# APPLY TO COLUMNS K:
bool_qns <-
c( program_change_qns_bool,
fundraise_qns_bool,
finance_chng_qns_bool,
leadership_chng_qns_bool )
COLUMNS <- bool_qns
# VALUES THAT NEED RECODING
RULES <- c(
" Yes =>> 1 ",
" No =>> 0 ",
" Unsure =>> 97 ",
" N/A =>> 98 ",
" -99 =>> 99 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 0, 1, 97, 98, 99 )
labels <- c( "No", "Yes", "Unsure", "Not Applicable", "N/A" )
missing <- c( 97, 98, 99 )
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )8 Relabeling Qualtrics Variables
Qualtrics uses some random values like -99 to encode for things like skipped questions, “not sure” categories, or “not applicable” answers. Currently one needs to a dictionary to look up every variable to understand what each value means.
In addition, some response categories are confusing because they equate to a missing response. For example, from an analytical perspective an answer of “not sure” and skipping the question completely both equate to a missing value when the response categories are yes or no.
The memisc package was designed for working with survey data. It allows you to label response categories in the data and also designate different types of missingness (e.g. Unsure, N/A, and -99 can all be coded as missing).
Several factor and boolean variables in this survey data set have inconsistent coding. They are recoded in this section and the decision criteria are documented for inspection and review.
9 Yes/No Questions
| Original Value | Description | Recode Value | Code as Missing ? |
|---|---|---|---|
| Yes | Yes | 1 | No |
| No | No | 0 | No |
| Unsure | Unsure | 97 | Yes |
| N/A | Not Applicable | 98 | Yes |
| -99 | Incomplete | 99 | Yes |
| NA | Unanswered | NA | Yes |
Example:
PrgSrvc_IncrNum — ‘TYPE: boolean’
“Indicates an increase in number of programs or services”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | 97, 98, 99 |
| Values and labels | N | Valid | Total | ||||||
| 0 | ‘No’ | 192 | 38 | . | 7 | 38 | . | 2 | |
| 1 | ‘Yes’ | 304 | 61 | . | 3 | 60 | . | 6 | |
| 98 | M | ‘Not Applicable’ | 3 | 0 | . | 6 | |||
| 99 | M | ‘N/A’ | 1 | 0 | . | 2 | |||
| NA | M | 2 | 0 | . | 4 | ||||
- QUESTION TXT:
- In the last year (between January 2022-December 2022), did your organization make any of the following changes to your Programs, as compared to 2021? - Increased the number of programs or services
| Original Value | Description | Recode Value | Code as Missing ? |
|---|---|---|---|
| Yes | Yes | 1 | No |
| No | No | 2 | No |
| Unsure | Unsure | 3 | Yes |
| N/A | Not Applicable | 98 | Yes |
| -99 | Incomplete | 99 | Yes |
| NA | Unanswered | NA | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- cares_qns_bool
# VALUES THAT NEED RECODING
RULES <- c( " Yes =>> 1 ",
" No =>> 2 ",
" Unsure =>> 3 ",
" N/A =>> 98 ",
" -99 =>> 99 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 1, 2, 3, 98, 99 )
labels <- c( "Yes", "No", "Unsure", "Not Applicable", "N/A" )
missing <- c( 3, 98, 99 )
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
CARES_Rcv — ‘TYPE: boolean’
“Indicates whether organization receive government funding from CARES act”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | 3, 98, 99 |
| Values and labels | N | Valid | Total | ||||||
| 1 | ‘Yes’ | 172 | 41 | . | 1 | 34 | . | 3 | |
| 2 | ‘No’ | 247 | 58 | . | 9 | 49 | . | 2 | |
| 3 | M | ‘Unsure’ | 16 | 3 | . | 2 | |||
| NA | M | 67 | 13 | . | 3 | ||||
- QUESTION TXT:
- In the last year (2022, or your last fiscal year), did your organization receive any government funding to help your organization respond to the COVID-19 pandemic (including funding from federal, state, or local government programs)?
10 Single Checkboxes
These questions are presented as a checkbox to the respondent. They indicate an affirmative answer to the question.
