feedback from stakeholders. Rönkkö has described how team politics and other organizational issues led to limitations of the personas method in one set of projects. Chapman, Love, Milham, Elrif, and Alford have demonstrated with survey data that descriptions with more than a few attributes (e.g., such as a persona) are likely to describe very few if any real people. They argued that personas cannot be assumed to be descriptive of actual customers. A study conducted by Long claimed support for Cooper, Pruitt et al. in the use of personas. In a partially controlled study, a group of students were asked to solve a design brief; two groups used personas while one group did not. The students who used personas were awarded higher course evaluations than the group who did not. Students who used personas were assessed as having produced designs with better usability attributes than students who did not use personas. The study also suggests that using personas may improve communication between design teams and facilitate user-focused design discussion. The study had several limitations: outcomes were assessed by a professor and students who were not blind to the hypothesis, students were assigned to groups in a non-random fashion, the findings were not replicated, and other contributing factors or expectation effects (e.g., the Hawthorne effect or Pygmalion effect) were not controlled for. Data-driven personas Data-driven personas (sometimes also called quantitative personas) have been suggested by McGinn and Kotamraju. These personas are claimed to address the shortcomings of qualitative persona generation (see Criticism). Academic scholars have proposed several methods for data-driven persona development, such as clustering, factor analysis, principal component analysis, latent semantic analysis, and non-negative matrix factorization. These methods generally take numerical input data, reduce its dimensionality, and output higher level abstractions (e.g., clusters, components, factors) that describe the patterns in the data. These patterns are typically interpreted as "skeletal" personas, and enriched with personified information (e.g., name, portrait picture). Quantitative personas can also be enriched with qualitative insights to generate mixed method personas (also called hybrid personas). See also Dave and Sue Digital identity Personalization Personal information Personal identity Scenario (computing) Use case User profile References Bibliography Humphrey, A (2017), Persona Studies, vol. 3, no. 2, pp 13–20. Human–computer interaction Usability Technical communication Market segmentation