hello I read your question, but I don't understand what you want to do and analyze. Tell me your design and the number of variables and its type, I will help as much as I can. If your design is experimental, why have you used scaling? You should check the results. If you use the test before and after the manipulation of the independent variable, you should analyze based on the number of groups and the time of the tests.

I will be happy to serve you, give me the necessary information

Thank you for your helping

I did what the articles have done. I adopted the manipulation scale from previous studies, and my design is experimental.

I have two groups. The first group was asked to read a scenario regarding economic compensations and then answered the following question: what is the strategy, they rated 1 for economic and 7 for symbolic. so they did well and rated it for 1 because they believed it was an economic strategy. I did the same for the second group, which was symbolic. In the research, they analyzed the manyplation by comparing mean like Anova or T-test, and if there is a difference in the mean, that means the manipulation is implemented successfully. Do I understand correctly?

To analyze the manipulation and check whether Group 1 perceived the recovery as economic and Group 2 perceived it as emotional, you need to assess whether the two groups responded differently to the respective scenarios (economic vs. emotional recovery). Here's how to approach this analysis:

1. Descriptive Statistics

• First, compute the mean and standard deviation of the responses for each group.
• Group 1 (economic recovery scenario): Calculate the mean ratings to see the overall agreement with the economic recovery.
• Group 2 (emotional recovery scenario): Calculate the mean ratings to see the overall agreement with the emotional recovery.

This gives you a basic understanding of how each group responded to the scenarios.

2. Independent Samples t-Test

Since you want to see if the ratings for the economic recovery differ from the ratings for the emotional recovery, an independent samples t-test is appropriate. This will test whether there is a statistically significant difference between the two groups' perceptions of recovery.

Steps:

• Null Hypothesis (H₀): There is no difference in the ratings between Group 1 (economic recovery) and Group 2 (emotional recovery).
• Alternative Hypothesis (H₁): There is a significant difference in the ratings between the two groups, with Group 1 agreeing more with economic recovery and Group 2 agreeing more with emotional recovery.

Perform the t-test to compare the means of the two groups:

• If p < 0.05, you reject the null hypothesis and conclude that the manipulation worked, meaning the two groups perceived recovery differently (Group 1 saw economic recovery, and Group 2 saw emotional recovery).
• If p ≥ 0.05, it suggests that the manipulation might not have worked as intended, as there is no significant difference between the two groups' perceptions.

3. Manipulation Check Using ANOVA (Optional)

If you have more than two conditions or more factors to consider (e.g., if you manipulated other variables), you could also conduct a one-way ANOVA to compare the group means across multiple conditions.

4. Visualize the Data

• Create bar charts or box plots to visualize the mean ratings for each group. This can help you clearly see how the groups responded to the different scenarios.

5. Interpret the Results

• If the t-test shows a significant difference, conclude that the manipulation successfully differentiated the perceptions of economic vs. emotional recovery between the two groups.
• If there is no significant difference, review your experimental design and consider whether the scenarios clearly reflected economic and emotional recovery, or whether the scales captured the intended responses accurately.

By applying these steps, you can analyze whether the manipulation of the recovery type (economic vs. emotional) was successful.