Mixed-worded scales require a more careful reading and answering process than scales with only one type of wording. The present study is about the unintended consequences of using such scales, because typically, not all respondents answer positively and negatively worded items consistently. This population heterogeneity-meaning that there are distinct groups of consistently and inconsistently answering respondents-may arguably underlie wording-related effects in mixed-worded scales. We formulated a constrained factor mixture analysis model that operationalized these two assumed classes of respondents. We investigated five data sets that contained four mixed-worded attitude scales, large-scale samples from three countries (Germany, Australia, and the U.S.), and two age groups (children and adolescents). The constrained factor mixture analysis model showed estimated parameter patterns in line with theoretical expectations and consistently outperformed its more traditional competitor, confirmatory factor analysis with one global and one orthogonal method factor across all used data sets. We found proportions of between 7% and 20% of respondents belonging to the inconsistent classes. To further substantiate and validate the interpretation of the proposed model, we related class membership to theoretically relevant respondent characteristics such as reading achievement, cognitive skills, or conscientiousness. Further, we undertook an initial exploration of the overlap in inconsistent respondents' class membership across time and across scales within a survey. The article discusses implications for future research as well as for the use of mixed-worded scales. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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一个约束因子混合分析模型，用于对混合词汇量表的一致和不一致的受访者进行分析。

与仅使用一种类型的措词的秤相比，混合措词的秤需要更仔细的阅读和回答过程。本研究是关于使用这样的量表的意外结果的，因为通常情况下，并非所有受访者都会一致地回答正面和负面的措词。这种人口异质性-意味着有不同组的一致和不一致回答的受访者-可以说是混合单词量表中与单词相关的影响的基础。我们制定了约束因素混合分析模型，用于对这两个假定的受访者类别进行操作。我们调查了五个数据集，其中包含四个混合词的态度量表，来自三个国家（德国，澳大利亚和美国）的大规模样本以及两个年龄组（儿童和青少年）。约束因子混合分析模型显示的估计参数模式符合理论预期，并且在所有使用的数据集中使用一个全局变量和一个正交方法因子，始终优于其更传统的竞争对手，验证性因子分析。我们发现不一致类别的受访者中有7％至20％的比例。为了进一步证实和验证所提出模型的解释，我们将班级成员与理论上相关的受访者特征相关，例如阅读成绩，认知技能或尽职调查。此外，我们对调查中跨时间和跨规模的不一致的受访者的班级成员之间的重叠进行了初步探索。这篇文章讨论了对未来研究以及混合词表的使用的意义。（PsycInfo数据库记录（c）2021 APA，保留所有权利）。