Correlation and regression

PROCESS Macro Online

Run Hayes’ PROCESS models in your browser without SPSS, SAS or R. Pick a model number or build your own, and get every regression, conditional effect, indirect effect and index of moderated mediation with bootstrap confidence intervals.

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) 1 selected

For serial models, select mediators in causal order.

Moderator W

Covariates (optional) 0 selected

Covariates enter every equation, as in PROCESS.

Bootstrap samples

Confidence level

Moderator values

Standard errors

Mean-center variables that define products

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

Table 1

Outcome: Trust

Predictor	coeff	se	t	p	LLCI	ULCI
constant	5.8411	0.8911	6.555	< .001	4.0859	7.5963
Leadership	-0.4143	0.1943	-2.132	.034	-0.7970	-0.0316
Autonomy	-0.8574	0.2244	-3.820	< .001	-1.2995	-0.4153
Leadership × Autonomy	0.2131	0.0492	4.330	< .001	0.1162	0.3100

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

Predictor	coeff	se	t	p	LLCI	ULCI
constant	2.2294	0.3267	6.824	< .001	1.5860	2.8729
Leadership	0.1793	0.0649	2.763	.006	0.0515	0.3071
Trust	0.4087	0.0718	5.695	< .001	0.2674	0.5501

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

Table 3

Direct effect of Leadership on Performance

Effect	se	t	p	LLCI	ULCI
0.1793	0.0649	2.763	.006	0.0515	0.3071

Table 4

Conditional indirect effect: Leadership → Trust → Performance

Autonomy	Effect	BootSE	BootLLCI	BootULCI
2.989	0.0910	0.0290	0.0390	0.1512
4.035	0.1821	0.0370	0.1124	0.2574
5.092	0.2741	0.0574	0.1673	0.3941

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

Table 5

Index of moderated mediation by Autonomy

Path	Effect	BootSE	BootLLCI	BootULCI
Leadership → Trust → Performance	0.0871	0.0250	0.0417	0.1397

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). PROCESS Macro Online [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/process-macro-online/

BibTeX

@software{xavier_processmacro_2026,
  author = {Xavier, Bibin},
  title = {PROCESS Macro Online},
  publisher = {FreeStatisticalTools.com},
  year = {2026},
  url = {https://freestatisticaltools.com/process-macro-online/},
  note = {Web application}
}

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

What is the PROCESS macro?

The PROCESS macro by Andrew Hayes estimates mediation, moderation and conditional process models with OLS regression and bootstrap confidence intervals. This browser version supports every numbered model from 1 to 76, including three-way interactions, parallel and serial mediators, covariates, HC3 standard errors and the Johnson-Neyman technique, plus custom models that moderate any combination of paths.

When to use the PROCESS macro

Use PROCESS when your hypothesis involves a mechanism (mediation), a boundary condition (moderation), or both (conditional process analysis). Choose the model number that matches your conceptual diagram, or use the custom option to moderate any combination of paths.

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 moderator w.
Set the options. Fill in process model, bootstrap samples, confidence level and moderator values.
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 the PROCESS macro in APA style

Report the model number, the number of bootstrap samples, each path coefficient, the (conditional) indirect effects with bootstrap CIs and, for moderated mediation, the index of moderated mediation. For example: “The index of moderated mediation was 0.11, 95% CI [0.04, 0.19] (PROCESS Model 7, 5,000 bootstrap samples).”

Frequently asked questions

Can I run PROCESS without SPSS?

Yes. This tool runs the same regression models and percentile bootstrap as PROCESS in your browser, so you do not need SPSS, SAS or R.

Which PROCESS model should I use?

Match the model to your conceptual diagram: Model 4 for mediation, Model 1 for moderation, Model 7 when a moderator affects the X → M path, Model 14 when it affects the M → Y path, and Model 58 or 59 when it affects several paths.

Will my results match SPSS PROCESS?

Coefficients, standard errors and conditional effects are identical because both use OLS. Bootstrap confidence intervals vary slightly between runs and programs because they depend on random resampling.

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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