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Physical Sciences and Mathematics Commons™
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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Forward And Backward Continuation Ratio Models For Ordinal Response Variables, Xing Liu, Haiyan Bai
Forward And Backward Continuation Ratio Models For Ordinal Response Variables, Xing Liu, Haiyan Bai
Journal of Modern Applied Statistical Methods
There are different types of continuation ratio (CR) models for ordinal response variables. The different model equations, corresponding parameterizations, and nonequivalent results are confusing. The purpose of this study is to introduce different types of forward and backward CR models, demonstrate how to implement these models using Stata, and compare the results using data from the Educational Longitudinal Study of 2002 (ELS:2002).
Fitting Stereotype Logistic Regression Models For Ordinal Response Variables In Educational Research (Stata), Xing Liu
Journal of Modern Applied Statistical Methods
The stereotype logistic (SL) model is an alternative to the proportional odds (PO) model for ordinal response variables when the proportional odds assumption is violated. This model seems to be underutilized. One major reason is the constraint of current statistical software packages. Statistical Package for the Social Sciences (SPSS) cannot perform the SL regression analysis, and SAS does not have the procedure developed to directly estimate the model. The purpose of this article was to illustrate the stereotype logistic (SL) regression model, and apply it to estimate mathematics proficiency level of high school students using Stata. In addition, it compared …
Fitting Proportional Odds Models To Educational Data With Complex Sampling Designs In Ordinal Logistic Regression, Xing Liu, Hari Koirala
Fitting Proportional Odds Models To Educational Data With Complex Sampling Designs In Ordinal Logistic Regression, Xing Liu, Hari Koirala
Journal of Modern Applied Statistical Methods
The conventional proportional odds (PO) model assumes that data are collected using simple random sampling by which each sampling unit has the equal probability of being selected from a population. However, when complex survey sampling designs are used, such as stratified sampling, clustered sampling or unequal selection probabilities, it is inappropriate to conduct ordinal logistic regression analyses without taking sampling design into account. Failing to do so may lead to biased estimates of parameters and incorrect corresponding variances. This study illustrates the use of PO models with complex survey data to predict mathematics proficiency levels using Stata and compare the …
Jmasm 32: Multiple Imputation Of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices?, Jennifer E. V. Lloyd, Jelena Obradović, Richard M. Carpiano, Frosso Motti-Stefanidi
Jmasm 32: Multiple Imputation Of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices?, Jennifer E. V. Lloyd, Jelena Obradović, Richard M. Carpiano, Frosso Motti-Stefanidi
Journal of Modern Applied Statistical Methods
A pedagogical tool is presented for applied researchers dealing with incomplete multilevel, longitudinal data. It explains why such data pose special challenges regarding missingness. Syntax created to perform a multiply-imputed growth modeling procedure in Stata Version 11 (StataCorp, 2009) is also described.
Ordinal Regression Analysis: Using Generalized Ordinal Logistic Regression Models To Estimate Educational Data, Xing Liu, Hari Koirala
Ordinal Regression Analysis: Using Generalized Ordinal Logistic Regression Models To Estimate Educational Data, Xing Liu, Hari Koirala
Journal of Modern Applied Statistical Methods
The proportional odds (PO) assumption for ordinal regression analysis is often violated because it is strongly affected by sample size and the number of covariate patterns. To address this issue, the partial proportional odds (PPO) model and the generalized ordinal logit model were developed. However, these models are not typically used in research. One likely reason for this is the restriction of current statistical software packages: SPSS cannot perform the generalized ordinal logit model analysis and SAS requires data restructuring. This article illustrates the use of generalized ordinal logistic regression models to predict mathematics proficiency levels using Stata and compares …
Ordinal Regression Analysis: Predicting Mathematics Proficiency Using The Continuation Ratio Model, Xing Liu, Ann A. O'Connell, Hari Koirala
Ordinal Regression Analysis: Predicting Mathematics Proficiency Using The Continuation Ratio Model, Xing Liu, Ann A. O'Connell, Hari Koirala
Journal of Modern Applied Statistical Methods
One commonly used model to analyze ordinal response data is the proportional odds (PO) model. However, if research interest is focused on a particular category and if an individual must pass through lower categories before achieving a higher level, the continuation ratio (CR) model is a more appropriate choice than the PO model. In addition, statistical software, such as Stata and SAS, may use different techniques to estimate the parameters. The CR model is used to illustrate the analysis of ordinal data in education using Stata and SAS and compares the results of fitting the CR model between these two …
Ordinal Regression Analysis: Fitting The Proportional Odds Model Using Stata, Sas And Spss, Xing Liu
Ordinal Regression Analysis: Fitting The Proportional Odds Model Using Stata, Sas And Spss, Xing Liu
Journal of Modern Applied Statistical Methods
Researchers have a variety of options when choosing statistical software packages that can perform ordinal logistic regression analyses. However, statistical software, such as Stata, SAS, and SPSS, may use different techniques to estimate the parameters. The purpose of this article is to (1) illustrate the use of Stata, SAS and SPSS to fit proportional odds models using educational data; and (2) compare the features and results for fitting the proportional odds model using Stata OLOGIT, SAS PROC LOGISTIC (ascending and descending), and SPSS PLUM. The assumption of the proportional odds was tested, and the results of the fitted models were …