10.1 Seek or Receive Fundraising Questions
These checkboxes are ticked by the respondent to indicate if they have sought or received funding from a specific source.
| Original Value | Description | Recode Label | Recode Value | Code as Missing? |
|---|---|---|---|---|
| 1 | Checkbox Checked | Yes | 1 | No |
| NA | Checkbox Unchecked | No | 0 | No |
| All NA | Unanswered | NA | NA | Yes |
ALL NA refers to no responses on all questions in this entire question group (i.e. the respondent skipped the section).
# APPLY TO COLUMNS K:
COLUMNS <- fundraise_skrcv_qns_bool
# ALL RESPONSES NA IN GROUP:
# indication that the question
# was skipped by the respondent
row.id <- apply( survey_df[ COLUMNS ],
MARGIN = 1,
FUN = function(x) all(is.na(x)) )
# NA means "no"
na_to_zero <- function(x){
x[ is.na(x) ] <- 0
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( na_to_zero )
# if they do not respond "yes"
# to at least one question then
# assume they skipped them all
zero_to_na <- function( x, rowid ){
x[ rowid ] <- NA
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( zero_to_na, row.id )
# VALUES THAT NEED RECODING
RULES <- c(
" 1 =>> 1 ",
" NA =>> 0 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( "0", "1" )
labels <- c( "No", "Yes" )
missing <- NA
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
FndRaise_LocGvtGrnt_Seek — ‘TYPE: boolean’
“Indicates if nonprofit sought a local government grant”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Percent | ||||
| 0 | ‘No’ | 228 | 45 | . | 4 | |
| 1 | ‘Yes’ | 274 | 54 | . | 6 | |
- QUESTION TXT:
- In the last year (2022), did your organization seek or receive revenue from any of the followi… - Did you SEEK or APPLY for this funding? - Local government grants - (select all that apply)
10.2 Regulation Checkboxes
These questions are presented as a checkbox to the user to indicate that a question is not applicable. “Yes” here means “Yes, this question is not applicable”.
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| 1 | Checkbox Checked | Yes | 1 | No |
| NA | Unanswered | No | 0 | No |
| 22 | Checkbox Checked | Yes | 1 | No |
| 21 | Unchecked Box | No | 0 | No |
# APPLY TO COLUMNS K:
# exclude OTHER TXT question
other <- which( regulation_qns == "Regulations_Other_TEXT" )
COLUMNS <- regulation_qns[ - other ]
# "did not contact any agencies":
# ~ NONE CONTACTED
dnc.rows <- survey_df$Regulations == 21
# all rows NA:
# question skipped by respondent
na.rows <- is.na( survey_df$Regulations )
# NA means "NO"
na_to_zero <- function(x){
x[ is.na(x) ] <- 0
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( na_to_zero )
# if they responded "NONE CONTACTED" (21)
# to the first question code
# the rest as "NONE CONTACTED"
zero_to_dnc <- function( x, rowid ){
x[ rowid ] <- "NONE"
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( zero_to_dnc, rowid=dnc.rows )
# if the first question was NA
# code all as missing ("no response")
zero_to_noresp <- function( x, rowid ){
x[ rowid ] <- "_NR_"
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( zero_to_noresp, rowid=na.rows )
# VALUES THAT NEED RECODING
RULES <- c( " 22 =>> 1 ",
" 21 =>> NONE ",
" NA =>> _NA_ " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( "0", "1", "NONE", "_NR_", "_NA_" )
labels <- c( "No", "Yes", "None Contacted", "No Response", "Missing" )
missing <- "_NA_"
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
Regulations — ‘TYPE: boolean’
“Indicates whether the nonprofit contacted federal, state, or local regulatory agencies in the past 2 years”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Percent | ||||
| NR | ‘No Response’ | 63 | 12 | . | 5 | |
| 0 | ‘No’ | 0 | 0 | . | 0 | |
| 1 | ‘Yes’ | 156 | 31 | . | 1 | |
| NONE | ‘None Contacted’ | 283 | 56 | . | 4 | |
- QUESTION TXT:
- Has your organization contacted federal, state or local regulatory agencies (IRS, State Attorney Generals, Secretaries of State, or other state or local charities regulation officials) in the past 2 years?
