The presence of satisficers among survey respondents threatens survey data quality. To identify such respondents, Oppenheimer et al. developed the Instructional Manipulation Check (IMC), which has been used as a tool to exclude observations from the analyses. However, this practice has raised concerns regarding its effects on the external validity and the substantive conclusions of studies excluding respondents who fail an IMC. Thus, more research on the differences between respondents who pass versus fail an IMC regarding sociodemographic and attitudinal variables is needed. This study compares respondents who passed versus failed an IMC both for descriptive and causal analyses based on structural equation modeling (SEM) using data from an online survey implemented in Spain in 2019. These data were analyzed by Rubio Juan and Revilla without taking into account the results of the IMC. We find that those who passed the IMC do differ significantly from those who failed for two sociodemographic and five attitudinal variables, out of 18 variables compared. Moreover, in terms of substantive conclusions, differences between those who passed and failed the IMC vary depending on the specific variables under study.

调查受访者中满意者的存在威胁着调查数据的质量。为了确定这些受访者，Oppenheimer 等人。开发了教学操作检查（IMC），它已被用作从分析中排除观察的工具。然而，这种做法引起了人们对其外部效度的影响以及排除未通过 IMC 的受访者的研究的实质性结论的担忧。因此，需要对通过与未通过 IMC 的受访者在社会人口统计学和态度变量方面的差异进行更多研究。本研究使用 2019 年在西班牙实施的在线调查中的数据，基于结构方程模型 (SEM) 对通过 IMC 和未通过 IMC 的受访者进行描述性和因果分析进行比较。这些数据由 Rubio Juan 和 Revilla 分析，没有考虑 IMC 的结果。我们发现，在比较的 18 个变量中，通过 IMC 的人确实与那些在两个社会人口统计变量和五个态度变量上失败的人存在显着差异。此外，在实质性结论方面，通过和未通过 IMC 的人之间的差异取决于所研究的具体变量。