Cross-Level Interaction Workflow

In mlmoderator: Probing, Plotting, and Interpreting Multilevel Interaction Effects

knitr::opts_chunk$set(
  collapse  = TRUE,
  comment   = "#>",
  fig.width  = 7,
  fig.height = 4.5,
  fig.align  = "center"
)

What is a cross-level interaction?

A cross-level interaction occurs when a level-2 variable (e.g., school climate) moderates the relationship between a level-1 predictor (e.g., student SES) and the outcome (e.g., math achievement).

Formally, the two-level model is:

Level 1: $$math_{ij} = \beta_{0j} + \beta_{1j} \cdot SES_{ij} + e_{ij}$$

Level 2: $$\beta_{0j} = \gamma_{00} + \gamma_{01} \cdot Climate_j + u_{0j}$$ $$\beta_{1j} = \gamma_{10} + \gamma_{11} \cdot Climate_j + u_{1j}$$

Substituting gives the composite model: $$math_{ij} = \gamma_{00} + \gamma_{10} SES_{ij} + \gamma_{01} Climate_j + \gamma_{11} SES_{ij} \times Climate_j + u_{0j} + u_{1j} SES_{ij} + e_{ij}$$

The term $\gamma_{11}$ is the cross-level interaction: it captures how much the SES slope varies as a function of school climate.

Setup

library(mlmoderator)
library(lme4)

data(school_data)

Centering strategy for cross-level interactions

For cross-level interactions, the recommended centering strategy is:

• Level-1 predictor (SES): group-mean center (within-school centering)
• Level-2 moderator (climate): grand-mean center

This separates within-school from between-school variance in SES, and makes the intercept interpretable as the school-average outcome at average climate.

dat <- mlm_center(school_data,
                  vars    = "ses",
                  cluster = "school",
                  type    = "group")   # within-school SES

dat <- mlm_center(dat,
                  vars = "climate",
                  type = "grand")      # grand-mean-centred climate

# Check
cat("Within-school SES mean (should be ~0):", round(mean(dat$ses_c), 4), "\n")
cat("Grand-mean climate (should be ~0):",     round(mean(dat$climate_c), 4), "\n")

Fit the random-slopes model

Because we hypothesise that the SES slope varies across schools (hence the cross-level interaction), we include a random slope for SES.

mod <- lmer(
  math ~ ses_c * climate_c + gender + (1 + ses_c | school),
  data = dat,
  REML = TRUE
)
summary(mod)

The ses_c:climate_c interaction is the cross-level interaction of interest.

Probe simple slopes

probe <- mlm_probe(mod,
                   pred        = "ses_c",
                   modx        = "climate_c",
                   modx.values = "mean-sd",
                   conf.level  = 0.95)
probe

Interpretation: At schools with above-average climate (+1 SD), the positive effect of SES on math is amplified. At schools with below-average climate (−1 SD), the SES effect is weaker (and may not be significant).

Johnson–Neyman region

The JN interval pinpoints the exact climate values where the SES effect transitions from non-significant to significant.

jn <- mlm_jn(mod, pred = "ses_c", modx = "climate_c")
jn

Visualise

mlm_plot(
  mod,
  pred         = "ses_c",
  modx         = "climate_c",
  modx.values  = "mean-sd",
  interval     = TRUE,
  x_label      = "Student SES (group-mean centred)",
  y_label      = "Mathematics Achievement",
  legend_title = "School Climate"
)

Full summary for reporting

mlm_summary(mod,
            pred        = "ses_c",
            modx        = "climate_c",
            modx.values = "mean-sd",
            jn          = TRUE)

Reporting guidance

When reporting a cross-level interaction, include:

1. The interaction coefficient — $\hat{\gamma}_{11}$, its SE, t-ratio, and p-value.
2. Simple slopes — the slope of the level-1 predictor at each chosen level of the moderator, with 95% CIs.
3. The JN region — the moderator value(s) at which the slope crosses significance.
4. An interaction plot — a figure showing predicted outcome lines across the level-1 predictor for each selected moderator value.

Example write-up:

There was a significant cross-level interaction between student SES and school climate, $\hat{\gamma}_{11}$ = [value], SE = [value], t([df]) = [value], p = [value]. Simple slope analysis indicated that the positive effect of SES on math achievement was significantly stronger at schools with high climate (+1 SD: $b$ = [value], 95% CI [lo, hi]) than at schools with low climate (−1 SD: $b$ = [value], 95% CI [lo, hi]). Johnson–Neyman analysis revealed that the SES slope was statistically significant for climate values above [JN boundary].

Assumptions to check

• Random slope variance — a non-trivial $\tau_{11}$ supports the rationale for the cross-level interaction.
• Sufficient level-2 sample size — cross-level interactions require at least 30–50 level-2 units for stable estimation.
• Centering — ensure level-1 predictors are group-mean centred if the level-2 variable moderates the within-cluster slope.

mlmoderator documentation built on April 4, 2026, 1:07 a.m.