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 With "empiricalresearch" we, here,mean the positivistic research tradition that relies on the analysis of (directlya nd indirectly) observable data.

Meritocratic matching solves the problem of cooperation by ensuring that only prosocial agents group together while excluding proselfs who are less inclined to cooperate. However, matching is less e ective when estimations of individual merit rely on group-level outcomes. Prosocials in uncooperative groups are unable to change the nature of the group and are themselves forced to defect to avoid exploitation. They are then indistinguishable from proselfs, preventing them from accessing cooperative groups. We investigate informal social networks as a potential solution. Interactions in dyadic network relations provide signals of individual cooperativeness which are easier to interpret. Network relations can thus help prosocials to escape from uncooperative groups. To test our intuitions, we develop an ABM modeling cooperative behavior based on a stochastic learning model with adaptive thresholds. We investigate both randomly and homophilously formed networks. We find that homophilous networks create conditions under which meritocratic matching can function as intended. Simulation experiments identify two underlying reasons. First, dyadic network interactions in homophilous networks di erentiate more between prosocials and proselfs. Second, homophilous networks create groups of prosocial agents who are aware of each other's behavior. The stronger this prosociality segregation is, the more easily prosocials cooperate in the group context. Further analyses also highlight a downside of homophilous networks. When prosocials successfully escape from uncooperative groups, noncooperatives have fewer encounters with prosocials, diminishing their chances to learn to cooperate through those encounters.

Peer feedback and collaboration intentionality (CI) are key prerequisites to advance in higher education. For learning, it is crucial that peers do not merely interact, but that students are willing to function as scaffolds by sharing their knowledge from different perspectives and asking each other for academic support. Peer feedback can only take place within a collaborative learning approach and when students are willing to initiate feedback relationships with their peers. Therefore, we analyze peer feedback networks (in terms of academic help and advice-seeking) and CI as an individual characteristic using an advanced statistical tool, namely stochastic actor-oriented models (SAOMs). In SAOMs, we control for selection and influence mechanisms. Selection comprises instances when feedback relations can be initiated based on CI, while influence builds upon existing feedback relations in affecting CI. One important selection mechanism is homophily, which means that individuals prefer to initiate a connection with someone else based on similarity in characteristics, attitudes, or behavior. In this chapter, we introduce this statistical technique within the higher education context and the added value for feedback research in education. We illustrate the SAOM methodology using two-wave peer feedback networks and CI data while controlling for gender and the Five-Factor Model personality traits. In this empirical example, we address the research question: To what extent does homophily of CI plays a role in selecting peers when seeking feedback and to what extent do feedback relationships influence CI? The SAOM shows an homophily effect, which implies that students preferentially seek feedback from others who are similar in CI. We also find an influence effect in which students who seek feedback from one another become more similar in terms of CI over time. Similarity in CI is driven by selection and influence mechanisms in peer feedback networks.

Decades of research show that (i) social value orientation (SVO) is related to important behavioral outcomes such as cooperation and charitable giving, and (ii) individuals differ in terms of SVO. A prominent scale to measure SVO is the social value orientation slider measure (SVOSM). The central premise is that SVOSM captures a stable trait. But it is unknown how reliable the SVOSM is over repeated measurements more than one week apart. To fill this knowledge gap, we followed a sample of N = 495 over 6 months with monthly SVO measurements. We find that continuous SVO scores are similarly distributed (Anderson-Darling k-sample p = 0.57) and highly correlated (r ≥ 0.66) across waves. The intra-class correlation coefficient of 0.78 attests to a high test-retest reliability. Using multilevel modeling and multiple visualizations, we furthermore find that one’s prior SVO score is highly indicative of SVO in future waves, suggesting that the slider measure consistently captures one’s SVO. Our analyses validate the slider measure as a reliable SVO scale.