2.3 Development of prediction model
D-TOFHLA
REGRESSION
The D-TOFHLA is created as whole sentences, where one or more words are missing, and the participant is asked to select word/words that best complete a sentence. Therefore, before modelling the DS-TOFHLA, the 50 reading comprehension items in the D-TOFHLA were grouped into meaningful sets of items (items that create a whole sentence) to ensure that the intended meaning were maintained. This resulted in 19 sets: 5 sets with 1 item, 7 sets with 2 items, 1 set with 3 items, 3 sets with 4 items, 2 sets with 5 items, and 1 set with 6 items.The DS-TOFHLA was based on the following MLR equation:
The model was developed using an exhaustive search strategy: for every model size (starting by a 1-item model and up until the quality criteria were met), all possible combinations of sets of comprehension items were tested. For each model, the root mean square error (RMS error or RMSE) between the DS-TOFHLA MLR and the D-TOFHLA score was used as the model fit criterion. After minimizing the RMSE by adjusting the regression coefficients for each model, the model with the highest Pearson’s correlation coefficient was identified.