LCL and UCL of percentages and difference of percentages

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LCL and UCL of percentages and difference of percentages

Art Kendall
I am trying to help a colleague cobble together a quick lesson for
policymakers.

3 questions.
1) how to get CTABLES to show LCL and UCL of percentages
2) Does CTABLES use asymmetric confidence limits.
3) what conceptual takeaways are missing?

* CNN data about Moderna Covid Vaccine copied down during broadcast.
* find 95% confidence interval of difference of proportions when proportions
are extreme.
* including zero hits on one DV.
* note the disciplinary difference in term 'cases'.
* note this an actual control grouping not just comparison grouping.
* further considerations at
* "https://www.bbc.com/news/health-54902908".
*
"https://www.cnn.com/2020/11/16/health/moderna-vaccine-results-coronavirus/index.html".
* The DV is a dichotomy a special instance of the level of measurement.
* Since there is only 1 possible interval, all intervals are identical.
* There are many ways to look at such data.
data list list /Group (f1)GroupSize(f5) Positive (f2) Severe(f2).
BEGIN DATA
1 15000 90 11
2 15000 5   0
END DATA.
VARIABLE LABELS
Group 'Treatment group'
GroupSize '# participants'
Positive '# hits - individuals with positive tests'
Severe '# hits  - individuals with severe symptoms'.
Value labels Group 1 'Placebo Control' 2 'Actual vaccine treatment'.
WEIGHT BY GroupSize.


* Custom Tables.
CTABLES
  /VLABELS VARIABLES=Group Positive DISPLAY=NAME
  /TABLE Group [COUNT F40.0 COLPCT.COUNT PCT40.1 COLPCT.COUNT.LCL PCT40.1
COLPCT.COUNT.UCL PCT40.1]
  BY Positive
  /CATEGORIES VARIABLES=Group ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CATEGORIES VARIABLES=Positive ORDER=A KEY=VALUE EMPTY=EXCLUDE
  /CRITERIA CILEVEL=95
  /COMPARETEST TYPE=PROP ALPHA=0.05 ADJUST=BONFERRONI ORIGIN=COLUMN
INCLUDEMRSETS=YES
    CATEGORIES=ALLVISIBLE MERGE=YES STYLE=APA SHOWSIG=NO.

* Are differences in treatment vs control results readily attributable to
chance?
* How precise is the estimate of effectiveness? Is the uncertainty due to
imprecision large enough to effect planning?

 



-----
Art Kendall
Social Research Consultants
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Art Kendall
Social Research Consultants
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Re: LCL and UCL of percentages and difference of percentages

Jon Peck
 COLPCT.COUNT.LCL  COLPCT.COUNT.UCL 
Yes, these are asymmetrical:
Lower bound ( pi ) = IDF.BETA(α/ 2 ,^w'i +.5 , W '−w'i +.5 ),
Upper bound ( pi ) = IDF.BETA(1− α / 2 ,w'i +.5 , W'−w'i +.5)
See Algorithms doc for unmangled formula

On Mon, Nov 16, 2020 at 8:28 AM Art Kendall <[hidden email]> wrote:
I am trying to help a colleague cobble together a quick lesson for
policymakers.

3 questions.
1) how to get CTABLES to show LCL and UCL of percentages
2) Does CTABLES use asymmetric confidence limits.
3) what conceptual takeaways are missing?

* CNN data about Moderna Covid Vaccine copied down during broadcast.
* find 95% confidence interval of difference of proportions when proportions
are extreme.
* including zero hits on one DV.
* note the disciplinary difference in term 'cases'.
* note this an actual control grouping not just comparison grouping.
* further considerations at
* "https://www.bbc.com/news/health-54902908".
*
"https://www.cnn.com/2020/11/16/health/moderna-vaccine-results-coronavirus/index.html".
* The DV is a dichotomy a special instance of the level of measurement.
* Since there is only 1 possible interval, all intervals are identical.
* There are many ways to look at such data.
data list list /Group (f1)GroupSize(f5) Positive (f2) Severe(f2).
BEGIN DATA
1 15000 90 11
2 15000 5   0
END DATA.
VARIABLE LABELS
Group 'Treatment group'
GroupSize '# participants'
Positive '# hits - individuals with positive tests'
Severe '# hits  - individuals with severe symptoms'.
Value labels Group 1 'Placebo Control' 2 'Actual vaccine treatment'.
WEIGHT BY GroupSize.


* Custom Tables.
CTABLES
  /VLABELS VARIABLES=Group Positive DISPLAY=NAME
  /TABLE Group [COUNT F40.0 COLPCT.COUNT PCT40.1 COLPCT.COUNT.LCL PCT40.1
COLPCT.COUNT.UCL PCT40.1]
  BY Positive
  /CATEGORIES VARIABLES=Group ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CATEGORIES VARIABLES=Positive ORDER=A KEY=VALUE EMPTY=EXCLUDE
  /CRITERIA CILEVEL=95
  /COMPARETEST TYPE=PROP ALPHA=0.05 ADJUST=BONFERRONI ORIGIN=COLUMN
INCLUDEMRSETS=YES
    CATEGORIES=ALLVISIBLE MERGE=YES STYLE=APA SHOWSIG=NO.

* Are differences in treatment vs control results readily attributable to
chance?
* How precise is the estimate of effectiveness? Is the uncertainty due to
imprecision large enough to effect planning?





-----
Art Kendall
Social Research Consultants
--
Sent from: http://spssx-discussion.1045642.n5.nabble.com/

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--
Jon K Peck
[hidden email]

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Re: LCL and UCL of percentages and difference of percentages

Bruce Weaver
Administrator
What Jon shows there is the Jeffreys method.  Some years ago, I wrote code to
compute CIs for binomial proportions using 5 methods, with Jeffreys as
method 5.  Here is a modified version of it using the binomial proportions
that I think Art is interested in.  HTH.

*  ======================================================== .
*  File:   ciprop.SPS .
*  Date:   30-Oct-2014 .
*  Author:  Bruce Weaver, [hidden email] .
*  ======================================================== .

* Compute the confidence interval for a binomial proportion using:
   [1] Clopper-Pearson "exact" method
   [2] Wald method
   [3] Adjusted Wald method (Agresti & Coull, 1998)
   [4] Wilson score method
   [6] Jeffreys method
.

* Another method proposed by Ghosh (1979) is equivalent
* to the the Wilson score method.

DATA LIST LIST /x(f8.0) n(f8.0) confid(f5.3) .
BEGIN DATA.
90 15000 .95
11 15000 .95
5 15000 .95
0 15000 .95
END DATA.

compute alpha = 1 - confid.
compute p = x/n.
compute q = 1-p.
compute z = probit(1-alpha/2).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Clopper-Pearson "exact" method.
* See http://www.sigmazone.com/binomial_confidence_interval.htm.

IF x EQ 0 lower1 = 0.
IF x EQ n upper1 = 1.
IF x GT 0 lower1 = 1 - idf.beta(1-alpha/2,n-x+1,x).
IF x LT n upper1 = 1 - idf.beta(alpha/2,n-x,x+1).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Wald method (i.e., the usual normal approximation).
* The Wald method breaks down if x = 0 or x = n.
* So only attempt to use if 0 < x < n.

NUMERIC lower2 upper2 (F5.4).
DO IF RANGE(x,1,n-1).
- COMPUTE #se = SQRT(p*q/n).
- COMPUTE lower2 = MAX(0,p - z*#se).
- COMPUTE upper2 = MIN(1,p + z*#se).
END IF.

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Adjusted Wald method due to Agresti & Coull (1998).

COMPUTE #p = (x + z**2/2) / (n + z**2).
COMPUTE #q = 1 - #p.
COMPUTE #se = SQRT(#p*#q/(n+z**2)).
COMPUTE lower3 = MAX(0, #p - z*#se).
COMPUTE upper3 = MIN(1, #p + z*#se).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Wilson score method (Method 3 in Newcombe, 1998) .
* Code adapted from Robert Newcombe's code posted here:
    http://archive.uwcm.ac.uk/uwcm/ms/Robert2.html .

COMPUTE #x1 = 2*n*p+z**2 .
COMPUTE #x2 = z*(z**2+4*n*p*(1-p))**0.5 .
COMPUTE #x3 = 2*(n+z**2) .
COMPUTE lower4 = (#x1 - #x2) / #x3 .
COMPUTE upper4 = (#x1 + #x2) / #x3 .

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Jeffreys method shown on the IBM-SPSS website at
* http://www-01.ibm.com/support/docview.wss?uid=swg21474963 .

compute lower5 = idf.beta(alpha/2,x+.5,n-x+.5).
compute upper5 = idf.beta(1-alpha/2,x+.5,n-x+.5).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Format variables and list the results of all methods .

FORMATS p q lower1 to upper5 (f5.4).

LIST var x n confid p lower1 to upper5 .

* Method 1:  Clopper-Pearson "exact" method.
* Method 2:  Wald method (i.e., the usual normal approximation) .
* Method 3:  Adjusted Wald method (using z**2/2 and z**2 rather than 2 and
4).
* Method 4:  Wilson score method (from Newcombe paper).
* Method 5:  Jeffreys method
(http://www-01.ibm.com/support/docview.wss?uid=swg21474963).
* Data from Newcombe (1998), Table I.

VARIABLE LABELS
 x "Successes"
 n "Trials"
 p "p(Success)"
 confid "Confidence Level"
 lower1 "Clopper-Pearson: Lower"
 upper1 "Clopper-Pearson: Upper"
 lower2 "Wald: Lower"
 upper2 "Wald: Upper"
 lower3 "Adj Wald: Lower"
 upper3 "Adj Wald: Upper"
 lower4 "Wilson score: Lower"
 upper4 "Wilson score: Upper"
 lower5 "Jeffreys: Lower"
 upper5 "Jeffreys: Upper"
.

* OMS.
OMS
  /SELECT TABLES
  /IF COMMANDS=['Summarize'] SUBTYPES=['Case Processing Summary']
  /DESTINATION VIEWER=NO.

SUMMARIZE
  /TABLES=x n p confid lower1 TO upper5
  /FORMAT=VALIDLIST NOCASENUM TOTAL
  /TITLE='Confidence Intervals for Binomial Proportions'
  /MISSING=VARIABLE
  /CELLS=NONE.

OMSEND.

*  ======================================================== .



Jon Peck wrote
> COLPCT.COUNT.LCL  COLPCT.COUNT.UCL
> Yes, these are asymmetrical:
> Lower bound ( pi ) = IDF.BETA(α/ 2 ,^w'i +.5 , W '−w'i +.5 ),
> Upper bound ( pi ) = IDF.BETA(1− α / 2 ,w'i +.5 , W'−w'i +.5)
> See Algorithms doc for unmangled formula
>
> On Mon, Nov 16, 2020 at 8:28 AM Art Kendall &lt;

> Art@

> &gt; wrote:
>
>> I am trying to help a colleague cobble together a quick lesson for
>> policymakers.
>>
>> 3 questions.
>> 1) how to get CTABLES to show LCL and UCL of percentages
>> 2) Does CTABLES use asymmetric confidence limits.
>> 3) what conceptual takeaways are missing?
>>
>> * CNN data about Moderna Covid Vaccine copied down during broadcast.
>> * find 95% confidence interval of difference of proportions when
>> proportions
>> are extreme.
>> * including zero hits on one DV.
>> * note the disciplinary difference in term 'cases'.
>> * note this an actual control grouping not just comparison grouping.
>> * further considerations at
>> * "https://www.bbc.com/news/health-54902908".
>> *
>> "
>> https://www.cnn.com/2020/11/16/health/moderna-vaccine-results-coronavirus/index.html
>> ".
>> * The DV is a dichotomy a special instance of the level of measurement.
>> * Since there is only 1 possible interval, all intervals are identical.
>> * There are many ways to look at such data.
>> data list list /Group (f1)GroupSize(f5) Positive (f2) Severe(f2).
>> BEGIN DATA
>> 1 15000 90 11
>> 2 15000 5   0
>> END DATA.
>> VARIABLE LABELS
>> Group 'Treatment group'
>> GroupSize '# participants'
>> Positive '# hits - individuals with positive tests'
>> Severe '# hits  - individuals with severe symptoms'.
>> Value labels Group 1 'Placebo Control' 2 'Actual vaccine treatment'.
>> WEIGHT BY GroupSize.
>>
>>
>> * Custom Tables.
>> CTABLES
>>   /VLABELS VARIABLES=Group Positive DISPLAY=NAME
>>   /TABLE Group [COUNT F40.0 COLPCT.COUNT PCT40.1 COLPCT.COUNT.LCL PCT40.1
>> COLPCT.COUNT.UCL PCT40.1]
>>   BY Positive
>>   /CATEGORIES VARIABLES=Group ORDER=A KEY=VALUE EMPTY=INCLUDE
>>   /CATEGORIES VARIABLES=Positive ORDER=A KEY=VALUE EMPTY=EXCLUDE
>>   /CRITERIA CILEVEL=95
>>   /COMPARETEST TYPE=PROP ALPHA=0.05 ADJUST=BONFERRONI ORIGIN=COLUMN
>> INCLUDEMRSETS=YES
>>     CATEGORIES=ALLVISIBLE MERGE=YES STYLE=APA SHOWSIG=NO.
>>
>> * Are differences in treatment vs control results readily attributable to
>> chance?
>> * How precise is the estimate of effectiveness? Is the uncertainty due to
>> imprecision large enough to effect planning?
>>
>>
>>
>>
>>
>> -----
>> Art Kendall
>> Social Research Consultants
>> --
>> Sent from: http://spssx-discussion.1045642.n5.nabble.com/
>>
>> =====================
>> To manage your subscription to SPSSX-L, send a message to
>>

> LISTSERV@.UGA

>  (not to SPSSX-L), with no body text except the
>> command. To leave the list, send the command
>> SIGNOFF SPSSX-L
>> For a list of commands to manage subscriptions, send the command
>> INFO REFCARD
>>
>
>
> --
> Jon K Peck

> jkpeck@

>
> =====================
> To manage your subscription to SPSSX-L, send a message to

> LISTSERV@.UGA

>  (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD





-----
--
Bruce Weaver
[hidden email]
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

--
Sent from: http://spssx-discussion.1045642.n5.nabble.com/

=====================
To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the
command. To leave the list, send the command
SIGNOFF SPSSX-L
For a list of commands to manage subscriptions, send the command
INFO REFCARD
--
Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.
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Re: LCL and UCL of percentages and difference of percentages

Jon Peck
The PROPOR extension command calculates binomial or Poisson CIs for proportions or differences in proportions.  

New in 27.0.1
is a PROPORTIONS procedure.  It is described as

Z-Tests and confidence intervals for Proportions and differences in Proportions: For One-Sample, Paired-Samples, Independent-Samples analyses.

Found under the Analyze > Compare Means menu, the new Proportions procedure allows users to test for differences in population proportions and construct confidence intervals on observed differences using a variety of methods for each type of analysis.

I haven't seen it yet as I am still on 27.0.0.


On Mon, Nov 16, 2020 at 1:06 PM Bruce Weaver <[hidden email]> wrote:
What Jon shows there is the Jeffreys method.  Some years ago, I wrote code to
compute CIs for binomial proportions using 5 methods, with Jeffreys as
method 5.  Here is a modified version of it using the binomial proportions
that I think Art is interested in.  HTH.

*  ======================================================== .
*  File:        ciprop.SPS .
*  Date:         30-Oct-2014 .
*  Author:  Bruce Weaver, [hidden email] .
*  ======================================================== .

* Compute the confidence interval for a binomial proportion using:
   [1] Clopper-Pearson "exact" method
   [2] Wald method
   [3] Adjusted Wald method (Agresti & Coull, 1998)
   [4] Wilson score method
   [6] Jeffreys method
.

* Another method proposed by Ghosh (1979) is equivalent
* to the the Wilson score method.

DATA LIST LIST /x(f8.0) n(f8.0) confid(f5.3) .
BEGIN DATA.
90 15000 .95
11 15000 .95
5 15000 .95
0 15000 .95
END DATA.

compute alpha = 1 - confid.
compute p = x/n.
compute q = 1-p.
compute z = probit(1-alpha/2).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Clopper-Pearson "exact" method.
* See http://www.sigmazone.com/binomial_confidence_interval.htm.

IF x EQ 0 lower1 = 0.
IF x EQ n upper1 = 1.
IF x GT 0 lower1 = 1 - idf.beta(1-alpha/2,n-x+1,x).
IF x LT n upper1 = 1 - idf.beta(alpha/2,n-x,x+1).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Wald method (i.e., the usual normal approximation).
* The Wald method breaks down if x = 0 or x = n.
* So only attempt to use if 0 < x < n.

