Likert Scales and summed scores

Question

I cannot seem to find consensus on the following so hopefully someone can shed some light on it. I have 5-point Likert scale Strongly Agree, Agree, Neither Agree nor disagree, disagree, strongly disagree.

My question is measuring privacy attitude using a construct that contains 10 privacy indicators (researched separately). I am trying to create a privacy score so I can later do a correlation analysis - those who have a higher privacy score are more likely going to have a higher privacy activism score (another construct).

Without going into to much detail I can't seem to figure out the correct way to do the score. The most common approach seems to be a sum all the items in the construct (measuring privacy). However I am concerned about the undecided (neither agree nor disagree) group.

How should I rank that subset of responses. Logic would have it to be coded as 0 given they have not really answered the question? other literature suggests it should be 3.

My concern is, if (hypothetically) I have 30% of undecided and 20% strongly agree or agree, then the privacy score isn't really reflecting a persons desire for privacy because the undecided is scored at 3, pushing up the score.

SO the question is what to score the undecided group so I can create a summed score that accurately represents the response

thus, 1 = Strongly Agree
2 - Agree
3 = undecided
4 - disagree
5 - Strongly disagree

I would be grateful for any literature or suggestions I could reads regarding this.

Answer 1

(more of a long comment)

How should I rank that subset of responses. Logic would have it to be coded as 0 given they have not really answered the question? other literature suggests it should be 3.

I guess we would need to know exactly how the questions was asked/worded. But I do not understand how you want to code undecided as zero, if it really is a non-answer it would be closer to missing? A related post is Ordinal variable with 'don't know'

Then, how do you make a score, summated or otherwise? Maybe first look at how the ten likert variables are correlated, maybe some are almost versions of each other? They would then get too much influence in a summated score ... have a look at How to choose between Pearson and Spearman correlation? and Under what conditions should Likert scales be used as ordinal or interval data?.

Then you could do an ordinal-data version of correspondence analysis, see Can principal component analysis be applied to datasets containing a mix of continuous and categorical variables? and How to understand optimal Scaling in R: The Package homals for novices. The scores on the first axis could be a candidate for a score, but I would not be surprised if that is highly correlated with the sum score.

Answer 2

Thanks to all who viewed or answered my post. For those struggling with the concept of aggregated or summed scales I have found some very good resources, that perhaps i should have found earlier, nonetheless it might help someone so posted here. Most obvious from Likert, The Method of Constructing a Likert scale, provided the basics of scaling. The most useful however is an article, How to Analyze Likert and other rating scale data. Finally, Summmated rating scale construction is a quick guide to defining and constructing scales. I am yet to discover the best way to weight each item. Some literature has touched on weighted factor analysis to do this however i need more reading time. The take away from the literature this far is understanding the difference between a Likert item and a Likert scale, what the two primary ways of scoring items, i.e., sum of means or sum of observed scores. I hope this is of help to the community.