A statistical model for the analysis of ordinal level dependent variables

McKelvey, Richard D.; Zavoina, William

doi:10.1080/0022250x.1975.9989847

Cited by 2,070 publications

(974 citation statements)

References 5 publications

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“…The index k, for example, may take values of "No injury" (k = 1), "Possible injury" (k = 2), "Non-incapacitating injury" (k = 3), "Incapacitating injury" (k = 4), and "Fatal injury" (k = 5). The equation system for the standard ordered response logit (ORL) model is (see McElvey and Zavoina, 1975, who first proposed the ORL model):…”

Section: The Standard Ordered Response Model and Its Limitationsmentioning

confidence: 99%

A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes

Eluru

Bhat

Hensher

2008

Accident Analysis & Prevention

506

229

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This paper proposes an econometric structure for injury severity analysis at the level of individual accidents that recognizes the ordinal nature of the categories in which injury severity are recorded, while also allowing flexibility in capturing the effects of explanatory variables on each ordinal category and allowing heterogeneity in the effects of contributing factors due to the moderating influence of unobserved factors. The model developed here, referred to as the mixed generalized ordered-response logit (MGORL) model, generalizes the standard ordered-response models used in the extant literature for injury severity analysis. To our knowledge, this is the first such formulation to be proposed and applied in the econometric literature in general, and in the safety analysis literature in particular.The MGORL model is applied to examine non-motorist injury severity in accidents in the USA, using the 2004 General Estimates System (GES) database. The empirical findings emphasize the inconsistent results obtained from the standard ordered response model. An important policy result from our analysis is that the general pattern and relative magnitude of elasticity effects of injury severity determinants are similar for pedestrians and bicyclists. The analysis also suggests that the most important variables influencing non-motorist injury severity are the age of the individual (the elderly are more injury-prone), the speed limit on the roadway (higher speed limits lead to higher injury severity levels), location of crashes (those at signalized intersections are less severe than those elsewhere), and time-of-day (darker periods lead to higher injury severity).

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Section: The Standard Ordered Response Model and Its Limitationsmentioning

confidence: 99%

A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes

Eluru

Bhat

Hensher

2008

Accident Analysis & Prevention

506

229

View full text Add to dashboard Cite

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“…The second measure is due to McKelvey and Zavoina (1975). This is of the 'explained variation class' of measures in that it mimics the standard R 2 in OLS since it can be interpreted as the ratio of explained sum of squares to the total sum of squares (Veall and Zimmermann, 1996).…”

Section: Ordinal Vs Cardinal Response Scale?mentioning

confidence: 99%

“…2. Pseudo-R 2 is the commonly reported McFadden (1973) measure based on the log likelihoods; M&Z-R 2 is due to McKelvey and Zavoina (1975) (see text for details). Table 6 Predicting finding a new job as good as the current one in Australia Table 3.…”

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confidence: 99%

Fears and realisations of employment insecurity

Dickerson

Green²

2012

Labour Economics

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Abstract:We investigate the validity of subjective data on expectations of job loss and on the probability of re-employment consequent on job loss, by examining associations between expectations and realisations. We find that subjective expectations data reveal private information about subsequent realisations of both job loss and of subsequent re-employment.As predictors of subsequent job loss, the expectations data perform better with numerical descriptors than with ordinal verbal descriptors. On average, employees overestimate the chance of losing their job; while they underestimate the difficulty of finding another job as good as the currently-held one. We recommend that survey items on employment insecurity should be explicit about each risk under investigation, and utilise a cardinal probability scale with discrete numerical descriptors.

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“…45 This technique is preferred to Ordinary Least Squares (OLS) because of the ordinal nature of the dependent variable, i.e. on a one to eight scale indicating utility or preference for the practice.…”

Section: Conjoint Analysis Questionnairementioning

confidence: 99%

“…The coef®cients of the ordered probit analysis were adjusted so that they could be interpreted in the same way as OLS coef®cients, and also on an interval scale. 45 It is these adjusted coef®cients that are used in the interpretation of the results.…”

Section: Conjoint Analysis Questionnairementioning

confidence: 99%

Using conjoint analysis to elicit the views of health service users: an application to the patient health card

Ryan

McIntosh

Shackley

1998

Health Expectations

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Objective To demonstrate the application of conjoint analysis (CA) for eliciting the views of health service users.Methods A CA study was conducted alongside a randomized controlled trial evaluating the introduction of a patient health card (PHC). The PHC was evaluated with respect to three other aspects of general practice: number of days between making a non-urgent appointment and seeing a doctor; waiting time in reception between the time of the appointment and seeing a doctor; and whether the patient is usually seen by the doctor of their choice. A postal questionnaire was sent to 100 individuals from a general practice in Inverurie, Scotland.Results Seventy-®ve individuals returned the questionnaire, of whom 51 answered the CA section. The PHC was the least important of the attributes considered. The number of days between making a non-urgent appointment and seeing a doctor was considered to be the most important. A 1-day reduction in the number of days to appointment was four and a half times more important than having a PHC; a 1-minute reduction in waiting time in the reception area was three and a half times more important than having a PHC; and seeing a doctor of choice was over three times more important than having a PHC. Satisfaction or utility scores for di erent ways of providing a general practice service also indicated that priority should be given to reducing waiting time to see a doctor or reducing waiting time in reception.Conclusions While the PHC is a signi®cant and positive predictor of satisfaction in general practice, it is less important than the other three attributes considered. More generally, CA appears to be a potentially useful instrument for eliciting the views of health service users.

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A statistical model for the analysis of ordinal level dependent variables

Cited by 2,070 publications

References 5 publications

A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes

A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes

Fears and realisations of employment insecurity

Using conjoint analysis to elicit the views of health service users: an application to the patient health card

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