Study design and data analysis

Karabiner

TS Contributor
#2
When you customize a material good (e.g., design a sneaker yourself), it becomes more self-expressive, more social and harder to compare.
Really the resulting good? Or rather the active process of customization?

My reasoning is that customization adds “experiential” qualities to a material good, thereby leaving you with a purchases combining characteristics from both the material and experiential world. In contrast, if customization has the same effect on experiences, I would just be adding more of the same things.
I must admit that I do not fully understand what you are saying here.
I hope this was somewhat clear. In any case I am currently working on my study and this is where I’m having trouble. Basically I’d like to do a 2x2 between-subjects design with happiness as the DV, and customization (customized vs. non customized) as well as purchase type (material vs. experiential) as the IV’s.
What exactely does " customization (customized vs. non customized) and purchase type
(material vs. experiential)" mean, what will be done in the experiment?

Apart from that, you might perhaps think about an "mixed design" with between-group and
repeated-measures factors. This way, subjects serve as their own control, and this reduces
statistical error and increases power.

What would be the easiest way to accomplish this?
Perhaps structural equation modeling.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.
Vanderweele, T. J., & Vansteelandt, S. (2014). Mediation analysis with multiple mediators. Epidemiologic Methods, 2, 95–115.

With kind regards

Karabiner
 
#4
Structural equation models are not for the faint of heart. After a year of studying them in a graduate program - I concluded they were complex. If you have not worked with it before and don't have a very strong statistical background I would be cautious about using that.
 

Karabiner

TS Contributor
#5
Structural equation models are not for the faint of heart. After a year of studying them in a graduate program - I concluded they were complex. If you have not worked with it before and don't have a very strong statistical background I would be cautious about using that.
Noetsi is right. Maybe Andrew Hayes has some alternatve to offer http://afhayes.com/spss-sas-and-r-macros-and-code.html

What do you mean by "a MANOVA with four IV's (happiness, self-expression, socialness, comparability). " ? Do you mean 4 dependent variables? That willl not answer yoiur research questions, as far as I can see.

With kind regards

Karabiner