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Preliminary Evaluation Of The Psychometric Quality Of HeightenTm Quantitative Literacy, Katrina C. Roohr, Hyesun Lee, Jun Xu, Ou Lydia Liu, Zhen Wang
Preliminary Evaluation Of The Psychometric Quality Of HeightenTm Quantitative Literacy, Katrina C. Roohr, Hyesun Lee, Jun Xu, Ou Lydia Liu, Zhen Wang
Numeracy
Quantitative literacy has been identified as an important student learning outcome (SLO) by both the higher education and workforce communities. This paper aims to provide preliminary evidence of the psychometric quality of the pilot forms for HEIghten quantitative literacy, a next-generation SLO assessment for students in higher education. We evaluated the psychometric quality of the test items (e.g., item analyses), individual- and group-level reliability, the relationship with student performance and related variables (e.g., grade point average) as well as student perceptions, and differences across college-related and demographic subgroups. Our study used data from a pilot test administered to over 1,500 …
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 …
Advancing Assessment Of Quantitative And Scientific Reasoning, Donna L. Sundre, Amy D. Thelk
Advancing Assessment Of Quantitative And Scientific Reasoning, Donna L. Sundre, Amy D. Thelk
Numeracy
Advancing Assessment of Quantitative and Scientific Reasoning is a four-year NSF Project (DUE-0618599) in part designed to evaluate the generalizability of quantitative (QR) and scientific reasoning (SR) assessment instruments created at James Madison University to four other four-year institutions with very distinct missions and student demographics. This article describes the methods, results, and findings we obtained in our studies. More specifically, we describe how to conduct content-alignment exercises in which faculty members map each item from a prospective test to the student learning objectives taught at the institution. Our results indicated that 92-100% of the QR and SR items were …
Quantitative Literacy Assessments: An Introduction To Testing Tests, Dorothy Wallace, Kim Rheinlander, Steven Woloshin, Lisa Schwartz
Quantitative Literacy Assessments: An Introduction To Testing Tests, Dorothy Wallace, Kim Rheinlander, Steven Woloshin, Lisa Schwartz
Numeracy
This paper describes how professional evaluators construct assessment instruments that work properly to measure the right thing. Constructing an assessment tool begins with getting feedback from relevant experts on the content of questions. The tool is developed and refined through comparison with existing instruments, focus groups and cognitive interviews. The final instrument is formally tested for content validity, usability, reliability and construct validity through a variety of statistical measures. This process of construction is illustrated by two examples relevant to quantitative literacy: the Medical Data Interpretation Test and the Math Attitudes Survey.