Self-reports are probably the most widely used method of measurement in the social sciences. Entire fields of research rely on the interpretability of self-reports. This is mainly because of their measurement efficiency, but also because self-reports can measure certain constructs and dimensions that hardly any other measurement method can capture. However, self-reports are also susceptible to certain biases. In part 1 of the session, David Grüning will provide an overview of these biases. We will categorize the biases and also discuss solutions to them. In the 2nd part, Christian Blötner will present one of the solutions in more detail: Inverting items. We will discuss advantages and disadvantages of inverted items and statistical procedures to improve latent model fit, as well as demonstrate these procedures using a scale we developed ourselves.