Latent variable modeling as a lens for psychometric theory is a popular tool for social scientists to examine measurement of constructs (Beaujean, 2014). Journals such as Assessment regularly publish articles supporting measures of latent constructs wherein a measurement model is established. Confirmatory factor analysis can be used to investigate the replicability and generalizability of the measurement model in new samples, while multi-group confirmatory factor analysis is used to examine the measurement model across groups within samples (Brown, 2015). With the rise of the replication crisis and “psychology’s renaissance” (Nelson et al., 2018), interest in divergence in measurement has increased, often focused on small parameter differences within the latent model. This manuscript outlines ways to visualize potential non-invariance, to supplement large numbers of tables that often overwhelm a reader within these published reports. Readers will learn how to interpret the impact and size of the proposed non-invariance in models. While it is tempting to suggest that problems with replication and generalizability are simply issues with measurement, it is crucial to remember that all models have variability and error, even those models estimating the differences between item functioning, such as multi-group confirmatory factor analysis.