by @oberski_evaluating_2017. The advantage of MTMM models is that they recognize not only the common variance due to measurement of the same phenomenon, but also any common biases that arise from the use of a common data source. For example, survey answers may correlate due to “acquiescence” and social desirability bias [@lavrakas_extending_2019], and electrocardiograms are susceptible to bias from manual annotators and placement of the electrodes [@zhu_fusing_2015]. MTMM models are designed to provide the researcher with an indication of the extent to which such biases are present and cause common correlation. At the same time, they determine implicit rules for the optimal guess regarding the true latent variable under study – dispensing with the commonly used ad-hoc rules of data fusion.

However, a disadvantage of latent variable models is that they are especially prone to problems of identification, nonconvergence, “inadmissible” estimates outside the acceptable range, and unstable estimates [@bartholomew_latent_2011]. MTMM models are especially well-known to suffer from such problems [@marsh_confirmatory_1989; @brannick_estimation_1990; @kenny_analysis_1992; @marsh_overcoming_1992; @bagozzi_assessing_1993; @revilla_split-ballot_2013]. Solutions to this problem have also been suggested. For example, @marsh_overcoming_1992 suggested omitting a model for all method factors while acconting for the resulting correlations; @eid_multitrait-multimethod_2000 suggested omitting one method factor from the model; and 