library(bruceR)
library(metafor) # standard meta-analyses
library(xlsx)
library(stringr)
library(robumeta) # package for RVE meta-analyses
library(clubSandwich)
# Yongxuan
data <- import("JIBSb6.csv") %>% setDT()
# Yongxuan https://docs.google.com/spreadsheets/d/12jjiYohd_QHxyAaX_S5jYxqZ6J4HnrzL/edit?usp=sharing&ouid=115346063709313800668&rtpof=true&sd=true
LBsR_aaa <- data[, .SD, .SDcols = patterns("aaa")]
# str(LBsR_aaa)
# 删除 ib1 变量中值为缺失的行
LBsR <- data[!is.na(ib1)] %>%
.[relation != ""] %>% # 清除不用的数据(Yongxuan)# 删除 relation 为空字符串的行
.[analysiscategory1 == "behavior", analysiscategory1 := "behaviors"] %>%
.[analysiscategory2 == "behavior", analysiscategory2 := "behaviors"] %>%
.[, analysiscategory1 := toupper(analysiscategory1)] %>%
.[, analysiscategory2 := toupper(analysiscategory2)] %>%
.[, aaa.analysisvariable1 := tolower(aaa.analysisvariable1)] %>%
.[, aaa.analysisvariable2 := tolower(aaa.analysisvariable2)] %>%
.[
aaa.analysisvariable1 %in% c("commitment", "job involvement", "withdraw"),
analysiscategory1 := "WORKPLACE ATTACHMENT"
] %>%
.[
aaa.analysisvariable1 %in% c("exchange fairness", "perception of support", "perceptions of support"),
analysiscategory1 := "SOCIAL EXCHANGE AT WORKPLACE"
] %>%
.[
aaa.analysisvariable2 %in% c("commitment", "job involvement", "withdraw"),
analysiscategory2 := "WORKPLACE ATTACHMENT"
] %>%
.[
aaa.analysisvariable2 %in% c("exchange fairness", "perception of support", "perceptions of support"),
analysiscategory2 := "SOCIAL EXCHANGE AT WORKPLACE"
] %>%
.[, XX := pmin(analysiscategory1, analysiscategory2)] %>%
.[, YY := pmax(analysiscategory1, analysiscategory2)] %>%
.[order(XX, YY)] %>%
# .[, cc("analysiscategory1,analysiscategory2") := NULL]%>%
# setnames(old = c("X", "Y"), new = cc("analysiscategory1,analysiscategory2"))%>%
.[analysiscategory1 == "DETERING INSTITUTIONAL FACTOR", aaa.analysisvariable1 := "detering institutional factor"] %>%
.[analysiscategory2 == "DETERING INSTITUTIONAL FACTOR", aaa.analysisvariable2 := "detering institutional factor"] %>%
.[analysiscategory1 == "ATTRACTING INSTITUTIONAL FACTOR", aaa.analysisvariable1 := "attracting institutional factor"] %>%
.[analysiscategory2 == "ATTRACTING INSTITUTIONAL FACTOR", aaa.analysisvariable2 := "attracting institutional factor"] %>%
.[, ordervariable1:= paste(aaa.analysisvariable1,analysiscategory1, sep = "@")]%>%
.[, ordervariable2:= paste(aaa.analysisvariable2,analysiscategory2, sep = "@")]%>%
.[, ordervariablefactor1:= paste(ordervariable1,aaa.variable1, sep = "&")]%>%
.[, ordervariablefactor2:= paste(ordervariable2,aaa.variable2, sep = "&")]%>%#包含variable，category，factor
.[, Xx := pmin(ordervariable1,ordervariable2)] %>%
.[, Yy := pmax(ordervariable1,ordervariable2)]#只包含variable，category
# .[, Xx := pmin(aaa.analysisvariable1, aaa.analysisvariable2)] %>%
# .[, Yy := pmax(aaa.analysisvariable1, aaa.analysisvariable2)] # %>%
# .[, Xx:= aaa.analysisvariable1] %>%
# .[, Yy:= aaa.analysisvariable2]
# .[order(Xx, Yy)]%>%
# .[, cc("aaa.analysisvariable1,aaa.analysisvariable2") := NULL]%>%
# setnames(old = c("Xx", "Yy"), new = cc("aaa.analysisvariable1,aaa.analysisvariable2"))
LBsR[LBsR == ""] <- NA
LBsR2 <- LBsR
# names(LBsR)LBsR <- LBsR[, CheckMissingR := rowSums(!is.na(.SD)), .SDcols = c("aaa.hsraw.r", "aaa.hscorrected.r", "aaa.horaw.r", "aaa.hocorrected.r")]
# [, CheckMissingR := rowSums(is.na(.SD)), .SDcols = c("aaa.hsraw.r", "aaa.hscorrected.r",
# "aaa.horaw.r", "aaa.hocorrected.r")]#%>%
Freq(LBsR$CheckMissingR)## Frequency Statistics:
## ───────────
## N %
## ───────────
## 1 668 49.0
## 2 694 51.0
## ───────────
## Total N = 1,362LBsR <- LBsR[, CategoriesVariablesCorrelates1 := paste(relation, analysiscategory1, aaa.analysisvariable1, aaa.variable1, sep = "#")] %>%
.[, CategoriesVariablesCorrelates2 := paste(relation, analysiscategory2, aaa.analysisvariable2, aaa.variable2, sep = "#")]
CategoriesVariables <- rbind(as.data.table(LBsR$CategoriesVariablesCorrelates1), as.data.table(LBsR$CategoriesVariablesCorrelates2))
## cat("TABLE 3. All countries")
# Table=FreqT(LBsR$RelationCorrelates)# Table=FreqT(CategoriesVariables[grepl("IB#|IBOB#", V1), ]$V1)
unique(CategoriesVariables[grepl("IB#|IBOB#", V1), ]$V1) %>% sort()## [1] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#Customer demand"
## [2] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#democratic institution"
## [3] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#FDI attractiveness"
## [4] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#institutional quality"
## [5] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#political stability"
## [6] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#Regulation"
## [7] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#rule of law"
## [8] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#Stakeholder norms"
## [9] "IB#ATTRACTING INSTITUTIONAL FACTOR#attracting institutional factor#Subsidy"
## [10] "IB#COMPETITIVE ADVANTAGES#organizational capability#Absorptive capacity(ACAP)"
## [11] "IB#COMPETITIVE ADVANTAGES#organizational capability#Dynamic capabilities"
## [12] "IB#COMPETITIVE ADVANTAGES#organizational capability#Efficiency"
## [13] "IB#COMPETITIVE ADVANTAGES#organizational capability#Firm Capabilities"
## [14] "IB#COMPETITIVE ADVANTAGES#organizational capability#governance structures"
## [15] "IB#COMPETITIVE ADVANTAGES#organizational capability#hierarchical governance"
## [16] "IB#COMPETITIVE ADVANTAGES#organizational capability#organizational capabilities"
## [17] "IB#COMPETITIVE ADVANTAGES#organizational capability#relational governance"
## [18] "IB#COMPETITIVE ADVANTAGES#organizational capability#Strategic Capabilities"
## [19] "IB#COMPETITIVE ADVANTAGES#organizational capability#technological capabilities"
## [20] "IB#COMPETITIVE ADVANTAGES#organizational resource#assest specificity"
## [21] "IB#COMPETITIVE ADVANTAGES#organizational resource#breadth of knowledge sources"
## [22] "IB#COMPETITIVE ADVANTAGES#organizational resource#Business ties"
## [23] "IB#COMPETITIVE ADVANTAGES#organizational resource#Cohesive Networks"
## [24] "IB#COMPETITIVE ADVANTAGES#organizational resource#Corporate Reputation"
## [25] "IB#COMPETITIVE ADVANTAGES#organizational resource#Diverse Networks"
## [26] "IB#COMPETITIVE ADVANTAGES#organizational resource#firm size"
## [27] "IB#COMPETITIVE ADVANTAGES#organizational resource#Firm Size"
## [28] "IB#COMPETITIVE ADVANTAGES#organizational resource#Inter-organizational Trust"
## [29] "IB#COMPETITIVE ADVANTAGES#organizational resource#Knowledge-Friendly Organisational Culture"
## [30] "IB#COMPETITIVE ADVANTAGES#organizational resource#Long-term institutional ownership"
## [31] "IB#COMPETITIVE ADVANTAGES#organizational resource#Media Visibility"
## [32] "IB#COMPETITIVE ADVANTAGES#organizational resource#Non-VRIN resources"
## [33] "IB#COMPETITIVE ADVANTAGES#organizational resource#organizational social capital"
## [34] "IB#COMPETITIVE ADVANTAGES#organizational resource#Political ties"
## [35] "IB#COMPETITIVE ADVANTAGES#organizational resource#Resources"
## [36] "IB#COMPETITIVE ADVANTAGES#organizational resource#VC investments"
## [37] "IB#COMPETITIVE ADVANTAGES#organizational resource#VRIN resources"
## [38] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Contextual distance"
## [39] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Corruption"
## [40] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Cultural differences"
## [41] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Cultural Distance"
## [42] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Firm Risk"
## [43] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#host country risk"
## [44] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Market Concentration"
## [45] "IB#DETERING INSTITUTIONAL FACTOR#detering institutional factor#Tax rates"
## [46] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#blockholders"
## [47] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#board independence"
## [48] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#board interlock"
## [49] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#CEO duality"
## [50] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#CEO pay"
## [51] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#executive compensation"
## [52] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#family firms"
## [53] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#family involvement"
## [54] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#insitutional investors"
## [55] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#Leadership"
## [56] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#managerial ownership"
## [57] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#Managerial ties"
## [58] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#Mindsets of top managers"
## [59] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic orientation#state ownership"
## [60] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#alignment mechanism"
## [61] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#alliance"
## [62] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Business planning"
## [63] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Corporate venturing"
## [64] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#environmental management practices"
## [65] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Environmental management system(EMS)"
## [66] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#High-performance work practices (HPWPs)"
## [67] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#HR practices"
## [68] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Interorganizational arrangements"
## [69] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#investment mechanism"
## [70] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#joint venture"
## [71] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#M&A"
## [72] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#monitoring mechanism"
## [73] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Proactive environmental strategies"
## [74] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Productivity Measurement and Enhancement System (ProMES)"
## [75] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Resource dependence"
## [76] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Social&Environmental disclosures"
## [77] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Strategic performance measurement systems"
## [78] "IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Strategic renewal"
## [79] "IB#ORGANIZATIONAL OUTCOME#general performance#Business Performance"
## [80] "IB#ORGANIZATIONAL OUTCOME#general performance#corporate performance"
## [81] "IB#ORGANIZATIONAL OUTCOME#general performance#family firm performance"
## [82] "IB#ORGANIZATIONAL OUTCOME#general performance#firm performance"
## [83] "IB#ORGANIZATIONAL OUTCOME#general performance#Firm performance"
## [84] "IB#ORGANIZATIONAL OUTCOME#general performance#Firm Performance"
## [85] "IB#ORGANIZATIONAL OUTCOME#general performance#General Performance"
## [86] "IB#ORGANIZATIONAL OUTCOME#general performance#organization performance"
## [87] "IB#ORGANIZATIONAL OUTCOME#general performance#Organizational performance"
## [88] "IB#ORGANIZATIONAL OUTCOME#general performance#Organizational Performance"
## [89] "IB#ORGANIZATIONAL OUTCOME#general performance#Overall performance"
## [90] "IB#ORGANIZATIONAL OUTCOME#general performance#performance"
## [91] "IB#ORGANIZATIONAL OUTCOME#general performance#Performance"
## [92] "IB#ORGANIZATIONAL OUTCOME#general performance#Performance-related consequences"
## [93] "IB#ORGANIZATIONAL OUTCOME#general performance#SME performance"
## [94] "IB#ORGANIZATIONAL OUTCOME#general performance#VC funded firm performance"
## [95] "IB#ORGANIZATIONAL OUTCOME#innovation#Environmental innovation"
## [96] "IB#ORGANIZATIONAL OUTCOME#innovation#green innovation"
## [97] "IB#ORGANIZATIONAL OUTCOME#innovation#innovation"
## [98] "IB#ORGANIZATIONAL OUTCOME#innovation#Innovation"
## [99] "IB#ORGANIZATIONAL OUTCOME#internationalization#Entry strategy"
## [100] "IB#ORGANIZATIONAL OUTCOME#internationalization#Export Market Orientation"
## [101] "IB#ORGANIZATIONAL OUTCOME#internationalization#foreign ownership strategy"
## [102] "IB#ORGANIZATIONAL OUTCOME#internationalization#International Business Combination"
## [103] "IB#ORGANIZATIONAL OUTCOME#internationalization#international strategic alliances(ISA) performance"
## [104] "IB#ORGANIZATIONAL OUTCOME#internationalization#Internationalization"
## [105] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#accounting performance"
## [106] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Customer Commitment"
## [107] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Customer Loyalty"
## [108] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Customer Trust"
## [109] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Economic performance"
## [110] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#financial performance"
## [111] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Financial Performance"
## [112] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Financial profitability"
## [113] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#Future Financial Performance"
## [114] "IB#ORGANIZATIONAL OUTCOME#marketing outcome#market performance"
## [115] "IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Aesthetic quality"
## [116] "IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Corporate social performance"
## [117] "IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Environment performance"
## [118] "IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Perceived value"
## [119] "IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#productivity"
## [120] "IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Strategy-related consequences"
## [121] "IB#OTHERS#others#Priori Financial Performance"
## [122] "IBOB#COMPETITIVE ADVANTAGES#organizational capability#Contractual governance"
## [123] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Behavioural intentions"
## [124] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Collective Employee Turnover"
## [125] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#collective turnover"
## [126] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Emotional intelligence"
## [127] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Employee performance"
## [128] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Employee satisfaction"
## [129] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Entrepreneurial Self Efficacy"
## [130] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Intra-organizational Trust"
## [131] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Management support"
## [132] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Organizational commitment"
## [133] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Trust"
## [134] "IBOB#ORGANIZATIONAL OUTCOME#general performance#firm performance"
## [135] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Firm performance"
## [136] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Firm Performance"
## [137] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Organizational performance"
## [138] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Organizational Performance"
## [139] "IBOB#ORGANIZATIONAL OUTCOME#innovation#Service innovation"
## [140] "IBOB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Aesthetic quality"
## [141] "IBOB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Core product quality"
## [142] "IBOB#ORGANIZATIONAL OUTCOME#non-marketing outcome#service quality"# Table=Freq(CategoriesVariables[grepl("OB#|IBOB.o#", V1), ]$V1)
unique(CategoriesVariables[grepl("OB#|IBOB.o#", V1), ]$V1) %>% sort()## [1] "IBOB.o#BEHAVIORS#discretionary performance#Service innovation"
## [2] "IBOB.o#BEHAVIORS#task performance#Employee performance"
## [3] "IBOB.o#HUMAN CAPITAL#ksao#Emotional intelligence"
## [4] "IBOB.o#MOTIVATION#motivation to work#Entrepreneurial Self Efficacy"
## [5] "IBOB.o#MOTIVATION#motivation to work#Intra-organizational Trust"
## [6] "IBOB.o#MOTIVATION#motivation to work#Trust"
## [7] "IBOB.o#MOTIVATION#satisfaction#Employee satisfaction"
## [8] "IBOB.o#ORGANIZATIONAL OUTCOME#general performance#firm performance"
## [9] "IBOB.o#ORGANIZATIONAL OUTCOME#general performance#Firm performance"
## [10] "IBOB.o#ORGANIZATIONAL OUTCOME#general performance#Firm Performance"
## [11] "IBOB.o#ORGANIZATIONAL OUTCOME#general performance#Organizational performance"
## [12] "IBOB.o#ORGANIZATIONAL OUTCOME#general performance#Organizational Performance"
## [13] "IBOB.o#ORGANIZATIONAL OUTCOME#non-marketing outcome#Aesthetic quality"
## [14] "IBOB.o#ORGANIZATIONAL OUTCOME#non-marketing outcome#Core product quality"
## [15] "IBOB.o#ORGANIZATIONAL OUTCOME#non-marketing outcome#service quality"
## [16] "IBOB.o#OTHERS#others#Behavioural intentions"
## [17] "IBOB.o#OTHERS#others#Contractual governance"
## [18] "IBOB.o#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#Management support"
## [19] "IBOB.o#WORKPLACE ATTACHMENT#commitment#Organizational commitment"
## [20] "IBOB.o#WORKPLACE ATTACHMENT#withdraw#Collective Employee Turnover"
## [21] "IBOB.o#WORKPLACE ATTACHMENT#withdraw#collective turnover"
## [22] "IBOB#COMPETITIVE ADVANTAGES#organizational capability#Contractual governance"
## [23] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Behavioural intentions"
## [24] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Collective Employee Turnover"
## [25] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#collective turnover"
## [26] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Emotional intelligence"
## [27] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Employee performance"
## [28] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Employee satisfaction"
## [29] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Entrepreneurial Self Efficacy"
## [30] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Intra-organizational Trust"
## [31] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Management support"
## [32] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Organizational commitment"
## [33] "IBOB#COMPETITIVE ADVANTAGES#organizational resource#Trust"
## [34] "IBOB#ORGANIZATIONAL OUTCOME#general performance#firm performance"
## [35] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Firm performance"
## [36] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Firm Performance"
## [37] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Organizational performance"
## [38] "IBOB#ORGANIZATIONAL OUTCOME#general performance#Organizational Performance"
## [39] "IBOB#ORGANIZATIONAL OUTCOME#innovation#Service innovation"
## [40] "IBOB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Aesthetic quality"
## [41] "IBOB#ORGANIZATIONAL OUTCOME#non-marketing outcome#Core product quality"
## [42] "IBOB#ORGANIZATIONAL OUTCOME#non-marketing outcome#service quality"
## [43] "OB#BEHAVIORS#discretionary performance#Communication"
## [44] "OB#BEHAVIORS#discretionary performance#counterproductive work behavior"
## [45] "OB#BEHAVIORS#discretionary performance#creativity"
## [46] "OB#BEHAVIORS#discretionary performance#Creativity"
## [47] "OB#BEHAVIORS#discretionary performance#Individual-level employee innovation"
## [48] "OB#BEHAVIORS#discretionary performance#innovation"
## [49] "OB#BEHAVIORS#discretionary performance#knowledge sharing"
## [50] "OB#BEHAVIORS#discretionary performance#Knowledge sharing"
## [51] "OB#BEHAVIORS#discretionary performance#Negotiation Performance"
## [52] "OB#BEHAVIORS#discretionary performance#OCB"
## [53] "OB#BEHAVIORS#discretionary performance#OCB-Inclusive role breadth"
## [54] "OB#BEHAVIORS#discretionary performance#OCBI"
## [55] "OB#BEHAVIORS#discretionary performance#OCBO"
## [56] "OB#BEHAVIORS#discretionary performance#open-minded discussion"
## [57] "OB#BEHAVIORS#discretionary performance#organization directed deviance"
## [58] "OB#BEHAVIORS#discretionary performance#organizational citizenship behavior"
## [59] "OB#BEHAVIORS#discretionary performance#Organizational citizenship behavior"
## [60] "OB#BEHAVIORS#discretionary performance#Pro-environmental behaviour"
## [61] "OB#BEHAVIORS#discretionary performance#Supervisor-directed deviance"
## [62] "OB#BEHAVIORS#discretionary performance#Voice"
## [63] "OB#BEHAVIORS#task performance#Individual performance"
## [64] "OB#BEHAVIORS#task performance#job performance"
## [65] "OB#BEHAVIORS#task performance#Job performance"
## [66] "OB#BEHAVIORS#task performance#Job Performance"
## [67] "OB#BEHAVIORS#task performance#Objective performance"
## [68] "OB#BEHAVIORS#task performance#Overall job performance"
## [69] "OB#BEHAVIORS#task performance#Performance"
## [70] "OB#BEHAVIORS#task performance#Subjective Performance"
## [71] "OB#BEHAVIORS#task performance#Subjective team performance"
## [72] "OB#BEHAVIORS#task performance#Subordinate work performance"
## [73] "OB#BEHAVIORS#task performance#Task performance"
## [74] "OB#BEHAVIORS#task performance#Task Performance"
## [75] "OB#CONSTRUCTIVE LEADERSHIP#abusive supervision#abusive supervision"
## [76] "OB#CONSTRUCTIVE LEADERSHIP#abusive supervision#leader mistreatment"
## [77] "OB#CONSTRUCTIVE LEADERSHIP#change-oriented leader behavior#perception of transformational/charismatic leadership"
## [78] "OB#CONSTRUCTIVE LEADERSHIP#change-oriented leader behavior#Romance of Leadership"
## [79] "OB#CONSTRUCTIVE LEADERSHIP#change-oriented leader behavior#supervisor transformational leadership"
## [80] "OB#CONSTRUCTIVE LEADERSHIP#change-oriented leader behavior#Transformational Leadership"
## [81] "OB#CONSTRUCTIVE LEADERSHIP#relational-oriented leader behavior#Leader Member Exchange"
## [82] "OB#CONSTRUCTIVE LEADERSHIP#relational-oriented leader behavior#leader-member exchange"
## [83] "OB#CONSTRUCTIVE LEADERSHIP#relational-oriented leader behavior#Leader–Member Exchange"
## [84] "OB#CONSTRUCTIVE LEADERSHIP#relational-oriented leader behavior#LMX"
## [85] "OB#CONSTRUCTIVE LEADERSHIP#relational-oriented leader behavior#Person-focused leader behaviors"
## [86] "OB#CONSTRUCTIVE LEADERSHIP#relational-oriented leader behavior#Person-focused leadership"
## [87] "OB#CONSTRUCTIVE LEADERSHIP#task-oriented behavior#Tasked-focused leadership"
## [88] "OB#CONSTRUCTIVE LEADERSHIP#value-based and moral leader behavior#Servant leadership"
## [89] "OB#HEALTH#stress#(reduced) personal accomplishment"
## [90] "OB#HEALTH#stress#burnout"
## [91] "OB#HEALTH#stress#depersonalisation"
## [92] "OB#HEALTH#stress#depersonalization"
## [93] "OB#HEALTH#stress#depression"
## [94] "OB#HEALTH#stress#emotional exhaustion"
## [95] "OB#HEALTH#stress#Emotional exhaustion"
## [96] "OB#HEALTH#stress#Emotional Exhaustion"
## [97] "OB#HEALTH#stress#Family interference with work"
## [98] "OB#HEALTH#stress#Home/family strain"
## [99] "OB#HEALTH#stress#Job demands "
## [100] "OB#HEALTH#stress#Negative family-to-work spillover"
## [101] "OB#HEALTH#stress#negative state"
## [102] "OB#HEALTH#stress#Negative work-to-family spillover"
## [103] "OB#HEALTH#stress#Overall Role Stress"
## [104] "OB#HEALTH#stress#Psychological Distress"
## [105] "OB#HEALTH#stress#Role ambiguity"
## [106] "OB#HEALTH#stress#Role Ambiguity"
## [107] "OB#HEALTH#stress#Role conflict"
## [108] "OB#HEALTH#stress#Role Conflict"
## [109] "OB#HEALTH#stress#Role Overload"
## [110] "OB#HEALTH#stress#self-regulatory capacity impairment"
## [111] "OB#HEALTH#stress#state negative affect"
## [112] "OB#HEALTH#stress#Stress"
## [113] "OB#HEALTH#stress#Work interference with family"
## [114] "OB#HEALTH#stress#Work strain"
## [115] "OB#HEALTH#well-being#Family Satisfaction"
## [116] "OB#HEALTH#well-being#family-work enrichment"
## [117] "OB#HEALTH#well-being#Family-Work Enrichment"
## [118] "OB#HEALTH#well-being#Home support"
## [119] "OB#HEALTH#well-being#Life satisfaction"
## [120] "OB#HEALTH#well-being#Mental health & well-being"
## [121] "OB#HEALTH#well-being#mental health problem"
## [122] "OB#HEALTH#well-being#Positive family-to-work spillover"
## [123] "OB#HEALTH#well-being#Positive work-to-family spillover"
## [124] "OB#HEALTH#well-being#Self-enjoyment"
## [125] "OB#HEALTH#well-being#Subjective Well-being"
## [126] "OB#HEALTH#well-being#Support from home"
## [127] "OB#HEALTH#well-being#Work-family enrichment"
## [128] "OB#HEALTH#well-being#Work-Family Enrichment"
## [129] "OB#HUMAN CAPITAL#ksao#Education"
## [130] "OB#HUMAN CAPITAL#ksao#Emotional Intelligence"
## [131] "OB#HUMAN CAPITAL#ksao#Psychological capital"
## [132] "OB#HUMAN CAPITAL#working experiences#Organization tenure"
## [133] "OB#HUMAN CAPITAL#working experiences#Organizational tenure"
## [134] "OB#HUMAN CAPITAL#working experiences#Position tenure"
## [135] "OB#MOTIVATION#motivation to work#achievement motivation"
## [136] "OB#MOTIVATION#motivation to work#Anticipated pay increase/promotion"
## [137] "OB#MOTIVATION#motivation to work#Autonomy"
## [138] "OB#MOTIVATION#motivation to work#entrepreneurial intention"
## [139] "OB#MOTIVATION#motivation to work#Entrepreneurial intention"
## [140] "OB#MOTIVATION#motivation to work#Intra-organizational Trust"
## [141] "OB#MOTIVATION#motivation to work#Job Control"
## [142] "OB#MOTIVATION#motivation to work#Proactive personality"
## [143] "OB#MOTIVATION#motivation to work#public service motivation"
## [144] "OB#MOTIVATION#motivation to work#Self-efficacy"
## [145] "OB#MOTIVATION#motivation to work#Social trust"
## [146] "OB#MOTIVATION#motivation to work#Trust"
## [147] "OB#MOTIVATION#perceived fit#Congruence"
## [148] "OB#MOTIVATION#perceived fit#Perceived overqualification"
## [149] "OB#MOTIVATION#perceived fit#Person–Group (P–G) Fit"
## [150] "OB#MOTIVATION#perceived fit#Person–Job (P–J) Fit"
## [151] "OB#MOTIVATION#perceived fit#Person–Organization (P–O) Fit"
## [152] "OB#MOTIVATION#perceived fit#Person–Supervisor (P–S) Fit"
## [153] "OB#MOTIVATION#satisfaction#Job & career satisfaction"
## [154] "OB#MOTIVATION#satisfaction#Job satisfaction"
## [155] "OB#MOTIVATION#satisfaction#Job Satisfaction"
## [156] "OB#MOTIVATION#satisfaction#Overall job satisfaction"
## [157] "OB#MOTIVATION#satisfaction#Satisfaction"
## [158] "OB#OTHERS#others#actual system use"
## [159] "OB#OTHERS#others#Place attachment"
## [160] "OB#OTHERS#others#subjective norm"
## [161] "OB#OTHERS#others#Subjective norm"
## [162] "OB#PERSONAL CHARACTERISTICS#demographics#age"
## [163] "OB#PERSONAL CHARACTERISTICS#demographics#Age"
## [164] "OB#PERSONAL CHARACTERISTICS#demographics#Female"
## [165] "OB#PERSONAL CHARACTERISTICS#demographics#gender"
## [166] "OB#PERSONAL CHARACTERISTICS#demographics#Gender"
## [167] "OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences"
## [168] "OB#PERSONAL CHARACTERISTICS#demographics#Gender Diversity"
## [169] "OB#PLASTICITY#extraversion#Extraversion"
## [170] "OB#PLASTICITY#openness to experience#Openness to Experience"
## [171] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#Distributive justice"
## [172] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#fairness"
## [173] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#Interactional justice"
## [174] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#Organizational Justice"
## [175] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#perceptions of unfairness"
## [176] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#Procedural justice"
## [177] "OB#SOCIAL EXCHANGE AT WORKPLACE#exchange fairness#Reciprocity"
## [178] "OB#SOCIAL EXCHANGE AT WORKPLACE#perception of support#Perceived organizational support"
## [179] "OB#SOCIAL EXCHANGE AT WORKPLACE#perception of support#Perceived supervisor support"
## [180] "OB#SOCIAL EXCHANGE AT WORKPLACE#perception of support#Rewards"
## [181] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#Idiosyncratic deals"
## [182] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#Organizational support"
## [183] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#perceived organizational support"
## [184] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#Service climate"
## [185] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#Support for change"
## [186] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#Support from work"
## [187] "OB#SOCIAL EXCHANGE AT WORKPLACE#perceptions of support#workplace Support"
## [188] "OB#STABILITY#agreeableness#Agreeableness"
## [189] "OB#STABILITY#conscientiousness#Conscientiousness"
## [190] "OB#STABILITY#emotional stability#Emotional Stability"
## [191] "OB#STABILITY#neuroticism#Neuroticism"
## [192] "OB#TEAM EFFECTIVENESS#team effectiveness#Collective turnover"
## [193] "OB#TEAM EFFECTIVENESS#team effectiveness#Overall team performance"
## [194] "OB#TEAM EFFECTIVENESS#team effectiveness#Team Effectiveness"
## [195] "OB#TEAM EFFECTIVENESS#team effectiveness#Team performance"
## [196] "OB#TEAM EFFECTIVENESS#team effectiveness#Team-level employee innovation"
## [197] "OB#TEAM INPUT#team composition#Cultural diversity"
## [198] "OB#TEAM INPUT#team structure#Competitive goal interdependence"
## [199] "OB#TEAM INPUT#team structure#Cooperative goal interdependence"
## [200] "OB#TEAM INPUT#team structure#Independent goal interdependence"
## [201] "OB#TEAM INPUT#team structure#Outcome interdependence"
## [202] "OB#TEAM INPUT#team structure#Task interdependence"
## [203] "OB#TEAM MEDIATOR#emerging state#collective attitudes/perception"
## [204] "OB#TEAM MEDIATOR#emerging state#Relationship quality"
## [205] "OB#TEAM MEDIATOR#emerging state#Social integration"
## [206] "OB#TEAM MEDIATOR#emerging state#Social network"
## [207] "OB#TEAM MEDIATOR#team process#Conflict"
## [208] "OB#TEAM MEDIATOR#team process#Relational team functioning"
## [209] "OB#TEAM MEDIATOR#team process#Task-focused team functioning"
## [210] "OB#WORKPLACE ATTACHMENT#commitment#affective commitment"
## [211] "OB#WORKPLACE ATTACHMENT#commitment#Affective commitment"
## [212] "OB#WORKPLACE ATTACHMENT#commitment#Affective commitment to change"
## [213] "OB#WORKPLACE ATTACHMENT#commitment#Affective Commitment to the Organization"
## [214] "OB#WORKPLACE ATTACHMENT#commitment#affective organizational commitment"
## [215] "OB#WORKPLACE ATTACHMENT#commitment#Commitment"
## [216] "OB#WORKPLACE ATTACHMENT#commitment#Commitment to change"
## [217] "OB#WORKPLACE ATTACHMENT#commitment#Continuance commitment"
## [218] "OB#WORKPLACE