Bibliothek

@article{Nuhfer2016,

title = {Random Number Simulations Reveal How Random Noise Affects the Measurements and Graphical Portrayals of Self-Assessed Competency},

author = {Edward Nuhfer and Christopher Cogan and Steven Fleisher and Eric C. Gaze and Karl Wirth},

url = {http://dx.doi.org/10.5038/1936-4660.9.1.4},

doi = {10.5038/1936-4660.9.1.4},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Numeracy},

volume = {9},

number = {4},

issue = {1},

abstract = {Self-assessment measures of competency are blends of an authentic self-assessment signal that researchers seek to measure and random disorder or "noise" that accompanies that signal. In this study, we use random number simulations to explore how random noise affects critical aspects of self-assessment investigations: reliability, correlation, critical sample size, and the graphical representations of self-assessment data. We show that graphical conventions common in the self-assessment literature introduce artifacts that invite misinterpretation. Troublesome conventions include: (y minus x) vs. (x) scatterplots; (y minus x) vs. (x) column graphs aggregated as quantiles; line charts that display data aggregated as quantiles; and some histograms. Graphical conventions that generate minimal artifacts include scatterplots with a best-fit line that depict (y) vs. (x) measures (self-assessed competence vs. measured competence) plotted by individual participant scores, and (y) vs. (x) scatterplots of collective average measures of all participants plotted item-by-item. This last graphic convention attenuates noise and improves the definition of the signal. To provide relevant comparisons across varied graphical conventions, we use a single dataset derived from paired measures of 1154 participants' self-assessed competence and demonstrated competence in science literacy. Our results show that different numerical approaches employed in investigating and describing self-assessment accuracy are not equally valid. By modeling this dataset with random numbers, we show how recognizing the varied expressions of randomness in self-assessment data can improve the validity of numeracy-based descriptions of self-assessment.},

keywords = {A, Dunning-Kruger effect, graphs, knowledge surveys, noise, numeracy, random number simulation, reliability, self-assessment, signal},

pubstate = {published},

tppubtype = {article}

}

@article{Kruger1999,

title = {Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments},

author = {Justin Kruger and David Dunning},

url = {https://doi.org/10.1037/0022-3514.77.6.1121},

doi = {10.1037/0022-3514.77.6.1121},

year  = {1999},

date = {1999-01-01},

urldate = {1999-01-01},

journal = {Journal of Personality and Social Psychology},

volume = {77},

issue = {6},

pages = {1121–1134},

abstract = {People tend to hold overly favorable views of their abilities in many social and intellectual domains. The authors suggest that this overestimation occurs, in part, because people who are unskilled in these domains suffer a dual burden: Not only do these people reach erroneous conclusions and make unfortunate choices, but their incompetence robs them of the metacognitive ability to realize it. Across 4 studies, the authors found that participants scoring in the bottom quartile on tests of humor, grammar, and logic grossly overestimated their test performance and ability. Although their test scores put them in the 12th percentile, they estimated themselves to be in the 62nd. Several analyses linked this miscalibration to deficits in metacognitive skill, or the capacity to distinguish accuracy from error. Paradoxically, improving the skills of the participants, and thus increasing their metacognitive competence, helped them recognize the limitations of their abilities. (APA PsycInfo Database Record (c) 2016 APA, all rights reserved)},

keywords = {A, Dunning-Kruger effect},

pubstate = {published},

tppubtype = {article}

}