Pretest Descriptive Results

Folder 'Data_and_Output' already exists. No action taken.
table(is.na(fullchoice$pref1)) ### should be all FALSE

FALSE 
  480 
table(fullchoice$block)  ### should always be a multiple of 10 and ideally equal

 1  2  3  4  5  6  7  8  9 10 
40 40 50 50 50 50 50 50 60 40 
table(fullchoice$arm)

  1   2   3   4 
130 120 110 120 
t<- fullchoice %>% group_by(RID) %>% summarize(n= n())

t_raw<- fullchoice_raw %>% group_by(RID) %>% summarize(n= n())

table(t$n) ### should all be 10, how many choice sets per respondent

10 
48 
table(fullchoice$pref1)   ## How many opt out choices

  1   2 
282 198 
table(data$exit_code) # must be zero

 0 
48 
table(data$status_comp) 

finished 
      48 
table(data$status_comp2)
< table of extent 0 >
# Where do we "loose" respondents?

table(raw_data$q1) # Do you want to participate in the survey?

 1  2 
77  5 
table(raw_data$status) # did api call started?

success 
     75 
sum(!is.na(raw_data$q10)) # How many are still there when we ask first attention question?
[1] 68
sum(!is.na(raw_data$q14)) # How many are still there when we ask third attention question?
[1] 64
table(raw_data$status_comp) # Is R studio server calculation done?

finished 
      64 
sum(!is.na(raw_data$q16)) # HNV map question
[1] 64
sum(!is.na(raw_data$q18)) # PA map question
[1] 63
table(t_raw$n) # We loose some respondents after the first choice card

 1  2  3  4  8  9 10 
 5  1  2  1  1  2 51 
loose_first_cc <- t_raw %>% filter(n==1)
ids_first_cc <- unique(loose_first_cc$RID)

loose_first_cc <- fullchoice_raw %>% filter(RID %in% ids_first_cc)

summary(loose_first_cc$pref1) # DCE display problem? Or do people just drop out?
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
     NA      NA      NA     NaN      NA      NA       5 
table(as.factor(loose_first_cc$is_mobile))

true 
   5 
sum(!is.na(raw_data$q27_1_1)) # First question after DCE
[1] 50

Meta data

Mobile device

Quality checks

Randomizations

Socio-demografics

# A tibble: 4 × 3
  educ                                     n percentage
  <fct>                                <int>      <dbl>
1 Mittlere Reife (Realschulabschluss)     18       37.5
2 Hochschulabschluss (Universität, FH)    14       29.2
3 Abitur                                  10       20.8
4 Hauptschulabschluss                      6       12.5

“Sonntagsfrage”


Tierschutzpartei 
               1

Attention questions

Access of Additional Information

< table of extent 0 >

1 
1

NSG and HNV Distribution

Spatial Distribution

Choice Experiment

Conditional Logit Models

Preference Space
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
beta_pa 0.071 0.022 3.261 0.034 2.103
beta_hnv 0.060 0.022 2.702 0.031 1.952
beta_pa_sq -0.0001 0.0001 -1.778 0.0002 -0.923
beta_hnv_sq -0.00004 0.0001 -0.593 0.0001 -0.319
pa_half_access 0.002 0.109 0.020 0.103 0.021
pa_full_access -0.046 0.103 -0.444 0.103 -0.444
hnv_visible 0.035 0.090 0.392 0.080 0.439
beta_cost -0.008 0.001 -10.024 0.001 -7.492
WTP Space
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
beta_pa 8.813 2.705 3.258 4.173 2.112
beta_hnv 7.489 2.712 2.761 3.803 1.969
beta_pa_sq -0.018 0.010 -1.751 0.020 -0.916
beta_hnv_sq -0.005 0.008 -0.588 0.016 -0.317
pa_half_access 0.310 13.570 0.023 12.879 0.024
pa_full_access -5.579 13.006 -0.429 13.035 -0.428
hnv_visible 4.501 11.215 0.401 10.044 0.448
beta_cost -0.008 0.001 -10.036 0.001 -7.502
WTP Space with ASC
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
beta_pa 5.418 2.775 1.952 3.981 1.361
beta_hnv 4.447 2.755 1.614 3.401 1.308
beta_pa_sq -0.016 0.009 -1.741 0.018 -0.906
beta_hnv_sq -0.005 0.008 -0.659 0.014 -0.353
pa_half_access -8.918 12.983 -0.687 11.930 -0.748
pa_full_access -18.082 13.079 -1.383 12.678 -1.426
hnv_visible -2.218 10.616 -0.209 9.305 -0.238
beta_cost -0.009 0.001 -10.152 0.001 -7.720
ASC_sq -39.594 15.756 -2.513 17.058 -2.321
All Dummies with ASC
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
beta_pa200 0.379 0.138 2.753 0.138 2.738
beta_pa300 0.355 0.145 2.444 0.122 2.911
beta_pa500 0.272 0.139 1.960 0.149 1.819
beta_pa800 0.425 0.129 3.299 0.119 3.577
beta_hnv200 0.063 0.137 0.461 0.147 0.430
beta_hnv300 -0.0005 0.137 -0.003 0.146 -0.003
beta_hnv500 -0.061 0.153 -0.397 0.152 -0.402
beta_hnv800 0.335 0.146 2.300 0.137 2.450
pa_half_access 0.032 0.118 0.271 0.106 0.301
pa_full_access -0.002 0.122 -0.016 0.118 -0.017
hnv_visible 0.002 0.094 0.024 0.079 0.028
beta_cost -0.008 0.001 -9.836 0.001 -7.361

