Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis

Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to interclass distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo–Mendell–Rubin test (LMR), adjusted LMR, bootstrap likelihood ratio test, Bayesian Information Criterion (BIC), and sample-size-adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. Akaike's Information Criterion and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.     

Creation and Assessment of an Active e-Learning Introductory Geology Course

The recent emphasis in higher education on both student engagement and online learning encouraged the authors to develop an active e-learning environment for an introductory geohazards course, which enrolls 70+ undergraduate students per semester. Instructors focused on replicating the achievements and addressing the challenges within an already established face-to-face student-centered class (Brudzinski and Sikorski 2010; Sit 2013). Through the use of a learning management system (LMS) and other available technologies, a wide range of course components were developed including online homework assignments with automatic grading and tailored feedback, video tutorials of software programs like Google Earth and Microsoft Excel, and more realistic scientific investigations using authentic and freely available data downloaded from the internet. The different course components designed to engage students and improve overall student learning and development were evaluated using student surveys and instructor reflection. Each component can be used independently and intertwined into a face-to-face course. Results suggest that significant opportunities are available in an online environment including the potential for improved student performance and new datasets for educational research. Specifically, results from pre and post-semester Geoscience Concept Inventory (GCI) testing in an active e-learning course show enhanced student learning gains compared to face-to-face lecture-based and student-centered courses.