重新审视不小心响应检测:直接和间接措施的准确性
Philippe Goldammer1, Peter Lucas Stöckli2, Yannik Andrea Escher3
1Military Academy at ETH Zurich, Birmensdorf, Switzerland. philippe.goldammer@milak.ethz.ch.
Behavior research methods
|August 15, 2024
概括
研究人员可以通过使用间接措施来检测不小心响应来提高数据质量. 这些方法,特别是人内一致性指数,往往比直接措施更有效地识别不注意的参与者.
科学领域:
- 心理测量 心理测量
- 研究方法研究方法研究方法学
- 数据质量保证 数据质量保证
背景情况:
- 对不小心响应的选对于保持研究中的数据完整性至关重要.
- 直接措施 (例如,假项目) 和间接措施 (例如,后期指数) 可用于检测不注意的参与者.
- 有限的研究比较了直接和间接检测方法的相对有效性.
研究的目的:
- 为了比较直接和间接指数的检测率,对不小心的响应.
- 研究情境因素 (响应模式严重性,项目键入,项目呈现) 对检测准确性的影响.
- 提供基于证据的建议,以选择适当的方法来识别不小心的参与者.
主要方法:
- 在受控条件下进行了五项实验研究,以诱导和检测疏忽反应集.
- 利用参与者对演员个性的评分 (研究1-2) 和自我评分 (研究3-5).
- 检查了各种直接和间接指数,操纵上下文因素来评估它们对检测率的影响.
主要成果:
- 大多数间接指数在各种条件下显示出比偶然的检测率更好的检测率.
- 间接指数通常在检测不小心响应方面表现和直接指标一样好,而且往往比直接指标更好.
- 特殊的间接指数,如最大长串,个体内响应可变性和个体对模型不适合的贡献是例外.
结论:
- 建议使用间接指数,特别是个人内一致性指标,而不是直接指标来检测不小心响应.
- 研究人员应优先使用间接指数,以提高研究结果的有效性和可靠性.
- 这些发现为改善心理学研究中参与者数据质量提供了实际指导.
相关概念视频
Naturalistic Observations
15.4K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.4K
The Availability Heuristic
5.9K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
5.9K
Statistical Analysis: Overview
6.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.3K
Cause and Effect
10.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.9K
Uncertainty in Measurement: Accuracy and Precision
73.6K
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.
73.6K
Strategies for Assessing and Addressing Confounding
87
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
87