Check Choice Patterns

# Relationship between Attribute Levels and Choices 

table((database$pa_att - database$sq_pa_area), database$pref1)
      
        1  2
  200  61 36
  400  59 35
  600  51 41
  1000 57 37
  1600 54 49
prop.table(table((database$pa_att - database$sq_pa_area), database$pref1), margin = 1)
      
               1         2
  200  0.6288660 0.3711340
  400  0.6276596 0.3723404
  600  0.5543478 0.4456522
  1000 0.6063830 0.3936170
  1600 0.5242718 0.4757282
table(database$Dummy_pa_half, database$pref1)
   
      1   2
  0 189 134
  1  93  64
prop.table(table(database$Dummy_pa_half, database$pref1), margin = 1)
   
            1         2
  0 0.5851393 0.4148607
  1 0.5923567 0.4076433
table(database$Dummy_pa_full, database$pref1)
   
      1   2
  0 176 141
  1 106  57
prop.table(table(database$Dummy_pa_full, database$pref1), margin = 1)
   
            1         2
  0 0.5552050 0.4447950
  1 0.6503067 0.3496933
table((database$hnv_att - database$sq_hnv_area), database$pref1)
      
        1  2
  200  59 40
  400  55 47
  600  56 32
  1000 58 28
  1600 54 51
prop.table(table((database$hnv_att - database$sq_hnv_area), database$pref1), margin = 1)
      
               1         2
  200  0.5959596 0.4040404
  400  0.5392157 0.4607843
  600  0.6363636 0.3636364
  1000 0.6744186 0.3255814
  1600 0.5142857 0.4857143
table(database$Dummy_hnv_visible, database$pref1)
   
      1   2
  0 153 100
  1 129  98
prop.table(table(database$Dummy_hnv_visible, database$pref1), margin = 1)
   
            1         2
  0 0.6047431 0.3952569
  1 0.5682819 0.4317181
table(database$cost_att, database$pref1)
     
       1  2
  5   13 43
  10  20 40
  40  29 29
  80  33 19
  120 41 12
  150 50 21
  200 43 17
prop.table(table(database$cost_att, database$pref1), margin = 1)
     
              1         2
  5   0.2321429 0.7678571
  10  0.3333333 0.6666667
  40  0.5000000 0.5000000
  80  0.6346154 0.3653846
  120 0.7735849 0.2264151
  150 0.7042254 0.2957746
  200 0.7166667 0.2833333
table((database$hnv_att - database$sq_hnv_area + database$pa_att - database$sq_pa_area), database$pref1)
      
        1  2
  400  12  6
  600  35 21
  800  43 16
  1000 25 11
  1200 22 19
  1400 18 18
  1600 15 10
  1800 20 21
  2000 32 27
  2200 18 25
  2600 22 16
  3200 20  8
prop.table(table((database$hnv_att - database$sq_hnv_area + database$pa_att - database$sq_pa_area), database$pref1), margin=1)
      
