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Model-based mediation and moderation analyses (i.e., using raw regression model objects with distinct R packages, BUT NOT with the SPSS PROCESS Macro, to estimate effects in mediation/moderation models).

NOTE: PROCESS() DOES NOT use or transform any code or macro from the original SPSS PROCESS macro developed by Hayes, though its output would link model settings to a PROCESS Model ID in Hayes's numbering system.

To use PROCESS() in publications, please cite not only bruceR but also the following R packages:

  • interactions::sim_slopes() is used to estimate simple slopes (and conditional direct effects) in moderation, moderated moderation, and moderated mediation models (for PROCESS Model IDs 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 58, 59, 72, 73, 75, 76).

  • mediation::mediate() is used to estimate (conditional) indirect effects in (moderated) mediation models (for PROCESS Model IDs 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 58, 59, 72, 73, 75, 76).

  • lavaan::sem() is used to perform serial multiple mediation analysis (for PROCESS Model ID 6).

Usage

PROCESS(
  data,
  y = "",
  x = "",
  meds = c(),
  mods = c(),
  covs = c(),
  clusters = c(),
  hlm.re.m = "",
  hlm.re.y = "",
  hlm.type = c("1-1-1", "2-1-1", "2-2-1"),
  med.type = c("parallel", "serial"),
  mod.type = c("2-way", "3-way"),
  mod.path = c("x-y", "x-m", "m-y", "all"),
  cov.path = c("y", "m", "both"),
  mod1.val = NULL,
  mod2.val = NULL,
  ci = c("boot", "bc.boot", "bca.boot", "mcmc"),
  nsim = 100,
  seed = NULL,
  center = TRUE,
  std = FALSE,
  digits = 3,
  file = NULL
)

Arguments

data

Data frame.

y, x

Variable name of outcome (Y) and predictor (X).

  • Can be: continuous (numeric) or dichotomous (factor)

meds

Variable name(s) of mediator(s) (M). Use c() to combine multiple mediators.

  • Can be: continuous (numeric) or dichotomous (factor)

  • Allows any number of mediators in parallel or 2~4 mediators in serial

  • Order matters when med.type="serial" (PROCESS Model 6: serial mediation)

mods

Variable name(s) of 0~2 moderator(s) (W). Use c() to combine multiple moderators.

  • Can be: continuous (numeric), dichotomous (factor), or multicategorical (factor)

  • Order matters when mod.type="3-way" (PROCESS Models 3, 5.3, 11, 12, 18, 19, 72, and 73)

  • Not applicable to med.type="serial" (PROCESS Model 6)

covs

Variable name(s) of covariate(s) (i.e., control variables). Use c() to combine multiple covariates.

  • Can be any type and any number of variables

clusters

HLM (multilevel) cluster(s): e.g., "School", c("Prov", "City"), c("Sub", "Item").

hlm.re.m, hlm.re.y

HLM (multilevel) random effect term of M model and Y model. By default, it converts clusters to lme4 syntax of random intercepts: e.g., "(1 | School)" or "(1 | Sub) + (1 | Item)".

You may specify these arguments to include more complex terms: e.g., random slopes "(X | School)", or 3-level random effects "(1 | Prov/City)".

hlm.type

HLM (multilevel) mediation type (levels of "X-M-Y"): "1-1-1" (default), "2-1-1" (indeed the same as "1-1-1" in a mixed model), or "2-2-1" (currently not fully supported, as limited by the mediation package). In most cases, no need to set this argument.

med.type

Type of mediator: "parallel" (default) or "serial" (only relevant to PROCESS Model 6). Partial matches with "p" or "s" also work. In most cases, no need to set this argument.

mod.type

Type of moderator: "2-way" (default) or "3-way" (relevant to PROCESS Models 3, 5.3, 11, 12, 18, 19, 72, and 73). Partial matches with "2" or "3" also work.

mod.path

Which path(s) do the moderator(s) influence? "x-y", "x-m", "m-y", or any combination of them (use c() to combine), or "all" (i.e., all of them). No default value.

cov.path

Which path(s) do the control variable(s) influence? "y", "m", or "both" (default).

mod1.val, mod2.val

By default (NULL), it uses Mean +/- SD of a continuous moderator (numeric) or all levels of a dichotomous/multicategorical moderator (factor) to perform simple slope analyses and/or conditional mediation analyses. You may manually specify a vector of certain values: e.g., mod1.val=c(1, 3, 5) or mod1.val=c("A", "B", "C").

ci

Method for estimating the standard error (SE) and 95% confidence interval (CI) of indirect effect(s). Defaults to "boot" for (generalized) linear models or "mcmc" for (generalized) linear mixed models (i.e., multilevel models).

  • "boot": Percentile Bootstrap

  • "bc.boot": Bias-Corrected Percentile Bootstrap

  • "bca.boot": Bias-Corrected and Accelerated (BCa) Percentile Bootstrap

  • "mcmc": Markov Chain Monte Carlo (Quasi-Bayesian)

Note that these methods never apply to the estimates of simple slopes. You should not report the 95% CIs of simple slopes as Bootstrap or Monte Carlo CIs, because they are just standard CIs without any resampling method.

nsim

Number of simulation samples (bootstrap resampling or Monte Carlo simulation) for estimating SE and 95% CI. Defaults to 100 for running examples faster. In formal analyses, however, nsim=1000 (or larger) is strongly suggested!

seed

Random seed for reproducible results. Defaults to NULL. Note that all mediation analyses include random processes (i.e., bootstrap resampling or Monte Carlo simulation). To reproduce results, you need to set a random seed. However, even if you set the same seed number, it is unlikely to get exactly the same results across different R packages (e.g., lavaan vs. mediation) and software (e.g., SPSS, Mplus, R, jamovi).

center

Centering numeric (continuous) predictors? Defaults to TRUE (suggested).

std

Standardizing variables to get standardized coefficients? Defaults to FALSE. If TRUE, it will standardize all numeric (continuous) variables before building regression models. However, it is not suggested to set std=TRUE for generalized linear (mixed) models.

digits

Number of decimal places of output. Defaults to 3.

file

File name of MS Word (".doc"). Currently, only regression model summary can be saved.

Value

Invisibly return a list of results:

process.id

PROCESS Model ID (in Hayes's numbering system).

process.type

PROCESS model type.

model.m

Mediator (M) model(s) (a list of multiple models).

model.y

Outcome (Y) model.

results

Effect estimates and other results (unnamed list object).

Output

Two parts of results are printed:

  • PART 1. Regression model summary

  • PART 2. Mediation/moderation effect estimates

Disclaimer

PROCESS() DOES NOT use or transform any code or macro from the original SPSS PROCESS macro developed by Hayes, though its output would link model settings to a PROCESS Model ID in Hayes's numbering system.

DO NOT state that "the bruceR package runs the PROCESS Model Code developed by Hayes (2018)" — it was not the truth. The bruceR package only links results to Hayes's numbering system but never uses his code.

Software Comparison

To perform mediation, moderation, and conditional process (moderated mediation) analyses, people may use Mplus, SPSS "PROCESS" macro, or SPSS "MLmed" macro. Some R packages and functions can also perform such analyses, in a somewhat complex way, including mediation::mediate(), interactions::sim_slopes(), and lavaan::sem().

Furthermore, some other R packages or scripts/modules have been developed, including jamovi module jAMM (by Marcello Gallucci, based on the lavaan package), R package processR (by Keon-Woong Moon, not official, also based on the lavaan package), and R script file "process.R" (the official PROCESS R code by Andrew F. Hayes, but it is not yet an R package).

