Previous research has found that children’s sharing with others relies on fairness norms, but also varies according to their social relationships. The current study focuses on the conflict between fairness and relationship, exploring their impacts across two resource allocation contexts. We used a parallel work task to explore the effect of relationship with different recipients (friend, stranger, or disliked peer) on three allocation patterns (generous, fair, or selfish), when children directly allocated resources (distributive allocation), or applied different procedures to recipients (procedural application). Participants consisted of 123 Chinese children between the ages of 6 and 12. We found that in the distributive allocation context, in which participants directly decided the outcome, children primarily considered their relationship with recipients when dividing resources, not fairness. However, in the procedural application context, in which children could choose different allocation procedures for recipients, children primarily preferred fairness, regardless of social relationship. Moreover, when making distributive allocations, 6‐ to 8‐year‐olds were more selfish toward their disliked peers, whereas 9‐ to 12‐year‐olds tended to be more fair and generous toward their friends and strangers. These findings shed light on the link between social relationship and fairness within different allocation contexts among children of Chinese cultural background.

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 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, thanks to the wide availability of R and its packages. This paper serves as a tutorial in testing for measurement invariance and cross-lagged panel models 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, conduct cross-lagged panel models, 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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