Is it reasonable to separate one scale into Part A and Part B, and ask the experimental group to fill in Part A, and the control group to fill in Part B?

You are most welcome!

Amazing.

Separating one scale into Part A and Part B and assigning them to different groups (experimental group filling Part A and control group filling Part B) may not be a recommended approach. This design could introduce confounding factors and limit the ability to draw valid conclusions about the intervention's effectiveness. Here are some reasons why this approach might not be reasonable:
1. Lack of pre-test data.: By assigning Part A to the experimental group and Part B to the control group, you would not have pre-test data for the control group. Pre-test data is crucial for establishing baseline measurements and understanding the initial status of participants before any intervention or treatment. Without pre-test data for the control group, it becomes challenging to assess the impact of the intervention accurately.
2. Potential bias: Assigning different parts of the scale to different groups based on their experimental or control status can introduce bias. This design may unintentionally create differences between the groups that are unrelated to the intervention itself. It's important to ensure that participants in both groups are comparable at the start of the evaluation, and any differences observed can be attributed to the intervention rather than the measurement design.
3. Comparability concerns: Dividing the scale into two parts and assigning them to different groups can make it difficult to compare and interpret the results. The responses in Part A and Part B may not be directly comparable due to differences in the items or constructs being measured. This could limit the ability to draw meaningful conclusions about the intervention's impact or make valid comparisons between the groups.
4. Reduced statistical power: By splitting the scale into two parts, you effectively reduce the number of items available for analysis in each group. This reduction in the number of items can impact the statistical power of the evaluation, potentially limiting the ability to detect significant differences or effects of the intervention.