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Point-Biserial Correlation in SPSS: Continuous and Binary Variables

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IBM SPSS Academy 27 File Edit View Data Transform Analyze Graphs Utilities Correlate ▶ ▶ Bivariate Menü Yolu: Analyze → Correlate → Bivariate Yukarıdaki menü yolunu takip ederek analiz penceresini açın

📸 Point-biserial correlation in SPSS — calculated as Pearson r

What Is Point-Biserial Correlation?

Point-biserial correlation (rpb) measures the strength of association between one continuous variable and one genuinely dichotomous binary variable (e.g., pass/fail, male/female, treatment/control). It is mathematically identical to Pearson r when the binary variable is coded as 0 and 1, so no special formula or SPSS menu is needed.

Running in SPSS

Step 1: Ensure the binary variable is coded 0/1.
Step 2: Analyze → Correlate → Bivariate. Add both the continuous and binary variables. Method: Pearson → OK. The resulting r IS the rpb.
SPSS Academy Output Viewer Correlations Exam Score Gender (0=M, 1=F) Exam Score 1.000 .312* Sig. .003 N 120 120 * p < .05 anlamlı sonuç gösterir

📸 rpb output — Pearson r with binary variable = point-biserial r

Converting rpb to Cohen's d

To compare with other effect size metrics: d = 2r / √(1-r²). For rpb=.312: d = 2(.312)/√(1-.097) = 0.654 — a medium effect.

APA Reporting

Point-biserial correlation indicated a significant association between gender and exam score, rpb(118)=.312, p=.003, 95% CI [.136, .470], with female students scoring significantly higher (M=74.2) than male students (M=68.4).

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