Joint assessment of handedness and footedness through latent class factor analysis.

Recently, some studies have demonstrated the effectiveness of two latent variable approaches in which hand preferences are analysed using either latent class methods or latent class factor (LCF) methods. The main aims of this study are: (i) to establish whether these approaches are adequate for assessing footedness, (ii) to evaluate their appropriateness when hand and foot preferences are jointly analysed, and (iii) to measure the association between handedness and footedness based on the examined latent variable models. To this end, a dataset providing information about the limb used to perform ten hand actions and three foot movements by 2236 young Italian sportspeople is analysed. The first aim is pursued through an exploratory analysis of the observed foot preferences; according to this analysis, footedness patterns are adequately described by two latent levels of footedness. As far as the second aim is concerned, a confirmatory analysis of foot and hand preferences is carried out; the best fit to the dataset is obtained using a two-dimensional LCF model with four latent levels of handedness and two latent levels of footedness. Finally, the association between handedness and footedness resulting from the employed methods is remarkably lower than that registered in other studies.

Assessment of handedness using latent class factor analysis.

Recently several studies in which handedness was evaluated as a latent construct have been performed. In those studies, handedness was modelled using a qualitative latent variable (latent class models), a continuous latent variable (factor models), or both a qualitative latent variable and a continuous latent trait (mixed Rasch models). The aim of this study was to explore the usefulness and effectiveness of an approach in which handedness is treated as a qualitatively scaled latent variable with ordered categories (latent class factor models). This aim was pursued through an exploratory analysis of a dataset containing information on the hand used by 2236 young Italian sportspeople to perform 10 tasks. For comparison purposes, a latent class analysis was carried out. A cross-validation procedure was implemented. The results of all the analyses revealed that the best fit to the observed handedness patterns was obtained using a latent class factor model. Through this model, individuals were assigned to one of four ordered levels of handedness, and a quantitative index of left-handedness for each individual was computed by taking into account the different effect of the 10 tasks. These results provide support for the use of the latent class factor approach for handedness assessment.

Evaluating patient satisfaction through latent class factor analysis.

This paper introduces Health and Place readers interested in studying the latent concept of satisfaction to the methodology of latent variable analysis. In particular, some suitable methods for analyzing individual opinions expressed on ordinal scales are illustrated. The basic theory behind these methods is explained and a step by step description of how they should be used in practice is given. The discussion of the subject starts with the simplest methods, in which opinions are grouped into two categories: typically positive and negative. Furthermore, more complex methods are presented to deal with opinions expressed on ordinal scales (e.g., very satisfied, somewhat satisfied, and not satisfied). All methods are described by showing various results obtained through the analysis of a dataset containing patients' opinions about their satisfaction with hospital care, collected through a survey conducted after their discharge from an Italian hospital. The database was created using a questionnaire covering different aspects of satisfaction and a five-point Likert scale. This represents an example of multi-level data: patients are clustered according to the hospital ward in which they were hospitalized. Thus, some specific latent variable methods able to deal with this particular structure of the data are also described.