Matches in SemOpenAlex for { <https://semopenalex.org/work/W1493193319> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W1493193319 endingPage "118" @default.
- W1493193319 startingPage "108" @default.
- W1493193319 abstract "Studies in many different fields of research suffer from the problem of missing data. With missing data, statistical tests will lose power, results may be biased, or analysis may not be feasible at all. There are several ways to handle the problem, for example through imputation. With imputation, missing values are replaced with estimated values according to an imputation method or model. In the k-nearest neighbour (k-NN) method, a case is imputed using values from the k most similar cases. In this paper, we present an evaluation of the k-NN method using Likert data in a software engineering context. We simulate the method with different values of k and for different percentages of missing data. Our findings indicate that it is feasible to use the k-NN method with Likert data. We suggest that a suitable value of k is approximately the square root of the number of complete cases. We also show that by relaxing the method rules with respect to selecting neighbours, the ability of the method remains high for large amounts of missing data without affecting the quality of the imputation." @default.
- W1493193319 created "2016-06-24" @default.
- W1493193319 creator A5037810527 @default.
- W1493193319 creator A5086299815 @default.
- W1493193319 date "2004-09-11" @default.
- W1493193319 modified "2023-09-23" @default.
- W1493193319 title "An evaluation of k-nearest neighbour imputation using Likert data" @default.
- W1493193319 cites W104412146 @default.
- W1493193319 cites W1574627325 @default.
- W1493193319 cites W1583121608 @default.
- W1493193319 cites W1606533552 @default.
- W1493193319 cites W2058128280 @default.
- W1493193319 cites W2092478087 @default.
- W1493193319 cites W2095778055 @default.
- W1493193319 cites W2096863518 @default.
- W1493193319 cites W2107634410 @default.
- W1493193319 cites W2111700774 @default.
- W1493193319 cites W2124732843 @default.
- W1493193319 cites W2136691316 @default.
- W1493193319 cites W2157542847 @default.
- W1493193319 cites W2159897062 @default.
- W1493193319 cites W2163952039 @default.
- W1493193319 cites W2173680996 @default.
- W1493193319 cites W2951941150 @default.
- W1493193319 cites W3017143921 @default.
- W1493193319 doi "https://doi.org/10.1109/metrics.2004.10" @default.
- W1493193319 hasPublicationYear "2004" @default.
- W1493193319 type Work @default.
- W1493193319 sameAs 1493193319 @default.
- W1493193319 citedByCount "22" @default.
- W1493193319 countsByYear W14931933192013 @default.
- W1493193319 countsByYear W14931933192014 @default.
- W1493193319 countsByYear W14931933192015 @default.
- W1493193319 countsByYear W14931933192016 @default.
- W1493193319 countsByYear W14931933192021 @default.
- W1493193319 crossrefType "proceedings-article" @default.
- W1493193319 hasAuthorship W1493193319A5037810527 @default.
- W1493193319 hasAuthorship W1493193319A5086299815 @default.
- W1493193319 hasConcept C105776082 @default.
- W1493193319 hasConcept C105795698 @default.
- W1493193319 hasConcept C119857082 @default.
- W1493193319 hasConcept C124101348 @default.
- W1493193319 hasConcept C154945302 @default.
- W1493193319 hasConcept C199360897 @default.
- W1493193319 hasConcept C2777904410 @default.
- W1493193319 hasConcept C2983946233 @default.
- W1493193319 hasConcept C33923547 @default.
- W1493193319 hasConcept C41008148 @default.
- W1493193319 hasConcept C58041806 @default.
- W1493193319 hasConcept C9357733 @default.
- W1493193319 hasConceptScore W1493193319C105776082 @default.
- W1493193319 hasConceptScore W1493193319C105795698 @default.
- W1493193319 hasConceptScore W1493193319C119857082 @default.
- W1493193319 hasConceptScore W1493193319C124101348 @default.
- W1493193319 hasConceptScore W1493193319C154945302 @default.
- W1493193319 hasConceptScore W1493193319C199360897 @default.
- W1493193319 hasConceptScore W1493193319C2777904410 @default.
- W1493193319 hasConceptScore W1493193319C2983946233 @default.
- W1493193319 hasConceptScore W1493193319C33923547 @default.
- W1493193319 hasConceptScore W1493193319C41008148 @default.
- W1493193319 hasConceptScore W1493193319C58041806 @default.
- W1493193319 hasConceptScore W1493193319C9357733 @default.
- W1493193319 hasLocation W14931933191 @default.
- W1493193319 hasOpenAccess W1493193319 @default.
- W1493193319 hasPrimaryLocation W14931933191 @default.
- W1493193319 hasRelatedWork W1550443206 @default.
- W1493193319 hasRelatedWork W1598553907 @default.
- W1493193319 hasRelatedWork W1983479840 @default.
- W1493193319 hasRelatedWork W2031668066 @default.
- W1493193319 hasRelatedWork W2044758663 @default.
- W1493193319 hasRelatedWork W2049633694 @default.
- W1493193319 hasRelatedWork W2058128280 @default.
- W1493193319 hasRelatedWork W2095778055 @default.
- W1493193319 hasRelatedWork W2096863518 @default.
- W1493193319 hasRelatedWork W2098168647 @default.
- W1493193319 hasRelatedWork W2118502261 @default.
- W1493193319 hasRelatedWork W2125055259 @default.
- W1493193319 hasRelatedWork W2126199450 @default.
- W1493193319 hasRelatedWork W2131378644 @default.
- W1493193319 hasRelatedWork W2136691316 @default.
- W1493193319 hasRelatedWork W2156267802 @default.
- W1493193319 hasRelatedWork W2157542847 @default.
- W1493193319 hasRelatedWork W2159798994 @default.
- W1493193319 hasRelatedWork W2172074277 @default.
- W1493193319 hasRelatedWork W3085162807 @default.
- W1493193319 isParatext "false" @default.
- W1493193319 isRetracted "false" @default.
- W1493193319 magId "1493193319" @default.
- W1493193319 workType "article" @default.