Development of factor-score-based models to explain and predict maximal box-lifting performance.

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The objectives of the study were threefold: (1) to develop factor-score-based models to predict maximum mass on a box-lifting task using multiple 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 men) completed a maximal box-lifting task and a maximal isoinertial lifting test on an Incremental Lifting Machine (ILM). Dynamic data collected 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 comprising the final solution, and multiple regression equations for men, women 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 using numerous data-level variables as predictors, i.e., all 32 lift parameters and ILM mass. In comparison, explanation was enhanced but predictive capabilities were reduced when linear models were formed using ILM mass and the factor scores derived from analyses of isoinertial lifting. The use of variables loading on the factors gave slightly increased predictive power than did the factor-score-based models. Similar trends 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 advantages and usefulness of factor-score-based models warranted their inclusion in future investigations of lifting performance.