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A variable in common (overlap) e.g. of form r(W,X) = r(W,Z).   A variable in common (overlap) e.g. of form r(W,X) = r(W,Z).
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A test for this comparison goes under various names the Williams test, Williams-Hotelling or Hotelling test.  A test for this comparison goes under various names the Williams test, Williams-Hotelling or Hotelling test.
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 This can be implemented using SPSS syntax provided at http://www.utexas.edu/its/rc/answers/general/gen28.html .    . This can be implemented using SPSS syntax provided at http://www.utexas.edu/its/rc/answers/general/gen28.html .
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* Dependent Correlation Comparison Program.
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* Dependent Correlation Comparison Program. * Compares correlation coefficients from the same sample.
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* Compares correlation coefficients from the same sample. * See Cohen & Cohen (1983), p. 57.
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* See Cohen & Cohen (1983), p. 57. * Sam Field, sfield@mail.la.utexas.edu , March 1, 2000.
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* Sam Field, sfield@mail.la.utexas.edu, March 1, 2000.
 
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/r12 r13  r23 nsize.  /r12 r13 r23 nsize.
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END DATA.  END DATA.
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                  /rvy = !tokens(1)   /rvy = !tokens(1) 
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                  /rxv = !tokens(1)   /rxv = !tokens(1) 
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                  /n = !tokens(1)).   /n = !tokens(1)).
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COMPUTE #diffr = !rxy - !rvy.  COMPUTE #diffr = !rxy - !rvy.
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*Calculate (rxy + rvy)^2 .  *Calculate (rxy + rvy)^2 .
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* Calculate numerator of t statistic.
COMPUTE #tnum = (#diffr) * (sqrt((!n-1)*(1 + !rxv))). 
* Calculate numerator of t statistic. COMPUTE #tnum = (#diffr) * (sqrt((!n-1)*(1 + !rxv))).
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COMPUTE #tden = sqrt(2*((!n-1)/(!n-3))*#detR + ((#rbar**2) * ((1-!rxv)**3))).  COMPUTE #tden = sqrt(2*((!n-1)/(!n-3))*#detR + ((#rbar**2) * ((1-!rxv)**3))).
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COMPUTE t= (#tnum/#tden).
COMPUTE df = !n - 3.
COMPUTE t= (#tnum/#tden). COMPUTE df = !n - 3.
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* Evaluate the value of the t statistic.  * Evaluate the value of the t statistic.
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* against a t distribution with n - 3 degrees if freedom for.  * against a t distribution with n - 3 degrees if freedom for.
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* statistical significance.  * statistical significance.
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COMPUTE p_1_tail = 1 - CDF.T(abs(t),df).
COMPUTE p_2_tail = (1 - CDF.T(abs(t),df))*2.
COMPUTE p_1_tail = 1 - CDF.T(abs(t),df). COMPUTE p_2_tail = (1 - CDF.T(abs(t),df))*2.
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* Print the results.  * Print the results.
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LIST t df    p_1_tail    p_2_tail. 
 
LIST t df p_1_tail p_2_tail.
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A variable in common (overlap) e.g. of form r(W,X) = r(W,Z).

A test for this comparison goes under various names the Williams test, Williams-Hotelling or Hotelling test.

An example of its use together with syntax is given below. Just cut and paste into a SPSS syntax window to use. You can also use the Williams-Hotelling test by typing equalcor at a UNIX prompt on a CBU machine.

* Dependent Correlation Comparison Program.

* Compares correlation coefficients from the same sample.

* See Cohen & Cohen (1983), p. 57.

* Sam Field, sfield@mail.la.utexas.edu , March 1, 2000.

******** this input is inputted in the macro call at end of this syntax*********

* Three pairs of correlations to compare*****

set format f10.5.

DATA LIST free

/r12 r13 r23 nsize.

BEGIN DATA

.50 .32 .65 50

.59 .31 .71 30

.80 .72 .89 26

END DATA.

***************macro and macro call**************

**** tests if rxy=rvy and outputs a t-statistic plus one and two-tailed p-values

define williams (rxy = !tokens(1)

  • /rvy = !tokens(1) /rxv = !tokens(1) /n = !tokens(1)).

COMPUTE #diffr = !rxy - !rvy.

COMPUTE #detR = (1 - !rxy **2 - !rvy**2 - !rxv**2)+ (2*!rxy*!rxv*!rvy).

*Calculate (rxy + rvy)^2 .

COMPUTE #rbar = (!rxy + !rvy)/2.

* Calculate numerator of t statistic. COMPUTE #tnum = (#diffr) * (sqrt((!n-1)*(1 + !rxv))).

COMPUTE #tden = sqrt(2*((!n-1)/(!n-3))*#detR + ((#rbar**2) * ((1-!rxv)**3))).

COMPUTE t= (#tnum/#tden). COMPUTE df = !n - 3.

* Evaluate the value of the t statistic.

* against a t distribution with n - 3 degrees if freedom for.

* statistical significance.

COMPUTE p_1_tail = 1 - CDF.T(abs(t),df). COMPUTE p_2_tail = (1 - CDF.T(abs(t),df))*2.

* Print the results.

LIST t df p_1_tail p_2_tail.

exe.

!enddefine.

*********************

williams rxy=r12 rvy=r13 rxv=r23 n=nsize.

None: FAQ/WilliamsSPSS (last edited 2021-04-09 11:33:29 by PeterWatson)