Massey Documents by Type

Permanent URI for this communityhttps://mro.massey.ac.nz/handle/10179/294

Browse

Start date: 2021Subject: search.filters.subject.490508 Statistical data science

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Using latent profile analysis to understand caregiver response to their rainbow children in Aotearoa New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Psychology at Massey University, Distance, Aotearoa New Zealand
    (Massey University, 2025) Westerbaan, Andrina Anne
    Parent and primary caregiver acceptance, rejection or a combination of acceptance and rejection of a child with a rainbow/LGBTQIA+ identity is influential in either reducing or contributing to poor health and well-being outcomes experienced by young rainbow people. Considering the persistent health and well-being disparities experienced by rainbow people and the influence of parent and caregiver response on their rainbow children, there is a need to better understand this nuanced group of parents and caregivers. This cross-sectional study replicated the work of Clark et al. (2022) who proposed that parents with rainbow children could be classified into three groups based on response to their rainbow child. It is unclear if the same classifications apply to Aotearoa New Zealand’s unique cultural context. Online survey responses from parents and caregivers (N = 140) living in Aotearoa New Zealand, with at least one rainbow identifying child under 30 years of age, was collected to clarify whether the same three classes exist. Descriptive statistics were run using SPSS. Latent profile analysis was run using tidyLPA and mclust in R Studio to identify parent and caregiver response classes, of which two were found: positive (n = 120, 85.71%) and mixed (n = 20, 14.29%). An examination of the differences between response class, demographics, time since and age at initial rainbow identity disclosure, caregiver attachment style, parenting style, religiosity and ethnic group acceptance were conducted using SPSS. Significant differences were detected between response classes based on participant gender identity, ethnicity, relationship to their rainbow child, parenting style, and religiosity. Results indicate that there is a distinct group of parents and caregivers in Aotearoa New Zealand who may benefit from additional support in addressing conflicting responses towards their rainbow child so that they can become a safer and more encouraging support for their rainbow child.
  • Thumbnail Image
    Item
    Contributions to food safety acceptance sampling plans : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics, Massey University, School of Mathematical and Computer Sciences
    (Massey University, 2023) Thevaraja, Mayooran
    An appropriate sampling inspection method is an essential tool for risk assessment in the food industry. A representative sampling approach will be helpful to reduce risk while minimising the sampling costs. Consequently, food manufacturers are employing efficient sampling approaches to assure food safety. In the food safety field, microbiological or other contamination often spreads unevenly across the production. Many factors are involved in the microbial risk assessment, such as (1) the amount of sample used for inspection, (2) what sampling methods were applied, (3) laboratory testing procedures, (4) physical sampling of materials from lots/batches of products and (5) the mixing of initially collected samples. This study focusses on improved sampling inspection approaches to reduce microbiological risk in food products. Part of this research also included developing open-source R packages to generate graphical displays for probabilistic risk assessment for practitioners. A single “wrapper” package is also provided to install all the newly developed packages in a single step.
  • Thumbnail Image
    Item
    Some statistical techniques for analysing Bluetooth tracking data in traffic modelling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
    (Massey University, 2021) Aslani, Ghazaleh
    The economy and the environment are both affected by traffic congestion. People spend time stuck in traffic, which limits their free time. Every city's road infrastructure is under increased pressure, particularly in large cities, due to population growth and vehicle ownership patterns. Therefore, traffic control and management are crucial to reducing traffic congestion problems and effectively using existing road infrastructure. Bluetooth is a commonly used wireless technology for short-distance data exchange. This technology allows all mobile phones, GPS systems, and in-vehicle applications such as navigation systems to connect with the personal devices of drivers and passengers. A Media Access Control (MAC) address is a unique electronic identifier used by each Bluetooth device. The concept is that, while a Bluetooth-equipped device travels along a road, its MAC address, detection time, and location can be detected anonymously at different locations. Bluetooth technology can be integrated into Intelligent Transportation Systems (ITS) to enable better and more effective traffic monitoring and management, hence reducing traffic congestion. This thesis aims to develop some statistical methods for analysing Bluetooth tracking data in traffic modelling. One of the challenges of using Bluetooth data, particularly for travel time estimation, is multiple Bluetooth detections, which occur when a Bluetooth sensor records a Bluetooth device several times while it passes through the detection zone. We employ cluster analysis to look at the possibility of extracting meaningful traffic information from multiple detections, and the observed gap distribution, which is the time difference between records when multiple detections occur. We also develop a novel regression method to investigate the relationship between data from Bluetooth and Automatic Traffic Counts (ATCs) through weighted regression analysis, in order to explore potential causes of bias in the representativeness of Bluetooth detections. Finally, we seek the practical objective of recovering ATC from Bluetooth data as a statistical calibration problem, following the development of a new time-varying coefficients Poisson regression model.