Goodness-of-Fit Measures for Probit and Logit

Hagle, Timothy M.; E., Mitchell Glenn

doi:10.2307/2111590

Cited by 215 publications

(132 citation statements)

References 11 publications

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“…We ran 4 logistic regression models for each population network to determine how well patch area and isolation predicted occupancy: (i) area only, (ii) isolation only, (iii) area ϩ isolation, and (iv) area ϫ isolation (see Methods). The amount of deviance (i.e., variation) in occupancy explained by each model (pseudoR 2 , or pR 2 ) represents the ability of patch area and/or isolation to predict species occurrence patterns; this statistic is analogous to the R 2 of linear regression (9). The pR 2 values from each of the 1,015 population networks created distributions that were skewed toward 0, and few species were strongly influenced by patch area or isolation (Fig.…”

Section: Resultsmentioning

confidence: 99%

Effect of habitat area and isolation on fragmented animal populations

Prugh

Hodges

Sinclair

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

635

485

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Habitat destruction has driven many once-contiguous animal populations into remnant patches of varying size and isolation. The underlying framework for the conservation of fragmented populations is founded on the principles of island biogeography, wherein the probability of species occurrence in habitat patches varies as a function of patch size and isolation. Despite decades of research, the general importance of patch area and isolation as predictors of species occupancy in fragmented terrestrial systems remains unknown because of a lack of quantitative synthesis. Here, we compile occupancy data from 1,015 bird, mammal, reptile, amphibian, and invertebrate population networks on 6 continents and show that patch area and isolation are surprisingly poor predictors of occupancy for most species. We examine factors such as improper scaling and biases in species representation as explanations and find that the type of land cover separating patches most strongly affects the sensitivity of species to patch area and isolation. Our results indicate that patch area and isolation are indeed important factors affecting the occupancy of many species, but properties of the intervening matrix should not be ignored. Improving matrix quality may lead to higher conservation returns than manipulating the size and configuration of remnant patches for many of the species that persist in the aftermath of habitat destruction.incidence function ͉ island biogeography ͉ logistic regression ͉ metaanalysis ͉ occupancy H abitat loss and fragmentation are major threats to terrestrial biodiversity (1). Globally, Ϸ40% of land has been converted for agricultural use (2), and regions as diverse as the eastern United States, the Philippines, and Ghana have lost Ͼ90% of their natural habitat (3, 4). Conservation theory and practice are founded on the principle that large habitat patches have more species than small ones and connected patches have more species than isolated ones (5). Although few would dispute this basic premise, we still do not know the general value of patch area and isolation as predictors of species occupancy in fragmented terrestrial systems. Despite hundreds of patch occupancy studies over Ͼ4 decades, there has been no quantitative synthesis of these findings. Several syntheses have examined species-area and diversity relationships (6, 7), but the species occupancy patterns that underlie diversity patterns in fragmented landscapes have been overlooked (8). How important is patch isolation relative to patch size in determining where species occur, and how consistent are these effects across diverse taxonomic groups? These are foundational, yet unanswered, questions for ecology and conservation biology.We synthesized patch occupancy data from 89 studies of terrestrial fauna on 6 continents (Table S1) to determine how patch area and isolation affect species' occurrence patterns. Collectively, these studies recorded the occurrence of 785 animal species (Table 1) in 1,015 population networks surveyed in 12,370 discrete habitat p...

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Section: Resultsmentioning

confidence: 99%

Effect of habitat area and isolation on fragmented animal populations

Prugh

Hodges

Sinclair

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

635

485

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“…Its sampling properties have been examined by simulation for the case in which the ordinal variable is dichotomous, and its performance compared quite favorably to that of several other pseudo-R 2 's (Hagle and Mitchell 1992). The simulations suggest it is a good estimate of the OLS R 2 associated with the continuous variable underlying the dichotomous variable in the simulations.…”

Section: Model With Measurement Errormentioning

confidence: 98%

The psychometric and empirical properties of measures of risk preferences

2017

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We examine the psychometric and empirical properties of some commonly used survey-based measures of risk preferences in a population-based sample of 11,000 twins. Using a model that provides a general framework for making inferences about the component of measured risk attitudes that is not due to measurement error, we show that measurement-error adjustment leads to substantially larger estimates of the predictive power of risk attitudes, of the size of the gender gap, and of the magnitude of the sibling correlation. Risk attitudes are predictive of investment decisions, entrepreneurship, and drinking and smoking behaviors; are robustly associated with cognitive ability and personality; and our estimates are often larger than those Uncertain (2017) 54:203-237 in the literature. Our results highlight the importance of adjusting for measurement error across a wide range of empirical settings.

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“…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). Simulations suggest that this measure most closely approximates the OLS R 2 for the underlying (unobserved) latent variable model (Hagle and Mitchell, 1992;Windmeijer, 1995) and hence it is an attractive choice. 17…”

Section: Ordinal Vs Cardinal Response Scale?mentioning

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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Goodness-of-Fit Measures for Probit and Logit

Cited by 215 publications

References 11 publications

Effect of habitat area and isolation on fragmented animal populations

Effect of habitat area and isolation on fragmented animal populations

The psychometric and empirical properties of measures of risk preferences

Fears and realisations of employment insecurity

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