## The Quality of a Composite Score of a Concept with Reflective Indicators

This situation is presented in the picture at the side. The observed answers to the three questions are represented by

*Y*1,

*Y*2,

*Y*3. These observed responses contain errors. The composite score (

*Ycs*) is the weighted sum of these three variables, therefore this composite score will also contain errors. So the question to answer is: how good is this composite score as measure for the complex concept satisfaction (

*F*) given the indicators

*F*1,

*F*2,

*F*3 and the systematic effects connected with the chosen method (

*M*)?

**Estimating the weights and the loadings**

From SQP, or otherwise, one can get information about the quality coefficients

*ri, vi, mi*, for all three or any number of single questions. That means that in the model only the effects of F on its indicators and the weights are unknown. However, the weights can be chosen to be 1 or unequal to 1, that is up to the researcher. Lawley and Maxwell (1971) have suggested an approach to determine the weights that maximize the quality (correlation) of the composite score for the complex concept. Given the weights the values of the composite score can be computed for all cases and the variance and standard deviation of these variables are also known. In order to take care that the composite score has a variance of 1 like all other variables, the chosen weights have to be divided by sycs which is the standard deviation of the composite score before standardization.

It can be shown that on the basis of the correlations between the observed variables

*Y*1,

*Y*2,

*Y*3 the correlations between the latent variables

*F*1,

*F*2,

*F*3 can be obtained.

On the basis of these correlations the effects λ_{i} between the concept of interest and the indicators can be estimated if there are at least 3 indicators. If there are only two questions used the λ_{1} and λ_{2} can only be estimated assuming that these two coefficients are equal. Because the indicators will not all prefectly represent the complex variables of interest, we introduce also unique components *(u _{1}, u_{2}, u_{3}) * for the indicators. They are another source of errors.

**The quality of Y _{CS}**

Given this specification of the model it follows

*i*because

q_{Ycs} = ∑_{i} r_{i} v_{i} λ_{i} is the effect of F on Y_{cs}

m_{Ycs} = ∑_{i} r_{i} m_{i} is the systematic method effect on Y_{cs}

The quality of the composite score as measure of F is equal to q_{Ycs2}

see above i-k and the weight