Emotion plays an ever-present role in human existence, impacting nearly every behavior and decision in some way. Research in the behavioral sciences is rife with exploration of emotion and the role it plays in everything from business decision making to health-related behavior. However, affect, the feeling or experience of emotion, is complex and presents many challenges to those interested in measuring it, often resulting in a disconnect between the way emotion is experienced and felt and the way that researchers measure it. This dissertation addresses the challenges of measuring emotion through the presentation of the design, development, and validation of a novel measure of affect. The Photographic Affect Meter, PAM, is a digital measure of affect in which subjects choose from a grid of photos the one that best represents their current emotional state. The objective of PAM is to provide researchers with a means of measuring affect that is brief, reliable, and effective when used in situ. PAM was developed through an extensive iterative design process anchored in Human-Computer Interaction research, drawing inspiration from Affective Computing and Design literature. PAM was then rigorously validated via three separate studies. In the first two studies, subjects were assessed using both PAM and one of three widely accepted measure of affect (PANAS, Russell's Affect Grid, or the Self Assessment Manikin). In the third study, subjects were induced with negative, neutral, or positive affect and then assessed using PAM. In each of the studies, PAM results were found to be consistent with expectation, establishing the validity and reliability of the measure. While the success of this approach has implications for researchers in Affective Computing, Emotion, Design, and Ecological Momentary Assessment, the primary contribution of this work is the introduction of a novel measure of affect that is ready to be deployed in a wide variety of studies.