NUMERIC lower2 upper2 (F5.4).
DO IF RANGE(x,1,n-1).
- COMPUTE #se = SQRT(p*q/n).
- COMPUTE lower2 = MAX(0,p - z*#se).
- COMPUTE upper2 = MIN(1,p + z*#se).
END IF.

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Adjusted Wald method due to Agresti & Coull (1998).

COMPUTE #p = (x + z**2/2) / (n + z**2).
COMPUTE #q = 1 - #p.
COMPUTE #se = SQRT(#p*#q/(n+z**2)).
COMPUTE lower3 = MAX(0, #p - z*#se).
COMPUTE upper3 = MIN(1, #p + z*#se).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Wilson score method (Method 3 in Newcombe, 1998) .
* Code adapted from Robert Newcombe's code posted here:
    http://archive.uwcm.ac.uk/uwcm/ms/Robert2.html .

COMPUTE #x1 = 2*n*p+z**2 .
COMPUTE #x2 = z*(z**2+4*n*p*(1-p))**0.5 .
COMPUTE #x3 = 2*(n+z**2) .
COMPUTE lower4 = (#x1 - #x2) / #x3 .
COMPUTE upper4 = (#x1 + #x2) / #x3 .

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Jeffreys method shown on the IBM-SPSS website at
* http://www-01.ibm.com/support/docview.wss?uid=swg21474963 .

compute lower5 = idf.beta(alpha/2,x+.5,n-x+.5).
compute upper5 = idf.beta(1-alpha/2,x+.5,n-x+.5).

*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

* Format variables and list the results of all methods .

FORMATS p q lower1 to upper5 (f5.4).

LIST var x n confid p lower1 to upper5 .

* Method 1:  Clopper-Pearson "exact" method.
* Method 2:  Wald method (i.e., the usual normal approximation) .
* Method 3:  Adjusted Wald method (using z**2/2 and z**2 rather than 2 and
4).
* Method 4:  Wilson score method (from Newcombe paper).
* Method 5:  Jeffreys method
(http://www-01.ibm.com/support/docview.wss?uid=swg21474963).
* Data from Newcombe (1998), Table I.

VARIABLE LABELS
 x "Successes"
 n "Trials"
 p "p(Success)"
 confid "Confidence Level"
 lower1 "Clopper-Pearson: Lower"
 upper1 "Clopper-Pearson: Upper"
 lower2 "Wald: Lower"
 upper2 "Wald: Upper"
 lower3 "Adj Wald: Lower"
 upper3 "Adj Wald: Upper"
 lower4 "Wilson score: Lower"
 upper4 "Wilson score: Upper"
 lower5 "Jeffreys: Lower"
 upper5 "Jeffreys: Upper"
.

* OMS.
OMS
  /SELECT TABLES
  /IF COMMANDS=['Summarize'] SUBTYPES=['Case Processing Summary']
  /DESTINATION VIEWER=NO.

SUMMARIZE
  /TABLES=x n p confid lower1 TO upper5
  /FORMAT=VALIDLIST NOCASENUM TOTAL
  /TITLE='Confidence Intervals for Binomial Proportions'
  /MISSING=VARIABLE
  /CELLS=NONE.

OMSEND.

*  ======================================================== .



Jon Peck wrote
> COLPCT.COUNT.LCL  COLPCT.COUNT.UCL
> Yes, these are asymmetrical:
> Lower bound ( pi ) = IDF.BETA(α/ 2 ,^w'i +.5 , W '−w'i +.5 ),
> Upper bound ( pi ) = IDF.BETA(1− α / 2 ,w'i +.5 , W'−w'i +.5)
> See Algorithms doc for unmangled formula
>
> On Mon, Nov 16, 2020 at 8:28 AM Art Kendall &lt;

