An Ordinal Regression Model Using Dealer Satisfaction Data

Staus, Alexander

doi:10.2139/ssrn.1007217

Cited by 5 publications

(3 citation statements)

References 24 publications

(16 reference statements)

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“…The Probit link function provides better predictions of the dependent variable when the responses are normally distributed. Finally, the Cauchit link function is mainly used when the dependent variable has many extreme values (Staus, 2008).…”

Section: Applied Methodsmentioning

confidence: 99%

“…To decide which link function gives good fits for the examined data, the distribution of values for the outcome variable was examined based on the histogram for the dependent variable. Based on (Staus, 2008) the Logit link function was selected to run the ordinal regression analysis of our models, since the middle category of the dependent variable was more probable. The higher and lower categories recorded less responses (Fig.…”

Section: Impact Of Social Media Use On Activity/mobility Planningmentioning

confidence: 99%

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Social influence and impact of social media on users’ mobility decisions

Karatsoli

Nathanail

2021

J. sustain. dev. transp. logist.

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Section: Applied Methodsmentioning

confidence: 99%

Section: Impact Of Social Media Use On Activity/mobility Planningmentioning

confidence: 99%

Social influence and impact of social media on users’ mobility decisions

Karatsoli

Nathanail

2021

J. sustain. dev. transp. logist.

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“…The major challenge of modeling is to select the most effective explanatory variables which need to be kept in the model and here in the ordinal regression model is to also select the link function such as logit link, complementary log-log link, and probit link since it has directly influence to the outcome. Hales and Chakavorty (2006) and Staus (2008) all discussed this topic in their study. The principal concentration of this paper was to formulate the ordinal regression model, ordinal regression analysis usage, and the result interpretations.…”

Section: Introductionmentioning

confidence: 99%

Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response

Kadir

Omer

2021

Cihan U Erbil J H SOC SCI

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Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher features for example, capacity and clarity to encourage the students to make them study of his subjects, capacity to establish the lesson also with trainings and lectures. These aspects are not only attributable to motivate students, but also age, gender, prior experiences, As more and more the effectiveness of teaching is becoming even more significant in school evaluation system, it is, indeed, essential to discover how to assess it and find the significant related factors for giving the best rank of feedback to their tutors. This paper focuses the assessment of teaching effectiveness forum from students’ perspective “Feedback”, examine the questionnaires provided to the students of Cihan University at the end of their courses and discover the most effected teacher’s characteristics. The outcome variable (Student’s Feedback) was recorded on well-ordered, five-point scale of Likert provided by the students, to a set of independent variables to teacher level. The major techniques elaborated in the model fitting for ordinal regression were stating which independent variables are most likely to be kept in the model and selecting the link function such as, (logit link, complementary log-log link, negative log-log link and probit link) which verified the model suitability. Further to that, several statistical diagnoses have conducted like the model fitting, classification accuracy and the validity assumptions of the model, which is parallel lines, were fundamentally calculated in choosing the best fitted model. The dataset implemented in the analysis entails of almost (21566) respondents to the formed questionnaire in relation to courses of Cihan University for the 2018 – 2019 academic year.

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Standard and Shuffled Halton Sequences in a Mixed Logit Model

Staus

2008

SSRN Journal

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Modeling consumer choice in different areas has lead to an increase use of discrete choice models. Probit or Multinomial Logit Models are often the base of further empirical research of consumer choice. In some of these models the equations to solve have no closed-form expression. They include multi-dimensional integrals which can not be solved analytically.Simulation methods have been developed to approximate a solution for these integrals. This paper describes the Standard Halton sequence and a modification of it, the Shuffled Halton sequence. Both are simulation methods which can reduce computational effort compared to a random sequence. We compare the simulation methods in their coverage of the multi-dimensional area and in their estimation results using data of consumer choice on grocery store formats.

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An Ordinal Regression Model Using Dealer Satisfaction Data

Cited by 5 publications

References 24 publications

Social influence and impact of social media on users’ mobility decisions

Social influence and impact of social media on users’ mobility decisions

Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response

Standard and Shuffled Halton Sequences in a Mixed Logit Model

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