title: Structural equation models: From paths to networks (Westland 2019)

authors: Sarstedt, Marko

subjects: Science & Technology

abstract: Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In the past decades, SEM has become a standard statistical analysis technique in behavioral, educational, psychological, and social science researchers’ repertoire.

From a technical perspective, SEM was developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) low-dimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.

text: Structural equation models: From paths to networks (Westland 2019) Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In the past decades, SEM has become a standard statistical analysis technique in behavioral, educational, psychological, and social science researchers’ repertoire.

From a technical perspective, SEM was developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) low-dimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.

year: 2020

title: Designing for Inaccessible People and Places

authors: Bowen, Judy

subjects: Science & Technology

abstract: New Zealand forestry has the highest number of accidents and fatalities than any other NZ industry. Worker fatigue, work environment, and worker demographics all contribute to these high numbers. We have been investigating ways of tackling these problems using wearable and sensor-based technology. Two of the challenges faced by this project are: the personal nature of data collected by wearable technology and the lack of regular access to workers to take part in user-centered design activities. In this paper, we describe the use of Lean UX methods with proxy participants and proxy technology to explore key aspects of a proposed technical solution. We show that from these experimental studies, we were able to draw appropriate conclusions on which to base the development of a prototype designed to support forestry worker safety.

text: Designing for Inaccessible People and Places New Zealand forestry has the highest number of accidents and fatalities than any other NZ industry. Worker fatigue, work environment, and worker demographics all contribute to these high numbers. We have been investigating ways of tackling these problems using wearable and sensor-based technology. Two of the challenges faced by this project are: the personal nature of data collected by wearable technology and the lack of regular access to workers to take part in user-centered design activities. In this paper, we describe the use of Lean UX methods with proxy participants and proxy technology to explore key aspects of a proposed technical solution. We show that from these experimental studies, we were able to draw appropriate conclusions on which to base the development of a prototype designed to support forestry worker safety.

year: 2021-01-01

title: Haumanu hauora: A commentary on strengthening health institution responsiveness to Māori health in the face of climate change

authors: Masters-Awatere, Bridgette

abstract: Climate change is the biggest threat to humanity through compounding ecological disasters. A focus on global averages tends to hide dramatic differences and mask health disparities that exist for Indigenous people. For 21 years, district health boards (DHBs) were responsible for providing or funding the provision of health services across Aotearoa. The introduction of the Pae Ora (Healthy Futures) Act 2022 disestablished DHBs and offered an opportunity to reshape health in ways that give serious consideration to climate change impacts. The need to give greater consideration to Indigenous people in climate change conversations is essential. The Waitangi Tribunal highlights areas where the Crown needs to work to improve Māori health outcomes. A clear deficit in existing policy process means a lack of preparedness for the intersecting health crises vulnerable Māori will experience in the face of climate change. Structural change is needed to strengthen health institution responsiveness to Māori health needs.

text: Haumanu hauora: A commentary on strengthening health institution responsiveness to Māori health in the face of climate change Climate change is the biggest threat to humanity through compounding ecological disasters. A focus on global averages tends to hide dramatic differences and mask health disparities that exist for Indigenous people. For 21 years, district health boards (DHBs) were responsible for providing or funding the provision of health services across Aotearoa. The introduction of the Pae Ora (Healthy Futures) Act 2022 disestablished DHBs and offered an opportunity to reshape health in ways that give serious consideration to climate change impacts. The need to give greater consideration to Indigenous people in climate change conversations is essential. The Waitangi Tribunal highlights areas where the Crown needs to work to improve Māori health outcomes. A clear deficit in existing policy process means a lack of preparedness for the intersecting health crises vulnerable Māori will experience in the face of climate change. Structural change is needed to strengthen health institution responsiveness to Māori health needs.

year: 2025