Estimating the Effect of Nonignorable Nonresponse in Sample Surveys
An Application of Rubin's Bayesian Method to the Estimation of Community Standards for Obscenity
Bibliographic Data
| ID | 10941508 |
|---|---|
| Authors | K C Land (0000-0002-9551-7314, Duke University, corresponding author), Patricia L Mccall (North Carolina State University) |
| Year | 1993 |
| Volume | 21 |
| Issue | 3 |
| Pages | 291-316 |
| Publication date | 1993-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0049124193021003001 |
| OpenAlex | W1980633920 |
| Language | EN |
| Citations received | 2 |
| References cited | 11 |
Rubin (1977) developed a method for estimating, in a subjective sense, the effect of nonignorable nonresponse in sample surveys. Based on Bayesian techniques, this method produces a subjective probability interval for the statistic, such as the mean of a response variable, that would have been calculated if all nonrespondents had responded. Demographic and socioeconomic background information that is recorded for both respondents and nonrespondents plays an important role in sharpening the subjective interval - through the adjustment of a regression equation that uses this information. In this article, Rubin's method-sometimes called the mixture modeling approach to drawing inferences from self-selected samples-is reviewed and applied to real survey and experimental data on community standards for sexually explicit material in which respondents were asked to judge the material's appeal to prurient interest and patent offensiveness (two of the three legal criteria for a determination of obscenity). A critically important substantive issue in this context is whether or not the sample self-selection processes governing the willingness of individuals to participate in the experiment have so truncated the frequency distributions of participant judgments about obscenity that they are grossly biased and inaccurate. It is shown how the mixture modeling approach sheds light on the possible extent of such biases
Bayesian probability · Context (archaeology · Econometrics · Geography · Non-response bias · Sample (material · Statistic · Statistics · Economic and Environmental Valuation · Mathematics · Psychology · Social Psychology · Survey Methodology and Nonresponse · Survey Sampling and Estimation Techniques
Inference and missing data
Sample Selection Bias as a Specification Error
The Relationship between Modified and Usual Multiple-Regression Approaches to the Analysis of Dichotomous Variables
What Do We Really Know about Wages? The Importance of Nonreporting and Census Imputation
Nonresponse Bias for Attitude Questions
Estimating Community Standars
An Introduction to Sample Selection Bias in Sociological Data
Theory Testing in a World of Constrained Research Design
Form effect in the measurement of feeling states
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,09 |
| Citation span | 2004 - 2004 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |