Representative Survey Data & Analysis Script for Determining Privacy Personas
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Provided are the survey data set and the script for the statistical analysis manuscript below: The corresponding survey questions can be found in the appendix of the manuscript. Answers were given on a 5-point Likert scale, with the following labels (Knowledge Items // Behavior Items): 1 – I disagree (Stimme nicht zu) // 1 - never (Niemals); 2 – I agree little (Stimme wenig zu) // 2 - rarely (Selten); 3 – I moderately agree (Stimme mittelmäßig zu) // 3 - occasionally (Gelegentlich); 4 – I rather agree (Stimme eher zu) // 4 - often (Oft); 5 – I strongly agree (Stimme sehr zu) // 5 - always (Immer); 6 – I don’t understand the question (Ich verstehe die Frage nicht); Established concise instruments to determine privacy personas -- typical privacy-related user groups -- are not available at present. Consequently, we aimed to identify privacy personas on a privacy knowledge--privacy behavior ratio based on a self-developed instrument. To achieve this end, we conducted an item analysis (N = 820) and a confirmatory factor analysis (CFA) (N = 656) of data based on an online study with German participants. Starting with 81 items, we reduced those to an eleven-item questionnaire with the two scales privacy knowledge and privacy behavior. A subsequent cluster analysis (N = 656) revealed three distinct user groups:(1) Fundamentalists scoring high in privacy knowledge and behavior,(2) Pragmatists scoring average in privacy knowledge and behavior and (3) Unconcerned scoring low in privacy knowledge and behavior. In a closer inspection of the used questionnaire, the CFAs supported the model with a close global fit based on RMSEA in a training and cross-validation sample. However, deficient local fit as well as validity and reliability coefficients well below generally accepted thresholds revealed that the questionnaire is not able to precisely describe privacy personas and needs to be revised. The results are discussed in terms of related persona conceptualizations, the importance of a methodologically sound investigation of corresponding privacy dimensions and our lessons learned in the evaluation process.
Paper available: https://petsymposium.org/popets/2022/popets-2022-0126.pdf
DFG subject classification407-05 Arbeitswissenschaft, Ergonomie, Mensch-Maschine-Systeme
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