We investigate two approaches for analyzing spatial coordinate responses using models inspired by Item Response Theory (IRT). In the first, we use a two-stage approach to first construct a pseudoresponse matrix using the spatial information and then apply standard IRT techniques to estimate proficiency and item parameters. In the second approach, we introduce the Spatial Error Model and use the spatial coordinates directly to infer information about the true locations and participant precision. As a motivating example, we use a study from forensic science designed to measure how fingerprint examiners use minutiae (small details in the fingerprint that form the basis for uniqueness) to come to an identification decision. The study found substantial participant variability, as different participants tend to focus on different areas of the image and some participants mark more minutiae than others. Using simulated data, we illustrate the relative strengths and weaknesses of each modeling approach, and demonstrate the advantages of modeling the spatial coordinates directly in the Spatial Error Model.<\/p>\n","protected":false},"excerpt":{"rendered":"
We investigate two approaches for analyzing spatial coordinate responses using models inspired by Item Response Theory (IRT). In the first, we use a two-stage approach to first construct a pseudoresponse […]<\/p>\n","protected":false},"author":5,"featured_media":13264,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","portfolio_category":[65],"portfolio_tag":[58,61],"acf":[],"yoast_head":"\n