Distinct from these existing tools, PROCESS() provides an integrative way for performing mediation/moderation analyses in R. This function supports 24 kinds of SPSS PROCESS models numbered by Hayes (2018) (but does not use or transform his code), and also supports multilevel mediation/moderation analyses. Overall, it supports the most frequently used types of mediation, moderation, moderated moderation (3-way interaction), and moderated mediation (conditional indirect effect) analyses for (generalized) linear or linear mixed models.

Specifically, PROCESS() fits regression models based on the data, variable names, and a few other arguments that users input (with no need to specify the PROCESS Model ID or manually mean-center the variables). The function can automatically link model settings to Hayes's numbering system.

Variable Centering

PROCESS() automatically conducts grand-mean centering, using grand_mean_center(), before model building, though it can be turned off by setting center=FALSE.

The grand-mean centering is important because it:

  1. makes the results of main effects accurate for interpretation (see my commentary on this issue: Bao et al., 2022);

  2. does not change any model fit indices (it only affects the interpretation of main effects);

  3. is only conducted in "PART 1" (for an accurate estimate of main effects) but not in "PART 2" because it is more intuitive and interpretable to use the raw values of variables for the simple-slope tests in "PART 2";

  4. is not conflicted with group-mean centering because after group-mean centering the grand mean of a variable will also be 0, such that the automatic grand-mean centering (with mean = 0) will not change any values of the variable.

Conduct group-mean centering, if necessary, with group_mean_center() before using PROCESS(). Remember that the automatic grand-mean centering never affects the values of a group-mean centered variable, which already has a grand mean of 0.

References

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis (second edition): A regression-based approach. Guilford Press.

Yzerbyt, V., Muller, D., Batailler, C., & Judd, C. M. (2018). New recommendations for testing indirect effects in mediational models: The need to report and test component paths. Journal of Personality and Social Psychology, 115(6), 929–943.

See also

lavaan_summary()

model_summary()

med_summary()

For more details and illustrations, see PROCESS-bruceR-SPSS (PDF and Markdown files).

Examples

#### NOTE ####
## In the following examples, I set nsim=100 to save time.
## In formal analyses, nsim=1000 (or larger) is suggested!

#### Demo Data ####
# ?mediation::student
data = mediation::student %>%
  dplyr::select(SCH_ID, free, smorale, pared, income,
                gender, work, attachment, fight, late, score)
names(data)[2:3] = c("SCH_free", "SCH_morale")
names(data)[4:7] = c("parent_edu", "family_inc", "gender", "partjob")
data$gender01 = 1 - data$gender  # 0 = female, 1 = male
# dichotomous X: as.factor()
data$gender = factor(data$gender01, levels=0:1, labels=c("Female", "Male"))
# dichotomous Y: as.factor()
data$pass = as.factor(ifelse(data$score>=50, 1, 0))

#### Descriptive Statistics and Correlation Analyses ####
Freq(data$gender)
#> Frequency Statistics:
#> ─────────────────
#>            N    %
#> ─────────────────
#> Female  5044 52.1
#> Male    4635 47.9
#> ─────────────────
#> Total N = 9,679
Freq(data$pass)
#> Frequency Statistics:
#> ────────────
#>       N    %
#> ────────────
#> 0  3856 39.8
#> 1  5823 60.2
#> ────────────
#> Total N = 9,679
Describe(data)     # file="xxx.doc"
#> Descriptive Statistics:
#> ──────────────────────────────────────────────────────────────────────
#>                N   Mean     SD | Median   Min    Max Skewness Kurtosis
#> ──────────────────────────────────────────────────────────────────────
#> SCH_ID      9679 285.50 164.45 | 285.00  1.00 568.00    -0.00    -1.21
#> SCH_free    9679   2.99   1.86 |   3.00  1.00   7.00     0.47    -0.97
#> SCH_morale  9679   4.02   0.75 |   4.00  2.00   5.00    -0.45    -0.08
#> parent_edu  9679   0.44   0.50 |   0.00  0.00   1.00     0.26    -1.93
#> family_inc  9679   9.26   2.34 |  10.00  1.00  16.00    -0.79     0.72
#> gender*     9679   1.48   0.50 |   1.00  1.00   2.00     0.08    -1.99
#> partjob     9679   0.39   0.49 |   0.00  0.00   1.00     0.47    -1.78
#> attachment  9679   0.89   0.32 |   1.00  0.00   1.00    -2.42     3.87
#> fight       9679   0.13   0.33 |   0.00  0.00   1.00     2.26     3.10
#> late        9679   2.24   1.13 |   2.00  1.00   5.00     0.90     0.29
#> score       9679  51.91   9.69 |  52.00 19.00  87.00    -0.11    -0.12
#> gender01    9679   0.48   0.50 |   0.00  0.00   1.00     0.08    -1.99
#> pass*       9679   1.60   0.49 |   2.00  1.00   2.00    -0.42    -1.83
#> ──────────────────────────────────────────────────────────────────────
#> 
#> NOTE: `gender`, `pass` transformed to numeric.
Corr(data[,4:11])  # file="xxx.doc"
#> NOTE: `gender` transformed to numeric.
#> 
#> Pearson's r and 95% confidence intervals:
#> ──────────────────────────────────────────────────────────
#>                            r       [95% CI]     p        N
#> ──────────────────────────────────────────────────────────
#> parent_edu-family_inc   0.38 [ 0.36,  0.40] <.001 *** 9679
#> parent_edu-gender       0.01 [-0.01,  0.03]  .320     9679
#> parent_edu-partjob     -0.02 [-0.04,  0.00]  .071 .   9679
#> parent_edu-attachment   0.03 [ 0.01,  0.05]  .012 *   9679
#> parent_edu-fight       -0.08 [-0.10, -0.06] <.001 *** 9679
#> parent_edu-late        -0.04 [-0.06, -0.02] <.001 *** 9679
#> parent_edu-score        0.28 [ 0.27,  0.30] <.001 *** 9679
#> family_inc-gender       0.03 [ 0.01,  0.05]  .008 **  9679
#> family_inc-partjob      0.02 [ 0.00,  0.04]  .044 *   9679
#> family_inc-attachment   0.00 [-0.02,  0.02]  .763     9679
#> family_inc-fight       -0.10 [-0.12, -0.08] <.001 *** 9679
#> family_inc-late        -0.07 [-0.09, -0.05] <.001 *** 9679
#> family_inc-score        0.33 [ 0.32,  0.35] <.001 *** 9679
#> gender-partjob          0.00 [-0.02,  0.02]  .842     9679
#> gender-attachment      -0.11 [-0.13, -0.09] <.001 *** 9679
#> gender-fight            0.18 [ 0.16,  0.20] <.001 *** 9679
#> gender-late             0.01 [-0.01,  0.03]  .582     9679
#> gender-score            0.08 [ 0.06,  0.10] <.001 *** 9679
#> partjob-attachment     -0.03 [-0.05, -0.01]  .001 **  9679
#> partjob-fight           0.06 [ 0.04,  0.08] <.001 *** 9679
#> partjob-late            0.04 [ 0.02,  0.06] <.001 *** 9679
#> partjob-score          -0.02 [-0.04, -0.00]  .047 *   9679
#> attachment-fight       -0.16 [-0.18, -0.14] <.001 *** 9679
#> attachment-late        -0.16 [-0.18, -0.14] <.001 *** 9679
#> attachment-score        0.06 [ 0.04,  0.08] <.001 *** 9679
#> fight-late              0.17 [ 0.15,  0.18] <.001 *** 9679
#> fight-score            -0.16 [-0.18, -0.14] <.001 *** 9679
#> late-score             -0.14 [-0.16, -0.12] <.001 *** 9679
#> ──────────────────────────────────────────────────────────
#> 

#> 

#### PROCESS Analyses ####

## Model 1 ##
PROCESS(data, y="score", x="late", mods="gender")  # continuous Y
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 1
#> Model Type : Simple Moderation
#> -    Outcome (Y) : score
#> -  Predictor (X) : late
#> -  Mediators (M) : -
#> - Moderators (W) : gender
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    score ~ late*gender
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ───────────────────────────────────────────
#>                  (1) score     (2) score   
#> ───────────────────────────────────────────
#> (Intercept)        51.912 ***    51.174 ***
#>                    (0.098)       (0.135)   
#> late               -1.174 ***    -0.947 ***
#>                    (0.087)       (0.122)   
#> genderMale                        1.545 ***
#>                                  (0.195)   
#> late:genderMale                  -0.462 ** 
#>                                  (0.173)   
#> ───────────────────────────────────────────
#> R^2                 0.019         0.026    
#> Adj. R^2            0.018         0.025    
#> Num. obs.        9679          9679        
#> ───────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Simple Moderation (Model 1)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effect on "score" (Y)
#> ──────────────────────────────────────
#>                   F df1  df2     p    
#> ──────────────────────────────────────
#> late * gender  7.14   1 9675  .008 ** 
#> ──────────────────────────────────────
#> 
#> Simple Slopes: "late" (X) ==> "score" (Y)
#> ───────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.       t     p             [95% CI]
#> ───────────────────────────────────────────────────────────
#>  Female   -0.947 (0.122)  -7.772 <.001 *** [-1.186, -0.708]
#>  Male     -1.409 (0.122) -11.513 <.001 *** [-1.649, -1.169]
#> ───────────────────────────────────────────────────────────
#> 
PROCESS(data, y="pass", x="late", mods="gender")   # dichotomous Y
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 1
#> Model Type : Simple Moderation
#> -    Outcome (Y) : pass
#> -  Predictor (X) : late
#> -  Mediators (M) : -
#> - Moderators (W) : gender
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    pass ~ late*gender
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ──────────────────────────────────────────────
#>                   (1) pass       (2) pass     
#> ──────────────────────────────────────────────
#> (Intercept)           0.418 ***      0.306 ***
#>                      (0.021)        (0.029)   
#> late                 -0.232 ***     -0.201 ***
#>                      (0.019)        (0.026)   
#> genderMale                           0.238 ***
#>                                     (0.042)   
#> late:genderMale                     -0.066    
#>                                     (0.037)   
#> ──────────────────────────────────────────────
#> McFadden's R^2        0.012          0.015    
#> Nagelkerke's R^2      0.022          0.027    
#> AIC               12859.980      12829.489    
#> BIC               12874.335      12858.200    
#> Num. obs.          9679           9679        
#> ──────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Simple Moderation (Model 1)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effect on "pass" (Y)
#> ─────────────────────────────────
#>                Chisq df     p    
#> ─────────────────────────────────
#> late * gender   3.18  1  .075 .  
#> ─────────────────────────────────
#> 
#> Simple Slopes: "late" (X) ==> "pass" (Y)
#> ───────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.       z     p             [95% CI]
#> ───────────────────────────────────────────────────────────
#>  Female   -0.201 (0.026)  -7.769 <.001 *** [-0.252, -0.151]
#>  Male     -0.268 (0.027) -10.082 <.001 *** [-0.320, -0.216]
#> ───────────────────────────────────────────────────────────
#> 

# (multilevel moderation)
PROCESS(data, y="score", x="late", mods="gender",  # continuous Y (LMM)
        clusters="SCH_ID")
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 1
#> Model Type : Simple Moderation
#> -    Outcome (Y) : score
#> -  Predictor (X) : late
#> -  Mediators (M) : -
#> - Moderators (W) : gender
#> - Covariates (C) : -
#> -   HLM Clusters : SCH_ID
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    score ~ late*gender + (1 | SCH_ID)
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ─────────────────────────────────────────────────────
#>                          (1) score      (2) score    
#> ─────────────────────────────────────────────────────
#> (Intercept)                 51.705 ***     50.986 ***
#>                             (0.204)        (0.221)   
#> late                        -0.932 ***     -0.763 ***
#>                             (0.082)        (0.114)   
#> genderMale                                  1.509 ***
#>                                            (0.182)   
#> late:genderMale                            -0.346 *  
#>                                            (0.160)   
#> ─────────────────────────────────────────────────────
#> Marginal R^2                 0.012          0.018    
#> Conditional R^2              0.209          0.214    
#> AIC                      70092.182      70027.142    
#> BIC                      70120.893      70070.208    
#> Num. obs.                 9679           9679        
#> Num. groups: SCH_ID        568            568        
#> Var: SCH_ID (Intercept)     18.549         18.392    
#> Var: Residual               74.298         73.764    
#> ─────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Simple Moderation (Model 1)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effect on "score" (Y)
#> ──────────────────────────────────────
#>                   F df1  df2     p    
#> ──────────────────────────────────────
#> late * gender  4.70   1 9356  .030 *  
#> ──────────────────────────────────────
#> 
#> Simple Slopes: "late" (X) ==> "score" (Y)
#> ──────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.      t     p             [95% CI]
#> ──────────────────────────────────────────────────────────
#>  Female   -0.763 (0.114) -6.676 <.001 *** [-0.987, -0.539]
#>  Male     -1.109 (0.114) -9.688 <.001 *** [-1.333, -0.885]
#> ──────────────────────────────────────────────────────────
#> 
PROCESS(data, y="pass", x="late", mods="gender",   # dichotomous Y (GLMM)
        clusters="SCH_ID")
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 1
#> Model Type : Simple Moderation
#> -    Outcome (Y) : pass
#> -  Predictor (X) : late
#> -  Mediators (M) : -
#> - Moderators (W) : gender
#> - Covariates (C) : -
#> -   HLM Clusters : SCH_ID
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    pass ~ late*gender + (1 | SCH_ID)
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ─────────────────────────────────────────────────────
#>                          (1) pass       (2) pass     
#> ─────────────────────────────────────────────────────
#> (Intercept)                  0.441 ***      0.314 ***
#>                             (0.044)        (0.049)   
#> late                        -0.224 ***     -0.202 ***
#>                             (0.021)        (0.029)   
#> genderMale                                  0.271 ***
#>                                            (0.047)   
#> late:genderMale                            -0.049    
#>                                            (0.041)   
#> ─────────────────────────────────────────────────────
#> AIC                      12227.697      12197.299    
#> BIC                      12249.231      12233.188    
#> Num. obs.                 9679           9679        
#> Num. groups: SCH_ID        568            568        
#> Var: SCH_ID (Intercept)      0.754          0.756    
#> ─────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Simple Moderation (Model 1)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effect on "pass" (Y)
#> ─────────────────────────────────
#>                Chisq df     p    
#> ─────────────────────────────────
#> late * gender   1.43  1  .232    
#> ─────────────────────────────────
#> 
#> Simple Slopes: "late" (X) ==> "pass" (Y)
#> ──────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.      z     p             [95% CI]
#> ──────────────────────────────────────────────────────────
#>  Female   -0.202 (0.029) -6.997 <.001 *** [-0.259, -0.145]
#>  Male     -0.251 (0.029) -8.541 <.001 *** [-0.308, -0.193]
#> ──────────────────────────────────────────────────────────
#> 

# (Johnson-Neyman (J-N) interval and plot)
PROCESS(data, y="score", x="gender", mods="late") -> P
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 1
#> Model Type : Simple Moderation
#> -    Outcome (Y) : score
#> -  Predictor (X) : gender (recoded: Female=0, Male=1)
#> -  Mediators (M) : -
#> - Moderators (W) : late
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    score ~ gender*late
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ───────────────────────────────────────
#>              (1) score     (2) score   
#> ───────────────────────────────────────
#> (Intercept)    51.912 ***    51.913 ***
#>                (0.098)       (0.097)   
#> gender          1.530 ***     1.545 ***
#>                (0.196)       (0.195)   
#> late                         -1.169 ***
#>                              (0.086)   
#> gender:late                  -0.462 ** 
#>                              (0.173)   
#> ───────────────────────────────────────
#> R^2             0.006         0.026    
#> Adj. R^2        0.006         0.025    
#> Num. obs.    9679          9679        
#> ───────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Simple Moderation (Model 1)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effect on "score" (Y)
#> ──────────────────────────────────────
#>                   F df1  df2     p    
#> ──────────────────────────────────────
#> gender * late  7.14   1 9675  .008 ** 
#> ──────────────────────────────────────
#> 
#> Simple Slopes: "gender" (X) ==> "score" (Y)
#> ───────────────────────────────────────────────────────────
#>  "late"       Effect    S.E.     t     p           [95% CI]
#> ───────────────────────────────────────────────────────────
#>  1.116 (- SD)  2.064 (0.275) 7.504 <.001 *** [1.525, 2.604]
#>  2.242 (Mean)  1.545 (0.195) 7.938 <.001 *** [1.163, 1.926]
#>  3.367 (+ SD)  1.025 (0.275) 3.727 <.001 *** [0.486, 1.564]
#> ───────────────────────────────────────────────────────────
#> 
P$results[[1]]$jn[[1]]       # Johnson-Neyman interval
#> JOHNSON-NEYMAN INTERVAL
#> 
#> When late is OUTSIDE the interval [4.03, 14.93], the slope of gender
#> is p < .05.
#> 
#> Note: The range of observed values of late is [1.00, 5.00]
#> 
P$results[[1]]$jn[[1]]$plot  # Johnson-Neyman plot (ggplot object)

GLM_summary(P$model.y)       # detailed results of regression
#> 
#> General Linear Model (OLS Regression)
#> 
#> Model Fit:
#> F(3, 9675) = 84.92, p = 3e-54 ***
#> R² = 0.02566 (Adjusted R² = 0.02535)
#> 
#> Unstandardized Coefficients:
#> Outcome Variable: score
#> N = 9679
#> ────────────────────────────────────────────────────────────────────
#>                   b    S.E.       t     p        [95% CI of b]   VIF
#> ────────────────────────────────────────────────────────────────────
#> (Intercept)  51.913 (0.097) 534.035 <.001 *** [51.723, 52.104]      
#> gender        1.545 (0.195)   7.938 <.001 *** [ 1.163,  1.926] 1.000
#> late         -1.169 (0.086) -13.519 <.001 *** [-1.338, -0.999] 1.001
#> gender:late  -0.462 (0.173)  -2.673  .008 **  [-0.800, -0.123] 1.001
#> ────────────────────────────────────────────────────────────────────
#> 
#> Standardized Coefficients (β):
#> Outcome Variable: score
#> N = 9679
#> ─────────────────────────────────────────────────────────────────────────────────
#>                   β    S.E.       t     p        [95% CI of β] r(partial) r(part)
#> ─────────────────────────────────────────────────────────────────────────────────
#> gender        0.080 (0.010)   7.938 <.001 *** [ 0.060,  0.099]      0.080   0.080
#> late         -0.136 (0.010) -13.519 <.001 *** [-0.155, -0.116]     -0.136  -0.136
#> gender:late  -0.027 (0.010)  -2.673  .008 **  [-0.047, -0.007]     -0.027  -0.027
#> ─────────────────────────────────────────────────────────────────────────────────
#> 

# (allows multicategorical moderator)
d = airquality
d$Month = as.factor(d$Month)  # moderator: factor with levels "5"~"9"
PROCESS(d, y="Temp", x="Solar.R", mods="Month")
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 1
#> Model Type : Simple Moderation
#> -    Outcome (Y) : Temp
#> -  Predictor (X) : Solar.R
#> -  Mediators (M) : -
#> - Moderators (W) : Month
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    Temp ~ Solar.R*Month
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ────────────────────────────────────────
#>                 (1) Temp     (2) Temp   
#> ────────────────────────────────────────
#> (Intercept)      78.116 ***   66.012 ***
#>                  (0.736)      (1.232)   
#> Solar.R           0.028 ***    0.027 *  
#>                  (0.008)      (0.011)   
#> Month6                        12.967 ***
#>                               (1.699)   
#> Month7                        17.366 ***
#>                               (1.742)   
#> Month8                        18.235 ***
#>                               (1.741)   
#> Month9                        11.129 ***
#>                               (1.721)   
#> Solar.R:Month6                 0.002    
#>                               (0.017)   
#> Solar.R:Month7                -0.009    
#>                               (0.018)   
#> Solar.R:Month8                 0.009    
#>                               (0.019)   
#> Solar.R:Month9                -0.014    
#>                               (0.019)   
#> ────────────────────────────────────────
#> R^2               0.076        0.549    
#> Adj. R^2          0.070        0.519    
#> Num. obs.       146          146        
#> ────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Simple Moderation (Model 1)
#> Sample Size : 146 (7 missing observations deleted)
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effect on "Temp" (Y)
#> ───────────────────────────────────────
#>                     F df1 df2     p    
#> ───────────────────────────────────────
#> Solar.R * Month  0.36   4 136  .838    
#> ───────────────────────────────────────
#> 
#> Simple Slopes: "Solar.R" (X) ==> "Temp" (Y)
#> ───────────────────────────────────────────────────────
#>  "Month" Effect    S.E.     t     p            [95% CI]
#> ───────────────────────────────────────────────────────
#>  5        0.027 (0.011) 2.432  .016 *   [ 0.005, 0.048]
#>  6        0.029 (0.013) 2.242  .027 *   [ 0.003, 0.054]
#>  7        0.017 (0.014) 1.186  .238     [-0.011, 0.046]
#>  8        0.035 (0.016) 2.202  .029 *   [ 0.004, 0.067]
#>  9        0.013 (0.015) 0.865  .389     [-0.017, 0.043]
#> ───────────────────────────────────────────────────────
#> 

