Detecting and Correcting Attrition Bias in Longitudinal Family Research

Abstract

Conducting longitudinal research has been encouraged among family scholars as a way to better understand family process and to measure changes in family structure and relationships over time. When doing longitudinal research, a common problem is the loss of sample members between the first wave of data collection and subsequent waves. Some subjects move between data points and cannot be located, whereas others refuse to continue their participation. Some members of the original sample, especially if it includes older adults, will have died or have become too disabled to participate further in the study. These losses represent an attrition of the original sample over time. These various forms of attrition of a sample result in a potential threat of bias if those who drop out have unique characteristics such that the remaining sample ceases to be representative of the original sample. This bias, known as attrition bias, is assumed to exist when there are significant differences between the initial and later samples. Because the loss of sample members between the first wave of data collection and subsequent waves is relatively common, researchers in longitudinal studies should check for attrition bias. Unchecked bias may be the major threat to longitudinal research (Markides, Dickson, & Pappas, 1982; Norris, 1985). Larzelere and Klein (1987) listed sample attrition as one of the three major problems that researchers face in analyzing longitudinal data. Despite the threat of attrition bias, however, it has received little attention in longitudinal family research. Indeed, few family researchers address this issue when reporting the results of their analyses of longitudinal data. There are two ways attrition can bias a sample. Firs, it may alter the characteristics of the sample, making it no longer representative of the original sample. Because the results of later waves of data are not generalizable to the original population that was sampled, the external validity of the study is threatened. For example, a longitudinal study examining the effects of divorce on the mental health of former spouses may fail to retain those subjects who have become too depressed to respond to the questionnaire. The nonparticipation of this subsample could bias the findings towards a minimization of depression as an outcome of divorce. The second way that selective attrition can bias longitudinal data is by altering the covariance of variables (Goudy, 1985; Norris, 1987). This problem occurs when the underrepresentation of some groups in the longitudinal sample leads to correlations between variables that are different than the true correlations in the original sample. Referring back to the previous example, the underrepresentation of depressed people in the second wave of the study may distort the statistical relation between length of marriage and depression. This poses a potentially severe threat to theory building because of the effect of these distortions on internal validity. TESTING FOR ATTRITION BIAS The most common method for detecting attrition bias in the characteristics of the sample is to use t tests to compare those subjects who responded to all waves of the study with those who dropped out after only one wave. The means of important variables from the first wave, such as socioeconomic status (SES), age, marital satisfaction, and depression, are compared between the two samples to determine if the differences are statistically significantly different. Significant differences in the means of one or more variables indicates the existence of attrition bias. Differences in categorical variables, such as race and marital status, can be determined by using the chi-square statistic. Logit analysis is a second method for detecting differences in characteristics of the sample. A logit equation is developed to estimate the probability that each first wave respondent will participate in later waves. …