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Moderated Mediation Analysis

Test whether an indirect effect depends on a moderator. Get conditional indirect effects at different moderator values and the index of moderated mediation with bootstrap confidence intervals.

Free · No sign-upReplaces SPSS PROCESS macroWorks with your data
Updated · By Bibin Xavier

Data 250 × 9

Paste from Excel, or drop a CSV, TSV or Excel file here.
Example data 250 rows, 9 variables. Data stays in this browser tab.
Employeetext · 250 levelsLeadershipnumericTrustnumericEngagementnumericAutonomynumericTenure_YearsnumericPerformancenumericAgenumericFemalenumeric · coded
E0014.563.944.123.214.95.36281
E0024.423.024.353.12104.26431
E0034.824.944.784.2319.44.06210
E0044.654.083.533.375.44.22210
E0056.434.585.353.3110.96.12440
E0064.865.495.414.6115.54.72380
E0072.93.795.552.8412.24.86350
E0084.64.113.54.372.35.09390

Variables and options

Mediators (M) 1 selected
For serial models, select mediators in causal order.
Covariates (optional) 0 selected
Covariates enter every equation, as in PROCESS.
Results update automatically when you change an option.

Results

250 rows · example data
PROCESS model7First-stage moderated mediation
Sample size250
Bootstrap samples5,000percentile 95% CI
Standard errorsOLS

Conceptual diagram: Model 7

W: AutonomyX: LeadershipY: PerformanceM: Trust
Table 1

Outcome: Trust

PredictorcoeffsetpLLCIULCI
constant5.84110.89116.555< .0014.08597.5963
Leadership-0.41430.1943-2.132.034-0.7970-0.0316
Autonomy-0.85740.2244-3.820< .001-1.2995-0.4153
Leadership × Autonomy0.21310.04924.330< .0010.11620.3100

Note. R = .5133, R² = .2634, MSE = 0.4572, F(3, 246) = 29.327, p < .001. Highest-order interaction test: Leadership × Autonomy: ΔR² = .0561, F(1, 246) = 18.748, p = < .001.

Table 2

Outcome: Performance

PredictorcoeffsetpLLCIULCI
constant2.22940.32676.824< .0011.58602.8729
Leadership0.17930.06492.763.0060.05150.3071
Trust0.40870.07185.695< .0010.26740.5501

Note. R = .4603, R² = .2119, MSE = 0.6371, F(2, 247) = 33.197, p < .001.

Table 3

Direct effect of Leadership on Performance

EffectsetpLLCIULCI
0.17930.06492.763.0060.05150.3071
Table 4

Conditional indirect effect: Leadership → Trust → Performance

AutonomyEffectBootSEBootLLCIBootULCI
2.9890.09100.02900.03900.1512
4.0350.18210.03700.11240.2574
5.0920.27410.05740.16730.3941

Note. Moderator values are the 16th, 50th and 84th percentiles (two codes for dichotomous moderators).

Table 5

Index of moderated mediation by Autonomy

PathEffectBootSEBootLLCIBootULCI
Leadership → Trust → Performance0.08710.02500.04170.1397

Note. Moderation of the indirect effect is supported when the bootstrap CI excludes zero.

Conditional indirect effect via Trust

Interpretation

The index of moderated mediation is 0.087 with a 95% bootstrap CI of [0.042, 0.140], so the indirect effect depends on Autonomy. Read the conditional indirect effects to see where it is significant.

The index of moderated mediation was significant, index = 0.087, 95% CI [0.042, 0.140] (PROCESS Model 7, 5,000 bootstrap samples).

How to cite this tool

Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.

APA 7

Xavier, B. (2026). Moderated Mediation Analysis [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/moderated-mediation-analysis/

BibTeX 

@software{xavier_moderatedmediation_2026,
  author = {Xavier, Bibin},
  title = {Moderated Mediation Analysis},
  publisher = {FreeStatisticalTools.com},
  year = {2026},
  url = {https://freestatisticaltools.com/moderated-mediation-analysis/},
  note = {Web application}
}

Suggested sentence for your methods section: “The analysis was carried out with the free Moderated Mediation Analysis tool on FreeStatisticalTools.com (Xavier, 2026).”

What is moderated mediation?

Moderated mediation (conditional process analysis) tests whether an indirect effect depends on a moderator. You get the conditional indirect effect at different moderator values and the index of moderated mediation, each with a bootstrap confidence interval.

When to use moderated mediation

Use moderated mediation when a moderator changes the first stage (Model 7), the second stage (Model 14), both (Models 21 and 58), or the direct effect as well (Models 8, 15, 59).

Assumptions

  • Linear relationships and correctly specified causal ordering (X before M before Y), justified by theory or design.
  • Independent observations and, for OLS standard errors, constant residual variance (choose HC3 if this is doubtful).
  • No unmeasured confounding of the paths between mediators and the outcome.

How to set up your data

One row per case and one numeric column per variable. Code dichotomous variables 0/1. Moderators with two values are probed at those two values; continuous moderators at the 16th, 50th and 84th percentiles, or the mean ± 1 SD.

How to use this tool

  1. Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
  2. Choose the variables. Select the x (predictor), y (outcome), mediators (m) and moderator w.
  3. Set the options. Fill in process model, bootstrap samples, confidence level and moderator values.
  4. Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.

How to report moderated mediation in APA style

Report the index of moderated mediation with its bootstrap CI, then the conditional indirect effects at low, middle and high values of the moderator.

Frequently asked questions

What is the index of moderated mediation?

It is the slope of the line linking the indirect effect to the moderator (Hayes, 2015). If its bootstrap CI excludes zero, the indirect effect depends on the moderator.

Model 7 or Model 14?

Use Model 7 when the moderator changes the X → M path (first stage) and Model 14 when it changes the M → Y path (second stage).

Why is there no index for Model 58?

When the same moderator affects two paths, the indirect effect is a curved function of the moderator, so PROCESS reports conditional indirect effects without a single index.

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PROCESS is a macro for SPSS, SAS and R written by Andrew F. Hayes. This tool is an independent implementation of the published regression methods and model numbering (Hayes, 2022) and is not affiliated with, endorsed by or derived from the PROCESS software.

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