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相关概念视频

Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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Every mathematical equation that connects separate distinct physical quantities must be dimensionally consistent, which implies it must abide by two rules. For this reason, the concept of dimension is crucial. The first rule is that an equation's expressions on either side of an equality must have the exact same dimension, i.e., quantities of the same dimension can be added or removed. The second rule stipulates that all popular mathematical functions, such as exponential, logarithmic, and...
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A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
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相关实验视频

Updated: Sep 11, 2025

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一个参数的物流模型可能是正确的,对一个单维测量仪器的概率为零:人们如何错误地去除不符合模型的物品.

Tenko Raykov1, Bingsheng Zhang2

  • 1Michigan State University, East Lansing, MI, USA.

Educational and psychological measurement
|August 11, 2025
PubMed
概括

一参数物流 (1PL) 模型或拉什模型可能不适合单维尺度. 删除不符合这些模型的项目可能会导致误导性的能力估计和在教育和行为研究中增加错误.

关键词:
拉什模型是拉什模型的一个例子.一个限制的约束.隐藏的维度是一个隐藏的维度.测量测量测量测量测量测量一个参数模型的模型.修复参数化的方法这是一个双参数模型.只有一个维度的单维性.

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相关实验视频

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科学领域:

  • 心理测量 心理测量 心理测量
  • 教育测量教育的测量
  • 行为研究 行为研究

背景情况:

  • 一参数后勤 (1PL) 模型和拉什模型是心理测量的基础,用于分析单维尺度.
  • 评估这些模型对二分项的概率是对尺度有效性至关重要的.

研究的目的:

  • 为了研究1PL或拉什模型对二元项的单维尺度的真实性的概率.
  • 探索删除不符合这些模型的物品的后果.

主要方法:

  • 对1PL/Rasch模型对一维尺度的坚持概率的分析.
  • 模拟研究使用大型数据集来检查项目消除效应.

主要成果:

  • 1PL或拉什模型正确的概率即使在单维尺度上也可能为零.
  • 删除不符合这些模型的项目可能会导致严重误导能力估计.
  • 消除项目的结果会导致潜在特征的标准错误和预测错误增加.

结论:

  • 研究人员必须谨慎地删除仅基于1PL/Rasch模型合适的项目.
  • 误导能力估计可能对教育和行为研究结果产生重大影响.
  • 为了确保准确的测量,可能需要采用其他尺度精细化的方法.