I found two different products, and for each product, I found a female and a male to sell them in different ways.
1. The respondents needed to answer the same questionnaire twice after the four sellers showing them the two different products.
2. I want to see the moderating effects (different products coded as 0 or 1 variable), but could not see the significant differences when respondents were facing the female or male sellers (coded as 0 or 1 variable as the antecedent) when purchasing two different products.
Specifically, I want to find out, for example, when selling the service products, female seller has greater influence than male seller. However, when selling the normal product, I want to figure out female seller's influence lower than the male seller.
I was wondering whether it was because of the moderator was not correct and I need to find a new moderator or I did not find the right method to run the analysis? I could not really see the influence was reduced.
Analyzing differences in consumers' perceptions towards two products when facing two sellers in an experimental research can be done through various methods. Here's a general outline of steps you could take:
By following these steps, you can effectively analyze and compare consumers' perceptions towards two products when facing two sellers in an experimental research setting.
Xi Fang, It seems like you're attempting to analyze the effects of both the product type and the gender of the seller on consumers' perceptions. Your aim is to see if there are differences in consumer perceptions when faced with different combinations of product type (service vs. normal product) and seller gender (female vs. male).
1. You're on the right track by considering interaction effects between the product type and the gender of the seller.
2. Make sure your model meets the assumptions of the statistical test you're using. For example, in regression analysis, you need to check for multicollinearity, normality of residuals, linearity, and homoscedasticity. Violations of these assumptions can affect the reliability of your results.
3. Ensure your sample size is adequate to detect the effects you're interested in. Small sample sizes can reduce statistical power, making it difficult to detect significant effects.
4. If you're still not finding significant results, consider exploring other potential moderators or factors that could influence consumer perceptions.
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