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class: center, middle
<span style="font-size: 60px;">第九章</span> <br>
<span style="font-size: 50px;">如何进行基本的数据分析</span> <br>
<span style="font-size: 50px;">中介和调节分析</span> <br>
<br>
<span style="font-size: 30px;">bruceR::PROCESS & lavaan</span> <br>
<br>
<br>
<span style="font-size: 30px;">胡传鹏</span> <br>
<span style="font-size: 30px;">2023/04/24</span> <br>
---
class: center, middle
<span style="font-size: 60px;">9.1 事前准备</span> <br>
---
# <h1 lang="zh-CN">相关知识点</h1>
<br>
<center>
<h2 lang="en">中介调节的鼻祖：Baron & Kenny (1986)</h2>
</center>
<br>
<div style="display:flex; align-items:center;">
<img src="http://3.bp.blogspot.com/-6OBEhzon_Bk/VfHWHZXlSYI/AAAAAAAAd_Q/chk77Yk2Ohc/s1600/B%2526K%2Bmediator.png" style="width:50%; height: 50%;">
<img src="http://1.bp.blogspot.com/-LoVbz4fImR8/VfHWKAH073I/AAAAAAAAd_Y/gPJHy1moJFk/s1600/B%2526K%2Bmoderator.png" style="width:50%;">
</div>
<br>
_<h5 lang="en">- Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173. </h5>_
---
# <h1 lang="zh-CN">相关知识点</h1>
<br>
<center>
<h2 lang="en">PROCESS的作者：Andrew F. Hayes </h2>
</center>
<br>
<div style="display:flex; align-items:center; justify-content:center;">
<img src="https://m.media-amazon.com/images/I/51h5ymu3dpL._SX348_BO1,204,203,200_.jpg" style="width:30%; height: 30%; margin-right: 20px;">
<img src="https://www.guilford.com/covers/large/9781462549030.jpg" style="width:30%; height: 30%;">
</div>
<br>
_<h5 lang="en">- Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications. </h5>_
---
# <h1 lang="zh-CN">相关的R包：</h1>
<br>
<center>
<h2 lang="en">bruceR::PROCESS & lavaan </h2>
</center>
<br>
<div style="display:flex; align-items:center; justify-content:center;">
<img src="https://raw.githubusercontent.com/psychbruce/bruceR/main/man/figures/logo.png" style="width:30%; height: 30%; margin-right: 40px;">
<img src="https://assets.datacamp.com/production/course_6419/shields/original/shield_image_course_6419_20180703-12-1hbh8vg?1530639951" style="width:30%; height: 30%;">
</div>
<br>
_<h5 lang="en">- Bao, H.-W.-S. (2022). bruceR: Broadly useful convenient and efficient R functions. R package version 0.8.x. https://CRAN.R-project.org/package=bruceR </h5>_
_<h5 lang="en">- Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software, 48(2), 1–36. doi:10.18637/jss.v048.i02. </h5>_
---
# <h1 lang="zh-CN">载入包 - 设置工作路径 - 导入原始数据</h1>
```r
# 检查是否已安装 pacman
if (!requireNamespace("pacman", quietly = TRUE)) {
install.packages("pacman") } # 如果未安装，则安装包
# 使用p_load来载入需要的包
pacman::p_load("tidyverse", "bruceR", "performance", "lavaan", "lavaanPlot")
```
```
##
## bruceR (version 0.8.9)
## BRoadly Useful Convenient and Efficient R functions
##
## Packages also loaded:
## √ dplyr √ emmeans √ ggplot2
## √ tidyr √ effectsize √ ggtext
## √ stringr √ performance √ cowplot
## √ forcats √ lmerTest √ see
## √ data.table
##
## Main functions of `bruceR`:
## cc() Describe() TTEST()
## add() Freq() MANOVA()
## .mean() Corr() EMMEANS()
## set.wd() Alpha() PROCESS()
## import() EFA() model_summary()
## print_table() CFA() lavaan_summary()
##
## https://psychbruce.github.io/bruceR/
##
## NEWS: A new version of bruceR (0.8.10) is available on 2023-03-03!
## ***** Please Update *****
## install.packages("bruceR", dep=TRUE)
##
## These R packages are dependencies of `bruceR` but not installed:
## lmtest, vars, phia, GGally, GPArotation
## ***** Please Install All Dependencies *****
## install.packages("bruceR", dep=TRUE)
```
---
# <h1 lang="zh-CN">载入包 - 设置工作路径 - 导入原始数据</h1>
---
```r
df.pg.raw <- read.csv('./data/penguin/penguin_rawdata_new.csv',
header = T, sep=",", stringsAsFactors = FALSE) %>%
dplyr::select(., age, language, avgtemp,socialdiversity, DEQ,
starts_with("ALEX"),
starts_with("ECR"),
starts_with("HOME"),
starts_with("KAMF"),
starts_with("SNI"),
)
# 这些包含原始题目的数据集，将在SEM lavaan中直接使用
```
<div id="htmlwidget-5ae54b8cc4e742daf14f" style="width:100%;height:auto;" class="datatables html-widget"></div>
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<th>SNI3<\/th>\n <th>SNI4<\/th>\n <th>SNI5<\/th>\n <th>SNI6<\/th>\n <th>SNI7<\/th>\n <th>SNI8<\/th>\n <th>SNI9<\/th>\n <th>SNI10<\/th>\n <th>SNI11<\/th>\n <th>SNI12<\/th>\n <th>SNI13<\/th>\n <th>SNI14<\/th>\n <th>SNI15<\/th>\n <th>SNI16<\/th>\n <th>SNI17<\/th>\n <th>SNI18<\/th>\n <th>SNI19<\/th>\n <th>SNI20<\/th>\n <th>SNI21<\/th>\n <th>SNI22<\/th>\n <th>SNI23<\/th>\n <th>SNI24<\/th>\n <th>SNI25<\/th>\n <th>SNI26<\/th>\n <th>SNI27<\/th>\n <th>SNI28<\/th>\n <th>SNI29<\/th>\n <th>SNI30<\/th>\n <th>SNI31<\/th>\n <th>SNI32<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,101,102,103,104,105]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
# <h1 lang="zh-CN">查找量表题目与维度的对应关系</h1>
ALEX：16题(TAS-20)<br> DIF 1,3,5,6,7,10,11; DDF 2,4,8,9; EOT 12,13,14,15,16 <br>
_<h5 lang="en">- Taylor, G. J., Bagby, R. M., & Parker, J. D. (2003). The 20-Item Toronto Alexithymia Scale: IV. Reliability and factorial validity in different languages and cultures. Journal of psychosomatic research, 55(3), 277-283. </h5>_
<br>
ECR：36题 1-18为焦虑，19-36为回避 <br>
_<h5 lang="en">- Fraley, R. C., Waller, N. G., & Brennan, K. A. (2000). An item response theory analysis of self-report measures of adult attachment. Journal of personality and social psychology, 78(2), 350. </h5>_
<br>
HOME：9题 <br>
_<h5 lang="zh-CN">来源未知</h5>_
<br>
KAMF：7题 <br>
_<h5 lang="zh-CN">来源未知</h5>_
<br>
在PROCESS中由于一个变量内的题目都会求平均分，所以是否知道题目与维度的对应并不重要<br>
但在lavaan(SEM)中，必须知道题目与维度的对应关系，才可以构建模型
---
# <h1 lang="zh-CN">清洗数据</h1>
# <h2 lang="zh-CN">对模型涉及变量的题目求均值</h2>
```r
# 如果你需要探究某个维度对其他变量的影响，可以按照维度求和，做更复杂的模型
df.pg.mean <- df.pg.raw %>%
dplyr::mutate(ALEX_mean = rowMeans(select(., starts_with("ALEX"))),
ECR_mean = rowMeans(select(., starts_with("ECR"))),
HOME_mean = rowMeans(select(., starts_with("HOME"))),
