Learning from friends: measuring influence in a dyadic computer instructional setting

@inproceedings{DeLay2014LearningFF,
  title={Learning from friends: measuring influence in a dyadic computer instructional setting},
  author={Dawn DeLay and Amy C. Hartl and Brett Laursen and Jill Denner and Linda L. Werner and Shannon Campe and Eloy Ortiz},
  year={2014}
}
Data collected from partners in a dyadic instructional setting are, by definition, not statistically independent. As a consequence, conventional parametric statistical analyses of change and influence carry considerable risk of bias. In this article, we illustrate a strategy to overcome this obstacle: the longitudinal actor-partner interdependence model (APIM). Participants included 60 girls and 100 boys enrolled in public middle schools, who ranged in age from 10 to 14 at the outset. Students… CONTINUE READING