Hundreds of studies have used the generalized Labeled Magnitude Scale (gLMS) to collect intensity data. Recent work on generalized affective scales like the Labeled Affective Magnitude (LAM) scale and Labeled Hedonic Scale (LHS) suggest a substantial proportion of participants fail to use the entire range of generalized scales, marking only at the adjective labels. This categorical behavior (i.e., clustering) is not limited to affective ratings, as it is well known anecdotally among users of the gLMS. One way to stop this behavior would be to retain a generalized top anchor and cross modal orientation procedure while stripping away the internal adjectives. Several published studies have already used this variant, the generalized Visual Analog Scale (gVAS). Because there are no reports directly comparing the gVAS and gLMS head to head, we did so in two experiments. In Experiment 1, participants (n=87) were randomized to 1 of 3 conditions to test effects of scaling instructions and scale structure. In Experiment 2, participants (n=58) assessed perceived ease of use and resolving power for each scale in a two-session crossover design. gLMS data showed evidence of categorical behavior, while gVAS data did not. Explicitly instructing participants to rate between adjectives did not reduce this behavior. The gLMS was easier to use according to participants, but resulted in non-normal data due to clustering near the adjective labels. gVAS data did not show categorical behavior, as there are no adjectives to cluster around, but the gVAS sacrifices semantic information about the magnitude of response. Regardless of scale type, participants felt the cross-modal orientation procedure helped them understand how to use the scale. Both scales were able to discriminate between sucrose samples in a concentration series. Relative tradeoffs between the two methods suggest the choice of one scale over the other depends on the specific goals and context of the project.

中文翻译：

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广义视觉模拟量表 (gVAS) 和通用标记震级量表 (gLMS) 的直接比较

数以百计的研究使用了广义标记量级 (gLMS) 来收集强度数据。最近关于广义情感量表的工作，如标记情感量表 (LAM) 量表和标记享乐量表 (LHS)，表明很大一部分参与者未能使用整个范围的广义量表，仅在形容词标签上进行标记。这种分类行为（即聚类）不限于情感评级，因为它在 gLMS 的用户中是众所周知的。阻止这种行为的一种方法是保留广义的顶部锚定和交叉模态定向过程，同时去除内部形容词。几项已发表的研究已经使用了这种变体，即广义视觉模拟量表 (gVAS)。因为没有直接比较 gVAS 和 gLMS 的报道，我们在两个实验中这样做了。在实验 1 中，参与者 (n=87) 被随机分配到 3 个条件中的 1 个，以测试缩放指令和量表结构的效果。在实验 2 中，参与者 (n=58) 评估了两个会话交叉设计中每个量表的感知易用性和分辨能力。gLMS 数据显示了分类行为的证据，而 gVAS 数据则没有。明确指示参与者在形容词之间评分并没有减少这种行为。根据参与者的说法，gLMS 更容易使用，但由于在形容词标签附近聚集，导致数据不正常。gVAS 数据没有显示分类行为，因为没有形容词可以聚集在一起，但 gVAS 牺牲了有关响应幅度的语义信息。无论何种秤型，参与者认为跨模式定向程序帮助他们了解如何使用量表。两种量表都能够区分浓度系列中的蔗糖样品。两种方法之间的相对权衡表明，选择一种规模而不是另一种规模取决于项目的具体目标和背景。