MCARMissing Completely At Random
MCARMilitary Construction, Army Reserve
MCARMarine Corps Air Station
MCARMachine Check Analysis and Recording
MCARMultiple Channel Aural Reception
MCARMultichannel Acoustic Relay
MCARMalaysian Civil Aviation Regulation (Malaysia)
MCARMobile Cosmetic Auto Repairs
MCARModel Civil Aviation Regulations
MCARModified Car and Rally (automotive show)
References in periodicals archive ?
In the first phase, observations were deleted at the ratio of 10% and 20% on the dependent continuous variable to create MAR and MCAR data sets.
However, the present work strongly recommends that the researchers had better use a Little's MCAR test, an omnibus statistical test, to find out whether or not the data are missing completely at random.
Had the data not been found to be MCAR, then the nurses' acceptance study researchers could not have determined that "no inputation was required.
Segun la prueba MCAR de Little, los valores perdidos no se distribuian completamente al azar ([[ji al cuadrado].
Little and Rubin [21] reported that MAR and MCAR are able to be ignorable because it is possible to adjust for the missingness.
Allison (2002) and Carter (2006) described advantages of using list-wise deletion for handling MCAR data.
MCAR assumes that the data is missing for completely random reasons, and that the probability of observing the value of a rating does not depend on the observed or unobserved values of the dependent variable.
Item responses were deleted from the full data set (n=385) with respect to the MCAR mechanism, and the missing data was generated through simple random selection from among all respondents with three missingness proportions (0.
Following suggestions for best practices for handling data that are not MCAR (Schlomer, Bauman, & Card, 2010), a multiple imputation (MI) procedure was used to estimate missing data values.
When the probability of missingness is independent of both unobserved and observed data, the missing data mechanism is called missing completely at random (MCAR; note that MCAR implies MAR).
If we then mindlessly input the resulting N values (including the imputed mean value for the m individuals who had missing data) into a software package designed to compute a 95% confidence interval for the population mean of Y, we will find the resulting interval width is only (about) R=n/N percent of what it should be even if we assume the MDM is MCAR and hence the point estimator [[bar.