Achievement goal research has grown increasingly complex with the number of proposed goal orientations that motivate students. As the number of proposed goal constructs proliferates, a variety of data analytic challenges have emerged, such as profiling students on different types of goal pursuit as well as evaluating the relationships of multiple goal pursuit with different educational outcomes. The purpose of the current article is to showcase the advantages of using latent profile analysis (LPA) over other traditional techniques (such as multiple regression and cluster analysis) when analyzing multidimensional data like achievement goals. Specifically, we review the advantages of LPA over traditional person- and variable-centered analyses and then provide a critical look at three different conceptualizations of goal orientation (2-, 3-, and 4-factor) using LPA.