Question
Asked 4 October 2017

Confused with numerator degrees of freedom. How to calculate sample size using ANCOVA measures in Gpower?

Can anyone please advised me on calculating sample size using Gpower? I want to see the effect of an intervention( independent variable: 2groups) on mental health (continuous) of participants and I have considered baseline outcome measure as a covariate? How can I calculate the sample size in this case? Furthermore, my two secondary outcomes (both are in continuous scale) are also affected by intervention according to the previous literature. In such case, do I have to consider them as a covariate? I am confused on numerator df and don't know what to put in both of these instances?

Most recent answer

Diksha Sapkota
Griffith University
Thank you

Popular answers (1)

The way I try to remeber this is to think of the denominator as the size of the cake and the numerator as the topping. The size is a matter of hard work, the topping is the design of the analysis. If you have two conditions and three diagnostic groups and would like to test the interaction of intervention by diagnosis with baseline-scores as co-variate, then in Gpower you get Numerator df= (2-1)*(3-1)=2, Number of groups=6, Number of covariates=1. With power .80 and alpha .05 and an expected effect of medium size you would need a total sample size of 128.
I don't see why you would include other secondary outcomes in this analysis if these outcomes are not correlated with your primary outcome. In another time and place you could have conducted seperate studies for your outcomes.
27 Recommendations

All Answers (14)

Mehmet Sinan Iyisoy
Necmettin Erbakan University
Numerator df should be # of levels of your factor-1 namely 2-1=1. Yes you can consider them as covariates.
1 Recommendation
The way I try to remeber this is to think of the denominator as the size of the cake and the numerator as the topping. The size is a matter of hard work, the topping is the design of the analysis. If you have two conditions and three diagnostic groups and would like to test the interaction of intervention by diagnosis with baseline-scores as co-variate, then in Gpower you get Numerator df= (2-1)*(3-1)=2, Number of groups=6, Number of covariates=1. With power .80 and alpha .05 and an expected effect of medium size you would need a total sample size of 128.
I don't see why you would include other secondary outcomes in this analysis if these outcomes are not correlated with your primary outcome. In another time and place you could have conducted seperate studies for your outcomes.
27 Recommendations
Mateu Servera
University of the Balearic Islands
Thanks, André, perfect answer. But I think the result of your example is 158, no 128. G*Power setting:
F-Test -> ANCOVA: Fixed effects, main effect and interactions.
A priori...
Effect size F = 0.25 (medium)
alfa = 0.05
Power = 0.80
Numerator df = 2
Number of groups = 6
Number of covariables = 1
...
Total Sample Size = 158
Mary Joy Secretario Taneo
Department of Health of the Philippines
You are all wonderful people, I just came across this as I was figuring out the same thing: numerator df.
Thank you all!
1 Recommendation
Nikolche Vasilevski
Bond University
Very helpful thread!!!
Ahmet Tolgay Akinci
Trakya University
Thank you all. I can't agree more that this thread is very helpful!
Noga Cohen
University of Haifa
Similarly to the question above, we have 3 groups and 2 covariates. Is the numerator df in this situation 2 (3-1)? or 5 (6-1)?
Alisa Johnson
University of Florida
Very helpful! Thank you!
Tahsin Khataei
University of Iowa
Thank you for your Q&A!
Ronja Haring
University of Pretoria
Reinaldo Requeiro
University of Cienfuegos
I think it is a serious and reliable tutorial.However You must view it with caution.
There are other materials that shorten the way.
In these times we need concreteness, speed, but certainty of what is right.
regards
Reinaldo
1 Recommendation
Simon Fryer
University of Gloucestershire
Really helpful, thank you!
Diksha Sapkota
Griffith University
Thank you

Similar questions and discussions

2x2 repeated measures (fully within-subjects) ANOVA power analysis in G*Power?
Question
3 answers
  • Lydia SearleLydia Searle
Hello,
I am trying to do a power analysis for a 2x2 repeated measures design to determine how many participants I need to achieve 80% power. I'm new to the world of power analysis and don't really have a strong stats background.
IV1 = face orientation
Level 1 = upright
Level 2 = inverted
IV2 = context
Level 1 = background present
Level 2 = background removed
This is a fully within-subjects design. I'm trying to use G*Power 3.1 to do the calculation. This is what I have entered into G*Power so far:
Test family: F tests
Statistical test: ANOVA: Repeated measures, within factors
Type of power analysis: A priori...
Effect size f = 0.25 (just assuming a medium effect)
Alpha err prob = 0.05
Power = 0.8
Number of groups = 1
Number of measurements = 4
Corr among rep measures = 0.5 (leaving it at the default)
Nonsphericity correction E = 1 (leaving it at the default)
The number of groups and number of measurements is the part I'm having an issue with. Will G*Power let me calculate n for a 2x2 within design, or is it assuming this is a 1x4 design? From what I've read and watched, number of groups comes into play if you have a between factor, which I don't, so I've set this to 1. As I have a 2x2 design, each participant is being measured 4 times, hence I've put number of measurements to 4.
Sometimes I've read/heard that G*Power DOES allow you to do a 2x2 within design, and sometimes I've read/heard that it does NOT allow you to do this.
I've had a look at GLIMMPSE 3.0.0. as an alternative but there are many fields it requires where I don't know the answer, mainly that there are a list of tests to choose from, none of which are a repeated measures ANOVA. It also wants me to put the means and SDs for each condition, but I haven't run the study yet, plus it's exploratory so I can't even really guess.
Can anyone with some stats / G*Power knowledge help?
Thank you,
Lydia
How do I report the results of a linear mixed models analysis?
Question
47 answers
  • Subina SainiSubina Saini
1) Because I am a novice when it comes to reporting the results of a linear mixed models analysis, how do I report the fixed effect, including including the estimate, confidence interval, and p-value in addition to the size of the random effects. I am not sure how to report these in writing. For example, how do I report the confidence interval in APA format and how do I report the size of the random effects?
2) How do you determine the significance of the size of the random effects (i.e. how do you determine if the size of the random effects is too large and how do you determine the implications of that size)?
3) Our study consisted of 16 participants, 8 of which were assigned a technology with a privacy setting and 8 of which were not assigned a technology with a privacy setting. Survey data was collected weekly. Our fixed effect was whether or not participants were assigned the technology. Our random effects were week (for the 8-week study) and participant. How do I justify using a linear mixed model for this study design? Is it accurate to say that we used a linear mixed model to account for missing data (i.e. non-response; technology issues) and participant-level effects (i.e. how frequently each participant used the technology; differences in technology experience; high variability in each individual participant's responses to survey questions across the 8-week period). Is this a sufficient justification? 
I am very new to mixed models analyses, and I would appreciate some guidance. 

Related Publications

Article
Full-text available
Meta‐analysis of individual participant data (IPD) is considered the "gold‐standard" for synthesizing clinical study evidence. However, gaining access to IPD can be a laborious task (if possible at all) and in practice only summary (aggregate) data are commonly available. In this work we focus on meta‐analytic approaches of comparative studies wher...
Article
Full-text available
Background In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates ma...
Data
Results of the ANCOVA of Change from Baseline HbA1c − Clinical Study Part B (Subjects with T2D taking Liraglutide). (DOCX)
Got a technical question?
Get high-quality answers from experts.