> Art@

> &gt; wrote:
>
>> I am trying to help a colleague cobble together a quick lesson for
>> policymakers.
>>
>> 3 questions.
>> 1) how to get CTABLES to show LCL and UCL of percentages
>> 2) Does CTABLES use asymmetric confidence limits.
>> 3) what conceptual takeaways are missing?
>>
>> * CNN data about Moderna Covid Vaccine copied down during broadcast.
>> * find 95% confidence interval of difference of proportions when
>> proportions
>> are extreme.
>> * including zero hits on one DV.
>> * note the disciplinary difference in term 'cases'.
>> * note this an actual control grouping not just comparison grouping.
>> * further considerations at
>> * "https://www.bbc.com/news/health-54902908".
>> *
>> "
>> https://www.cnn.com/2020/11/16/health/moderna-vaccine-results-coronavirus/index.html
>> ".
>> * The DV is a dichotomy a special instance of the level of measurement.
>> * Since there is only 1 possible interval, all intervals are identical.
>> * There are many ways to look at such data.
>> data list list /Group (f1)GroupSize(f5) Positive (f2) Severe(f2).
>> BEGIN DATA
>> 1 15000 90 11
>> 2 15000 5   0
>> END DATA.
>> VARIABLE LABELS
>> Group 'Treatment group'
>> GroupSize '# participants'
>> Positive '# hits - individuals with positive tests'
>> Severe '# hits  - individuals with severe symptoms'.
>> Value labels Group 1 'Placebo Control' 2 'Actual vaccine treatment'.
>> WEIGHT BY GroupSize.
>>
>>
>> * Custom Tables.
>> CTABLES
>>   /VLABELS VARIABLES=Group Positive DISPLAY=NAME
>>   /TABLE Group [COUNT F40.0 COLPCT.COUNT PCT40.1 COLPCT.COUNT.LCL PCT40.1
>> COLPCT.COUNT.UCL PCT40.1]
>>   BY Positive
>>   /CATEGORIES VARIABLES=Group ORDER=A KEY=VALUE EMPTY=INCLUDE
>>   /CATEGORIES VARIABLES=Positive ORDER=A KEY=VALUE EMPTY=EXCLUDE
>>   /CRITERIA CILEVEL=95
>>   /COMPARETEST TYPE=PROP ALPHA=0.05 ADJUST=BONFERRONI ORIGIN=COLUMN
>> INCLUDEMRSETS=YES
>>     CATEGORIES=ALLVISIBLE MERGE=YES STYLE=APA SHOWSIG=NO.
>>
>> * Are differences in treatment vs control results readily attributable to
>> chance?
>> * How precise is the estimate of effectiveness? Is the uncertainty due to
>> imprecision large enough to effect planning?
>>
>>
>>
>>
>>
>> -----
>> Art Kendall
>> Social Research Consultants
>> --
>> Sent from: http://spssx-discussion.1045642.n5.nabble.com/
>>
>> =====================
>> To manage your subscription to SPSSX-L, send a message to
>>

> LISTSERV@.UGA

>  (not to SPSSX-L), with no body text except the
>> command. To leave the list, send the command
>> SIGNOFF SPSSX-L
>> For a list of commands to manage subscriptions, send the command
>> INFO REFCARD
>>
>
>
> --
> Jon K Peck

> jkpeck@

>
> =====================
> To manage your subscription to SPSSX-L, send a message to

> LISTSERV@.UGA

>  (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD





-----
--
Bruce Weaver
[hidden email]
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly.
To send me an e-mail, please use the address shown above.

--
Sent from: http://spssx-discussion.1045642.n5.nabble.com/

=====================
To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the
command. To leave the list, send the command
SIGNOFF SPSSX-L
For a list of commands to manage subscriptions, send the command
INFO REFCARD


--
Jon K Peck
[hidden email]

===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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Re: LCL and UCL of percentages and difference of percentages

Art Kendall
In reply to this post by Jon Peck
Thanks.

I should download v 27.
I'll just leave v24 on my PC and have both here.



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Art Kendall
Social Research Consultants
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Re: LCL and UCL of percentages and difference of percentages

Art Kendall
In reply to this post by Bruce Weaver
Thanks again.  

I passed along the previous version with just the notes/comments.

I'll send this since the briefing is in about 1/2 hour.

The SPSSX-l list members came to the rescue again.





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Art Kendall
Social Research Consultants
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Sent from: http://spssx-discussion.1045642.n5.nabble.com/

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Art Kendall
Social Research Consultants