# Power Analysis for Latent Variable Mediation

#### 2025-08-31

# Introduction

This article is a brief illustration of how to use `power4test()` from the package power4mome to do power analysis for the mediation effect among latent factors in a model to be fitted by structural equation model modeling using `lavaan`.

# Prerequisite

Basic knowledge about fitting models by `lavaan` is required. Readers are also expected to have basic knowledge of mediation and structural equation modeling.

# Scope

To make this vignette self-contained, some sections from `vignette("power4mome")` are repeated here.

To do power analysis for a mediation effect in a path model with no latent factors, please refer to `vignette("power4mome")`.

# Package

This introduction only needs the following package:

    library(power4mome)

# Workflow

Two functions are sufficient for estimating power given a model, population values, sample size, and the test to be used. This is the workflow:

1. Specify the model syntax for the population model, in `lavaan` style, and set the population values of the model parameters.

2. Call `power4test()` to examine the setup and the datasets generated. Repeat this and previous steps until the model is specified correctly.

3. Call `power4test()` again, with the test to do specified.

4. Call `rejection_rates()` to compute the power.

# Mediation

Let’s consider a simple mediation model with three factors. We would like to estimate the power of testing the mediation effect by Monte Carlo confidence interval.

## Specify the Population Model

For a model with latent factor, we only need to specify the model syntax for the factors. No need to include the measurement part and the indicators.

This is the model syntax:

    mod <-
    "
    fm ~ fx
    fy ~ fm + fx
    "

The latent variables are `fx`, `fm`, and `fy`. There is an indirect path from `fx` to `fy`, through `fm`.

Note that, even if we are going to test a mediation effect, we do not need to add any labels to this model. This will be taken care of by the test functions, through the use of the package `manymome` (Cheung & Cheung, 2024).

## Specify The Population Values

There are two approaches to do this:

- Using named vectors or lists.

- Using a multiline string similar to `lavaan` model syntax.

The second approach is demonstrated below.

Suppose we want to estimate the power when:

- The path from `fx` to `fm` are “large” in strength.

- The path from `fm` to `fy` are “large” in strength.

- The path from `fx` to `fm` are “small” in strength.

By default, `power4mome` use this convention for regression path and correlation:1

- Small: .10 (or -.10)

- Medium: .30 (or -.30)

- Large: .50 (or -.50)

All these values are for the standardized solution (the correlations and so-called “betas”).

The following string denotes the desired values:

    mod_es <-
    "
    fm ~ fx: l
    fy ~ fm: l
    fy ~ fx: s
    "

Each line starts with a tag, which is the parameter presented in `lavaan` syntax. The tag ends with a colon, `:`.

After the colon is population value, which can be:

- A string denoting the value. By default:

  - `s`: Small. (`-s` for small and negative.)

  - `m`: Medium. (`-m` for medium and negative.)

  - `l`: Large. (`-l` for large and negative.)

  - `nil`: Zero.

All other regression coefficients and covariances, if not specified in this string, are set to zero.

## Specify the Measurement Part

Power analysis is usually conducted before data collection. We rarely know in advance the factor loadings of all items. For the purpose of power analysis, which is not intended to be conducted with the knowledge of all factor loadings, we believe that, instead of specifying all the loadings, it is sufficient to specify two values for each factor:

- The number of indicators.

- The population reliability.

This is the approach used in `power4mome`.

For each factor, the population standardized factor loadings for each indicator will be derived automatically from the hypothesized (or expected) population reliability and the number of indicators, assuming that all indicators have equal loadings.

Although the equal-loading assumption is unrealistic, in a priori power analysis, it is difficult, if not impossible, to specify the pattern of factor loadings. This level of details is also not necessary because the power estimated is merely used to guide the planning of data collection, instead of estimating the “true” power after data is collected.

Two arguments will be used to set the number of indicators and the reliability.

- `number_of_indicators`: This should be a named vector of the number of indicators for each factor. The names are the names of the factors as appeared in the model syntax, and the values are the number of indicators.

- `reliability`: This should be a named vector of the reliability for each factor. The names are the names of the factors as appeared in the model syntax, and the values are population reliability.

