Start with your research question, then follow the relevant guide.
For a first analysis, complete Get
started; it uses wsMed() throughout. All models here
require two within-subject conditions with paired measurements.
Prepare your data
Use one row per participant, separate columns for the two conditions, and the same mediator order in both conditions. Decide which condition is subtracted from which before fitting: wsMed uses condition 2 minus condition 1.
- Column layout: a small worked example.
- Within- and between-subject covariates: analysis options.
- Category coding and reference groups: categorical predictors.
Choose your model
| Research structure | form |
Meaning / next step |
|---|---|---|
| Parallel pathways | "P" |
Each mediator forms a separate pathway; start with the quick start. |
| Serial pathways | "CN" |
Each mediator predicts every later mediator in the supplied order. |
| Serial-parallel structure | "CP" |
The first mediator (M1) predicts all remaining
mediators. |
| Parallel-serial structure | "PC" |
All remaining mediators predict the first mediator
(M1). |
| User-defined pathways | "UD" |
Set form = "UD" and list each mediator-to-mediator or
mediator-to-outcome arrow in paths; see the example
below. |
The four predefined models are shown below. Each includes paths from
the condition to every mediator and to the outcome, and from every
mediator to the outcome. Choose the structure and mediator order from
your research design; CP and PC place
M1 at opposite ends of the mediator connections.
Click a diagram to enlarge it. In these difference-score diagrams,
diff denotes condition 2 minus condition 1 and
avg denotes the centered average; the triangle represents
the constant used to estimate condition effects.
For user-defined models, M1,
M2, and so on refer to the positions in M_C1
and M_C2; Y refers to the pair supplied
through Y_C1 and Y_C2. For example, with
M_C1 = c("A1", "B1", "C1") and
M_C2 = c("A2", "B2", "C2"), the following specifies A to C,
C to the outcome, and B to the outcome:
custom_paths <- c(
"M1 -> M3", # A to C
"M3 -> Y", # C to the outcome
"M2 -> Y" # B to the outcome
)Pass this vector as paths = custom_paths together with
form = "UD" in wsMed(). Write one arrow per
string using these model labels, not the raw column names. Only the
listed mediator connections are included: this example omits
M1 -> Y. Condition-to-mediator paths and the direct
condition-to-outcome path are added automatically. The worked UD example shows the complete
call, input rules, and resulting indirect effects.
To add a moderator to this UD model, keep custom_paths
and add W = "D3" and MP = c("b_1_3", "b3") to
the call. Here, D3 is the moderator column; b_1_3 and
b3 select the M1-to-M3 and M3-to-outcome slopes. Use
coefficient labels in MP, not the arrow strings used in
paths. The UD moderation
example shows the complete call, how to choose other paths, and how
to read conditional effects at selected moderator values.
Add moderation
| Moderator | Guide | What to report |
|---|---|---|
| Continuous | Specify moderated paths | Conditional effects at meaningful raw values, with a curve or selected-level plot. |
| Categorical | Fit and compare groups | Reference coding, group-specific effects and intervals for group differences. |
Declare categorical between-subject covariates and moderators as unordered factors before using either interface. Set factor levels explicitly; the first level is the reference group. The categorical predictor guide shows binary and multicategory coding. Specify which paths are moderated rather than assuming that every path includes an interaction.
Handle missing data
| Situation | Where to start |
|---|---|
| Complete data or deliberate complete-case analysis |
Na = "DE"; inspect the number of retained
participants. |
| Full-information maximum likelihood |
Na = "FIML"; see options and
restrictions. |
| Multiple imputation |
Na = "MI"; see imputation
options. |
| Multiple imputation with moderation interactions | Model-compatible imputation, including external completed datasets. |
The choice depends on the missing-data assumptions and variable types. Default main-effects imputation may not preserve moderation interactions. Do not treat successful fitting or reproducible output as proof that an imputation model is suitable. Missing categorical predictors have additional restrictions.
Read, compare and present results
- Read and export results: all core effects in one table.
- Standardized effects: with or without moderation.
- Plot gallery: forest plots, curves, group effects and differences.
Go further when you need it
| Task | Guide |
|---|---|
| Specify, fit, infer, and inspect or extract at each stage | Staged workflow |
| Inspect generated SEM syntax | Model syntax |
| Check warnings, diagnostics and reproducibility | Diagnostics and migration |
| Read mathematical definitions and software comparisons | All articles |
Next: run a first analysis or read its results.