Attention and manipulation checks are widely used in survey experiments to assess respondents’ attentiveness and engagement with treatment, respectively. Yet these checks are subject to measurement error, which may itself depend on treatment when checks are administered after treatment exposure. Restricting analyses to respondents who pass these checks can therefore introduce substantial bias. We propose a general statistical framework for analyzing survey experiments with pre- or post-treatment attention and manipulation checks while accounting for measurement error. We show that three separate checks suffice to identify the proportion of respondents who would be attentive or successfully manipulated under treatment, provided that the checks are conditionally independent among treated respondents given their true, unobserved attentiveness or manipulation status and pre-treatment covariates. Under additional mild assumptions, this result also identifies the average treatment effect among respondents who would be attentive or successfully manipulated under treatment. Building on these identification results, we propose an estimator that allows flexible covariate modeling and establish its asymptotic normality. Applications to framing and digital media literacy experiments demonstrate how accounting for measurement error yields treatment effect estimates that are substantively different from those obtained using conventional approaches.