I am a Master student and for my graduation I must perform some statistical analysis. I created the questionnaire myself: it begins (section 1) with asking characteristics about the person without using the Likert scale (public/private; previous collaborations; amount of partners..) then the questionnaire is divided into 3 further sections. The second section is divided into 9 themes, and each theme contains between 1 and 4 statements (some negative and some positive - actually, will this be problematic?) to which respondents have to answer using the Likert scale and say whether they experienced this "theme" during collaboration. After, the third section, they are asked about their satisfaction in relation to the nine themes (1 statement per theme) as a measure of effectiveness. Finally, in the fourth section, which is very small, I ask them about whether benefits outweigh the drawbacks and whether they see the collaboration as helping them achieve sustainable development goals (SDGs) as a further measure of effectiveness.
1. How is it possible for me to look at whether their characteristics (independent variables, section 1) affect their perception of these 9 themes (section 2)?
2. How can I see if their perception of these nine themes (section 2) is correlated with their satisfaction (per theme, section 3)? And whether their separate characteristics (section 1) are correlated with their satisfaction (section 3)?
3. How can I see if their satisfaction (section 3) is correlated with their perception of whether the collab is helping achieve SDGs (section 4) and their overall thoughts on benefits/drawbacks (still section 4). I.e. if effectiveness can be measured by satisfaction, as well as via the perception of whether the collab helps achieve SDGs
Lots here. Let's start with the first one.
"1. How is it possible for me to look at whether their characteristics (independent variables, section 1) affect their perception of these 9 themes (section 2)?"
First, you have phrased this as a causal question, how one set affects another. Without further information about the study it is difficult to know if/how you can address this. But if you want to know how one set of variables is associated with another, depending on what you mean this is possible. Are you interested in how each individual item is set A is associated with each individual item in set B, or are you interested in, for example, what is the highest correlation between some summary of the items in set A with some summary of the items in set B? Which of these you are interested in will lead you down a different analytic path.
I won't look at the other questions you have yet. Worth taking steps one at a time!
It sounds like the first step you need to take in your analysis is to assess whether your 9 scales (themes?) have sufficient internal inconsistency, which involves calculating coefficient alpha (Cronbach's alpha) for each scale. Here is a link that describes how to do this in R.
https://mattchoward.com/calculating-cronbachs-alpha-in-r/
Daniel Wright Hi Daniel, Thank you for your response. I am indeed interested in finding out whether the individual partner characteristics (about 7 characteristics in Sect A) are associated with a "greater perception of each of the 9 themes" (taken separately) present in Sec B. Does that make any sense?
So for example: a characteristic could be "having worked for 7 years in this project". From there, the questionnaire is divided into 9 themes (per each theme there are some statements to which they can respond using a Likert scale).
For example: Each theme is concerning a different advantage of collaboration, lets say Connections to make it a bit more concrete. So I am interested in seeing (an example taken from my study) if people who have worked for longer than 7 years (1 question of Sec A) perceive the advantage of "finding connections" better than someone who has only worked 3 years. Does this make it a bit more clear?
David L Morgan Hi David, thank you for your response and the very informative link. That is a great idea! I will definitely do that. Should this always be done at the beginning of each statistical analysis (when working with Likert scales)?
Yes, coefficient alpha is a first step in determining whether your scales are working as intended.
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