检查与体重相关的招聘信息对参与者特征的影响:一个随机的实地实验
Christy Wang1, Jeffrey M Hunger2, Joseph Liao1
1Department of Psychology University of California Los Angeles California USA.
Obesity science & practice
|February 5, 2024
概括
招聘传单强调体重并没有改变参与者的特征,尽管招聘时间更长. 提供详细的研究信息赋予参与者权力,而不会引入偏见.
科学领域:
- 行为科学 行为科学
- 健康心理学 心理健康心理学
- 招聘策略 招聘策略
背景情况:
- 招募参与者参加研究,特别是那些涉及体重的研究,面临挑战,因为潜在的偏见.
- 研究信息在招聘材料中呈现的方式可以影响谁参与.
- 体重指数 (BMI) 高的人可能会避免研究强调与体重相关的程序.
研究的目的:
- 调查招聘材料中与体重相关信息的措辞如何影响参与者的特征.
- 为了确定一个体重较低的飞行员是否会影响与中性飞行员相比招募的个人类型.
主要方法:
- 两张传单,一个是重量值的,一个是中性的,被用来招募300名参与者参加调查.
- 参与者完成了关于内化体重偏差,体重耻辱的经历,饮食习惯和人口统计数据的测量.
- 传单被贴在大学校园,并记录了招聘时间.
主要成果:
- 与中性飞行员相比,减肥飞行员在招募目标样本大小方面花费了18.5天的时间.
- 在参与者的内化体重偏见,体重耻辱,饮食失调,BMI或感知体重方面,在传单之间没有发现显著差异.
- 所有参与者的测量变量的总体水平都很低.
结论:
- 提交详细的研究信息,即使与体重相关,似乎不会对参与者的特征产生差异性影响.
- 告知潜在参与者研究程序提高了他们的自主权,而不会引入抽样偏见.
- 这表明,招聘材料中的透明沟通是有益的.
相关概念视频
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
00:57Weighted Mean
5.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.2K
01:12Coefficient of Correlation
6.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.1K