knitr::opts_chunk$set(echo = TRUE)
ds <- read.csv("/Users/corbinwalls/Library/CloudStorage/OneDrive-american.edu/Graduate Assistantship/Donor Impact on Collaboration/LebanonCollabData.csv")
#install.packages("tidyverse")
#install.packages("dplyr")
#install.packages("modelsummary")
#install.packages("kableExtra")
#install.packages("pandoc")
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## group_rowslibrary(pandoc)colnames(ds)## [1] "ID" "SECTOR" "Q1" "Q2_1" "Q2_2" "Q2_3" "Q2_4" "Q2_5" "Q2_6" "Q2_7" "Q2_8" "Q2_9"
## [13] "Q2_10" "Q2_11" "Q2_12" "Q2_13" "Q2_14" "Q2_15" "Q2_16" "Q3" "Q4" "Q5_1" "Q5_2" "Q5_3"
## [25] "Q5_4" "Q5_5" "Q5_6" "Q5_7" "Q5_8" "Q5_9" "Q5_10" "Q5_11" "Q5_12" "Q5_13" "Q5_14" "Q5_15"
## [37] "Q5_16" "Q5_17" "Q5_18" "Q5_19" "Q6" "Q7" "Q8" "Q9_1" "Q9_2" "Q9_3" "Q9_4" "Q9_5"
## [49] "Q9_6" "Q9_7" "Q9_8" "Q9_9" "Q9_10" "Q9_11" "Q9_12" "Q9_13" "Q9_14" "Q9_15" "Q9_16" "Q9_17"
## [61] "Q9_18" "Q9_19" "Q10_1" "Q10_2" "Q10_3" "Q10_4" "Q10_5" "Q10_6" "Q10_7" "Q10_8" "Q10_9" "Q10_10"
## [73] "Q10_11" "Q10_12" "Q10_13" "Q10_14" "Q11_1" "Q11_2" "Q11_3" "Q11_4" "Q11_5" "Q11_6" "Q12" "Q13"
## [85] "Q14_1" "Q14_2" "Q14_3" "Q14_4" "Q14_5" "Q14_6" "Q15" "Q16" "Q17" "Q18" "Q19" "Q20_1"
## [97] "Q20_2" "Q20_3" "Q20_4" "Q20_5" "Q20_6" "Q21_1" "Q21_2" "Q22_1" "Q22_2" "Q23_1" "Q23_2" "Q23_3"
## [109] "Q23_4" "Q23_5" "Q23_6" "Q23_7" "Q23_8" "Q23_9" "Q23_10" "Q23_11" "Q23_12" "Q23_13" "Q23_14" "Q23_15"
## [121] "Q24_1" "Q24_2" "Q24_3" "Q24_4" "Q24_5" "Q24_6" "Q24_7" "Q24_8" "Q24_9" "Q24_10" "Q24_11" "Q24_12"
## [133] "Q25" "Q26" "Q27_1" "Q27_2" "Q27_3" "Q27_4" "Q27_5" "Q27_6" "Q27_7" "Q27_8" "Q27_9" "Q27_10"
## [145] "Q27_11" "Q27_12" "Q27_13" "Q27_14" "Q27_15" "Q27_16" "Q28_1" "Q28_2" "Q28_3" "Q28_4" "Q28_5" "Q28_6"
## [157] "Q28_7" "Q28_8" "Q28_9" "Q29" "Q30_1" "Q30_2" "Q30_3" "Q30_4" "Q30_5" "Q30_6" "Q31" "Q32_1"
## [169] "Q32_2" "Q32_3" "Q32_4" "Q32_5" "Q32_6" "Q33_1" "Q33_2" "Q33_3" "Q33_4" "Q33_5.NGO.ONLY." "Q34_1" "Q34_2"
## [181] "Q34_3" "Q34_4" "Q35" "Q36" "Q37" "Q38_1" "Q38_2" "Q38_3" "Q38_4" "Q38_5" "Q38_6" "Q39"
## [193] "Q40" "Q41"ds <- ds %>% rename(id = ID)
ds <- ds %>% rename(sector = SECTOR)
ds <- ds %>% rename(collab = Q1)
ds <- ds %>% rename(nocollab_rsn_staff = Q2_1)
ds <- ds %>% rename(nocollab_rsn_noben = Q2_2)
ds <- ds %>% rename(nocollab_rsn_noint_other = Q2_3)
ds <- ds %>% rename(nocollab_rsn_notrust = Q2_4)
ds <- ds %>% rename(nocollab_rsn_priv = Q2_5)
ds <- ds %>% rename(nocollab_rsn_cost = Q2_6)
ds <- ds %>% rename(nocollab_rsn_dscntu = Q2_7)
ds <- ds %>% rename(nocollab_rsn_other = Q2_8)
ds <- ds %>% rename(nocollab_rsn_otherspecify = Q2_9)
ds <- ds %>% rename(nocollab_rsn_funds = Q2_10)
ds <- ds %>% rename(nocollab_rsn_noint_resp = Q2_11)
ds <- ds %>% rename(nocollab_rsn_trble = Q2_12)
ds <- ds %>% rename(nocollab_rsn_noopp = Q2_13)
ds <- ds %>% rename(nocollab_rsn_collab = Q2_14)
ds <- ds %>% rename(nocollab_rsn_goalachvd = Q2_15)
ds <- ds %>% rename(nocollab_rsn_partner = Q2_16)
ds <- ds %>% rename(consulted_others = Q3)
ds <- ds %>% rename(future_interest = Q4)
ds <- ds %>% rename(future_interest_arts = Q5_1)
ds <- ds %>% rename(future_interest_cmntyact = Q5_2)
ds <- ds %>% rename(future_interest_seniors = Q5_3)
ds <- ds %>% rename(future_interest_health = Q5_4)
ds <- ds %>% rename(future_interest_disaster = Q5_5)
ds <- ds %>% rename(future_interest_transp = Q5_6)
ds <- ds %>% rename(future_interest_enforcelaw = Q5_7)
ds <- ds %>% rename(future_interest_youth = Q5_8)
ds <- ds %>% rename(future_interest_lib = Q5_9)
ds <- ds %>% rename(future_interest_fire = Q5_10)
ds <- ds %>% rename(future_interest_finfund = Q5_11)
ds <- ds %>% rename(future_interest_econdev = Q5_12)
ds <- ds %>% rename(future_interest_humserv = Q5_13)
ds <- ds %>% rename(future_interest_house = Q5_14)
ds <- ds %>% rename(future_interest_parks = Q5_15)
ds <- ds %>% rename(future_interest_edu = Q5_16)
ds <- ds %>% rename(future_interest_env = Q5_17)
ds <- ds %>% rename(future_interest_other = Q5_18)
ds <- ds %>% rename(future_interest_otherspecify = Q5_19)
ds <- ds %>% rename(funds_gaveorgot = Q6)
ds <- ds %>% rename(funds_gaveorgot_rsn = Q7)