KAMF_mean = rowMeans(select(., starts_with("KAMF"))),) %>%
dplyr::select(age, language, avgtemp,
ALEX_mean, ECR_mean, HOME_mean, KAMF_mean)
```
<div id="htmlwidget-8511246f49d4efafb6e9" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-8511246f49d4efafb6e9">{"x":{"filter":"none","vertical":false,"fillContainer":true,"data":[["1","2","3","4","5","6","7","8","9","10"],[1922,1940,1945,1948,1948,1951,1952,1952,1952,1953],[7,5,5,5,2,1,5,5,5,4],[36.75,35.4,35.1,35.95,36.08333333,35.6,35.4,35.8,35.85,35.6],[1.9375,1.4375,2.125,2.5,1.5,2.1875,2.3125,1.4375,2.875,1.375],[3.5,1.75,2.88888888888889,2.19444444444444,2.63888888888889,2.36111111111111,3.33333333333333,3.41666666666667,4.30555555555556,2.83333333333333],[3.22222222222222,2.33333333333333,4,4.44444444444444,4.11111111111111,4.33333333333333,3.88888888888889,4.77777777777778,4.44444444444444,4.66666666666667],[2.28571428571429,1,3.28571428571429,3.42857142857143,3.28571428571429,4.85714285714286,2.71428571428571,4,3.85714285714286,4.42857142857143]],"container":"<table class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>age<\/th>\n <th>language<\/th>\n <th>avgtemp<\/th>\n <th>ALEX_mean<\/th>\n <th>ECR_mean<\/th>\n <th>HOME_mean<\/th>\n <th>KAMF_mean<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
class: center, middle
<span style="font-size: 60px;">9.2 PROCESS in R</span> <br>
---
# <h1 lang="en">Penguin Model 4</h1>
<div style="display:flex; align-items:center;">
<img src="picture/chp9/model4_hayse.png" style="width:45%; height: 45%;margin-right: 90px;">
<font size=20> Model 4 </font>
</div>
---
# <h1 lang="en">Penguin Model 4</h1>
<br>
<div style="display:flex; align-items:center;">
<img src="picture/chp9/model4.png" style="width:100%; height: 100%;"> <br>
</div>
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">CODE</h2>
```r
result.PROCESS <- capture.output({
# capture.output 可以将这段代码的结果存储进一个变量中
bruceR::PROCESS(df.pg.raw,
x = "DEQ", #自变量
y = "avgtemp", # 因变量
meds = c("socialdiversity"), # 中介变量
#mods = c(), # 调节变量
#covs = c(), # 控制变量（协变量）
#clusters = c(), #类似调节变量 分组变量
#hlm.re.m = "",hlm.re.y = "",hlm.type = c("1-1-1", "2-1-1", "2-2-1"),
#med.type = c("parallel", "serial"), # 中介变量之间是并列还是连续
#mod.type = c("2-way", "3-way"), # 两项交互还是三项交互
#mod.path = c(), # 调节哪条路径
cov.path = c("both"), # 控制变量进入几个方程，both意味着中介和因变量都会考虑控制变量
#mod1.val = NULL,mod2.val = NULL, # 调节变量高低水平对应的值
ci = c("boot"), # 置信区间使用bootstrap 或其他抽样方法
nsim = 1000, # 抽样的次数
#seed = NULL, # 设置seed可以让每次的抽样结果都一样
center = FALSE, # 是否对自变量和调节变量中心化。非中心化在解释时更方便
std = FALSE, # 是否在计算钱对所有变量进行标准化
digits = 3, # 保留的小数位数
#nsmall = digits,file = NULL
)
})
writeLines(result.PROCESS, "./output/chp9/PROCESS_model_4.md") # .md最整齐
```
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_4_1.png" width="90%">
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_4_2.png" width="100%">
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_4_3.png" width="100%">
---
# <h1 lang="zh-CN">想~~编~~一个模型</h1>
<div style="display:flex; align-items:center;">
<img src="picture/chp9/model7_hayse.png" style="width:45%; height: 45%;margin-right: 90px;">
<font size=20> Model 7 </font>
</div>
---
# <h1 lang="zh-CN">想~~编~~一个模型</h1>
<div style="display:flex; align-items:center;">
<img src="picture/chp9/model7.png" style="width:100%; height: 100%;"> <br>
</div>
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">CODE</h2>
```r
result.PROCESS <- capture.output({
# capture.output 可以将这段代码的结果存储进一个变量中
bruceR::PROCESS(df.pg.mean,
x = "ALEX_mean", #自变量
y = "HOME_mean", # 因变量
meds = c("ECR_mean"), # 中介变量
mods = c("KAMF_mean"), # 调节变量
covs = c("age", "language", "avgtemp"), # 控制变量（协变量）
#clusters = c(), #类似调节变量 分组变量
#hlm.re.m = "",hlm.re.y = "",hlm.type = c("1-1-1", "2-1-1", "2-2-1"),
#med.type = c("parallel", "serial"), # 中介变量之间是并列还是连续
#mod.type = c("2-way", "3-way"), # 两项交互还是三项交互
mod.path = c("x-m"), # 调节哪条路径
cov.path = c("both"), # 控制变量进入几个方程，both意味着中介和因变量都会考虑控制变量
#mod1.val = NULL,mod2.val = NULL, # 调节变量高低水平对应的值
ci = c("boot"), # 置信区间使用bootstrap 或其他抽样方法
nsim = 100, # 抽样的次数
#seed = NULL, # 设置seed可以让每次的抽样结果都一样
center = FALSE, # 是否对自变量和调节变量中心化。非中心化在解释时更方便
std = FALSE, # 是否在计算钱对所有变量进行标准化
digits = 3, # 保留的小数位数
#nsmall = digits,file = NULL
)
})
writeLines(result.PROCESS, "./output/chp9/PROCESS_model_7.md") # .md最整齐
```
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_7_1.png" width="100%">
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_7_2.png" width="80%">
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_7_3.png" width="100%">
---
# <h1 lang="en">bruceR::PROCESS</h1>
## <h2 lang="en">OUTPUT</h2>
<img src="./picture/chp9/PROCESS_model_7_4.png" width="100%">
---
class: center, middle
<span style="font-size: 60px;">9.3 SEM in R (lavaan)</span> <br>
---
# <h1 lang="en">lavaan::cfa</h1>
# <h2 lang="en">CODE</h2>
```r
result.CFA <- capture.output({
CFA<- 'DIDF =~ ALEX1 + ALEX3 + ALEX5 + ALEX6 + ALEX7 + ALEX10 + ALEX11 + ALEX2 + ALEX4 + ALEX8 + ALEX9
EOF =~ ALEX12 + ALEX13 + ALEX14 + ALEX15 + ALEX16
ANX =~ ECR1 + ECR2 + ECR3 + ECR4 + ECR5 + ECR6 + ECR7 + ECR8 + ECR9 + ECR10 + ECR11 + ECR12 + ECR13 + ECR14 + ECR15 + ECR16 + ECR17 + ECR18
AVO =~ ECR19 + ECR20 + ECR21 + ECR22 + ECR23 + ECR24 + ECR25 + ECR26 + ECR27 + ECR28 + ECR29 + ECR30 + ECR31 + ECR32 + ECR33 + ECR34 + ECR35 + ECR36
HOME =~ HOME1 + HOME2 + HOME3 + HOME4 + HOME5 + HOME6 + HOME7 + HOME8 + HOME9
KAMF =~ KAMF1 + KAMF2 + KAMF3 + KAMF4 + KAMF5 + KAMF6 + KAMF7' %>%
# ALEX =~ DIDF + EOF
# ECR =~ ANX + AVO
# 为了简化，这里没有做二阶
lavaan::cfa(.,data=df.pg.raw,estimator="ML")
lavaan::summary(CFA,fit.measures=TRUE,standardized=TRUE)
})
writeLines(result.CFA, "./output/chp9/CFA.md")
```
_<h4 lang="zh-CN">- 这里并不是完全正确的做法，该论文penguin对原始量表做了一些修改</h4>_
_<h4 lang="zh-CN">- 原始量表中ALEX是一个二阶潜变量，由三个一阶潜变量DIF, DDF, EOF构成</h4>_
_<h4 lang="zh-CN">- 但是DIF和DDF被合并为DIDF了（penguin没有说这是为什么）</h4>_
_<h4 lang="zh-CN">- 如果你每个维度单独做CFA，就能找到问题所在（是因为这两个维度共线性太高）</h4>_
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Model Fit</h2>
<img src="./picture/chp9/CFA_1.png" width="100%">
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Model Fit</h2>
<img src="./picture/chp9/CFA_2.png" width="60%">
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Factor Loading</h2>
<img src="./picture/chp9/CFA_3.png" width="100%">
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Factor Loading</h2>
<img src="./picture/chp9/CFA_4.png" width="100%">
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Factor Loading</h2>
<img src="./picture/chp9/CFA_5.png" width="100%">
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Factor Loading</h2>
<img src="./picture/chp9/CFA_6.png" width="100%">
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Covariances</h2>
<img src="./picture/chp9/CFA_7.png" width="70%">
<br>
如果有两个变量之间协方差（相关）过高，则说明存在共线性，可以删除其中一个
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">OUTPUT: Variances</h2>
<img src="./picture/chp9/CFA_8.png" width="70%">
<br>
残差和方差必须为正且显著，否则模型可能不收敛
_<h5 lang="en">- Heywood Case</h3>_
_<h5 lang="en">- Van Driel, O. P. (1978). On various causes of improper solutions in maximum likelihood factor analysis. Psychometrika, 43, 225-243.</h5>_
---
# <h1 lang="en">lavaan::cfa</h1>
## <h2 lang="en">Plot</h2>
```r
lavaanPlot(model = CFA, edge_options = list(color = "grey"))
```
由于模型的题目比较多，这里的图片已经看不清了，就不打印在PPT上了<br>
lavaan的画图比较鸡肋，AMOS在画图上会更好看一些(如下图) <br>
有兴趣的同学可以跑一下试试看<br>
<img src="./picture/chp9/AMOS.png" width="70%">
---
# <h1 lang="en">lavaan::sem</h1>
## <h2 lang="en">CODE</h2>
```r
# 这里拿model 4举例
# 因为lavaan的调节变量不能是潜变量. 如果是潜变量作为调节变量，需要手动与其他变量相乘
result.SEM <- capture.output({
SEM<- 'DIDF =~ ALEX1 + ALEX3 + ALEX5 + ALEX6 + ALEX7 + ALEX10 + ALEX11 + ALEX2 + ALEX4 + ALEX8 + ALEX9
EOF =~ ALEX12 + ALEX13 + ALEX14 + ALEX15 + ALEX16
ALEX =~ DIDF + EOF
ANX =~ ECR1 + ECR2 + ECR3 + ECR4 + ECR5 + ECR6 + ECR7 + ECR8 + ECR9 + ECR10 + ECR11 + ECR12 + ECR13 + ECR14 + ECR15 + ECR16 + ECR17 + ECR18
AVO =~ ECR19 + ECR20 + ECR21 + ECR22 + ECR23 + ECR24 + ECR25 + ECR26 + ECR27 + ECR28 + ECR29 + ECR30 + ECR31 + ECR32 + ECR33 + ECR34 + ECR35 + ECR36
ECR =~ ANX + AVO
HOME =~ HOME1 + HOME2 + HOME3 + HOME4 + HOME5 + HOME6 + HOME7 + HOME8 + HOME9
ECR ~ a*ALEX + age + language + avgtemp
HOME ~ b*ECR + age + language + avgtemp
ind_eff:=a*b ' %>%
lavaan::sem(.,data=df.pg.raw,estimator="ML")
lavaan::summary(SEM,fit.measures=TRUE, standardized=TRUE, ci=TRUE)
})
writeLines(result.SEM, "./output/chp9/SEM.md")
```
---
# <h1 lang="en">lavaan::sem</h1>
## <h2 lang="en">OUTPUT: Path Analysis and Indirect Effect</h2>
<img src="./picture/chp9/SEM_1.png" width="100%"> <br>
<img src="./picture/chp9/SEM_2.png" width="100%"> <br>
大多数结果与CFA类似，但是多了路径系数，以及我们设定的中介效应值
---
class: center, middle
<span style="font-size: 60px;">9.4 Others</span> <br>
---
# <h1 lang="zh-CN">其他有用的包</h1>
1. 如果你的SEM模型中一定用latent moderator，你可以使用Mplus，或者回到PROCESS <br>
2. Mplus某种意义上也是一个R包，install.packages("MplusAutomation") <br>
3. CFA得到之后，可以借助semTools这个包来提取矩阵lambda, theta, psi, beta. 这些矩阵用于制作收敛和区别效度表 <br>
其他SEM的细节就不在R课里讲述了
---
# <h1 lang="en">Videos</h1>
<div style="display:flex; align-items:center;">
<img src="./picture/chp9/SEM_bilibili.png" style="width:100%; height: 100%;">
</div>
https://www.bilibili.com/video/BV1Bt4y127xq/?spm_id_from=333.999.0.0&vd_source=4b61fa5a135da9b04ce5af0535b1e28c
<br>
有兴趣的同学可以去看这个视频学习怎么做SEM
---
# <h1 lang="en">Videos</h1>
<div style="display:flex; align-items:center;">
<img src="./picture/chp9/SEM_youtube.png" style="width:100%; height: 100%;">
</div>
https://www.youtube.com/watch?v=fGdsiugwO0k&list=PLliBbGBc5nn3m8bXQ4CmOep3UmQ_5tVlC&index=1&ab_channel=SachaEpskamp
<br>
这是原地址
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