Exploring the police use of force continuum with a partial proportional odds model

PurposeThe present study examines a range of police force on the continuum (firearms, TASER/chemical spray and physical force) to see whether they are associated with individual (subject and officer), situational and/or neighborhood factors.Design/methodology/approachA partial proportional odds model is used to analyze police use of force data from 2003 to 2016 in Dallas. Independent variables are allowed for varying effects across the different cumulative dichotomizations of the dependent variable (firearms vs TASER/chemical spray and physical force and firearms and TASER/chemical spray vs physical force).FindingsMost officer demographic and situational factors are consistently significant across the cumulative dichotomizations of police force. In addition, suspect race/ethnicity (Hispanic) and violent crime rates play significant roles when officers make decisions to use firearms, as opposed to TASER/chemical spray and physical force. Overall, situational variables (subject gun possession and contact types) play greater roles than other variables in affecting police use of force.Originality/valueDespite the large body of police use of force research, little to no research has used the partial proportional odds model to examine the ordinal nature of police force from physical to intermediate to deadly force. The current findings can provide important implications for policy and research.

The Association Between Multimorbidity and Health-Related Quality of Life Among Clients Attending Chronic Outpatient Medical Care in Bahir Dar, Northwest Ethiopia: The Application of Partial Proportional Odds Model

Background Multimorbidity, the presence of two or more chronic non-communicable diseases (NCDs) in a given person affects all aspects of individuals’ lives. Poor quality of life (QoL) is one of the major consequences of living with multimorbidity. Although healthcare aims to support multimorbid individuals to achieve better quality of life, little is known about the effect of multimorbidity on quality of life of patients attending chronic outpatient medical care in Ethiopia. Objectives This study aimed to determine the association between multimorbidity and quality of life among clients attending chronic outpatient medical care in Bahir Dar city, Northwest Ethiopia. Methods A multi-centered facility-based study was conducted among 1440 participants aged 40+ years attending chronic outpatient medical care. Two complementary methods (interview and review of medical records) were employed to collect data on sociodemographic characteristics and presence of chronic diseases. We used the short form (SF-12 V2) instrument to measure quality of life. The data were analyzed by STATA V.16 and multivariate partial proportional odds model was fitted to identify covariates associated with quality of life, adjusting for relevant confounding factors. Statistical significance was considered at p-value <0.05.Results Multimorbidity was identified in 54.8% (95% CI=52.2%-57.4%) of the sample. A significant proportion (33.5%) of the study participants had poor quality of life and one fourth (25.8%) of them had moderate quality of life. Advanced age and living with multimorbidity were associated with poor quality of life. Conversely, being female, strong social support, high socioeconomic status, and adequate functioning and satisfaction with care were the variables positively associated with higher categories of quality of life.Conclusion The magnitude of multimorbidity in this study was high and individuals living with multimorbidity had a relatively poor quality of life than those without multimorbidity. Care of people with chronic multiple conditions may need to be oriented to the realities in multimorbidity burden and its implication on quality of life. Interventions targeting modifiable associated factors and studies exploring the longitudinal effect of multimorbidity on quality of life are needed.

Cluster verification for listening learner strategies

<p>This thesis presents a comparison of statistical methodologies for cluster verification on ordinal response variables. Methodologies will be applied to a Listening Strategy dataset collected by the Language Learner Strategies research team at the National Institute of Education in Singapore. From this listening dataset, eight clusters suggested by Linguistics theory require verification. The methodologies undertaken is to find which listening strategies have been formed well. Methods used includes the proportional odds model, confirmatory factor analysis and ordinal agreement model. The proportional odds model is used to establish how well each cluster of questions is built. This is established by checking how similar questions within clusters are. The confirmatory factor analysis is used to verify how well the overall listening clusters have been built. This will be compared to clusters proposed by a statistical method. Lastly, the ordinal agreement model is applied to see how much agreement there is within each of the listening clusters. This will be able to show us which clusters is built better than the other clusters for this listening questionnaire. Results show that the prediction listening strategy has the highest level of agreement as well as no difference between questions within this cluster. The Socio-affective listening strategy has the lowest level of agreement and very strong evidence of a difference between questions within the cluster. This suggests that the prediction cluster has been formed better than the Socio-affective cluster.</p>

