ADJUSTING FOR NONRESPONSE IN A TELEPHONE SUBSCRIBER SURVEY

Introduction Each month , GTE adminis ters a sizable number of telephone surveys of subscribers to its local telephone service, in order to gauge the extent of customer satisfaction with the service, to assess the impact of various service improvemen t programs, and to diagnose perceived problems which may lie outs ide the scope of the Corporation's internal measurements. From its billing records, each individual telephone company selects simple random samples of its residential and its business customers each month, from which a specified quota of individuals complete a fairly lengthy telephone-administered interview. Given the desire that the completed interviews accurately r ep resen t the pe rcep t ions of the genera l populations of GTE telephone subscribers, concerns have been raised over high levels of nonresponse, currently running at roughly 40-45% for residential customers and 25-60% for various segments of business customers. Despite these high rates of nonresponse, there is hope that the nonresponse problem can be controlled through a combination of increasing i n t e r v i e w a t t e m p t s , l o o s e n i n g i n t e r v i e w qualifications, and post-hoc data adjustments. The latter remedy is often applied by locating or defining some variable which "explains", or is p laus ib ly associa ted with , an ind iv idua l ' s nonresponse and whose data are available for the entire sample or the entire population from which the completed interviews are gathered. In the surveys we consider, candidate variables for this sort of use are available at the individual level through the telephone repair and service billing i n f o rma t ion GTE records as par t of its administrative information. These surveys are a major part of GTE's performance monitoring program, both in their extent and in the seriousness with which the results are interpreted. Consequent cost and schedul ing concerns effectively eliminate the possibility of any casual deviation from the threecall rule by which the current survey interviews are adminis tered. Thus, one must evaluate the consequences of uni t nonresponse w i th ou t recourse to any form of complete data. The interim strategy we have adopted during this study is to attempt several types of nonresponse adjustment with existing data and compare the adjusted estimates to gain an informal notion of the sensi t ivi ty of our survey est imates to the adjustment techniques. We attempt three major types of nonresponse adjus tment . For the first, we search for administrative data which are available for each unit in the sample, to be used to form weighting classes, as described by Oh and Scheuren (1983), for example. We note that, in this survey one might reasonably hope for variables which are associated with both the response mechanism and with survey responses, since telephone company data are likely to be related to telephone usage and hence to telephone survey availability, while that same data may well be also related to the telephone service evaluation that is the survey's subject. The second major type of adjustment, the administrative data are used to construct an explicit dichotomous variable model to predict response, which is then used as part of a selection bias model. The third adjustment strategy exploits the fact that these surveys are conducted by telephone with specific callback policies. Numbers of responses at each call are used to estimate response probabilities within (fairly flexibly defined) categories, from which adjustments are made. In support of the comparison of the estimates based on these strategies, we devote the next section to a description of some of the characteristics of calling dispositions for our sample. In subsequent sections, the adjustment strategies are applied and compared. Calling Histories Seven hundred thirty nine households were selected as the target sample for the TEL-CEL General Services survey of February 1988. Each household was called by telephone up to three times, generally using a day-night-weekend calling sequence. Each call was classified according to its survey response result based on nine different categories. To condense the possible call patterns over the three-or-fewer calls, each household was uniquely categorized as one of the following: