UNDERSTANDING MAIL SURVEY RESPONSE BEHAVIOR A META-ANALYSIS

A meta-analysis of prior studies of techniques designed to inducemail survey response rates was conducted. Research encompassing184 effects (study outcomes) in 115 studies (articles) for 17predictors of response rate was examined. The average effectsize across all manipulations was r=.065, indicating an averageincrease of about 6.5 percent in response rates for manipulations.Effect sizes for specific predictors and two potential moderatorsof effects were examined. Results indicated that repeated contactsin the form of preliminary notification and follow-ups, appeals,inclusion of a return envelope, postage, and monetary incentives,were effective in increasing survey response rates. Significanteffect sizes for the predictors ranged from an increase in responseof 2 percent to 31 percent. Implications of the results forthe conduct of mail surveys and future research on mail surveyresponse behavior are discussed.     

Nonresponse weighting adjustment using estimated response probability

To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator.     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.     

Organizational Compatibility and Workplace Drug Testing: Modeling the Adoption of Innovative Social Control Practices

Few researchers have examined organizational variation in the adoption of workplace drug testing, but innovation theory suggests that adoption is more likely when it is compatible with an organization's values, previously introduced ideas, and needs. Using data from the 1997 National Employee Survey, this research models the effects of organizational compatibility, industry, and establishment size on the adoption of drug testing. The data reveal that compatibility, as measured by an organization's rules orientation, presence of an employee assistance program, and mechanization, is associated with the adoption of drug testing. As predicted, the adoption of drug testing varies across industries and by establishment size.