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dc.contributor.authorLucas A Othuon
dc.date.accessioned2020-08-12T10:22:12Z
dc.date.available2020-08-12T10:22:12Z
dc.date.issued1993
dc.identifier.urihttps://repository.maseno.ac.ke/handle/123456789/2037
dc.description.abstractDescription Public examinations have been used in Kenya for decades as selection instruments for further education and training. The Kenya Certificate of Primary Education examination (KCPE) is the first of such selection examinations. A basic assumption is that those who pass the examination and are selected to join secondary school have a good chance of succeeding in secondary school. However, evidence that may verify such an assumption, that is, a study of the predictive validity of KCPE, has received little attention. The purpose of this study was to determine the extent to which KCPE predicts success in secondary school. Success in secondary school was measured by the level of examinee achievement in the Kenya Certificate of Secondary Education examination (KCSE). Stratified random sampling was used to select 26 secondary schools within a single district in Kenya. The 1991 KCSE data for 781 examinees in the sample were used in the analysis. The KCSE records for examinees in the sample were matched with corresponding 1987 KCPE records. The nature of the relationship between KCPE and KCSE was determined by use of Hierarchical Linear Models (HLM). The influence of selected moderator variables on the relationship between KCPE and KCSE was investigated as well. These variables were age, gender, repetition of Standard 8 (ie, writing KCPE more than once), and school size. A moderate linear relationship between KCPE and KCSE was found. The predictive validity did not significantly vary from one school to the other. Of the three pupil-level moderator variables used in this study, only age showed a …en_US
dc.publisherUniversity of British Columbiaen_US
dc.subjectprimary education, K.C.S.E, K.C.P.Een_US
dc.titleA study of the predictive validity of the Kenya certificate of primary education examination: application of hierarchical linear modelsen_US
dc.typeArticleen_US


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