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May 23, 2017 at 12:39 history edited CommunityBot
replaced http://stackoverflow.com/ with https://stackoverflow.com/
Mar 18, 2016 at 6:59 comment added Karthik Sharma Thanks, I will look into it. I have one last doubt, regarding missing values. How to replace missing values with mean? I mean I did google, but I got confused regarding it, so I am asking for some explanation.
Mar 16, 2016 at 13:09 comment added Giuseppe Biondi-Zoccai I recommend you to use the meta R package: metacont for continuous outcomes and metabin for dichotomous variables. You find plenty of examples here: cran.r-project.org/web/packages/meta/meta.pdf.
Mar 16, 2016 at 4:33 comment added Karthik Sharma Okay. What are the best tools you can advise on to do it?
Mar 15, 2016 at 23:13 comment added Giuseppe Biondi-Zoccai I still don't follow. Break down your goals into several simple steps. First identify 1 dependent variable, and then 1 independent variable. You need a precision estimate for the dependent variable as well (might be inverse of sample size, but the standard error of the dependent variable would be better). Then run this simple bivariate approach separately for all your potential moderators. If you want then to do meta-regression with multiple moderators simultaneously, you theorically can do it, but usually it is fraught with overfitting.
Mar 15, 2016 at 12:43 comment added Karthik Sharma TF1, TF2, OF1, OF2, EF1 EF2 are my moderators. The primary outcome is to do hypothesis checking by these moderators. Like TF1 moderates the relationship between IT variables and IT Adoption. For calculating precision feature I have issue with sample size. As each paper has around 10 studies as I have multiple estimates from same study which are correlated. So how do I solve it is my question?
Mar 15, 2016 at 8:42 comment added Giuseppe Biondi-Zoccai I am not sure what you need or want to do. Give me some additional context. Also clarify better what is the primary outcome, which are the secondary outcomes, and which are the moderators. You need a precision feature (eg standard error) for each unit of the analysis for the outcomes, but you may skip it for the moderators.
Mar 14, 2016 at 22:26 history edited Giuseppe Biondi-Zoccai CC BY-SA 3.0
Integrated with additional content
Mar 14, 2016 at 22:21 comment added Giuseppe Biondi-Zoccai If you don't have the standard errors of the correlation coefficients (most likely your outcome of interest), then you have to back calculate them relying on the sample size. See edited version of my entry.
Mar 14, 2016 at 7:34 comment added Karthik Sharma I have edited the image, can you tell me. The effect size and size of the study are given. I am still stuck at entering values into my database. Like my only confusion is for the correlation values. How do I enter into the sheet? To make it simple I have totally 6 factors to be considered. So where should I enter each of the correlation value in the table is what I am confused about.
Mar 14, 2016 at 5:29 comment added Karthik Sharma Once we have the effect sizes, how do we calculate the measure of precision or weighing factor?
Mar 13, 2016 at 22:29 history edited Giuseppe Biondi-Zoccai CC BY-SA 3.0
Improved accuracy of my entry (focusing on precision rather than effect size)
Mar 13, 2016 at 22:27 comment added Giuseppe Biondi-Zoccai I have edited my entry. The effect of interest might indeed be there as correlation factor. What is missing is a measure of precision / weighting factor (e.g. its standard error).
Mar 13, 2016 at 14:41 comment added Karthik Sharma Effect size is the correlation i.e r value. Correlation value is the effect size right?
Mar 13, 2016 at 14:22 comment added Karthik Sharma Regarding the example its a sample help I got. Its random structure and I am confused how to enter data or the value of r into my table. Observation means size of the sample right?
Mar 13, 2016 at 13:43 history answered Giuseppe Biondi-Zoccai CC BY-SA 3.0