The anchoring effect examples software#
The anchoring effect examples code#

Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Data were collected from university students for a transportation mode selection. In this study, the existence of anchoring bias-people's tendency to rely on, evaluate, and decide based on the first piece of information they receive-is examined in two multi-attribute decision-making (MADM) methods, simple multi-attribute rating technique (SMART), and Swing.

The anchoring effect examples code#

We discuss examples of design decisions and contextual factors in the conduct of code comprehension experiments that can induce an anchoring effect, and recommend the use of more robust comprehension measures in code comprehension studies to enhance the validity of results. This affects students and professionals alike. An initial hint of hard to understand code leads participants to assess the code as harder to understand than participants who received no hint or a hint of easy to understand code. Our results show that participants are significantly influenced by the initial scenario description in their assessment of code comprehensibility. Accordingly, it is important to understand how robust these measures are to cognitive biases such as the anchoring effect. Subjective code evaluations are a commonly used measure for how well a developer in a code comprehension study understood code.

The anchoring effect examples software#

In this study, we investigated in a randomized, controlled experiment with 256 participants (50 software professionals and 206 computer science students) whether a hint about the difficulty of the code to be understood in a task description anchors participants in their own code comprehensibility ratings. In particular, describing or even mentioning the difficulty of a code comprehension task might anchor participants and their perception of the task itself. Describing experiment scenarios and experimental tasks will influence participants in ways that are sometimes hard to predict and control. In code comprehension experiments, participants are usually told at the beginning what kind of code comprehension task to expect.