## Model 2 ##
PROCESS(data, y="score", x="late",
        mods=c("gender", "family_inc"),
        mod.type="2-way")  # or omit "mod.type", default is "2-way"
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 2
#> Model Type : Parallel Moderation (2 mods; 2-way)
#> -    Outcome (Y) : score
#> -  Predictor (X) : late
#> -  Mediators (M) : -
#> - Moderators (W) : gender, family_inc
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    score ~ late*gender + late*family_inc
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ───────────────────────────────────────────
#>                  (1) score     (2) score   
#> ───────────────────────────────────────────
#> (Intercept)        51.912 ***    51.255 ***
#>                    (0.098)       (0.127)   
#> late               -1.174 ***    -0.836 ***
#>                    (0.087)       (0.115)   
#> genderMale                        1.375 ***
#>                                  (0.184)   
#> family_inc                        1.339 ***
#>                                  (0.040)   
#> late:genderMale                  -0.301    
#>                                  (0.163)   
#> late:family_inc                   0.007    
#>                                  (0.034)   
#> ───────────────────────────────────────────
#> R^2                 0.019         0.129    
#> Adj. R^2            0.018         0.129    
#> Num. obs.        9679          9679        
#> ───────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Parallel Moderation (2 mods; 2-way) (Model 2)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effects on "score" (Y)
#> ───────────────────────────────────────────
#>                        F df1  df2     p    
#> ───────────────────────────────────────────
#> late * gender       3.40   1 9673  .065 .  
#> late * family_inc   0.04   1 9673  .849    
#> (All Interactions)  1.72   2 9673  .179    
#> ───────────────────────────────────────────
#> 
#> Simple Slopes: "late" (X) ==> "score" (Y)
#> ────────────────────────────────────────────────────────────────────────
#>  "family_inc"  "gender" Effect    S.E.      t     p             [95% CI]
#> ────────────────────────────────────────────────────────────────────────
#>  6.923 (- SD)  Female   -0.851 (0.137) -6.219 <.001 *** [-1.119, -0.583]
#>  6.923 (- SD)  Male     -1.152 (0.138) -8.369 <.001 *** [-1.422, -0.882]
#>  9.258 (Mean)  Female   -0.836 (0.115) -7.238 <.001 *** [-1.062, -0.609]
#>  9.258 (Mean)  Male     -1.137 (0.116) -9.792 <.001 *** [-1.365, -0.909]
#>  11.594 (+ SD) Female   -0.820 (0.144) -5.699 <.001 *** [-1.103, -0.538]
#>  11.594 (+ SD) Male     -1.122 (0.144) -7.778 <.001 *** [-1.405, -0.839]
#> ────────────────────────────────────────────────────────────────────────
#> 

## Model 3 ##
PROCESS(data, y="score", x="late",
        mods=c("gender", "family_inc"),
        mod.type="3-way")
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 3
#> Model Type : Moderated Moderation (2 mods; 3-way)
#> -    Outcome (Y) : score
#> -  Predictor (X) : late
#> -  Mediators (M) : -
#> - Moderators (W) : gender, family_inc
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    score ~ late*gender*family_inc
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ──────────────────────────────────────────────────────
#>                             (1) score     (2) score   
#> ──────────────────────────────────────────────────────
#> (Intercept)                   51.912 ***    51.266 ***
#>                               (0.098)       (0.127)   
#> late                          -1.174 ***    -0.818 ***
#>                               (0.087)       (0.116)   
#> genderMale                                   1.348 ***
#>                                             (0.185)   
#> family_inc                                   1.375 ***
#>                                             (0.055)   
#> late:genderMale                             -0.339 *  
#>                                             (0.164)   
#> late:family_inc                              0.088    
#>                                             (0.050)   
#> genderMale:family_inc                       -0.069    
#>                                             (0.079)   
#> late:genderMale:family_inc                  -0.153 *  
#>                                             (0.069)   
#> ──────────────────────────────────────────────────────
#> R^2                            0.019         0.130    
#> Adj. R^2                       0.018         0.129    
#> Num. obs.                   9679          9679        
#> ──────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Moderated Moderation (2 mods; 3-way) (Model 3)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effects on "score" (Y)
#> ───────────────────────────────────────────────────
#>                                F df1  df2     p    
#> ───────────────────────────────────────────────────
#> late * gender               4.26   1 9671  .039 *  
#> late * family_inc           3.11   1 9671  .078 .  
#> gender * family_inc         0.75   1 9671  .386    
#> late * gender * family_inc  4.95   1 9671  .026 *  
#> (All Interactions)          2.33   4 9671  .054 .  
#> ───────────────────────────────────────────────────
#> 
#> Conditional Interaction Effects on "score" (Y)
#> ────────────────────────────────────────────────────
#>  "family_inc"    Interaction    F df1  df2     p    
#> ────────────────────────────────────────────────────
#>  6.923 (- SD)  late * gender 0.01   1 9671  .938    
#>  9.258 (Mean)  late * gender 4.26   1 9671  .039 *  
#>  11.594 (+ SD) late * gender 8.56   1 9671  .003 ** 
#> ────────────────────────────────────────────────────
#> 
#> Simple Slopes: "late" (X) ==> "score" (Y)
#> ────────────────────────────────────────────────────────────────────────
#>  "family_inc"  "gender" Effect    S.E.      t     p             [95% CI]
#> ────────────────────────────────────────────────────────────────────────
#>  6.923 (- SD)  Female   -1.022 (0.157) -6.506 <.001 *** [-1.330, -0.714]
#>  6.923 (- SD)  Male     -1.005 (0.155) -6.470 <.001 *** [-1.309, -0.700]
#>  9.258 (Mean)  Female   -0.818 (0.116) -7.068 <.001 *** [-1.044, -0.591]
#>  9.258 (Mean)  Male     -1.157 (0.117) -9.927 <.001 *** [-1.385, -0.928]
#>  11.594 (+ SD) Female   -0.613 (0.170) -3.604 <.001 *** [-0.947, -0.280]
#>  11.594 (+ SD) Male     -1.308 (0.166) -7.894 <.001 *** [-1.633, -0.983]
#> ────────────────────────────────────────────────────────────────────────
#> 
PROCESS(data, y="pass", x="gender",
        mods=c("late", "family_inc"),
        mod1.val=c(1, 3, 5),     # moderator 1: late
        mod2.val=seq(1, 15, 2),  # moderator 2: family_inc
        mod.type="3-way")
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 3
#> Model Type : Moderated Moderation (2 mods; 3-way)
#> -    Outcome (Y) : pass
#> -  Predictor (X) : gender (recoded: Female=0, Male=1)
#> -  Mediators (M) : -
#> - Moderators (W) : late, family_inc
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Outcome:
#> -    pass ~ gender*late*family_inc
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ────────────────────────────────────────────────────
#>                         (1) pass       (2) pass     
#> ────────────────────────────────────────────────────
#> (Intercept)                 0.414 ***      0.442 ***
#>                            (0.021)        (0.022)   
#> gender                      0.228 ***      0.219 ***
#>                            (0.042)        (0.044)   
#> late                                      -0.216 ***
#>                                           (0.019)   
#> family_inc                                 0.259 ***
#>                                           (0.010)   
#> gender:late                               -0.046    
#>                                           (0.039)   
#> gender:family_inc                         -0.022    
#>                                           (0.020)   
#> late:family_inc                            0.005    
#>                                           (0.009)   
#> gender:late:family_inc                    -0.032    
#>                                           (0.018)   
#> ────────────────────────────────────────────────────
#> McFadden's R^2              0.002          0.073    
#> Nagelkerke's R^2            0.004          0.127    
#> AIC                     12989.483      12080.387    
#> BIC                     13003.839      12137.809    
#> Num. obs.                9679           9679        
#> ────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘interactions’ (v1.2.0)
#> Effect Type : Moderated Moderation (2 mods; 3-way) (Model 3)
#> Sample Size : 9679
#> Random Seed : -
#> Simulations : -
#> 
#> Interaction Effects on "pass" (Y)
#> ──────────────────────────────────────────────
#>                             Chisq df     p    
#> ──────────────────────────────────────────────
#> gender * late                1.43  1  .231    
#> gender * family_inc          1.17  1  .279    
#> late * family_inc            0.32  1  .573    
#> gender * late * family_inc   3.18  1  .075 .  
#> (All Interactions)           5.72  4  .221    
#> ──────────────────────────────────────────────
#> 
#> Conditional Interaction Effects on "pass" (Y)
#> ──────────────────────────────────────────────
#>  "family_inc"   Interaction Chisq df     p    
#> ──────────────────────────────────────────────
#>  1.000        gender * late  2.08  1  .149    
#>  3.000        gender * late  1.73  1  .188    
#>  5.000        gender * late  1.15  1  .285    
#>  7.000        gender * late  0.22  1  .642    
#>  9.000        gender * late  0.97  1  .323    
#>  11.000       gender * late  3.93  1  .047 *  
#>  13.000       gender * late  4.34  1  .037 *  
#>  15.000       gender * late  4.19  1  .041 *  
#> ──────────────────────────────────────────────
#> 
#> Simple Slopes: "gender" (X) ==> "pass" (Y)
#> ────────────────────────────────────────────────────────────────────
#>  "family_inc" "late" Effect    S.E.      z     p            [95% CI]
#> ────────────────────────────────────────────────────────────────────
#>  1.000        1.000   0.131 (0.253)  0.518  .605     [-0.365, 0.627]
#>  1.000        3.000   0.561 (0.201)  2.795  .005 **  [ 0.168, 0.954]
#>  1.000        5.000   0.991 (0.441)  2.247  .025 *   [ 0.127, 1.855]
#>  3.000        1.000   0.166 (0.196)  0.850  .395     [-0.217, 0.550]
#>  3.000        3.000   0.470 (0.155)  3.029  .002 **  [ 0.166, 0.774]
#>  3.000        5.000   0.773 (0.341)  2.270  .023 *   [ 0.106, 1.440]
#>  5.000        1.000   0.202 (0.141)  1.435  .151     [-0.074, 0.477]
#>  5.000        3.000   0.378 (0.111)  3.402 <.001 *** [ 0.160, 0.596]
#>  5.000        5.000   0.555 (0.244)  2.275  .023 *   [ 0.077, 1.033]
#>  7.000        1.000   0.237 (0.092)  2.576  .010 **  [ 0.057, 0.418]
#>  7.000        3.000   0.287 (0.072)  3.962 <.001 *** [ 0.145, 0.429]
#>  7.000        5.000   0.337 (0.159)  2.124  .034 *   [ 0.026, 0.649]
#>  9.000        1.000   0.272 (0.066)  4.109 <.001 *** [ 0.143, 0.402]
#>  9.000        3.000   0.196 (0.052)  3.775 <.001 *** [ 0.094, 0.298]
#>  9.000        5.000   0.120 (0.114)  1.049  .294     [-0.104, 0.343]
#>  11.000       1.000   0.308 (0.087)  3.544 <.001 *** [ 0.138, 0.478]
#>  11.000       3.000   0.105 (0.069)  1.529  .126     [-0.030, 0.239]
#>  11.000       5.000  -0.098 (0.151) -0.650  .515     [-0.395, 0.198]
#>  13.000       1.000   0.343 (0.134)  2.564  .010 *   [ 0.081, 0.606]
#>  13.000       3.000   0.014 (0.106)  0.128  .898     [-0.194, 0.221]
#>  13.000       5.000  -0.316 (0.234) -1.351  .177     [-0.775, 0.143]
#>  15.000       1.000   0.379 (0.188)  2.009  .045 *   [ 0.009, 0.748]
#>  15.000       3.000  -0.078 (0.150) -0.520  .603     [-0.371, 0.215]
#>  15.000       5.000  -0.534 (0.330) -1.619  .105     [-1.181, 0.113]
#> ────────────────────────────────────────────────────────────────────
#> 

## Model 4 ##
PROCESS(data, y="score", x="parent_edu",
        meds="family_inc", covs="gender",
        ci="boot", nsim=100, seed=1)
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 4
#> Model Type : Simple Mediation
#> -    Outcome (Y) : score
#> -  Predictor (X) : parent_edu
#> -  Mediators (M) : family_inc
#> - Moderators (W) : -
#> - Covariates (C) : gender
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Mediator:
#> -    family_inc ~ gender + parent_edu
#> Formula of Outcome:
#> -    score ~ gender + parent_edu + family_inc
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ───────────────────────────────────────────────────────
#>              (1) score     (2) family_inc  (3) score   
#> ───────────────────────────────────────────────────────
#> (Intercept)    51.206 ***     9.207 ***      51.262 ***
#>                (0.130)       (0.030)         (0.126)   
#> genderMale      1.474 ***     0.108 *         1.358 ***
#>                (0.188)       (0.044)         (0.182)   
#> parent_edu      5.537 ***     1.793 ***       3.594 ***
#>                (0.190)       (0.044)         (0.199)   
#> family_inc                                    1.083 ***
#>                                              (0.042)   
#> ───────────────────────────────────────────────────────
#> R^2             0.087         0.146           0.145    
#> Adj. R^2        0.086         0.146           0.145    
#> Num. obs.    9679          9679            9679        
#> ───────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘mediation’ (v4.5.1)
#> Effect Type : Simple Mediation (Model 4)
#> Sample Size : 9679
#> Random Seed : set.seed(1)
#> Simulations : 100 (Bootstrap)
#> 
#> Warning: nsim=1000 (or larger) is suggested!
#> 
#> Running 100 simulations...
#> Indirect Path: "parent_edu" (X) ==> "family_inc" (M) ==> "score" (Y)
#> ─────────────────────────────────────────────────────────────
#>                Effect    S.E.      z     p      [Boot 95% CI]
#> ─────────────────────────────────────────────────────────────
#> Indirect (ab)   1.943 (0.089) 21.759 <.001 *** [1.752, 2.095]
#> Direct (c')     3.594 (0.220) 16.319 <.001 *** [3.155, 3.981]
#> Total (c)       5.537 (0.203) 27.249 <.001 *** [5.084, 5.898]
#> ─────────────────────────────────────────────────────────────
#> Percentile Bootstrap Confidence Interval
#> (SE and CI are estimated based on 100 Bootstrap samples.)
#> 
#> Note. The results based on bootstrapping or other random processes
#> are unlikely identical to other statistical software (e.g., SPSS).
#> To make results reproducible, you need to set a seed (any number).
#> Please see the help page for details: help(PROCESS)
#> Ignore this note if you have already set a seed. :)
#> 

