Quantifying habitat quality is dependent on measuring a site’s relative contribution to population growth rate. This is challenging for studies of waterbirds, whose high mobility can decouple demographic rates from local habitat conditions and make sustained monitoring of individuals near-impossible. To overcome these challenges, biologists have used many direct and indirect proxies of waterbird habitat quality. However, consensus on what methods are most appropriate for a given scenario is lacking. We undertook a structured literature review of the methods used to quantify waterbird habitat quality, and provide a synthesis of the context-dependent strengths and limitations of those methods. Our structured search of the Web of Science database returned a sample of 398 studies, upon which our review was based. The reviewed studies assessed habitat quality by either measuring habitat attributes (e.g., food abundance, water quality, vegetation structure), or measuring attributes of the waterbirds themselves (e.g., demographic parameters, body condition, behaviour, distribution). Measuring habitat attributes, although they are only indirectly related to demographic rates, has the advantage of being unaffected by waterbird behavioural stochasticity. Conversely, waterbird-derived measures (e.g., body condition, peck rates) may be more directly related to demographic rates than habitat variables, but may be subject to greater stochastic variation (e.g., behavioural change due to presence of conspecifics). Therefore, caution is needed to ensure that the measured variable does influence waterbird demographic rates. This assumption was usually based on ecological theory rather than empirical evidence. Our review highlighted that there is no single best, universally applicable method to quantify waterbird habitat quality. Individual project specifics (e.g., time frame, spatial scale, funding) will influence the choice of variables measured. Where possible, practitioners should measure variables most directly related to demographic rates. Generally, measuring multiple variables yields a better chance of accurately capturing the relationship between habitat characteristics and demographic rates.