A classification of response scale characteristics that affect data quality: a literature review

Abstract

Quite a lot of research is available on the relationships between survey response scales’ characteristics and the quality of responses. However, it is often difficult to extract practical rules for questionnaire design from the wide and often mixed amount of empirical evidence. The aim of this study is to provide first a classification of the characteristics of response scales, mentioned in the literature, that should be considered when developing a scale, and second a summary of the main conclusions extracted from the literature regarding the impact these characteristics have on data quality. Thus, this paper provides an updated and detailed classification of the design decisions that matter in questionnaire development, and a summary of what is said in the literature about their impact on data quality. It distinguishes between characteristics that have been demonstrated to have an impact, characteristics for which the impact has not been found, and characteristics for which research is still needed to make a conclusion.

Comparison of Response Scales as Measures of Indoor Environmental Perception in Combined Thermal and Acoustic Conditions

“…Capturing true responses regarding occupants' comfort in and acceptance of an indoor environment is crucial when evaluating a building's performance. The impact of the questionnaire design characteristics on the quality of the responses should be minimized to achieve true responses [1]. Determining the characteristics of the response scale is often the most important decision in ensuring good measurement properties [2][3][4].…”

“…Determining the characteristics of the response scale is often the most important decision in ensuring good measurement properties [2][3][4]. DeCastellarnau [1], in her latest review article, classified 23 different characteristics of response scales and provided their effects on data quality. Ten characteristics out of the 23 have been found to affect data quality: the scales' evaluative dimensions, the types of scales, scale length, verbal labels, number of fixed reference points, order of numerical labels, correspondence between numerical and verbal labels, scale illustrative format, scale layout display, and the label visual separation.…”

Data Quality Methods and Applications in Health Care System: A Systematic Literature Review

“…The term data quality means the ability of data and information to respond optimally to the intended purpose; in particular, we often refer to a process characterized above all by a precise knowledge of the elements, and secondly by their management and analysis (Davenport, 1998). The main phase of data quality assessment, however, is the verification of all data management phases, the ultimate goal is to identify any deficiencies and increase their quality by reducing the costs of non-quality (Batini & Scannapieco, 2006;Wills, 2014;Biancone, Secinaro & Brescia;2018).…”

Interpreting Foreign Smiles: Language Context and Type of Scale in the Assessment of Perceived Happiness and Sadness

“…Furthermore, in the case of pain assessment, this type of scale provides responses that are less contaminated by other factors, such as unpleasantness (Thong et al, 2018). Nevertheless, several studies have suggested that these scales do not necessarily provide an improvement over verbal scales (DeCastellarnau, 2018), making the effects of verbal versus non-verbal scales quite unclear. A similar type of scale that has received less attention is color intensity.…”