DEVELOPING AND TESTING THE CONSTRUCT VALIDITY INSTRUMENT OF TADZKIYATUN NAFS

Rijal Firdaos

Abstract


This research aims to develop and test the construct validity of the tadzkiyatun nafs instrument at the Tarbiyah and Teacher Training Faculty of UIN Raden Intan Lampung. The Population of this research was the students of Tarbiyah and Teacher Training Faculty that consisted of 150 students. The method employed in this research was survey technique by using Likert Scale parameter. The result of the empirical research states that 37 items of the Tadzkiyatun Nafs instrument are spread out into five dimensions. The result of construct validity by using factor analysis proved that several numbers of items that have been estimated are 37 indicators with the value of > 0.30, out of 39 items, assuming that all items are estimated in accordance with the theory being constructed. The two other indicators are < 0.30 which then eliminated. The two items that were eliminated are item A4 on the Tauhid dimension, and point A11 on the shalat dimension. The implications of this research are expected can contribute to the progress of Islamic civilization, especially in relation to scientific integration by integrating Islamic sciences as its subject, and exact science as its methodology.

Keywords


Developing; Construct Validity; Tadzkiyatun Nafs

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DOI: http://dx.doi.org/10.21043/addin.v11i2.2497

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