Analyzing Ordered Responses: A Review of the Ordered Probit Model

By A. Daykin, P. Moffatt. 2002

274 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/293bcd0978e189ed439062882e6e49bfb74755b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Xinyu Cao
2. S. Handy
3. Patricia L. Mokhtarian
4. J. Jordan
5. J. Thies
6. David R. Lee
7. M. Wollni
8. Chris Zegras

In Statistics and Probability

1. J. Jordan
2. J. Thies
3. Xinyu Cao
4. David R. Lee
5. S. Handy
6. M. Wollni
7. Patricia L. Mokhtarian

Citations in the corpus

• Skills for creative industries graduate success (2011, unknown): “An ordered probit model is appropriate when the outcome variable is ordinal, as ordinary linear regression models make the assumption that the differences between categories of the outcome variable are equal, which is not necessarily the case when a Likert scale is used (Daykin & Moffatt, 2002).”
• Modeling Ordered Choices: A Primer and Recent Developments (2008, unknown): “…science oriented introductions to the ordered choice model appear in journal articles such as Winship and Mare (1984), Becker and Kennedy (1992), Daykin and Moffatt (2002) and Boes and Winkelmann (2006a), and in textbook treatments including Maddala (1983), DeMaris (2004), Long (1997), Long and…”
• Food Waste and the ‘Green’ Consumer (2017, background): “It has some drawbacks, such as the complexity of method (Baum, 2006), but it is surprisingly easy to estimate with modern specialist software and interpretation of the results is also straightforward (Daykin and Moffatt, 2002).”
• Conservation agriculture, organic marketing, and collective action in the Honduran hillsides (2010, methodology): “The ordinal nature of the dependent variable motivates the use of an ordered probit model (Daykin and Moffatt, 2002; Greene, 2008).”
• Policy feedback and support for the welfare state (2013, background): “Ordered probit is an extension of the standard probit model that allows for the existence of multiple values of the dependent variable through the calculation of unique intercepts (or thresholds) for each category of the dependent variable (Daykin and Moffatt, 2002).”
• Modeling Ordered Choices: A Primer (2010, unknown): “, Daykin and Moffatt (2002).] There is no natural conditional mean function in the model.”
• Are Female-Headed Households More Food Insecure? Evidence from Bangladesh (2010, unknown): “The ordered probit model overcomes this problem because it estimates the parameters of the underlying distribution, rather than the response itself (Daykin & Moffatt, 2002, p. 159).”
• Cross-Sectional and Quasi-Panel Explorations of the Connection between the Built Environment and Auto Ownership (2007, background): “This assumes an underlying latent continuous variable, Y , representing a household's propensity to own cars (Daykin and Moffatt, 2002).”