Applied Ordinal Logistic Regression Using Stata
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software.
Applied Ordinal Logistic Regression Using Stata
Applied Ordinal Logistic Regression Using Stata by Xing Liu is an approachable introduction to ordinal logistic regression for students and applied researchers in education, the behavioral sciences, the social sciences, and related fields. This book is a practical guide to understanding and implementing a variety of models for ordinal data.
Liu first focuses on the use of Stata, including an overview of Stata's interface features, command syntax, and help system. Readers are also introduced to commands for data management, graphics, and basic statistics. Then the discussion turns to logistic regression for binary outcomes and ordinal logistic regression for ordered outcomes. Beyond this, Liu covers more advanced models such as generalized ordinal logit, continuation ratio, adjacent categories logit, stereotype logit, and multilevel ordinal logit models.
Below we use the ologit command to estimate an ordered logistic regressionmodel. The i. before pared indicates that pared is a factorvariable (i.e.,categorical variable), and that it should be included in the model as a seriesof indicator variables. The same goes for i.public.
In the output above the results are displayed as proportional odds ratios.We would interpret these pretty much as we would odds ratios from a binarylogistic regression. For pared, we would say that for a one unit increasein pared, i.e., going from 0 to 1, the odds of high apply versus the combinedmiddle and low categories are 2.85 greater, given that all of the othervariables in the model are held constant. Likewise, the odds of thecombined middle and high categories versus low apply is 2.85 times greater,given that all of the other variables in the model are held constant. For a one unitincrease in gpa, the odds of the high category of applyversus the low and middle categories of apply are 1.85 times greater, given that theother variables in the model are held constant. Because of theproportional odds assumption (see below for more explanation), the sameincrease, 1.85 times, is found between low apply and the combinedcategories of middle and high apply.
Both of the above tests indicate that we have not violated the proportionalodds assumption. If we had, we would want to run our model as ageneralized ordered logistic model using gologit2. You need to download gologit2 by typing search gologit2.
This chapter introduces logistic regression models for binary data. It will introduce concepts of odds, odds ratio, and goodness-of-fit statistics of the model; describe how to test significance of predictors; and show how to interpret parameter estimates. Following the description of data, two logistic regression models using Stata will be illustrated with step-by-step instructions. Stata commands and output will be explained in detail. The focus of this chapter is on fitting binary logistic regression models using Stata, as well as on interpreting and presenting the results. After reading this chapter, you should be able to
This chapter introduces proportional odds models for ordinal response variables. It starts with an introduction of the model followed by a discussion of the odds and odds ratios in the model, goodness-of-fit statistics of the model, the Brant test of the proportional odds assumption, and how to interpret parameter estimates. After a description of the data, two proportional odds models using Stata are illustrated with step-by-step instructions. Stata commands and output are explained in detail. This chapter focuses on fitting proportional odds models using Stata, as well as on interpreting and presenting the results. After reading this chapter, you should be able to
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 the results from fitting PO models and generalized ordinal logistic regression models.
To account for the ordinal nature of outcomes, various ordinal logistic regression models exist. The logits of these various ordinal regression models are formed in a variety of ways, for instance, POM (cumulated higher categories compared with remaining cumulated lower categories), CRM (cumulated higher categories compared to lower category only), and ACM (between any of two consecutive categories). As a result, each form of the logit has advantages and disadvantages; one can utilize the models based on their requirements. The proportional odds model (POM) is frequently utilized in epidemiological and biological applications. However, the continuation ratio model is also utilized on occasion [14, 38]. Our research objective of the statistical inquiry is centered on the decision of POM and CRM models. As is clear, the interpretation we do under POM would be more rational and understandable in the case of SES. If the condition of proportionality is breached, the model of PPOM could be a preferable option [39, 40]. Furthermore, the likelihood ratio test and AIC were used to evaluate the choice between POM and PPOM.
In the univariable analysis, the covariates of gender, age of HOF, saving habit, education status of HOF, and family size were found to be statistically significant at the univariable level. This indicates that they are important factors that might affect the SES of the household. However, religion and marital status were not significant factors for the SES of households at a 25% level of significance. Therefore, based on this result, it is better to ignore the religion and marital status covariate and shall do our multivariable ordinal logistic analysis using the remaining factors. Hence, the effects of the covariates of gender, age of HOF, saving habit, education status of HOF, and family size on the SES of households shall better be interpreted using the multivariable ordinal logistic regression analysis.
Five variables were chosen for the stepwise regression from seven available variables based on their crude association at a 25% level of significance. Before developing the multivariable ordinal logistic regression model, we have checked the collinearity and the first-order effect modifier was evaluated. However, in the current dataset, they were not present.
This study looked at identifying factors associated with socioeconomic status (SES) by applying ordinal logistic regression. According to the findings of this study, ordinal regression may be a better alternative in the case of the ordinal form of the outcome. Furthermore, PPOM may be a preferable option if any of the covariates violate the proportionality requirement. This almost certainly ensures that the result and the inferences and implications that follow are correct. Finally, the most likely associated indicators with the SES of families in Tepi town, Southwest Ethiopia, were family size, age, saving habit, and education level. This suggests that the application of the OLR model for ordered outcomes is a preferable option, and in addition, improvement of SES based on significant covariates is needed.
Introduction: We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex.
Methods: We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings.
Discussion: The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.
I am writing a research protocol which requires an ordinal logistic regression to be done (Kleinbaum and Klein, 2002). my outcome is type of neonatal admission with three levels, namely, NICU admission, High Risk admission, and Well Baby admission. My my predictors are number of antenatal care visits, age of gestation, birth weight, and two interaction terms number of visit x age of gestation and number of visit x birth weight. 041b061a72