“2021MetaPOQ”的版本间的差异
来自OBHRM百科
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Lichaoping（讨论 | 贡献） |
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write.csv(total.rwa, file = "Table5_6_相对权重分析结果.csv") # 表5与表6 | write.csv(total.rwa, file = "Table5_6_相对权重分析结果.csv") # 表5与表6 | ||
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| + | ==纳入元分析的文献== | ||
| + | <pre> | ||
| + | 纳入元分析的参考文献： | ||
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</pre> | </pre> | ||
2021年1月22日 (五) 07:56的版本
文章信息
杨伟文 李超平. 资质过剩感对个体绩效的作用效果及机制：基于情绪-认知加工系统与文化情境的元分析研究. 心理学报, 53(X)，P-P
文章数据
文章R代码
#加载安装包。如果没有安装，请自行安装。安装方法见：http://www.obhrm.net/index.php/R
library(psychmeta)
library(tidyr)
library(magrittr)
library(dplyr)
library(tidyverse)
library(rio)
library(metaSEM)
library(plyr)
library(meta)
library(psych)
setwd("D:\\") #请将2021MetaPOQ.xlsx放在D盘根目录下，如果放在其他目录下，请在此做相应调整。
################## 全部数据准备 ######################
data <- import("2021MetaPOQ.xlsx")
correct_main <- correct_r(correction = c("meas", "uvdrr_x", "uvdrr_y", "uvirr_x",
"uvirr_y", "bvdrr", "bvirr"),
rxyi = data$rxy, ux = 1, uy = 1,
rxx = data$rxx,
ryy = data$ryy, ux_observed = TRUE, uy_observed = TRUE,
rxx_restricted = TRUE, rxx_type = "alpha", k_items_x = NA,
ryy_restricted = TRUE, ryy_type = "alpha", k_items_y = NA,
sign_rxz = 1, sign_ryz = 1, n = NULL, conf_level = 0.95,
correct_bias = FALSE)
data$rtp = correct_main[["correlations"]][["rtp"]]
orig.data <- subset(data.frame(data, correct_main$correlations), rtp <1)
# 此处获取研究样本信息
k.article <- length(unique(paste(orig.data$author, orig.data$title)))
k.inde.sample <- length(unique(orig.data$id))
k.eff.size <- nrow(unique(data.frame(orig.data$title, orig.data$rxy, orig.data$x, orig.data$y)))
sample.size <- unique(data.frame(orig.data$title, orig.data$n))
n.sample.size <- as.vector(sample.size$orig.data.n)%>%sum
descr <- data.frame(c("文献", "独立样本", "效应值", "样本规模"),
c(k.article, k.inde.sample, k.eff.size, n.sample.size))%>%
set_colnames(c("类型", "数量"))
descr
#### 1………………直接效应分析………………####
#### 发表偏倚检验 ####
d <- subset(orig.data, orig.data$x.frame.all == "资质过剩感")
d$zr <- 0.5*log((1+d$rxy)/(1-d$rxy))
zr <- split(d$zr, d$y.coding.t)
n <- split(d$n, d$y.coding.t)
t <- list()
for (i in 1:length(n)){
t[[i]] <- zr[[i]]*sqrt(n[[i]]-3)
i = i + 1}
nfs <- vector()
for (i in 1:length(zr)){
nfs[[i]] <- abs(round(sum(t[[i]])^2/2.706 - length(zr[[i]])))
i = i + 1}
final.nfs = data.frame(names(zr),nfs) %>% set_colnames(c("name", "nfs"))
zr.t <- split(d$zr, d$y.frame.all)
n.t <- split(d$n, d$y.frame.all)
for (i in 1:length(n.t)){
t[[i]] <- zr.t[[i]]*sqrt(n.t[[i]]-3)
i = i + 1}
nfs <- vector()
for (i in 1:length(zr.t)){
nfs[[i]] <- abs(round(sum(t[[i]])^2/2.706 - length(zr.t[[i]])))
i = i + 1}
final.nfs.t = data.frame(names(zr.t),nfs)%>% set_colnames(c("name", "nfs"))
nfs = rbind(final.nfs, final.nfs.t)
order <- data.frame(c("消极情绪", "愤怒", "无聊",
"积极自我概念", "一般效能感", "其他效能感", "一般自尊", "基于组织的自尊",
"消极行为", "反生产行为", "离职意愿",
"积极行为", "创新", "工作重塑", "建言", "前瞻行为",
"知识共享", "组织公民行为", "任务绩效")) %>% set_colnames("order")
t.nfs = left_join(order, nfs, by=c("order"="name"))
#### 主要变量直接效应 ####
main_r <- ma_r(rxyi = orig.data$rxy, n = orig.data$n,
