Ask Power - Recent questions in Use of WebPower

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Fri, 29 May 2026 02:01:02 +0000

clarification of input for moderated mediation model 7

Thu, 06 Mar 2025 07:49:13 +0000

Dear WebPower team,

Thanks for this great tool!

I would like to ask clarification on the following inputs for model 7 moderated mediation.

1. Are the regression coefficients supposed to be standardized betas?

2. How to decide on the moderator value?

More generally, it is not always clear to me in WebPower which measures of effect size I should input.

Thanks very much in advance.

Best wishes,

Lukas Van Oudenhove

WebPower removed from CRAN?

Wed, 20 Jul 2022 15:13:31 +0000

Hello,

I noticed WebPower was removed from CRAN on 7/19/2022. Is there a plan to add it to CRAN again?

Thanks

Power Curve for multigroup mediation models, non normal data and a continious moderator

Sun, 23 Jan 2022 12:48:29 +0000

- Is it possible to calculate a power curve for multigroup mediation models in R?

- Is it possible to take account for non normal data in R when x is dichotomous?

- Is there another way to calculate power for an moderated mediation model when the moderator is continious than to use +/- 1SD of the moderator (w)? I could calculate Johnson-Neyman intervals for the x-w-y realationship and put the group split at the first significant value of the moderator for example.

Thank you and kind regards,

Alex

A question on the syntax for power analysis using the "wp.mc.sem.basic" function

Mon, 24 May 2021 08:03:16 +0000

Please let me illustrate my question with a concrete example of power analysis for the following model (some irrelevant parts have been omitted to save space):

library (WebPower)

model.2f <- '

F1 =~ a*x1 + start (.55)*x1 + start (.7)*x2 + start (.7)*x3

F2 =~ b*x4 + start (.6)*x4 + start (.6)*x5 + start (.5)*x6

F1 ~~ start (1)*F1

F2 ~~ start (1)*F2

F1 ~~ start (.3)*F2

x1 ~~ start(.6)*x1

…

power.2f <- wp.mc.sem.basic(model = model.2f, indirect = NULL, nobs = 100, nrep = 5000)

summary (power.2f)

In the outputs, the power analysis results (MSE, SD, Power, Coverage) for the indicator variables, x1 and x4, of the two respective factors in the model are not available, though I have set both factors’ variances as 1 and the loadings of x1 and x4 as freely estimated. By “not available” I mean their corresponding output rows are like:

True Estimate MSE SD Power Coverage

F1 =~ x1 (a) 0.550 0.550 0.000 0.000 NaN 0.000

…

F2 =~ x4 (b) 0.600 0.600 0.000 0.000 NaN 0.000

Grateful if you could advise how the power analysis results can be displayed for ALL the indicator variables (x1 – x6 in this case) of a measurement model when the latent variables they indicate have been set to have unit variances.

Power analysis for data-model fit using a Monte-Carlo approach

Mon, 24 May 2021 02:47:09 +0000

Grateful if your team could advise whether the WebPower or any other R package(s) may provide power analysis facilities using a Monte-Carlo approach for the fit indices (e.g., AIC, SRMR) of a given structural equation model. For example, Mplus provides Monte Carlo analysis outputs for the expected and observed distributions of the fit indices of a specified model. It will be great if you can point me to similar facilities/functionalities available via WebPower or other R packages that are free. (I understand the functions of WebPower like "wp.sem.chisq", "wp.sem.rmsea" provide power analysis for fit statistics/indices - I am specifically interested in the availability of the corresponding facilities using a Monte Carlo approach).

how to perform sensitivity power analysis for independent samples t-test using our effect size?

Sun, 23 May 2021 16:04:49 +0000

hi there,

i used one way between groups ANOVA for my data, 3 groups, the overall result was non significant, so I did not follow any planned contrasts ( 2 planned contrasts).

however, I was told that I may need to run 2 a priori analysis for indenpendent samples t test for two groups, two tailed , but because the data is already collected then I have to run sensitivity power based on the sample I was given. I have 2 effect size for both groups ( anger group 0.08, fear group, 0.10)

I uploaded my data file www.webpower.psychstat.org/models/means03/effectsize.php

regards

how to determinate ng and nm in wp.rmanova

Mon, 17 May 2021 23:56:36 +0000

Hi,

I am calculating the needed sample size for a new experiment using wp.rmanova function. The experiment is a fully crossed within-subject 2x2 design. I have two factors, and each factor has two levels. It is a signal detection type of experiment. For each condition, it has 180 trials. So, total 180 * 4 = 720 trials for each participant.

