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Ravi wrote:

Nonetheless, if you want to do this you have to use computed variables at the moment (we are finishing up an easier way to recode variables but it's not completely finished yet). Here's a blog post about computed variables: https://blog.jamovi.org/2017/11/28/jamovi-formulas.html.

You need to use a series of IF statements to recode the continuous variables into quartile labels as follows:

Screenshot from 2018-09-04 11-14-13.png

So you have to use the values you get from the descriptives to recode your variable. Here's the example code:

Hope this will help you.

Nonetheless, if you want to do this you have to use computed variables at the moment (we are finishing up an easier way to recode variables but it's not completely finished yet). Here's a blog post about computed variables: https://blog.jamovi.org/2017/11/28/jamovi-formulas.html.

You need to use a series of IF statements to recode the continuous variables into quartile labels as follows:

Screenshot from 2018-09-04 11-14-13.png

So you have to use the values you get from the descriptives to recode your variable. Here's the example code:

- Code:
`IF(x1 < 4.17, 'Q1',`

IF(x1 < 5.00, 'Q2',

IF(x1 < 5.67, 'Q3', 'Q4')))

Hope this will help you.

I chose jamovi over R for this reason

anyway, I'll try! thanks!

Statistics: Posted by ste — Tue Sep 04, 2018 6:27 pm

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You need to use a series of IF statements to recode the continuous variables into quartile labels as follows:

Screenshot from 2018-09-04 11-14-13.png

So you have to use the values you get from the descriptives to recode your variable. Here's the example code:

- Code:
`IF(x1 < 4.17, 'Q1',`

IF(x1 < 5.00, 'Q2',

IF(x1 < 5.67, 'Q3', 'Q4')))

Hope this will help you.

Statistics: Posted by Ravi — Tue Sep 04, 2018 6:18 pm

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I am both an absolute beginner in meta-analysis and in using jamovi. So my naive question is whether the requested effect size in the jamovi MAJOR package can also be an unstandardized regression coefficient? Background: I have a set of individual-level data (10 studies) and would like to estimate the average regression weight across the 10 studies. Because the operationalisations of the dependent variable differ across studies, I think that using a meta-analysis (rather than a multigroup SEM) is appropriate. Many thanks for your replies!

Best

Empisoz

Statistics: Posted by empisoz — Tue Sep 04, 2018 1:15 pm

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Ravi wrote:

Hi,

I'm not completely sure what you want to do with the IQR. Could you elaborate on this sentence:

Cheers,

Ravi

Hi,

I'm not completely sure what you want to do with the IQR. Could you elaborate on this sentence:

This mean I have to find evidence against the null hypothesis by percentile (25th, 50th, 75th) comparing the scores by height and weight and BMI.

Cheers,

Ravi

Yes, sure!

80 people performed resuscitation manoeuvres on a mannequin. The mannequin elaborates some data such as mean rate (frequency) and mean depth of chest compressions.

In different publications, weight, height and BMI are characteristics able to impact the performance. This is why it's necessary to compare the results (such as mean depth and mean rate) by weight, height and BMI. Weight and height (as BMI) are grouped into IQR and than compared with scores.

You can check out this article: Contri E, Cornare S "Complete chest recoil during laypersons' CPR: Is it a matter of weight?" American Journal of Emergency Medicine, 2017

Statistics: Posted by ste — Tue Sep 04, 2018 8:19 am

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I'm not completely sure what you want to do with the IQR. Could you elaborate on this sentence:

This mean I have to find evidence against the null hypothesis by percentile (25th, 50th, 75th) comparing the scores by height and weight and BMI.

Cheers,

Ravi

Statistics: Posted by Ravi — Tue Sep 04, 2018 6:17 am

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I have done 2 random effects meta-analysis using Empirical Bayes estimator. I have plotted the two corresponding funnel plots.

However I have some doubts interpreting the two side-arms of the triangle of the funnel plot. I have plotted it with Standard error on y-axis v/s Mean difference on x-axis.

What is the formula for creating and calculating these two side-arms of the triangle in the funnel plot ? Any help would be appreciated.

Thanks & Regards,

Sauvik Das Gupta

Statistics: Posted by sauvik_dasgupta — Mon Sep 03, 2018 10:33 am

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I am new to statistics and I was wondering if you could help me.

I have collected weight and height of 80 people, those have performed a test twice. This mean, I have two final scores for each person (160 scores).

With jamovi I got the IQR of weight and height, and I need to understand if these variables could impact the scores. This mean I have to find evidence against the null hypothesis by percentile (25th, 50th, 75th) comparing the scores by height and weight and BMI.

How do I proceed?

Thanks for your support

Statistics: Posted by ste — Sat Sep 01, 2018 6:54 pm

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Thank you so much for sharing this detail. Much helpful!

Warm Regards,

Prasad

Statistics: Posted by prasadkrec — Wed Aug 29, 2018 7:28 am

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the sd1 of 2.71 looks to be based on the full 93 observations of LDLF, however LDHF has two missing values, and so these two cases need to excluded from LDLF as well.

if i take the sd1 value of 2.67 from the t-test descriptives, i get the same results.

(for the record MOTE is a *super-rough* package at this stage - i had to roll back a few commits to find a version which worked. maybe pick a package that's at least on CRAN next time )

Screen Shot 2018-08-29 at 07.41.39.png

Statistics: Posted by jonathon — Tue Aug 28, 2018 9:46 pm

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I am a naive use of JAMOVI. I tried to calculate the effect size for a paired t-test using JAMOVI. I checked the cohen's d using MOTE package and these numbers are quite different. Below is the code that replicates my output:

library(jmv)

data('bugs', package = 'jmv')

jmv::ttestPS(bugs, pairs = list(list(i1 = 'LDLF', i2 = 'LDHF')), hypothesis = "twoGreater", meanDiff = TRUE, effectSize = TRUE, ci = TRUE, desc = TRUE, plots = FALSE)

OUTPUT SHOWS cohen's d= -0.697

library(MOTE)

d.dep.t.avg(m1 =5.72 , m2 = 7.38, sd1 = 2.71, sd2 = 2.52 , n = 91, a = .05)

OUTPUT SHOWS cohen's d= -0.6347992

This not so much a rounding errors. Kindly advise

Cheers

Prasad

Statistics: Posted by prasadkrec — Tue Aug 28, 2018 1:38 pm

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Cheers,

Ravi

Statistics: Posted by Ravi — Tue Jul 24, 2018 10:50 pm

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I am wondering if the JAMOVI is capble of including Hazard Ratio (HR) with associated confidence interval for use in a meta-analysis. That is each study reports HR with 95%Confidence Interval and sample size. Any feedback is apprecaited.

Best

Amir

Statistics: Posted by AmirSepehry — Mon Jul 23, 2018 5:46 pm

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For effect sizes - will you be putting up Cohen's d?

Thanks in advance

Nick

Statistics: Posted by nickbehn — Thu Jul 19, 2018 4:16 pm

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