# (allows an infinite number of multiple mediators in parallel)
PROCESS(data, y="score", x="parent_edu",
        meds=c("family_inc", "late"),
        covs=c("gender", "partjob"),
        ci="boot", nsim=100, seed=1)
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 4
#> Model Type : Parallel Multiple Mediation (2 meds)
#> -    Outcome (Y) : score
#> -  Predictor (X) : parent_edu
#> -  Mediators (M) : family_inc, late
#> - Moderators (W) : -
#> - Covariates (C) : gender, partjob
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Mediator:
#> -    family_inc ~ gender + partjob + parent_edu
#> -    late ~ gender + partjob + parent_edu
#> Formula of Outcome:
#> -    score ~ gender + partjob + parent_edu + family_inc + late
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ─────────────────────────────────────────────────────────────────────
#>              (1) score     (2) family_inc  (3) late      (4) score   
#> ─────────────────────────────────────────────────────────────────────
#> (Intercept)    51.206 ***     9.207 ***       2.235 ***    51.254 ***
#>                (0.130)       (0.030)         (0.016)       (0.125)   
#> genderMale      1.475 ***     0.107 *         0.013         1.374 ***
#>                (0.188)       (0.044)         (0.023)       (0.181)   
#> partjob        -0.301         0.132 **        0.091 ***    -0.353    
#>                (0.193)       (0.045)         (0.023)       (0.186)   
#> parent_edu      5.531 ***     1.796 ***      -0.092 ***     3.545 ***
#>                (0.190)       (0.044)         (0.023)       (0.197)   
#> family_inc                                                  1.057 ***
#>                                                            (0.042)   
#> late                                                       -0.957 ***
#>                                                            (0.081)   
#> ─────────────────────────────────────────────────────────────────────
#> R^2             0.087         0.146           0.003         0.158    
#> Adj. R^2        0.087         0.146           0.003         0.157    
#> Num. obs.    9679          9679            9679          9679        
#> ─────────────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘mediation’ (v4.5.1)
#> Effect Type : Parallel Multiple Mediation (2 meds) (Model 4)
#> Sample Size : 9679
#> Random Seed : set.seed(1)
#> Simulations : 100 (Bootstrap)
#> 
#> Warning: nsim=1000 (or larger) is suggested!
#> 
#> Running 100 simulations...
#> Indirect Path: "parent_edu" (X) ==> "family_inc" (M) ==> "score" (Y)
#> ─────────────────────────────────────────────────────────────
#>                Effect    S.E.      z     p      [Boot 95% CI]
#> ─────────────────────────────────────────────────────────────
#> Indirect (ab)   1.898 (0.089) 21.262 <.001 *** [1.711, 2.055]
#> Direct (c')     3.545 (0.218) 16.280 <.001 *** [3.089, 3.937]
#> ─────────────────────────────────────────────────────────────
#> Percentile Bootstrap Confidence Interval
#> (SE and CI are estimated based on 100 Bootstrap samples.)
#> 
#> Running 100 simulations...
#> Indirect Path: "parent_edu" (X) ==> "late" (M) ==> "score" (Y)
#> ─────────────────────────────────────────────────────────────
#>                Effect    S.E.      z     p      [Boot 95% CI]
#> ─────────────────────────────────────────────────────────────
#> Indirect (ab)   0.088 (0.022)  3.946 <.001 *** [0.054, 0.136]
#> Direct (c')     3.545 (0.218) 16.280 <.001 *** [3.089, 3.937]
#> ─────────────────────────────────────────────────────────────
#> Percentile Bootstrap Confidence Interval
#> (SE and CI are estimated based on 100 Bootstrap samples.)
#> 
#> Note. The results based on bootstrapping or other random processes
#> are unlikely identical to other statistical software (e.g., SPSS).
#> To make results reproducible, you need to set a seed (any number).
#> Please see the help page for details: help(PROCESS)
#> Ignore this note if you have already set a seed. :)
#> 

# (multilevel mediation)
PROCESS(data, y="score", x="SCH_free",
        meds="late", clusters="SCH_ID",
        ci="mcmc", nsim=100, seed=1)
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 4
#> Model Type : Simple Mediation
#> -    Outcome (Y) : score
#> -  Predictor (X) : SCH_free
#> -  Mediators (M) : late
#> - Moderators (W) : -
#> - Covariates (C) : -
#> -   HLM Clusters : SCH_ID
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Mediator:
#> -    late ~ SCH_free + (1 | SCH_ID)
#> Formula of Outcome:
#> -    score ~ SCH_free + late + (1 | SCH_ID)
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ────────────────────────────────────────────────────────────────────
#>                          (1) score      (2) late       (3) score    
#> ────────────────────────────────────────────────────────────────────
#> (Intercept)                 51.853 ***      2.245 ***     51.858 ***
#>                             (0.162)        (0.017)        (0.159)   
#> SCH_free                    -1.611 ***      0.049 ***     -1.566 ***
#>                             (0.085)        (0.009)        (0.084)   
#> late                                                      -0.902 ***
#>                                                           (0.082)   
#> ────────────────────────────────────────────────────────────────────
#> Marginal R^2                 0.095          0.007          0.106    
#> Conditional R^2              0.201          0.073          0.208    
#> AIC                      69944.810      29498.368      69828.530    
#> BIC                      69973.521      29527.079      69864.419    
#> Num. obs.                 9679           9679           9679        
#> Num. groups: SCH_ID        568            568            568        
#> Var: SCH_ID (Intercept)      9.935          0.084          9.592    
#> Var: Residual               75.201          1.176         74.339    
#> ────────────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘mediation’ (v4.5.1)
#> Effect Type : Simple Mediation (Model 4)
#> Sample Size : 9679
#> Random Seed : set.seed(1)
#> Simulations : 100 (Monte Carlo)
#> 
#> Warning: nsim=1000 (or larger) is suggested!
#> 
#> Running 100 simulations...
#> Indirect Path: "SCH_free" (X) ==> "late" (M) ==> "score" (Y)
#> ────────────────────────────────────────────────────────────────
#>                Effect    S.E.       z     p        [MCMC 95% CI]
#> ────────────────────────────────────────────────────────────────
#> Indirect (ab)  -0.044 (0.009)  -5.155 <.001 *** [-0.061, -0.030]
#> Direct (c')    -1.573 (0.078) -20.230 <.001 *** [-1.720, -1.440]
#> Total (c)      -1.618 (0.078) -20.624 <.001 *** [-1.769, -1.480]
#> ────────────────────────────────────────────────────────────────
#> Monte Carlo (Quasi-Bayesian) Confidence Interval
#> (Effect, SE, and CI are estimated based on 100 Monte Carlo samples.)
#> 
#> Note. The results based on bootstrapping or other random processes
#> are unlikely identical to other statistical software (e.g., SPSS).
#> To make results reproducible, you need to set a seed (any number).
#> Please see the help page for details: help(PROCESS)
#> Ignore this note if you have already set a seed. :)
#> 

