Linear multiple regression models were fitted for the
SLCT scores. The residuals were sufficiently normally
distributed and no heteroscedasticity was observed. VIFs
of the predictors in the regression models had a maximum
value of 1.001, which is well below the cut-off value of
10. The outliers had virtually no effect (maximum Cook’s
distance 0.04). Table 1 presents the mean and standard
deviation stratified by age and sex. Table 2 represents the
regression models. Age and sex had a significantly positive
and negative (P < 0.001) influence on the predicted SLCT
scores.
Combining these regression models with the standard
deviations of the residuals provides normative data.
First, the predicted values of the scores (predicted yi) for
the SLCT are calculated by inserting the coded values
of the predictor variables in the regression models
[Table 2]. Next, the residuals of both scores are calculated
(ei = observed yi – predicted yi) and then standardized (Zi
= ei / SD (residual). The SD (residual) equals 7.82 for the
SLCT scores.
Multiple linear regressions provided a multiple R value of
0.538 with a corresponding R2 determination index of 0.29,
indicating that 29% of the score variance was explained by
the combination of age and sex. The model equation was:
SLCT score = -4.307 + 2.545 × Age – 4.25 × Sex. This
indicates that for each progressive year of age, the SLCT
scores increase, on average, by 2.545 and decrease by -4.25
for each sex. These coefficients allowed us to calculate the
correction scores to apply to individual subjects to consider
the effects of age and sex. Table 3 provides normative SLCT
data based on the regression models in Table 3, stratified
by age and sex with percentile values.
