3  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.

4 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

# APPLY TO COLUMNS K:
bool_qns <- 
  c( program_change_qns_bool, 
     fundraise_qns_bool, 
     cares_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 )

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’	268	40	.	1	38	.	8
1		‘Yes’	400	59	.	9	57	.	9
97	M	‘Unsure’	1				0	.	1
98	M	‘Not Applicable’	6				0	.	9
99	M	‘N/A’	11				1	.	6
NA	M		5				0	.	7

QUESTION TXT:

3. In the last year (between January 2021-December 2021), did your organization make any of the following changes to your Programs, as compared to 2020? - Increased the number of programs or services

5 Single Checkboxes

These questions are presented as a checkbox to the respondent. They indicate an affirmative answer to the question.

5.1 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?
(select all that apply)	Checkbox Checked	Yes	1	No
-99	Checkbox Unchecked	No	0	No
NA	Unanswered	NA	NA	Yes

# APPLY TO COLUMNS K:
COLUMNS <-  fundraise_skrcv_qns_bool

# VALUES THAT NEED RECODING

RULES <- c(    
  
  "  (select all that apply)    =>>    1    ", 
  "                      -99    =>>    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 <- "UNSURE"

# 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:	UNSURE

Values and labels			N	Valid			Total
0		‘No’	317	46	.	8	45	.	9
1		‘Yes’	361	53	.	2	52	.	2
NA	M		13				1	.	9

QUESTION TXT:

9. In the last year (2021), 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)

5.2 Race and Gender Checkboxes

These checkboxes are ticked by the respondent to indicate if their CEO or board chair belong to a specified race or gender identity.

Original Value	Description	Recode Label	Recode Value	Code as Missing ?
Asian/Pacific Islander	Checkbox Checked	Yes	1	No
Black/African American	Checkbox Checked	Yes	1	No
Latinx/Hispanic	Checkbox Checked	Yes	1	No
Native American/American Indian	Checkbox Checked	Yes	1	No
White	Checkbox Checked	Yes	1	No
Man	Checkbox Checked	Yes	1	No
Woman	Checkbox Checked	Yes	1	No
Trans	Checkbox Checked	Yes	1	No
Gender non-conforming/Non-Binary	Checkbox Checked	Yes	1	No
Other (please specify)	Checkbox Checked	Yes	1	No
0	Checkbox Unchecked	No	0	No
-99	Incomplete	Incomplete	99	Yes
NA	Unanswered	NA	NA	Yes

# APPLY TO COLUMNS K:
COLUMNS <-  race_gender_qns_bool

# VALUES THAT NEED RECODING

RULES <- c(    
  
          "          Asian/Pacific Islander    =>>     1   ",
          "          Black/African American    =>>     1   ",
          "                 Latinx/Hispanic    =>>     1   ",
          " Native American/American Indian    =>>     1   ",
          "                           White    =>>     1   ",
          "                             Man    =>>     1   ",
          "                           Woman    =>>     1   ",
          "                           Trans    =>>     1   ",
          "Gender non-conforming/Non-Binary    =>>     1   ",
          "         Other (please specify):    =>>     1   ",
          "                             -99    =>>     x   ",
          "                               0    =>>     0   "     )


rules <- parse_rules( RULES )          
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]

# MEMISC LABELS AND MISSING VALUE CODES 
values  <- c( "0", "1", "x" )
labels  <- c( "No", "Yes", "Incomplete" )
missing <- "x"

# RECODE VARIABLES 
survey_df <- 
  survey_df %>% 
  recode_columns( k=COLUMNS, pattern, replace, values, labels, missing )

Example:

CEOrace_AAPI — ‘TYPE: boolean’

“race of CEO”

Storage mode:	character
Measurement:	nominal
Missing values:	x

Values and labels			N	Valid			Total
0		‘No’	48	98	.	0	6	.	9
1		‘Yes’	1	2	.	0	0	.	1
x	M	‘Incomplete’	2				0	.	3
NA	M		640				92	.	6

QUESTION TXT:

15.a. Which of the following best describes the race/ethnicity for your organization’s current Chief Executive (i.e., Executive Director or CEO)? (Select all that apply) - Selected Choice - Asian/Pacific Islander

5.3 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”.

Original Value	Description	Recode Label	Recode Value	Code as Missing ?
C	Yes, this question is not applicable	Yes	1	No
N/A	Yes, this question is not applicable	Yes	1	No
-99	Incomplete	No	0	No
NA	Unanswered	NA	NA	Yes

na_bool_qns <- 
  c( staff_qns_bool, 
     reserve_qns_bool, 
     people_served_qns_bool )


# APPLY TO COLUMNS K:
COLUMNS <-  na_bool_qns

# VALUES THAT NEED RECODING

RULES <- c(    
          
             "    C    =>>     1   ", 
             "  -99    =>>     0   ", 
             "  N/A    =>>     1   "     )


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 <- NULL

# 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

Values and labels			N	Valid			Total
0		‘No’	638	92	.	9	92	.	3
1		‘Yes’	49	7	.	1	7	.	1
NA	M		4				0	.	6

QUESTION TXT:

STAFF & VOLUNTEERS      6. 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

6 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.