Cluster verification for listening learner strategies

<p>This thesis presents a comparison of statistical methodologies for cluster verification on ordinal response variables. Methodologies will be applied to a Listening Strategy dataset collected by the Language Learner Strategies research team at the National Institute of Education in Singapore. From this listening dataset, eight clusters suggested by Linguistics theory require verification. The methodologies undertaken is to find which listening strategies have been formed well. Methods used includes the proportional odds model, confirmatory factor analysis and ordinal agreement model. The proportional odds model is used to establish how well each cluster of questions is built. This is established by checking how similar questions within clusters are. The confirmatory factor analysis is used to verify how well the overall listening clusters have been built. This will be compared to clusters proposed by a statistical method. Lastly, the ordinal agreement model is applied to see how much agreement there is within each of the listening clusters. This will be able to show us which clusters is built better than the other clusters for this listening questionnaire. Results show that the prediction listening strategy has the highest level of agreement as well as no difference between questions within this cluster. The Socio-affective listening strategy has the lowest level of agreement and very strong evidence of a difference between questions within the cluster. This suggests that the prediction cluster has been formed better than the Socio-affective cluster.</p>

Perceptions of Natural Resources Use in Rwanda - A Partial Proportional Odds Model

The scarcity of natural resources is a challenge in Rwanda. Although Rwanda has improved water supplies, projections show a further increase in water demand. Particularly, agriculture continues to place further demands on water resources through intensification and industrialization. Similarly, although the dependence on biomass for cooking has improved over the past two decades in Rwanda, the ratio is still high and is projected to increase. Unfortunately, the heavy dependence on biomass is damaging to the environment in general, forests in particular. As the consumption of water and charcoal increases, it is important to study how people perceive their consumption. Research shows that people who perceive their consumption of natural resources are more likely to conserve them as they can see how much they are consuming. This study investigated perceptions of water and charcoal consumption among farmers in northern Rwanda. A survey was used to collect data from 323 farmers involved in a poultry development project in the district of Musanze, northern Rwanda. A Partial Proportional Odds Model (PPOM) was used to analyse the effect of different factors on the perception of natural resource consumption. Results indicate that the perception of charcoal consumption was associated with three variables: living in the urban section of the district, the amount of feed consumed by chicken, and elevation at which the coop is located. Results from this study can improve how food security projects are implemented by incorporating people’s perceptions of their consumption of natural resources.

Exploring the Fundamental Risk Factors of Child Malnutrition: An Application of Proportional Odds Model (POM)

Background: This study was designed with the objective to evaluate the fundamental risk factors of malnutrition among children in Lahore city and its outskirts, Pakistan. Methods: Data of four hundred and five children with age group less than 5 years was collected from Children hospital and Jinnah hospital, Lahore. We used weight-for-height anthropometric index, designed by World Health Organization (WHO), to measure the nutritional status of children. Preliminary analysis was performed by using Pearson Chi-square and Gamma statistics to check the association between factors (demographic, socio-economic and health related variables) and response variable i.e., nutritional status. Then, proportional odds model (POM) was fitted to the information collected. Results: Of 405 children included in the study, 33% (135) were found malnourished, whereas, approximately 67% (270) were found nourished. Findings of the study revealed that child’s age, maternal education level, socio-economic status, maternal age at the birth of child, low birth spacing, status of antenatal-postnatal care, incomplete vaccinations, exclusive breastfeeding, prevalence of fever, cough and recurrent episodes of diarrhea were the significant risk factors of malnutrition. Prevalence of acute respiratory infection (ARI) was turned out to be insignificant risk factor. In this study, parallel lines assumption was satisfied and proportional odds model was fitted well with p-value greater than 5% level of significance. Conclusion: This paper highlights the significant risk factors that are affecting the child’s health. Recognizing and understanding the identified risk factors would be very helpful in prevention of malnutrition in Pakistan.