KORELASI (Kuliah Minggu ke-13)

  1. Diperkenalkan oleh Karl Pearson pada 1895.
  2. Pearson’s  r (Product Moment…) digunakan untuk melihat arah hubungan dan kekuatan hubungan antara dua pembolehubah.
  3. Kedua-dua pembolehubah yang diuji mestilah selanjar (continuous) iaitu sama ada data jenis sela atau nisbah.
  4. Sekiranya data jenis ordinal, maka ujian korelasi Spearman’s Rho digunakan (tidak dibincang lanjut dalam tulisan ini).
  5. Korelasi Pearson tidak hanya menunjukkan arah serta kekuatan hubungan, namun tidak menyatakan sebab-akibat berlakunya perkara tersebut.

Terdapat 3 kemungkinan sebab-akibat. Mungkin A penyebab kepada B, atau mungkin B penyebab kepada A, dan atau terdapat pemboleh ubah lain C yang menjadi faktor penyebab.

Contoh:       Kemungkinan pencapaian adalah disebabkan oleh motivasi, atau mungkin juga pencapaian menyebabkan wujudnya motivasi , atau wujudnya faktor lain (cth.gaya pengajaran) menyebabkan pencapaian meningkat.

Terdapat 3 kemungkinan arah hubungan – positif, negatif, dan tiada hubungan r=0 (curvilinear).

Alias Baba (1992) mencadangkan anggaran kekuatan hubungan 2 pembolehubah;

±0.01 – ±0.20  Sangat Lemah

±0.21 – ±0.40  Lemah

±0.41 – ±0.60 Sederhana

±0.61 – ±0.80 Kuat

±0.81 – ±1.00  Sangat Kuat

Cohen (1988) pula mencadangkan nilai korelasi seperti berikut:

±0.01 – ±0.29  Kecil

±0.30 – ±0.49  Sederhana

±0.50 – ±1.00  Besar

Korelasi sangat sensitif kepada ‘outliers’ serta sampel yang besar!

Terdapat kemungkinan sampel yang besar akan menunjukkan terdapatnya korelasi, TETAPI pada tahap yang lemah.

“Signifikan, tapi lemah”. Maka, ABAIKAN!
Dua perkara yang paling penting dalam korelasi adalah ARAH dan KEKUATAN, signifikan dalam kes yang diterangkan di atas menunjukkan ianya bukan perkara utama.

Example of research question: Is there a relationship between the amount of control people have over their internal states and their levels of perceived stress? Do people with high levels of perceived control experience lower levels of perceived stress?

What you need: Two variables: both continuous, or one continuous and the other dichotomous (two values).

What it does: Correlation describes the relationship between two continuous variables, in terms of both the strength of the relationship and the direction.

Langkah-langkah melakukan ujian korelasi menggunakan SPSS:

Procedure for requesting Pearson r or Spearman rho

  1. From the menu at the top of the screen, click on Analyze, then select Correlate, then Bivariate.
  2. Select your two variables and move them into the box marked Variables (e.g. Total perceived stress: tpstress, Total PCOISS: tpcoiss). If you wish you can list a whole range of variables here, not just two. In the resulting matrix, the correlation between all possible pairs of variables will be listed. This can be quite large if you list more than just a few variables.
  3. In the Correlation Coefficients section, the Pearson box is the default option. If you wish to request the Spearman rho (the non-parametric alternative), tick this box instead (or as well).
  4. Click on the Options button. For Missing Values, click on the Exclude cases pairwise box. Under Options, you can also obtain means and standard deviations if you wish.
  5. Click on Continue and then on OK.

 Procedure for generating a scatter plot

  1. From the menu at the top of the screen, click on Graphs, then select Legacy Dialogs.
  2. Click on Scatter/Plot and then Simple Scatter. Click Define.
  3. Click on the first variable and move it into the Y-axis box (this will run vertically). By convention, the dependent variable is usually placed along the Y-axis (e.g. Total perceived stress: tpstress).
  4. Click on the second variable and move to the X-axis box (this will run across the page). This is usually the independent variable (e.g. Total PCOISS: tpcoiss).
  5. In the Label Cases by: box, you can put your ID variable so that outlying points can be identified.
  6. Click on OK.

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