Regulations_Local — ‘TYPE: boolean’
“Indicates whether the nonprofit has contacted local regulatory agencies in the past 2 years”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Percent | ||||
| NR | ‘No Response’ | 63 | 12 | . | 5 | |
| 0 | ‘No’ | 60 | 12 | . | 0 | |
| 1 | ‘Yes’ | 96 | 19 | . | 1 | |
| NONE | ‘None Contacted’ | 283 | 56 | . | 4 | |
- QUESTION TXT:
- Please indicate below the types of agencies (check all that apply): - Local
10.3 Staffing Plans
These questions are presented as a checkbox to the user to indicate that a question is not applicable. “Yes” here means “Yes, this question is not applicable”.
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| 1 | Checkbox Checked | Yes | 1 | No |
| NA | Unanswered | No | 0 | No |
# APPLY TO COLUMNS K:
COLUMNS <- staffing_plans
# ALL RESPONSES NA IN GROUP:
# indication that the question
# was skipped by the respondent
row.id <- apply( survey_df[ COLUMNS ],
MARGIN = 1,
FUN = function(x) all(is.na(x)) )
# VALUES THAT NEED RECODING
RULES <- c(
" 1 =>> 1 ",
" NA =>> 0 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# NA means "no"
na_to_zero <- function(x){
x[ is.na(x) ] <- 0
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( na_to_zero )
# if they do not respond "yes"
# to at least one question then
# assume they skipped them all
zero_to_na <- function( x, rowid ){
x[ rowid ] <- NA
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( zero_to_na, row.id )
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( "0", "1" )
labels <- c( "No", "Yes" )
missing <- NA
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
StaffingPlans_HireDifferent — ‘TYPE: boolean’
“Indicates whether the nonprofit has plans to hire new staff with different skills than existing staff in the next 12 months”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Percent | ||||
| 0 | ‘No’ | 329 | 65 | . | 5 | |
| 1 | ‘Yes’ | 173 | 34 | . | 5 | |
- QUESTION TXT:
- Does your organization have plans to change its staffing in the next 12 months? (select all that apply) - Hire new staff with different skills than existing staff
10.4 N/A Checkboxes
These questions are presented as a checkbox to the user to indicate that a question is not applicable. “Yes” here means “Yes, this question is not applicable”.
10.4.1 Staff Forecasts
We see three four cases:
- All answered
- Only answer for 2022 but don’t forecast 2023
- Answer “does not apply to me”
- Answer “does apply” but then don’t report numbers
We create an imputed version of 2023 numbers using 2022 numbers for missing cases (new variable x_2023 is saved as x_2023_imp for “imputed” ).
Replace “OK” in the not applicable field with “UNSURE” if they report no staff numbers. These will become NA (missing) when the dataset is saved.
Staff_Fulltime_2022 Staff_Fulltime_2023 Staff_Fulltime_NA
142 100 110 0
144 1 1 0
145 210 225 0
143 1 NA 0
146 0 NA 0
141 NA NA 1
150 NA NA 0After imputation of 2023 responses (use 2022 values):
Staff_Fulltime_2022 Staff_Fulltime_2023 Staff_Fulltime_NA
142 100 110 0
144 1 1 0
145 210 225 0
143 1 1 0
146 0 0 0
141 NA NA 1
150 NA NA 99impute_staff <- function( df, v2022, v2023, vNA ){
x.2022 <- df[[ v2022 ]]
x.2023 <- df[[ v2023 ]]
x.NA <- df[[ vNA ]]
na.2022 <- is.na( x.2022 )
na.2023 <- is.na( x.2023 )
both.na <- na.2022 & na.2023
# Not applicable is "unsure"
x.NA[ is.na( x.NA ) ] <- 0
x.NA[ x.NA == 0 & both.na ] <- 99
# impute missing 2023 values with 2022 values
x.2023[ na.2023 ] <- x.2022[ na.2023 ]
v2023i <- paste0( v2023, "_imp" )
df[[ v2023i ]] <- x.2023
df[[ vNA ]] <- x.NA
return(df)
}
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_Fulltime_2022",
v2023="Staff_Fulltime_2023",
vNA="Staff_Fulltime_NA" )
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_Parttime_2022",
v2023="Staff_Parttime_2023",
vNA="Staff_Parttime_NA" )
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_Boardmmbr_2022",
v2023="Staff_Boardmmbr_2023",
vNA="Staff_Boardmmbr_NA" )
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_RegVlntr_2022",
v2023="Staff_RegVlntr_2023",
vNA="Staff_RegVlntr_NA" )
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_EpsdVltnr_2022",
v2023="Staff_EpsdVltnr_2023",
vNA="Staff_EpsdVltnr_NA" )
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_AmerVlntr_2022",
v2023="Staff_AmerVlntr_2023",
vNA="Staff_AmerVlntr_NA" )
survey_df <-
survey_df %>%
impute_staff( v2022="Staff_PdCnslt_2022",