               1         2
  400  0.6666667 0.3333333
  600  0.6250000 0.3750000
  800  0.7288136 0.2711864
  1000 0.6944444 0.3055556
  1200 0.5365854 0.4634146
  1400 0.5000000 0.5000000
  1600 0.6000000 0.4000000
  1800 0.4878049 0.5121951
  2000 0.5423729 0.4576271
  2200 0.4186047 0.5813953
  2600 0.5789474 0.4210526
  3200 0.7142857 0.2857143
# Has radius an impact?

table(database$radius, database$pref1)
       
          1   2
  15000  67  43
  20000  60  50
  25000  41  39
  30000 114  66
prop.table(table(database$radius, database$pref1), margin = 1)
       
                1         2
  15000 0.6090909 0.3909091
  20000 0.5454545 0.4545455
  25000 0.5125000 0.4875000
  30000 0.6333333 0.3666667
# Has DCE order an impact?

table(database$dce_version, database$pref1)
   
      1   2
  3 116 114
  4 166  84
prop.table(table(database$dce_version, database$pref1), margin = 1)
   
            1         2
  3 0.5043478 0.4956522
  4 0.6640000 0.3360000
# Check if it is a recoding issue

table(database$dce_version, database$pref1, database$cost_att)
, ,  = 5

   
     1  2
  3  2 21
  4 11 22

, ,  = 10

   
     1  2
  3 12 24
  4  8 16

, ,  = 40

   
     1  2
  3 18 13
  4 11 16

, ,  = 80

   
     1  2
  3 12 10
  4 21  9

, ,  = 120

   
     1  2
  3 18  6
  4 23  6

, ,  = 150

   
     1  2
  3 17 15
  4 33  6

, ,  = 200

   
     1  2
  3 18 13
  4 25  4
# Has the order of attributes an impact?

table(database$order, database$pref1)
           
              1   2
  1,2,3,4,5  67  63
  3,4,1,2,5 103  57
  5,1,2,3,4  54  46
  5,3,4,1,2  58  32
prop.table(table(database$order, database$pref1), margin = 1)
           
                    1         2
  1,2,3,4,5 0.5153846 0.4846154
  3,4,1,2,5 0.6437500 0.3562500
  5,1,2,3,4 0.5400000 0.4600000
  5,3,4,1,2 0.6444444 0.3555556
# Do correct responses have an impact?
table(database$corr_all, database$pref1)
   
      1   2
  0 238 162
  1  44  36
prop.table(table(database$corr_all, database$pref1), margin = 1)
   
        1     2
  0 0.595 0.405
  1 0.550 0.450
#Does length of payment have an impact
table(database$arm, database$pref1)
   
     1  2
  1 86 44
  2 62 58
  3 56 54
  4 78 42
prop.table(table(database$arm, database$pref1), margin = 1)
   
            1         2
  1 0.6615385 0.3384615
  2 0.5166667 0.4833333
  3 0.5090909 0.4909091
  4 0.6500000 0.3500000

Protester

CV

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
    0.0    12.5    30.0   852.6    80.0 25000.0      17

Travel Cost

Comments

print(unique(data$feedback_43))
 [1] NA                                                                                                                                                                                                                                                                                                                                                                                                                          
 [2] "Die Grünen sollten abtreten"                                                                                                                                                                                                                                                                                                                                                                                               
 [3] "Nichts, vielen Dank"                                                                                                                                                                                                                                                                                                                                                                                                       
 [4] "Nice"                                                                                                                                                                                                                                                                                                                                                                                                                      
 [5] "keine"                                                                                                                                                                                                                                                                                                                                                                                                                     
 [6] "Nein"                                                                                                                                                                                                                                                                                                                                                                                                                      
 [7] "Gut"                                                                                                                                                                                                                                                                                                                                                                                                                       
 [8] "Nein, danke"                                                                                                                                                                                                                                                                                                                                                                                                               
 [9] "-/-"                                                                                                                                                                                                                                                                                                                                                                                                                       
[10] "Ähnlich wie bei der Abgabe zur Deich-und Sielacht sollte man von jedem Haushalt eine Abgabe in Höhe von 14 Euro für alle Haushalte bundesweit erheben. Voraussetzung wäre allerdings keine Erhöhungen dieses Betrages. ( Meistens ist es so , das wenn eine Abgabe erst einmal eingeführt ist, die Begehrlichkeiten mehr werden und Beiträge erhöht werden. Das ist bei dem Beitrag zur Deich-Und Sielacht nicht der Fall."
[11] "Es war sehr interessant."