91Ó°ÊÓ

In experimental design, the response variable is what the researcher measures to determine the outcome of an experiment. It is the focus of the experiment's results and reflects the biological, psychological, or general changes due to experimental factors.
For example, in Carol Dweck's study, the response variable was the choice of task made by students: selecting either a challenging task or an easy one. This response showed the impact of the different types of praise given to the students. The goal was to see how praise affects students' willingness to challenge themselves.
The response variable is crucial because it provides insight into how effective the experimental treatments are. When analyzing results, the researcher looks for differences in the response variable to understand the impact of the experiment.

An explanatory variable is an element that the researcher manipulates in an experiment to observe its effect on the response variable. It helps understand how different conditions can cause changes in the outcome.
In Dweck's study, the explanatory variable was the type of praise the students received. Students were praised either for their effort or for their intelligence. By changing how students were praised, researchers could observe how this affected their choice of tasks, thereby linking the type of praise (explanatory variable) to the task choice (response variable).
This concept is essential in experiments because it allows researchers to establish cause-and-effect relationships. By controlling and adjusting the explanatory variable, they can isolate its direct impact on the response variable, making the data more reliable.

A randomized controlled trial (RCT) is a type of experiment where participants are randomly assigned to different groups to receive different treatments. This style of experiment is considered the gold standard in research because it minimizes biases and ensures that the groups are similar except for the treatment they receive.
In the study conducted by Carol Dweck, students were randomly divided into two groups. One group received praise for effort, while the other received praise for intelligence. Randomization helped ensure that any variations between groups were due to the intervention rather than pre-existing differences among students.

• Reduces bias in group allocation
• Provides reliable results by controlling variables
• Ensures each participant has an equal chance of receiving any treatment

The strength of an RCT lies in its ability to control external variables and focus solely on the treatment's effects, providing robust evidence for researchers.