ds <- ds %>% rename(Funds_gaveorgot_amnt = Q8)
ds <- ds %>% rename(collab_arts = Q9_1)
ds <- ds %>% rename(collab_cmntyact = Q9_2)
ds <- ds %>% rename(collab_seniors = Q9_3)
ds <- ds %>% rename(collab_health = Q9_4)
ds <- ds %>% rename(collab_disaster = Q9_5)
ds <- ds %>% rename(collab_transp = Q9_6)
ds <- ds %>% rename(collab_enforcelaw = Q9_7)
ds <- ds %>% rename(collab_youth = Q9_8)
ds <- ds %>% rename(collab_lib = Q9_9)
ds <- ds %>% rename(collab_fire = Q9_10)
ds <- ds %>% rename(collab_finfund = Q9_11)
ds <- ds %>% rename(collab_econdev = Q9_12)
ds <- ds %>% rename(collab_humserv = Q9_13)
ds <- ds %>% rename(collab_house = Q9_14)
ds <- ds %>% rename(collab_parks = Q9_15)
ds <- ds %>% rename(collab_edu = Q9_16)
ds <- ds %>% rename(collab_env = Q9_17)
ds <- ds %>% rename(collab_other = Q9_18)
ds <- ds %>% rename(collab_otherspecify = Q9_19)
ds <- ds %>% rename(collab_mthd_grants = Q10_1)
ds <- ds %>% rename(collab_mthd_servdlvry = Q10_2)
ds <- ds %>% rename(collab_mthd_infoexchg = Q10_3)
ds <- ds %>% rename(collab_mthd_sharewrkspce = Q10_4)
ds <- ds %>% rename(collab_mthd_rcrtmnt = Q10_5)
ds <- ds %>% rename(collab_mthd_casemngmnt = Q10_6)
ds <- ds %>% rename(collab_mthd_frmlservcntrct = Q10_7)
ds <- ds %>% rename(collab_mthd_govequioment = Q10_8)
ds <- ds %>% rename(collab_mthd_fndrsng = Q10_9)
ds <- ds %>% rename(collab_mthd_prgmdev = Q10_10)
ds <- ds %>% rename(collab_mthd_purchasing = Q10_11)
ds <- ds %>% rename(collab_mthd_advcy = Q10_12)
ds <- ds %>% rename(collab_mthd_other = Q10_13)
ds <- ds %>% rename(collab_mthd_otherspecify = Q10_14)
ds <- ds %>% rename(collab_initiate_lclgov = Q11_1)
ds <- ds %>% rename(collab_initiate_ngo = Q11_2)
ds <- ds %>% rename(collab_initiate_cntlgov = Q11_3)
ds <- ds %>% rename(collab_initiate_dnrs = Q11_4)
ds <- ds %>% rename(collab_initiate_other = Q11_5)
ds <- ds %>% rename(collab_initiate_otherspecify = Q11_6)
ds <- ds %>% rename(collabs_nmbr = Q12)
ds <- ds %>% rename(collab_orgsnmbr = Q13)
ds <- ds %>% rename(collab_rltnshp_supp = Q14_1)
ds <- ds %>% rename(collab_rltnshp_coord = Q14_2)
ds <- ds %>% rename(collab_rltnshp_partner = Q14_3)
ds <- ds %>% rename(collab_rltnshp_norltnshp = Q14_4)
ds <- ds %>% rename(collab_rltnshp_tension = Q14_5)
ds <- ds %>% rename(collab_rltnshp_tension_rsn = Q14_6)
ds <- ds %>% rename(collab_effectiveness = Q15)
ds <- ds %>% rename(collab_mstactvservarea = Q16)
ds <- ds %>% rename(collab_age_mstactvservarea = Q17)
ds <- ds %>% rename(collab_nmbr_in_mstactvservarea = Q18)
ds <- ds %>% rename(collab_addtnlcollabs_2016_mstactvservarea = Q19)
ds <- ds %>% rename(fundscollab_lclgov = Q20_1)
ds <- ds %>% rename(fundscollab_ngo = Q20_2)
ds <- ds %>% rename(fundscollab_cntlgov = Q20_3)
ds <- ds %>% rename(fundscollab_donors = Q20_4)
ds <- ds %>% rename(fundscollab_other = Q20_5)
ds <- ds %>% rename(fundscollab_otherspecify = Q20_6)
ds <- ds %>% rename(collab_primcrdntr = Q21_1)
ds <- ds %>% rename(collab_primcrdntr_otherspecify = Q21_2)
ds <- ds %>% rename(collab_decisionauthty = Q22_1)
ds <- ds %>% rename(collab_decisionauthty_otherspecify = Q22_2)
ds <- ds %>% rename(collab_motive_reg = Q23_1)
ds <- ds %>% rename(collab_motive_fund = Q23_2)
ds <- ds %>% rename(collab_motive_servquality = Q23_3)
ds <- ds %>% rename(collab_motive_cmntyrelations = Q23_4)
ds <- ds %>% rename(collab_motive_costeffective = Q23_5)
ds <- ds %>% rename(collab_motive_sharedgoals = Q23_6)
ds <- ds %>% rename(collab_motive_cmntyaccess = Q23_7)
ds <- ds %>% rename(collab_motive_donorrqurmnts = Q23_8)
ds <- ds %>% rename(collab_motive_avoidcompetition = Q23_9)
ds <- ds %>% rename(collab_motive_buildcmnty = Q23_10)
ds <- ds %>% rename(collab_motive_buildrltnshps = Q23_11)
ds <- ds %>% rename(collab_motive_expertise = Q23_12)
ds <- ds %>% rename(collab_motive_addressproblems = Q23_13)
ds <- ds %>% rename(collab_motive_other = Q23_14)
ds <- ds %>% rename(collab_motive_otherspecify = Q23_15)
ds <- ds %>% rename(collab_outcome_savedfinresources = Q24_1)
ds <- ds %>% rename(collab_outcome_incrsecmntyserviceslevel = Q24_2)
ds <- ds %>% rename(collab_outcome_incrsecmntyservicesquality = Q24_3)
