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Comprehensive SPSS Application for Statistical Analysis: Reliability and Principal Component Analysis

This document outlines the use of SPSS for conducting statistical analyses, focusing on reliability analysis through Cronbach's Alpha and principal component analysis (PCA). The reliability analysis discusses relevant statistics including mean, standard deviation, and the reliability coefficients across various test items. Furthermore, the PCA section features a rotated component matrix to facilitate interpretation of underlying factors in the data. The application adheres to rigorous statistical methodologies, benefiting researchers aiming for precise data analytics.

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Comprehensive SPSS Application for Statistical Analysis: Reliability and Principal Component Analysis

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  1. Sesi 13 Aplikasi SPSS Untuk Analisis Statistik

  2. ****** Method 1 (space saver) will be used for this analysis ****** • R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) • Mean Std Dev Cases • 1. P1 3.4000 1.1738 10.0 • 2. P2 4.1000 .8756 10.0 • 3. P3 4.7000 .4830 10.0 • 4. P4 3.8000 1.3166 10.0 • 5. K1 3.3000 1.4181 10.0 • 6. K2 4.6000 .5164 10.0 • 7. K3 4.1000 1.1005 10.0 • 8. K4 3.2000 1.0328 10.0 • 9. M1 3.3000 1.1595 10.0 • 10. M2 4.5000 .9718 10.0 • 11. M3 3.5000 1.3540 10.0 • 12. M4 4.7000 .6749 10.0 • Reliability Coefficients • N of Cases = 10.0 N of Items = 12 • Alpha = .5196

  3. Rotated Component Matrix(a) • Component • 1 2 3 • P1 .162 -.798 -.243 • P2 .790 .084 -.128 • P3 .839 .279 .416 • P4 -.087 -.705 -.093 • K1 .414 .350 -.686 • K2 .766 -.067 .275 • K3 .440 .642 -.257 • K4 -.064 .750 -.049 • M1 .111 .066 .730 • M2 .837 -.008 -.282 • M3 .763 -.106 .253 • M4 .296 .266 .857 • Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. • a Rotation converged in 6 iterations.

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