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| === Using linear regression as an alternative to logistic regression === | |
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| [[attachment:hellevik.pdf | Hellevik (2009) ]] suggests that linear regression can be used with a binary outcome as opposed to logistic regression particularly for large samples. P-values between the two types of regression were found to be very close despite the ordinary least squares approach not incorporating heterogeneity of variance (where the variance of the proportions depends on the proportion) which is taken into account by logistic regression.[[attachment:mood.pdf | Mood (2010)]] shows almost identical results using linear regression and logistic regression in Table 5 of the paper and also suggests linear regressions may be used on a binary outcome e.g. to make the results more interpretable. | |
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| == Linear regression as an alternative to logistic regression == | On a related theme [[attachment:jaeger.pdf | this research report]] by Jaeger suggests using mixed random effect models as opposed to the arcsine transformation when analyzing proportions in repeated measures ANOVAs. |
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| [[attachment:hellevik.odf | This paper]] suggests that linear regression can be used with a binary outcome as opposed to logistic regression particularly for large samples. Heterogeneity of variance (where the variance of the proportions depends on the proportion) could which is not taken into account by ordinary linear regression could, however, lead to problems with inference. | __Reference__ Hellevik, O. (2009) Linear versus logistic regression when the dependent variable is a dichotomy. ''Qual Quant'' '''43''' 59-74. Mood, C. (2010) Logistic regression: why we cannot do what we think we can do, and what we can do about it. ''European Sociological Review'' '''26(1)''' 67-82. |
Using linear regression as an alternative to logistic regression
Hellevik (2009) suggests that linear regression can be used with a binary outcome as opposed to logistic regression particularly for large samples. P-values between the two types of regression were found to be very close despite the ordinary least squares approach not incorporating heterogeneity of variance (where the variance of the proportions depends on the proportion) which is taken into account by logistic regression.Mood (2010) shows almost identical results using linear regression and logistic regression in Table 5 of the paper and also suggests linear regressions may be used on a binary outcome e.g. to make the results more interpretable.
On a related theme this research report by Jaeger suggests using mixed random effect models as opposed to the arcsine transformation when analyzing proportions in repeated measures ANOVAs.
Reference
Hellevik, O. (2009) Linear versus logistic regression when the dependent variable is a dichotomy. Qual Quant 43 59-74.
Mood, C. (2010) Logistic regression: why we cannot do what we think we can do, and what we can do about it. European Sociological Review 26(1) 67-82.