Correlation and regression

Mediation Analysis Calculator

Test whether the effect of X on Y runs through one or more mediators. Indirect effects are tested with percentile bootstrap confidence intervals, as in Hayes’ PROCESS Models 4 and 6, with pairwise contrasts and standardized effects.

Free · No sign-upReplaces SPSS PROCESS macroWorks with your data

Updated 13 October 2026 · By Bibin Xavier

Data 250 × 9

Paste from Excel, or drop a CSV, TSV or Excel file here.Upload file

Example data 250 rows, 9 variables. Data stays in this browser tab.

Employeetext · 250 levels	Leadershipnumeric	Trustnumeric	Engagementnumeric	Autonomynumeric	Tenure_Yearsnumeric	Performancenumeric	Agenumeric	Femalenumeric · coded
E001	4.56	3.94	4.12	3.21	4.9	5.36	28	1
E002	4.42	3.02	4.35	3.12	10	4.26	43	1
E003	4.82	4.94	4.78	4.23	19.4	4.06	21	0
E004	4.65	4.08	3.53	3.37	5.4	4.22	21	0
E005	6.43	4.58	5.35	3.31	10.9	6.12	44	0
E006	4.86	5.49	5.41	4.61	15.5	4.72	38	0
E007	2.9	3.79	5.55	2.84	12.2	4.86	35	0
E008	4.6	4.11	3.5	4.37	2.3	5.09	39	0

Variables and options

PROCESS model

X (predictor)

Y (outcome)

Mediators (M) 2 selected

For serial models, select mediators in causal order.

Covariates (optional) 0 selected

Covariates enter every equation, as in PROCESS.

Bootstrap samples

Confidence level

Standard errors

Results update automatically when you change an option.

Results

250 rows · example data

PROCESS model4Simple or parallel mediation

Sample size250

Bootstrap samples5,000percentile 95% CI

Standard errorsOLS

Conceptual diagram: Model 4

Table 1

Outcome: Trust

Predictor	coeff	se	t	p	LLCI	ULCI
constant	2.6007	0.2372	10.963	< .001	2.1335	3.0679
Leadership	0.3941	0.0517	7.627	< .001	0.2923	0.4959

Note. R = .4359, R² = .1900, MSE = 0.4988, F(1, 248) = 58.173, p < .001.

Table 2

Outcome: Engagement

Predictor	coeff	se	t	p	LLCI	ULCI
constant	2.6448	0.3194	8.281	< .001	2.0157	3.2738
Leadership	0.4575	0.0696	6.575	< .001	0.3204	0.5945

Note. R = .3853, R² = .1485, MSE = 0.9041, F(1, 248) = 43.237, p < .001.

Table 3

Outcome: Performance

Predictor	coeff	se	t	p	LLCI	ULCI
constant	1.7918	0.3279	5.465	< .001	1.1461	2.4376
Leadership	0.0991	0.0647	1.531	.127	-0.0284	0.2266
Trust	0.3281	0.0711	4.613	< .001	0.1880	0.4682
Engagement	0.2447	0.0528	4.631	< .001	0.1406	0.3487

Note. R = .5245, R² = .2751, MSE = 0.5884, F(3, 246) = 31.113, p < .001.

Table 4

Total effect of Leadership on Performance

Effect	se	t	p	LLCI	ULCI
0.3404	0.0620	5.490	< .001	0.2183	0.4625

Table 5

Direct effect of Leadership on Performance

Effect	se	t	p	LLCI	ULCI
0.0991	0.0647	1.531	.127	-0.0284	0.2266

Table 6

Indirect effect: Leadership → Trust → Performance

Effect	BootSE	BootLLCI	BootULCI	Completely standardized
0.1293	0.0311	0.0699	0.1923	0.1251

Table 7

Indirect effect: Leadership → Engagement → Performance

Effect	BootSE	BootLLCI	BootULCI	Completely standardized
0.1119	0.0265	0.0648	0.1691	0.1083

Table 8

Total indirect effect

Effect	BootSE	BootLLCI	BootULCI
0.2413	0.0411	0.1640	0.3257

Table 9

Pairwise contrasts between indirect effects

Contrast	Effect	BootSE	BootLLCI	BootULCI
(Leadership → Trust → Performance) − (Leadership → Engagement → Performance)	0.0174	0.0407	-0.0632	0.0956

Note. A CI excluding zero means the two indirect effects differ.

Interpretation

All of 2 indirect effects are significant: Leadership → Trust → Performance (0.129); Leadership → Engagement → Performance (0.112). The direct effect of Leadership is not statistically significant, which indicates indirect-only (full) mediation.

The indirect effect Leadership → Trust → Performance was significant, b = 0.129, 95% CI [0.070, 0.192]; The indirect effect Leadership → Engagement → Performance was significant, b = 0.112, 95% CI [0.065, 0.169], based on 5,000 bootstrap samples (PROCESS Model 4).

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). Mediation Analysis Calculator [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/mediation-analysis-calculator/

BibTeX

@software{xavier_mediationanalysis_2026,
  author = {Xavier, Bibin},
  title = {Mediation Analysis Calculator},
  publisher = {FreeStatisticalTools.com},
  year = {2026},
  url = {https://freestatisticaltools.com/mediation-analysis-calculator/},
  note = {Web application}
}

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

What is mediation analysis?

Mediation analysis tests whether the effect of X on Y works through one or more mediators. This tool runs Hayes’ PROCESS Model 4 (simple and parallel mediation) and Model 6 (serial mediation) with percentile bootstrap confidence intervals, pairwise contrasts of indirect effects, completely standardized effects and covariates.

When to use mediation analysis

Use mediation analysis to test a mechanism: whether X affects Y by changing M. Model 4 handles one or several parallel mediators; Model 6 handles mediators in a causal chain. Bootstrap CIs are now preferred to the Baron and Kenny steps and the Sobel test.

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

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.
Choose the variables. Select the x (predictor), y (outcome), mediators (m) and covariates (optional).
Set the options. Fill in process model, bootstrap samples, confidence level and standard errors.
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 mediation analysis in APA style

Report the a, b, c′ and c paths and each indirect effect with its bootstrap CI, for example “The indirect effect through trust was significant, b = 0.15, 95% CI [0.08, 0.23].”

Frequently asked questions

How do I know if mediation is significant?

An indirect effect is significant when its 95% bootstrap confidence interval does not include zero.

What is the difference between parallel and serial mediation?

In parallel mediation (Model 4) the mediators do not affect each other. In serial mediation (Model 6) they form a chain, X → M1 → M2 → Y, and each specific indirect effect is tested.

Do I still need the Baron and Kenny steps?

No. Current guidance (Hayes, 2022; Zhao, Lynch and Chen, 2010) tests the indirect effect directly; a significant total effect is not required for mediation.

Related tools

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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