Effect of Rancher’s Management Philosophy, Grazing Practices, and Personal Characteristics on Sustainability Indices for North Central Texas Rangeland Page: 62
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There are numerous ways to deal with missing data. Wayman (2003) stated, "It is
important to understand that once data are missing, it is impossible not to treat them -once data
are missing, any subsequent procedure with that data set represents a response in some form to
the missing data problem. As a result, there are many different methods of managing missing
data". Abrams (2007) suggested, "If there are missing values for several cases on different
variables, omission of the variable or the case would lead to data being lost." He then implied
that some form of analysis would be recommended to impute missing values. Imputation of
missing values can be done by several methods. According to Gelman and Hill (2006) the last
value could be carried forward, information from related observations could be used, indicator
variables for "missingness" of categorical or continuous predictors is an option, or imputation
could be done based on logical rules. However, when more than just a "trivial fraction" of data
contains missing information, it is preferred to use some form of random imputation.
Garson (2011 e) recommends checking for data missing completely at random (MCAR)
versus missing at random (MAR). He states most often data is (MAR). Additionally he states,
"If data are MCAR, then the researcher may choose listwise or pairwise deletion of cases. If data
are not MCAR, missing values should be imputed."
MCAR can be confirmed by dividing respondents into those with and without missing
data, then using t-tests of mean differences on income, age, gender, and other key variables to
establish that the two groups do not differ significantly (Garson, 2011 e). Therefore, an
independent t-test was used to check for differences between groups of respondents with missing
information and those without.
The random imputation approach was taken during this analysis. Variables and cases
were subjected to analysis by the R package 'seqKnn' in version 2.13.0. This package estimates
missing values sequentially from the gene, that has least missing rate in microarray data, using
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Becker, Wayne. Effect of Rancher’s Management Philosophy, Grazing Practices, and Personal Characteristics on Sustainability Indices for North Central Texas Rangeland, dissertation, December 2011; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc103289/m1/73/: accessed July 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .