The article describes a general two-step procedure for the numerical translation of vague linguistic terms (LTs). The suggested procedure consists of empirical and model components, including (1) participants' estimates of numerical values corresponding to verbal terms and (2) modeling of the empirical data using fuzzy membership functions (MFs), respectively. The procedure is outlined in two studies for data from N = 89 and N = 109 participants, who were asked to estimate numbers corresponding to 11 verbal frequency expressions (e.g., sometimes). Positions and shapes of the resulting MFs varied considerably in symmetry, vagueness, and overlap and are indicative of the different meanings of the vague frequency expressions. Words were not distributed equidistantly across the numerical scale. This has important implications for the many questionnaires that use verbal rating scales, which consist of frequency expressions and operate on the premise of equidistance. These results are discussed for an exemplar questionnaire (COPSOQ). Furthermore, the variation of the number of prompted LTs (5 vs. 11) showed no influence on the words' interpretations.Keywords Translation procedure . Linguistic terms . Frequency expressions . Vagueness . Fuzzy membership functions . Verbal rating scale Since the 1960s, researchers in different scientific areas have sustained an interest in studying the relationship between verbal and numerical expressions-particularly, probability words and quantifiers (Bocklisch, Bocklisch, & Krems, 2010;Dhami & Wallsten, 2005;Lichtenstein & Newman, 1967;Teigen & Brun, 2003). Moreover, expressions of intensity or frequency of occurrence (e.g., sometimes or often) are of interest with regard to their wide application in questionnaires. Several studies consistently showed that people prefer to use words instead of numbers to indicate their opinions and uncertainty (e.g., Wallsten, Budescu, Zwick, & Kemp, 1993). Even experts such as doctors or lawyers frequently use qualitative rather than quantitative terms to express their beliefs, on the grounds that words are more natural and are easier to understand and communicate. Words are especially useful in most everyday situations when subjective belief or uncertainty cannot be precisely verbalized in quantitative terms. Therefore, while it may be more natural for people to use language to express their beliefs, it is also potentially more advantageous to use numerical estimates: Their standard interpretation renders them easily comparable, and they form the basis of calculations and computational inferences. Accordingly, many researchers have developed translation procedures (e.g., Beyth-Marom, 1982;Bocklisch et al., 2010;Budescu, Karelitz, & Wallsten, 2003) and have established numerical equivalents for common linguistic expressions (for a broader literature review, see Teigen & Brun, 2003). One outcome of these efforts is that linguistic terms have often been conceptualized as fuzzy sets and mathematically described using fuzzy membership functions...