The study aimed to identify methodological confounding factors affecting patient satisfaction survey results. The data gathered from CINAHL and PubMed databases consisted of 355 surveys published from 2006 to 2012. Linear regression and Bayesian models, with seven potential survey-related confounders together with patient age and gender as explanatory variables, were constructed. According to the linear model, up to 12% of the original variation in patient satisfaction was explained by confounding variables, not by the actual variation in satisfaction. The presence of an interviewer resulted in lower satisfaction levels, and the satisfaction results correlated negatively with the number of items in the questionnaire. According to the Bayesian model, if patients were over 60 years old and the questionnaire consisted mainly of positively phrased items, the probability of rating their experiences as very satisfied was 75%. The Bayesian and linear models endorsed each other and revealed specifically that the surveys reporting high patient satisfaction could be predicted on the basis of confounding variables. The following recommendations are given for constructing a patient satisfaction survey: use neutral rather than negatively or positively phrased items, and use enough items to increase the likelihood that the least satisfactory care components are also included in order to better enable comparisons across sporadic surveys.