The SPSS missing value analysis (MVA) was used to analyze the data for both MAR and MCAR data loss using an expectation maximization technique. Little’s (Little & Rubin, 2002) MCAR resulted in a Chi-square = 1852.25 (p = 0.099; df = 1778). This significance denotes that the missing data is MCAR and the data loss pattern is not systematic.
The SPSS MVA module also incorporates an expectation-maximization (EM) algorithm for generation of imputed values used to fill in all the missing data. Since the data is MCAR, listwise deletion is a better alternative than pairwise deletion which may cause covariance matrix issues due to unequal numbers of cases (Kline, 2005).
The AMOS application is unique in that it can be used to analyze data that includes missing data. AMOS incorporates a special form of maximum likelihood estimation (Special ML) which partitions all cases with the same missing data patterns. Peters and Enders (2002) found that this method for analyzing datasets with incomplete data “outperformed traditional (available case) methods” (cited in Kline, 2005, p. 56). Tabachnick and Fidell (2001) suggest using both methods (with and without missing data) but favor the EM imputation method and listwise methods (if data is ignorable) over mean substitution or pairwise deletion. Tabachnick and Fidell (2001) state, “The decision about to handle missing data is important. At best, the decision is among several bad alternatives” (p. 59).
Caution should be exercised with any method using a dataset with a high percentage of missing values (> 5%). Nunnally and Bernstein (1994) suggest that when there is a high percentage of missing values any of these methods may be unsatisfactory. Incorporating listwise deletion may be the best option for MCAR data since EM imputation may cause distorted coefficients of association and correlations (Kalton & Kasprzyk, 1982). In the present data set, listwise deletion resulted in a final sample size of 286 respondents.
Click here for dissertation statistics help