For example, suppose we will use the following vectors:

    k <- c(fm = 3,
           fx = 4,
           fy = 5)

    mod_rel <- c(fy = .70,
                 fm = .60,
                 fx = .50)

The numbers of indicators for `fx`, `fm`, and `fy` are 4, 3, and 5, respectively.

The population reliability coefficients for `fx`, `fm`, and `fy` are .50, .60, and .70, respectively. In real research, reliability as low as .50 can be problematic. We chose these values merely for illustration

The orders are intentionally arbitrary, to demonstrate the order does not matter. The names will be used to interpret the numbers correctly.

## Call `power4test()` to Check the Model

We are all set and can call `power4test()` to check the model:

    out <- power4test(nrep = 2,
                      model = mod,
                      pop_es = mod_es,
                      number_of_indicators = k,
                      reliability = mod_rel,
                      n = 50000,
                      iseed = 1234)

These are the arguments used:

- `nrep`: The number of replications. In this stage, a small number can be used. It is more important to have a large sample size than to have many replications.

- `model`: The model syntax.

- `pop_es`: The string setting the population values.

- `number_of_indicators`: A named vector of the number of indicators for each factor, described in the previous section.

- `reliability`: A named vector of the population reliability for each factor, described in the previous section.

- `n`: The sample size in each replications. In this stage, just for checking the model and the data generation, this number can be set to a large one unless the model is slow to fit when the sample size is large.

- `iseed`: If supplied, it is used to set the seed for the random number generator. It is advised to always set this to an arbitrary integer, to make the results reproducible.2

The population values can be shown by printing this object:

    out
    #>
    #> ====================== Model Information ======================
    #>
    #> == Model on Factors/Variables ==
    #>
    #> fm ~ fx
    #> fy ~ fm + fx
    #>
    #> == Model on Variables/Indicators ==
    #>
    #> fm ~ fx
    #> fy ~ fm + fx
    #>
    #> fm =~ fm1 + fm2 + fm3
    #> fx =~ fx1 + fx2 + fx3 + fx4
    #> fy =~ fy1 + fy2 + fy3 + fy4 + fy5
    #> ====== Population Values ======
    #>
    #> Regressions:
    #>                    Population
    #>   fm ~
    #>     fx                0.500
    #>   fy ~
    #>     fm                0.500
    #>     fx                0.100
    #>
    #> Variances:
    #>                    Population
    #>    .fm                0.750
    #>    .fy                0.690
    #>     fx                1.000
    #>
    #> (Computing indirect effects for 2 paths ...)
    #>
    #> == Population Conditional/Indirect Effect(s) ==
    #>
    #> == Indirect Effect(s) ==
    #>
    #>                  ind
    #> fx -> fm -> fy 0.250
    #> fx -> fy       0.100
    #>
    #>  - The 'ind' column shows the indirect effect(s).
    #>
    #> ==== Population Reliability ====
    #>
    #>   fy  fm  fx
    #>  0.7 0.6 0.5
    #>
    #> == Population Standardized Loadings ==
    #>
    #>     fm    fx    fy
    #>  0.661 0.522 0.408
    #> ======================= Data Information =======================
    #>
    #> Number of Replications:  2
    #> Sample Sizes:  50000
    #>
    #> Call print with 'data_long = TRUE' for further information.
    #>
    #> ==================== Extra Element(s) Found ====================
    #>
    #> - fit
    #>
    #> === Element(s) of the First Dataset ===
    #>
    #> ============ <fit> ============
    #>
    #> lavaan 0.6-19 ended normally after 44 iterations
    #>
    #>   Estimator                                         ML
    #>   Optimization method                           NLMINB
    #>   Number of model parameters                        27
    #>
    #>   Number of observations                         50000
    #>
    #> Model Test User Model:
    #>
    #>   Test statistic                                38.042
    #>   Degrees of freedom                                51
    #>   P-value (Chi-square)                           0.910

By the default, the population model will be fitted to each dataset, hence the section `<fit>`. The fit is not “perfect” because the model is not a saturated model. However, the p-value is high and not significant (because, with the population model fitted, the chance of significant is close to .05).

Although we specify only the structure for the latent factors, we can see the automatically generated measurement part syntax in the section `Model on Variables/Indicators`:

    #> fm =~ fm1 + fm2 + fm3
    #> fx =~ fx1 + fx2 + fx3 + fx4
    #> fy =~ fy1 + fy2 + fy3 + fy4 + fy5

It confirmed that we specified the measurement part correctly.

We then check the section `Population Values` to see whether the values are what we expected.

- In this example, the population values for the regression paths are what we specified.

If they are different from what we expect, check the string for `pop_es` to see whether we set the population values correctly.

- The section `Variances:` shows the variances or error variances of the latent factors. Because `fm` and `fy` are endogenous factors, the values presented, next to `.fm` and `.fy`, are error variances, and that’s why they are not 1, unlike `fx`.

The section `Population Reliability` shows the population reliability coefficients:

    #> ==== Population Reliability ====
    #>
    #>   fy  fm  fx
    #>  0.7 0.6 0.5

They are the values we set to `reliability`.

The section `Population Standardized Loadings` shows the standardized factor loadings for each factor. Only one value for each factor because the loadings are assumed to be the same for all items:

    #> == Population Standardized Loadings ==
    #>
    #>     fm    fx    fy
    #>  0.661 0.522 0.408

If necessary, we can check the data generation by adding `data_long = TRUE` when printing the output:

    print(out,
          data_long = TRUE)

Two new sections will be printed. This is the first one:

    #> ==== Descriptive Statistics ====
    #>
    #>     vars     n mean   sd  skew kurtosis se
    #> fm1    1 1e+05 0.00 1.00  0.01     0.00  0
    #> fm2    2 1e+05 0.01 1.00  0.01    -0.01  0
    #> fm3    3 1e+05 0.00 1.01  0.00    -0.02  0
    #> fx1    4 1e+05 0.00 1.00  0.00     0.00  0
    #> fx2    5 1e+05 0.00 1.00 -0.01     0.00  0
    #> fx3    6 1e+05 0.00 1.00 -0.01    -0.02  0
    #> fx4    7 1e+05 0.00 1.00  0.00     0.00  0
    #> fy1    8 1e+05 0.00 1.00  0.01     0.01  0
    #> fy2    9 1e+05 0.00 1.00  0.01     0.00  0
    #> fy3   10 1e+05 0.00 1.00  0.00     0.01  0
    #> fy4   11 1e+05 0.01 1.00  0.01     0.00  0
    #> fy5   12 1e+05 0.00 1.00  0.02    -0.01  0

The section `Descriptive Statistics`, generated by `psych::describe()`, shows basic descriptive statistics for indicators. As expected, they have means close to zero and standard deviations close to one, because the datasets were generated using the standardized model.

This is the second one:

    #> fy5   12 1e+05 0.00 1.00  0.02    -0.01  0
    #>
    #> ===== Parameter Estimates Based on All 2 Samples Combined =====
    #>
    #> Total Sample Size: 100000
    #>
    #> ==== Standardized Estimates ====
    #>
    #> Variances and error variances omitted.
    #>
    #> Latent Variables:
    #>                     est.std
    #>   fm =~
    #>     fm1               0.578
    #>     fm2               0.579
    #>     fm3               0.578
    #>   fx =~
    #>     fx1               0.445
    #>     fx2               0.443
    #>     fx3               0.445
    #>     fx4               0.445
    #>   fy =~
    #>     fy1               0.559
    #>     fy2               0.560
    #>     fy3               0.563
    #>     fy4               0.565
    #>     fy5               0.569
    #>
    #> Regressions:
    #>                     est.std
    #>   fm ~
    #>     fx                0.504
    #>   fy ~
    #>     fm                0.505
    #>     fx                0.097

The section `Parameter Estimates Based on` shows the parameter estimates when the population model is fitted to all the datasets combined. When the total sample size is large, these estimates should be close to the population values.

The results show that we have specified the population model correctly. We can proceed to specify the test and estimate the power.

## Call `power4test()` to Do the Target Test

We can now do the simulation to estimate power. A large number of datasets (e.g., 400) of the target sample size are to be generated, and then the target test will be conducted in each of these datasets.

