DEVELOPMENT OF FACTOR-SCORE-BASED MODELS TO EXPLAIN AND PREDICT MAXIMAL BOX-LIFTING PERFORMANCE

The objectives of the study were threefold: (1) to develop factor-scor e-based models to predict maximum mass on a box-lifting task using mul tiple regressions; (2) to compare predictive and explanatory powers of factor-score-based models to models derived from data-level variables ; and (3) to apply these findings to ergonomic research and practical problem-solving situations. Forty-eight volunteers (25 women and 23 me n) completed a maximal box-lifting task and a maximal isoinertial lift ing test on an Incremental Lifting Machine (ILM). Dynamic data collect ed during isoinertial testing were summarized into 32 lift parameters, and then subjected to principal components analyses using the 'FACTOR PROCEDURE' from the Statistical Analysis System (SAS). Factor scores were calculated for each participant on each of the four factors compr ising the final solution, and multiple regression equations for men, w omen and combined data were generated using the 'GENERAL LINEAR MODELS ' procedure from SAS. Results revealed that prediction of box-lifting performance was optimized when regression equations were developed usi ng numerous data-level variables as predictors, i.e., all 32 lift para meters and ILM mass. In comparison, explanation was enhanced but predi ctive capabilities were reduced when linear models were formed using I LM mass and the factor scores derived from analyses of isoinertial Lif ting. The use of variables loading on the factors gave slightly increa sed predictive power than did the factor-score-based models. Similar t rends in predictive and explanatory powers appeared when the data were analysed according to gender. Ergonomic applications of factor-score- based models were discussed with regard to ongoing research as well as to practical problem-solving situations. It was concluded that the ad vantages and usefulness of factor-score-based models warranted their i nclusion in future investigations of lifting performance.