v2023="Staff_PdCnslt_2023",
vNA="Staff_PdCnslt_NA" )| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| 1 | Yes, this question is not applicable | Not Applicable | 1 | No |
| NA | No, this question applies to me | OK | 1 | No |
| All NA | N/A response conflicts | Unsure | 99 | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- staff_qns_bool
# VALUES THAT NEED RECODING
RULES <- c(
" 1 =>> 1 ",
" 0 =>> 0 ",
" 99 =>> 99 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( "0", "1", "99" )
labels <- c( "OK", "N/A", "UNSURE" )
missing <- "UNSURE"
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
Staff_Fulltime_NA — ‘TYPE: boolean’
“Number of full time staff not applicable”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | UNSURE |
| Values and labels | N | Percent | ||||
| 0 | ‘OK’ | 456 | 90 | . | 8 | |
| 1 | ‘N/A’ | 40 | 8 | . | 0 | |
| 99 | ‘UNSURE’ | 6 | 1 | . | 2 | |
- QUESTION TXT:
- How many (paid and unpaid) people in your organization will have worke… - Check here if not applicable for your organization - Full-time paid staff (35 or more hours /week) - C
10.4.2 Reserve Questions
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| 1 | Yes, this question is not applicable | N/A | 1 | No |
| NA | This question applies to me | 0 | OK | No |
| All NA | N/A response conflicts | Unsure | 99 | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- reserve_qns_bool
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( na_to_zero )
# did not respond to the reserve questions
no.resp <- is.na( survey_df$Reserves_Est )
survey_df$Reserves_NA_X[ no.resp ] <- 99
# VALUES THAT NEED RECODING
RULES <- c(
" 1 =>> 1 ",
" 0 =>> 0 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( "0", "1", "99" )
labels <- c( "OK", "N/A", "Did Not Answer" )
missing <- "99"
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
Reserves_NA_X — ‘TYPE: boolean’
“Estimated total operating cash and unrestricted liquid assets in reserve is not applicable”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | 99 |
| Values and labels | N | Valid | Total | ||||||
| 0 | ‘OK’ | 355 | 95 | . | 9 | 70 | . | 7 | |
| 1 | ‘N/A’ | 15 | 4 | . | 1 | 3 | . | 0 | |
| 99 | M | ‘Did Not Answer’ | 132 | 26 | . | 3 | |||
- QUESTION TXT:
- Approximately how much total operating cash and unrestricted liquid assets in reserve did you… - Select here if your organization does not have a reserve fund - Total operating cash and unrestricted liquid assets in reserve - C
10.4.3 People Served Questions
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| 1 | Yes, this question is not applicable | N/A | 1 | No |
| NA | This question applies to me | 0 | OK | No |
| All NA | N/A response conflicts | Unsure | 99 | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- people_served_qns_bool
# ALL RESPONSES NA IN GROUP:
# indication that the question
# was skipped by the respondent
people_qns <- c( "PplSrv_NumServed", "PplSrv_NumWait" )
row.id <- apply( survey_df[ people_qns ],
MARGIN = 1,
FUN = function(x){ all(is.na(x)) } )
# NA means "no"
na_to_zero <- function(x){
x[ is.na(x) ] <- 0
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( na_to_zero )
# if they do not respond "yes"
# to at least one question then
# assume they skipped them all
zero_to_99 <- function( x, rowid ){
x[ rowid ] <- 99
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( zero_to_99, row.id )
# VALUES THAT NEED RECODING
RULES <- c( " 1 =>> 1 ",
" 0 =>> 0 ",
" NA =>> _NA_ " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( "0", "1", "99", "_NA_" )
labels <- c( "OK", "N/A", "No response", "No response" )
missing <- c( "99", "_NA_" )
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
PplSrv_NumServed_NA_X — ‘TYPE: boolean’
“Number of people served not applicable”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | 99, NA |
| Values and labels | N | Valid | Total | ||||||
| 0 | ‘OK’ | 444 | 99 | . | 6 | 88 | . | 4 | |
| 1 | ‘N/A’ | 2 | 0 | . | 4 | 0 | . | 4 | |
| 99 | M | ‘No response’ | 56 | 11 | . | 2 | |||
- QUESTION TXT:
- Approximately how many people do you estimate your organization served and… - Check here if not applicable for your organization - Number of People Served - N/A
11 Multi-selection Inputs
These questions offer the user with multiple options to select one from. Since the options are ordered categories, they are coded on an ordinal scale.
