Experimental Analysis and Design Report Writing - IT Assignment Help

Assignment Task

Task    

Questions to include in your report
Q1. Test whether age and gender differ between the “ugly” labeling and no labeling conditions
Q2. Test H1 and report the results consistent with the result section in the corresponding lecture
Q3. Test H2 and report the results consistent with the result section in the corresponding lecture
Q4. Test H3 and report the results consistent with the result section in the corresponding lecture

Dependent variable (measured)
“likelihood”: Likelihood to purchase unattractive produce measured on a 5-point scale ranging from 1 = “Definitely Box A” to 5 = “Definitely Box B”, with 3 = “Indifferent”
Hypotheses H1: “Ugly” labeling (vs. no specific label) increases the likelihood that consumers purchase unattractive produce
H2: The positive effect of “ugly” labeling on purchase is moderated by the depth of price discount, such that “ugly” labeling is most effective when associated with a moderate (vs. steep) discount
H3: The positive effect of “ugly” labeling on purchase is moderated by vegetable consumption frequency, such that “ugly” labeling is most effective when vegetable consumption frequency is high (vs. low

Part A: Design
Experimental design and data collection plan

• Defining the objective and the experimental outcome or DV (e.g., but not limited to, clicks, willingness to pay, purchase frequency, compliance rate, etc.). Note that the outcome needs to be an interval or ratio variable. It cannot be a nominal outcome (e.g., recycle vs. not recycle).
• Experimental design (between-subject experiment). The experiment can take place in any type of settings such as (but not limited to) an online experiment, a laboratory experiment, a field experiment, etc. Please provide enough information to make it clear how the data would be collected. Imagine that you have no financial constraint to run the experiment so you can be creative with this part.
• Experimental manipulations or IV. You need exactly two experimental conditions (e.g. intervention condition vs. control condition).
• One moderating variable M (e.g., either manipulated or measured). This variable can be binary (like “steep” in part A) or quantitative (like “veggie_freq” in part A). Its manipulation or measure must be clearly detailed (note that in case you choose to manipulate the moderator, you need to add extra experimental conditions (e.g., steep discount ("steep"=1) experimental conditions in part A)).

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