ATTACHMENT#commitment#Continuance commitment to change"
## [219] "OB#WORKPLACE ATTACHMENT#commitment#Continuance Commitment to the Organization"
## [220] "OB#WORKPLACE ATTACHMENT#commitment#Normative commitment"
## [221] "OB#WORKPLACE ATTACHMENT#commitment#Normative commitment to change"
## [222] "OB#WORKPLACE ATTACHMENT#commitment#Normative Commitment to the Organization"
## [223] "OB#WORKPLACE ATTACHMENT#commitment#Occupational commitment"
## [224] "OB#WORKPLACE ATTACHMENT#commitment#Organization Commitment"
## [225] "OB#WORKPLACE ATTACHMENT#commitment#Organizational commitment"
## [226] "OB#WORKPLACE ATTACHMENT#commitment#Organizational Commitment"
## [227] "OB#WORKPLACE ATTACHMENT#commitment#Overall/attitudinal commitment"
## [228] "OB#WORKPLACE ATTACHMENT#job involvement#Job involvement"
## [229] "OB#WORKPLACE ATTACHMENT#job involvement#Work engagement"
## [230] "OB#WORKPLACE ATTACHMENT#job involvement#Work Engagement"
## [231] "OB#WORKPLACE ATTACHMENT#withdraw#absences"
## [232] "OB#WORKPLACE ATTACHMENT#withdraw#Intent to quit"
## [233] "OB#WORKPLACE ATTACHMENT#withdraw#Overall withdrawal cognition"
## [234] "OB#WORKPLACE ATTACHMENT#withdraw#personnel changes"
## [235] "OB#WORKPLACE ATTACHMENT#withdraw#Turnover behavior"
## [236] "OB#WORKPLACE ATTACHMENT#withdraw#turnover intention"
## [237] "OB#WORKPLACE ATTACHMENT#withdraw#Turnover intention"
## [238] "OB#WORKPLACE ATTACHMENT#withdraw#Turnover Intention"# # 定义需要反向编码的组合列表（4个部分的字符串）
# reverse_list_obvariables <- c(
# # "IBOB.o#COMPETITIVE ADVANTAGES#organizational resource#Collective Employee Turnover",#在文字中做过了
# # "IBOB.o#COMPETITIVE ADVANTAGES#organizational resource#collective turnover",
# "OB#BEHAVIORS#contextual performance#counterproductive work behavior",
# "OB#BEHAVIORS#contextual performance#organization directed deviance",
# "OB#BEHAVIORS#contextual performance#Supervisor-directed deviance",
# "OB#SOCIAL EXCHANGE AT WORKPLACE#justice#perceptions of unfairness",
# "OB#TEAM EFFECTIVENESS#team effectiveness#Collective turnover",
# "OB#TEAM MEDIATOR#team process#Conflict"
# )
#
#
# # 增加新列 reverse.VariablesOBX，默认赋值为 1
# LBsR[, reverse.VariablesOBX := 1]
# LBsR[, reverse.VariablesOBY := 1]
# #
# LBsR[CategoriesVariablesCorrelates1 %in% reverse_list_obvariables, reverse.VariablesOBX := -1]
# LBsR[CategoriesVariablesCorrelates2 %in% reverse_list_obvariables, reverse.VariablesOBY := -1]# 1. 先把需要“反向编码”的组合放在一个向量中
reverse_pairs <- c(
"contextual performance#counterproductive work behavior",
"contextual performance#organization directed deviance",
"contextual performance#Supervisor-directed deviance",
"exchange fairness#perceptions of unfairness",
"team effectiveness#Collective turnover",
"team process#Conflict",
"organizational resource#Collective Employee Turnover",
"organizational resource#collective turnover",
"perceived fit#Perceived overqualification",
"well-being#mental health problem"
)
# 2. 给 LBsR 增加一个新列 reverse.VariablesOBX
# 先全部初始化为 0（也可以是 NA 或者其他值）
LBsR$reverse.VariablesOBX <- 1
LBsR$reverse.VariablesOBY <- 1
# 3. 对于那些 (Xx, aaa.variable1) 字符串用 "#" 连接后
# 恰好出现在 reverse_pairs 向量里的行，赋值为 -1
LBsR$reverse.VariablesOBX[
paste(LBsR$aaa.analysisvariable1, LBsR$aaa.variable1, sep = "#") %in% reverse_pairs
] <- -1
LBsR$reverse.VariablesOBY[
paste(LBsR$aaa.analysisvariable2, LBsR$aaa.variable2, sep = "#") %in% reverse_pairs
] <- -1# 1. 先把需要“反向编码”的组合放在一个向量中
reverse_pairs_IB <- c(
"organizational resource#Collective Employee Turnover",
"organizational resource#collective turnover"
)
# 2. 给 LBsR 增加一个新列 reverse.VariablesOBX
# 先全部初始化为 0（也可以是 NA 或者其他值）
LBsR$reverse.VariablesIBX <- 1
LBsR$reverse.VariablesIBY <- 1
# 3. 对于那些 (Xx, aaa.variable1) 字符串用 "#" 连接后
# 恰好出现在 reverse_pairs 向量里的行，赋值为 -1
LBsR$reverse.VariablesIBX[
paste(LBsR$aaa.analysisvariable1, LBsR$aaa.variable1, sep = "#") %in% reverse_pairs_IB
] <- -1
LBsR$reverse.VariablesIBY[
paste(LBsR$aaa.analysisvariable2, LBsR$aaa.variable2, sep = "#") %in% reverse_pairs_IB
] <- -1# 构造所有需要查看的 caseid 值的向量
selected_cases <- c(2000,484)
# 筛选出 articleid 在 selected_cases 中的行
subset_data <- LBsR[aaa.articleid %in% selected_cases]
# 查看结果
print(subset_data)## caseid aaa.articleid Coder Coder2 Note Note2 tableno
## <char> <char> <char> <char> <char> <lgcl> <char>
## 1: 7505 2000 张娅坤 巫彤彤 效应值是d NA 2
## 2: 7506 2000 张娅坤 巫彤彤 <NA> NA 2
## 3: 7507 2000 张娅坤 巫彤彤 <NA> NA 2
## 4: 7508 2000 张娅坤 <NA> <NA> NA 2
## 5: 7509 2000 张娅坤 <NA> <NA> NA 2
## 6: 7510 2000 张娅坤 <NA> <NA> NA 2
## 7: 7511 2000 张娅坤 巫彤彤 <NA> NA 2
## 8: 7512 2000 张娅坤 巫彤彤 <NA> NA 2
## 9: 7513 2000 张娅坤 巫彤彤 <NA> NA 2
## 10: 7514 2000 张娅坤 <NA> <NA> NA 2
## 11: 7515 2000 张娅坤 <NA> <NA> NA 2
## 12: 7516 2000 张娅坤 <NA> <NA> NA 2
## 13: 6802 484 张娅坤 颉碧艳 <NA> NA 4
## 14: 6803 484 张娅坤 颉碧艳 <NA> NA 4
## 15: 6804 484 张娅坤 颉碧艳 <NA> NA 4
## 16: 6805 484 张娅坤 颉碧艳 <NA> NA 4
## subgroup.country ImputeNote
## <char> <char>
## 1: <NA> <NA>
## 2: <NA> <NA>
## 3: <NA> <NA>
## 4: <NA> <NA>
## 5: <NA> <NA>
## 6: <NA> <NA>
## 7: <NA> <NA>
## 8: <NA> <NA>
## 9: <NA> <NA>
## 10: <NA> <NA>
## 11: <NA> <NA>
## 12: <NA> <NA>
## 13: United States <NA>
## 14: Germany and Switzerland average(Ger and Swit)
## 15: Netherlands <NA>
## 16: Sweden <NA>
## subgroup.culture ib1 powerdistance
## <char> <num> <num>
## 1: high Hofstede’s Individualism 1.0 NA
## 2: middle Hofstede’s Individualism 1.0 NA
## 3: low Hofstede’s Individualism 1.0 NA
## 4: high GLOBE’s Assertiveness Practice 0.0 NA
## 5: middle GLOBE’s Assertiveness Practice 0.0 NA
## 6: low GLOBE’s Assertiveness Practice 0.0 NA
## 7: high GLOBE’s Ingroup Collectivism Practice 0.0 NA
## 8: middle GLOBE’s Ingroup Collectivism Practice 0.0 NA
## 9: low GLOBE’s Ingroup Collectivism Practice 0.0 NA
## 10: high Schwartz’s Harmony 0.0 NA
## 11: middle Schwartz’s Harmony 0.0 NA
## 12: low Schwartz’s Harmony 0.0 NA
## 13: <NA> 1.0 40.0
## 14: <NA> 0.5 34.5
## 15: <NA> 1.0 38.0
## 16: <NA> 1.0 31.0
## individulism.collectivism masculinity.femininity uncertaintyavoidance
## <num> <num> <num>
## 1: NA NA NA
## 2: NA NA NA
## 3: NA NA NA
## 4: NA NA NA
## 5: NA NA NA
## 6: NA NA NA
## 7: NA NA NA
## 8: NA NA NA
## 9: NA NA NA
## 10: NA NA NA
## 11: NA NA NA
## 12: NA NA NA
## 13: 91.0 62 46.0
## 14: 67.5 68 61.5
## 15: 80.0 14 53.0
## 16: 71.0 5 29.0
## longterm indulgence.restraint aaa.hl.powerdistance
## <num> <num> <char>
## 1: NA NA <NA>
## 2: NA NA <NA>
## 3: NA NA <NA>
## 4: NA NA <NA>
## 5: NA NA <NA>
## 6: NA NA <NA>
## 7: NA NA <NA>
## 8: NA NA <NA>
## 9: NA NA <NA>
## 10: NA NA <NA>
## 11: NA NA <NA>
## 12: NA NA <NA>
## 13: 26.0 68 Low
## 14: 78.5 53 Low
## 15: 67.0 68 Low
## 16: 53.0 78 Low
## aaa.individulism.collectivism aaa.masculinity.femininity
## <char> <char>
## 1: Individulism <NA>
## 2: middle <NA>
## 3: Collectivism <NA>
## 4: <NA> <NA>
## 5: <NA> <NA>
## 6: <NA> <NA>
## 7: <NA> <NA>
## 8: <NA> <NA>
## 9: <NA> <NA>
## 10: <NA> <NA>
## 11: <NA> <NA>
## 12: <NA> <NA>
## 13: Individulism Masculinity
## 14: Individulism Masculinity
## 15: Individulism Femininity
## 16: Individulism Femininity
## aaa.hl.uncertaintyavoidance aaa.longterm.shortterm aaa.indulgence.restraint
## <char> <char> <char>
## 1: <NA> <NA> <NA>
## 2: <NA> <NA> <NA>
## 3: <NA> <NA> <NA>
## 4: <NA> <NA> <NA>
## 5: <NA> <NA> <NA>
## 6: <NA> <NA> <NA>
## 7: <NA> <NA> <NA>
## 8: <NA> <NA> <NA>
## 9: <NA> <NA> <NA>
## 10: <NA> <NA> <NA>
## 11: <NA> <NA> <NA>
## 12: <NA> <NA> <NA>
## 13: Low ShortTermOrientation Indulgence
## 14: High LongTermOrientation Indulgence
## 15: High LongTermOrientation Indulgence
## 16: Low LongTermOrientation Indulgence
## aaa.theory
## <char>
## 1: <NA>
## 2: <NA>
## 3: <NA>
## 4: <NA>
## 5: <NA>
## 6: <NA>
## 7: <NA>
## 8: <NA>
## 9: <NA>
## 10: <NA>
## 11: <NA>
## 12: <NA>
## 13: dynamic capability theory
## 14: dynamic capability theory
## 15: dynamic capability theory
## 16: dynamic capability theory
## aaa.variable1
## <char>
## 1: Gender Differences
## 2: Gender Differences
## 3: Gender Differences
## 4: Gender Differences
## 5: Gender Differences
## 6: Gender Differences
## 7: Gender Differences
## 8: Gender Differences
## 9: Gender Differences
## 10: Gender Differences
## 11: Gender Differences
## 12: Gender Differences
## 13: Productivity Measurement and Enhancement System (ProMES)
## 14: Productivity Measurement and Enhancement System (ProMES)
## 15: Productivity Measurement and Enhancement System (ProMES)
## 16: Productivity Measurement and Enhancement System (ProMES)
## aaa.analysisvariable1 analysiscategory1 OBorIB1
## <char> <char> <char>
## 1: demographics PERSONAL CHARACTERISTICS OB
## 2: demographics PERSONAL CHARACTERISTICS OB
## 3: demographics PERSONAL CHARACTERISTICS OB
## 4: demographics PERSONAL CHARACTERISTICS OB
## 5: demographics PERSONAL CHARACTERISTICS OB
## 6: demographics PERSONAL CHARACTERISTICS OB
## 7: demographics PERSONAL CHARACTERISTICS OB
## 8: demographics PERSONAL CHARACTERISTICS OB
## 9: demographics PERSONAL CHARACTERISTICS OB
## 10: demographics PERSONAL CHARACTERISTICS OB
## 11: demographics PERSONAL CHARACTERISTICS OB
## 12: demographics PERSONAL CHARACTERISTICS OB
## 13: strategic process EFFECTIVENESS OF STRATEGIC MANAGEMENT IB
## 14: strategic process EFFECTIVENESS OF STRATEGIC MANAGEMENT IB
## 15: strategic process EFFECTIVENESS OF STRATEGIC MANAGEMENT IB
## 16: strategic process EFFECTIVENESS OF STRATEGIC MANAGEMENT IB
## aaa.variable2 aaa.analysisvariable2 analysiscategory2
## <char> <char> <char>
## 1: Negotiation Performance discretionary performance BEHAVIORS
## 2: Negotiation Performance discretionary performance BEHAVIORS
## 3: Negotiation Performance discretionary performance BEHAVIORS
## 4: Negotiation Performance discretionary performance BEHAVIORS
## 5: Negotiation Performance discretionary performance BEHAVIORS
## 6: Negotiation Performance discretionary performance BEHAVIORS
## 7: Negotiation Performance discretionary performance BEHAVIORS
## 8: Negotiation Performance discretionary performance BEHAVIORS
## 9: Negotiation Performance discretionary performance BEHAVIORS
## 10: Negotiation Performance discretionary performance BEHAVIORS
## 11: Negotiation Performance discretionary performance BEHAVIORS
## 12: Negotiation Performance discretionary performance BEHAVIORS
## 13: productivity non-marketing outcome ORGANIZATIONAL OUTCOME
## 14: productivity non-marketing outcome ORGANIZATIONAL OUTCOME
## 15: productivity non-marketing outcome ORGANIZATIONAL OUTCOME
## 16: productivity non-marketing outcome ORGANIZATIONAL OUTCOME
## OBorIB2 relation Note3 N aaa.k aaa.hsraw.r
## <char> <char> <char> <char> <char> <char>
## 1: OB OB <NA> 17627 118 <NA>
## 2: OB OB <NA> 1728 22 <NA>
## 3: OB OB <NA> 3727 40 <NA>
## 4: OB OB <NA> 15866 104 <NA>
## 5: OB OB <NA> 2714 28 <NA>
## 6: OB OB <NA> 2523 30 <NA>
## 7: OB OB <NA> 3113 38 <NA>
## 8: OB OB <NA> 2133 27 <NA>
## 9: OB OB <NA> 15857 97 <NA>
## 10: OB OB <NA> 2202 27 <NA>
## 11: OB OB <NA> 5775 52 <NA>
## 12: OB OB <NA> 14860 96 <NA>
## 13: IB IB 变量间的关系从文章中找到 1647 33 0.93
## 14: IB IB <NA> 364 23 1.18
## 15: IB IB <NA> 236 13 1.39
## 16: IB IB <NA> 317 11 1.35
## hsraw.sdcorr hsraw.sdartifact hsraw.sdtrue hsraw.var.obs.total
## <num> <lgcl> <lgcl> <char>
## 1: NA NA NA <NA>
## 2: NA NA NA <NA>
## 3: NA NA NA <NA>
## 4: NA NA NA <NA>
## 5: NA NA NA <NA>
## 6: NA NA NA <NA>
## 7: NA NA NA <NA>
## 8: NA NA NA <NA>
## 9: NA NA NA <NA>
## 10: NA NA NA <NA>
## 11: NA NA NA <NA>
## 12: NA NA NA <NA>
## 13: 1.68 NA NA <NA>
## 14: 0.98 NA NA <NA>
## 15: 0.68 NA NA <NA>
## 16: 1.67 NA NA <NA>
## hsraw.var.error hsraw.var.true hsraw.95lci hsraw.95uci hs.LCI90 hs.UCI90
## <char> <char> <num> <num> <num> <num>
## 1: <NA> <NA> NA NA NA NA
## 2: <NA> <NA> NA NA NA NA
## 3: <NA> <NA> NA NA NA NA
## 4: <NA> <NA> NA NA NA NA
## 5: <NA> <NA> NA NA NA NA
## 6: <NA> <NA> NA NA NA NA
## 7: <NA> <NA> NA NA NA NA
## 8: <NA> <NA> NA NA NA NA
## 9: <NA> <NA> NA NA NA NA
## 10: <NA> <NA> NA NA NA NA
## 11: <NA> <NA> NA NA NA NA
## 12: <NA> <NA> NA NA NA NA
## 13: 0.1195 <NA> 0.58 1.72 NA NA
## 14: 0.3431 <NA> 0.78 1.58 NA NA
## 15: 0.4464 <NA> 1.13 1.87 NA NA
## 16: 0.939 <NA> 1.22 3.20 NA NA
## hsraw.10cv hsraw.90cv hs.LCV5note hs.UCV95note hs.LCV2.5note hs.UCV97.5note
## <num> <num> <num> <num> <num> <num>
## 1: NA NA NA NA NA NA
## 2: NA NA NA NA NA NA
## 3: NA NA NA NA NA NA
## 4: NA NA NA NA NA NA
## 5: NA NA NA NA NA NA
## 6: NA NA NA NA NA NA
## 7: NA NA NA NA NA NA
## 8: NA NA NA NA NA NA
## 9: NA NA NA NA NA NA
## 10: NA NA NA NA NA NA
## 11: NA NA NA NA NA NA
## 12: NA NA NA NA NA NA
## 13: NA NA NA NA NA NA
## 14: NA NA NA NA NA NA
## 15: NA NA NA NA NA NA
## 16: NA NA NA NA NA NA
## hsraw.se hsraw.acc aaa.hscorrected.r hscorrected.sdcorr
## <num> <num> <char> <num>
## 1: NA NA <NA> NA
## 2: NA NA <NA> NA
## 3: NA NA <NA> NA
## 4: NA NA <NA> NA
## 5: NA NA <NA> NA
## 6: NA NA <NA> NA
## 7: NA NA <NA> NA
## 8: NA NA <NA> NA
## 9: NA NA <NA> NA
## 10: NA NA <NA> NA
## 11: NA NA <NA> NA
## 12: NA NA <NA> NA
## 13: 0.29 NA <NA> NA
## 14: 0.20 NA <NA> NA
## 15: 0.19 NA <NA> NA
## 16: 0.50 NA <NA> NA
## hscorrected.sdartifact hscorrected.sdtrue hscorrected.var.obs.total
## <lgcl> <num> <num>
## 1: NA NA NA
## 2: NA NA NA
## 3: NA NA NA
## 4: NA NA NA
## 5: NA NA NA
## 6: NA NA NA
## 7: NA NA NA
## 8: NA NA NA
## 9: NA NA NA
## 10: NA NA NA
## 11: NA NA NA
## 12: NA NA NA
## 13: NA NA NA
## 14: NA NA NA
## 15: NA NA NA
## 16: NA NA NA
## hscorrected.var.error hscorrected.var.true hscorrected.95lci
## <num> <num> <char>
## 1: NA NA <NA>
## 2: NA NA <NA>
## 3: NA NA <NA>
## 4: NA NA <NA>
## 5: NA NA <NA>
## 6: NA NA <NA>
## 7: NA NA <NA>
## 8: NA NA <NA>
## 9: NA NA <NA>
## 10: NA NA <NA>
## 11: NA NA <NA>
## 12: NA NA <NA>
## 13: NA NA <NA>
## 14: NA NA <NA>
## 15: NA NA <NA>
## 16: NA NA <NA>
## hscorrected.95uci hscorrected.90lci hscorrected.90uci hscorrected.10cv
## <num> <num> <num> <char>
## 1: NA NA NA <NA>
## 2: NA NA NA <NA>
## 3: NA NA NA <NA>
## 4: NA NA NA <NA>
## 5: NA NA NA <NA>
## 6: NA NA NA <NA>
## 7: NA NA NA <NA>
## 8: NA NA NA <NA>
## 9: NA NA NA <NA>
## 10: NA NA NA <NA>
## 11: NA NA NA <NA>
## 12: NA NA NA <NA>
## 13: NA NA NA <NA>
## 14: NA NA NA <NA>
## 15: NA NA NA <NA>
## 16: NA NA NA <NA>
## hscorrected.90cv hscorrected.5cv hscorrected.95cv hscorrected.se
## <char> <num> <num> <num>
## 1: <NA> NA NA NA
## 2: <NA> NA NA NA
## 3: <NA> NA NA NA
## 4: <NA> NA NA NA
## 5: <NA> NA NA NA
## 6: <NA> NA NA NA
## 7: <NA> NA NA NA
## 8: <NA> NA NA NA
## 9: <NA> NA NA NA
## 10: <NA> NA NA NA
## 11: <NA> NA NA NA
## 12: <NA> NA NA NA
## 13: <NA> NA NA NA
## 14: <NA> NA NA NA
## 15: <NA> NA NA NA
## 16: <NA> NA NA NA
## hscorrected.acc aaa.horaw.r horaw.95uci horaw.95lci horaw.q horaw.p horaw.t
## <num> <char> <num> <num> <char> <char> <lgcl>
## 1: NA 0.31 0.24 0.37 408.54 <0.001 NA
## 2: NA -0.07 -0.41 0.27 223.85 <0.001 NA
## 3: NA -0.27 -0.43 -0.11 175.5 <0.001 NA
## 4: NA 0.29 0.21 0.36 385.88 <0.001 NA
## 5: NA 0.23 0.11 0.36 60.56 <0.001 NA
## 6: NA -0.34 -0.61 -0.07 278.33 <0.001 NA
## 7: NA -0.28 -0.50 -0.06 294.99 <0.001 NA
## 8: NA 0.2 0.05 0.35 66.64 <0.001 NA
## 9: NA 0.32 0.24 0.39 359.1 <0.001 NA
## 10: NA 0.18 0.03 0.33 68.21 <0.001 NA
## 11: NA -0.11 -0.27 0.05 374.72 <0.001 NA
## 12: NA 0.3 0.21 0.40 548.86 <0.001 NA
## 13: NA <NA> NA NA <NA> <NA> NA
## 14: NA <NA> NA NA <NA> <NA> NA
## 15: NA <NA> NA NA <NA> <NA> NA
## 16: NA <NA> NA NA <NA> <NA> NA
## horaw.t2 horaw.I horaw.i2 aaa.hocorrected.r hocorrected.95uci
## <num> <lgcl> <num> <num> <num>
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## 6: NA NA NA NA NA
## 7: NA NA NA NA NA
## 8: NA NA NA NA NA
## 9: NA NA NA NA NA
## 10: NA NA NA NA NA
## 11: NA NA NA NA NA
## 12: NA NA NA NA NA
## 13: NA NA NA NA NA
## 14: NA NA NA NA NA
## 15: NA NA NA NA NA
## 16: NA NA NA NA NA
## hocorrected.95lci hocorrected.q hocorrected.p hocorrected.t hocorrected.t2
## <num> <num> <char> <char> <num>
## 1: NA NA <NA> <NA> NA
## 2: NA NA <NA> <NA> NA
## 3: NA NA <NA> <NA> NA
## 4: NA NA <NA> <NA> NA
## 5: NA NA <NA> <NA> NA
## 6: NA NA <NA> <NA> NA
## 7: NA NA <NA> <NA> NA
## 8: NA NA <NA> <NA> NA
## 9: NA NA <NA> <NA> NA
## 10: NA NA <NA> <NA> NA
## 11: NA NA <NA> <NA> NA
## 12: NA NA <NA> <NA> NA
## 13: NA NA <NA> <NA> NA
## 14: NA NA <NA> <NA> NA
## 15: NA NA <NA> <NA> NA
## 16: NA NA <NA> <NA> NA
## hocorrected.I hocorrected.i2 XX
## <lgcl> <num> <char>
## 1: NA NA BEHAVIORS
## 2: NA NA BEHAVIORS
## 3: NA NA BEHAVIORS
## 4: NA NA BEHAVIORS
## 5: NA NA BEHAVIORS
## 6: NA NA BEHAVIORS
## 7: NA NA BEHAVIORS
## 8: NA NA BEHAVIORS
## 9: NA NA BEHAVIORS
## 10: NA NA BEHAVIORS
## 11: NA NA BEHAVIORS
## 12: NA NA BEHAVIORS
## 13: NA NA EFFECTIVENESS OF STRATEGIC MANAGEMENT
## 14: NA NA EFFECTIVENESS OF STRATEGIC MANAGEMENT
## 15: NA NA EFFECTIVENESS OF STRATEGIC MANAGEMENT
## 16: NA NA EFFECTIVENESS OF STRATEGIC MANAGEMENT
## YY
## <char>
## 1: PERSONAL CHARACTERISTICS
## 2: PERSONAL CHARACTERISTICS
## 3: PERSONAL CHARACTERISTICS
## 4: PERSONAL CHARACTERISTICS
## 5: PERSONAL CHARACTERISTICS
## 6: PERSONAL CHARACTERISTICS
## 7: PERSONAL CHARACTERISTICS
## 8: PERSONAL CHARACTERISTICS
## 9: PERSONAL CHARACTERISTICS
## 10: PERSONAL CHARACTERISTICS
## 11: PERSONAL CHARACTERISTICS
## 12: PERSONAL CHARACTERISTICS
## 13: ORGANIZATIONAL OUTCOME
## 14: ORGANIZATIONAL OUTCOME
## 15: ORGANIZATIONAL OUTCOME
## 16: ORGANIZATIONAL OUTCOME
## ordervariable1
## <char>
## 1: demographics@PERSONAL CHARACTERISTICS
## 2: demographics@PERSONAL CHARACTERISTICS
## 3: demographics@PERSONAL CHARACTERISTICS
## 4: demographics@PERSONAL CHARACTERISTICS
## 5: demographics@PERSONAL CHARACTERISTICS
## 6: demographics@PERSONAL CHARACTERISTICS
## 7: demographics@PERSONAL CHARACTERISTICS
## 8: demographics@PERSONAL CHARACTERISTICS
## 9: demographics@PERSONAL CHARACTERISTICS
## 10: demographics@PERSONAL CHARACTERISTICS
## 11: demographics@PERSONAL CHARACTERISTICS
## 12: demographics@PERSONAL CHARACTERISTICS
## 13: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT
## 14: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT
## 15: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT
## 16: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT
## ordervariable2
## <char>
## 1: discretionary performance@BEHAVIORS
## 2: discretionary performance@BEHAVIORS
## 3: discretionary performance@BEHAVIORS
## 4: discretionary performance@BEHAVIORS
## 5: discretionary performance@BEHAVIORS
## 6: discretionary performance@BEHAVIORS
## 7: discretionary performance@BEHAVIORS
## 8: discretionary performance@BEHAVIORS
## 9: discretionary performance@BEHAVIORS
## 10: discretionary performance@BEHAVIORS
## 11: discretionary performance@BEHAVIORS
## 12: discretionary performance@BEHAVIORS
## 13: non-marketing outcome@ORGANIZATIONAL OUTCOME
## 14: non-marketing outcome@ORGANIZATIONAL OUTCOME
## 15: non-marketing outcome@ORGANIZATIONAL OUTCOME
## 16: non-marketing outcome@ORGANIZATIONAL OUTCOME
## ordervariablefactor1
## <char>
## 1: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 2: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 3: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 4: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 5: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 6: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 7: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 8: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 9: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 10: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 11: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 12: demographics@PERSONAL CHARACTERISTICS&Gender Differences
## 13: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT&Productivity Measurement and Enhancement System (ProMES)
## 14: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT&Productivity Measurement and Enhancement System (ProMES)
## 15: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT&Productivity Measurement and Enhancement System (ProMES)
## 16: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT&Productivity Measurement and Enhancement System (ProMES)
## ordervariablefactor2
## <char>
## 1: discretionary performance@BEHAVIORS&Negotiation Performance
## 2: discretionary performance@BEHAVIORS&Negotiation Performance
## 3: discretionary performance@BEHAVIORS&Negotiation Performance
## 4: discretionary performance@BEHAVIORS&Negotiation Performance
## 5: discretionary performance@BEHAVIORS&Negotiation Performance
## 6: discretionary performance@BEHAVIORS&Negotiation Performance
## 7: discretionary performance@BEHAVIORS&Negotiation Performance
## 8: discretionary performance@BEHAVIORS&Negotiation Performance
## 9: discretionary performance@BEHAVIORS&Negotiation Performance
## 10: discretionary performance@BEHAVIORS&Negotiation Performance
## 11: discretionary performance@BEHAVIORS&Negotiation Performance
## 12: discretionary performance@BEHAVIORS&Negotiation Performance
## 13: non-marketing outcome@ORGANIZATIONAL OUTCOME&productivity
## 14: non-marketing outcome@ORGANIZATIONAL OUTCOME&productivity
## 15: non-marketing outcome@ORGANIZATIONAL OUTCOME&productivity
## 16: non-marketing outcome@ORGANIZATIONAL OUTCOME&productivity
## Xx
## <char>
## 1: demographics@PERSONAL CHARACTERISTICS
## 2: demographics@PERSONAL CHARACTERISTICS
## 3: demographics@PERSONAL CHARACTERISTICS
## 4: demographics@PERSONAL CHARACTERISTICS
## 5: demographics@PERSONAL CHARACTERISTICS
## 6: demographics@PERSONAL CHARACTERISTICS
## 7: demographics@PERSONAL CHARACTERISTICS
## 8: demographics@PERSONAL CHARACTERISTICS
## 9: demographics@PERSONAL CHARACTERISTICS
## 10: demographics@PERSONAL CHARACTERISTICS
## 11: demographics@PERSONAL CHARACTERISTICS
## 12: demographics@PERSONAL CHARACTERISTICS
## 13: non-marketing outcome@ORGANIZATIONAL OUTCOME
## 14: non-marketing outcome@ORGANIZATIONAL OUTCOME
## 15: non-marketing outcome@ORGANIZATIONAL OUTCOME
## 16: non-marketing outcome@ORGANIZATIONAL OUTCOME
## Yy CheckMissingR
## <char> <num>
## 1: discretionary performance@BEHAVIORS 1
## 2: discretionary performance@BEHAVIORS 1
## 3: discretionary performance@BEHAVIORS 1
## 4: discretionary performance@BEHAVIORS 1
## 5: discretionary performance@BEHAVIORS 1
## 6: discretionary performance@BEHAVIORS 1
## 7: discretionary performance@BEHAVIORS 1
## 8: discretionary performance@BEHAVIORS 1
## 9: discretionary performance@BEHAVIORS 1
## 10: discretionary performance@BEHAVIORS 1
## 11: discretionary performance@BEHAVIORS 1
## 12: discretionary performance@BEHAVIORS 1
## 13: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT 1
## 14: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT 1
## 15: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT 1
## 16: strategic process@EFFECTIVENESS OF STRATEGIC MANAGEMENT 1
## CategoriesVariablesCorrelates1
## <char>
## 1: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 2: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 3: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 4: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 5: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 6: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 7: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 8: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 9: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 10: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 11: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 12: OB#PERSONAL CHARACTERISTICS#demographics#Gender Differences
## 13: IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Productivity Measurement and Enhancement System (ProMES)
## 14: IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Productivity Measurement and Enhancement System (ProMES)
## 15: IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Productivity Measurement and Enhancement System (ProMES)
## 16: IB#EFFECTIVENESS OF STRATEGIC MANAGEMENT#strategic process#Productivity Measurement and Enhancement System (ProMES)
## CategoriesVariablesCorrelates2
## <char>
## 1: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 2: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 3: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 4: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 5: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 6: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 7: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 8: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 9: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 10: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 11: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 12: OB#BEHAVIORS#discretionary performance#Negotiation Performance
## 13: IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#productivity
## 14: IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#productivity
## 15: IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#productivity
## 16: IB#ORGANIZATIONAL OUTCOME#non-marketing outcome#productivity
## reverse.VariablesOBX reverse.VariablesOBY reverse.VariablesIBX
## <num> <num> <num>
## 1: 1 1 1
## 2: 1 1 1
## 3: 1 1 1
## 4: 1 1 1
## 5: 1 1 1
## 6: 1 1 1
## 7: 1 1 1
## 8: 1 1 1
## 9: 1 1 1
## 10: 1 1 1
## 11: 1 1 1
## 12: 1 1 1
## 13: 1 1 1
## 14: 1 1 1
## 15: 1 1 1
## 16: 1 1 1
## reverse.VariablesIBY
## <num>
## 1: 1
## 2: 1
## 3: 1
## 4: 1
## 5: 1
## 6: 1
## 7: 1
## 8: 1
## 9: 1
## 10: 1
## 11: 1
## 12: 1
## 13: 1
## 14: 1
## 15: 1
## 16: 1library(data.table)
# 先确保aaa.hsraw.r和aaa.horaw.r这两列为数值型
LBsR[, `aaa.hsraw.r` := as.numeric(`aaa.hsraw.r`)]
LBsR[, `aaa.horaw.r` := as.numeric(`aaa.horaw.r`)]
# 对于 articleid==484 的行：使用 aaa.hsraw.r 计算转换后的 r 值，结果存入 aaa.hscorrected.r
LBsR[aaa.articleid == 484, `aaa.hscorrected.r` := `aaa.hsraw.r` / sqrt(`aaa.hsraw.r`^2 + 4)]
# 对于 articleid==2000 的行：使用 aaa.horaw.r 计算转换后的 r 值，结果存入 aaa.hocorrected.r
LBsR[aaa.articleid == 2000, `aaa.hocorrected.r` := `aaa.horaw.r` / sqrt(`aaa.horaw.r`^2 + 4)]
# 删除（或清空）原始的 d 值：将原始存储位置设置为 NA
LBsR[aaa.articleid == 484, `aaa.hsraw.r` := NA]
LBsR[aaa.articleid == 2000, `aaa.horaw.r` := NA]
# 检查结果
LBsR[aaa.articleid %in% c(484, 2000), .(aaa.articleid, `aaa.hsraw.r`, `aaa.hscorrected.r`, `aaa.horaw.r`, `aaa.hocorrected.r`)]raw_vars <- c("hsraw.sdcorr", "hsraw.sdartifact", "hsraw.sdtrue",
"hsraw.var.obs.total", "hsraw.var.error", "hsraw.var.true",
"hsraw.95lci", "hsraw.95uci", "hs.LCI90", "hs.UCI90",
"hsraw.10cv", "hsraw.90cv", "hs.LCV5note", "hs.UCV95note",
"hs.LCV2.5note", "hs.UCV97.5note", "hsraw.se", "hsraw.acc")
corrected_vars <- c("hscorrected.sdcorr", "hscorrected.sdartifact", "hscorrected.sdtrue",
"hscorrected.var.obs.total", "hscorrected.var.error", "hscorrected.var.true",
"hscorrected.95lci", "hscorrected.95uci", "hscorrected.LCI90", "hscorrected.UCI90",
"hscorrected.10cv", "hscorrected.90cv", "hscorrected.LCV5note", "hscorrected.UCV95note",
"hscorrected.LCV2.5note", "hscorrected.UCV97.5note", "hscorrected.se", "hscorrected.acc")
stopifnot(length(raw_vars) == length(corrected_vars))
for(i in seq_along(raw_vars)) {
raw_col <- raw_vars[i]
cor_col <- corrected_vars[i]
# 先把原始列转换为数值型，以免出现字符/因子类型运算报错
LBsR[, (raw_col) := as.numeric(as.character(get(raw_col)))]
# 复制（仅对 articleid=484 的行）
LBsR[aaa.articleid == 484, (cor_col) := get(raw_col)]
# 清空原始列
LBsR[aaa.articleid == 484, (raw_col) := NA]
}
# 查看最终结果（示例：只选出相应列）
cols_to_show <- c("aaa.articleid", raw_vars, corrected_vars)
LBsR[aaa.articleid == 484, ..cols_to_show]# 定义 ho 列和对应 hocorrected 列的映射关系
ho_raw_vars <- c( "horaw.95uci", "horaw.95lci",
"horaw.q", "horaw.p", "horaw.t", "horaw.t2", "horaw.I", "horaw.i2")
ho_corrected_vars <- c( "hocorrected.95uci", "hocorrected.95lci",
"hocorrected.q", "hocorrected.p", "hocorrected.t", "hocorrected.t2", "hocorrected.I", "hocorrected.i2")
# 确保两者数量一致
stopifnot(length(ho_raw_vars) == length(ho_corrected_vars))
# 循环遍历每对列（仅对 articleid==2000 的记录进行操作）
for(i in seq_along(ho_raw_vars)) {
raw_var <- ho_raw_vars[i]
cor_var <- ho_corrected_vars[i]
# 将原始列转换成数值型，防止因字符或因子问题影响复制
LBsR[, (raw_var) := as.numeric(as.character(get(raw_var)))]
# 对 articleid 为2000的行，复制原始ho列的值到对应的hocorrected列
LBsR[aaa.articleid == 2000, (cor_var) := get(raw_var)]
# 清空原始ho列的值（设为 NA）
LBsR[aaa.articleid == 2000, (raw_var) := NA]
}
# 检查结果：只显示文章编号为2000的记录中相关的ho和hocorrected列
cols_to_show <- c(ho_raw_vars, ho_corrected_vars)
print(LBsR[aaa.articleid == 2000, ..cols_to_show])## horaw.95uci horaw.95lci horaw.q horaw.p horaw.t horaw.t2 horaw.I horaw.i2
## <num> <num> <num> <num> <num> <num> <num> <num>
## 1: NA NA NA NA NA NA NA NA
## 2: NA NA NA NA NA NA NA NA
## 3: NA NA NA NA NA NA NA NA
## 4: NA NA NA NA NA NA NA NA
## 5: NA NA NA NA NA NA NA NA
## 6: NA NA NA NA NA NA NA NA
## 7: NA NA NA NA NA NA NA NA
## 8: NA NA NA NA NA NA NA NA
## 9: NA NA NA NA NA NA NA NA
## 10: NA NA NA NA NA NA NA NA
## 11: NA NA NA NA NA NA NA NA
## 12: NA NA NA NA NA NA NA NA
## hocorrected.95uci hocorrected.95lci hocorrected.q hocorrected.p
## <num> <num> <num> <char>
## 1: 0.24 0.37 408.54 <NA>
## 2: -0.41 0.27 223.85 <NA>
## 3: -0.43 -0.11 175.50 <NA>
## 4: 0.21 0.36 385.88 <NA>
## 5: 0.11 0.36 60.56 <NA>
## 6: -0.61 -0.07 278.33 <NA>
## 7: -0.50 -0.06 294.99 <NA>
## 8: 0.05 0.35 66.64 <NA>
## 9: 0.24 0.39 359.10 <NA>
## 10: 0.03 0.33 68.21 <NA>
## 11: -0.27 0.05 374.72 <NA>
## 12: 0.21 0.40 548.86 <NA>
## hocorrected.t hocorrected.t2 hocorrected.I hocorrected.i2
## <char> <num> <lgcl> <num>
## 1: <NA> NA NA NA
## 2: <NA> NA NA NA
## 3: <NA> NA NA NA
## 4: <NA> NA NA NA
## 5: <NA> NA NA NA
## 6: <NA> NA NA NA
## 7: <NA> NA NA NA
## 8: <NA> NA NA NA
## 9: <NA> NA NA NA
## 10: <NA> NA NA NA
## 11: <NA> NA NA NA
## 12: <NA> NA NA NAlibrary(metafor) # standard meta-analyses
library(xlsx)
library(stringr)
library(robumeta) # package for RVE meta-analyses
library(clubSandwich)SOOutput <- function(data, NewVariables = NULL, Order = "", title = "",
TName = FALSE, note = FALSE, Cnote = FALSE, digits = FALSE) {
# 输出中文说明（可选）
if (Cnote) {
instructionText_cn <- "SOOutput() 函数用于提取二阶元分析结果，并整理为一个数据表。
主要提取的变量包括：
- XCategory, YCategory：构建变量
- Nmeta：元分析中包含的研究数量
- sumk：效应量总数
- sumN：样本量总和
- C6_Mean.R, C6_SE：效应量及其标准误
- CI.LL, CI.UL：95%置信区间下、上限
- CV.LL, CV.UL：信用区间下、上限
- C7_Sampling.Error.Variance：抽样误差方差
- C8_Total.Variance：总方差
- C9_True.Variance：真实方差
- C10_Prop.of.Error.Variance：误差方差所占比例
- C11_Reliability：可靠性指标
额外参数：
- NewVariables：可额外增加的变量
- Order：指定排序列（例如 '-sumk' 表示降序排序）
- title：输出表格标题
- TName：是否将变量重命名为更直观的名称
- digits：如果为 TRUE，则除“XCategory”和“YCategory”外的数值列保留3位小数且不采用科学计数法；为 FALSE 时保持原样。"
wrapTextForChinese(instructionText_cn, maxWidth = 55)
}
# 输出英文说明（可选）
if (note) {
instructionText_en <- "SOOutput() is an R function for extracting and organizing second-order meta-analysis results.