11.1 Increase - Decrease Questions
There 2 questions that ask respondents to define changes via an increase or decrease relative to previous years. They are recoded on an ordinal scale.
11.2 Changes in Demand Questions
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| Increase | Increase | Increase | 2 | No |
| Stay the same | Stay the same | Unchanged | 1 | No |
| Decrease | Decrease | Decrease | 0 | No |
| -99 | Incomplete | No | 99 | Yes |
| NA | Unanswered | NA | NA | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- demand_fct_qns
# VALUES THAT NEED RECODING
RULES <- c(
" 0 =>> 0 ",
" 1 =>> 1 ",
" 98 =>> 2 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 2, 1, 0 )
labels <- c( "Increase", "Unchanged", "Decrease" )
missing <- NA
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
Dmnd_NxtYear — ‘TYPE: factor’
“Demand for programs or services next year”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Valid | Total | ||||||
| 0 | ‘Decrease’ | 126 | 25 | . | 2 | 25 | . | 1 | |
| 1 | ‘Unchanged’ | 7 | 1 | . | 4 | 1 | . | 4 | |
| 2 | ‘Increase’ | 367 | 73 | . | 4 | 73 | . | 1 | |
| NA | M | 2 | 0 | . | 4 | ||||
- QUESTION TXT:
- In the next year, do you anticipate the demand for your programs to decrease, stay the same, or increase?
11.3 Changes in Fundraising Questions
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| Increased significantly (by more than 10%) | Largest Increase | Increase Significantly | 5 | No |
| Increased moderately (by less than 10%) | Second Largest Increase | Increase Moderately | 4 | No |
| Stayed more or less the same | Third Largest Increase | Unchanged | 3 | No |
| Decreased moderately (by less than 10%) | Fourth Largest Increase | Decrease Moderately | 2 | No |
| Decreased significantly (by more than 10%) | Fifth Largest Increase | Decrease Significantly | 1 | No |
| Unsure | Unsure | Unsure | 99 | Missing |
| -99 | Incomplete | Incomplete | 98 | Yes |
| N/A | Not Applicable | Not Applicable | 97 | Yes |
| NA | Unanswered | NA | NA | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- fundraise_change_qns_fct
# VALUES THAT NEED RECODING
row.id <- apply( survey_df[ COLUMNS ],
MARGIN = 1,
FUN = function(x) all(is.na(x)) )
RULES <- c(
" Increased significantly (by more than 10%) =>> 5 ",
" Increased moderately (by less than 10%) =>> 4 ",
" Stayed more or less the same =>> 3 ",
" Decreased moderately (by less than 10%) =>> 2 ",
" Decreased significantly (by more than 10%) =>> 1 ",
" 98 =>> 0 ",
" 99 =>> X " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
all_na <- function( x, rowid ){
x[ rowid ] <- NA
return(x)
}
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( all_na, row.id )
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 5, 4, 3, 2, 1, 0, "X" )
labels <- c( "Increase Significantly", "Increase Moderately",
"Unchanged", "Decrease Moderately", "Decrease Significantly",
"Unsure", "Not Applicable" )
missing <- c( "X" )
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
FndRaise_Overall_Chng — ‘TYPE: factor’
“Change in overall donations”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | X |
| Values and labels | N | Valid | Total | ||||||
| 0 | ‘Unsure’ | 10 | 2 | . | 2 | 2 | . | 0 | |
| 1 | ‘Decrease Significantly’ | 62 | 13 | . | 4 | 12 | . | 4 | |
| 2 | ‘Decrease Moderately’ | 41 | 8 | . | 9 | 8 | . | 2 | |
| 3 | ‘Unchanged’ | 140 | 30 | . | 4 | 27 | . | 9 | |
| 4 | ‘Increase Moderately’ | 136 | 29 | . | 5 | 27 | . | 1 | |
| 5 | ‘Increase Significantly’ | 72 | 15 | . | 6 | 14 | . | 3 | |
| X | M | ‘Not Applicable’ | 10 | 2 | . | 0 | |||
| NA | M | 31 | 6 | . | 2 | ||||
- QUESTION TXT:
- To the best of your knowledge, has each of the following categories of donations to your org… - In 2022 compared to 2021 - Overall donations
12 Level of Importance Questions
There are 2 questions that ask respondents to rank the importance of volunteers and donors respectively. However, both sets of options’ are not identical. Hence, they are recoded to common values for reproducibility.
