Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.

ObjectiveWe aimed to describe eating patterns among home-dwelling older subjects to establish typologies of eaters at higher or lower risk of malnutrition.DesignCross-sectional study between June and September 2015 using a standardised questionnaire. The questionnaire was given to home-help employees (responsible for delivering meals to home-dwelling older persons and helping them to eat). The employees were asked to complete the questionnaire three times during the same week, for the same older adults, in order to identify the totality of their food intake.SettingRegistered customers of the home meal delivery company ‘Azaé’ (France).Participants605 older home-dwelling persons were randomly selected among customers served by the home meal delivery company.OutcomesMultiple factor analysis was used to understand the different modes of food consumption and to establish eating profiles. Hierarchical classification was performed to construct eating profiles corresponding to the dietary habits of the respondents.ResultsAverage age of the older adults was 85.3 years; 73.5% were women. Overall, 59% of participants reported that they ate out of habit, while 33.7% said they ate for pleasure. We identified four different groups of eaters, at varying levels of risk for malnutrition. Individuals in group 4 had the highest food intake in terms of quantity; and were less dependent than individuals in group 1 (p=0.05); group 1 was at highest risk of malnutrition.ConclusionImproved understanding of eating habits can help detect risky behaviours and help caregivers to promote better nutrition among home-dwelling older subjects.

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The appropriateness of sum scores as estimates of factor scores in the multiple factor analysis of ordered-categorical responses.

10.31124/advance.12417902.v1 ◽

2020 ◽

Author(s):

Pere Ferrando ◽

Urbano Lorenzo-Seva

Keyword(s):

Factor Analysis ◽

Unit Weight ◽

Factor Scores ◽

Multiple Factor Analysis ◽

Non Linear ◽

Categorical Responses ◽

Sum Scores ◽

Biased Estimates ◽

Ordered Categorical ◽

Multiple Factor

<p>Unit-weight sum scores (UWSSs) are routinely used as estimates of factor scores on the basis of solutions obtained with the non-linear exploratory factor analysis (EFA) model for ordered-categorical responses. Theoretically, this practice results in a loss of information and accuracy, and is expected to lead to biased estimates. However, the practical relevance of these limitations is far from clear. In this article we adopt an empirical view, and propose indices and procedures (some of them new) for assessing the appropriateness of UWSSs in non-linear EFA applications. A new automated approach for obtaining UWSSs that maximize fidelity and correlational accuracy is proposed. The appropriateness of UWSSs under different conditions and the behavior of the present proposal in comparison with other more common approaches are assessed with a simulation study. A tutorial for interested practitioners is presented using an illustrative example based on a well-known personality questionnaire. All the procedures proposed in the article have been implemented in a well-known noncommercial EFA program. </p>

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