First, you should not add items with 5-point and 7-point scoring, so hopefully those are in different scales. If not, you should standardize the items before adding them.

It doesn't make any difference in the analysis whether you simply add the items or take an average, because averaging simply divides the summed score by a constant (the number of items). The main reason for averaging is that it gives you a more interpretable "metric" by returning the summed version to a score that ranges from 1 to 5 or 1 to 7.

Mean-centering involves nothing more that subtracting out a constant, so it also will no effect on the analysis other than to make the results easier to interpret (e.g., the R-sq will be identical, with and without mean-centering, so it will not reduce multi-collinearity).

Given the complexity of what you are doing, you might consider using Structural Equation Modeling as an alternative to simple regression. With SPSS, this requires using the additional AMOS module.

I have following comments to offer:

1. As a first step, there is a need to calculate sum of scores by each construct.

2. There is also a need to calculate the total score for each individual.

3. Based on total score, you can calculate the individuals into three major groups like Low, Medium and High ( let us term it as status)

4. Then analysis can be attempted to see whether the scores differ significantly by each construct by the status.

5. As few items are on 5 point scale and few are on 7 point scale, there is a need to standardize it. For quick reference see how the standardize scores can be obtained.

Rescaling of 5 points and 7 points scale to 10 points scale

5 Point Rescaled to 7 Point Rescaled to

10 point 10 point

1 1.00 1 1.0

2 3.25 2 2.5

3 5.50 3 4.0

4 7.75 4 5.5

5 10.00 5 7.0

6 8.5

7 10.0

6. There are few constructs with few items and other with more items. To standardize it, may be mean score which need to be calculated for each construct and for total score.

7. The mean standadrized score will allow you to decide which construct is more important than the other (by status)

In short, there are two things which you have to remember . First to standardize the scores so that comparison become valid between the the constructs. Second, there is a need to calculate mean scores so that the comparison can be made by the status.

First of all, I do not think a 7-point scale can should "rescaled" into a 10-point. And if it could, it would have to maintain its ordinality, so a value for 6 (8.5) could not be lower than a value for 5 (10.0).

I am an expert on rating scales statistics and have developed statistical methods that take account of the non-metric properties of ordered categorical data.

The fact is that assessments made on rating scales consist of ordered categorical data. Irrespective of the type of labelling of the scale categories, numerical (Likert), verbal or continuous, the data do not represent a mathematical value, except for ordering. This means that data from scale assessments have rank-invariant properties only without information regarding distance and standardized magnitude. Hence calculations of sum scores and differences are not appropriate analytical operations. However, there are statistical metods that take these properties into account which in turn will result in valid results.

Some examples:

Svensson E. Guidelines to statistical evaluation of data from ratings scales and questionnaires. Journal of Rehabilitation Medicine 2001;33 (1): 47-8. doi:

10.1080/165019701300006542

Svensson E. Comparison of the quality of assessments using continuous and discrete ordinal rating scales. Biometrical Journal 2000;42:417-34 doi.org/10.1002/1521-4036(200008)42:43.0.CO;2-Z.

2. Svensson E. Concordance between ratings using different scales for the same variable. Statistics in Medicine 2000;19(24):3483-96.

Svensson E. Different ranking approaches defining association and agreement measures of paired ordinal data. Statistics in Medicine, 2012;31:3104-3117. (wileyonlinelibrary.com) DOI:10.1002/sim.5382.

Svensson E., Nyström B., Goldie I., Landrø N.I., Sidén Å., Staff P., Schillberg B., Taube A. Superior outcomes following cervical fusion vs. multimodal rehabilitation in a subgroup of randomized Whiplash-Associated-Disorders (WAD) patients indicating somatic pain origin. Comparison of outcome assessments made by four examiners from different disciplines. Scand J of Pain 2018; 18(2):175-186. https://doi.org/10.1515/sjpain-2017-0180.

2. Haddeland K, Slettebö Å, Svensson E, Carstens P, Fossum M. Validity of a questionnaire developed to measure the impact of a high-fidelity simulation intervention: A feasibility study. J Adv Nurs. 2019;00:1-10. https://doi.org/10.1111/jan.14077.

Svensson E. Recent developments in analysis of paired ordinal data. Discussion of the paper Ivy Liu and Alan Agresti: The analysis of ordered categorical data: An overview and a survey of recent development. TEST 2005;14(1):1-73 pp 44-46.