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意大利原版伦敦塔测验的规范数据:速度和准确性得分的二元分析。

Italian Normative Data for the Original Version of the Tower of London Test: A Bivariate Analysis on Speed and Accuracy Scores.

机构信息

Istituto Auxologico Italiano, IRCCS, U.O. di Neurologia e Neuroriabilitazione, Ospedale S. Giuseppe, Piancavallo, Italy.

Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.

出版信息

Assessment. 2022 Mar;29(2):209-224. doi: 10.1177/1073191120961834. Epub 2020 Sep 29.

DOI:10.1177/1073191120961834
PMID:32993361
Abstract

The Tower of London (ToL) test is traditionally used to assess strategical reasoning, problem-solving, and mental planning in clinical populations. Here, we provide the Italian standardization norms for the original, 12-problem version of the ToL test. The performance of 216 Italian individuals ranging 18 to 89 in age was scored in terms of both () and -the time, and the number of attempts, necessary to find a solution. We performed univariate analyses on separate and scores, using in years, in years, and (male vs. female) as predictors. scores and equivalent scores were provided. Moreover, we performed a bivariate analysis for the assessment of individuals' performance in terms of and simultaneously. This standardization allows clinicians to use the original, most widespread version of ToL with the Italian population, thus optimizing comparability with other clinical and experimental research worldwide. Critically, this article offers a new statistical perspective on how and scores, which are typically related to each other, can be combined to obtain a single, consistent clinical categorization that captures most of the information contained in the patient's performance.

摘要

伦敦塔测验(ToL)传统上用于评估临床人群的策略推理、问题解决和心理规划能力。在这里,我们提供了原始的 12 个问题版本的 ToL 测验的意大利标准化规范。216 名年龄在 18 至 89 岁的意大利个体的表现,根据解决问题所需的时间、尝试次数和找到解决方案的次数进行了评分。我们对 得分和 得分进行了单变量分析,使用年龄(以年为单位)、年龄(以年为单位)和性别(男性与女性)作为预测因素。我们提供了 得分和等效得分。此外,我们还进行了双变量分析,以评估个体在 和 方面的表现。该标准化允许临床医生使用原始的、最广泛应用的 ToL 版本与意大利人群进行比较,从而优化了与全球其他临床和实验研究的可比性。至关重要的是,本文提供了一种新的统计方法,用于组合 和 得分,这两个得分通常相互关联,从而获得一个单一的、一致的临床分类,该分类可以捕获患者表现中包含的大部分信息。

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