africa in March 2023 using Meta advertisements as a recruitment method (Ghana n = 527, Kenya n = 2,843, and South Africa n =313) to benchmarks from the cross-national Demographics and Health Survey (DHS). The benchmarks will be socio-demographics and health-related variables such as HIV knowledge. In univariate comparison, bias is measured as the relative bias for every variable and, on an aggregated level, the average absolute relative bias (AARB). In bivariate estimation, we compare Pearson’s r values against each other, and in multivariate comparison, different regression models are compared against each other. Results Our poster will show examples of output from the package, including visualizations and tables for each comparison level. While the focus will be on figures, tables can also be useful for documentation and more detailed inspection. As to the specific content of our example, we will see that the social media surveys show a high amount of bias on a univariate level. In contrast, the bias is less pronounced on a bivariate or multivariate level. We will also report country differences in sample accuracy. Added Value Our R-Package will provide an easy-to-use toolkit to perform bias analyses and survey comparisons and, therefore, will be a valuable tool in the social research workflow. Using the same or similar procedures and visualizations for the various comparisons will increase comparability and standardization. The visualization is based on the commonly used R-package ggplot2, making it easily customizable.
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