People's values provide a decision-making framework that helps guide their everyday actions. Most popular methods of assessing values show tenuous relationships with everyday behaviors. Using a new Amazon Mechanical Turk dataset(N = 767) consisting of people's language, values, and behaviors, we explore the degree to which attaining "ground truth" is possible with regards to such complicated mental phenomena. We then apply our findings to a corpus of Facebook user (N = 130; 828) status updates in order to understand how core values influence the personal thoughts and behaviors that users share through social media. Our findings suggest that self-report questionnaires for abstract and complex phenomena, such as values, are inadequate for painting an accurate picture of individual mental life. Free response language data and language modeling show greater promise for understanding both the structure and content of concepts such as values and, additionally, exhibit a predictive edge over self-report questionnaires.