sample_id = orig.data$id, citekey = orig.data$id,
construct_x = orig.data$x.frame.all,
construct_y = orig.data$y.frame.all,
moderators = orig.data$y.coding.t,
data = orig.data)
main_rho <- ma_r(rxyi = orig.data$rtp, n = orig.data$n,
sample_id = orig.data$id, citekey = orig.data$id,
construct_x = orig.data$x.frame.all,
construct_y = orig.data$y.frame.all,
moderators = orig.data$y.coding.t,
data = orig.data)
# 此处获取 %Var 信息
hetero <- heterogeneity(main_rho)%>%get_heterogeneity
data.hetero = data.frame(matrix(NA, nrow = length(hetero), ncol=2))
names(data.hetero) = c("id","var")
data.hetero$id = colnames(sapply(hetero, function(x) x[[1]]))
data.hetero$var = sapply(hetero, function(x) x[[1]]$percent_var_accounted[[1]])
#### 主效应分析表制作 ####
main_r <- main_r %>% summary %>% get_metatab
main_rho <- main_rho %>% summary %>% get_metatab
maineffects <- data.frame(main_r$analysis_id,
main_r$construct_x,
main_r$construct_y,
main_r$`orig.data$y.coding.t`,
main_r$k, main_r$N,
round(cbind(main_r$mean_r, main_r$sd_r,
main_rho$mean_r,main_rho$sd_r),2),
paste("[", round(main_rho$CI_LL_95, 2), ", ",
round(main_rho$CI_UL_95, 2), "]"),
paste("[", round(main_rho$CR_LL_80, 2), ", ",
round(main_rho$CR_UL_80, 2),"]"),
data.hetero) %>%
set_colnames(c("id", "x", "y", "dim", "k", "n",
"r", "SD_r","rho", "SD_rho",
"95% CI", "80% CV",
"var%"))
main.poq <- subset(maineffects, maineffects$x == "资质过剩感" &
maineffects$y != "个人工作匹配"&
maineffects$y != "个人组织匹配"&
maineffects$y != "健康"&
maineffects$y != "对工作的态度"&
maineffects$y != "对组织的态度")
main.poq$`5k+10` = main.poq$k * 5 + 10
maineffects.poq = left_join(main.poq, t.nfs, by=c("dim"="order")) %>%
left_join(t.nfs, by=c("y"="order")) %>% unique
mainpoq <- data.frame(maineffects.poq[, c(3:7,9:12)],
round(unlist(maineffects.poq$`var%`),2),
maineffects.poq[, 14:16])%>%set_colnames(
c("y", "dim", "k", "n", "r", "rho",
"SD_rho", "95%CI", "80%CV",
"var", "5k+10", "nfs.dim", "nfs.all"))
write.csv(mainpoq, file = "Table1_poq直接效应及发表偏倚检验.csv") #表1
maineffects$word <- paste("(", maineffects$r,", ", maineffects$rho, ")","\n",
"(", maineffects$k,", ", maineffects$n, ")","\n",
maineffects$`95% CI`,"\n",
maineffects$`80% CV`)
#### 2………………中介效应检验………………####
#### 数据准备：全模型数据 ####
varnames <- c("POQ","NE", "PS", "NB", "PB", "JP")
poq.ne <- subset(maineffects, maineffects$x == "资质过剩感" &
maineffects$y == "消极情绪" &
maineffects$dim == "All Levels")
poq.ps <- subset(maineffects, maineffects$x == "资质过剩感" &
maineffects$y == "积极自我概念" &
maineffects$dim == "All Levels")
poq.nb <- subset(maineffects, maineffects$x == "资质过剩感" &
maineffects$y == "消极行为" &
maineffects$dim == "All Levels")
poq.pb <- subset(maineffects, maineffects$x == "资质过剩感" &
maineffects$y == "积极行为" &
maineffects$dim == "All Levels")
poq.jp <- subset(maineffects, maineffects$x == "资质过剩感" &
maineffects$y == "任务绩效" &
maineffects$dim == "All Levels")
ne.ps <- subset(maineffects, (maineffects$x == "消极情绪" &
maineffects$y == "积极自我概念" &
maineffects$dim == "All Levels")|
(maineffects$y == "消极情绪" &
maineffects$x == "积极自我概念" &
maineffects$dim == "All Levels"))
ne.nb <- subset(maineffects, (maineffects$x == "消极情绪" &
maineffects$y == "消极行为" &
maineffects$dim == "All Levels")|
(maineffects$y == "消极情绪" &
maineffects$x == "消极行为" &
maineffects$dim == "All Levels"))
ne.pb <- subset(maineffects, (maineffects$x == "消极情绪" &
maineffects$y == "积极行为" &
maineffects$dim == "All Levels")|
(maineffects$y == "消极情绪" &
maineffects$x == "积极行为" &
maineffects$dim == "All Levels"))
ne.jp <- subset(maineffects, (maineffects$x == "消极情绪" &
maineffects$y == "任务绩效" &
maineffects$dim == "All Levels")|
(maineffects$y == "消极情绪" &
maineffects$x == "任务绩效" &
maineffects$dim == "All Levels"))
ps.nb <- subset(maineffects, (maineffects$x == "积极自我概念" &
maineffects$y == "消极行为" &
maineffects$dim == "All Levels")|
(maineffects$y == "积极自我概念" &
maineffects$x == "消极行为" &
maineffects$dim == "All Levels"))
ps.pb <- subset(maineffects, (maineffects$x == "积极自我概念" &
maineffects$y == "积极行为" &
maineffects$dim == "All Levels")|
(maineffects$y == "积极自我概念" &
maineffects$x == "积极行为" &
maineffects$dim == "All Levels"))
ps.jp <- subset(maineffects, (maineffects$x == "积极自我概念" &
maineffects$y == "任务绩效" &
maineffects$dim == "All Levels")|
(maineffects$y == "积极自我概念" &
maineffects$x == "任务绩效" &
maineffects$dim == "All Levels"))
nb.pb <- subset(maineffects, (maineffects$x == "消极行为" &
maineffects$y == "积极行为" &
maineffects$dim == "All Levels")|
(maineffects$y == "消极行为" &
maineffects$x == "积极行为" &
maineffects$dim == "All Levels"))
nb.jp <- subset(maineffects, (maineffects$x == "消极行为" &
maineffects$y == "任务绩效" &
maineffects$dim == "All Levels")|
(maineffects$y == "消极行为" &
maineffects$x == "任务绩效" &
maineffects$dim == "All Levels"))
pb.jp <- subset(maineffects, (maineffects$x == "积极行为" &
maineffects$y == "任务绩效" &
maineffects$dim == "All Levels")|
(maineffects$y == "积极行为" &
maineffects$x == "任务绩效" &
maineffects$dim == "All Levels"))
all.matrix <- vec2symMat(c(poq.ne$rho, poq.ps$rho, poq.nb$rho, poq.pb$rho, poq.jp$rho,
ne.ps$rho, ne.nb$rho, ne.pb$rho, ne.jp$rho,
ps.nb$rho, ps.pb$rho, ps.jp$rho,
nb.pb$rho, nb.jp$rho,
pb.jp$rho),
diag = FALSE, byrow = FALSE) %>%
set_colnames(varnames) %>%
set_rownames(varnames)
all_dat <- list(all.matrix)
main_in_r <- ma_r(rxyi = orig.data$rxy, n = orig.data$n,
sample_id = orig.data$id, citekey = orig.data$id,
construct_x = orig.data$x.frame.all,
construct_y = orig.data$y.frame.all,
moderators = orig.data$indi,
data = orig.data)
main_in_rho <- ma_r(rxyi = orig.data$rtp, n = orig.data$n,
sample_id = orig.data$id, citekey = orig.data$id,
construct_x = orig.data$x.frame.all,
construct_y = orig.data$y.frame.all,
moderators = orig.data$indi,
data = orig.data)
hetero_in <- heterogeneity(main_in_rho)%>%get_heterogeneity
data.hetero_in = data.frame(matrix(NA, nrow = length(hetero_in), ncol=2))
names(data.hetero_in) = c("id","var")
data.hetero_in$id = colnames(sapply(hetero_in, function(x) x[[1]]))
data.hetero_in$var = sapply(hetero_in, function(x) x[[1]]$percent_var_accounted[[1]])
main_in_r <- main_in_r %>% summary %>% get_metatab %>% na.omit
main_in_rho <- main_in_rho %>% summary %>% get_metatab %>% na.omit
main_indi <- data.frame(main_in_r$analysis_id,
main_in_r$construct_x,
main_in_r$construct_y,
main_in_r$`orig.data$indi`,
main_in_r$k, main_in_r$N,
round(cbind(main_in_r$mean_r, main_in_r$sd_r,
main_in_rho$mean_r,main_in_rho$sd_r),2),
paste("[", round(main_in_rho$CI_LL_95, 2), ", ",
round(main_in_rho$CI_UL_95, 2), "]"),
paste("[", round(main_in_rho$CR_LL_80, 2), ", ",
round(main_in_rho$CR_UL_80, 2),"]")) %>%
set_colnames(c("id", "x", "y", "indi", "k", "n",
"r", "SD_r","rho", "SD_rho",
"95% CI", "80% CV"))
#### 数据准备：低个人主义（高集体主义）数据 ####
poq.ne.l <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "消极情绪" &
main_indi$indi == "L")
poq.ps.l <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "积极自我概念" &
main_indi$indi == "L")
poq.nb.l <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "消极行为" &
main_indi$indi == "L")
poq.pb.l <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "积极行为" &
main_indi$indi == "L")
poq.jp.l <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "任务绩效" &
main_indi$indi == "L")
ne.ps.l <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "积极自我概念" &
main_indi$indi == "L")|
(main_indi$y == "消极情绪" &
main_indi$x == "积极自我概念" &
main_indi$indi == "L"))
ne.nb.l <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "消极行为" &
main_indi$indi == "L")|
(main_indi$y == "消极情绪" &
main_indi$x == "消极行为" &
main_indi$indi == "L"))
ne.pb.l <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "积极行为" &
main_indi$indi == "L")|
(main_indi$y == "消极情绪" &
main_indi$x == "积极行为" &
main_indi$indi == "L"))
ne.jp.l <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "任务绩效" &
main_indi$indi == "L")|
(main_indi$y == "消极情绪" &
main_indi$x == "任务绩效" &
main_indi$indi == "L"))
ps.nb.l <- subset(main_indi, (main_indi$x == "积极自我概念" &
main_indi$y == "消极行为" &
main_indi$indi == "L")|
(main_indi$y == "积极自我概念" &
main_indi$x == "消极行为" &
main_indi$indi == "L"))
ps.pb.l <- subset(main_indi, (main_indi$x == "积极自我概念" &
main_indi$y == "积极行为" &
main_indi$indi == "L")|
(main_indi$y == "积极自我概念" &
main_indi$x == "积极行为" &
main_indi$indi == "L"))
ps.jp.l <- subset(main_indi, (main_indi$x == "积极自我概念" &
main_indi$y == "任务绩效" &
main_indi$indi == "L")|
(main_indi$y == "积极自我概念" &
main_indi$x == "任务绩效" &
main_indi$indi == "L"))
nb.pb.l <- subset(main_indi, (main_indi$x == "消极行为" &
main_indi$y == "积极行为" &
main_indi$indi == "L")|
(main_indi$y == "消极行为" &
main_indi$x == "积极行为" &
main_indi$indi == "L"))
nb.jp.l <- subset(main_indi, (main_indi$x == "消极行为" &
main_indi$y == "任务绩效" &
main_indi$indi == "L")|
(main_indi$y == "消极行为" &
main_indi$x == "任务绩效" &
main_indi$indi == "L"))
pb.jp.l <- subset(main_indi, (main_indi$x == "积极行为" &
main_indi$y == "任务绩效" &
main_indi$indi == "L")|
(main_indi$y == "积极行为" &
main_indi$x == "任务绩效" &
main_indi$indi == "L"))
all.matrix.l <- vec2symMat(c(poq.ne.l$rho, poq.ps.l$rho, poq.nb.l$rho, poq.pb.l$rho, poq.jp.l$rho,
ne.ps.l$rho, ne.nb.l$rho, ne.pb.l$rho, ne.jp.l$rho,
ps.nb.l$rho, ps.pb.l$rho, ps.jp.l$rho,
nb.pb.l$rho, nb.jp.l$rho,
pb.jp.l$rho),
diag = FALSE, byrow = FALSE) %>%
set_colnames(varnames) %>%
set_rownames(varnames)
all_dat.l <- list(all.matrix.l)
#### 数据准备：高个人主义（低集体主义）数据 ####
poq.ne.h <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "消极情绪" &
main_indi$indi == "H")
poq.ps.h <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "积极自我概念" &
main_indi$indi == "H")
poq.nb.h <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "消极行为" &
main_indi$indi == "H")
poq.pb.h <- subset(main_indi, x == "资质过剩感" &
main_indi$y == "积极行为" &
main_indi$indi == "H")
poq.jp.h <- subset(main_indi, main_indi$x == "资质过剩感" &
main_indi$y == "任务绩效" &
main_indi$indi == "H")
ne.ps.h <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "积极自我概念" &
main_indi$indi == "H")|
(main_indi$y == "消极情绪" &
main_indi$x == "积极自我概念" &
main_indi$indi == "H"))
ne.nb.h <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "消极行为" &
main_indi$indi == "H")|
(main_indi$y == "消极情绪" &
main_indi$x == "消极行为" &
main_indi$indi == "H"))
ne.pb.h <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "积极行为" &
main_indi$indi == "H")|
(main_indi$y == "消极情绪" &
main_indi$x == "积极行为" &
main_indi$indi == "H"))
ne.jp.h <- subset(main_indi, (main_indi$x == "消极情绪" &
main_indi$y == "任务绩效" &
main_indi$indi == "H")|
(main_indi$y == "消极情绪" &
main_indi$x == "任务绩效" &
main_indi$indi == "H"))
ps.nb.h <- subset(main_indi, (main_indi$x == "积极自我概念" &
main_indi$y == "消极行为" &
main_indi$indi == "H")|
(main_indi$y == "积极自我概念" &
main_indi$x == "消极行为" &
main_indi$indi == "H"))
ps.pb.h <- subset(main_indi, (main_indi$x == "积极自我概念" &
main_indi$y == "积极行为" &
main_indi$indi == "H")|
(main_indi$y == "积极自我概念" &
main_indi$x == "积极行为" &
main_indi$indi == "H"))
ps.jp.h <- subset(main_indi, (main_indi$x == "积极自我概念" &
main_indi$y == "任务绩效" &
main_indi$indi == "H")|
(main_indi$y == "积极自我概念" &
main_indi$x == "任务绩效" &
main_indi$indi == "H"))
nb.pb.h <- subset(main_indi, (main_indi$x == "消极行为" &
main_indi$y == "积极行为" &
main_indi$indi == "H")|
(main_indi$y == "消极行为" &
main_indi$x == "积极行为" &
main_indi$indi == "H"))
nb.jp.h <- subset(main_indi, (main_indi$x == "消极行为" &
main_indi$y == "任务绩效" &
main_indi$indi == "H")|
(main_indi$y == "消极行为" &
main_indi$x == "任务绩效" &
main_indi$indi == "H"))
pb.jp.h <- subset(main_indi, (main_indi$x == "积极行为" &
main_indi$y == "任务绩效" &
main_indi$indi == "H")|
(main_indi$y == "积极行为" &
main_indi$x == "任务绩效" &
main_indi$indi == "H"))
all.matrix.h <- vec2symMat(c(poq.ne.h$rho, poq.ps.h$rho, poq.nb.h$rho, poq.pb.h$rho, poq.jp.h$rho,
ne.ps.h$rho, ne.nb.h$rho, ne.pb.h$rho, ne.jp.h$rho,
ps.nb.h$rho, ps.pb.h$rho, ps.jp.h$rho,
nb.pb.h$rho, nb.jp.h$rho,
pb.jp.h$rho),
diag = FALSE, byrow = FALSE) %>%
set_colnames(varnames) %>%
set_rownames(varnames)
all_dat.h <- list(all.matrix.h)
#### 调和平均数 ####
all_n <- c(poq.ne$n, poq.ps$n, poq.nb$n, poq.pb$n, poq.jp$n,
ne.ps$n, ne.nb$n, ne.pb$n, ne.jp$n,
ps.nb$n, ps.pb$n, ps.jp$n,
nb.pb$n, nb.jp$n,
pb.jp$n) %>% harmonic.mean %>% round
n_l <- c(poq.ne.l$n, poq.ps.l$n, poq.nb.l$n, poq.pb.l$n, poq.jp.l$n,
ne.ps.l$n, ne.nb.l$n, ne.pb.l$n, ne.jp.l$n,
ps.nb.l$n, ps.pb.l$n, ps.jp.l$n,
nb.pb.l$n, nb.jp.l$n,
pb.jp.l$n) %>% harmonic.mean %>% round
n_h <- c(poq.ne.h$n, poq.ps.h$n, poq.nb.h$n, poq.pb.h$n, poq.jp.h$n,
ne.ps.h$n, ne.nb.h$n, ne.pb.h$n, ne.jp.h$n,
ps.nb.h$n, ps.pb.h$n, ps.jp.h$n,
nb.pb.h$n, nb.jp.h$n,
pb.jp.h$n) %>% harmonic.mean %>% round
####模型设置 ####
model_all <- 'NB ~ POQ + PS + NE
PB ~ POQ + PS + NE
JP ~ POQ + PS + NE
NE ~ POQ
PS ~ POQ
NE ~~ 1*NE
PS ~~ 1*PS
POQ ~~ 1*POQ
NE ~~ 0.1*PS
NB ~~ 0.1*NB
PB ~~ 0.1*PB
JP ~~ 0.1*JP
NB ~~ 0.1*PB
NB ~~ 0.1*JP
JP ~~ 0.1*PB'
RAM_all <- lavaan2RAM(model_all, obs.variables = varnames)
#### SEM全部数据及调和平均数 ####
fin_dat <- list(all_dat, all_dat.l, all_dat.h)
fin_n <- c(all_n, n_l, n_h)
stage1 <- list()
stage2 <- list()
stage2_LB <- list()
stage2_Z <- list()
ind_LB <- list()
ind_Z <- list()
fitness <- list()
path <- list()
ind <- list()
sem <- list()
name <- c("all", "low individualism", "high individualism")
for (i in 1:length(fin_dat)){
#### stage1 ####
stage1[[i]] <- tssem1(Cov = fin_dat[[i]], n = fin_n[i], method = "FEM",
RE.type = "Diag", RE.startvalues=0.1,
acov = "weighted")
summary(stage1[[i]])
#### stage2 ####
stage2[[i]] <- tssem2(stage1[[i]], Amatrix = RAM_all$A, Smatrix = RAM_all$S)
summary(stage2[[i]])
stage2_LB[[i]] <- tssem2(stage1[[i]], Amatrix = RAM_all$A, Smatrix = RAM_all$S,
diag.constraints = TRUE, intervals.type = "LB",
mx.algebras = list(
#POQ-NE-Y
IndNE_NB = mxAlgebra(NEONPOQ*NBONNE, name = "IndNE_NB"),
IndNE_PB = mxAlgebra(NEONPOQ*PBONNE, name = "IndNE_PB"),
IndNE_JP = mxAlgebra(NEONPOQ*JPONNE, name = "IndNE_JP"),
#POQ-PS-Y
IndPS_NB = mxAlgebra(PSONPOQ*NBONPS, name = "IndPS_NB"),
IndPS_PB = mxAlgebra(PSONPOQ*PBONPS, name = "IndPS_PB"),
IndPS_JP = mxAlgebra(PSONPOQ*JPONPS, name = "IndPS_JP"),
#POQ-NE+PS-Y
Indall_NB = mxAlgebra(NEONPOQ*NBONNE + PSONPOQ*NBONPS, name = "Indall_NB"),
Indall_PB = mxAlgebra(NEONPOQ*PBONNE + PSONPOQ*PBONPS, name = "Indall_PB"),
Indall_JP = mxAlgebra(NEONPOQ*JPONNE + PSONPOQ*JPONPS, name = "Indall_JP"),
#POQ-NE+PS-Y PLUS POQ-Y
Total_NB = mxAlgebra(NEONPOQ*NBONNE + PSONPOQ*NBONPS + NBONPOQ, name = "Total_NB"),
Total_PB = mxAlgebra(NEONPOQ*PBONNE + PSONPOQ*PBONPS + PBONPOQ, name = "Total_PB"),
Total_JP = mxAlgebra(NEONPOQ*JPONNE + PSONPOQ*JPONPS + JPONPOQ, name = "Total_JP"),
#Diff POQ-(NE-PS)-Y
DIF_NEminusPS_NB= mxAlgebra(NEONPOQ*NBONNE - PSONPOQ*NBONPS, name = "DIF_NEminusPS_NB"),
DIF_NEminusPS_PB= mxAlgebra(NEONPOQ*PBONNE - PSONPOQ*PBONPS, name = "DIF_NEminusPS_PB"),
DIF_NEminusPS_JP= mxAlgebra(NEONPOQ*JPONNE - PSONPOQ*JPONPS, name = "DIF_NEminusPS_JP")))
stage2_LB[[i]] <- rerun(stage2_LB[[i]])
ind_LB[[i]] <- summary(stage2_LB[[i]])
stage2_Z[[i]] <- tssem2(stage1[[i]], Amatrix = RAM_all$A, Smatrix = RAM_all$S,
diag.constraints = FALSE, intervals.type = "z",
mx.algebras = list(
IndNE_NB = mxAlgebra(NEONPOQ*NBONNE, name = "IndNE_NB"),
IndNE_PB = mxAlgebra(NEONPOQ*PBONNE, name = "IndNE_PB"),
IndNE_JP = mxAlgebra(NEONPOQ*JPONNE, name = "IndNE_JP"),
IndPS_NB = mxAlgebra(PSONPOQ*NBONPS, name = "IndPS_NB"),
IndPS_PB = mxAlgebra(PSONPOQ*PBONPS, name = "IndPS_PB"),
IndPS_JP = mxAlgebra(PSONPOQ*JPONPS, name = "IndPS_JP"),
Indall_NB = mxAlgebra(NEONPOQ*NBONNE + PSONPOQ*NBONPS, name = "Indall_NB"),
Indall_PB = mxAlgebra(NEONPOQ*PBONNE + PSONPOQ*PBONPS, name = "Indall_PB"),
Indall_JP = mxAlgebra(NEONPOQ*JPONNE + PSONPOQ*JPONPS, name = "Indall_JP"),
Total_NB = mxAlgebra(NEONPOQ*NBONNE + PSONPOQ*NBONPS + NBONPOQ, name = "Total_NB"),
Total_PB = mxAlgebra(NEONPOQ*PBONNE + PSONPOQ*PBONPS + PBONPOQ, name = "Total_PB"),
Total_JP = mxAlgebra(NEONPOQ*JPONNE + PSONPOQ*JPONPS + JPONPOQ, name = "Total_JP"),
DIF_NEminusPS_NB= mxAlgebra(NEONPOQ*NBONNE - PSONPOQ*NBONPS, name = "DIF_NEminusPS_NB"),
DIF_NEminusPS_PB= mxAlgebra(NEONPOQ*PBONNE - PSONPOQ*PBONPS, name = "DIF_NEminusPS_PB"),
DIF_NEminusPS_JP= mxAlgebra(NEONPOQ*JPONNE - PSONPOQ*JPONPS, name = "DIF_NEminusPS_JP")))
stage2_Z[[i]] <- rerun(stage2_Z[[i]])
ind_Z[[i]] <- summary(stage2_Z[[i]])
#### 拟合系数 ####
fitness[[i]] <- ind_LB[[i]][["stat"]]%>%as.data.frame%>%round(2)%>%
set_colnames("Goodness-of-fit indices")
#### 路径系数 ####
path[[i]] <- data.frame(round(ind_Z[[i]][["coefficients"]][,1], 2),
paste("[", round(ind_Z[[i]][["coefficients"]][,3], 2),",",
round(ind_Z[[i]][["coefficients"]][,4], 2), "]"),
round(ind_Z[[i]][["coefficients"]][,2], 2),
round(ind_Z[[i]][["coefficients"]][,5], 2),
round(ind_Z[[i]][["coefficients"]][,6], 2)) %>%
set_colnames(c("β", "95% CI", "SE", "z", "p")) %>%
set_rownames(c("NE->JP", "POQ->JP", "PS->JP",
"NE->NB", "POQ->NB", "PS->NB",
"POQ->NE", "NE->PB", "POQ->PB",
"PS->PB", "POQ->PS"))
#### 中介效应 ####
ind[[i]] <- data.frame(round(ind_LB[[i]][["mx.algebras"]][,2],2),
paste("[", round(ind_LB[[i]][["mx.algebras"]][,1],2), ",",
round(ind_LB[[i]][["mx.algebras"]][,3],2), "]")) %>%
set_colnames(c("β", "95%CI"))%>%
set_rownames(c("IndNE_NB", "IndNE_PB", "IndNE_JP",
"IndPS_NB", "IndPS_PB", "IndPS_JP",
"Indall_NB", "Indall_PB", "Indall_JP",
"Total_NB", "Total_PB", "Total_JP",
"DIF_NEminusPS_NB", "DIF_NEminusPS_PB", "DIF_NEminusPS_JP"))
sem[[i]] <- list(name = name[i],
fit=fitness[[i]], path = path[[i]], ind = ind[[i]])
sem[[i]]
i = i + 1}
#### 相关系数文本 ####
sem.matrix <- cbind(c(poq.ne$word, poq.ps$word, poq.nb$word, poq.pb$word, poq.jp$word),
c(" ", ne.ps$word, ne.nb$word, ne.pb$word, ne.jp$word),
c(" ", " ", ps.nb$word, ps.pb$word,ps.jp$word),
c(" ", " ", " ", nb.pb$word, nb.jp$word),
c(" ", " "," ", " ", pb.jp$word))%>%
set_colnames(c("资质过剩感", "消极情绪", "积极自我概念", "消极角色外行为", "积极角色外行为"))%>%
set_rownames(c("消极情绪", "积极自我概念", "消极角色外行为", "积极角色外行为", "任务绩效"))
write.csv(sem.matrix, "Table2_变量相关系数矩阵.csv") # 表2
#### 结构方程分析结果 ####
# 此处获取结构方程拟合结果
final_fit <- cbind(sem[[1]][["fit"]][c(1,7,4),],
sem[[2]][["fit"]][c(1,7,4),],
sem[[3]][["fit"]][c(1,7,4),])%>%
set_rownames(c("n", "Chi", "p"))%>%
set_colnames(c("全样本", "高集体主义", "低集体主义"))
# 此处获取图2到4信息 ####
final_path <- rbind(c("全样本", " "," ", " ", " ",
"高集体主义", " "," ", " ", " ",
"低集体主义", " "," ", " ", " "),
rep(c("β", "CI", "SE", "z", "p"), 3),
cbind(rbind(sem[[1]][["path"]]),
rbind(sem[[2]][["path"]]),
rbind(sem[[3]][["path"]])))%>%set_colnames(c(1:15))
write.csv(final_path, "Figure2_4_模型1-3路径系数.csv") # 图2、图3、图4
final_ind <- rbind(rep(c("β", "95%CI"),3),
cbind(sem[[1]][["ind"]],
sem[[2]][["ind"]],
sem[[3]][["ind"]]))%>%set_colnames(c(1:6))
final_ind_final <- rbind(final_ind[c(1:7, 14:16, 11:13),])
write.csv(final_ind_final, "Table3_情绪认知间接效应检验.csv") # 表3
#### 3………………概念冗余度检验………………####
#### 相关矩阵构建 ####
matrix <- data.frame(maineffects$x, maineffects$y, maineffects$dim,
paste( "(", maineffects$k, ",", maineffects$n, ")" ) ,
paste( "(", maineffects$r, ",", maineffects$rho, ")" ) ,
maineffects$`95% CI`, maineffects$`80% CV`, maineffects$rho)%>%
set_colnames(c("x", "dim", "y", "(k, n)", "(r, ρ)", "95% CI", "80% CV", "rho"))
matrix$all = matrix$`(r, ρ)`
poq_pj <- subset(matrix, matrix$x == "资质过剩感" & matrix$y == "个人工作匹配")
poq_po <- subset(matrix, matrix$x == "资质过剩感" & matrix$y == "个人组织匹配")
poq_y <- subset(matrix, (matrix$y == "压力（反向）"|
matrix$y == "组织承诺"|
matrix$y == "工作满意度"|
matrix$y == "创新"|
matrix$y == "组织公民行为"|
matrix$y == "反生产行为"|
matrix$y == "离职意愿"|
matrix$y == "任务绩效")&
matrix$x == "资质过剩感")
pj_po <- subset(matrix, matrix$x == "个人工作匹配" &
matrix$dim == "个人组织匹配")
pj_y<- subset(matrix, (matrix$x == "个人工作匹配"&
(matrix$y == "压力（反向）"|
matrix$y == "组织承诺"|
matrix$y == "工作满意度"))|
(matrix$dim == "个人工作匹配"&
(matrix$y == "创新"|
matrix$y == "组织公民行为"|
matrix$y == "反生产行为"|
matrix$y == "离职意愿"|
matrix$y == "任务绩效")))
po_y <- subset(matrix, (matrix$x == "个人组织匹配"&
(matrix$y == "压力（反向）"|
matrix$y == "组织承诺"|
matrix$y == "工作满意度"))|
(matrix$dim == "个人组织匹配"&
(matrix$y == "创新"|
matrix$y == "组织公民行为"|
matrix$y == "反生产行为"|
matrix$y == "离职意愿"|
matrix$y == "任务绩效")))
#### 相关矩阵表格 ####
ynames <- c("创新", "组织公民行为", "反生产行为", "离职意愿",
"任务绩效", "压力（反向）", "工作满意度", "组织承诺")
rwa.text <- data.frame(ynames, poq_y= poq_y$all, pj_y = pj_y$all, po_y = po_y$all,
poq_pj = poq_pj$all, poq_po = poq_po$all,
pj_po = pj_po$all)
write.csv(rwa.text, "Table4_poq及匹配与结果变量相关性.csv") # 表4
#### 数据准备：相对权重分析 ####
data.rwa <- data.frame(ynames, poq_y= poq_y$rho, pj_y = pj_y$rho, po_y = po_y$rho,
poq_pj = poq_pj$rho, poq_po = poq_po$rho,
pj_po = pj_po$rho)
varnames <- c("y", "poq", "pj", "po")
lables <- list(varnames, varnames)
cordat <- list()
for (i in 1:nrow(data.rwa)){
cordat[[i]] <- vec2symMat(as.matrix(data.rwa[i,2:ncol(data.rwa)]),
diag = FALSE, byrow = FALSE)
dimnames(cordat[[i]]) <- lables}
#### 相对权重分析函数 ####
#注：参考https://relativeimportance.davidson.edu/，根据本研究做相应调整
multRegress<-function(mydata){
numVar<<-NCOL(mydata)
Variables<<- names(mydata)[2:numVar]
RXX<-mydata[2:numVar,2:numVar]
RXY<-mydata[2:numVar,1]
RXX.eigen<-eigen(RXX)
D<-diag(RXX.eigen$val)
delta<-sqrt(D)
lambda<-RXX.eigen$vec%*%delta%*%t(RXX.eigen$vec)
lambdasq<-lambda^2
beta<-solve(lambda)%*%RXY
rsquare<<-sum(beta^2)
RawWgt<-lambdasq%*%beta^2
import<-(RawWgt/rsquare)*100
result<-data.frame(Variables, Raw.W=round(RawWgt,2),
R.perc=round(import,2),
R2 = round(rsquare,2))
}
#### POQ fit 相对权重计算 ####
rawdata <- list()
thedata <- list()
Labels <- list()
a <- list()
title <- list()
R_square <- list()
rwa.results <- list()
for (i in 1:length(cordat)){
rawdata[[i]]<- data.frame(cordat[[i]])
attach(rawdata[[i]])
thedata[[i]] <- data.frame(y, poq, pj, po)
Labels[[i]] <- names(thedata[[i]])[2:length(thedata[[i]])]
a[[i]] <- multRegress(thedata[[i]]) %>% set_colnames(c("变量", "W", "%R", "R2"))
title[[i]] <- data.frame("", ynames[[i]], "", "") %>% set_colnames(c("变量", "W", "%R", "R2"))
R_square[[i]] <- data.frame("R2", unique(a[[i]][,4]), "", "") %>% set_colnames(c("变量", "W", "%R", "R2"))
rwa.results[[i]] <- rbind(a[[i]], R_square[[i]], title[[i]],
data.frame("变量", "W", "%R", "R2") %>%
set_colnames(c("变量", "W", "%R", "R2")))[, 1:3]
rwa.results[[i]] <- data.frame(id = c(1:nrow(rwa.results[[i]])), rwa.results[[i]])
}
total<-merge(rwa.results[[1]],
rwa.results[2:length(rwa.results)], by="id")%>%t%>%unique%>%t
total.rwa <- rbind(total[5:6,], total[1:4,])
write.csv(total.rwa, file = "Table5_6_相对权重分析结果.csv") # 表5与表6
纳入元分析的文献
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