Suppose we would like to estimate the power of using Monte Carlo confidence interval to test the indirect effect from `fx` to `fy` through `fm`, when sample size is 150. This is the call:

    out <- power4test(nrep = 400,
                      model = mod,
                      pop_es = mod_es,
                      number_of_indicators = k,
                      reliability = mod_rel,
                      n = 150,
                      R = 2000,
                      ci_type = "mc",
                      test_fun = test_indirect_effect,
                      test_args = list(x = "fx",
                                       m = "fm",
                                       y = "fy",
                                       mc_ci = TRUE),
                      iseed = 1234,
                      parallel = TRUE)

These are the new arguments used:

- `R`: The number of replications used to generate the Monte Carlo simulated estimates, 2000 in this example. In real studies, this number should be 10000 or even 20000 for Monte Carlo confidence intervals. However, 2000 is sufficient because the goal is to estimate power by generating many intervals, rather than to have one single stable interval.

- `ci_type`: The method used to generate estimates. Support both Monte Carlo (`"mc"`) and nonparametric bootstrapping (`"boot"`).3 Although bootstrapping is usually used to test an indirect effect, it is very slow to do `R` bootstrapping in `nrep` datasets (the model will be fitted `R * nrep` times). Therefore, it is preferable to use Monte Carlo to do the initial estimation.

- `test_fun`: The function to be used to do the test for each replication. Any function following a specific requirement can be used, and `power4mome` comes with several built-in functions for some common tests. The function `test_indirect_effect()` is used to test an indirect effect in the model.

- `test_args`: A named list of arguments to be supplied to `test_fun`. For `test_indirect_effect()`, it is a named list specifying the predictor (`x`), the mediator(s) (`m`), and the outcome (`y`). A path with any number of mediators can be supported. Please refer to the help page of `test_indirect_effect()`.4

- `parallel`: If the test to be conducted is slow, which is the case for tests done by Monte Carlo or nonparametric bootstrapping confidence interval, it is advised to enable parallel processing by setting `parallel` to `TRUE`.5

For `nrep = 400`, the 95% confidence limits for a power of .80 are about .04 below and above .80. This should be precise enough for determining whether a sample size has sufficient power.

This is the default printout:

    out
    #>
    #> ====================== Model Information ======================
    #>
    #> == Model on Factors/Variables ==
    #>
    #> fm ~ fx
    #> fy ~ fm + fx
    #>
    #> == Model on Variables/Indicators ==
    #>
    #> fm ~ fx
    #> fy ~ fm + fx
    #>
    #> fm =~ fm1 + fm2 + fm3
    #> fx =~ fx1 + fx2 + fx3 + fx4
    #> fy =~ fy1 + fy2 + fy3 + fy4 + fy5
    #> ====== Population Values ======
    #>
    #> Regressions:
    #>                    Population
    #>   fm ~
    #>     fx                0.500
    #>   fy ~
    #>     fm                0.500
    #>     fx                0.100
    #>
    #> Variances:
    #>                    Population
    #>    .fm                0.750
    #>    .fy                0.690
    #>     fx                1.000
    #>
    #> (Computing indirect effects for 2 paths ...)
    #>
    #> == Population Conditional/Indirect Effect(s) ==
    #>
    #> == Indirect Effect(s) ==
    #>
    #>                  ind
    #> fx -> fm -> fy 0.250
    #> fx -> fy       0.100
    #>
    #>  - The 'ind' column shows the indirect effect(s).
    #>
    #> ==== Population Reliability ====
    #>
    #>   fy  fm  fx
    #>  0.7 0.6 0.5
    #>
    #> == Population Standardized Loadings ==
    #>
    #>     fm    fx    fy
    #>  0.661 0.522 0.408
    #> ======================= Data Information =======================
    #>
    #> Number of Replications:  400
    #> Sample Sizes:  150
    #>
    #> Call print with 'data_long = TRUE' for further information.
    #>
    #> ==================== Extra Element(s) Found ====================
    #>
    #> - fit
    #> - mc_out
    #>
    #> === Element(s) of the First Dataset ===
    #>
    #> ============ <fit> ============
    #>
    #> lavaan 0.6-19 ended normally after 38 iterations
    #>
    #>   Estimator                                         ML
    #>   Optimization method                           NLMINB
    #>   Number of model parameters                        27
    #>
    #>   Number of observations                           150
    #>
    #> Model Test User Model:
    #>
    #>   Test statistic                                69.181
    #>   Degrees of freedom                                51
    #>   P-value (Chi-square)                           0.046
    #>
    #> =========== <mc_out> ===========
    #>
    #>
    #> == A 'mc_out' class object ==
    #>
    #> Number of Monte Carlo replications: 2000
    #>
    #>
    #> ====================== Test(s) Conducted ======================
    #>
    #> - test_indirect: fx->fm->fy
    #>
    #> Call print() and set 'test_long = TRUE' for a detailed report.

