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Social hidden groups size analyzing: application of count regression models for excess zeros
Maryam Jalali1, Ali Nikfarjam, Ali Akbar Haghdoost
1Regional Knowledge Hub for HIV/AIDS Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran. jalali3944@yahoo.com.
Journal of Research in Health Sciences
|October 1, 2013
Summary
For sensitive questions, Poisson regression (P) struggles with zero-inflated data. Negative Binomial (NB) and Zero Inflated Negative Binomial (ZINB) models offer better fits for count data, especially concerning hidden populations.
Area of Science:
- Statistics
- Biostatistics
- Social Sciences
Background:
- Count data analysis often uses Poisson regression (P), but it performs poorly with zero-inflated and over-dispersed data.
- Sensitive questions, like those about hidden populations, frequently yield zero counts, posing challenges for standard models.
- Respondent characteristics may influence the reporting of sensitive information, necessitating robust statistical approaches.
Purpose of the Study:
- Compare the performance of alternative count regression models for sensitive data.
- Investigate the impact of respondent characteristics on reporting sensitive information.
- Determine the best-fitting statistical models for analyzing counts of individuals in hidden groups.
Main Methods:
- Fitted five regression models (Logistic, P, Negative Binomial (NB), Zero Inflated Poisson (ZIP), Zero Inflated Negative Binomial (ZINB)) to data from 700 participants.
- Assessed model performance using Likelihood Ratio Test (LRT), Vuong statistic, AIC, and Sum Square of Error (SSE).
- Collected data on the number of alcoholics, methadone users, and Female Sex Workers (FSWs) known by respondents.
Main Results:
- High percentages of zero responses were observed: 35% for alcoholics, 50% for methadone users, and 65% for FSWs.
- The Zero Inflated Negative Binomial (ZINB) model best fit data for alcoholics.
- Negative Binomial (NB) model provided the best fit for methadone users and FSWs. Younger, male, and less educated respondents were more likely to report sensitive information.
Conclusions:
- Standard Poisson regression (P) is often inadequate for zero-inflated count data from sensitive questions.
- Negative Binomial (NB) and Zero Inflated Negative Binomial (ZINB) models demonstrate superior goodness-of-fit for analyzing sensitive count data.
- Respondent demographics significantly influence the reporting of sensitive information, highlighting the need for advanced modeling techniques.
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