Dear all, I would like to use the metafor package [escalc function] to calculate my effect size. I saw that the metafor escalc function measure = MN is for raw means. However, I am looking to calculate a "standardized mean". In my studies, only a single outcome is reported (?recalled number of words?). The problem is that sometimes this is reported as a proportion (e.g., percentage recalled words of all possible study words, percentage of recalled words of possible condition-specific words) and sometimes as raw numbers. Thus, I only have one quantitative outcome variable, which is sometimes operationalized in different scales across studies. This is why I had the idea to use some sort of ?standardized mean? instead of the raw mean. Is this possible? And if so, is there an escalc function measure for this? Many thanks in advance. Best wishes, Sera
[R-meta] Escalc function: Possible to compute single standardized mean as outcomes?
4 messages · Michael Dewey, Sera Wiechert
Dear Sera Can I just clarify this In the first group of studies each person recalls n words and the authors calculated a proportion of possible words for each person and then report the mean (and hopefully the standard deviation/error) of those proportions. In the second group the authors just report the mean of the number of words again hopefully with a standard deviation/error. Is it possible for the second group to impute the denominator which they would have used if they had reported in the same way as the first group? If they have then you can convert to mean proportions. Apologies if that has already occurred to you but you have discarded it as impossible. Michael
On 24/11/2021 10:03, Sera Wiechert wrote:
Dear all, I would like to use the metafor package [escalc function] to calculate my effect size. I saw that the metafor escalc function measure = MN is for raw means. However, I am looking to calculate a "standardized mean". In my studies, only a single outcome is reported (?recalled number of words?). The problem is that sometimes this is reported as a proportion (e.g., percentage recalled words of all possible study words, percentage of recalled words of possible condition-specific words) and sometimes as raw numbers. Thus, I only have one quantitative outcome variable, which is sometimes operationalized in different scales across studies. This is why I had the idea to use some sort of ?standardized mean? instead of the raw mean. Is this possible? And if so, is there an escalc function measure for this? Many thanks in advance. Best wishes, Sera [[alternative HTML version deleted]]
_______________________________________________ R-sig-meta-analysis mailing list R-sig-meta-analysis at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
Dear Michael, Thanks for your thoughts on this. Most of them do report their standard deviation/error or CIs, so that should be no problem. But for your main question; this is indeed something I thought about, too. But I am afraid that that will not be possible in most of the cases as the ?word-pool? and thus, the denominator they use, is often not reported properly and many researchers seem to use different group totals to calculate their proportions. Would there be, in this situation, a way to standardize the two in order to not be dependent on the denominator information? Many thanks. Best, Sera Von: Michael Dewey <lists at dewey.myzen.co.uk> Datum: Mittwoch, 24. November 2021 um 17:27 An: Sera Wiechert <s.wiechert at uva.nl>, r-sig-meta-analysis at r-project.org <r-sig-meta-analysis at r-project.org> Betreff: Re: [R-meta] Escalc function: Possible to compute single standardized mean as outcomes? Dear Sera Can I just clarify this In the first group of studies each person recalls n words and the authors calculated a proportion of possible words for each person and then report the mean (and hopefully the standard deviation/error) of those proportions. In the second group the authors just report the mean of the number of words again hopefully with a standard deviation/error. Is it possible for the second group to impute the denominator which they would have used if they had reported in the same way as the first group? If they have then you can convert to mean proportions. Apologies if that has already occurred to you but you have discarded it as impossible. Michael
On 24/11/2021 10:03, Sera Wiechert wrote:
Dear all,
I would like to use the metafor package [escalc function] to calculate my effect size.
I saw that the metafor escalc function measure = MN is for raw means. However, I am looking to calculate a "standardized mean". In my studies, only a single outcome is reported (?recalled number of words?). The problem is that sometimes this is reported as a proportion (e.g., percentage recalled words of all possible study words, percentage of recalled words of possible condition-specific words) and sometimes as raw numbers. Thus, I only have one quantitative outcome variable, which is sometimes operationalized in different scales across studies.
This is why I had the idea to use some sort of ?standardized mean? instead of the raw mean. Is this possible? And if so, is there an escalc function measure for this?
Many thanks in advance.