It extracts the following variables:
XCategory, YCategory, Nmeta, sumk, sumN,
C6_Mean.R, C6_SE, CI.LL, CI.UL, CV.LL, CV.UL,
C7_Sampling.Error.Variance, C8_Total.Variance, C9_True.Variance,
C10_Prop.of.Error.Variance, C11_Reliability.
Additional parameters:
- NewVariables: Optional additional variables to include.
- Order: Specifies the sorting column (e.g., '-sumk' for descending order).
- title: Title for the output table.
- TName: If TRUE, renames the output columns to more intuitive names.
- digits: If TRUE, all numeric columns (except XCategory and YCategory) will be rounded to 3 decimal places and displayed in fixed notation; if FALSE, they remain unchanged."
wrapText(instructionText_en, width = 80)
}
# 定义默认需要提取的变量
default_vars <- c("XCategory", "YCategory", "Nmeta", "sumk", "sumN",
"C6_Mean R", "C6_SE", "CI.LL", "CI.UL", "CV.LL", "CV.UL",
"C7_Sampling Error Variance", "C8_Total Variance", "C9_True Variance",
"C10_Prop of Error Variance", "C11_Reliability")
# 合并额外变量（如果有的话）
if (!is.null(NewVariables)) {
selected_vars <- c(default_vars, NewVariables)
} else {
selected_vars <- default_vars
}
# 确保数据为 data.table 格式
data <- as.data.table(data)
# 检查所需列是否存在，缺失则警告并移除
missing_vars <- setdiff(selected_vars, names(data))
if (length(missing_vars) > 0) {
warning("以下变量在数据中未找到，将被移除: ", paste(missing_vars, collapse = ", "))
selected_vars <- intersect(selected_vars, names(data))
}
# 若没有任何有效的列，则停止运行
if (length(selected_vars) == 0) {
stop("没有找到任何有效的列，请检查输入数据和NewVariables参数。")
}
# 提取所需变量构建输出表
OutputTable <- data[, ..selected_vars]
# 自动转换所有列为数值型，除了 "XCategory" 和 "YCategory"
for (col in names(OutputTable)) {
if (!(col %in% c("XCategory", "YCategory"))) {
OutputTable[[col]] <- suppressWarnings(as.numeric(OutputTable[[col]]))
}
}
# 根据 Order 参数进行排序（支持负号表示降序）
if (nzchar(Order)) {
order_col <- gsub("^-", "", Order) # 去除负号
if (!(order_col %in% names(OutputTable))) {
warning("排序列 ", order_col, " 不存在于输出表中，跳过排序。")
} else {
order_desc <- grepl("^-", Order) # 检查是否降序
setorderv(OutputTable, cols = order_col, order = if (order_desc) -1 else 1)
}
}
# 根据 TName 参数重命名输出列为更直观的名称
if (TName) {
rename_map <- list(
"XCategory" = "X Category",
"YCategory" = "Y Category",
"Nmeta" = "Number of Studies",
"sumk" = "Total k",
"sumN" = "Total N",
"C6_Mean.R" = "Mean R",
"C6_SE" = "SE",
"CI.LL" = "CI Lower",
"CI.UL" = "CI Upper",
"CV.LL" = "CV Lower",
"CV.UL" = "CV Upper",
"C7_Sampling.Error.Variance" = "Sampling Error Var",
"C8_Total.Variance" = "Total Var",
"C9_True.Variance" = "True Var",
"C10_Prop.of.Error.Variance" = "Prop. Error Var",
"C11_Reliability" = "Reliability"
)
common_names <- intersect(names(rename_map), names(OutputTable))
setnames(OutputTable, old = common_names, new = unlist(rename_map[common_names]))
}
# 当 digits 为 TRUE 时，对数值型列（除 XCategory 和 YCategory 外）进行 round() 四舍五入至3位小数，
# 返回的依然是数值型的 data.table（如需避免科学计数法显示，可在外部设置 options(scipen = 999)）
if (digits) {
numeric_cols <- names(OutputTable)[sapply(OutputTable, is.numeric)]
if (length(numeric_cols) > 0) {
OutputTable[, (numeric_cols) := lapply(.SD, round, 3), .SDcols = numeric_cols]
}
}
# 如果指定了标题并且存在打印函数，则打印输出表格
if (nzchar(title) && exists("print_table")) {
print_table(OutputTable, title = title)
}
# 强制确保返回对象为 data.table
setDT(OutputTable)
return(OutputTable)
}###A. Print Chinese text for notes
wrapTextForChinese <- function(text, maxWidth = 40) {
# PURPOSE: This function is used to bring the Chinese text when knit rmd for html format.
#EXAMPLE: For illustrative example, please refer to the section 1.3.6 and 2.2.2 of 'Auto.Me Illustration' file in this link: https://rpubs.com/EasonZhang.
lines <- unlist(strsplit(text, split = "\n")) # 首先按原有换行符分割文本
wrappedLines <- c()
for (line in lines) {
while (nchar(line, type = "width") > maxWidth) {
# 查找在 maxWidth 限制内的最后一个可能的断行点
breakPosition <- maxWidth
if (grepl("!", substr(line, 1, maxWidth))) {
# 如果在当前行的前maxWidth个字符中存在"!"，找到最后一个"!"的位置作为断行点
breakPosition <- max(unlist(gregexpr("!", substr(line, 1, maxWidth))))
}
# 添加当前断行点之前的文本到wrappedLines
wrappedLines <- c(wrappedLines, substr(line, 1, breakPosition))
# 移除已经添加的部分
line <- substr(line, breakPosition + 1, nchar(line))
}
# 添加剩余的部分（如果有）
if (nchar(line) > 0) {
wrappedLines <- c(wrappedLines, line)
}
}
# 使用cat打印最终结果
cat(paste(wrappedLines, collapse = "\n"))
}
wrapText <- function(text, width = 80) {
# PURPOSE: This function is used to bring the English text when knit rmd for html format.
#EXAMPLE: For illustrative example, please refer to the section 1.3.6 and 2.2.2 of 'Auto.Me Illustration' file in this link: https://rpubs.com/EasonZhang.
words <- unlist(strsplit(text, split = " ")) # 将文本拆分为单词
wrappedText <- ""
currentLineLength <- 0
for (word in words) {
# 检查添加当前单词是否会超过宽度限制
if (currentLineLength + nchar(word, type="width") <= width) {
# 当前行添加单词
wrappedText <- ifelse(currentLineLength > 0, paste0(wrappedText, " ", word), paste0(wrappedText, word))
currentLineLength <- currentLineLength + nchar(word, type="width") + 1 # 加1因为空格
} else {
# 超过宽度限制，从新行开始
wrappedText <- paste0(wrappedText, "\n", word)
currentLineLength <- nchar(word, type="width")
}
}
cat(wrappedText)
}
trim.author <- function(author.v) {
author.v <- sub(",.*", " et al.", author.v)
author.v <- sub("，.*", " et al.", author.v)
save.id <- grep("De", author.v)
author.sv <- author.v[save.id]
author.v <- gsub("
.*$", "", author.v)
author.v[save.id] <- author.sv
return(author.v)
}
# trim.metaid <- function(idv){
# idv = sub('\\(.*','',idv)
# idv = sub('（.*','',idv)
# idv = sub('_','',idv)
# return(as.numeric(idv))
# }
trim.metaid <- function(idv) {
idv <- sub("\\(.*", "", idv)
idv <- sub("（.*", "", idv)
idv <- sub("_", "", idv)
# 如果字符串中包含字母，比如OB中"Liang2022"，则替换成 "20252022"
idv <- ifelse(grepl("Liang2022", idv), "20252022", idv)
# 如果字符串中包含字母，比如IB中"LiuJOM"，则替换成 "20252023"
idv <- ifelse(grepl("LiuJOM", idv), "20252023", idv)
return(as.numeric(idv))
}
LabChange <- function(labM, labv) {
nr <- nrow(labM)
labv.new <- labv
for (i in 1:nr) {
labv.new[labv.new == labM[i, 1]] <- labM[i, 2]
}
return(labv.new)
}
# myrma.des <- function(yi, vi, sid, method = "MLM", tau2 = NULL) {
# # method: MLM or RVE
# del.tmp <- which(is.na(yi) == T)
# nna <- length(del.tmp)
# if (nna > 0 && nna < length(yi)) {
# yi <- yi[-del.tmp]
# vi <- vi[-del.tmp]
# sid <- sid[-del.tmp]
# }
#
# nes <- length(yi) # number of effect sizes. 有多少个效应量
# nmeta <- length(unique(sid)) # number of 1st-order meta 有多少个元分析
# esid <- 1:length(sid) # effect size id 效应量的id，从1到nmeta，有多少个元分析有多少效应量
# dat.tmp <- data.frame(yi = yi, vi = vi, sid = sid) # 效应量 yi、对应的抽样方差 vi 以及研究标识符 sid合并成一个数据框
# fit <- list(yi = yi, vi = vi)
# if (nmeta == 1) { # All effect sizes are from one meta-analysis
# # RVE
# fit.rve <- try(robu(
# formula = yi ~ 1, data = dat.tmp,
# studynum = sid, var.eff.size = vi,
# modelweights = "CORR", small = TRUE
# )) # 使用稳健方差估计
# if (inherits(fit.rve, "try-error")) {
# fit <- list(
# b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA,
# k = nes, vi = rep(NA, nes)
# )
# } else {
# fit$b <- fit.rve$reg_table$b.r # 总体平均效应量
# fit$se <- fit.rve$reg_table$SE # 总体平均效应量的标准误
# fit$sigma2 <- fit.rve$mod_info$tau.sq # 模型提供的 tau² 值（即异质性指标）
# fit$se.tau2 <- NA # 总体效应量方差的标准误？
# fit$ci.lb <- fit.rve$reg_table$CI.L # 总体平均效应量的置信区间下限
# fit$ci.ub <- fit.rve$reg_table$CI.U # 总体平均效应量的置信区间上限
# fit$k <- fit.rve$k # 元分析的个数，效应量数
# print(fit.rve$mod_info$tau.sq)
# cat("RVE meta-analysis with one meta-analysis\n",
# "Overall effect size: ", fit$b, "\n",
# "Standard error: ", fit$se, "\n",
# "Heterogeneity (tau^2): ", fit$sigma2, "\n",
# "95% CI: [", fit$ci.lb, ", ", fit$ci.ub, "]\n")
# }
# } else {
# if (nmeta == nes) { # no dependent effect sizes
# fit <- try(rma(yi = yi, vi = vi))
# if (inherits(fit, "try-error")) { # 如果 rma 函数拟合出错，则将 fit 赋值为一个包含关键统计量均为 NA 的列表
# fit <- list(b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA)
# } else {
# fit$sigma2 <- fit$tau2
# }
# } else { # With dependent effect sizes
# if (method == "RVE") { # robust variance estimation
# fit.rve <- try(robu(
# formula = yi ~ 1, data = dat.tmp,
# studynum = sid, var.eff.size = vi,
# modelweights = "CORR", small = TRUE
# ))
# if (inherits(fit, "try-error")) {
# fit <- list(
# b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA,
# k = nes, vi = rep(NA, nes)
# )
# } else {
# fit$b <- fit.rve$reg_table$b.r
# fit$se <- fit.rve$reg_table$SE
# fit$sigma2 <- fit.rve$mod_info$tau.sq
# fit$se.tau2 <- NA
# fit$ci.lb <- fit.rve$reg_table$CI.L
# fit$ci.ub <- fit.rve$reg_table$CI.U
# fit$k <- fit.rve$k
# }
# } else if (method == "MLM") {
# if(length(yi) <= 1){
# # 当只有一个效应量时，直接构造结果
# fit <- list(
# b = yi,
# se = sqrt(vi),
# sigma2 = NA, # 无法估计异质性
# se.tau2 = NA,
# ci.lb = yi - 1.96 * sqrt(vi),
# ci.ub = yi + 1.96 * sqrt(vi),
# k = 1,
# vi = vi,
# nmeta = nmeta
# )
# } else {
# fit.mlm <- try(rma.mv(yi = yi, V = vi,
# random = list(~1 | sid, ~1 | esid),# 考虑两层随机效应——一个是基于研究编号（sid），另一个是基于效应量的编号（esid）
# cvvc = TRUE))
# if(inherits(fit.mlm, 'try-error')) {
# fit <- list(b = NA, se = NA, sigma2 = rep(NA, 2),
# ci.lb = NA, ci.ub = NA, k = nes, vi = rep(NA, nes))
# } else {
# fit <- fit.mlm
# }
# }
# }
# }
# }
#
# fit$nmeta <- nmeta
# return(fit)
# }
# 基于rma.uni的
myrma.des <- function(yi, vi, sid, method = "MLM", tau2 = NULL) {
# method: MLM or RVE
del.tmp <- which(is.na(yi) == T)
nna <- length(del.tmp)
if (nna > 0 && nna < length(yi)) {
yi <- yi[-del.tmp]
vi <- vi[-del.tmp]
sid <- sid[-del.tmp]
}
nes <- length(yi) # number of effect sizes. 有多少个效应量
nmeta <- length(unique(sid)) # number of 1st-order meta 有多少个元分析
esid <- 1:length(sid) # effect size id 效应量的id，从1到nmeta，有多少个元分析有多少效应量
dat.tmp <- data.frame(yi = yi, vi = vi, sid = sid) # 效应量 yi、对应的抽样方差 vi 以及研究标识符 sid合并成一个数据框
fit <- list(yi = yi, vi = vi)
if (nmeta == 1) { # All effect sizes are from one meta-analysis
# RVE
fit.rma <- try(
rma.uni(yi = yi, vi = vi, data = dat.tmp, method = "REML") )# 使用稳健方差估计
if (inherits(fit.rma, "try-error")) {
fit <- list(
b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA,
k = nes, vi = rep(NA, nes)
)
} else {
print("运行成功")
fit$b <- fit.rma$b # 总体平均效应量
fit$se <- fit.rma$se # 总体平均效应量的标准误
fit$sigma2 <- fit.rma$tau2 # 模型提供的 tau² 值（即异质性指标）
fit$se.tau2 <- fit.rma$se.tau2 # 总体效应量方差的标准误？
fit$ci.lb <- fit.rma$ci.lb# 总体平均效应量的置信区间下限
fit$ci.ub <- fit.rma$ci.ub # 总体平均效应量的置信区间上限
fit$k <-fit.rma$k# 元分析的个数，效应量数
# cat("结果:\n",
# " 平均效应量 (b):", fit$b, "\n",
# " 标准误 (SE):", fit$se, "\n",
# " 异质性 (tau²):", fit$sigma2, "\n",
# " tau² 的标准误 (SE):", fit$se.tau2, "\n",
# " 置信区间下限:", fit$ci.lb, "\n",
# " 置信区间上限:", fit$ci.ub, "\n",
# " 效应量数量 (k):", fit$k, "\n"
# )
}
} else {
