Analysis of Covariance in Repeated Measurement Designs to Evaluate Treatment Effects on Tea Production

Wiwit Widiyanti, Suwanda Suwanda, Siti Sunendiari

Abstract


When in the experiments there are noncontrollable concomitant variable but can be observed along with the response variable and there is a linear relationship between the concomitant variable and the response variable then it becomes the basis for performing the analysis of covariance, whereas in response to the same experimental unit measured at several different times were reviewed on repeated measurement designs. In repeated measurement designs, consider the sum of the squares between-subjects and within-subjects so that the sum of squares of error becomes reduced and the test becomes more sensitive in determining the small effect difference. This paper discusses analysis of covariance with repeated measurements applied to evaluate the effects of treatment on tea production (gram), the response is measured at 13 different times. There are 6 levels of treatment that is control, standard garden, mineral, organo mineral, bio organo mineral, and bio mineral and a concomitant variable is the number of pecco shoots. The results show that only a significant time effect whereas the treatment and interaction effects of treatment with time are not significant.


Keywords


concomitant variable, analysis of covariance, repeated measurement designs, evaluate the effects

References


Ceurvorst, R.W. dan Stock, W.A. (1978). Comments On The Analysis Of Covariance With Repeated Measures Designs. Multivariate Behavioral Research, 509-512.

Davis, C.S. (2002). “Statistical Methods for the Analysis of Repeated Measurements”. Springer, California.

Montgomery, D.C. (2001). “Design and Analysis of Experiments”. John Wiley & Sons, Inc, New York.

Suwanda. (2011). “Desain Eksperimen untuk Penelitian Ilmiah”. Alpabeta, Bandung.

Winer, B.J. (1991). “Statistical Principles in Experimental Design”. McGraw-Hill, Inc. Printed in the United States of America, New York.




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