I am confused about what the inputs for ng and nm in this situation are. According to the manual, ng seems to be the number of between-subject factors, and nm seems to be the levels of the within-subject factors.

Thus, should the parameters be ng = 1 and nm = 4, ng = 1 and nm = 2, or considering each trial is one measurement, ng = 1 and nm = 720?

Thank you for your help!

Power calculation for model with ordinal outcome variable

Fri, 01 May 2020 16:06:40 +0000

Hello,
I am conducting a power calculation for a Stan model using brm() (https://www.rdocumentation.org/packages/brms/versions/2.12.0/topics/brm) and wp.regression(). My model has an ordered factor as the outcome variable and therefore uses family ‘cumulative’. However, I am having trouble calculating the model's R^2 statistic because the function bayes_R2() does not accept input from a model with an ordinal outcome variable / cumulative family type. Is there another way to compute the R^2 statistic for this type of model?

Does wp.regression() alpha parameter specify a one-tailed or two-tailed test?

Fri, 01 May 2020 15:56:27 +0000

I am using the function wp.regression() and setting the alpha parameter to 0.05. However it's not clear whether it is computing a one-tailed or two-tailed test. Which test is default, and can I specify that one-tailed is required?

Effect size in RM ANOVA: f or d?

Thu, 23 Apr 2020 19:16:33 +0000

Hi,

I have been trying to determine the sample size for a repeated measures design with one repeated measures variable with two levels.

First, I tried with the t-test (https://webpower.psychstat.org/models/means01/), with power = 0.80, alpha = 0.05, two sided, type = paired, and a medium effect size of d = 0.50 (I assumed that the effect size was Cohen's d because of the outcome of the analysis and the man page of the R package). The result is n = 33.37, which is consistent with pwr package in R.

I also tried to replicate the analysis above with a repeated measures ANOVA (https://webpower.psychstat.org/models/means05/). My understanding is that for a within participants design with one variable with two levels, if parameters are the same the results (i.e., the expected n) should be the same as with the t-test.

For one group and two measures (that is, only one RM factor with two levels), power = 0.80, type of effect = within, and leaving sample size empty, for a medium effect of Cohen's f = 0.25 (equivalent to a d = 0.50) the outcome is that I would need 127.5 participants. Interestingly, if I enter f = 0.50 the outcome is n = 33.37, as with the t-test.

It also happens the other way round: in the t-test power analysis, if we introduce d = 0.25 the resulting n is 127.5. Thus, it seems that Cohen's d in the t-test and Cohen's f in the RM ANOVA are the same, when actually they are different (f = d / 2).

I wonder if I misunderstood the effect size of the RM ANOVA and, instead of f, it is a d, or there is any other difference between the t-test and the RM ANOVA in this particular case. Could you, please, confirm whether in the RM ANOVA we should enter Cohen's f or Cohen's d as effect size? If it's Cohen's f, why the discrepant results between the t-test and the RM ANOVA?

Thanks for your time.

post hoc power analysis

Tue, 18 Feb 2020 20:43:46 +0000

Hi

I tested the simple mediation model in the manual (page 287):

Initially, I defined the parameter estimates as follows:

a @ .39 b @ .39 and cp @ 0, but the program gave an error.

later

a? .39 b? .39 and cp? 0, but the program gave an error again.

I gathered data from a small group to determine the sample size for my study (n = 117). Firstly, I analyzed this data. I want to add the parameter estimates I obtained from this analysis as fixed values in the model, and I want to calculate power according to these values.

@ or ? instead of using *, the program runs, but the parameter estimates on the output page are not what I initially defined; Estimates close to 0.

What do you suggest in this case? I want to know if I should add to my sample by doing post hoc power analysis.

Thank you.

Zeynep

Please send us the URL to your analysis day=20200207&base=67d262748ad87d3963c776f660248cad so that we can fix it.

Fri, 07 Feb 2020 14:32:10 +0000

Hello,

I want to calculate power for the 65th model of Dr. Hayes. I used Hayes'symbols. I added following interactions to "Power parameter section":

a1b3: = a1 * b3

a1b4: = a1 * b4

a3b1: = a3 * b1

a3b3: = a3 * b3

a3b4: = a3 * b4

I also added covariance to among the independent variables in the model, described all paths and covariances as "? .10".

Webpower gives a calculation error. What could be the source of the problem?

Thank you.

