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Article: A nonparametric item analysis of a selected item subset of the Learning Process Questionnaire

TitleA nonparametric item analysis of a selected item subset of the Learning Process Questionnaire
Authors
Issue Date2003
PublisherThe British Psychological Society. The Journal's web site is located at http://www.bps.org.uk/publications/jEP_1.cfm
Citation
British Journal Of Educational Psychology, 2003, v. 73 n. 3, p. 395-423 How to Cite?
AbstractBackground. Although there have been numerous studies conducted on the psychometric properties of Biggs' Learning Process Questionnaire (LPQ), these have involved the use of traditional omnibus measures of scale quality such as corrected item total correlations, internal consistency estimates of reliability, and factor analysis. However, these omnibus measures of scale quality are sample dependent and fail to model item responses as a function of trait level. And since the item trait relationship is typically nonlinear, traditional factor analytic methods are inappropriate. Aims. The purpose of this study was to identify a unidimensional subset of LPQ items and examine the effectiveness of these items and their options in discriminating between changes in the underlying trait level. In addition to assessing item quality, we were interested in assessing overall scale quality with non-sample dependent measures. Method. The sample was split into two nearly equal halves, and a undimensional subset of items was identified in one of these samples and cross-validated in the other. The nonlinear relationship between the probability of endorsing an item option and the underlying trait level was modelled using a nonparametric latent trait technique known as kernel smoothing and implemented with the program TestGraf. After item and scale quality were established, maximum likelihood estimates of participants' trait level were obtained and used to examine grade and gender differences. Results. A undimensional subset of 16 deep and achieving items was identified. Slightly more than half of these items needed some of their options combined so that the probability of endorsing an item option as a function of increasing trait level corresponded to the ideal rank ordering of the item options. With this adjustment, scale quality as measured by the information function and standard error function was found to be good. However, no statistically significant gender differences were observed and, although statistically significant grade differences were observed, they were not substantively meaningful. Conclusions. The use of nonparametric kernel-smoothing techniques is advocated over parametric latent trait methods for the analysis of attitudinal and psychological measures involving polychotomous ordered-response categories. It is also suggested that latent trait methods are more appropriate than traditional test-based measures for studying differential item functioning both within and between cultures. Nonparametric kernel-smoothing techniques hold particular promise in identifying and understanding cross-cultural differences in student approaches to learning at both the item and scale level.
Persistent Identifierhttp://hdl.handle.net/10722/84997
ISSN
2015 Impact Factor: 2.0
2015 SCImago Journal Rankings: 1.304
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorSachs, Jen_HK
dc.contributor.authorLaw, YKen_HK
dc.contributor.authorChan, CKKen_HK
dc.date.accessioned2010-09-06T08:59:37Z-
dc.date.available2010-09-06T08:59:37Z-
dc.date.issued2003en_HK
dc.identifier.citationBritish Journal Of Educational Psychology, 2003, v. 73 n. 3, p. 395-423en_HK
dc.identifier.issn0007-0998en_HK
dc.identifier.urihttp://hdl.handle.net/10722/84997-
dc.description.abstractBackground. Although there have been numerous studies conducted on the psychometric properties of Biggs' Learning Process Questionnaire (LPQ), these have involved the use of traditional omnibus measures of scale quality such as corrected item total correlations, internal consistency estimates of reliability, and factor analysis. However, these omnibus measures of scale quality are sample dependent and fail to model item responses as a function of trait level. And since the item trait relationship is typically nonlinear, traditional factor analytic methods are inappropriate. Aims. The purpose of this study was to identify a unidimensional subset of LPQ items and examine the effectiveness of these items and their options in discriminating between changes in the underlying trait level. In addition to assessing item quality, we were interested in assessing overall scale quality with non-sample dependent measures. Method. The sample was split into two nearly equal halves, and a undimensional subset of items was identified in one of these samples and cross-validated in the other. The nonlinear relationship between the probability of endorsing an item option and the underlying trait level was modelled using a nonparametric latent trait technique known as kernel smoothing and implemented with the program TestGraf. After item and scale quality were established, maximum likelihood estimates of participants' trait level were obtained and used to examine grade and gender differences. Results. A undimensional subset of 16 deep and achieving items was identified. Slightly more than half of these items needed some of their options combined so that the probability of endorsing an item option as a function of increasing trait level corresponded to the ideal rank ordering of the item options. With this adjustment, scale quality as measured by the information function and standard error function was found to be good. However, no statistically significant gender differences were observed and, although statistically significant grade differences were observed, they were not substantively meaningful. Conclusions. The use of nonparametric kernel-smoothing techniques is advocated over parametric latent trait methods for the analysis of attitudinal and psychological measures involving polychotomous ordered-response categories. It is also suggested that latent trait methods are more appropriate than traditional test-based measures for studying differential item functioning both within and between cultures. Nonparametric kernel-smoothing techniques hold particular promise in identifying and understanding cross-cultural differences in student approaches to learning at both the item and scale level.en_HK
dc.languageengen_HK
dc.publisherThe British Psychological Society. The Journal's web site is located at http://www.bps.org.uk/publications/jEP_1.cfmen_HK
dc.relation.ispartofBritish Journal of Educational Psychologyen_HK
dc.rightsReproduced with permission from British Journal of Educational Psychology © The British Psychological Society 2003-
dc.subject.meshAptitude Tests - statistics and numerical data-
dc.subject.meshFactor Analysis, Statistical-
dc.subject.meshLearning-
dc.subject.meshPsychometrics-
dc.subject.meshStatistics, Nonparametric-
dc.titleA nonparametric item analysis of a selected item subset of the Learning Process Questionnaireen_HK
dc.typeArticleen_HK
dc.identifier.emailLaw, YK: yklaw@hkucc.hku.hken_HK
dc.identifier.emailChan, CKK: ckkchan@hku.hken_HK
dc.identifier.authorityLaw, YK=rp00922en_HK
dc.identifier.authorityChan, CKK=rp00891en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1348/000709903322275902en_HK
dc.identifier.pmid14672151-
dc.identifier.scopuseid_2-s2.0-0141678059en_HK
dc.identifier.hkuros78572en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0141678059&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume73en_HK
dc.identifier.issue3en_HK
dc.identifier.spage395en_HK
dc.identifier.epage423en_HK
dc.identifier.isiWOS:000185592200007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridSachs, J=7101926178en_HK
dc.identifier.scopusauthoridLaw, YK=7006095372en_HK
dc.identifier.scopusauthoridChan, CKK=27170802100en_HK

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