Cluster Analysis for Diminishing Heterogeneous Opinions of Service Quality Public Transport Passengers

[EN] One of the principal measures that public transport administrations are following for reaching a sustainable transportation in the cities consists on attract a higher number of citizens towards the use of public transport modes, by offering high quality services. Collecting users opinions is the best way of detecting where the service is failing and which aspects are been provided successfully. The main problem that has to be faced for analyzing service quality is the subjective nature of its measurement, offering heterogeneous assessments among passengers about the service. Stratifying the sample of users on segments of passengers which have more uniform opinions about the service can help to reduce this heterogeneity. This stratification usually is conducted based on the social and demographic characteristics of the passengers. However, there are more advance techniques that permits to identify more homogeneous groups of users. One of these techniques is the Cluster Analysis, which is a data mining technique that can be used for segmenting the sample of passengers on groups that share some common characteristics, and that have more homogeneous perceptions about the service. This technique has been applied in other fields of transport engineering but it has never been applied for searching homogeneous groups of users with regards to service quality evaluation in a public transport service. For this reason, the aim of this work is to find groups of passengers that perceive the quality of the service in a more homogeneous way, and to apply to this clusters a suitable statistic technique that permit us to discover which are the variables that more influence the passengers¿ overall evaluation about the service. The comparison among the results of each cluster will show considerable differences among them and also with the results obtained using the global sample.This study is sponsored by the Consejería de Innovación, Ciencia y Economía of the Junta de Andalucía (Spain) through the Excellence Research Project denominated Q-METROBUS-Quality of service indicator for METROpolitan public BUS transport services . The authors also acknowledge the Granada Consorcio de Transportes for making the data set available for this study. Likewise, Griselda López wishes to express her acknowledgement to the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for their scholarship to train teachers and researchers in Deficit AreasDe Oña, R.; López-Maldonado, G.; Díez De Los Ríos, F.; De Oña, J. (2014). Cluster Analysis for Diminishing Heterogeneous Opinions of Service Quality Public Transport Passengers. Procedia - Social and Behavioral Sciences. 162:459-466. https://doi.org/10.1016/j.sbspro.2014.12.227S45946616

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions

Prime movers : mechanochemistry of mitotic kinesins

Mitotic spindles are self-organizing protein machines that harness teams of multiple force generators to drive chromosome segregation. Kinesins are key members of these force-generating teams. Different kinesins walk directionally along dynamic microtubules, anchor, crosslink, align and sort microtubules into polarized bundles, and influence microtubule dynamics by interacting with microtubule tips. The mechanochemical mechanisms of these kinesins are specialized to enable each type to make a specific contribution to spindle self-organization and chromosome segregation

Recruitment of the mitotic exit network to yeast centrosomes couples septin displacement to actomyosin constriction

The Mitotic Exit Network (MEN) promotes mitotic exit and cytokinesis but if and how MEN independently controls these two processes is unclear. Here, the authors report that MEN displaces septins from the cell division site to promote actomyosin ring constriction, independently of MEN control of mitotic exit

Early life patterns of common infection: a latent class analysis

Early life infection has been implicated in the aetiology of many chronic diseases, most often through proxy measures. Data on ten infectious symptoms were collected by parental questionnaire when children were 6 months old as part of the Avon Longitudinal Study of Parents and Children, United Kingdom. A latent class analysis was used to identify patterns of infection and their relationship to five factors commonly used as proxies: sex, other children in the home, maternal smoking, breastfeeding and maternal education. A total of 10,032 singleton children were included in the analysis. Five classes were identified with differing infectious disease patterns and children were assigned to the class for which they had a highest probability of membership based on their infectious symptom profile: ‘general infection’ (n = 1,252, 12.5%), ‘gastrointestinal’ (n = 1,902, 19.0%), ‘mild respiratory’ (n = 3,560, 35.5%), ‘colds/ear ache’ (n = 462, 4.6%) and ‘healthy’ (n = 2,856, 28.5%). Females had a reduced risk of being in all infectious classes, other children in the home were associated with an increased risk of being in the ‘general infection’, ‘mild respiratory’ or ‘colds/ear ache’ class. Breastfeeding reduced the risk of being in the ‘general infection’ and ‘gastrointestinal’ classes whereas maternal smoking increased the risk of membership. Higher maternal education was associated with an increased risk of being in the ‘mild respiratory’ group. Other children in the home had the greatest association with infectious class membership. Latent class analysis provided a flexible method of investigating the relationship between multiple symptoms and demographic and lifestyle factors