This presents a comprehensive review of the empirical literature bearing on the effects of cognitive feedback (CFB) on multiple measures of performance. CEB refers to the process of presenting the person information about the relations in the environment (task information [TI]), relations perceived by the person (cognitive information [CIj), and relations between the environment and the person's perceptions of the environment (functional validity information [FVI]). Overall, CFB does improve performance on judgment tasks. Specifically, the research indicates that TI rather than CI is the aspect of CFB that influences performance. Factors influencing the effects of CFB on performance are discussed, and both current and potential applications of CFB are explored.A major theme emerging from extensive work in cognitive psychology is that people are limited in their ability to process information in uncertain environments (Nisbett & Ross, 1980), notably so with respect to human judgment and decision making (Kahneman, Slovic, & Tversky, 1982). Research has shown that people have difficulty inferring environmental relationships from unaided experience (Brehmer, 1980) and often lack sufficient insight into their judgment strategies to permit them to communicate those strategies (Balke, Hammond, & Meyer, 1973). More effective judgment and decision making would enhance the lives of individuals and the performance of organizations, and researchers have devoted considerable attention to investigating means of improving these cognitive activities

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In longitudinal studies involving multiple latent variables, researchers often seek to predict how iterations of latent variables measured at early time points predict iterations measured at later time points. Cross-lagged panel modeling, a form of structural equation modeling, is a useful way to conceptualize and test these relationships. However, prior to making causal claims, researchers must first ensure that the measured constructs are indeed equivalent between time points. To do this, they test for measurement invariance, constructing and comparing a series of increasingly strict and parsimonious models, each making more constraints across time than the last. This comparison process, though challenging, is an important prerequisite to interpretation of results. Fortunately, testing for measurement invariance in cross-lagged panel models has become easier in recent years, thanks to the wide availability of R and its packages. This paper serves as a tutorial in testing for measurement invariance using the lavaan package. Using real data from an openly available study on perfectionism and drinking problems, we provide a step-by-step guide of how to test for longitudinal measurement invariance and interpret the results. Original data source with materials: https://osf.io/gduy4/. Project website with data/syntax for the tutorial: https://osf.io/hwkem/.

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