ds <- ds %>% rename(collab_outcome_newfunds_respondent = Q24_4)
ds <- ds %>% rename(collab_outcome_newfunds_partners = Q24_5)
ds <- ds %>% rename(collab_outcome_reducedcompetition = Q24_6)
ds <- ds %>% rename(collab_outcome_incrsesatisfytrust = Q24_7)
ds <- ds %>% rename(collab_outcome_trustinprtnr = Q24_8)
ds <- ds %>% rename(collab_outcome_emply_attitudestwrdothersector = Q24_9)
ds <- ds %>% rename(collab_outcome_pltcn_attitudestwrdothersector = Q24_10)
ds <- ds %>% rename(collab_outcome_other = Q24_11)
ds <- ds %>% rename(collab_outcome_otherspecify = Q24_12)
ds <- ds %>% rename(goal_agreement = Q25)
ds <- ds %>% rename(goal_effectiveness = Q26)
ds <- ds %>% rename(anticollab_rsn_competition = Q27_1)
ds <- ds %>% rename(anticollab_rsn_finresources = Q27_2)
ds <- ds %>% rename(anticollab_rsn_staff = Q27_3)
ds <- ds %>% rename(anticollab_rsn_negattitudes = Q27_4)
ds <- ds %>% rename(anticollab_rsn_prfrcntlgov = Q27_5)
ds <- ds %>% rename(anticollab_rsn_servquality_sometimes = Q27_6)
ds <- ds %>% rename(anticollab_rsn_servquality_usually = Q27_7)
ds <- ds %>% rename(anticollab_rsn_weakrltnshp = Q27_8)
ds <- ds %>% rename(anticollab_rsn_representation = Q27_9)
ds <- ds %>% rename(anticollab_rsn_ngolose = Q27_10)
ds <- ds %>% rename(anticollab_rsn_lclgovlose = Q27_11)
ds <- ds %>% rename(anticollab_rsn_prsnlrltnshp = Q27_12)
ds <- ds %>% rename(anticollab_rsn_notrust = Q27_13)
ds <- ds %>% rename(anticollab_rsn_cnstrndauthrty = Q27_14)
ds <- ds %>% rename(anticollab_rsn_other = Q27_15)
ds <- ds %>% rename(anticollab_rsn_otherspecify = Q27_16)
ds <- ds %>% rename(otherorgs_charity = Q28_1)
ds <- ds %>% rename(otherorgs_coop = Q28_2)
ds <- ds %>% rename(otherorgs_familyassociation = Q28_3)
ds <- ds %>% rename(otherorgs_clubs = Q28_4)
ds <- ds %>% rename(otherorgs_mutualfund = Q28_5)
ds <- ds %>% rename(otherorgs_municipalunion = Q28_6)
ds <- ds %>% rename(otherorgs_muhafiz_qaimmaqam = Q28_7)
ds <- ds %>% rename(otherorgs_other = Q28_8)
ds <- ds %>% rename(otherorgs_otherspecify = Q28_9)
ds <- ds %>% rename(ingo_interact_nmbr = Q29)
ds <- ds %>% rename(ingo_rltnshp_support = Q30_1)
ds <- ds %>% rename(ingo_rltnshp_coord = Q30_2)
ds <- ds %>% rename(ingo_rltnshp_partner = Q30_3)
ds <- ds %>% rename(ingo_rltnshp_norltnshp = Q30_4)
ds <- ds %>% rename(ingo_rltnshp_tension = Q30_5)
ds <- ds %>% rename(ingo_rltnshp_tension_specify = Q30_6)
ds <- ds %>% rename(ingo_rltnshp_effectiveness = Q31)
ds <- ds %>% rename(ingo_v_collaborator_better = Q32_1)
ds <- ds %>% rename(ingo_v_collaborator_better_specify = Q32_2)
ds <- ds %>% rename(ingo_v_collaborator_same = Q32_3)
ds <- ds %>% rename(ingo_v_collaborator_same_specify = Q32_4)
ds <- ds %>% rename(ingo_v_collaborator_worse = Q32_5)
ds <- ds %>% rename(ingo_v_collaborator_worse_specify = Q32_6)
ds <- ds %>% rename(relative_budget = Q33_1)
ds <- ds %>% rename(relative_staff = Q33_2)
ds <- ds %>% rename(relative_urbanness = Q33_3)
ds <- ds %>% rename(ngo_relative_operationlevel = Q33_4)
ds <- ds %>% rename(ngo_relative_operationfield = Q33_5.NGO.ONLY.)
ds <- ds %>% rename(member_localnetwork = Q34_1)
ds <- ds %>% rename(member_intnlnetwork = Q34_2)
ds <- ds %>% rename(nmbr_intnl_collabs = Q34_3)
ds <- ds %>% rename(nmbr_liaisons = Q34_4)
ds <- ds %>% rename(respondent_position = Q35)
ds <- ds %>% rename(respondent_position_tenure = Q36)
ds <- ds %>% rename(respondent_org_tenure = Q37)
ds <- ds %>% rename(respondent_exp_business = Q38_1)
ds <- ds %>% rename(respondent_exp_otherngos = Q38_2)
ds <- ds %>% rename(respondent_exp_cntlgov = Q38_3)
ds <- ds %>% rename(respondent_exp_lclgov = Q38_4)
ds <- ds %>% rename(respondent_exp_other = Q38_5)
ds <- ds %>% rename(respondent_exp_otherspecify = Q38_6)
ds <- ds %>% rename(respondent_edu = Q39)
ds <- ds %>% rename(respondent_fem = Q40)
ds <- ds %>% rename(respondent_member_ngolclgov = Q41)
write.csv(ds, "/Users/corbinwalls/Library/CloudStorage/OneDrive-american.edu/Graduate Assistantship/Donor Impact on Collaboration/LebanonCollabData_ClearNames.csv")One observation in the original dataset contained a value of FALSE. This is transformed to 0.
ds <- ds %>% mutate(collab = case_when(
collab == 0 ~ 0,
collab == FALSE ~ 0,
collab == 1 ~ 1))ds_collabed <- ds %>% filter(collab == 1)Organizations have low external dependence if they have either ngo funding or local government funding and have no funding from external sources (donors, central gov, or other). Organizations have medium external dependence if they have either ngo funding or local government funding and also have funding from at least one external source. Organizations have high dependence if they have no ngo or local government funding and do have funding from external sources.
ds <- ds %>% mutate(fundscollab_lclgov = case_when(
fundscollab_lclgov == 1 ~ 1,
collab == 1 & is.na(fundscollab_lclgov) ~ 0,
collab == 0 & is.na(fundscollab_lclgov) ~ NA))
ds <- ds %>% mutate(fundscollab_ngo = case_when(
fundscollab_ngo == 1 ~ 1,
collab == 1 & is.na(fundscollab_ngo) ~ 0,
collab == 0 & is.na(fundscollab_ngo) ~ NA))
ds <- ds %>% mutate(fundscollab_cntlgov = case_when(
fundscollab_cntlgov == 1 ~ 1,
collab == 1 & is.na(fundscollab_cntlgov) ~ 0,
collab == 0 & is.na(fundscollab_cntlgov) ~ NA))
ds <- ds %>% mutate(fundscollab_donors = case_when(
fundscollab_donors == 1 ~ 1,
collab == 1 & is.na(fundscollab_donors) ~ 0,
collab == 0 & is.na(fundscollab_donors) ~ NA))
ds <- ds %>% mutate(fundscollab_other = case_when(
fundscollab_other == 1 ~ 1,
collab == 1 & is.na(fundscollab_other) ~ 0,
collab == 0 & is.na(fundscollab_other) ~ NA))
ds <- ds %>% mutate(external_dependence_lab = case_when(
(fundscollab_lclgov == 1 | fundscollab_ngo == 1) & (fundscollab_cntlgov == 0 & fundscollab_donors == 0 & fundscollab_other == 0) ~ "Low",
(fundscollab_lclgov == 1 | fundscollab_ngo == 1) & (fundscollab_cntlgov == 1 | fundscollab_donors == 1 | fundscollab_other == 1) ~ "Medium",
(fundscollab_lclgov == 0 & fundscollab_ngo == 0) & (fundscollab_cntlgov == 1 | fundscollab_donors == 1 | fundscollab_other == 1) ~ "High"))
ds <- ds %>% mutate(external_dependence = case_when(
external_dependence_lab == "Low" ~ 1,
external_dependence_lab == "Medim" ~ 2,
external_dependence_lab == "High" ~ 3))
ds$external_dependence_lab <- factor(ds$external_dependence_lab, levels = c("Low", "Medium", "High"))If the respondent’s organization is primarily responsible for coordination, then this variable takes a value of 1. If anything other than this condition is met, it takes a value of 0.
ds$collab_primcrdntr## [1] NA NA NA NA 1 NA 1 1 1 NA NA 1 1 1 NA NA NA NA NA 3 2 1 NA 1 NA NA NA 1 NA NA 1 NA NA NA NA NA NA NA NA 3 1 NA NA 1 1 3 1 3 2 2 1 1 1 1 1 NA 1 NA NA 1 2 2 1 NA 2 NA 1 1 NA 3 1 NA 1 NA
## [75] NA 1 NA 1 1 NA 1 1 NA 2 NA 1 1 2 1 NA 1 1 NA 1 1 NA 1 2 NA 1 2 1 1 3 1 2 2 2 NA 2 2 NA NA 2 1 2 2 1 1 2 2 1 NA 2 NA 1 1 2 3 NA 1 1 3 1 NA 1 NA 1 1 NA NA 1 NA NA NA 1 2 2
## [149] 1 2 1 NA NA 1 NA NA 2 2 2 1 NA 3 1 2 3 NA 3 2 1 NA 2 NA 1 NA 2 1 2 1 1 1 1 NA NA NA NA NA 1 NA 1 1 1 NA NA 1 1 1 NA NA NA NA 2 1 NA 1 NA NA NA NA NA NA NA NA 2 1 NA 2 1 1 NA NA 1 NA
## [223] NA NA NA NA 1 NA 1 1 NA NA 1 1 NA 1 NA NA 1 1 1 1 NA NA NA 1 NA NA 3 3 2 NA 3 NA 3 NA NA 2 NA NA NA NA NA 3 3 2 NA 2 NA 3 2 NA 3 2 NA NA NA 3 2 NA 2 NA 2 2 3 NA NA 2 NA 2 NA NA 1 NA 2 2
## [297] 3 NA 2 2 NA NA NA NA 1 2 NA 3 NA 2 NA 2 NA 3 1 3 NA 2 NA NA NA NA 3 3 2 4 NA NA 3 2 3 NA 4 2 3 2 4 2 NA 3 NA NA 1 NA 2 2 2 2 3 3 2 NA 2 2 NA NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA
## [371] NA 2 NA NA NA NA NA NA NA NA NA NA NA NA NA 2 NA NA 1 NA NA NA NA NA NA NA NA 2 NA NA NA 2 NA NA NA NA NA NA NA 2 NA NA NA 2 NA NA 2 NA NA NA NA NA NA 2 NA NA 1 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [445] NA NA NA NA NA NA 2 2 4 4 2 2 2 2 4 2 NA 4 1 2 1 2 1 2 2 2 2ds <- ds %>% mutate(coord_power = case_when(
collab == 1 & sector == 1 & collab_primcrdntr == 1 ~ 1,
collab == 1 & sector == 0 & collab_primcrdntr == 2 ~ 1,
collab == 1 & is.na(collab_primcrdntr) ~ NA_real_,
collab == 1 & TRUE ~ 0,
collab == 0 ~ NA_real_))
table(ds$coord_power)##
## 0 1
## 85 146If a collaboration is motivated by donor requirements, value of 1. If not, value of 0. In the original data, the variable only takes 1 or NA, so we are assuming NAs for organizations that do collaborate equal 0.
