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📖 Analisis Korelasi di SPSS: Pendekatan 5W1H

 

📖 Analisis Korelasi di SPSS: Pendekatan 5W1H

1. What (Apa Itu Analisis Korelasi?)

Analisis korelasi adalah teknik statistik yang digunakan untuk mengukur hubungan antara dua atau lebih variabel (Field, 2020). Nilai korelasi biasanya ditunjukkan dengan koefisien korelasi (r), yang berkisar antara -1 hingga +1.

  • r positif → semakin tinggi X, semakin tinggi Y.
  • r negatif → semakin tinggi X, semakin rendah Y.
  • r = 0 → tidak ada hubungan.

SPSS (Statistical Package for the Social Sciences) memudahkan perhitungan korelasi baik Pearson (parametrik) maupun Spearman (nonparametrik), sehingga sering dipakai dalam penelitian sosial, psikologi, pendidikan, dan ekonomi.

2. Who (Siapa yang Menggunakan Analisis Korelasi?)

Analisis korelasi digunakan oleh:

  • Mahasiswa & Peneliti → untuk skripsi, tesis, atau disertasi, khususnya saat ingin mengetahui hubungan antar variabel seperti motivasi belajar dengan prestasi akademik.
  • Dosen & Akademisi → menguji model teoritis dengan melihat keterkaitan antar konsep.
  • Praktisi → misalnya HRD menguji hubungan kepuasan kerja dengan kinerja karyawan (Hair et al., 2019).

Contoh: Peneliti di bidang ekonomi bisa menguji apakah ada korelasi antara inflasi dan tingkat pengangguran.

3. When (Kapan Analisis Korelasi Digunakan?)

Analisis korelasi digunakan ketika:

  1. Penelitian ingin menguji hubungan, bukan sebab-akibat.
  2. Variabel bersifat interval/rasio (Pearson) atau ordinal (Spearman).
  3. Data sudah melalui uji asumsi klasik seperti normalitas (untuk Pearson).
  4. Peneliti ingin memprediksi arah hubungan antar variabel sebelum melangkah ke regresi.

Contoh: Mahasiswa melakukan penelitian tentang hubungan lama belajar dengan nilai ujian. Korelasi dipakai sebelum melanjutkan ke analisis regresi.

4. Where (Di Mana Analisis Korelasi Dilakukan?)

Analisis korelasi dilakukan di:

  • SPSS Software → menu Analyze → Correlate → Bivariate.
  • Bidang Penelitian Sosial → misalnya hubungan antara media sosial dan kecemasan remaja.
  • Bidang Ekonomi → korelasi GDP per kapita dengan indeks kebahagiaan.
  • Bidang Kesehatan → hubungan indeks massa tubuh (BMI) dengan tekanan darah (Pallant, 2020).

SPSS menjadi pilihan utama karena tampilannya sederhana dan hasil outputnya langsung menampilkan nilai koefisien, signifikansi, dan interpretasi.

5. Why (Mengapa Analisis Korelasi Penting?)

Analisis korelasi penting karena:

  1. Memberikan gambaran awal keterkaitan antar variabel.
  2. Menjadi dasar untuk analisis lebih lanjut seperti regresi atau SEM.
  3. Menghindari kesalahan dalam pemilihan model penelitian.
  4. Membantu memahami pola data dalam penelitian sosial dan eksperimental.

Tanpa korelasi, peneliti sulit memastikan apakah dua variabel layak dikaji lebih lanjut dalam model prediktif (Tabachnick & Fidell, 2019).

6. How (Bagaimana Cara Melakukan Analisis Korelasi di SPSS?)

Langkah praktis:

  1. Masukkan data ke SPSS (variabel X dan Y).
  2. Klik Analyze → Correlate → Bivariate.
  3. Pilih metode:
    • Pearson → untuk data interval/rasio & normal.
    • Spearman → untuk data ordinal/tidak normal.
  4. Centang two-tailed (uji dua arah).
  5. Klik OK → Output menampilkan tabel korelasi.

📊 Contoh Output SPSS:

  • Korelasi Pearson antara X (Motivasi) dan Y (Prestasi Akademik) = 0.65, Sig. (2-tailed) = 0.001.
    Interpretasi: ada hubungan positif kuat dan signifikan.

📊 Tabel Perbedaan Pearson vs Spearman di SPSS

Jenis Korelasi Data yang Digunakan Kelebihan Kekurangan Contoh Penggunaan
Pearson Interval/rasio, normal Tepat & detail Butuh asumsi normalitas Korelasi jam belajar dengan nilai ujian
Spearman Ordinal/tidak normal Fleksibel, tidak butuh normalitas Kurang akurat untuk data interval Korelasi ranking siswa dengan motivasi
Kendall’s Tau Data ordinal kecil Stabil untuk sampel kecil Jarang dipakai di SPSS Hubungan preferensi produk dengan ranking minat

✅ Kelebihan Analisis Korelasi

  1. Mudah dipahami & cepat dihitung.
  2. Dapat dipakai di berbagai disiplin ilmu.
  3. Memberikan gambaran awal hubungan variabel.
  4. Bisa dijadikan dasar sebelum uji lanjutan.

⚠️ Kekurangan Analisis Korelasi

  1. Tidak bisa membuktikan sebab-akibat.
  2. Rentan dipengaruhi variabel luar.
  3. Hanya menggambarkan hubungan linear.
  4. Bisa menimbulkan salah interpretasi jika data bias.

📌 Contoh Kasus Nyata

Penelitian di bidang pendidikan:

  • Variabel X = Intensitas Belajar Online
  • Variabel Y = Prestasi Akademik

Hasil SPSS menunjukkan korelasi Pearson = 0.58 (p < 0.01).
Interpretasi → ada hubungan positif cukup kuat: semakin tinggi intensitas belajar online, semakin baik prestasi.

💬 Ajakan Diskusi

Bagaimana pengalaman Anda menggunakan analisis korelasi di SPSS? Apakah sering menemukan hasil signifikan, atau justru variabelnya tidak berhubungan?
Yuk diskusi lebih lanjut di laacademic.com 🚀

📚 Referensi (APA, 2019–2024, Scopus & Books)

  • Field, A. (2020). Discovering statistics using SPSS (5th ed.). Sage.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
  • Pallant, J. (2020). SPSS survival manual (7th ed.). Routledge.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
  • Zhang, Y., & Lu, Y. (2021). Digital literacy and technology adoption in education. Computers & Education, 170, 104224.


Please check our service, if you need help to publish your article in Scopus, Copernicus or even Sinta journal. 

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