pseudo_johnson_neyman {manymome}

Pseudo Johnson-Neyman Probing

Description

Use the pseudo Johnson-Neyman approach (Hayes, 2022) to find the range of values of a moderator in which the conditional effect is not significant.

Usage

pseudo_johnson_neyman(
  object = NULL,
  w_lower = NULL,
  w_upper = NULL,
  optimize_method = c("uniroot", "optimize"),
  extendInt = c("no", "yes", "downX", "upX"),
  tol = .Machine$double.eps^0.25
)

## S3 method for class 'pseudo_johnson_neyman'
print(x, digits = 3, ...)

Arguments

object

A cond_indirect_effects-class object, which is the output of cond_indirect_effects().

w_lower

The smallest value of the moderator when doing the search. If set to ⁠NULL,⁠ the default, it will be 10 standard deviations below mean, which should be small enough.

w_upper

The largest value of the moderator when doing the search. If set to ⁠NULL,⁠ the default, it will be 10 standard deviations above mean, which should be large enough.

optimize_method

The optimization method to be used. Either "uniroot" (the default) or "optimize", corresponding to stats::uniroot() and stats::optimize(), respectively.

extendInt

Used by stats::uniroot(). If "no", then search will be conducted strictly within c(w_lower, w_upper). Otherwise, the range is extended based on this argument if the solution is not found. Please refer to stats::uniroot() for details.

tol

The tolerance level used by both stats::uniroot() and stats::optimize().

x

The output of pseudo_johnson_neyman().

digits

Number of digits to display. Default is 3.

...

Other arguments. Not used.

Details

This function uses the pseudo Johnson-Neyman approach proposed by Hayes (2022) to find the values of a moderator at which a conditional effect is "nearly just significant" based on confidence interval. If an effect is moderated, there will be two such points (though one can be very large or small) forming a range. The conditional effect is not significant within this range, and significant outside this range, based on the confidence interval.

This function receives the output of cond_indirect_effects() and search for, within a specific range, the two values of the moderator at which the conditional effect is "nearly just significant", that is, the confidence interval "nearly touches" zero.

Note that numerical method is used to find the points. Therefore, strictly speaking, the effects at the end points are still either significant or not significant, even if the confidence limit is very close to zero.

Supported Methods

This function supports models fitted by lm(), lavaan::sem(), and semTools::sem.mi(). This function also supports both bootstrapping and Monte Carlo confidence intervals. It also supports conditional direct paths (no mediator) and conditional indirect paths (with one or more mediator), with x and/or y standardized.

Requirements

To be eligible for using this function, one form of confidence intervals (e.g, bootstrapping or Monte Carlo) must has been requested (e.g., setting boot_ci = TRUE or mc_ci = TRUE) when calling cond_indirect_effects().

The confidence level of the confidence intervals adopted when calling cond_indirect_effects() will be used by this function.

Possible failures

Even if a path has only one moderator, it is possible that no solution, or more than one solution, is/are found if the relation between this moderator and the conditional effect is not linear.

Solution may also be not found if the conditional effect is significant over a wide range of value of the moderator.

It is advised to use plot_effect_vs_w() to examine the relation between the effect and the moderator first before calling this function.

Speed

Note that, for conditional indirect effects, the search can be slow because the confidence interval needs to be recomputed for each new value of the moderator.

Limitations

• This function currently only supports a path with only one moderator,

• This function does not yet support multigroup models.

Value

A list of the class pseudo_johnson_neyman (with a print method, print.pseudo_johnson_neyman()). It has these major elements:

• cond_effects: An output of cond_indirect_effects() for the two levels of the moderator found.

• w_min_valid: Logical. If TRUE, the conditional effect is just significant at the lower level of the moderator found, and so is significant below this point. If FALSE, then the lower level of the moderator found is just the lower bound of the range searched, that is, w_lower.

• w_max_valid: Logical. If TRUE, the conditional effect is just significant at the higher level of the moderator found, and so is significant above this point. If FALSE, then the higher level of the moderator found is just the upper bound of the range searched, that is, w_upper.

Methods (by generic)

• print(pseudo_johnson_neyman): Print method for output of pseudo_johnson_neyman().

References

Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (Third edition). The Guilford Press.

See Also

cond_indirect_effects()

Examples

library(lavaan)

dat <- data_med_mod_a
dat$wx <- dat$x * dat$w
mod <-
"
m ~ x + w + wx
y ~ m + x
"
fit <- sem(mod, dat)

# In real research, R should be 2000 or even 5000
# In real research, no need to set parallel and progress to FALSE
# Parallel processing is enabled by default and
# progress is displayed by default.
boot_out <- do_boot(fit,
                    R = 50,
                    seed = 4314,
                    parallel = FALSE,
                    progress = FALSE)
out <- cond_indirect_effects(x = "x", y = "y", m = "m",
                             wlevels = "w",
                             fit = fit,
                             boot_ci = TRUE,
                             boot_out = boot_out)

# Visualize the relation first
plot_effect_vs_w(out)

out_jn <- pseudo_johnson_neyman(out)
out_jn

# Plot the range
plot_effect_vs_w(out_jn$cond_effects)