12.1 Volunteer Importance
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| Essential - we depend entirely on volunteers to carry out our mission and goals | Maximum Importance | Essential | 1 | No |
| Very important - we depend on volunteers for a wide range of tasks, but not all | Second Most Important | Very Important | 2 | No |
| Somewhat important - we depend on volunteers for several key tasks | Third Most Important | Somewhat Important | 3 | No |
| Not very important - we depend on volunteers for only non-essential tasks | Fourth Most Important | Not Very Important | 4 | No |
| Not at all important - we could carry out our mission and goals without using volunteers | Fifth Most Important | Not At All Important | 5 | No |
| We do not use volunteers | Sixth Most Important | Not Used | 99 | No |
| NA | Unanswered | NA | NA | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- volimportance_qns_fct
# VALUES THAT NEED RECODING
RULES <- c(
" Essential - we depend entirely on volunteers to carry out our mission and goals =>> 1 ",
" Very important - we depend on volunteers for a wide range of tasks, but not all =>> 2 ",
" Somewhat important - we depend on volunteers for several key tasks =>> 3 ",
" Not very important - we depend on volunteers for only non-essential tasks =>> 4 ",
" Not at all important - we could carry out our mission and goals without using volunteers =>> 5 ",
" We do not use volunteers =>> 99 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 1, 2, 3, 4, 5, 99 )
labels <- c( "Essential", "Very Important", "Somewhat Important",
"Not Very Important", "Not At All Important", "Not Used" )
missing <- NA
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
VolImportance — ‘TYPE: factor’
“Importance of volunteers to organization”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Valid | Total | ||||||
| 1 | ‘Essential’ | 111 | 22 | . | 2 | 22 | . | 1 | |
| 2 | ‘Very Important’ | 134 | 26 | . | 7 | 26 | . | 7 | |
| 3 | ‘Somewhat Important’ | 149 | 29 | . | 7 | 29 | . | 7 | |
| 4 | ‘Not Very Important’ | 54 | 10 | . | 8 | 10 | . | 8 | |
| 5 | ‘Not At All Important’ | 22 | 4 | . | 4 | 4 | . | 4 | |
| 99 | ‘Not Used’ | 31 | 6 | . | 2 | 6 | . | 2 | |
| NA | M | 1 | 0 | . | 2 | ||||
- QUESTION TXT:
- How important were volunteers – other than board members – to the work your organization conducted in 2021?
