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Paired Measures Of Competence And Confidence Illuminate Impacts Of Privilege On College Students, Rachel M. Watson, Edward Nuhfer, Kali Nicholas Moon, Steven Fleisher, Paul Walter, Karl Wirth, Christopher Cogan, Ami Wangeline, Eric Gaze
Paired Measures Of Competence And Confidence Illuminate Impacts Of Privilege On College Students, Rachel M. Watson, Edward Nuhfer, Kali Nicholas Moon, Steven Fleisher, Paul Walter, Karl Wirth, Christopher Cogan, Ami Wangeline, Eric Gaze
Numeracy
We seek to understand how the experiences of groups that differ in gender, ethnicity, and sexual orientation produce college-level educational performances that differ from the experiences of the dominant majority group. We employ two datasets: a National Database of 24,701 participants and a Paired-Measures Database with 3,323 participants. Both datasets provide demographic information, socioeconomic conditions of status as first-generation student, English as a first language, and interest in majoring in science, and competency scores on understanding science as a way of knowing obtained from the Science Literacy Concept Inventory. The Paired-Measures Database includes additional self-assessed competence ratings that enabled quantifying …
How Random Noise And A Graphical Convention Subverted Behavioral Scientists' Explanations Of Self-Assessment Data: Numeracy Underlies Better Alternatives, Edward Nuhfer, Steven Fleisher, Christopher Cogan, Karl Wirth, Eric Gaze
How Random Noise And A Graphical Convention Subverted Behavioral Scientists' Explanations Of Self-Assessment Data: Numeracy Underlies Better Alternatives, Edward Nuhfer, Steven Fleisher, Christopher Cogan, Karl Wirth, Eric Gaze
Numeracy
Despite nearly two decades of research, researchers have not resolved whether people generally perceive their skills accurately or inaccurately. In this paper, we trace this lack of resolution to numeracy, specifically to the frequently overlooked complications that arise from the noisy data produced by the paired measures that researchers employ to determine self-assessment accuracy. To illustrate the complications and ways to resolve them, we employ a large dataset (N = 1154) obtained from paired measures of documented reliability to study self-assessed proficiency in science literacy. We collected demographic information that allowed both criterion-referenced and normative-based analyses of self-assessment data. …
Random Number Simulations Reveal How Random Noise Affects The Measurements And Graphical Portrayals Of Self-Assessed Competency, Edward Nuhfer, Christopher Cogan, Steven Fleisher, Eric Gaze, Karl Wirth
Random Number Simulations Reveal How Random Noise Affects The Measurements And Graphical Portrayals Of Self-Assessed Competency, Edward Nuhfer, Christopher Cogan, Steven Fleisher, Eric Gaze, Karl Wirth
Numeracy
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 …