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Using Node-Level Regression Model for Count Data?

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Shimi Zhou All Messages By This Member #6999 Hello all, I am a PhD student currently working on understanding how co-authorship network centralities influence an author's publication count. My question is: Can I utilize the node-level regression model in UCINET to perform this analysis? Given that for variables involving count data, we often opt for Poisson regression or Negative Binomial Regression, would it still be appropriate to employ node-level regression models if the dependent variable is count data in the context of my research?? I'd really appreciate any insights or guidance on this matter. Thank you in advance for your time and assistance! Best. Shimi
helixed2 All Messages By This Member #7000 Hi Shimi, It depends partly on the distribution of your data. If the mean of the counts is relatively high, then a square root transformation of the outcome may suffice. Meanwhile, if there is considerable overdispersion in the outcome, there is not necessarily a good transformation that equates to a negative binomial. Good luck. On Wednesday, September 13, 2023 at 10:43:43 PM EDT, Shimi Zhou <shimizhou25@...> wrote: Hello all, I am a PhD student currently working on understanding how co-authorship network centralities influence an author's publication count. My question is: Can I utilize the node-level regression model in UCINET to perform this analysis? Given that for variables involving count data, we often opt for Poisson regression or Negative Binomial Regression, would it still be appropriate to employ node-level regression models if the dependent variable is count data in the context of my research?? I'd really appreciate any insights or guidance on this matter. Thank you in advance for your time and assistance! Best. Shimi
Shimi Zhou All Messages By This Member #7001 开云体育 Hello, Thank you so much for advice!? Based on your feedback, I’d like to ask several follow-up questions: 1. Does UCINET provide tools or functionalities that support the analysis of count data using either Poisson or Negative Binomial Regression? 2. Can UCINET determine which of these regressions is more suitable based on the characteristics of the data, such as its distribution, mean, and potential overdispersion? 3. Are there any best practices or guidelines within UCINET for data transformations, especially when considering count data? Thank you for your time and expertise! Best, Shimi toggle quoted message Show quoted text On Sep 14, 2023, at 7:07 AM, helixed2 via groups.io <helixed2@...> wrote: Hi Shimi, It depends partly on the distribution of your data. If the mean of the counts is relatively high, then a square root transformation of the outcome may suffice. Meanwhile, if there is considerable overdispersion in the outcome, there is not necessarily a good transformation that equates to a negative binomial. Good luck. On Wednesday, September 13, 2023 at 10:43:43 PM EDT, Shimi Zhou <shimizhou25@...> wrote: Hello all, I am a PhD student currently working on understanding how co-authorship network centralities influence an author's publication count. My question is: Can I utilize the node-level regression model in UCINET to perform this analysis? Given that for variables involving count data, we often opt for Poisson regression or Negative Binomial Regression, would it still be appropriate to employ node-level regression models if the dependent variable is count data in the context of my research?? I'd really appreciate any insights or guidance on this matter. Thank you in advance for your time and assistance! Best. Shimi

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