12.2 Donor Importance
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| Essential, we depend entirely on individual donations to carry out our mission and goals | Maximum Importance | Essential | 1 | No |
| Very important, we depend on individual donations for a wide range of activities, but not all | Very Important | 2 | No | |
| Important, we depend on individual donations for several key activities | Third Most Important | Somewhat Important | 3 | No |
| Not very important, we depend on individual donations for only non-essential activities | Fourth Most Important | Not Very Important | 4 | No |
| Not at all important, we could carry out our mission and goals without donations from individuals | Fifth Most Important | Not At All Important | 5 | No |
| We do not receive donations from individuals | Sixth Most Important | Not Used | 6 | No |
| -99 | Incomplete | Incomplete | 0 | Yes |
| NA | Unanswered | NA | NA | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- donimportance_qns_fct
# VALUES THAT NEED RECODING
RULES <- c(
" Essential, we depend entirely on individual donations to carry out our mission and goals =>> 1 ",
" Very important, we depend on individual donations for a wide range of activities, but not all =>> 2 ",
" Important, we depend on individual donations for several key activities =>> 3 ",
" Not very important, we depend on individual donations for only non-essential activities =>> 4 ",
" Not at all important, we could carry out our mission and goals without donations from individuals =>> 5 ",
" We do not receive donations from individuals =>> 99 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 1, 2, 3, 4, 5, 99 )
labels <- c( "Essential", "Very Important",
"Somewhat Important", "Not Very Important",
"Not At All Important", "Not Used" )
missing <- NA
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
DonImportance — ‘TYPE: factor’
“Importance of donors to organization”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Valid | Total | ||||||
| 1 | ‘Essential’ | 125 | 25 | . | 0 | 24 | . | 9 | |
| 2 | ‘Very Important’ | 218 | 43 | . | 5 | 43 | . | 4 | |
| 3 | ‘Somewhat Important’ | 97 | 19 | . | 4 | 19 | . | 3 | |
| 4 | ‘Not Very Important’ | 33 | 6 | . | 6 | 6 | . | 6 | |
| 5 | ‘Not At All Important’ | 16 | 3 | . | 2 | 3 | . | 2 | |
| 99 | ‘Not Used’ | 12 | 2 | . | 4 | 2 | . | 4 | |
| NA | M | 1 | 0 | . | 2 | ||||
- QUESTION TXT:
- How important were donations from individuals (including direct donations and indirect donations through federated giving programs, United Way, and donor advised funds, or in-kind donations) to the work of your organization in 2021?
13 Frequency Questions
These questions ask respondents to rank the frequency at which they engage in an activity.
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| More Often Than Not | Most Frequent | More Often Than Not | 5 | No |
| Frequently | Second Most Frequent | Frequently | 4 | No |
| Occasionally | Third Most Frequent | Occasionally | 3 | No |
| Rarely | Fourth Most Frequent | Rarely | 2 | No |
| Never | Fifth Most Frequent | Occasionally | 1 | No |
| -99 | Incomplete | Incomplete | 99 | Yes |
| NA | Unanswered | NA | NA | Yes |
# APPLY TO COLUMNS K:
COLUMNS <- extaffairs_qns_fct
# VALUES THAT NEED RECODING
RULES <- c(
" More Often Than Not =>> 5 ",
" Frequently =>> 4 ",
" Occasionally =>> 3 ",
" Rarely =>> 2 ",
" Never =>> 1 " )
rules <- parse_rules( RULES )
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]
# MEMISC LABELS AND MISSING VALUE CODES
values <- c( 5, 4, 3, 2, 1 )
labels <- c( "More often than not", "Frequently",
"Occasionally", "Rarely", "Never" )
missing <- NA
# RECODE VARIABLES
survey_df <-
survey_df %>%
recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )Example:
ExtAffairs_GenEd — ‘TYPE: factor’
“Frequency of activities aimed at educating the general public about a specific policy issue and/or the interests of certain groups”
| Storage mode: | character |
| Measurement: | nominal |
| Missing values: | NA |
| Values and labels | N | Valid | Total | ||||||
| 1 | ‘Never’ | 139 | 31 | . | 7 | 27 | . | 7 | |
| 2 | ‘Rarely’ | 60 | 13 | . | 7 | 12 | . | 0 | |
| 3 | ‘Occasionally’ | 119 | 27 | . | 1 | 23 | . | 7 | |
| 4 | ‘Frequently’ | 91 | 20 | . | 7 | 18 | . | 1 | |
| 5 | ‘More often than not’ | 30 | 6 | . | 8 | 6 | . | 0 | |
| NA | M | 63 | 12 | . | 5 | ||||
- QUESTION TXT:
- During the last two years (2022-2023), how often did your organization conduct the following activities? (Including at the local, county, state, or federal level). - Educate the general public about a specific policy issue and/or the interests of certain groups
14 Integer Inputs
These questions accept an integer input from users to indicate the number of staff they have, people they served, or donors they have.
