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Apa Itu Analisis Faktor?


What (Apa itu Analisis Faktor?)

Analisis faktor adalah teknik statistik yang digunakan untuk mengidentifikasi struktur laten (faktor-faktor tersembunyi) dari sejumlah variabel yang saling berkorelasi. Metode ini bertujuan untuk mereduksi data, sehingga banyak variabel dapat disederhanakan menjadi beberapa faktor utama (Hair et al., 2020).

Why (Mengapa digunakan?)

Analisis faktor digunakan untuk:

  • Menyederhanakan data yang kompleks menjadi faktor-faktor inti.
  • Mengidentifikasi pola hubungan antar variabel.
  • Membantu pembuatan konstruk penelitian (misalnya dalam psikologi, pemasaran, atau manajemen).
  • Menguji validitas dan reliabilitas instrumen penelitian (Liu et al., 2021).

Who (Siapa yang menggunakan?)

  • Peneliti sosial & psikologi: untuk menguji validitas kuesioner.
  • Ekonom & manajer: untuk menganalisis perilaku konsumen atau pasar.
  • Ahli kesehatan & pendidikan: untuk mengelompokkan indikator kesehatan atau kualitas belajar.
  • Data scientist: untuk reduksi dimensi dalam machine learning.

Where (Di mana digunakan?)

  • Psikologi & pendidikan: mengukur konstruk seperti motivasi, kecerdasan, kepuasan belajar.
  • Ekonomi & bisnis: segmentasi konsumen, preferensi produk.
  • Kesehatan: pengelompokan gejala penyakit atau indikator kesehatan.
  • Data science: reduksi variabel sebelum pemodelan machine learning (Feng et al., 2020).

How (Bagaimana cara dilakukan?)

Tahapan umum analisis faktor:

  1. Uji kelayakan data: KMO (Kaiser-Meyer-Olkin) dan Bartlett’s Test.
  2. Ekstraksi faktor: Principal Component Analysis (PCA) atau Common Factor Analysis.
  3. Rotasi faktor: Varimax atau Promax untuk memperjelas struktur faktor.
  4. Interpretasi faktor: memberi label pada faktor yang terbentuk.
  5. Validasi: menguji reliabilitas dengan Cronbach’s Alpha.

Contoh Kasus

Seorang peneliti melakukan survei kepuasan mahasiswa dengan 20 item pertanyaan.
Setelah analisis faktor:

  • Item menyusut menjadi 3 faktor utama, yaitu Kualitas Pengajaran, Fasilitas Kampus, dan Pelayanan Administrasi.
  • Ketiga faktor ini menjelaskan 70% variansi dari seluruh data.

Kesimpulan

Analisis faktor adalah metode multivariat yang esensial untuk menyederhanakan data, menemukan pola laten, serta meningkatkan kualitas instrumen penelitian. Teknik ini menjadi pilar utama dalam ilmu sosial, bisnis, kesehatan, dan data science di era big data.

📚 Referensi (APA Style, Scopus 5 Tahun Terakhir)

  • Feng, C., Wang, H., Lu, N., Chen, T., He, H., Lu, Y., & Tu, X. M. (2020). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 32(1), 58–63. https://doi.org/10.11919/j.issn.1002-0829.219045
  • Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2020). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage.
  • Liu, Y., Yu, Y., & Wang, Y. (2021). Application of exploratory and confirmatory factor analysis in psychological scale development. Frontiers in Psychology, 12, 626153. https://doi.org/10.3389/fpsyg.2021.626153
  • Beavers, A. S., Lounsbury, J. W., Richards, J. K., Huck, S. W., Skolits, G. J., & Esquivel, S. L. (2020). Practical considerations for using exploratory factor analysis in educational research. Practical Assessment, Research, and Evaluation, 25(1), 2. https://doi.org/10.7275/tdrn-qj48
  • Watkins, M. W. (2021). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 47(8), 959–980. https://doi.org/10.1177/00957984211017072


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