Chapter 6 Week6

2023-08-27 23:22| 来源: 网络整理| 查看: 265

Chapter 6 Week6_1: Lavaan Lab 4 Mediated Moderation & Moderated Mediation

In this lab, we will learn how to:

Estimate the mediated moderation model Estimate the moderated mediation model Bootstrap the effects Conduct simple slope analyses 6.1 PART 1: Mediated Moderation (Indirect Conditional effect) 6.1.1 Step 1: Read-in Data

Imagine that we extended our CBT study by adding a mediator: the average number of daily negative thoughts reported at the end of six weeks.

The hypothesis we will test is that NegThoughts mediates the CBT*NeedCog -> Depression path

Let’s read this dataset in:

cbtData |z|) ci.lower ci.upper ## CBTxNeedCogCont ~~ ## NeedCogCont 0.480 0.027 18.055 0.000 0.428 0.532 ## CBTDummy -0.008 0.011 -0.764 0.445 -0.030 0.013 ## NeedCogCont ~~ ## CBTDummy -0.020 0.016 -1.249 0.212 -0.051 0.011 ## ## Intercepts: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper ## .NegThoughts 0.523 0.045 11.618 0.000 0.434 0.611 ## .Depression 2.104 0.038 55.199 0.000 2.029 2.179 ## CBTxNeedCogCnt -0.017 0.022 -0.764 0.445 -0.060 0.026 ## NeedCogCont 0.006 0.032 0.188 0.851 -0.056 0.068 ## CBTDummy 0.500 0.016 31.623 0.000 0.469 0.531 ## ## Variances: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper ## .NegThoughts 1.010 0.045 22.361 0.000 0.921 1.098 ## .Depression 0.639 0.029 22.361 0.000 0.583 0.695 ## CBTxNeedCogCnt 0.480 0.021 22.361 0.000 0.438 0.522 ## NeedCogCont 0.994 0.044 22.361 0.000 0.907 1.081 ## CBTDummy 0.250 0.011 22.361 0.000 0.228 0.272 ## ## Defined Parameters: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper ## MedMod_ab -4.858 0.127 -38.211 0.000 -5.107 -4.609 ## TotalMod -4.967 0.113 -43.943 0.000 -5.188 -4.745

Are we done?

6.1.4 Step 4: Bootstrap Version

We need to request Bootstrap because this involves testing a mediation effect MedMod_ab.

Remember to set a seed:

set.seed(2022) ex1Boot 80% 6.2 PART 2: Moderated Mediation (Conditional Indirect effect)

In this lab, we’ll test this first-stage moderated mediation model in which NeedCog moderates the CBT -> NegThoughts path

6.2.1 Step 1: Product Term

We already have the product term in the dataset:

cbtData$CBTxNeedCogCont Depression path, then we center NegThoughts and create a product term between centered NegThoughts*NeedCogCont (making sense?)

6.2.2 Step 2: Write the syntax and Fit the model ex2ModMediationBasic Depression indirect effect through moderating the first stage of the indirect effect

Since we expect the effect of CBT on Depression to be negative (CBT reduces Depression) And IndexOfModMed is also negative We’ll say NeedCogCont strengthens the indirect effect of CBT on Depression through NegThoughts The higher need for cognition, the stronger the indirect effect, and the more effect mediated by NegThoughts 6.2.4 Step 4: Simple Slopes

As a follow-up analysis to a significant moderation effect, we conduct simple slope anlaysis:

Let’s use pick-a-point (Rogosa, 1980) and plot the indirect effects at designated levels of NeedCogCont:

mean(cbtData$NeedCogCont) #0 ## [1] 0.005925852 sd(cbtData$NeedCogCont) # 1 ## [1] 0.9974319

Three representative levels:

mean(cbtData$NeedCogCont) - sd(cbtData$NeedCogCont) # -1 ## [1] -0.991506 mean(cbtData$NeedCogCont) #0 ## [1] 0.005925852 mean(cbtData$NeedCogCont) + sd(cbtData$NeedCogCont) # 1 ## [1] 1.003358

let’s define the Conditional Indirect Effects in the syntax:

ex3ModMediation NegThoughts (a path) are all negative at three levels of the moderator

the indirect effects of CBT -> NegThoughts -> Depression (ab) are all negative at three levels of the moderator

6.2.5 Step 5 JOHNSON-NEYMAN INTERVAL

Although johnson_neyman() does not work on lavaan fitted object (yet), one can use a try-and-error approach to figure out the region of significance:

First, obtain the minimum and maximum of the moderator NeedCogCont:

min(cbtData$NeedCogCont) # -2.83 ## [1] -2.829197 max(cbtData$NeedCogCont) # 3.31 ## [1] 3.310095 ex4_JN

【本文地址】

Chapter 6 Week6

Chapter 6 Week6

今日新闻

推荐新闻