A methodology for analyzing general categorical data with misclassification errors with an application in studying seat belt effectiveness. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Sources of information on federal programs providing categorical aid for local school districts and other educational agencies.

"July, 1971."

Summaries of categorical pretreatment standards /

"November, 1987."

Multiple classification analysis ; a report on a computer program for multiple regression using categorical predictors /

Bibliographical footnotes.

A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

Measuring work group diversity of categorical variables

The article presents abstracts of papers for a conference on research methods including "On the Folly of Rewarding A While Hoping for B: A Critical Assessment of Theory Development," "All That Jazz: A Methodological Story of Stories," and "An Accounting of Counting: Universalism, Particularism, and the Counting of Qualitative Data."

Classification of Chenopodium Genus Populations and Species Based on Continuous and Categorical Variables

2000 Mathematics Subject Classification: 62P10, 62H30

Categorical Differences in Scores of Students with Disabilities on the Florida Comprehensive Assessment Test

Students with disabilities participate in statewide assessments as required by federal law, and the success of schools is contingent on this group making Adequate Yearly Progress. This report on research includes the findings of differences between disability categories scoring proficient on the FCAT from the years 2005 through 2010.

Sedimentology, structure and age estimate of five continental slope submarine landslides, eastern Australia

Sedimentological and accelerator mass spectrometry (AMS) 14C data provide estimates of the structure and age of five submarine landslides (∼0.4–3 km3) present on eastern Australia's continental slope between Noosa Heads and Yamba. Dating of the post-slide conformably deposited sediment indicates sediment accumulation rates between 0.017 m ka–1 and 0.2 m ka–1, which is consistent with previous estimates reported for this area. Boundary surfaces were identified in five continental slope cores at depths of 0.8 to 2.2 m below the present-day seafloor. Boundary surfaces present as a sharp colour-change across the surface, discernible but small increases in sediment stiffness, a slight increase in sediment bulk density of 0.1 g cm–3, and distinct gaps in AMS 14C ages of at least 25 ka. Boundary surfaces are interpreted to represent a slide plane detachment surface but are not necessarily the only ones or even the major ones. Sub-bottom profiler records indicate that: (1) the youngest identifiable sediment reflectors upslope from three submarine landslides terminate on and are truncated by slide rupture surfaces; (2) there is no obvious evidence for a post-slide sediment layer draped over, or burying, slide ruptures or exposed slide detachment surfaces; and (3) the boundary surfaces identified within the cores are unlikely to be near-surface slide surfaces within an overall larger en masse dislocation. These findings suggest that these submarine landslides are geologically recent (<25 ka), and that the boundary surfaces are either: (a) an erosional features that developed after the landslide, in which case the boundary surface age provides a minimum age for the landslide; or (b) detachment surfaces from which slabs of near-surface sediment were removed during landsliding, in which case the age of the sediment above the boundary surface indicates the approximate age of landsliding. While an earthquake-triggering mechanism is favoured for the initiation of submarine landslides on the eastern Australian margin, further evidence is required to confirm this interpretation.

Güímar and la Orotava mega-landslides (Tenerife) and Tsunamis deposits in Canary Islands

[EN] More than 20 mega-landslides have been described in the Canary Islands affecting the flanks of the volcanic edifices. Güímar and La Orotava landslides, in Tenerife, are two exceptional cases due to their huge dimensions and outstanding geomorphological features. The estimated volume of these landslides exceed tens of cubic km. Tsunami deposits have been also identified in some of the islands of the archipelago probably associated to the large landslides of the islands flanks. An investigation has been carried out to explain the causes of these large instability processes and their failure mechanisms.

A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method.