Assuring Measurement Quality in the Social Sciences – new standards for quality documentation 2
|Convenor||Professor Beatrice Rammstedt (GESIS - Leibniz Institute for the Social Sciences )|
|Coordinator 1||Dr Natalja Menold (GESIS - Leibniz Institute for the Social Sciences)|
|Coordinator 2||Dr Constanze Beierlein (GESIS - Leibniz Institute for the Social Sciences)|
Psychological constructs have attracted increasing attention as predictors of social phenomena. However, many psychological measures include too many items to be useful in large-scale surveys. In order to tackle this problem, researchers often remove items from these measures, but psychometricians have shown that well-known item-selection procedures impair the quality of these scales. Recently, psychometricians have developed new and advanced short-scale construction methods. In our talk we will compare different old and new “top-down” and “bottom-up”-item-selection techniques using simulated and empirical data. We will give hands-on recommendations for practical applications in surveys.
Using samples of the Swiss household panel (SHP), this study investigates the quality and validity of two different short scales of the Big Five personality traits: the 10-item Big Five Inventory Ten introduced in the SHP in 2009, and the 15-item Big-Five-Inventory-Short version introduced in a subsample of the SHP in 2014.
The measurement models postulated by the authors of both scales have been evaluated using confirmatory factor analyses. Results demonstrate that the SHP data do not support the five factors structure postulated by the theory of both scales.
Measuring educational attainment in cross-national surveys correctly is challenging. We know that substantial inconsistencies in the distribution of education coded in the International Standard Classification of Education (ISCED) exist. They are mainly caused through inconsistent coding of country-specific education categories into ISCED. In the paper the processing of education variables of four cross-national surveys is analyzed. Based on these analyses, general recommendations on data harmonization and processing for quality assurance will be proposed. In the project “Computer-Assisted Measurement and Coding of Educational Qualifications in Surveys” (CAMCES) these recommendations are taken up systematically.