Effect Size for 3-arm cluster randomized trial

Sun, 17 Mar 2019 08:35:08 +0000

Hi,

I am in the process of calculating the effect size for a 3-arm cluster randomized trial. I was successful in calculating it using your software. However, I would like to see the equations that enabled the calculation, so I can repeat it in SAS and references for the equations as I need it for a paper I am writing.

Thanks!

Representing interaction terms in montecarlo SEM power

Tue, 05 Mar 2019 16:37:41 +0000

Hello,

I've been rethinking my previous question and I think, I would be able to solve that one if I knew how to represent interaction terms between exogenous variables with lavaan based syntax for a sem montecarlo power estimation.

Is there anyway I can do it?

For example when dealing with moderated mediation?

Sample size for a 3-way moderation and moderated mediation

Tue, 05 Mar 2019 11:22:48 +0000

Good morning,

Could you please help me out in a setting a template (R syntax) to simulate power at various sample sizes for

mod3 <- '
# Model 3 PROCESS Hayes - 3way interaction
# XW, XZ, WZ, XWZ are products of predictors/moderators
Y ~ b1*X + b2*W + b3*Z + b4*XW + b5*XZ + b6*WZ + b7* XWZ
'
fit<-lavaan::sem(mod3, df, se = "boot")

X, W and Y are continouse
Z is a 2 level factor, coding experimental conditions (treatment vs control)

I fitted this model on pilot study data where a 3way interaction was hypothesized. Of course I had to manually compute product variables for interaction terms

Now I'd like to run a sample size simulation for an other study, where both Z and W will be experimental conditions (2x2 between subjects) and the effect size is expected to by small-medium.
I can't figure out, how to handle the fact that XZ, XW, ZW, XWZ are not distinct measured variables but products.

I'd appreciate your help in sorting this out.

The lab i'm working in, deals with (in Andrew Hayes PROCESS terminology) moderations (model1 and 3), simple mediation (model4 - that's easy), and moderated mediations (model 7 and model 14).

I'd love to sort out a workable simulation template for doing sample size calculations at (conservatively) low effect sizes.

Thank you

multilevel modeling power analysis

Sat, 29 Sep 2018 17:06:48 +0000

Hi, I have a two-level nested-design dataset for power analysis. Level 1 represent goals we have subjects listed; level 2 represent differences between individuals. So it is goal nested in person design. The tricky thing is that we do not have treatment/control group; all variables are continuous. Not sure if wecan use WebPower to calculate power.

accounting for clustered data

Mon, 30 Jul 2018 20:42:06 +0000

Hi! I'd like to do a power analysis for latent-variable path model (SEM). I will be using clustered data (assessment scores from about 260 children who were in clusters of about 6 children per classroom). I plan to use the option in Mplus to adjust standard errors for clustering, as I'd like to avoid multilevel modeling. Is there a way to account for this clustered data in webpower? Thank you!

Could you please guide me in calculating skewness and kurtosis (Mardia's multivariate coefficient): load data, etc

Sun, 27 Aug 2017 00:17:28 +0000

I have been struggling to upload my data to enable me calculate mulitivariate normality using Mardia's coefficient. Could you please guide or help me with instructions on how to do this?

Many thanks,

Swala

Where can I download the WebPower package for R?

Thu, 20 Apr 2017 21:04:34 +0000

I would like to download the WebPower package so that I can incorporate the power analysis code directly into my R scripts. Is this possible or does it only run on a server? If it is possible where I download the package?

But why you equalize S with the standard error (S.E.)?

Mon, 14 Mar 2016 08:08:57 +0000

In the formulae of Sp, S_1^2 and S_2^2 it refers to standard deviation of each population, why you equalize to the standard error?.

Thanks for your time Johnny!!!

but in that case, where do I put the mean values of my samples?

Thu, 10 Mar 2016 14:47:48 +0000

Thanks Johnny!....but, If I leave the effect size blank, where do I put the values of my means and the standard deviation?.

Can I estimate the detectable differences with Web Power?

Thu, 10 Mar 2016 13:48:04 +0000

Hello, Im trying to perform a post-hoc analysis to estimate the detectable differences (percentage difference of detectable

treatment means relative to control means) for a = 0.05 and a power of 80%. Does Web Power do this?...I have been

trying but cant find the correct way. Want to estimate the detectable differences of a t-test (two tailed).

If anyone can help me it would be great!!

My data are:

sample 1) N= 16; mean= 122.4; S.E ± 8.38

Sample 2) N = 20; mean= 111.4; S.E. ± 6.35

Thanks in advance!

Matías

How do I calculate sample size using a power

Tue, 23 Sep 2014 15:55:11 +0000