This research paper investigated the importance of conducting measurement invariance analysis in developing measurement tools for assessing differences between and among study variables. Most of the studies, which tended to develop an inventory to assess the existence of an attitude, behavior, belief, IQ, or an intuition in a person's characterological profile, ignored testing measurement invariance for equivalency between comparable variables. With this finding, measurers lack in true validity and reliability and suffer methodological bias and have little or no chance to figure out the true differences between variables being studied. This article, therefore, explains the necessity and use of measurement invariance analysis when a researcher wanted to develop a new measurement tool or adapt a tool from one source language to another target language. The types of measurement invariance levels mentioned in this study are configural-invariance, scalar invariance, metric invariance and structural invariance analysis. The approaches used to conduct those invariance models and the way they have been interpreted were all discussed in great detail with a robust collection of supportive literature.