Best wishes,
Sera
[[alternative HTML version deleted]]
_______________________________________________ R-sig-meta-analysis mailing list R-sig-meta-analysis at r-project.org https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-meta-analysis&data=04%7C01%7Cs.wiechert%40uva.nl%7Cd4a75b2fbbd34b78ce3b08d9af67387e%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637733680423807743%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=LZz5D2aLew697HwCTeqotWRkDRCCqsqLDkA2t%2F1%2F4pM%3D&reserved=0
-- Michael https://eur04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.dewey.myzen.co.uk%2Fhome.html&data=04%7C01%7Cs.wiechert%40uva.nl%7Cd4a75b2fbbd34b78ce3b08d9af67387e%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637733680423807743%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=YH7VNeUCH6mhALPp%2Fo23zsZ2%2F7CF%2BC2BU%2FAdnzj8j6I%3D&reserved=0
Dear Sera I feared that might be the case. One further thing occurs to me. You say you want to analyse means which implies each primary study gives rise to one mean only. That is an unusual sort of study, it would be more usual to have two or more groups and a comparative design. If that had been the case there might be a way forward of course. Michael
On 25/11/2021 08:01, Sera Wiechert wrote:
Dear Michael, Thanks for your thoughts on this. Most of them do report their standard deviation/error or CIs, so that should be no problem. But for your main question; this is indeed something I thought about, too. But I am afraid that that will not be possible in most of the cases as the ?word-pool? and thus, the denominator they use, is often not reported properly and many researchers seem to use different group totals to calculate their proportions. Would there be, in this situation, a way to standardize the two in order to not be dependent on the denominator information? Many thanks. Best, Sera *Von: *Michael Dewey <lists at dewey.myzen.co.uk> *Datum: *Mittwoch, 24. November 2021 um 17:27 *An: *Sera Wiechert <s.wiechert at uva.nl>, r-sig-meta-analysis at r-project.org <r-sig-meta-analysis at r-project.org> *Betreff: *Re: [R-meta] Escalc function: Possible to compute single standardized mean as outcomes? Dear Sera Can I just clarify this In the first group of studies each person recalls n words and the authors calculated a proportion of possible words for each person and then report the mean (and hopefully the standard deviation/error) of those proportions. In the second group the authors just report the mean of the number of words again hopefully with a standard deviation/error. Is it possible for the second group to impute the denominator which they would have used if they had reported in the same way as the first group? If they have then you can convert to mean proportions. Apologies if that has already occurred to you but you have discarded it as impossible. Michael On 24/11/2021 10:03, Sera Wiechert wrote:
Dear all, I would like to use the metafor package [escalc function] to calculate my effect size. I saw that the metafor escalc function measure = MN is for raw means. However, I am looking to calculate a "standardized mean". In my studies, only a single outcome is reported (?recalled number of words?). The problem is that sometimes this is
reported as a proportion (e.g., percentage recalled words of all possible study words, percentage of recalled words of possible condition-specific words) and sometimes as raw numbers. Thus, I only have one quantitative outcome variable, which is sometimes operationalized in different scales across studies.
This is why I had the idea to use some sort of ?standardized mean?instead of the raw mean. Is this possible? And if so,
is there an escalc function measure for this?
Many thanks in advance. Best wishes, Sera ??????? [[alternative HTML version deleted]]
_______________________________________________ R-sig-meta-analysis mailing list R-sig-meta-analysis at r-project.org https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-meta-analysis&data=04%7C01%7Cs.wiechert%40uva.nl%7Cd4a75b2fbbd34b78ce3b08d9af67387e%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637733680423807743%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=LZz5D2aLew697HwCTeqotWRkDRCCqsqLDkA2t%2F1%2F4pM%3D&reserved=0
<https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-meta-analysis&data=04%7C01%7Cs.wiechert%40uva.nl%7Cd4a75b2fbbd34b78ce3b08d9af67387e%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637733680423807743%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=LZz5D2aLew697HwCTeqotWRkDRCCqsqLDkA2t%2F1%2F4pM%3D&reserved=0>
-- Michael https://eur04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.dewey.myzen.co.uk%2Fhome.html&data=04%7C01%7Cs.wiechert%40uva.nl%7Cd4a75b2fbbd34b78ce3b08d9af67387e%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637733680423807743%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=YH7VNeUCH6mhALPp%2Fo23zsZ2%2F7CF%2BC2BU%2FAdnzj8j6I%3D&reserved=0 <https://eur04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.dewey.myzen.co.uk%2Fhome.html&data=04%7C01%7Cs.wiechert%40uva.nl%7Cd4a75b2fbbd34b78ce3b08d9af67387e%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637733680423807743%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=YH7VNeUCH6mhALPp%2Fo23zsZ2%2F7CF%2BC2BU%2FAdnzj8j6I%3D&reserved=0> <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virus-free. www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>