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: Population trends, convictions and imprisonment: Demographic divergence, dichotomy and diversity

authors: Pool, Ian

subjects: convictions

abstract: The links between population patterns and trends, and policy and planning for the justice system is important. The trends in the number of convictions and imprisonments by regional councils are investigated for the period 1986 to 2001. This does not just focus on Custodial sentences but also looks at other types of sentences such as monetary and community sentences. Additionally, a regional estimate of the muster in prison is derived to give “normal” place of residence of those in prison. The relationship of imprisonment to other factors such as income, unemployment, sickness/invalid benefit rates, labour force participation rates and ethnicity is investigated. Some policy implications of these findings are presented.

text: Population trends, convictions and imprisonment: Demographic divergence, dichotomy and diversity The links between population patterns and trends, and policy and planning for the justice system is important. The trends in the number of convictions and imprisonments by regional councils are investigated for the period 1986 to 2001. This does not just focus on Custodial sentences but also looks at other types of sentences such as monetary and community sentences. Additionally, a regional estimate of the muster in prison is derived to give “normal” place of residence of those in prison. The relationship of imprisonment to other factors such as income, unemployment, sickness/invalid benefit rates, labour force participation rates and ethnicity is investigated. Some policy implications of these findings are presented.

year: 2006-02

title: Breaking the browsing barrier for historic search of newspaper texts

authors: Keegan, Te Taka Adrian Gregory

subjects: computer science

abstract: Traditionally, to find information in a newspaper collection it has been necessary to painstakingly browse through the individual issues hoping that relevant words will catch your eye. Researchers of New Zealand’s encounter history received a boost when, in 1996, the Alexander Turnbull Library produced a collection on microfiche totaling almost 18,000 pages and covering a printing period from 1842 to 1933. While having all the newspapers in a single collection was a large step forward, browsing or searching for information in this microfiche collection was still time consuming.
By incorporating the collection into a digital library with an Internet interface, and by enabling full-text search, we have broken this browsing barrier. Making the information available this way is even more significant as the majority of the collection is written in the Māori language; we have created a Māori language resource that is sorely needed by education and research institutes, and one that provides quick and accurate access to the previously obscure source. The process involved in developing this unique digital library collection, the advantages of traditional newspaper stored in this medium, and the possibilities that we intend to investigate in the future, will be discussed in this paper.

text: Breaking the browsing barrier for historic search of newspaper texts Traditionally, to find information in a newspaper collection it has been necessary to painstakingly browse through the individual issues hoping that relevant words will catch your eye. Researchers of New Zealand’s encounter history received a boost when, in 1996, the Alexander Turnbull Library produced a collection on microfiche totaling almost 18,000 pages and covering a printing period from 1842 to 1933. While having all the newspapers in a single collection was a large step forward, browsing or searching for information in this microfiche collection was still time consuming.
By incorporating the collection into a digital library with an Internet interface, and by enabling full-text search, we have broken this browsing barrier. Making the information available this way is even more significant as the majority of the collection is written in the Māori language; we have created a Māori language resource that is sorely needed by education and research institutes, and one that provides quick and accurate access to the previously obscure source. The process involved in developing this unique digital library collection, the advantages of traditional newspaper stored in this medium, and the possibilities that we intend to investigate in the future, will be discussed in this paper.

year: 2001