subsidiary variable

简明释义

辅助变量

英英释义

例句

1.The team decided to control for the subsidiary variable to ensure the accuracy of their findings.

团队决定控制这个附属变量以确保研究结果的准确性。

2.The researchers found that the subsidiary variable had a significant effect on the primary variable.

研究人员发现这个附属变量对主要变量有显著影响。

3.In statistical analysis, a subsidiary variable may serve as a confounding factor that needs to be addressed.

在统计分析中，附属变量可能作为一个混杂因素需要被处理。

4.In our research, we identified a subsidiary variable that influenced the main outcome, which was surprising.

在我们的研究中，我们发现一个附属变量影响了主要结果，这让人感到惊讶。

5.Understanding the role of the subsidiary variable can help improve the overall model fit.

理解附属变量的作用可以帮助提高整体模型的拟合度。

作文

In the field of research and data analysis, understanding various types of variables is crucial for drawing accurate conclusions. One such type of variable is the subsidiary variable, which plays a significant role in the broader context of a study. A subsidiary variable can be defined as a secondary factor that may influence the primary variable being studied but is not the main focus of the investigation. These variables can provide additional insights and help researchers understand the complexities of the relationships between different factors.For instance, consider a study aimed at determining the impact of exercise on weight loss. The primary variable here would be the amount of weight lost, while a subsidiary variable might be the type of diet a participant follows. Although the primary focus is on weight loss, the diet can significantly affect the results. By examining both the primary and subsidiary variables, researchers can gain a more comprehensive understanding of how different factors interact with one another.The importance of subsidiary variables cannot be overstated. They allow researchers to control for additional factors that could skew results if left unexamined. For example, in a clinical trial assessing the effectiveness of a new medication, age could serve as a subsidiary variable. If older adults respond differently to the medication than younger adults, failing to account for age could lead to misleading conclusions about the medication's overall effectiveness.Moreover, analyzing subsidiary variables can also reveal trends and patterns that may not be immediately evident from the primary variable alone. In social science research, for example, a study investigating the relationship between education level and income might find that geographical location serves as a subsidiary variable. By exploring how income varies across different regions, researchers can uncover important nuances that contribute to the overall findings.In summary, subsidiary variables are essential components of research that enhance our understanding of complex phenomena. They provide context, control for confounding factors, and reveal underlying patterns that enrich the analysis. By recognizing and incorporating subsidiary variables into their studies, researchers can produce more robust and reliable results. As we continue to explore various fields of inquiry, the careful consideration of both primary and subsidiary variables will undoubtedly lead to deeper insights and advancements in knowledge.In conclusion, the role of subsidiary variables is pivotal in research design and analysis. They allow for a more nuanced understanding of the relationships between different factors. As researchers strive for accuracy and depth in their studies, the inclusion of subsidiary variables will remain a fundamental aspect of effective research methodology.

在研究和数据分析领域，理解各种类型的变量对于得出准确的结论至关重要。其中一种变量是附属变量，它在研究的更广泛背景中起着重要作用。附属变量可以定义为可能影响正在研究的主要变量的次要因素，但不是调查的主要焦点。这些变量可以提供额外的见解，并帮助研究人员理解不同因素之间关系的复杂性。例如，考虑一项旨在确定锻炼对减肥影响的研究。这里的主要变量将是减掉的体重，而附属变量可能是参与者遵循的饮食类型。尽管主要关注的是减肥，但饮食会显著影响结果。通过研究主要和附属变量，研究人员可以更全面地理解不同因素之间的互动。附属变量的重要性不容低估。它们使研究人员能够控制可能会扭曲结果的额外因素。例如，在评估新药物有效性的临床试验中，年龄可能作为附属变量。如果老年人对药物的反应与年轻人不同，那么不考虑年龄可能导致对药物整体有效性的误导性结论。此外，分析附属变量还可以揭示一些可能不会立即从主要变量中显现的趋势和模式。在社会科学研究中，例如，研究教育水平与收入之间关系的研究可能发现地理位置作为附属变量。通过探索不同地区收入的变化，研究人员可以揭示对整体发现贡献的重要细微差别。总之，附属变量是增强我们对复杂现象理解的研究的重要组成部分。它们提供背景，控制混杂因素，并揭示丰富分析的潜在模式。通过识别和纳入附属变量，研究人员可以产生更强大和可靠的结果。随着我们继续探索各个研究领域，仔细考虑主要和附属变量无疑将导致更深刻的见解和知识的进步。最后，附属变量在研究设计和分析中的角色是关键的。它们允许对不同因素之间关系的更细致理解。随着研究人员在研究中追求准确性和深度，纳入附属变量将始终是有效研究方法的基本方面。