6.1 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.

6.2 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(    
          
             "        Decrease    =>>     0   ",
             "   Stay the same    =>>     1   ", 
             "        Increase    =>>     2   ", 
             "             -99    =>>     x   "    )


rules <- parse_rules( RULES )          
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]

# MEMISC LABELS AND MISSING VALUE CODES 
values  <- c( 2, 1, 0, "x" )
labels  <- c( "Increase", "Unchanged", "Decrease", "Incomplete" )
missing <- "x"

# 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:	x

Values and labels			N	Valid			Total
0		‘Decrease’	17	2	.	5	2	.	5
1		‘Unchanged’	148	21	.	9	21	.	4
2		‘Increase’	512	75	.	6	74	.	1
x	M	‘Incomplete’	10				1	.	4
NA	M		4				0	.	6

QUESTION TXT:

5. In the next year, do you anticipate the demand for your programs to decrease, stay the same, or increase?

6.3 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

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   ",
     "                                       Unsure    =>>     0   ",
     "                                          -99    =>>     X   ",
     "                                          N/A    =>>   N/A   "  )
 
rules <- parse_rules( RULES )          
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]

# MEMISC LABELS AND MISSING VALUE CODES 
values  <- c( 5, 4, 3, 2, 1, 0, "X", "N/A" )
labels  <- c( "Increase Significantly", "Increase Moderately", 
              "Unchanged", "Decrease Moderately", "Decrease Significantly", 
              "Unsure", "Incomplete", "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’	12	1	.	9	1	.	7
1		‘Decrease Significantly’	80	12	.	6	11	.	6
2		‘Decrease Moderately’	77	12	.	1	11	.	1
3		‘Unchanged’	161	25	.	4	23	.	3
4		‘Increase Moderately’	169	26	.	7	24	.	5
5		‘Increase Significantly’	133	21	.	0	19	.	2
N/A		‘Not Applicable’	2	0	.	3	0	.	3
X	M	‘Incomplete’	33				4	.	8
NA	M		24				3	.	5

QUESTION TXT:

8.c. To the best of your knowledge, has each of the following categories of donations to your org… - In 2021 compared to 2020 - Overall donations

7 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.

7.1 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	5	No
Very important - we depend on volunteers for a wide range of tasks, but not all	Second Most Important	Very Important	4	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	2	No
Not at all important - we could carry out our mission and goals without using volunteers	Fifth Most Important	Not At All Important	1	No
We do not use volunteers	Sixth Most Important	Not Used	0	No
-99	Incomplete	Incomplete	99	Yes
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    =>>     5   ",
"           Very important - we depend on volunteers for a wide range of tasks, but not all    =>>     4   ",
"                        Somewhat important - we depend on volunteers for several key tasks    =>>     3   ",
"                 Not very important - we depend on volunteers for only non-essential tasks    =>>     2   ",
"  Not at all important - we could carry out our mission and goals without using volunteers    =>>     1   ",
"                                                                  We do not use volunteers    =>>     0   ",
"                                                                                       -99    =>>     X   "    )

rules <- parse_rules( RULES )          
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]

# MEMISC LABELS AND MISSING VALUE CODES 
values  <- c( 5, 4, 3, 2, 1, 0, "X" )
labels  <- c( "Essential", "Very Important", "Somewhat Important", 
              "Not Very Important", "Not At All Important", "Not Used", 
              "Incomplete" )
missing <- c( "X" )

# 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:	X

Values and labels			N	Valid			Total
0		‘Not Used’	45	6	.	6	6	.	5
1		‘Not At All Important’	39	5	.	7	5	.	6
2		‘Not Very Important’	74	10	.	9	10	.	7
3		‘Somewhat Important’	188	27	.	6	27	.	2
4		‘Very Important’	200	29	.	4	28	.	9
5		‘Essential’	135	19	.	8	19	.	5
X	M	‘Incomplete’	6				0	.	9
NA	M		4				0	.	6

QUESTION TXT:

7. How important were volunteers – other than board members – to the work your organization conducted in 2021?

7.2 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	5	No
Very important, we depend on individual donations for a wide range of activities, but not all	Very Important	4	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	2	No
Not at all important, we could carry out our mission and goals without donations from individuals	Fifth Most Important	Not At All Important	1	No
We do not receive donations from individuals	Sixth Most Important	Not Used	0	No
-99	Incomplete	Incomplete	99	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  =>>  5  ",
  "     Very important, we depend on individual donations for a wide range of activities, but not all  =>>  4  ",
  "                           Important, we depend on individual donations for several key activities  =>>  3  ",
  "           Not very important, we depend on individual donations for only non-essential activities  =>>  2  ",
  " Not at all important, we could carry out our mission and goals without donations from individuals  =>>  1  ",
  "                                                      We do not receive donations from individuals  =>>  0  ",
  "                                                                                               -99  =>>  X  "  )


rules <- parse_rules( RULES )          
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]

# MEMISC LABELS AND MISSING VALUE CODES 
values  <- c(5, 4, 3, 2, 1, 0, "X" )
labels  <- c( "Essential", "Very Important", 
              "Somewhat Important", "Not Very Important", 
              "Not At All Important", "Not Used", "Incomplete")
missing <- c( "X" )

# 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:	X

Values and labels			N	Valid			Total
0		‘Not Used’	18	2	.	6	2	.	6
1		‘Not At All Important’	24	3	.	5	3	.	5
2		‘Not Very Important’	56	8	.	2	8	.	1
3		‘Somewhat Important’	132	19	.	4	19	.	1
4		‘Very Important’	282	41	.	5	40	.	8
5		‘Essential’	168	24	.	7	24	.	3
X	M	‘Incomplete’	7				1	.	0
NA	M		4				0	.	6

QUESTION TXT:

8. 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?

8 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 ?
Almost all the time	Most Frequent	Most of the time	4	No
Frequently	Second Most Frequent	Frequently	3	No
Occasionally	Third Most Frequent	Occasionally	2	No
Rarely	Fourth Most Frequent	Rarely	1	No
Never	Fifth Most Frequent	Occasionally	0	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(    

     "    Almost all the time    =>>     4   ",
     "             Frequently    =>>     3   ",
     "           Occasionally    =>>     2   ",
     "                 Rarely    =>>     1   ",
     "                  Never    =>>     0   ",
     "                    -99    =>>     X   "   )
     
rules <- parse_rules( RULES )          
pattern <- rules[[ "pattern" ]]
replace <- rules[[ "replace" ]]

# MEMISC LABELS AND MISSING VALUE CODES 
values  <- c( 4, 3, 2, 1, 0, "X" )
labels  <- c( "Most of the time", "Frequently",  
              "Occasionally", "Rarely", "Never", "Incomplete" )
missing <- c( "X" )

# 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:	X

Values and labels			N	Valid			Total
0		‘Never’	167	29	.	0	24	.	2
1		‘Rarely’	98	17	.	0	14	.	2
2		‘Occasionally’	149	25	.	9	21	.	6
3		‘Frequently’	116	20	.	2	16	.	8
4		‘Most of the time’	45	7	.	8	6	.	5
X	M	‘Incomplete’	10				1	.	4
NA	M		106				15	.	3

QUESTION TXT:

EXTERNAL AFFAIRS 16. During the last two years (2020-2021), 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

9 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_2021 — ‘TYPE: integer’

“Number of full time staff”

Storage mode:	double
Measurement:	interval
Missing values:	Inf

Values and labels			N	Percent
M		(unlab.mss.)	61	8	.	8
NA	M		4	0	.	6

Min:	0	.	000
Max:	3000	.	000
Mean:	17	.	119
Std.Dev.:	122	.	704

QUESTION TXT:

STAFF & VOLUNTEERS      6. How many (paid and unpaid) people in your organization will have worke… - 2021 - Full-time paid staff (35 or more hours /week) - (best estimate)

10 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
M		(unlab.mss.)	18	2	.	6
NA	M		25	3	.	6

Min:	0	.	010
Max:	1000000	.	000
Mean:	5935	.	894
Std.Dev.:	44801	.	714

QUESTION TXT:

8.b. What is the smallest donation amount that your organization would consider to be a major gift? - $

11 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_2021 'TYPE: text'

   "Description of other staff"

--------------------------------------------------------------------------------

   Storage mode: character
   Measurement: nominal

   Min:                     "Book keeper"
   Max: "we are a part time organization"

   QUESTION TXT:
       STAFF & VOLUNTEERS      6. How many (paid and unpaid) people in
       your organization will have worke... - 2021 - Other (please
       specify): - Text

Example:

Staff_Other_Text_2021 — ‘TYPE: text’

“Description of other staff”

Storage mode:	character
Measurement:	nominal

Min:	“Book keeper”
Max:	“we are a part time organization”

QUESTION TXT:

STAFF & VOLUNTEERS      6. How many (paid and unpaid) people in your organization will have worke… - 2021 - Other (please specify): - Text