unstandardized

简明释义

英[ʌnˈstændəraɪzd]美[ʌnˈstændərˌdaɪzd]

不标准的

不规范的

英英释义

单词用法

同义词

反义词

例句

1.The content of language normalization is very rich, it is not only to criticize the unstandardized u…

汉语规范化工作的内容是丰富的，不仅仅是一味地指责语病。

2.The main problems of the reeducation through labor in our country are the unlawful bases of law, uncertain natures of acts, unreason -ble deadlines, unstandardized procedures, etc.

我国劳动教养制度的主要问题表现在法律依据不合法、行为性质不明确、期限不合理、程序不规范等方面。

3.The main problems of the reeducation through labor in our country are the unlawful bases of law, uncertain natures of acts, unreason -ble deadlines, unstandardized procedures, etc.

我国劳动教养制度的主要问题表现在法律依据不合法、行为性质不明确、期限不合理、程序不规范等方面。

4.In order to draw meaningful conclusions, we must convert the unstandardized scores into a common format.

为了得出有意义的结论，我们必须将未标准化的分数转换为通用格式。

5.The unstandardized results of the experiment raised questions about its reliability.

实验的未标准化结果引发了对其可靠性的质疑。

6.Researchers often face challenges when working with unstandardized metrics in their analysis.

研究人员在分析中经常面临使用未标准化指标时的挑战。

7.The data collected from the survey was unstandardized, making it difficult to compare with other studies.

从调查中收集的数据是未标准化的，这使得与其他研究进行比较变得困难。

8.The unstandardized approach to measuring performance can lead to inconsistent evaluations.

对绩效的未标准化测量方法可能导致评估不一致。

作文

In the realm of research and data analysis, the term unstandardized refers to measurements or variables that have not been adjusted to a common scale. This can often lead to challenges in interpreting results, especially when comparing different datasets. For instance, consider a study that examines the academic performance of students across various schools. If one school uses a grading system based on a 100-point scale while another uses a pass/fail system, the resulting data would be unstandardized and thus difficult to compare directly. The lack of standardization can obscure meaningful insights and lead to erroneous conclusions.Standardization is crucial in many fields, including psychology, education, and health sciences. When researchers collect data, they often strive to convert unstandardized measures into standardized scores, such as z-scores or percentiles. This process allows for a more accurate comparison between groups or individuals. For example, if we look at test scores from different populations, converting these unstandardized scores into a common metric helps in understanding relative performance.Moreover, in the context of statistical analysis, using unstandardized coefficients in regression models can lead to misinterpretations. These coefficients represent the change in the dependent variable for a one-unit change in the independent variable, but without standardization, the magnitude of these changes can be misleading. Researchers often prefer standardized coefficients because they provide a clearer picture of the relative importance of each predictor in the model.However, there are situations where unstandardized measures can be beneficial. For example, in qualitative research, the richness of unstandardized data can provide deeper insights into participants' experiences and perspectives. In these cases, the focus is less on numerical comparison and more on understanding the nuances of individual stories. This approach highlights the importance of context and the subjective nature of human experience, which may be lost in overly standardized data.In conclusion, while unstandardized data can present challenges, it also offers unique opportunities for exploration and understanding. Researchers must carefully consider the implications of using unstandardized measures in their work. Whether striving for standardization to facilitate comparison or embracing the richness of unstandardized data to capture individual experiences, the choice ultimately depends on the research objectives. Balancing the need for standardization with the value of unstandardized insights is essential for producing meaningful and impactful research outcomes.

在研究和数据分析领域，术语unstandardized指的是未调整为共同标准的测量或变量。这通常会导致解释结果时的挑战，尤其是在比较不同数据集时。例如，考虑一项研究，该研究考察了不同学校学生的学业表现。如果一所学校使用基于100分制的评分系统，而另一所学校使用及格/不及格系统，那么得到的数据将是unstandardized的，因此难以直接比较。缺乏标准化可能会掩盖有意义的见解，并导致错误的结论。在心理学、教育和健康科学等多个领域，标准化至关重要。当研究人员收集数据时，他们通常努力将unstandardized的测量转换为标准化分数，例如z分数或百分位数。这个过程使得在组或个体之间进行更准确的比较成为可能。例如，如果我们查看来自不同人群的测试分数，将这些unstandardized分数转换为一个共同的度量有助于理解相对表现。此外，在统计分析的背景下，在回归模型中使用unstandardized系数可能会导致误解。这些系数表示自变量变化一个单位时因变量的变化，但如果不进行标准化，这些变化的大小可能会产生误导。研究人员通常更喜欢使用标准化系数，因为它们提供了每个预测变量在模型中相对重要性的更清晰图景。然而，在某些情况下，unstandardized的测量可能是有益的。例如，在定性研究中，unstandardized数据的丰富性可以提供更深入的见解，了解参与者的经历和观点。在这些情况下，重点不在于数值比较，而在于理解个体故事的细微差别。这种方法突显了背景的重要性和人类经验的主观性，这可能在过度标准化的数据中丢失。总之，虽然unstandardized数据可能带来挑战，但也为探索和理解提供了独特的机会。研究人员必须仔细考虑在其工作中使用unstandardized测量的影响。无论是努力实现标准化以便于比较，还是拥抱unstandardized数据的丰富性以捕捉个体经历，最终选择取决于研究目标。在产生有意义和有影响力的研究成果时，平衡标准化的需求与unstandardized见解的价值是至关重要的。