## Compute the Power

The power estimate is simply the proportion of significant results, the rejection rate, because the null hypothesis is false. The rejection rate can be retrieved by `rejection_rates()`.

    out_power <- rejection_rates(out)
    out_power
    #> [test]: test_indirect: fx->fm->fy
    #> [test_label]: Test
    #>     est   p.v reject r.cilo r.cihi
    #> 1 0.336 1.000  0.492  0.444  0.541
    #> Notes:
    #> - p.v: The proportion of valid replications.
    #> - est: The mean of the estimates in a test across replications.
    #> - reject: The proportion of 'significant' replications, that is, the
    #>   rejection rate. If the null hypothesis is true, this is the Type I
    #>   error rate. If the null hypothesis is false, this is the power.
    #> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
    #>   on normal approximation.
    #> - Refer to the tests for the meanings of other columns.

In the example above, the estimated power of the test of the indirect effect, conducted by Monte Carlo confidence interval, is 0.492, under the column `reject`.

`p.v` is the proportion of valid results across replications. `1.000` means that the test conducted normally in all replications.

By default, the 95% confidence interval of the rejection rate (power) based on normal approximation is also printed, under the column `r.cilo` and `r.cihi`. In this example, the 95% confidence interval is [0.444; 0.541].

# Repeat a Simulation With A Different Sample Size

The function `power4test()` also supports redoing an analysis using a new value for the sample size (or population effect sizes set to `pop_es`). Simply

- set the output of `power4test` as the first argument, and

- set the new value for `n`.

For example, we can repeat the simulation for the test of indirect effect, but for a smaller sample size of 200. We simply call `power4test()` again, set the previous output (`out` in the example above) as the first argument, and set `n` to a new value (200 in this example):

    out_new_n <- power4test(out,
                            n = 200)
    out_new_n

This is the estimated power when the sample size is 200.

    out_new_n_reject <- rejection_rates(out_new_n)
    out_new_n_reject
    #> [test]: test_indirect: fx->fm->fy
    #> [test_label]: Test
    #>     est   p.v reject r.cilo r.cihi
    #> 1 0.352 1.000  0.815  0.777  0.853
    #> Notes:
    #> - p.v: The proportion of valid replications.
    #> - est: The mean of the estimates in a test across replications.
    #> - reject: The proportion of 'significant' replications, that is, the
    #>   rejection rate. If the null hypothesis is true, this is the Type I
    #>   error rate. If the null hypothesis is false, this is the power.
    #> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
    #>   on normal approximation.
    #> - Refer to the tests for the meanings of other columns.

The estimated power is 0.815, 95% confidence interval [0.777; 0.853], when the sample size is 200.

This technique can be repeated to find the required sample size for a target power.

# Repeat a Simulation With Different Numbers of Indicators or Reliability

We can also redo an analysis using a new value for reliability. For example, we may want to see whether we can have a higher power if we use more reliable scales.

As in the previous example, we just call `power4test()` one the original output, but set `reliability` to a new vector.

Assume that we want to know the power for the same scenario, but with all scales having a population reliability of .80:

    out_new_rel <- power4test(out,
                              reliability = c(fx = .80,
                                              fm = .80,
                                              fy = .80))
    out_new_rel

This is the estimated power with higher population reliability:

    out_new_rel_reject <- rejection_rates(out_new_rel)
    out_new_rel_reject
    #> [test]: test_indirect: fx->fm->fy
    #> [test_label]: Test
    #>     est   p.v reject r.cilo r.cihi
    #> 1 0.235 1.000  0.985  0.973  0.997
    #> Notes:
    #> - p.v: The proportion of valid replications.
    #> - est: The mean of the estimates in a test across replications.
    #> - reject: The proportion of 'significant' replications, that is, the
    #>   rejection rate. If the null hypothesis is true, this is the Type I
    #>   error rate. If the null hypothesis is false, this is the power.
    #> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
    #>   on normal approximation.
    #> - Refer to the tests for the meanings of other columns.

