The document discusses three correlation coefficients used to measure the strength of correlation between dichotomous and continuous or ordinal variables. The Point-Biserial coefficient (rpb) measures correlation between a dichotomous variable and a continuous variable. The Rank-Biserial coefficient (rbr) measures correlation between a dichotomous variable and an ordinal variable. The Phi coefficient measures correlation between two dichotomous variables. Examples of dichotomous and continuous/ordinal variables that could be used with each coefficient are provided. Formulas for calculating the Point-Biserial and Rank-Biserial coefficients are also shown.

2.  Measures the strength of correlation
between a dichotomous (1,0) variable
and a continuous variable (R).

3. Dichotomous Variable Continuous
Variable
Expected Results
Sex (male (1), Female
(0))
IQ score Inferences about the
relationship between sex
and IQ
Education ( private (1),
public ((0)
Math score Inference about the type
of schooling and
performance on Math
Marital Status (Married
(1), Single (0))
Cholesterol Level Inferences about marital
status and health
Location of residence
(Urban(1), rural (0))
Level of literacy Inferences about location
of residence and literacy
College degree (
graduated (1), no
college degree (0))
Income Inferences about level of
education and income

4.  Measures the strength of correlation
between a dichotomous variable (1,0)
and an ordinal variable (rank).

5. Dichotomous Variable Ordinal Variable Expected results
Sex (male 1, female 0) TV networks
preferences
Inferences about sex
and preferred TV
networks
Education ( private 1,
public 0)
TV program
preferences
Inferences about type
of schooling and
preferred TV programs
Marital Status ( married
1, single 0)
Senatorial Candidate
preferences
Inferences about
marital status and
preferred senatorial
candidate
Location of residence
(Urban 1, rural 0)
Bb Pilipinas candidates
preferences
Inferences about
location of residence
and preferred BB.
Pilpinas candidates

6.  Measures the strength of correlation
between a dichotomous variable (1,0)
and another dichotomous variable (1,0).

7. Dichotomous Variable Dichotomous
Variable
Expected Results
Sex (male (1), Female
(0))
College Education
(Graduate 1, Non-
graduate 0)
Inferences about sex and
educational attainment
Education ( private (1),
public ((0)
Sports involvement
(involved 1, not
involved 0)
Inference about the type
of schooling and sports
involvement
Marital Status (Married
(1), Single (0))
Financial status
(Poor 1, not poor)
Inferences about marital
status and affluence
Location of residence
(Urban(1), rural (0))
Financial status
(Poor 1, not poor
0)
Inferences about location
of residence and
affluence
College degree (
graduated (1), no
college degree (0))
Employment
(Employed 1, not
employed 0)
Inferences about level of
education and
employment

8. Name X Y statistic
Point Biserial
Coefficient
Dichotomous
variable
Continuous
Variable
Rank Biserial
Coefficient
Dichotomous
variable
Ordinal
Variable
(Ranking)
Phi
coefficient
Dichotomous
variable
Dichotomous
variable
pbr
rbr

9. Let X be a dichotomous variable and Y the
continuous Variable
 1
01
1
01



 nn
nn
s
MM
r
n
pb
 
 


n
i
in YY
n
s
1
2
1
1
1
Sample standard
deviation
M1 – the mean value of
observation in the continuous
variable Y for group 1
M0 – the mean value of
observation in the continuous
variable Y for group 2 (0)
n1 – size of sample for group 1
n0 – size of sample for group 2 (0)
n – total sample size