if (nmeta == nes) { # no dependent effect sizes
fit <- try(rma(yi = yi, vi = vi))
if (inherits(fit, "try-error")) { # 如果 rma 函数拟合出错，则将 fit 赋值为一个包含关键统计量均为 NA 的列表
fit <- list(b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA)
} else {
fit$sigma2 <- fit$tau2
}
} else { # With dependent effect sizes
if (method == "RVE") { # robust variance estimation
fit.rve <- try(robu(
formula = yi ~ 1, data = dat.tmp,
studynum = sid, var.eff.size = vi,
modelweights = "CORR", small = TRUE
))
if (inherits(fit, "try-error")) {
fit <- list(
b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA,
k = nes, vi = rep(NA, nes)
)
} else {
fit$b <- fit.rve$reg_table$b.r
fit$se <- fit.rve$reg_table$SE
fit$sigma2 <- fit.rve$mod_info$tau.sq
fit$se.tau2 <- NA
fit$ci.lb <- fit.rve$reg_table$CI.L
fit$ci.ub <- fit.rve$reg_table$CI.U
fit$k <- fit.rve$k
}
} else if (method == "MLM") {
if(length(yi) <= 1){
# 当只有一个效应量时，直接构造结果
fit <- list(
b = yi,
se = sqrt(vi),
sigma2 = NA, # 无法估计异质性
se.tau2 = NA,
ci.lb = yi - 1.96 * sqrt(vi),
ci.ub = yi + 1.96 * sqrt(vi),
k = 1,
vi = vi,
nmeta = nmeta
)
} else {
fit.mlm <- try(rma.mv(yi = yi, V = vi,
random = list(~1 | sid, ~1 | esid),# 考虑两层随机效应——一个是基于研究编号（sid），另一个是基于效应量的编号（esid）
cvvc = TRUE))
if(inherits(fit.mlm, 'try-error')) {
fit <- list(b = NA, se = NA, sigma2 = rep(NA, 2),
ci.lb = NA, ci.ub = NA, k = nes, vi = rep(NA, nes))
} else {
fit <- fit.mlm
}
}
}
}
}
fit$nmeta <- nmeta
return(fit)
}
# Organize results
# i.e., return the 6th-11th columns in Table 2 in Schmidt paper
meta2nd.Table2SO <- function(fit, K, NK, nmeta2) {
# fit: an rma object
# meta2.d: data for the 2nd order meta-analysis
# rbar: 1st order meta-analytic correlations
# vi: sampling variances of rbar
# rel: study reliability (sqrt(rxx*ryy) or rbar/rhobar)
# K: number of effect sizes within 1st order meta-analyses
# NK: Total number of participants within 1st order meta-analyses
if (is.null(fit$nmeta)) {
nmeta <- nmeta2
} else {
nmeta <- fit$nmeta
}
nes <- length(K) # number of effect sizes
if (is.null(fit$k)) {
nes.used <- 0
} else {
nes.used <- fit$k
}
# #如果多效应量情况，确保必需字段存在
# if (nes.used > 1) {
# if (is.null(fit$vi)) fit$vi <- rep(NA_real_, nes)
# if (is.null(fit$yi)) fit$yi <- rep(NA_real_, nes)
# if (is.null(fit$b)) fit$b <- NA_real_
# if (is.null(fit$se)) fit$se <- NA_real_
# if (is.null(fit$ci.lb)) fit$ci.lb <- NA_real_
# if (is.null(fit$ci.ub)) fit$ci.ub <- NA_real_
# } else {
# if (is.null(fit$rbar)) fit$rbar <- NA_real_
# if (is.null(fit$vi)) fit$vi <- NA_real_
# if (is.null(fit$sigma2)) fit$sigma2 <- NA_real_
# }
if (nes.used > 1) {
## Compute Second order sampling error: ESr2
wi <- 1 / fit$vi # weight for each first order meta-analysis
rbar.1st <- fit$yi
# 1st order meta-analyses with missing values
se.tau2 <- se.tau2.1.2nd <- se.tau2.2.2nd <- NA
if (is.null(fit$se.tau2) == 0) {
se.tau2 <- fit$se.tau2
}
del2 <- unique(which(is.na(cbind(rbar.1st, fit$vi)), arr.ind = T)[, 1])
if (length(del2) == nes) { # 如果所有一阶元分析都有缺失则都为NA
rbar.2nd <- ESr2 <- tau2.2nd <- tau2.1.2nd <- tau2.2.2nd <- NA
Srbar2 <- ProVar <- rel <- NA
CI95 <- CV80 <- rep(NA, 2)
} else {
if (length(del2) > 0) {
ESr2 <- length(wi[-del2]) / sum(wi[-del2]) # 有缺失的，用未缺失部分计算
} else {
ESr2 <- length(wi) / sum(wi)
} # Eq.6e 二阶抽样误差方差：m/sum(wi) m为权重个数
rbar.2nd <- fit$b[1] # estimated population mean correlation
if (length(fit$sigma2) == 2) {
tau2.1.2nd <- fit$sigma2[1] # between-meta variance
tau2.2.2nd <- fit$sigma2[2] # within-meta variance
tau2.2nd <- sum(fit$sigma2) # total between-effect-size variance
if ((sum(is.na(fit$vvc)) == 0) && (nrow(fit$vvc) == 2)) {
se.tau2 <- as.numeric(sqrt(t(c(1, 1)) %*% fit$vvc %*% c(1, 1)))
se.tau2.1.2nd <- sqrt(fit$vvc[1, 1])
se.tau2.2.2nd <- sqrt(fit$vvc[2, 2])
}
} else {
tau2.2nd <- fit$sigma2
tau2.1.2nd <- tau2.2.2nd <- NA
}
CI95 <- c(fit$ci.lb, fit$ci.ub)
CV80 <- c(
rbar.2nd - qnorm(.90) * sqrt(tau2.2nd),
rbar.2nd + qnorm(.90) * sqrt(tau2.2nd)
)
Srbar2 <- ESr2 + tau2.2nd
ProVar <- ESr2 / Srbar2
rel <- 1 - ProVar
}
res6_11 <- c(
nmeta, nes, nes.used, sum(K, na.rm = T), sum(NK, na.rm = T), rbar.2nd,
fit$se, CI95, CV80, ESr2, Srbar2, tau2.2nd, se.tau2, tau2.2.2nd, tau2.1.2nd,
se.tau2.1.2nd, se.tau2.2.2nd, ProVar, rel
)
} else {
print("nes.used <= 1, 1st order meta-analyses are not used.")
# rbar 取 fit$rbar 或 fit$b
if (!is.null(fit$rbar) && !is.na(fit$rbar)) {
rbar <- fit$rbar
} else if (!is.null(fit$b) && !is.na(fit$b)) {
rbar <- fit$b
} else {
rbar <- NA
warning("fit$rbar and fit$b are both missing.")
}
# rbar.se 取 sqrt(fit$vi) 或 sqrt(fit$se)
if (!is.null(fit$vi) && length(fit$vi) > 0 && !any(is.na(fit$vi))) {
rbar.se <- sqrt(fit$vi)
} else if (!is.null(fit$se) && length(fit$se) > 0 && !any(is.na(fit$se))) {
rbar.se <- sqrt(fit$se)
} else {
rbar.se <- NA
warning("fit$vi and fit$se are both missing.")
}
tau2 <- fit$sigma2
CI95 <- c(rbar - 1.96 * rbar.se, rbar + 1.96 * rbar.se) # Handbook p.206
if (nes > 1) {
K <- sum(K, na.rm = T)
NK <- sum(NK, na.rm = T)
}
if (!is.null(fit$vi) && length(fit$vi) > 0) {
Sr2 <- fit$vi * K
} else if (!is.null(fit$se) && length(fit$se) > 0) {
Sr2 <- (fit$se^2) * K
} else {
warning("Both fit$vi and fit$se are missing or empty.")
}
Srbar2 <- Sr2 + tau2
ProVar <- Sr2 / Srbar2
rel <- 1 - ProVar
CV80 <- c(rbar - qnorm(.90) * sqrt(tau2), rbar + qnorm(.90) * sqrt(tau2))
res6_11 <- c(
nmeta, nes, nes.used, K, NK, rbar, rbar.se, CI95, CV80, Sr2, Srbar2, tau2,
NA, NA, NA, NA, NA, ProVar, rel
)
}
return(res6_11)
}
DiffTest <- function(est, se) {
d <- est[1] - est[2]
sed <- sqrt(sum(se^2))
zd <- abs(d / sed)
pvalue <- 2 * (1 - pnorm(zd))
return(c(d, sed, zd, pvalue))
}# Data preparation
## Sample sizes and the indexing matrix
NK <- as.numeric(data$N) # 1st order meta-analyses 中的参与者总数
K <- as.numeric(data$aaa.k) # 1st order meta-analyses 中的效应大小数量
Nbar <- NK / K # 每个原始研究的平均参与者数量
Nmeta <- length(NK) # 1st order meta-analyses 的数量
# Replace missing within-study sample sizes with the median Nbar
median.Nbar <- median(Nbar, na.rm = TRUE) # 计算 Nbar 的中位数（忽略缺失值）
median.K <- median(K, na.rm = TRUE) # 计算 K 的中位数（忽略缺失值）
NK[is.na(Nbar)] <- median.Nbar * K[is.na(Nbar)] # 对于 Nbar 缺失的样本，用中位数 Nbar 乘以对应的 K 替换 NK
NK[is.na(NK)] <- median.Nbar * median.K # 对于 NK 仍缺失的值，用中位数 Nbar 与中位数 K 的乘积替换
Nbar[is.na(Nbar)] <- median.Nbar # 用中位数 Nbar 替换 Nbar 中缺失的值
K[is.na(K)] <- median.K # 用中位数 K 替换 K 中缺失的值
NK <- round(NK, 0)
indM <- matrix(1, Nmeta, 6)
colnames(indM) <- c("ind.rbar", "ind.rhobar", "ind.Sr2", "ind.Src2", "ind.tau2.r", "ind.tau2.rho")
# rbar: Meta-analytic mean correlation (raw)
# 1: HS average correlation 1: HS 平均相关系数
# 2: HO average correlation 2: HO 平均相关系数
# 3: mid point of the prediction interval 3: 预测区间的中点
# 4: no results
# rhobar: Meta-analytic mean correlation (With unreliability correction)
# 1: HS average correlation
# 2: sample size weighted average correlation 2: 样本大小加权平均相关系数
# 3: no results
# 4: rbar and rhobar have different signs rbar 和 rhobar 的符号不同
# 5: rhobar is smaller than its rbar rhobar 小于其 rbar
# Sr2: Total variance of the observed correlations Sr2：观察到的相关系数的总方差
# Without unreliability correction
# 1: Directly report total。 1：直接报告总数
# 2: Compute total variance from Q/NK 2：从 Q/NK 计算观察到的总方差
# 3: Total Variance = Average Sampling Variance + Heterogeneity 3：总方差=平均抽样方差+异质性
# 4: Total Variance = Average Sampling Variance/(1-Percentage)。 4：总方差=平均抽样方差/(1-百分比)
# 5: Reported total variance smaller than Average Sampling variance (<0)
# 6: Computed Sr2 greater than 1
# 7: Computed Sr2 from Src2
# 8: Total Variance = Average Sampling Variance + Heterogeneity (Imputed)
# With unreliability correction
# 1: Directly report with unreliability correction 1：直接报告带有不可靠性校正的值
# 2: Compute total variance from Q/NK 2：从 Q/NK 计算观察到的总方差
# 3: Total Variance = Average Sampling Variance + Heterogeneity
# 4: Total Variance = Average Sampling Variance/(1-Percentage)
# 5: Reported total variance smaller than Average Sampling variance
# 6: Computed Src2 greater than 1
# 7: Compute Src2 from Sr2
# 8: Total Variance = Average Sampling Variance + Heterogeneity (Imputed)
# tau2.r:
# 1: Directly report tau2
# 2: Tau2 computed based on credibility intervals 2：基于可信区间计算的 tau2
# 3: Tau2 computed based on the formula: Total Var-Average Error Var 3：基于公式计算的 tau2：总方差-平均误差方差
# 4: Tau2 imputed using 0.0144 4：使用 0.0144 补充的 tau2
# tau2.rho (with unreliability correction):
# 1: Directly report tau2
# 2: Tau2 computed based on credibility intervals
# 3: Tau2 computed based on the formula: Total Var-Average Error Var
# 4: Tau2 imputed using 0.0144# Meta-analytic mean correlation (raw)
#--------------------------------------
# 1: HS average correlation
rbar <- data$aaa.hsraw.r
rbar <- as.numeric(rbar)
# 2: HO average correlation
indM[is.na(rbar), 1] <- 2
aaa.horaw.r <- as.numeric(data$aaa.horaw.r)
rbar[is.na(rbar)] <- aaa.horaw.r[is.na(rbar)]
# 3: mid point of the prediction interval
indM[is.na(rbar), 1] <- 3
rbar[is.na(rbar)] <- ((as.numeric(data$hsraw.10cv) + as.numeric(data$hsraw.90cv)) / 2)[is.na(rbar)]
# 4: no results
indM[is.na(rbar), 1] <- 4# Meta-analytic mean correlation (With unreliability correction)
#-----------------------------------------------------------------
# 1: HS average correlation
if (!is.null(data$aaa.hscorrected.r)) {
rhobar <- suppressWarnings(as.numeric(data$aaa.hscorrected.r))
} else {
rhobar <- rep(NA, nrow(data))
}
# 2: HO average correlation
indM[is.na(rhobar), 2] <- 2
if (!is.null(data$aaa.hocorrected.r)) {
hocorr <- suppressWarnings(as.numeric(data$aaa.hocorrected.r))
rhobar[is.na(rhobar)] <- hocorr[is.na(rhobar)]
}
# 3: no results
indM[is.na(rhobar), 2] <- 3# 先将 rbar 和 rhobar 转换为字符型
# rbar <- as.character(rbar)
# rhobar <- as.character(rhobar)
# 替换 rbar 中包含 "<-0.1" 的值为 NA
rbar[grepl("<-0.1", rbar)] <- NA
# 替换 rhobar 中包含 "/" 的值为 NA
rhobar[grepl("/", rhobar)] <- NA
# 转换为数值型
rbar <- as.numeric(rbar)
rhobar <- as.numeric(rhobar)# Missing data handling: assume a reliability, i.e., 0.8
# fill missing values with corresponding raw mean correlations
# --------------------------------------------------------------------------
rhobar[is.na(rhobar)] <- rbar[is.na(rhobar)] / 0.8
rhobar[which(rhobar > 1)] <- NA # 超过1的情况，置为NA
# fill missing values with corresponding corrected mean correlations
rbar[is.na(rbar)] <- rhobar[is.na(rbar)] * 0.8
# Reliability estimates for each 1st meta-analysis
# 计算 rbar/rhobar 或直接设为 0.8
# rbar/rhobar or set to 0.8
rel.meta <- rbar / rhobar
rel.meta[rhobar == 0] <- 0.8 # 当无法用 rbar/rhobar 估计时，设为 0.8
rel.meta[rbar == 0] <- 0.8 # 当无法用 rbar/rhobar 估计时，设为 0.8
rel.meta[is.na(rel.meta)] <- 0.8
# Data cleaning
# 对于可靠性不合理的研究，替换 rhobar
# 1. 当 rbar 与 rhobar 符号不一致时
sel <- which(rel.meta < 0) # 处理 rbar 和 rhobar 符号不一致
indM[sel, 2] <- 4
rhobar[sel] <- rbar[sel] / 0.8
rel.meta[sel] <- 0.8
# 2. 当 rhobar 小于对应的 rbar 时（计算出来的可靠性大于1）
# rhobar is smaller than its rbar
sel <- which(rel.meta > 1)
indM[sel, 2] <- 5
rhobar[sel] <- rbar[sel] / 0.8
rel.meta[sel] <- 0.8
# 3. 对于可靠性小于 0.6 的情况，直接设为 0.6，并重新计算 rhobar
# reliability smaller than 0.6 is set to 0.6
sel <- which(rel.meta < 0.6)
rel.meta[sel] <- 0.6
rhobar[sel] <- rbar[sel] / rel.meta[sel]### tau2 of raw correlations
# tau2: 1 Directly report tau2
#-------------------------------------------------
tau2.r <- (data$hsraw.sdtrue)^2 # 使用原始数据中的标准差（平方得到变异数）
tau2.r[is.na(tau2.r)] <- data$hsraw.var.true[is.na(tau2.r)]
tau2.r[is.na(tau2.r)] <- (data$horaw.t^2)[is.na(tau2.r)]
tau2.r[is.na(tau2.r)] <- data$horaw.t2[is.na(tau2.r)]
tau2.r <- as.numeric(tau2.r)
# 2: Tau2 computed based on credibility intervals
#---------------------------------------------------------------
indM[is.na(tau2.r), 5] <- 2
# credibility level: 80%
UCV <- as.numeric(data$hsraw.90cv)
LCV <- as.numeric(data$hsraw.10cv)
CV <- (UCV - LCV) / 2 / qnorm(0.90) # 标准正态分布在 90% 分位处的值
tau2.r[is.na(tau2.r)] <- (CV[is.na(tau2.r)])^2data$hocorrected.t <- as.numeric(data$hocorrected.t)
data$hocorrected.t2 <- as.numeric(data$hocorrected.t2)
data$hscorrected.sdtrue <- as.numeric(data$hscorrected.sdtrue)
# tau2: 1 Directly report tau2
#-----------------------------------------------------------------------
if ((is.null(data$aaa.hscorrected.r) == F) | (is.null(data$aaa.hocorrected.r) == F)) {
tau2.rho <- (data$hscorrected.sdtrue)^2 # Heterogeneity based on HS corrected SD
tau2.rho[is.na(tau2.rho)] <- data$hscorrected.var.true[is.na(tau2.rho)]
# 如果 HO 校正的 T2 值存在，则使用它填补缺失
if (!is.null(data$hocorrected.t2)) {
tau2.rho[is.na(tau2.rho)] <- data$hocorrected.t2[is.na(tau2.rho)]
}
# 如果 HO 校正的 T 值存在，则使用其平方填补缺失
if (!is.null(data$hocorrected.t)) {
tau2.rho[is.na(tau2.rho)] <- (data$hocorrected.t^2)[is.na(tau2.rho)]
}
# Tau2: 2 Tau2 computed based on credibility intervals
#----------------------------------------------------------------------
indM[is.na(tau2.rho), 6] <- 2
# credibility level: 80%
UCV <- as.numeric(data$hscorrected.90cv)
LCV <- as.numeric(data$hscorrected.10cv)
CV <- (UCV - LCV) / 2 / qnorm(0.90)
tau2.rho[is.na(tau2.rho)] <- (CV[is.na(tau2.rho)])^2
} else {
tau2.rho <- rep(NA, nrow(data))
}## Total variance of observed correlations Sr2
### Total variance of raw observed correlations
# Sr2: 1 Directly report
#------------------------------------------------------------------------
Sr2 <- (data$hsraw.sdcorr)^2
# 如果 hsraw.var.obs.total 不为空，则用它填补 Sr2 中的 NA
if (!is.null(data$hsraw.var.obs.total)) {
Sr2[is.na(Sr2)] <- data$hsraw.var.obs.total[is.na(Sr2)]
}
# Sr2: 2 Compute total variance from Q/NK
#-------------------------------------------------------------------------
indM[is.na(Sr2), 3] <- 2
Q <- as.numeric(data$horaw.q)
Sr2.tmp <- (Q - (K - 1)) / (NK - NK / K) # Handbook p.271 Equ. 14.23
Sr2.tmp[Sr2.tmp < 0] <- NA
Sr2[is.na(Sr2)] <- Sr2.tmp[is.na(Sr2)]
# Sr2: 3 Total Variance = Average Sampling Variance + Heterogeneity
#-------------------------------------------------------------------------
indM[is.na(Sr2), 3] <- 3
# Average Sampling Variance (参考 Hunter & Schmidt p.101)
# 这里的 rbar、Nbar 需要在前面代码里已正确计算
AveSig2e <- ((1 - rbar^2)^2) / (Nbar - 1)
# 如果上一步失败或 NA，可以使用 hsraw.sdartifact 的平方
Sig2e <- (data$hsraw.sdartifact)^2
AveSig2e[is.na(AveSig2e)] <- Sig2e[is.na(AveSig2e)]
# 如果依旧是 NA，可以使用 hsraw.var.error
AveSig2e[is.na(AveSig2e)] <- data$hsraw.var.error[is.na(AveSig2e)]
# 去除小于 0 的异常值
AveSig2e[AveSig2e < 0] <- NA
AveSig2e <- as.numeric(AveSig2e)
tau2.r <- as.numeric(tau2.r)
# 计算总方差 = 平均抽样方差 + 异质性方差
TotVar <- AveSig2e + tau2.r
Sr2[is.na(Sr2)] <- TotVar[is.na(Sr2)]
# Sr2: 4 Total Variance = Average Sampling Variance/(1 - Percentage)
#-------------------------------------------------------------------------
indM[is.na(Sr2), 4] <- 4
Sr2[is.na(Sr2)] <- (AveSig2e / (1 - data$horaw.i2))[is.na(Sr2)]
# Sr2: 5 Reported total variance smaller than Average Sampling variance
#-------------------------------------------------------------------------
Sr2 <- as.numeric(Sr2)
indM[which(Sr2 - AveSig2e < 0), 3] <- 5
Sr2[which(Sr2 - AveSig2e < 0)] <- NA
Sr2[Sr2 < 0] <- NA
# Sr2: 6 Computed Sr2 greater than 1
#-------------------------------------------------------------------------
indM[which(Sr2 > 1), 3] <- 6
Sr2[which(Sr2 > 1)] <- NAif ((is.null(data$aaa.hscorrected.r) == F) | (is.null(data$aaa.hocorrected.r) == F)) {
# 1: Directly report
#-------------------------------------------------------------------------
Src2 <- (data$hscorrected.sdcorr)^2
if (is.null(data$hscorrected.var.obs.total) == F) {
Src2[is.na(Src2)] <- data$hscorrected.var.obs.total[is.na(Src2)]
}
# 2: Compute total variance from Q/NK
#-------------------------------------------------------------------------
indM[is.na(Src2), 4] <- 2
Q <- data$hocorrected.q
Src2.tmp <- ((Q - (K - 1)) / (NK - NK / K)) # Handbook p.271 Equ. 14.23
Src2.tmp[Src2.tmp < 0] <- NA
Src2[is.na(Src2)] <- Src2.tmp[is.na(Src2)]
# 3: Total Variance = Average Sampling Variance + Heterogeneity
#-------------------------------------------------------------------------
indM[is.na(Src2), 4] <- 3
AveSig2e.rho <- ((1 - rbar^2)^2) / (Nbar - 1) / (rel.meta^2) # Hunter & Schmidt p.144
AveSig2e.rho[is.na(AveSig2e.rho)] <- ((data$hscorrected.sdartifact)^2)[is.na(AveSig2e.rho)]
if (is.null(data$hscorrected.varerror) == F) {
AveSig2e.rho[is.na(AveSig2e.rho)] <- data$hscorrected.varerror[is.na(AveSig2e.rho)]
}
AveSig2e.rho[which(AveSig2e.rho < 0)] <- NA
TotVar.rho <- AveSig2e.rho + tau2.rho
Src2[is.na(Src2)] <- TotVar.rho[is.na(Src2)]
# 4: Total Variance = Average Sampling Variance/(1 - Percentage)
#-------------------------------------------------------------------------
indM[is.na(Src2), 4] <- 4
Src2[is.na(Src2)] <- (AveSig2e.rho / (1 - data$hocorrected.i2))[is.na(Src2)]
# 5: Reported total variance smaller than Average Sampling variance
#-------------------------------------------------------------------------
indM[which(Src2 - AveSig2e.rho < 0), 4] <- 5
Src2[Src2 - AveSig2e.rho < 0] <- NA
Src2[Src2 < 0] <- NA
# 6: Computed Src2 greater than 1
#-------------------------------------------------------------------------
indM[which(Src2 > 1), 4] <- 6
Src2[which(Src2 > 1)] <- NA
} else {
Src2 <- AveSig2e.rho <- rep(NA, nrow(data))
}# Method 1: tau2 = Total Variance - Average Error Variance
#-------------------------------------------------------------------------
# Raw correlations
tau2.r.f <- tau2.r
indM[is.na(tau2.r), 5] <- 3
tau2.r.f[is.na(tau2.r.f)] <- Sr2[is.na(tau2.r.f)] - AveSig2e[is.na(tau2.r.f)]
tau2.r.f[tau2.r.f == 0] <- NA
tau2.r.f[(Sr2 - tau2.r.f) == 0] <- NA
# tau2.r.f[tau2.r.f > 1] = NA # 如果tau2超过1，可按需要置为NA
# Corrected correlations
tau2.rho.f <- tau2.rho
indM[is.na(tau2.rho), 6] <- 3
tau2.rho.f[is.na(tau2.rho.f)] <- Src2[is.na(tau2.rho.f)] - AveSig2e.rho[is.na(tau2.rho.f)]
# tau2.rho.f[tau2.rho.f < 0] = NA
tau2.rho.f[tau2.rho.f == 0] <- NA
tau2.rho.f[tau2.rho.f > 1] <- NA
# Method 2: tau2 = 0.0144 from a large meta-analysis on heterogeneity in psychological research
# Method 2: 采用0.0144（来源于心理学领域大规模meta分析的异质性估计）
#-------------------------------------------------------------------------
# Raw correlations
# indM[is.na(tau2.r.f), 5] = 4
# tau2.r.f[is.na(tau2.r.f)] = 0.0144
# Corrected correlations
# indM[is.na(tau2.rho.f), 6] = 4
# tau2.rho.f[is.na(tau2.rho.f)] = 0.0144# Total Variance = Average Sampling Variance + Heterogeneity (Imputed)
# Raw correlations
Sr2.f <- Sr2
indM[is.na(Sr2.f), 3] <- 8
TotVar <- AveSig2e + tau2.r.f
Sr2.f[is.na(Sr2.f)] <- TotVar[is.na(Sr2.f)]
# Corrected correlations
Src2.f <- Src2
indM[is.na(Src2.f), 4] <- 8
TotVar <- AveSig2e.rho + tau2.rho.f
Src2.f[is.na(Src2.f)] <- TotVar[is.na(Src2.f)]
Sr2.f[is.na(Sr2.f)] <- mean(Sr2.f, na.rm = TRUE)
indM[is.na(Sr2.f), 3] <- 9
Src2.f[is.na(Src2.f)] <- mean(Src2.f, na.rm = TRUE)
indM[is.na(Src2.f), 4] <- 9library(readxl)
library(data.table)
# 读取 Excel 文件中名为 "国商reference" 的 sheet
meta_coding <- as.data.table(read_excel("meta analysis coding.xlsx", sheet = "国商reference"))
data[aaa.articleid == "Liang2022", aaa.articleid := 20252022]
data[aaa.articleid == "LiuJOM", aaa.articleid := 20252023]
data <- merge(data,
meta_coding[, .(aaa.articleid, Author, year)],
by = "aaa.articleid",
all.x = TRUE,
sort = FALSE)#Reverse coding(OB)
# data$reverse.VariablesOBX <- as.numeric(data$reverse.VariablesOBX)
# data$reverse.VariablesOBY <- as.numeric(data$reverse.VariablesOBY)
#
# # 第二步：将 rbar、rhobar 分别乘以这两个数值
# rbar <- rbar * data$reverse.VariablesOBX * data$reverse.VariablesOBY
# rhobar <- rhobar * data$reverse.VariablesOBX * data$reverse.VariablesOBY
# # # Reverse coding(IB)
data$reverse.VariablesIBX <- as.numeric(data$reverse.VariablesIBX)
data$reverse.VariablesIBY <- as.numeric(data$reverse.VariablesIBY)
# 第二步：将 rbar、rhobar 分别乘以这两个数值
rbar <- rbar * data$reverse.VariablesIBX * data$reverse.VariablesIBY
rhobar <- rhobar * data$reverse.VariablesIBX * data$reverse.VariablesIBY
#
meta.id <- trim.metaid(data$aaa.articleid) # 提取并排序 meta.id
# 先去除两边空格
data$Xx <- str_trim(data$Xx)
data$Yy <- str_trim(data$Yy)
# 将 "perception of support" 替换为 "perceptions of support"
data$Xx <- sub("^perceptions of support(@.*)", "perception of support\\1", data$Xx)
data$Yy <- sub("^perceptions of support(@.*)", "perception of support\\1", data$Yy)
# IB中需要替换的名称
columns_to_replace <- c("Xx", "Yy")
# # 要替换的值和新值
# values_to_replace <- c("financial outcome", "general performance",
# "innovation", "Innovation",
# "internationalization", "Internationalization",
# "non-marketing outcome","marketing outcome","Organizational Outcome")
# new_value <- "organizational outcome"
#
# # 替换指定列中的值
#
# data=data[, (columns_to_replace) := lapply(.SD, function(x) {
# ifelse(x %in% values_to_replace, new_value, x)
# }), .SDcols = columns_to_replace]
# 要替换的列
columns_to_replace <- c("Xx", "Yy")
# 要替换的值
values_to_replace <- c("financial outcome", "general performance",
"innovation", "Innovation",
"internationalization", "Internationalization",
"non-marketing outcome", "marketing outcome", "Organizational Outcome")
# 替换后的统一值
new_value <- "organizational outcome"
#
data$Xx <- sub("^strategic orientation(@.*)", "strategic leadership\\1", data$Xx)
#
data$Yy <- sub("^strategic orientation(@.*)", "strategic leadership\\1", data$Yy)
# 替换逻辑：保留后缀部分（@xxx）
for (old_value in values_to_replace) {
pattern <- paste0("^", old_value, "(@.*)?") # 匹配以 old_value 开头，后面可跟 @...
