Please check it up:

http://spssx-discussion.1045642.n5.nabble.com/standardized-values-in-multilevel-td5117328.html

Good Luck.

The term mixed model refers to the use of both fixed and random effects in the same analysis. Fixed effects have levels that are of primary interest and would be used again if the experiment were repeated. Random effects have levels that are not of primary interest, but rather are thought of as a random selection from a much larger set of levels. Subject effects are almost always random effects, while treatment levels are almost always fixed effects. A correlation value close to 0 indicates no association between the variables. Since the formula for calculating the correlation coefficient standardizes the variables, changes in scale or units of measurement (standardization) will not affect its value.

Besides when do you standardize variables?

You should standardize the variables when your regression model contains polynomial terms or interaction terms. While these types of terms can provide extremely important information about the relationship between the response and predictor variables, they also produce excessive amounts of multicollinearity.

Multicollinearity is a problem because it can hide statistically significant terms, cause the coefficients to switch signs, and make it more difficult to specify the correct model.

If you have a continuous predictor x_1 for an outcome variable y and estimate a linear model (irrespectively of whether it is multilevel or not), you get the slope parameter b_1. This coefficient means:

"one unit change on x_1 causes a change of b_1 on y_1". Of course, if you transform x_1, b_1 is effected. That may be what you want, but when x_1 has a commonly understood scale of measurement (minutes, meter, count), this is lost in the transformation and communicating the results will be less intuitive.