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From Methodological Individualism to Methodological Relationism: Implications for Organization Theories
How the CCP Has Failed to Obtain Control over China’s Collective Memory on the 1950s<br><br>from Part III - Legitimacy and Local Agencies
Zwischen Vision und Wirklichkeit - Herausforderungen und Potenziale des KI-Einsatzes in der öffentlichen Verwaltung
Public Purpose Reporting in Dutch and German Municipally Owned Corporations: Accountability Deficits and Research Perspectives
Κατευθυντήριες γραμμές για την εισαγωγή και τη χρήση της τεχνητής νοημοσύνης στο Κοινοβούλιο
Models for multicategorical responses - Testing the hypothesis of constant probability
Categorical responses are ubiquitous in social and political research. Binary logit and probit models already appear elusive to may researchers, not the least because of their intrinsic non-linearity. Even more elusive are models of responses with more than two categories. Here the values and, possibly, the statistical significance of coefficients depend on the choice of the baseline category of the response. To address this, a recent paper published in Political Analysis recommends to look at the statistical significance of differences in probabilities rather than the statistical significance of individual coefficients. I argue that such a recommendation does not help with practitioners’ confusion about these models as it conflates inference with interpretation. I further show that there are established techniques of inferences in the statistician’s toolbox that avoid such conflation. Third, I show that probability changes always occur if any of the coefficients in a multinomial logit model are non-zero. Finally, I present a two-stage logit model that allows for some of the response probabilities to remain constant while others vary with the values of independentvariables