Application of Weighting Strategies for the Combination of Data from a Dual-frame Telephone Survey
With the widespread use of cell phones and changes in life style, conventional telephone surveys, which are conducted using mainly household landline numbers, have encountered the problems of coverage and representativeness. As the use of landline numbers cannot reach people who only use cell phones, data quality of telephone surveys is further affected. Dual-frame telephone surveys have been applied in recent years to include both landlines and cell-phone users to reduce undercoverage bias due to the incompleteness of the landline frame. With a fixed budget, previous studies have suggested adopting an overlapping sampling design for dual-frame surveys. The weighting procedures and the combination of data from dual frame surveys, which can be seen as unequal weighting methods, therefore, become a challenging issue. In particular, data combination for those who use both landlines and cell-phones in the overlapped sampling domain has drawn researchers’ attention. As sample characteristics derived from cell phone and landline samples differ in many aspects, it is important to employ suitable weighting strategies for combining data from dual-frame telephone surveys.
Previous studies have developed various weighting approaches, with the consideration of non-sampling or nonresponse errors. Due to the absence of external population estimates for telephone usage, several weighting strategies are not suitable to be applied to dualframe telephone surveys in Taiwan. This study aims to examine different procedures for incorporating unequal weights for dual-frame telephone survey using data from the 2020 Adult Smoking Behavior Survey (ASBS). Two types of weighting procedure, which are modified based on previously developed approaches, are examined. One is to apply weights to landline and cell-phone samples separately before the combination of the dual samples. The other is to combine the dual samples first with the consideration of sample characteristics in the cross-tabulation of age and gender, education, and geographic areas. During the weighting procedures, the proportion of different telephone users, including “landline only”, “cell phone only” and “dual users”, derived from the landline and cell-phone samples, is used to adjust the combined data, and final weights are applied using poststratification raking. We use bootstrapping to evaluate the robustness of sample estimates, with different sample allocations of the randomly selected resamples from the 2020 ASBS. Auxiliary information of labor participation rate and the distributions of ethnicity groups in Taiwan is used as bench mark to assess the bootstrapping estimates.
A total of 26,065 complete landline cases is obtained with a response rate (AAPOR RR1) of 18.3%. For cell-phone sample, a total of 4,299 complete cases is obtained, with a response rate of 10.8%. Consistent with previous studies, the unweighted cell-phone sample included higher proportions of younger respondents and those with a college degree, while more females and the elderly responded to the landline survey. The proportions of different telephone users, i.e. landline-only, dual users, and cell phone only, slightly differed between the two weighting strategies. The bootstrapping results indicated that sample estimates derived from the first weighting strategy performed somewhat better than the second one. Although an optimal weighting approach for the data combination of dual-frame telephone surveys is not available, it is important to obtain relatively low standard errors for sample estimates. We discuss the limitations of this study and call for more research on this issue.