![]() ![]() The model is estimated using panel data exhibiting attrition.ĪTTRITION EFFECTS ON REGRESSION ESTIMATES The nodal correcting for attrition bias simultaneously determines the correlates of attrition and the structural nodal parameters. It is demonstrated that the bias is actually specification error in the structural model being estimated. This paper describes the conditions under which parameter bias can occur from attrition and develops a model to correct for the bias. The effect of attrition on the parameters of linear models has been ignored in studies using panel data. Such applications have been in the areas of evaluating advertising effectiveness (Prasad and Ring 1976, Winer 1980), market segmentation (Frank, Massy, and Boyd 1967, McCann 1974), testing general models of buyer behavior (Farley and Ring 1970, Farley, Howard, and Lehmann 1976), sales management (Futrell and Jenkins 1978), and others. Therefore, assuming an initial probability sample or an alternative sampling approach attempting to represent the population studied, the sample left at the end of the final wave may be biased with respect to the population.Įconometric or other linear models have often been used to analyze panel data. ![]() Ferber (1966) discovered disproportionately high attrition rates related to older age, lower education, self-employment, and high personal asset value. Buckles and Carman (1967) also found the interest factor and its correlates to be highly related to attrition. For example, in a panel study of economic attitude study and change covering the years 1954-1957, Sobol (1959) found that renters, people with low income, and people disinterested in the study tended to drop out. There has been considerable interest in the possibility of panel bias occurring due to attrition. The panel utilized by Farley, Howard, and Lehmann (1976) exhibited a 43% drop-out rate over four waves of interviewing spanning fifteen months. For example, Charlton and Ehrenberg (1976) report that 88% of their initial sample completed the 25-weak panel. Except for panels operating under tightly controlled conditions, panel attrition is unavoidable. One of these problems is panel attrition or mortality. However, panel studies other than those using consumer panel data have occurred frequently in the marketing literature.Īlthough panels have been widely used, they are not without problems (Carman 1974). To marketing researchers, the tern panel data usually connotes consumer panel data which are records of household purchasing behavior over time. Such data have been referred to in various literatures as longitudinal, cross-sectional, time-series and panel. Researchers in marketing and other social science disciplines often conduct studies where a sample of individuals is interviewed at two or more points in time. The results indicated that the impact of attrition tended to be on the medal's exogenous variables and not on the endogenous variables that were exogenous in the equations. The model is illustrated using a simultaneous equation structural model and panel data that have an attrition problem. In this paper, a model is developed that corrects structural econometric models estimated using panel data for possible attrition bias. However, a common problem with panel data is attrition. Panel data are often used to estimate the parameters of econometric or other linear models. ![]() Monroe, Ann Abor, MI : Association for Consumer Research, Pages: 220-226.Īdvances in Consumer Research VolPages 220-226ĪTTRITION BIAS IN THE ESTIMATION OF ECONOMETRIC MODELS FROM PANEL DATA Winer (1981) ,"Attrition Bias in the Estimation of Econometric Models From Panel Data", in NA - Advances in Consumer Research Volume 08, eds. ABSTRACT - Panel data are often used to estimate the parameters of econometric or other linear models. ![]()
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