The estimated power is 0.985, 95% confidence interval [0.973; 0.997], much higher than the original scenario.

# Find the Sample Size With Desired Power

There are several more efficient ways to find the sample size with the desired power.

## Using `n_region_from_power()`

The function `n_region_from_power()` can be used to find the region of sample sizes likely to have the desired power. If the default settings are to be used, then it can be called directly on the output of `power4test()`:

    out2_region <- n_region_from_power(out2,
                                       seed = 2345)

This is the recommended way for sample size planning, when there is no predetermined range of sample sizes.

See the templates for examples on using `n_region_from_power()` for common models.

## Using `power4test_by_n()`

First, the function `power4test_by_n()` can be used To estimate the power for a sequence of sample sizes. For example, suppose we do not know that the power is about .80 with a sample size of 200. We can estimate the power in the mediation model above for these sample sizes: 175, 200, 225, 250.

    out_several_ns <- power4test_by_n(out,
                                      n = c(175, 200, 225, 250),
                                      by_seed = 4567)

The first argument is the output of `power4test()` for an arbitrary sample size.

The argument `n` is a numeric vector of sample sizes to examine. For each `n`, `nrep` datasets will be generated. Although there is no limit on the number of sample sizes to try, it is recommended to restrict the number of sample sizes to 5 or less.

The argument `by_seed`, if set to an integer, try to make the results reproducible.

The call will take some time to run because it is equivalent to calling `power4test()` once for each sample size.

The rejection rates for each sample size can be retrieved by `rejection_rates()` too:

    rejection_rates(out_several_ns)
    #> [test]: test_indirect: fx->fm->fy
    #> [test_label]: Test
    #>     n   est   p.v reject r.cilo r.cihi
    #> 1 175 0.327 1.000  0.703  0.658  0.747
    #> 2 200 0.329 1.000  0.830  0.793  0.867
    #> 3 225 0.340 1.000  0.892  0.862  0.923
    #> 4 250 0.330 1.000  0.902  0.873  0.932
    #> Notes:
    #> - n: The sample size in a trial.
    #> - p.v: The proportion of valid replications.
    #> - est: The mean of the estimates in a test across replications.
    #> - reject: The proportion of 'significant' replications, that is, the
    #>   rejection rate. If the null hypothesis is true, this is the Type I
    #>   error rate. If the null hypothesis is false, this is the power.
    #> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
    #>   on normal approximation.
    #> - Refer to the tests for the meanings of other columns.

The results show that, to have a power of about .800 to detect the mediation effect, a sample size of about 200 is needed.

This approach is used when the range of sample sizes has already been decided and the levels of power are needed to determine the final sample size.

Please refer to the help page of `power4test_by_n()` for other examples.

## Using `x_from_power()`

The function `x_from_power()` can be used to systematically search within an interval the sample size with the target power. This takes longer to run but, instead of manually trying different sample size, this function do the search automatically.

This approach can be used when the goal is to find the probable minimum or maximum sample size with the desired level of power. The first approach, using `n_region_from_power()`, simply uses this approach twice to find the region of sample sizes.

See this article for an illustration of how to use `x_from_power()`.

# Other Scenarios

For other scenarios, such as moderation and moderated mediation, please refer to `vignette("power4mome")`.

# References

Cheung, S. F., & Cheung, S.-H. (2024). Manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models. Behavior Research Methods, 56(5), 4862–4882. https://doi.org/10.3758/s13428-023-02224-z

1. Users can specify the values directly if necessary.↩︎

2. The functions used are `parallel::clusterSetRNGStream()` for parallel processing, and `set.seed()` for serial processing.↩︎

3. They are implemented by `manymome::do_mc()` and `manymome::do_boot()`, respectively.↩︎

4. The test is implemented by `manymome::indirect()`.↩︎

5. The number of cores is determined automatically but can be set directly by the `ncores` argument.↩︎