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listwise and pairwise deletion procedures

Croatian translation: postupci brisanja po popisu i po parovima

GLOSSARY ENTRY (DERIVED FROM QUESTION BELOW)
English term or phrase:listwise and pairwise deletion procedures
Croatian translation:postupci brisanja po popisu i po parovima
Entered by: Nives

16:24 Apr 15, 2008
English to Croatian translations [PRO]
Medical - Mathematics & Statistics
English term or phrase: listwise and pairwise deletion procedures
Mplus is the statistical application preferred because it is able to use the full information maximum likelihood procedure in concert with the Satorra-Bentler correction for non-normal data [see the work of McArdle and Cattell (1994) and Graham, Hofer, Donaldson, MacKinnon, and Schafer (1997) for discussion of the advantages of maximum likelihood-based incomplete data methods over more traditional listwise and pairwise deletion procedures].
Nives
Bosnia and Herzegovina
Local time: 17:32
postupci brisanja po popisu i po parovima
Explanation:
Po mom mišljenju, ovaj prijevod je odgovarajući. Nažalost, nisam našao odgovarajuće citate na hrvatskom.

Evo par citata na engleskom kako biste vidjeli o čemu se radi:

Listwise deletion – SPSS will not include cases (subjects) that have missing values on the variable(s) under analysis. If you are only analyzing one variable, then listwise deletion is simply analyzing the existing data. If you are analyzing multiple variables, then listwise deletion removes cases (subjects) if there is a missing value on any of the variables. The disadvantage is a loss of data because you are removing all data from subjects who may have answered some of the questions, but not others (e.g., the missing data).

Pairwise deletion – SPSS will include all available data. Unlike listwise deletion which removes cases (subjects) that have missing values on any of the variables under analysis, pairwise deletion only removes the specific missing values from the analysis (not the entire case). In other words, all available data is included. For example: If you are conducting a correlation on multiple variables, then SPSS will conduct the bivariate correlation between all available data points, and ignore only those missing values if they exist on some variables. In this case, pairwise deletion will result in different sample sizes for each correlation. Pairwise deletion is useful when sample size is small or missing values are large because there are not many values to begin with, so why omit even more with listwise deletion.

http://www.psychwiki.com/wiki/Dealing_with_Missing_Data

The most obvious method for dealing with incomplete data is
to let the computer program discard all cases with any missing
values and then use the remaining records to compute results.
For most statistical programs, this occurs by default. However,
a serious limitation of this approach is that relevant data are
frequently discarded (Kim & Curry, 1977; Raymond & Roberts,
1987).

Pairwise deletion is an attractive alternative when there
are a small number of missing cases on each variable relative to
the total sample size, and a large number of variables are
involved (Kim & Curry, 1977). With this piecemeal method, all
available observations for each particular variable are used to
compute means and variances, while all available pairs of values
are used to compute covariances (Raymond & Robert, 1987). Thus,
correlations are computed using only those observations that
have nonmissing values on both variables.
http://ericae.net/ft/tamu/cool1.pdf
Selected response from:

Aleksandar Medić
Local time: 17:32
Grading comment
tnx
4 KudoZ points were awarded for this answer



Summary of answers provided
3 +1postupci brisanja po popisu i po parovima
Aleksandar Medić


  

Answers


4 hrs   confidence: Answerer confidence 3/5Answerer confidence 3/5 peer agreement (net): +1
postupci brisanja po popisu i po parovima


Explanation:
Po mom mišljenju, ovaj prijevod je odgovarajući. Nažalost, nisam našao odgovarajuće citate na hrvatskom.

Evo par citata na engleskom kako biste vidjeli o čemu se radi:

Listwise deletion – SPSS will not include cases (subjects) that have missing values on the variable(s) under analysis. If you are only analyzing one variable, then listwise deletion is simply analyzing the existing data. If you are analyzing multiple variables, then listwise deletion removes cases (subjects) if there is a missing value on any of the variables. The disadvantage is a loss of data because you are removing all data from subjects who may have answered some of the questions, but not others (e.g., the missing data).

Pairwise deletion – SPSS will include all available data. Unlike listwise deletion which removes cases (subjects) that have missing values on any of the variables under analysis, pairwise deletion only removes the specific missing values from the analysis (not the entire case). In other words, all available data is included. For example: If you are conducting a correlation on multiple variables, then SPSS will conduct the bivariate correlation between all available data points, and ignore only those missing values if they exist on some variables. In this case, pairwise deletion will result in different sample sizes for each correlation. Pairwise deletion is useful when sample size is small or missing values are large because there are not many values to begin with, so why omit even more with listwise deletion.

http://www.psychwiki.com/wiki/Dealing_with_Missing_Data

The most obvious method for dealing with incomplete data is
to let the computer program discard all cases with any missing
values and then use the remaining records to compute results.
For most statistical programs, this occurs by default. However,
a serious limitation of this approach is that relevant data are
frequently discarded (Kim & Curry, 1977; Raymond & Roberts,
1987).

Pairwise deletion is an attractive alternative when there
are a small number of missing cases on each variable relative to
the total sample size, and a large number of variables are
involved (Kim & Curry, 1977). With this piecemeal method, all
available observations for each particular variable are used to
compute means and variances, while all available pairs of values
are used to compute covariances (Raymond & Robert, 1987). Thus,
correlations are computed using only those observations that
have nonmissing values on both variables.
http://ericae.net/ft/tamu/cool1.pdf

Aleksandar Medić
Local time: 17:32
Specializes in field
Native speaker of: Native in SerbianSerbian
PRO pts in category: 24
Grading comment
tnx

Peer comments on this answer (and responses from the answerer)
agree  Larisa Djuvelek-Ruggiero (X)
3 days 20 hrs
  -> Hvala.
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