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This paper presents an overview of methods that can be used to collect and analyse data on user responses to spoken dialogue system components intended to increase human-likeness, and to evaluate how well the components succeed in reaching that goal. Wizard-of-Oz variations, human-human data manipulation, and micro-domains are discussed in this context, as is the use of third-party reviewers to get a measure of the degree of human-likeness. We also present the two-way mimicry target, a model for measuring how well a human-computer dialogue mimics or replicates some aspect of human-human dialogue, including human flaws and inconsistencies. Although we have added a measure of innovation, none of the techniques is new in its entirety. Taken together and described from a human-likeness perspective, however, they form a set of tools that may widen the path towards human-like spoken dialogue systems.