Principal component and multiple correspondence analysis for handling mixed variables in the smoothed location model

The issue of classifying objects into groups when the measured variables are mixtures of continuous and binary variables has attracted the attention of statisticians. Among the discriminant methods in classification, Smoothed Location Model (SLM) is used to handle data that contains both continuous and binary variables simultaneously. However, this model is infeasible if the data is having a large number of binary variables. The presence of huge binary variables will create numerous multinomial cells that will later cause the occurrence of large number of empty cells. Past studies have shown that the occurrence of many empty cells affected the performance of the constructed smoothed location model. In order to overcome the problem of many empty cells due to large number of measured variables (mainly binary), this study proposes four new SLMs by combining the existing SLM with Principal Component Analysis (PCA) and four types of Multiple Correspondence Analysis (MCA). PCA is used to handle large continuous variables whereas MCA is used to deal with huge binary variables. The performance of the four proposed models, SLM+PCA+Indicator MCA, SLM+PCA+Burt MCA, SLM+PCA+Joint Correspondence Analysis (JCA), and SLM+PCA+Adjusted MCA are compared based on the misclassification rate. Results of a simulation study show that SLM+PCA+JCA model performs the best in all tested conditions since it successfully extracted the smallest amount of binary components and executed with the shortest computational time. Investigations on a real data set of full breast cancer also showed that this model produces the lowest misclassification rate. The next lowest misclassification rate is obtained by SLM+PCA+Adjusted MCA followed by SLM+PCA+Burt MCA and SLM+PCA+Indicator MCA models. Although SLM+PCA+Indicator MCA model gives the poorest performance but it is still better than a few existing classification methods. Overall, the developed smoothed location models can be considered as alternative methods for classification tasks in handling large number of mixed variables, mainly the binary

The use of multiple correspondence analysis to explore associations between categories of qualitative variables in healthy ageing

In pressPopulation studies are often characterized by a plethora of data that includes quantitative to qualitative variables. The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions (based on the continuous neurocognitive test variables) and MCA to detect and explore relationships of cognitive, clinical, physical and lifestyle categorical variables across the low-dimensional space. Altogether the technique allows to not only simplify complex data, providing a detailed description of the data and yielding a simple and exhaustive analysis, but also to handle a large and diverse dataset comprised of quantitative, qualitative, objective and subjective data. Two PCA dimensions were identified (general cognition/executive function and memory) and two main MCA dimensions were retained. As expected, poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators and presence of pathology. Interestingly, the first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics within each of the identified dimensions. Following MCA findings, the weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing not only if a relationship exists between variables but also how they are related, offering at the same time statistical results can be seen both analytically and visually.EC -European Commissio

Process-systemic approach to quality cost modelling

Циљ рада је да се најпре спозна стање у области трошкова квалитета у пракси, као и да се утврди потреба за дефинисањем модела трошкова квалитета процесно-системске оријентације. Истраживање је спроведенo над 186 организационих система, различитих делатности. Само организациони системи који су упознати са терминологијом и основном идејом трошкова квалитета селектовани су за ово истраживање. База таквих организационих система формирана је на основу три индикатора њихове упознатости са облашћу трошкова квалитета. Резултати показују да је присутан висок ниво свести о значају трошкова квалитета као и тренд раста броја организационих система који почињу да практикују менаџмент трошкова квалитета. Издвојени су фактори који утичу на системе менаџмента трошкова квалитета и анализиране су везе између варијабли које описују ове системе. Осим тога, издвојени су захтеви стандарда ISO 9001:2015 који су, у односу на став који организације имају о утицају њиховог испуњења на адекватност менаџмента трошкова квалитета, у статистички значајној вези са варијаблама које описују системе менаџмента трошкова квалитета. С обзиром да су резултати истраживања указали и на потребу за дефинисањем модела трошкова квалитета процесно-системске оријентације, у раду је PAF модел употребљен на нивоу процеса, с даљим циљем израде модела трошкова квалитета, у оквиру ког се елементи трошкова квалитета посматрају у односу на излазе из процеса и где се примењује принцип да један процес утиче на квалитет других процеса преко својих излаза.The objective of the paper is to expand the level of knowledge about quality costing in current practice, and to determine the need for defining а quality cost model in the contex of process-systemic approach. The paper presents a study that was conducted on 186 companies, from different industries. Only companies that are familiar with quality costs were selected for the research. The database of companies for the research was formed using three indicators of the companies’ familiarity with quality costs. The results show that there is a high level of awareness of quality costs importance, and that there is an increase in the number of companies managing these costs. Factors affecting quality costs management systems were pointed out, and associations among variables which define those systems are analysed. In addition, the requirements of standard ISO 9001:2015, which are in statistically significant association with the variables defining quality costs management, were selected according to the companies’ standpoint towards the importance of their fulfillment. Given that the research results point to the need for defining а quality cost model in the contex of process-systemic approach, the PAF model is used in this paper on the process level, in order to propose a quality cost model where the elements of quality costs are considered in relation to the outputs of the processes, and where the principle that one process affects the quality of another process by its outputs is taken into consideration. In the model, quality costs are determined for each process in two moments: the current (before taking measurs) and expected (after taking measurs)