ds <- ds %>% mutate(collab_motive_donorrqurmnts = case_when(
collab_motive_donorrqurmnts == 1 ~ 1,
collab == 1 & is.na(collab_motive_donorrqurmnts) ~ 0,
collab == 0 & is.na(collab_motive_donorrqurmnts) ~ NA_real_))
ds <- ds %>% rename(donor_motivated_collab = collab_motive_donorrqurmnts)sum(is.na(ds_collabed$goal_agreement))/298## [1] 0.204698About 20% of the orgs that collaborated still have NAs for this goal agreement variable.
sum(is.na(ds_collabed$goal_effectiveness))/298## [1] 0.204698About 20% of the orgs that collaborated still have NAs for this goal effectiveness variable.
If a collaboration has a formal contract, value of 1. If not, value of 0. In the original data, the variable only takes 1 or NA, so we are assuming NAs among orgs that collaborate equal 0.
ds <- ds %>% mutate(collab_mthd_frmlservcntrct = case_when(
collab == 1 & collab_mthd_frmlservcntrct == 1 ~ 1,
collab == 1 & is.na(collab_mthd_frmlservcntrct) ~ 0,
collab == 0 ~ NA_real_))
ds <- ds %>% rename(formal_collab = collab_mthd_frmlservcntrct)The original variable is a 3-point ordinal scale with values of “not at all”, “to some extent”, and “to a great extent” in response to the question “In this particular service area, please indicate the extent to which your [municipality’s/NGO’s] collaboration with [NGOs/municipalities] has accomplished the following: increased citizen satisfaction or trust in our [municipality/NGO].” This variable is recoded as binary, where values of “not at all” take a value of 0 and values of either “to some extent” or “to a great extent” take a value of 1.
ds <- ds %>% mutate(satisfaction = case_when(
collab == 1 & collab_outcome_incrsesatisfytrust == 1 ~ 0,
collab == 1 & collab_outcome_incrsesatisfytrust == 2 ~ 1,
collab == 1 & collab_outcome_incrsesatisfytrust == 3 ~ 1,
collab == 0 ~ NA_real_,
collab == 1 & is.na(collab_outcome_incrsesatisfytrust) ~ NA_real_))The original variable is a 3-point ordinal scale with values of “not at all”, “to some extent”, and “to a great extent” in response to the question “In this particular service area, please indicate the extent to which your [municipality’s/NGO’s] collaboration with [NGOs/municipalities] has accomplished the following: saved financial resources.” This variable is recoded as binary, where values of “not at all” take a value of 0 and values of either “to some extent” or “to a great extent” take a value of 1.
ds <- ds %>% mutate(costs_saved = case_when(
collab == 1 & collab_outcome_savedfinresources == 1 ~ 0,
collab == 1 & collab_outcome_savedfinresources == 2 ~ 1,
collab == 1 & collab_outcome_savedfinresources == 3 ~ 1,
collab == 0 ~ NA_real_,
collab == 1 & is.na(collab_outcome_savedfinresources) ~ NA_real_))This worked. Just note, there are a good amount of NAs. For this variable, about 50% (49.89%) of the observations are blank in the overall dataset. When subset to only those who collaborated, about 22% (21.81%) of the observations have a value of NA.
sum(is.na(ds$costs_saved))/298## [1] 0.7986577sum(is.na(ds_collabed$costs_saved))/298## [1] 0ds <- ds %>% mutate(improved_attitudes = case_when(
collab == 1 & collab_outcome_emply_attitudestwrdothersector == 1 ~ 0,
collab == 1 & collab_outcome_emply_attitudestwrdothersector == 2 ~ 1,
collab == 1 & collab_outcome_emply_attitudestwrdothersector == 3 ~ 1,
collab == 0 ~ NA_real_,
collab == 1 & is.na(collab_outcome_emply_attitudestwrdothersector) ~ NA_real_))ds <- ds %>% rename(staff_size = relative_staff)
ds <- ds %>% mutate(staff_size_lab = case_when(
staff_size == 1 ~ "Small",
staff_size == 2 ~ "Medium",
staff_size == 3 ~ "Large"))
ds$staff_size_lab <- factor(ds$staff_size_lab, levels = c("Small", "Medium", "Large")) ds$relative_urbanness## [1] 1 1 1 1 2 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2
## [75] 1 1 1 1 1 1 3 2 1 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 3 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 3 2 1 3 1 3 1 2 1 3 1 3 2 3 1 3 3 1 2 1 1 1 2 2 1 3 1 1 3 3 1 1 2 1 1 1 3 1 2 1 2 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 3 2 1 1 1 2
## [223] 1 1 1 1 1 2 2 3 1 2 1 1 1 1 2 1 1 3 2 1 1 2 1 1 3 1 1 1 1 2 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 3 3 1 1 1 1 2 1 2 1 2 1 1 1 1 3 2
## [297] 1 2 1 2 3 2 3 3 1 1 2 1 3 2 2 2 2 1 1 1 1 1 2 2 1 1 1 1 2 2 1 1 1 1 1 3 2 1 1 2 3 2 1 2 2 3 2 2 2 2 1 1 1 2 1 3 1 1 3 3 3 1 1 1 NA NA NA 3 3 1 3 2 1 2
## [371] 3 3 3 3 1 3 2 2 3 1 1 2 2 1 1 3 3 1 1 3 3 3 1 2 3 1 1 2 2 2 2 2 1 1 3 3 3 3 2 1 3 3 3 2 3 2 3 2 3 3 3 1 3 1 1 1 3 1 3 3 2 1 1 3 2 1 1 1 2 2 1 2 3 2
## [445] 1 1 1 3 3 3 2 2 3 1 3 3 1 1 1 2 1 1 1 2 2 2 3 2 2 1 1ds <- ds %>% rename(urbanness = relative_urbanness)
ds <- ds %>% mutate(urbanness_lab = case_when(
urbanness == 1 ~ "Rural",
urbanness == 2 ~ "Suburban",
urbanness == 3 ~ "Urban"))
ds$urbanness_lab <- factor(ds$urbanness_lab, levels = c("Rural", "Suburban", "Urban"))ds <- ds %>% rename(local_gov = sector)
ds <- ds %>% mutate(sector = case_when(
local_gov == 0 ~ "NGO",
local_gov == 1 ~ "Local Government"))| Variable Name | Variable Description | Variable Measurement |
|---|---|---|
sector & local_gov. |
The sector of the respondent’s o rganization. | sector: “NGO” & “Local Government”. local_gov: 0 = NGO; 1 = Local Government |
collab |
Whether the organization collaborated or not | 0 = no; 1 = yes |
|
The level of external resource dependence for the collaboration. Low dependence means the collaboration is only funded by the members of the collaboration (the NGO or the local government), medium dependence means that the collaboration is funded by both the members of the collaboration and external sources, and high dependence means that the collaboration is only funded by external sources and is not funded by members of the collaboration. |
|
coord_power |
Whether the respondent was the primary coordinator for the collaboration. | 0 = no; 1 = yes |
de c i sion_authority |
Whether the donor held primary decision making authority in the collaboration. | 0 = no; 1 = yes |
donor_ m o tivated_collab |
Whether the respondent indicated that the collaboration was motivated by the donor requirements. | 0 = no; 1 = yes |
goal_agreement |
A measure of the perceived extent to which the members of the collaboration agree on the collaboration’s goals. | 1 = Not at all; 2; 3; 4; 5; 6; 7 = To a great extent |
go a l _effectiveness |
A measure of the perceived effectiveness of the collaboration. | 1 = Not at all effective; 2; 3; 4; 5; 6; 7 = Very effective |
formal_collab |
Whether the collaboration included a formal contract between the collaborators. | 0 = no; 1 = yes |
satisfaction |
Whether the respondent indicated that they perceived increased public satisfaction and trust as a result of the collaboration. | 0 = no; 1 = yes |
costs_saved |
Whether the respondent indicated that the collaboration resulted in saved financial resources for the organization. | 0 = no; 1 = yes |
im p r oved_attitudes |
Whether the respondent indicated that the collaboration resulted in increased attitudes toward the sector of the other organization in the collaboration among employees of the respondent’s organization. | 0 = no; 1 = yes |
staff_size & staff_size_lab |
A measure of the perceived staff size of the respondent’s organization relative to other organizations in the same sector within the country. |
|
urbanness & urbanness_lab |
A measure of the perceived urbanness of the area that the respondent’s organization primarily serves relative to other organizations within the same sector within the country. |
|
ds %>% drop_na(goal_effectiveness) %>% drop_na(goal_agreement) %>% summarize(
effectiveness_n = length(goal_effectiveness),
effectiveness_mean = mean(goal_effectiveness, na.rm = TRUE),
effectiveness_sd = sd(goal_effectiveness, na.rm = TRUE),
effectiveness_min = min(goal_effectiveness, na.rm = TRUE),
effectiveness_max = max(goal_effectiveness, na.rm = TRUE),
agreement_n = length(goal_agreement),
agreement_mean = mean(goal_agreement, na.rm = TRUE),
agreement_sd = sd(goal_agreement, na.rm = TRUE),
agreement_min = min(goal_agreement, na.rm = TRUE),
agreement_max = max(goal_agreement, na.rm = TRUE))## effectiveness_n effectiveness_mean effectiveness_sd effectiveness_min effectiveness_max agreement_n agreement_mean agreement_sd agreement_min agreement_max
## 1 240 5.133333 1.466121 1 7 240 5.304167 1.418343 1 7ds %>%
drop_na(external_dependence_lab) %>%
summarize(
external_dependence_lab_n =
length(external_dependence_lab),
externalal_dependence_high_n =
sum(external_dependence_lab == "High"),
external_dependence_high_perc =
round(sum(external_dependence_lab == "High")/external_dependence_lab_n,
digits = 4)*100,
external_dependence_med_n =
sum(external_dependence_lab == "Medium"),
external_dependence_med_perc =
round(sum(external_dependence_lab == "Medium")/external_dependence_lab_n,
digits = 4)*100,
external_dependence_low_n =
sum(external_dependence_lab == "Low"),
external_dependence_low_perc =
round(sum(external_dependence_lab == "Low")/external_dependence_lab_n,
digits = 4)*100,
sum = sum(external_dependence_low_perc,
external_dependence_med_perc,
external_dependence_high_perc))## external_dependence_lab_n externalal_dependence_high_n external_dependence_high_perc external_dependence_med_n external_dependence_med_perc external_dependence_low_n external_dependence_low_perc sum
## 1 234 64 27.35 75 32.05 95 40.6 100ds %>%
drop_na(donor_motivated_collab) %>%
summarize(
donor_induce_n =
length(donor_motivated_collab),
donor_induce_yes_n =
sum(donor_motivated_collab == 1),
donor_induce_perc =
round(sum(donor_motivated_collab)/donor_induce_n,
digits = 4)*100,
donor_induce_no_n =
sum(donor_motivated_collab == 0),
donor_induce_perc_no =
100 - donor_induce_perc,
sum = sum(donor_induce_perc, donor_induce_perc_no))## donor_induce_n donor_induce_yes_n donor_induce_perc donor_induce_no_n donor_induce_perc_no sum
## 1 298 47 15.77 251 84.23 100ds %>%
drop_na(decision_authority) %>%
summarize(
decision_authority_n =
length(decision_authority),
decision_authority_yes_n =
sum(decision_authority == 1),
decision_authority_perc =
round(sum(decision_authority)/decision_authority_n,
digits = 4)*100,
decision_authority_no_n =
sum(decision_authority == 0),
decision_authority_perc_no =
100 - decision_authority_perc,
sum = sum(
decision_authority_perc,
decision_authority_perc_no))## decision_authority_n decision_authority_yes_n decision_authority_perc decision_authority_no_n decision_authority_perc_no sum
## 1 237 4 1.69 233 98.31 100ds %>%
drop_na(satisfaction) %>%
summarize(
satisfaction_n =
length(satisfaction),
satisfaction_yes_n =
sum(satisfaction == 1),
satisfaction_perc =
round(sum(satisfaction)/satisfaction_n,
digits = 4)*100,
satisfaction_no_n =
sum(satisfaction == 0),
satisfaction_perc_no =
100 - satisfaction_perc,
sum = sum(
satisfaction_perc,
satisfaction_perc_no))## satisfaction_n satisfaction_yes_n satisfaction_perc satisfaction_no_n satisfaction_perc_no sum
## 1 245 227 92.65 18 7.35 100ds %>%
drop_na(improved_attitudes) %>%
summarize(
improved_attitudes_n =
length(improved_attitudes),
improved_attitudes_yes_n =
sum(improved_attitudes == 1),
improved_attitudes_perc =
round(sum(improved_attitudes)/improved_attitudes_n,
digits = 4)*100,
improved_attitudes_no_n =
sum(improved_attitudes == 0),
improved_attitudes_perc_no =
100 - improved_attitudes_perc,
sum = sum(
improved_attitudes_perc,
improved_attitudes_perc_no))## improved_attitudes_n improved_attitudes_yes_n improved_attitudes_perc improved_attitudes_no_n improved_attitudes_perc_no sum
## 1 239 202 84.52 37 15.48 100ds %>%
drop_na(formal_collab) %>%
summarize(
formal_collab_n =
length(formal_collab),
formal_collab_yes_n =
sum(formal_collab == 1),
formal_collab_perc =
round(sum(formal_collab)/formal_collab_n,
digits = 4)*100,
formal_collab_no_n =
sum(formal_collab == 0),
formal_collab_perc_no =
100 - formal_collab_perc,
sum = sum(
formal_collab_perc,
formal_collab_perc_no))## formal_collab_n formal_collab_yes_n formal_collab_perc formal_collab_no_n formal_collab_perc_no sum
## 1 298 14 4.7 284 95.3 100ds %>%
drop_na(coord_power) %>%
summarize(
coord_power_n =
length(coord_power),
coord_power_yes_n =
sum(coord_power == 1),
coord_power_perc =
round(sum(coord_power)/coord_power_n,
digits = 4)*100,
coord_power_no_n =
sum(coord_power == 0),
coord_power_perc_no =
100 - coord_power_perc,
sum = sum(
coord_power_perc,
coord_power_perc_no))## coord_power_n coord_power_yes_n coord_power_perc coord_power_no_n coord_power_perc_no sum
## 1 231 146 63.2 85 36.8 100ds %>%
drop_na(costs_saved) %>%
summarize(
costs_saved_n =
length(costs_saved),
costs_saved_yes_n =
sum(costs_saved == 1),
costs_saved_perc =
round(sum(costs_saved)/costs_saved_n,
digits = 4)*100,
costs_saved_no_n =
sum(costs_saved == 0),
costs_saved_perc_no =
100 - costs_saved_perc,
sum = sum(
costs_saved_perc,
costs_saved_perc_no))## costs_saved_n costs_saved_yes_n costs_saved_perc costs_saved_no_n costs_saved_perc_no sum
## 1 233 181 77.68 52 22.32 100ds %>%
drop_na(staff_size_lab) %>%
summarize(
staff_size_lab_n =
length(staff_size_lab),
staff_size_lg_n =
sum(staff_size_lab == "Large"),
staff_size_lg_perc =
round(sum(staff_size_lab == "Large")/staff_size_lab_n,
digits = 4)*100,
staff_size_md_n =
sum(staff_size_lab == "Medium"),
staff_size_md_perc =
round(sum(staff_size_lab == "Medium")/staff_size_lab_n,
digits = 4)*100,
staff_size_sm_n =
sum(staff_size_lab == "Small"),
staff_size_sm_perc =
round(sum(staff_size_lab == "Small")/staff_size_lab_n,
digits = 4)*100,
sum = sum(
staff_size_sm_perc,
staff_size_md_perc,
staff_size_lg_perc))## staff_size_lab_n staff_size_lg_n staff_size_lg_perc staff_size_md_n staff_size_md_perc staff_size_sm_n staff_size_sm_perc sum
## 1 468 27 5.77 113 24.15 328 70.09 100.01ds %>%
drop_na(urbanness_lab) %>%
summarize(
urbanness_lab_n =
length(urbanness_lab),
urbanness_ub_n =
sum(urbanness_lab == "Urban"),
urbanness_ub_perc =
round(sum(urbanness_lab == "Urban")/urbanness_lab_n,
digits = 4)*100,
urbanness_sb_n =
sum(urbanness_lab == "Suburban"),
urbanness_sb_perc =
round(sum(urbanness_lab == "Suburban")/urbanness_lab_n,
digits = 4)*100,
urbanness_rl_n =
sum(urbanness_lab == "Rural"),
urbanness_rl_perc =
round(sum(urbanness_lab == "Rural")/urbanness_lab_n,
digits = 4)*100,
sum = sum(
urbanness_rl_perc,
urbanness_sb_perc,
urbanness_ub_perc))## urbanness_lab_n urbanness_ub_n urbanness_ub_perc urbanness_sb_n urbanness_sb_perc urbanness_rl_n urbanness_rl_perc sum
## 1 468 74 15.81 103 22.01 291 62.18 100ds %>%
drop_na(sector) %>%
summarize(
sector_n =
length(sector),
sector_ngo_n =
sum(sector == "NGO"),
sector_ngo_perc =
round(sum(sector == "NGO")/sector_n,
digits = 4)*100,
sector_lclgov_n =
sum(sector == "Local Government"),
sector_lclgov_perc =
round(sum(sector == "Local Government")/sector_n,
digits = 4)*100,
sum = sum(
sector_ngo_perc,
sector_lclgov_perc))## sector_n sector_ngo_n sector_ngo_perc sector_lclgov_n sector_lclgov_perc sum
## 1 471 223 47.35 248 52.65 100ds_ngo <- ds %>% filter(local_gov == 0)
ds_lclgov <- ds %>% filter(local_gov == 1)lm_full_donor_motivated <- lm(donor_motivated_collab ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_decision_authority <- lm(decision_authority ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_satisfaction <- lm(satisfaction ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_formal_collab <- lm(formal_collab ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_coord_power <- lm(coord_power ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_effectiveness <- lm(goal_effectiveness ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_goal_agreement <- lm(goal_agreement ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_improved_attitudes <- lm(improved_attitudes ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)
lm_full_costs_saved <- lm(costs_saved ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds)modelsummary(list("Donor Motivated Collaboration" = lm_full_donor_motivated,
"Held Decision Authority" = lm_full_decision_authority,