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| Whole Number | Number of Staff, People or Donors | NA | Integer Value | No |
| N/A | Not Applicable | NA | -1 | Yes |
| -99 | Incomplete | NA | -2 | Yes |
| NA | Unanswered | NA | NA | Yes |
int_qns <-
c( staff_qns_int,
people_served_qns_int,
fundraise_donor_qns_int )
COLUMNS <- int_qns
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( recode_x, pattern=c("N/A","-99"), replace=c("Inf","Inf") )
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( as.numeric )
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( memisc::as.item, missing.values=Inf )
survey_df[ COLUMNS ] <- purrr::map( COLUMNS, add_q_details, survey_df )Example:
Staff_Fulltime_2022 — ‘TYPE: integer’
“Number of full time staff”
| Storage mode: | double |
| Measurement: | interval |
| Missing values: | Inf |
| Values and labels | N | Percent | ||||
| NA | M | 43 | 8 | . | 6 | |
| Min: | 0 | . | 000 |
| Max: | 3800 | . | 000 |
| Mean: | 20 | . | 950 |
| Std.Dev.: | 179 | . | 420 |
- QUESTION TXT:
- How many (paid and unpaid) people in your organization will have worke… - 2022 - Full-time paid staff (35 or more hours /week) - (best estimate)
15 Numeric Inputs
These questions accept a numeric input from users to denote dollar values. For some variables, additional processing is done to remove commas and “$” signs.
| Original Value | Description | Recode Label | Recode Value | Code as Missing ? |
|---|---|---|---|---|
| Dollar Amount | Dollar Amount | NA | Numeric Value | No |
| -99 | Incomplete | NA | -1 | Yes |
| NA | Unanswered | NA | NA | Yes |
numeric_qns <-
c( majorgift_qn_num,
reserve_qns_num,
cares_qns_num,
finance_revenue_qns_num )
COLUMNS <- numeric_qns
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( keep_numbers )
survey_df[ COLUMNS ] <-
survey_df[ COLUMNS ] %>%
lapply( memisc::as.item, missing.values=Inf )
survey_df[ COLUMNS ] <- purrr::map( COLUMNS, add_q_details, survey_df )Example:
FndRaise_MajGift_Amt — ‘TYPE: numeric’
“Donation amount considered a major gift”
| Storage mode: | double |
| Measurement: | interval |
| Missing values: | Inf |
| Values and labels | N | Percent | ||||
| NA | M | 35 | 7 | . | 0 | |
| Min: | 0 | . | 000 |
| Max: | 1000000 | . | 000 |
| Mean: | 6178 | . | 546 |
| Std.Dev.: | 47376 | . | 540 |
- QUESTION TXT:
- What is the smallest donation amount that your organization would consider to be a major gift? - $
16 Text Inputs
These questions allow the user to enter raw text as answers. All non-text values are converted to NAs.
text_qns <-
c( staff_qns_text,
finance_chng_qns_text,
finance_revenue_qns_text,
fundraise_qns_text,
leadership_chng_qns_text,
primary_cncrn_qn_text,
program_change_qns_txt,
race_gender_qns_text)
survey_df[ text_qns ] <-
survey_df[ text_qns ] %>%
lapply( recode_x, pattern="-99", replace=NA )
survey_df[ text_qns ] <-
survey_df[ text_qns ] %>%
lapply( memisc::as.item )
survey_df[ text_qns ] <- purrr::map( text_qns, add_q_details, survey_df )
codebook( survey_df[ text_qns[1] ] )================================================================================
Staff_Other_Text_2022 'TYPE: text'
"Description of other staff"
--------------------------------------------------------------------------------
Storage mode: character
Measurement: nominal
Min: "1099 artists"
Max: "teaching artists & conductors"
QUESTION TXT:
How many (paid and unpaid) people in your organization will have
worke... - 2022 - Other (please specify): - TextExample:
Staff_Other_Text_2022 — ‘TYPE: text’
“Description of other staff”
| Storage mode: | character |
| Measurement: | nominal |
| Min: | “1099 artists” | ||
| Max: | “teaching artists & conductors” |
- QUESTION TXT:
- How many (paid and unpaid) people in your organization will have worke… - 2022 - Other (please specify): - Text