Sentence examples for models for ordinal from inspiring English sources

Focuses on making students familiar with the most important quantitative methods (e.g., logit/probit models, models for ordinal and nominal outcomes, count models, event history models).

1998 Sobel, M. E. "Some Log-Linear and Log-Nonlinear Models for Ordinal Scales with Midpoints, with an Application to Public Opinion Data". Sociological Methodology, 1998: 263-292.

Regression models for ordinal data were used to analyze their variability, and its effect on growth patterns was evaluated by comparing parameters of the resulting growth models.

Most popular models for ordinal response fall outside the multiplicative intercept class and one should be cautious while performing a naive prospective analysis of such data as the bias could be substantial.

Using mixed models for ordinal data, this study investigated how to optimally analyze FMS data, and then used the resulting models to examine the development of symptoms over time in detail.

The daily spawning pattern of the European anchovy, Engraulis encrasicolus, was assessed using a novel methodology that included the analysis of the hourly distribution of imminent and recent spawning stages using a combination of generalised additive models (GAMs) and of multinomial models for ordinal categorical data.

To deal with multicollinearity, we also ran a proportional odds model for ordinal logistic regression to assess the marginal effects of the relationship between carer and care-recipient health given that carer health was a five-modality categorical variable.

Despite the differences in the factor loadings, which seemed to suggest a two-parameter (2PL) model, the items met the common criterion of fit to the Rasch model for ordinal data (hypothesis III) as calculated using ACER ConQuest 2.0: partial credit model (Masters 1982), N = 338, mean of latent ability distribution = 0.

By fitting an ordinary ORM with robust standard errors for clustered data instead of fitting a variance components model (a multilevel model for ordinal responses), we were treating the within-cluster dependence as a "nuisance" and not as a phenomenon that we were interested in [36].

For predictive modelling, we adopted a recently established statistical technique L1-penalised continuation-ratio model for ordinal outcomes[ 28].

The expected level of the two SRH measurements was modelled using a cumulative probit model for ordinal data.