"Increased Satisfacion and Trust" = lm_full_satisfaction,
"Formal Collaboration Agreement Present" = lm_full_formal_collab,
"Held Primary Coordination Power" = lm_full_coord_power,
"Perceived Effectiveness" = lm_full_effectiveness,
"Perceived Goal Agreement" = lm_full_goal_agreement,
"Improved Attitudes Toward Other Sector" = lm_full_improved_attitudes,
"Saved Financial Resources" = lm_full_costs_saved),
title = "Relationship between External Resource Dependence and Collaboration Outcomes and Processes - Full Sample",
stars = TRUE,
statistic = c("std.error"),
coef_map = c(
"external_dependence_labMedium" = "Medium External Dependence",
"external_dependence_labHigh" = "High External Dependence",
"staff_size_labMedium" = "Medium Staff Size",
"staff_size_labLarge" = "Large Staff Size",
"urbanness_labSuburban" = "Suburban Service Area",
"urbanness_labUrban" = "Urban Service Area"
),
gof_omit = "AIC|BIC|Log.Lik.|RMSE",
output = "/Users/corbinwalls/Library/CloudStorage/OneDrive-american.edu/Graduate Assistantship/Donor Impact on Collaboration/Full Sample OLS Models - Donor Impact on Collaboration.docx")lm_ngo_donor_motivated <- lm(donor_motivated_collab ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_decision_authority <- lm(decision_authority ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_satisfaction <- lm(satisfaction ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_formal_collab <- lm(formal_collab ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_coord_power <- lm(coord_power ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_effectiveness <- lm(goal_effectiveness ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_goal_agreement <- lm(goal_agreement ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_improved_attitudes <- lm(improved_attitudes ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)
lm_ngo_costs_saved <- lm(costs_saved ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_ngo)modelsummary(list("Donor Motivated Collaboration" = lm_ngo_donor_motivated,
"Held Decision Authority" = lm_ngo_decision_authority,
"Increased Satisfacion and Trust" = lm_ngo_satisfaction,
"Formal Collaboration Agreement Present" = lm_ngo_formal_collab,
"Held Primary Coordination Power" = lm_ngo_coord_power,
"Perceived Effectiveness" = lm_ngo_effectiveness,
"Perceived Goal Agreement" = lm_ngo_goal_agreement,
"Improved Attitudes Toward Other Sector" = lm_ngo_improved_attitudes,
"Saved Financial Resources" = lm_ngo_costs_saved),
title = "Relationship between External Resource Dependence and Collaboration Outcomes and Processes - NGO Sample",
stars = TRUE,
statistic = c("std.error"),
coef_map = c(
"external_dependence_labMedium" = "Medium External Dependence",
"external_dependence_labHigh" = "High External Dependence",
"staff_size_labMedium" = "Medium Staff Size",
"staff_size_labLarge" = "Large Staff Size",
"urbanness_labSuburban" = "Suburban Service Area",
"urbanness_labUrban" = "Urban Service Area"
),
gof_omit = "AIC|BIC|Log.Lik.|RMSE",
output = "/Users/corbinwalls/Library/CloudStorage/OneDrive-american.edu/Graduate Assistantship/Donor Impact on Collaboration/NGO Sample OLS Models - Donor Impact on Collaboration.docx")lm_lclgov_donor_motivated <- lm(donor_motivated_collab ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_decision_authority <- lm(decision_authority ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_satisfaction <- lm(satisfaction ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_formal_collab <- lm(formal_collab ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_coord_power <- lm(coord_power ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_effectiveness <- lm(goal_effectiveness ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_goal_agreement <- lm(goal_agreement ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_improved_attitudes <- lm(improved_attitudes ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)
lm_lclgov_costs_saved <- lm(costs_saved ~ external_dependence_lab + staff_size_lab + urbanness_lab, data = ds_lclgov)modelsummary(list("Donor Motivated Collaboration" = lm_lclgov_donor_motivated,
"Held Decision Authority" = lm_lclgov_decision_authority,
"Increased Satisfacion and Trust" = lm_lclgov_satisfaction,
"Formal Collaboration Agreement Present" = lm_lclgov_formal_collab,
"Held Primary Coordination Power" = lm_lclgov_coord_power,
"Perceived Effectiveness" = lm_lclgov_effectiveness,
"Perceived Goal Agreement" = lm_lclgov_goal_agreement,
"Improved Attitudes Toward Other Sector" = lm_lclgov_improved_attitudes,
"Saved Financial Resources" = lm_lclgov_costs_saved),
title = "Relationship between External Resource Dependence and Collaboration Outcomes and Processes - Local Government Sample",
stars = TRUE,
statistic = c("std.error"),
coef_map = c(
"external_dependence_labMedium" = "Medium External Dependence",
"external_dependence_labHigh" = "High External Dependence",
"staff_size_labMedium" = "Medium Staff Size",
"staff_size_labLarge" = "Large Staff Size",
"urbanness_labSuburban" = "Suburban Service Area",
"urbanness_labUrban" = "Urban Service Area"
),
gof_omit = "AIC|BIC|Log.Lik.|RMSE",
output = "/Users/corbinwalls/Library/CloudStorage/OneDrive-american.edu/Graduate Assistantship/Donor Impact on Collaboration/Local Gov Sample OLS Models - Donor Impact on Collaboration.docx")