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Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.


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Book ChapterDOI
26 May 2014
TL;DR: In this article, a reformulation of the PLS algorithm, named Ordinal PLS (OrdPLS), is introduced, which properly deals with ordinal variables, in particular when the number of categories of the items in the questionnaire is small (4 or 5).
Abstract: The partial least squares (PLS) is a popular path modeling technique commonly used in social sciences. The traditional PLS algorithm deals with variables measured on interval scales while data are often collected on ordinal scales. A reformulation of the algorithm, named Ordinal PLS (OrdPLS), is introduced, which properly deals with ordinal variables. Some simulation results show that the proposed technique seems to perform better than the traditional PLS algorithm applied to ordinal data as they were metric, in particular when the number of categories of the items in the questionnaire is small (4 or 5) which is typical in the most common practical situations.

13 citations

Journal ArticleDOI
TL;DR: In this paper, three different estimation methods on real data were performed with ordinal variables and empirical results obtained from the different estimations on given real large sample educational data were investigated and compared to recent simulation results.
Abstract: In the behavioral sciences, response variables are often non-continuous, ordinal variables. Conventional structural equation models (SEMs) have been generalized to accommodate ordinal responses. In this study, three different estimation methods on real data were performed with ordinal variables. Empirical results obtained from the different estimation methods on given real large sample educational data were investigated and compared to recent simulation results. As a result, even very large sample is available, model estimations and fits for ordinal data are affected from inconvenient estimation methods thus it is concluded that asymptotically distribution free estimation method specialized for ordinal variables is more convenient way to model ordinal variables.

13 citations

Journal ArticleDOI
TL;DR: In this paper, it is shown that using conventional multivariate procedures for evaluating ordinal data should imply a shift from a metric space to a topological data space; as such the use of ordinal datasets does not represent a serious methodological error, provided that results are interpreted accordingly.
Abstract: In a recent Forum paper, it is argued that, in most studies, ordinal data such as the Braun-Blanquet abundance/dominance scale are not properly treated by multivariate methods. This is because conventional multivariate methods are generally adequate for ratio-scale variables only, while for ordinal variables differences between states and their ratios are not interpreted. Conversely, in this paper it is shown that using conventional multivariate procedures for evaluating ordinal data should imply a shift from a metric space to a topological data space; as such the use of ordinal data does not represent a serious methodological error, provided that results are interpreted accordingly.

13 citations

Journal ArticleDOI
01 Jul 2002
TL;DR: In this paper, the effects of ordinal regressors in linear regression models and in limited dependent variable models are investigated, and it is shown that using ordinal indicators only leads to correct answers in a few special cases.
Abstract: This paper investigates the effects of ordinal regressors in linear regression models and in limited dependent variable models. Each ordered categorical variable is interpreted as a rough measurement of an underlying continuous variable as it is often done in microeconometrics for the dependent variable. It is shown that using ordinal indicators only leads to correct answers in a few special cases. In most situations, the usual estimators are biased. In order to estimate the parameters of the model consistently, the indirect estimation procedure suggested by Gourieroux et al. (1993) is applied. To demonstrate this method, first a simulation study is performed and then in a second step, two real data sets are used. In the latter case, continuous regressors are transformed into categorical variables to study the behavior of the estimation procedure. The method is extended to the case of limited dependent variable models. In general, the indirect estimators lead to adequate results.

13 citations

Journal ArticleDOI
TL;DR: This paper proposes an approach that defines an assigning score system for an ordinal categorical variable based on underlying continuous latent distribution with interpretation by using three case study examples and shows that the proposed score system is well for skewed ordinals categorical data.
Abstract: Ordinal data are the most frequently encountered type of data in the social sciences. Many statistical methods can be used to process such data. One common method is to assign scores to the data, convert them into interval data, and further perform statistical analysis. There are several authors who have recently developed assigning score methods to assign scores to ordered categorical data. This paper proposes an approach that defines an assigning score system for an ordinal categorical variable based on underlying continuous latent distribution with interpretation by using three case study examples. The results show that the proposed score system is well for skewed ordinal categorical data.

13 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023102
2022191
202188
202093
201979
201873