## Model 6 ##
PROCESS(data, y="score", x="parent_edu",
        meds=c("family_inc", "late"),
        covs=c("gender", "partjob"),
        med.type="serial",
        ci="boot", nsim=100, seed=1)
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 6
#> Model Type : Serial Multiple Mediation (2 meds)
#> -    Outcome (Y) : score
#> -  Predictor (X) : parent_edu
#> -  Mediators (M) : family_inc, late
#> - Moderators (W) : -
#> - Covariates (C) : gender, partjob
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Mediator:
#> -    family_inc ~ gender + partjob + parent_edu
#> -    late ~ gender + partjob + parent_edu + family_inc
#> Formula of Outcome:
#> -    score ~ gender + partjob + parent_edu + family_inc + late
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ─────────────────────────────────────────────────────────────────────
#>              (1) score     (2) family_inc  (3) late      (4) score   
#> ─────────────────────────────────────────────────────────────────────
#> (Intercept)    51.206 ***     9.207 ***       2.516 ***    51.254 ***
#>                (0.130)       (0.030)         (0.051)       (0.125)   
#> genderMale      1.475 ***     0.107 *         0.017         1.374 ***
#>                (0.188)       (0.044)         (0.023)       (0.181)   
#> partjob        -0.301         0.132 **        0.096 ***    -0.353    
#>                (0.193)       (0.045)         (0.023)       (0.186)   
#> parent_edu      5.531 ***     1.796 ***      -0.037         3.545 ***
#>                (0.190)       (0.044)         (0.025)       (0.197)   
#> family_inc                                   -0.030 ***     1.057 ***
#>                                              (0.005)       (0.042)   
#> late                                                       -0.957 ***
#>                                                            (0.081)   
#> ─────────────────────────────────────────────────────────────────────
#> R^2             0.087         0.146           0.007         0.158    
#> Adj. R^2        0.087         0.146           0.006         0.157    
#> Num. obs.    9679          9679            9679          9679        
#> ─────────────────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘lavaan’ (v0.6.19)
#> Effect Type : Serial Multiple Mediation (2 meds) (Model 6)
#> Sample Size : 9679
#> Random Seed : set.seed(1)
#> Simulations : 100 (Bootstrap)
#> 
#> Warning: nsim=1000 (or larger) is suggested!
#> 
#> Running 100 simulations (lavaan model)...
#> LAVAAN Syntax:
#> family_inc ~ gender + partjob + a1*parent_edu
#> late ~ gender + partjob + a2*parent_edu + d12*family_inc
#> score ~ gender + partjob + c.*parent_edu + b1*family_inc + b2*late
#> Indirect_All := a1*b1 + a2*b2 + a1*d12*b2
#> Ind_X_M1_Y := a1*b1
#> Ind_X_M2_Y := a2*b2
#> Ind_X_M1_M2_Y := a1*d12*b2
#> Direct := c.
#> Total := c. + a1*b1 + a2*b2 + a1*d12*b2
#> ────────────────────────────────────────────────────────────────────────
#>                  Estimate    S.E.      z     p       [Boot 95% CI]  Beta
#> ────────────────────────────────────────────────────────────────────────
#>   Indirect_All      1.986 (0.088) 22.644 <.001 *** [ 1.798, 2.135] 0.102
#>   Ind_X_M1_Y        1.898 (0.083) 22.816 <.001 *** [ 1.724, 2.058] 0.097
#>   Ind_X_M2_Y        0.036 (0.027)  1.327  .185     [-0.011, 0.086] 0.002
#>   Ind_X_M1_M2_Y     0.052 (0.011)  4.774 <.001 *** [ 0.034, 0.078] 0.003
#>   Direct            3.545 (0.178) 19.879 <.001 *** [ 3.179, 3.825] 0.182
#>   Total             5.531 (0.171) 32.290 <.001 *** [ 5.130, 5.857] 0.283
#> ────────────────────────────────────────────────────────────────────────
#> Percentile Bootstrap Confidence Interval
#> (SE and CI are estimated based on 100 Bootstrap samples.)
#> 
#> Note. The results based on bootstrapping or other random processes
#> are unlikely identical to other statistical software (e.g., SPSS).
#> To make results reproducible, you need to set a seed (any number).
#> Please see the help page for details: help(PROCESS)
#> Ignore this note if you have already set a seed. :)
#> 

## Model 8 ##
PROCESS(data, y="score", x="fight",
        meds="late",
        mods="gender",
        mod.path=c("x-m", "x-y"),
        ci="boot", nsim=100, seed=1)
#> 
#> ****************** PART 1. Regression Model Summary ******************
#> 
#> PROCESS Model ID : 8
#> Model Type : Moderated Mediation
#> -    Outcome (Y) : score
#> -  Predictor (X) : fight
#> -  Mediators (M) : late
#> - Moderators (W) : gender
#> - Covariates (C) : -
#> -   HLM Clusters : -
#> 
#> All numeric predictors have been grand-mean centered.
#> (For details, please see the help page of PROCESS.)
#> 
#> Formula of Mediator:
#> -    late ~ fight*gender
#> Formula of Outcome:
#> -    score ~ fight*gender + late
#> 
#> CAUTION:
#>   Fixed effect (coef.) of a predictor involved in an interaction
#>   denotes its "simple effect/slope" at the other predictor = 0.
#>   Only when all predictors in an interaction are mean-centered
#>   can the fixed effect be interpreted as "main effect"!
#>   
#> Model Summary
#> 
#> ──────────────────────────────────────────────────────────
#>                   (1) score     (2) late      (3) score   
#> ──────────────────────────────────────────────────────────
#> (Intercept)         51.912 ***     2.272 ***    50.824 ***
#>                     (0.097)       (0.016)       (0.136)   
#> fight               -4.585 ***     0.646 ***    -6.188 ***
#>                     (0.293)       (0.062)       (0.527)   
#> genderMale                        -0.057 *       2.129 ***
#>                                   (0.023)       (0.196)   
#> fight:genderMale                  -0.103         2.305 ***
#>                                   (0.074)       (0.633)   
#> late                                            -0.950 ***
#>                                                 (0.087)   
#> ──────────────────────────────────────────────────────────
#> R^2                  0.025         0.028         0.050    
#> Adj. R^2             0.025         0.028         0.049    
#> Num. obs.         9679          9679          9679        
#> ──────────────────────────────────────────────────────────
#> Note. * p < .05, ** p < .01, *** p < .001.
#> 
#> ************ PART 2. Mediation/Moderation Effect Estimate ************
#> 
#> Package Use : ‘mediation’ (v4.5.1), ‘interactions’ (v1.2.0)
#> Effect Type : Moderated Mediation (Model 8)
#> Sample Size : 9679
#> Random Seed : set.seed(1)
#> Simulations : 100 (Bootstrap)
#> 
#> Warning: nsim=1000 (or larger) is suggested!
#> 
#> Interaction Effect on "score" (Y)
#> ────────────────────────────────────────
#>                     F df1  df2     p    
#> ────────────────────────────────────────
#> fight * gender  13.24   1 9674 <.001 ***
#> ────────────────────────────────────────
#> 
#> Simple Slopes: "fight" (X) ==> "score" (Y)
#> (Conditional Direct Effects [c'] of X on Y)
#> ───────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.       t     p             [95% CI]
#> ───────────────────────────────────────────────────────────
#>  Female   -6.188 (0.527) -11.742 <.001 *** [-7.221, -5.155]
#>  Male     -3.883 (0.359) -10.821 <.001 *** [-4.587, -3.180]
#> ───────────────────────────────────────────────────────────
#> 
#> Interaction Effect on "late" (M)
#> ───────────────────────────────────────
#>                    F df1  df2     p    
#> ───────────────────────────────────────
#> fight * gender  1.90   1 9675  .168    
#> ───────────────────────────────────────
#> 
#> Simple Slopes: "fight" (X) ==> "late" (M)
#> (Conditional Effects [a] of X on M)
#> ────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.      t     p           [95% CI]
#> ────────────────────────────────────────────────────────
#>  Female    0.646 (0.062) 10.485 <.001 *** [0.525, 0.766]
#>  Male      0.543 (0.042) 12.989 <.001 *** [0.461, 0.625]
#> ────────────────────────────────────────────────────────
#> 
#> Running 100 * 2 simulations...
#> Indirect Path: "fight" (X) ==> "late" (M) ==> "score" (Y)
#> (Conditional Indirect Effects [ab] of X through M on Y)
#> ──────────────────────────────────────────────────────────
#>  "gender" Effect    S.E.      z     p        [Boot 95% CI]
#> ──────────────────────────────────────────────────────────
#>  Female   -0.614 (0.086) -7.117 <.001 *** [-0.807, -0.477]
#>  Male     -0.516 (0.060) -8.538 <.001 *** [-0.645, -0.411]
#> ──────────────────────────────────────────────────────────
#> Percentile Bootstrap Confidence Interval
#> (SE and CI are estimated based on 100 Bootstrap samples.)
#> 
#> Note. The results based on bootstrapping or other random processes
#> are unlikely identical to other statistical software (e.g., SPSS).
#> To make results reproducible, you need to set a seed (any number).
#> Please see the help page for details: help(PROCESS)
#> Ignore this note if you have already set a seed. :)
#> 

## For more examples and details, see:
## https://github.com/psychbruce/bruceR/tree/main/note