<![CDATA[Conventional methods of clustering become weak when meet measured objects with qualitative or categorical data.Latent class logistic analysis can bean alternative method of clustering to overcome this problem. This research is aim to see the application of latent class logistic analysis to clusterthe measured objects with qualitative and quantitative variable andat once to find out backgrounds of the clusters. The objects in this research are 2171 eight grade students from 133 schools in Indonesia. There are two resultsinthis research; first in clustering and second in logistic analysis. In clustering, the students have beenclustered intofour ideal clusters,e.g.39.16 percent students were in cluster1, 32.42 percent in cluster2, 21.46 percent in cluster3, and 6.97 percent in cluster4.Each cluster representsthe students with very low, low,medium, andhigh ability in mathematics. In logistic analysis, overall, eachcluster has been explained well by covariatese.g. student’s interest, attitude, aptitudeand motivation on mathematics, parent’ssocial-economic condition, parent’s highest education level, teacher’s highest education level,teacher’s major study of mathematics and educations,teacher’s perceptions on schools,school’s facilities, etc.]]>
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