Dear Lisa,

You just need to run an ANOVA on your perceived quality scores with content source and disclosure as two IVs. Yourl design analysis may include the main effect of the two IVs along with their interaction (full factorial interaction design) to test a possible moderating effect.

Hope this helps. Best,

Based on your description, the variable "disclosure vs non-disclosure" is what's commonly referred to as a manipulation check or a treatment condition. It's a way to ensure that participants are indeed exposed to the intended experimental conditions. In your case, it seems like it's a manipulation of whether participants are informed about the source of the content they are viewing (AI vs. human).

Here's how you can classify it:

Regarding visualization, you can indeed create a matrix including both of the independent variables. Here's how you can visualize it:

Disclosure Non-Disclosure

__________________ __________________

| | |

| AI Content | AI Content |

|__________________|__________________|

| | |

| Human-generated | Human-generated |

| Content | Content |

|__________________|__________________|

Each cell in the matrix represents a unique combination of the two independent variables. Participants are randomly assigned to one of these conditions.

For data analysis, you would conduct a two-way analysis of variance (ANOVA) to examine the main effects of content source and disclosure, as well as any interaction effects between them, on the dependent variables (content quality measures).

This will allow you to determine if there are statistically significant differences in content quality based on the content source, the disclosure condition, or their combination.

Remember to conduct appropriate post-hoc tests if you find significant effects, to further explore the nature of these effects.

Also, ensure that your experiment design and statistical analysis plan are aligned with your research questions and hypotheses. If in doubt, consulting with your professor or a research methodologist can provide further guidance.

Dear Marios Didachos great explanation. I want to seek clarity. I partially accept that "disclosure vs non-disclosure" is being considered as a manipulation check; however, don't you think it in itself a variable, i.e. "knowledge about the source of content", which may increase or decrease the perceived quality on the basis of its absence or presence? Hence, it can be considered as a moderator. Please correct me.

Dear Bharti Sharma Thank you for your message and for delving deeper into the classification of the variable "disclosure vs non-disclosure". Your perspective raises some interesting considerations, and I appreciate the opportunity to clarify.

Upon reflection, I understand your point about the potential role of "knowledge about the source of content" as a moderator rather than solely a manipulation check. It's indeed a variable that could influence the perception of content quality based on its presence or absence.

Here's a revised classification based on your insights:

Independent Variable:

• Content Source (AI vs. Human)

Moderating Variable:

• Knowledge about the Source of Content (Disclosure vs. Non-disclosure)

Dependent Variables:

• Measures of Content Quality (e.g., engagement, credibility, satisfaction)

In this revised classification, the variable "disclosure vs non-disclosure" indeed acts as a moderator, as it may interact with the effect of content source on content quality. Participants' knowledge about the source of the content could impact how they perceive and evaluate its quality.