השוואת שיטות

	מתאם חלקי (Partial Correlation)	מתאם פירסון מסוג "מכפלת מומנטים"	מקדם המתאם של ספירמן
תחום	סטטיסטיקה	סטטיסטיקה	סטטיסטיקה
משפחה	Hypothesis test	Hypothesis test	Hypothesis test
שנת המקור	1924	1895	1904
הוגה השיטה	R. A. Fisher	Karl Pearson	Charles Spearman
סוג	Parametric correlation with covariate control	Parametric correlation	Nonparametric rank-based correlation
מקור מכונן	Fisher, R.A. (1924). The Distribution of the Partial Correlation Coefficient. Metron, 3, 329–332.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.	Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.
כינויים	partial r, controlled correlation, Kısmi Korelasyon (Partial Correlation)	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi	Spearman's rho, Spearman rank-order correlation, Spearman Sıra Korelasyonu
קשורות	2	4	4
תקציר	Partial correlation measures the linear relationship between two continuous variables after removing the shared influence of one or more control variables. The technique was formalised by R. A. Fisher in 1924 and is the standard approach whenever a researcher suspects that a third variable inflates or suppresses the observed association between two variables of interest.	The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association.	The Spearman rank correlation coefficient (ρ) is a nonparametric measure of the monotonic association between two variables. Introduced by Charles Spearman in 1904, it converts raw observations to ranks and measures how consistently one variable increases as the other increases, without assuming a normal distribution or a linear relationship.