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Many missing data studies have simulated data, randomly deleted values, and investigated the method of handling the missing values that would most closely approximate the original data. Regression procedures have emerged as the most recommended methods. If the values are missing randomly, these procedures are effective. If, however, the values are not missing randomly, the use of regression procedures to impute values for missing data is questionable. The purpose of this study was to determine if values were missing randomly in samples selected from the National Education Longitudinal Study of 1988. Four samples were selected: 2 samples of 8 variables, average inter-correlation of 0.2 and 0.4 respectively; and 2 samples of 4 variables, average inter-correlation of 0.2 and 0.4 respectively. All cases containing one or more missing values were selected, and the pattern of missing values for each was determined. Chi square analysis indicated that the missing values are not missing randomly (p<.001). Implications of the use of regression procedures to handle non-randomly missing values are discussed. Appendix A contains four tables of descriptive statistics. (Contains 12 references and 2 tables.) (Author/SLD)

Descriptors: Chi Square, Longitudinal Studies, National Surveys, Regression (Statistics), Research Methodology, Simulation











Author: Witta, E. Lea

Source: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=12568&id=ED389727



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