预测健康个体在计算机化威斯康星州卡片排序测试中的规范数据,使用回归模型
Samet Çelik1, Vural Yıldırım2, Züleyha Damla Güler1
1Department of Psychology, Bartin University, Bartin, Turkey.
NeuroRehabilitation
|December 25, 2023
概括
这项研究在健康成年人中产生了计算机化的威斯康星州卡片排序测试 (WCST-CV) 的规范性数据. 年龄和教育显著影响WCST-CV的表现,需要根据年龄和教育调整的规范进行准确的评估.
科学领域:
- 神经心理学 神经心理学
- 认知评估 认知评估
- 心理测量 心理测量 心理测量
背景情况:
- 计算机化神经心理测试提供了效率,但与手动版本相比,可能会显示性能差异.
- 威斯康星州卡片排序测试 (WCST) 是这样一个测试,手动和计算机版本显示性能差异.
- 对于手动WCST的现有规范数据不能应用于计算机化版本,需要新的规范数据.
研究的目的:
- 在健康的成年人群中建立计算机化的威斯康星州卡片排序测试 (WCST-CV) 的规范值.
- 开发回归模型,用于为WCST-CV生成年龄和教育调整的规范.
- 确定影响WCST-CV性能的人口因素.
主要方法:
- 共有422名年龄在18-78岁之间的健康成年人参与了这项研究.
- 用回归分析来建模基于年龄和教育水平的WCST-CV子分数.
- 通常最小平方 (OLS) 和加权最小平方 (WLS) 用于基于统计假设的得分估计.
主要成果:
- 对于WCST 2,WCST 3,WCST 4,WCST 10和WCST 11分数的回归模型被发现是显著的.
- 用p值表示的错误率随着年龄和教育水平的提高而增加.
- 没有发现性别是WCST-CV子分数的重要因素.
结论:
- 用回归分析成功生成了WCST-CV的规范性数据,用于18-78岁的成年人.
- 年龄和教育水平被确定为解释WCST-CV表现的关键人口统计变量.
- 这项研究强调了对计算机化神经心理测试 (如WCST-CV) 进行特定规范性数据的需求.
相关概念视频
Regression 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
Mechanistic Models: Compartment Models in Individual and Population Analysis
43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
Wald-Wolfowitz Runs Test II
244
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
244
Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K