replacement <- paste0(new_value, "\\1") # 用新值替换，并保留原后缀
data[, (columns_to_replace) := lapply(.SD, function(x) sub(pattern, replacement, x)),
.SDcols = columns_to_replace]
}
# 替换factor为&Market Concentration的variable和category
columns_to_replace <- c("ordervariablefactor1", "ordervariablefactor2")
#
# 正确的 pattern：匹配整串（从头到 &Market Concentration）
pattern <- "^[^@&]+@[^@&]+(&Market Concentration)"
#
# 正确的 replacement：完全替换成新的前缀和中间部分，只保留 & 后的内容
replacement <- "organizational capability@COMPETITIVE ADVANTAGES\\1"
# 应用替换
data[, (columns_to_replace) := lapply(.SD, function(x) gsub(pattern, replacement, x)),
.SDcols = columns_to_replace]
#
#
#
# 替换 strategic orientation-> strategic leadership，以及COMPETITIVE ADVANTAGES
#
data$ordervariablefactor1 <- sub("^strategic orientation(@.*)", "strategic leadership\\1", data$ordervariablefactor1)
#
data$ordervariablefactor2 <- sub("^strategic orientation(@.*)", "strategic leadership\\1", data$ordervariablefactor2)
data[ordervariablefactor1 == "organizational capability@COMPETITIVE ADVANTAGES&Market Concentration",
`:=`(
Xx = Yy,
Yy = "organizational capability@COMPETITIVE ADVANTAGES"
)]
# factorX = data$aaa.variable1
# factorY = data$aaa.variable2
#
# # Xx 和 Yy 的所属的类别
# XAnalysisCategory <- data$XX
# YAnalysisCategory <- data$YY
OrderVariableFactor1 <- data$ordervariablefactor1
OrderVariableFactor2 <- data$ordervariablefactor2
#Author and Year
Author <- data$Author
Year <- data$year
aaa.hl.powerdistance = data$aaa.hl.powerdistance
aaa.individulism.collectivism = data$aaa.individulism.collectivism
aaa.masculinity.femininity = data$aaa.masculinity.femininity
aaa.hl.uncertaintyavoidance = data$aaa.hl.uncertaintyavoidance
aaa.longterm.shortterm = data$aaa.longterm.shortterm
aaa.indulgence.restraint= data$aaa.indulgence.restraint
# Extact and get order the X and Y categories
# 提取并排序 X 和 Y 的类别
XCategory <- data$Xx
XCategory <- str_trim(XCategory)
XCategory.names <- names(table(XCategory)) # X 类别中所有不重复的名称
YCategory <- data$Yy
YCategory <- str_trim(YCategory)
YCategory.names <- vector("list", length = length(XCategory.names))
names(YCategory.names) <- XCategory.names
for (i in 1:length(XCategory.names)) {
sel <- which(XCategory == XCategory.names[i])
YCategory.sub <- YCategory[sel]
tmp <- names(table(YCategory.sub))
YCategory.names[[i]] <- tmp
}
YCategory.names2 <- unique(unlist(YCategory.names))
# 计算置信区间（H&S book p.230，标准误设置为 sqrt(Src2.f/K)）
CI95_LL <- rhobar - 1.96 * sqrt(Src2.f / K)
CI95_UL <- rhobar + 1.96 * sqrt(Src2.f / K)
# 计算80%信用区间（基于 tau2.rho.f）
CR80_LL <- rhobar - qnorm(0.9) * sqrt(tau2.rho.f)
CR80_UL <- rhobar + qnorm(0.9) * sqrt(tau2.rho.f)
First.Meta <- data.frame(
meta.id, # 由 trim.metaid(data$aaa.articleid) 得到
Author, # 来自 data$Author
Year, # 来自 data$Year
# group, # 来自 data$subgroup.country
XCategory, # 来自 data$Xx（已 str_trim 处理）
YCategory, # 来自 data$Yy（已 str_trim 处理）
OrderVariableFactor1, # 来自 data$ordervariablefactor1
OrderVariableFactor2, # 来自 data$ordervariablefactor2
aaa.hl.powerdistance, # 来自 data$aaa.hl.powerdistance
aaa.individulism.collectivism, # 来自 data$aaa.individulism.collectivism
aaa.masculinity.femininity, # 来自 data$aaa.masculinity.femininity
aaa.hl.uncertaintyavoidance, # 来自 data$aaa.hl.uncertaintyavoidance
aaa.longterm.shortterm, # 来自 data$aaa.longterm.shortterm
aaa.indulgence.restraint, # 来自 data$aaa.indulgence.restraint
K, # 第一阶 meta 分析中的效应量数
NK, # 总样本量
rbar, # 原始平均相关
Sr2.f, # 补充后的原始总方差
tau2.r.f, # 原始异质性估计
rhobar, # 原始校正后的相关系数
tau2.rho.f, # 校正后异质性估计
Src2.f, # 补充后的校正总方差
CI95_LL, # 95%置信下限
CI95_UL, # 95%置信上限
CR80_LL, # 80%信用区间下限
CR80_UL, # 80%信用区间上限
rel.meta, # 可靠性指标
AveSig2e, # 平均抽样方差（raw）
AveSig2e.rho, # 平均抽样方差（校正）
indM # 填补或修正方法记录矩阵/数据框
)
write.xlsx(First.Meta, "VariablesIB1stOrdInput.xlsx", col.names = TRUE, row.names = FALSE)# 计算原始和校正相关的采样方差
vi <- Sr2.f / K
vic <- Src2.f / K
vi <- as.numeric(vi)
vic <- as.numeric(vic)
## Table 2 (Schmidt & Oh, 2008)
T2.col.names <- c(
"XCategory", "YCategory", "Nmeta", "Nes", "Nes.used", "sumk", "sumN",
"C6_Mean R", "C6_SE", "CI.LL", "CI.UL", "CV.LL", "CV.UL",
"C7_Sampling Error Variance", "C8_Total Variance", "C9_True Variance", "C9_SE",
"L1_Within-Meta Variance", "L2_Between-Meta Variance",
"SE_L1_Within-Meta Variance", "SE_L2_Between-Meta Variance",
"C10_Prop of Error Variance", "C11_Reliability"
)
# 每个X类别下有多少种Y类别
n.ycat.l <- lapply(YCategory.names, function(x) length(x))
# 总的 X-Y 组合数
n.xycat <- sum(unlist(n.ycat.l))
# X类别的唯一数量
n.xcat <- length(XCategory.names)
# 初始化Table2的矩阵
Table2SO.uc.y <- matrix(NA, n.xycat, 23)
colnames(Table2SO.uc.y) <- T2.col.names
rownames(Table2SO.uc.y) <- paste0("CatXY", 1:n.xycat)
Table2SO.c.y <- Table2SO.uc.y
# 初始化计数器
counti <- 0
branch_counter <- 0
for (j in 1:n.xcat) {
crit1 <- (XCategory == XCategory.names[j])
for (w in 1:n.ycat.l[[j]]) {
counti <- counti + 1
Table2SO.uc.y[counti, 1] <- XCategory.names[j]
Table2SO.c.y[counti, 1] <- XCategory.names[j]
Table2SO.uc.y[counti, 2] <- YCategory.names[[j]][w]
Table2SO.c.y[counti, 2] <- YCategory.names[[j]][w]
crit2 <- (YCategory == YCategory.names[[j]][w])
sel <- which(crit1 * crit2 == 1)
nes <- length(sel)
nes.narm <- min(c(nes, length(na.omit(rbar[sel]))))
sel.narm <- sel[is.na(rbar[sel]) == FALSE]
sid <- meta.id[sel]
nmeta <- length(unique(sid))
if (nes.narm > 1) { # multiple effect sizes from multiple 1st-order meta analyses
# If all effect sizes come from one 1st-order meta
# MLM method does not work
# We use RVE methods and assume the between-effect-size correlation = 0.8
fit <- myrma.des(yi = rbar[sel], vi = vi[sel], sid, method = "MLM")
fitc <- myrma.des(yi = rhobar[sel], vi = vic[sel], sid, method = "MLM")
if (is.na(fit$b) | is.null(fit$b)) {
fit <- list(rbar = NA_real_, sigma2 = NA_real_, vi = NA_real_, k = 0, nmeta = nmeta)
}
if (is.na(fitc$b) | is.null(fitc$b)) {
fitc <- list(rbar = NA_real_, sigma2 = NA_real_, vi = NA_real_, k = 0, nmeta = nmeta)
}
if ((is.null(fit$rbar) - 1) + (is.null(fitc$rbar) - 1) < 0) {
# 更新计数器，并打印当前组合信息及累计次数
branch_counter <- branch_counter + 1
cat(
"执行rma分支的组合：", XCategory.names[j], "和", YCategory.names[[j]][w],
"，累计执行次数：", branch_counter, "\n"
)
fit <- try(rma(yi = rbar[sel], vi = vi[sel]))
fitc <- try(rma(yi = rhobar[sel], vi = vic[sel]))
if (inherits(fit, "try-error")) {
print("yyx fit error")
fit <- list(b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA)
} else {
fit$sigma2 <- fit$tau2
cat("fit$k:", fit$k, "\n")
if (fit$k == 1) {
cat("打印fit结果:\n")
print(fit$b)
print(fit$se)
print(fit$sigma2)
print(fit)
}
}
if (inherits(fitc, "try-error")) {
print("yyx fit error")
fitc <- list(b = NA, se = NA, sigma2 = NA, se.tau2 = NA, ci.lb = NA, ci.ub = NA)
} else {
fitc$sigma2 <- fitc$tau2
cat("fitc$k:", fitc$k, "\n")
if (fitc$k == 1) {
cat("打印fitc结果:\n")
print(fitc$b)
print(fitc$se)
print(fit$sigma2)
print(fitc)
}
}
}
} else if (nes.narm == 1) { # only one effect size in this category
print("只有一个效应量")
fit <- list(
rbar = rbar[sel.narm], sigma2 = tau2.r.f[sel.narm],
vi = vi[sel.narm], k = 1, nmeta = nmeta
)
fitc <- list(
rbar = rhobar[sel.narm], sigma2 = tau2.rho.f[sel.narm],
vi = vic[sel.narm], k = 1, nmeta = nmeta
)
} else { # zero effect in this category
fit <- list(rbar = NA, sigma2 = NA, vi = NA, k = 0, nmeta = nmeta)
fitc <- list(rbar = NA, sigma2 = NA, vi = NA, k = 0, nmeta = nmeta)
}
Ksub <- K[sel]
NKsub <- NK[sel]
if (nes.narm != nes) {
Ksub[is.na(rbar[sel]) == TRUE] <- NA
NKsub[is.na(rbar[sel]) == TRUE] <- NA
}
Table2SO.uc.y[counti, 3:23] <- meta2nd.Table2SO(fit, Ksub, NKsub, nmeta)
Table2SO.c.y[counti, 3:23] <- meta2nd.Table2SO(fitc, Ksub, NKsub, nmeta)
# result <- meta2nd.Table2SO(fit, Ksub, NKsub, nmeta)
# result2 <- meta2nd.Table2SO(fitc, Ksub, NKsub, nmeta)
}
}## [1] "运行成功"
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## [1] "运行成功"# 转换为 data.table
Table2SO.uc.y <- as.data.table(Table2SO.uc.y)
Table2SO.c.y <- as.data.table(Table2SO.c.y)
# 拆分 XCategory 和 YCategory 列
Table2SO.uc.y[, c("XCategory", "XAnalysisCategory") := tstrsplit(XCategory, "@", fixed = TRUE)]
Table2SO.uc.y[, c("YCategory", "YAnalysisCategory") := tstrsplit(YCategory, "@", fixed = TRUE)]
Table2SO.c.y[, c("XCategory", "XAnalysisCategory") := tstrsplit(XCategory, "@", fixed = TRUE)]
Table2SO.c.y[, c("YCategory", "YAnalysisCategory") := tstrsplit(YCategory, "@", fixed = TRUE)]
# 重新排列列顺序
setcolorder(Table2SO.uc.y,
c("XCategory", "XAnalysisCategory", "YCategory", "YAnalysisCategory",
setdiff(names(Table2SO.uc.y), c("XCategory", "XAnalysisCategory", "YCategory", "YAnalysisCategory")))
)
setcolorder(Table2SO.c.y,
c("XCategory", "XAnalysisCategory", "YCategory", "YAnalysisCategory",
setdiff(names(Table2SO.c.y), c("XCategory", "XAnalysisCategory", "YCategory", "YAnalysisCategory")))
)
write.xlsx(Table2SO.uc.y, "VariablesIB_Uncorrected.xlsx",
col.names = T,
row.names = T, sheetName = "Overall", append = T
)
write.xlsx(Table2SO.c.y, "VariablesIB_Corrected.xlsx",
col.names = T,
row.names = T, sheetName = "Overall", append = T
)# SOOutput(Table2SO.uc.y, NewVariables = NULL, Order = "-sumk", title = "Uncorrected Variables", TName = TRUE, note = TRUE, Cnote = FALSE, digits = FALSE)
SOOutput(Table2SO.uc.y, NewVariables = NULL, Order = "-sumk", title = "Uncorrected Variables", TName = TRUE, note = TRUE, Cnote = FALSE, digits =TRUE)## SOOutput() is an R function for extracting and organizing second-order
## meta-analysis results.
## It extracts the following variables:
## XCategory, YCategory,
## Nmeta, sumk, sumN,
## C6_Mean.R, C6_SE, CI.LL, CI.UL, CV.LL, CV.UL,
##
## C7_Sampling.Error.Variance, C8_Total.Variance, C9_True.Variance,
##
## C10_Prop.of.Error.Variance, C11_Reliability.
## Additional parameters:
## -
## NewVariables: Optional additional variables to include.
## - Order: Specifies the
## sorting column (e.g., '-sumk' for descending order).
## - title: Title for the
## output table.
## - TName: If TRUE, renames the output columns to more intuitive
## names.
## - digits: If TRUE, all numeric columns (except XCategory and YCategory)
## will be rounded to 3 decimal places and displayed in fixed notation; if FALSE,
## they remain unchanged.Uncorrected Variables
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## X Category Y Category Number of Studies Total k Total N C6_Mean R S.E. CI Lower CI Upper CV Lower CV Upper C7_Sampling Error Variance C8_Total Variance C9_True Variance C10_Prop of Error Variance Reliability
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 1 organizational outcome strategic leadership 6.000 4284.000 10860990.000 0.085 (0.044) -0.001 0.171 -0.054 0.224 0.000 0.012 0.012 0.003 0.997
## 2 organizational outcome strategic process 11.000 2744.000 3345094.000 0.204 (0.033) 0.139 0.270 0.053 0.355 0.000 0.014 0.014 0.032 0.968
## 3 organizational outcome organizational resource 14.000 2669.000 5870231.000 0.236 (0.042) 0.153 0.318 0.022 0.450 0.000 0.028 0.028 0.014 0.986
## 4 organizational outcome organizational outcome 11.000 1161.000 1172445.000 0.242 (0.034) 0.175 0.308 0.094 0.390 0.001 0.014 0.013 0.061 0.939
## 5 detering institutional factor organizational outcome 4.000 754.000 1070059.000 -0.019 (0.016) -0.050 0.012 -0.109 0.071 0.000 0.005 0.005 0.072 0.928
## 6 organizational outcome organizational capability 5.000 349.000 205931.000 0.359 (0.060) 0.242 0.476 0.179 0.539 0.004 0.023 0.020 0.153 0.847
## 7 attracting institutional factor detering institutional factor 1.000 189.000 59589.000 -0.076 (0.023) -0.121 -0.032 -0.118 -0.034 0.000 0.002 0.001 0.299 0.701
## 8 attracting institutional factor attracting institutional factor 1.000 184.000 58013.000 0.135 (0.075) -0.011 0.281 -0.132 0.402 0.000 0.044 0.043 0.008 0.992
## 9 strategic process strategic process 1.000 165.000 1514613.000 0.026 (0.008) 0.011 0.040 0.010 0.041 0.000 0.000 0.000 0.562 0.438
## 10 organizational capability organizational resource 2.000 120.000 73507.000 0.081 (0.034) 0.014 0.148 -0.042 0.204 0.001 0.010 0.009 0.100 0.900
## 11 attracting institutional factor organizational outcome 1.000 59.000 229268.000 0.273 (0.042) 0.192 0.355 0.192 0.355 0.006 0.010 0.004 0.605 0.395
## 12 attracting institutional factor strategic process 1.000 50.000 13908.000 0.217 (0.058) 0.102 0.331 0.089 0.344 0.002 0.012 0.010 0.172 0.828
## 13 organizational capability strategic process 1.000 50.000 11500.000 0.490 (0.045) 0.402 0.578 0.334 0.646 0.002 0.017 0.015 0.125 0.875
## 14 organizational resource organizational resource 1.000 38.000 12907.000 0.175 (0.037) 0.102 0.247 0.112 0.238 0.003 0.005 0.002 0.516 0.484
## 15 strategic leadership strategic process 2.000 33.000 21783.000 0.167 (0.125) -0.078 0.412 -0.057 0.391 0.001 0.031 0.031 0.020 0.980
## 16 organizational resource others 1.000 26.000 14065.000 0.151 (0.065) 0.024 0.278 0.065 0.237 0.004 0.008 0.005 0.455 0.545
## 17 detering institutional factor organizational resource 1.000 11.000 3862.000 -0.197 (0.045) -0.285 -0.108 -0.197 -0.197 0.004 0.004 0.000 1.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────SOOutput(Table2SO.c.y, NewVariables = NULL, Order = "-sumk", title = "Uncorrected Variables", TName = TRUE, note = TRUE, Cnote = FALSE, digits = TRUE)## SOOutput() is an R function for extracting and organizing second-order
## meta-analysis results.
## It extracts the following variables:
## XCategory, YCategory,
## Nmeta, sumk, sumN,
## C6_Mean.R, C6_SE, CI.LL, CI.UL, CV.LL, CV.UL,
##
## C7_Sampling.Error.Variance, C8_Total.Variance, C9_True.Variance,
##
## C10_Prop.of.Error.Variance, C11_Reliability.
## Additional parameters:
## -
## NewVariables: Optional additional variables to include.
## - Order: Specifies the
## sorting column (e.g., '-sumk' for descending order).
## - title: Title for the
## output table.
## - TName: If TRUE, renames the output columns to more intuitive
## names.
## - digits: If TRUE, all numeric columns (except XCategory and YCategory)
## will be rounded to 3 decimal places and displayed in fixed notation; if FALSE,
## they remain unchanged.Uncorrected Variables
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## X Category Y Category Number of Studies Total k Total N C6_Mean R S.E. CI Lower CI Upper CV Lower CV Upper C7_Sampling Error Variance C8_Total Variance C9_True Variance C10_Prop of Error Variance Reliability
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## 1 organizational outcome strategic leadership 6.000 4284.000 10860990.000 0.100 (0.050) 0.002 0.199 -0.059 0.260 0.000 0.016 0.016 0.003 0.997
## 2 organizational outcome strategic process 11.000 2744.000 3345094.000 0.257 (0.042) 0.174 0.340 0.064 0.449 0.001 0.023 0.023 0.030 0.970
## 3 organizational outcome organizational resource 14.000 2669.000 5870231.000 0.286 (0.049) 0.189 0.383 0.036 0.537 0.001 0.039 0.038 0.015 0.985
## 4 organizational outcome organizational outcome 11.000 1161.000 1172445.000 0.305 (0.044) 0.218 0.391 0.111 0.498 0.001 0.024 0.023 0.049 0.951
## 5 detering institutional factor organizational outcome 4.000 754.000 1070059.000 -0.025 (0.019) -0.063 0.013 -0.136 0.085 0.001 0.008 0.007 0.074 0.926
## 6 organizational outcome organizational capability 5.000 349.000 205931.000 0.439 (0.065) 0.312 0.566 0.238 0.640 0.004 0.029 0.025 0.141 0.859
## 7 attracting institutional factor detering institutional factor 1.000 189.000 59589.000 -0.095 (0.028) -0.151 -0.039 -0.147 -0.043 0.001 0.002 0.002 0.299 0.701
## 8 attracting institutional factor attracting institutional factor 1.000 184.000 58013.000 0.169 (0.093) -0.014 0.351 -0.165 0.502 0.001 0.068 0.068 0.008 0.992
## 9 strategic process strategic process 1.000 165.000 1514613.000 0.032 (0.009) 0.014 0.051 0.013 0.052 0.000 0.001 0.000 0.563 0.437
## 10 organizational capability organizational resource 2.000 120.000 73507.000 0.097 (0.042) 0.016 0.179 -0.053 0.248 0.002 0.015 0.014 0.103 0.897
## 11 attracting institutional factor organizational outcome 1.000 59.000 229268.000 0.340 (0.052) 0.239 0.441 0.215 0.465 0.006 0.016 0.010 0.394 0.606
## 12 attracting institutional factor strategic process 1.000 50.000 13908.000 0.294 (0.075) 0.148 0.440 0.134 0.454 0.004 0.019 0.016 0.198 0.802
## 13 organizational capability strategic process 1.000 50.000 11500.000 0.575 (0.044) 0.489 0.661 0.424 0.726 0.002 0.016 0.014 0.146 0.854
## 14 organizational resource organizational resource 1.000 38.000 12907.000 0.205 (0.045) 0.116 0.294 0.123 0.288 0.004 0.008 0.004 0.483 0.517
## 15 strategic leadership strategic process 2.000 33.000 21783.000 0.227 (0.175) -0.115 0.570 -0.086 0.541 0.001 0.061 0.060 0.017 0.983
## 16 organizational resource others 1.000 26.000 14065.000 0.195 (0.050) 0.096 0.294 0.195 0.195 0.005 0.005 0.000 1.000 0.000
## 17 detering institutional factor organizational resource 1.000 11.000 3862.000 -0.252 (0.057) -0.365 -0.139 -0.252 -0.252 0.007 0.007 0.000 1.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────