fuzzy fsQCA bestfit displaying few cases

[Rosa Blau]

Hello Statalist,

I am conducting a fuzzy-set QCA using the 'fuzzy' package written by Longest & Vaisey (2008). I am looking at the following outcome variable:

* Example generated by -dataex-. To install: ssc install dataex
clear
input float A
0
0
1
1
1
0
0
0
1
1
1
1
0
0
0
1
1
0
0
0
0
0
1
1
0
1
1
1
0
0
0
1
0
0
1
0
0
0
1
1
1
1
0
0
1
0
0
1
0
1
1
1
1
0
1
1
1
1
0
1
1
1
1
1
1
1
1
0
1
1
1
0
0
1
1
0
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
0
0
1
1
1
1
0
end
label values A A
label def A 0 "Does not provided extended maternity leave", modify
label def A 1 "Provides extended maternity leave", modify

This outcome variable has 65 observations that take the value of 1:

. tab A, nolab

   Extended |
  maternity |
      leave |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         39       37.50       37.50
          1 |         65       62.50      100.00
------------+-----------------------------------
      Total |        104      100.00

In my fsQCA I am examining the following five conditions:

* Example generated by -dataex-. To install: ssc install dataex
clear
input float S
0
1
1
0
0
0
0
1
1
0
0
0
1
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
1
1
1
0
1
0
1
1
1
1
1
0
0
1
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
1
0
0
1
0
1
0
0
0
0
1
0
0
0
1
0
0
1
0
0
1
1
1
0
0
0
0
1
end
label values S S
label def S 0 "California", modify
label def S 1 "New York", modify

* Example generated by -dataex-. To install: ssc install dataex
clear
input float O
.67
.33
.67
.33
 .5
.33
  1
 .5
.33
.67
  1
 .5
 .5
.33
 .5
.33
 .5
.33
.33
 .5
 .5
  1
 .5
 .5
 .5
.33
.67
 .5
.33
 .5
.33
 .5
 .5
 .5
.33
  1
.67
 .5
 .5
.67
 .5
.67
.67
.67
.33
.67
  1
 .5
.33
.67
 .5
 .5
.67
  1
.33
  1
  1
 .5
.67
  1
  1
 .5
 .5
  1
.33
  1
 .5
  1
  1
  1
.33
.67
.67
 .5
 .5
 .5
 .5
.67
.33
 .5
  1
  1
 .5
.33
.67
 .5
  1
.67
.67
.67
 .5
.33
 .5
  1
.67
.67
.33
.67
.67
  1
end

* Example generated by -dataex-. To install: ssc install dataex
clear
input float F
 1
.5
.5
 1
.5
 1
 1
.5
.5
 0
.5
.5
.5
 0
.5
.5
.5
.5
 0
 0
 0
.5
.5
.5
.5
.5
 1
 0
.5
.5
.5
 0
.5
 0
 0
.5
.5
.5
 0
.5
.5
 1
.5
.5
.5
 0
.5
.5
.5
 1
 1
 0
.5
.5
 0
.5
 0
 1
.5
.5
 0
.5
.5
.5
 1
.5
.5
.5
.5
 1
.5
.5
 1
 1
 1
.5
.5
.5
 0
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
 1
 0
.5
.5
 1
end

* Example generated by -dataex-. To install: ssc install dataex
clear
input float T
.
1
1
1
0
1
1
0
1
1
1
0
1
1
1
1
0
1
1
.
1
1
1
1
0
1
0
1
1
1
0
0
1
1
0
1
1
0
0
1
1
0
1
0
1
1
1
1
0
1
0
0
1
1
1
1
0
0
1
1
0
1
0
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
0
0
1
1
1
1
1
0
1
1
1
0
1
1
1
1
1
1
1
1
1
1
end
label values T tightness
label def tightness 0 "not tight (high skills easy to fill)", modify
label def tightness 1 "tight (high skills hard to fill)", modify

* Example generated by -dataex-. To install: ssc install dataex
clear
input float I
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
0
0
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
end
label values I I
label def I 0 "Mixed Industry Skill-Profile", modify
label def I 1 "High-General Industry Skill-Profile", modify

In a first step I am trying to assess which configurations of conditions contain the greatest number of cases. To this end, I used the following code:

fuzzy A S O F T I
tabulate bestfit, sort

The result I get is

. fuzzy A S O F T I

. tabulate bestfit, sort 

    bestfit |      Freq.     Percent        Cum.
------------+-----------------------------------
      sOFTI |          4       16.67       16.67
      SOFTI |          2        8.33       25.00
      SoFTI |          2        8.33       33.33
      sOfti |          2        8.33       41.67
      soFTi |          2        8.33       50.00
      sofTI |          2        8.33       58.33
      sofTi |          2        8.33       66.67
      softI |          2        8.33       75.00
      SOFtI |          1        4.17       79.17
      SOfTI |          1        4.17       83.33
      SofTi |          1        4.17       87.50
      sOFti |          1        4.17       91.67
      sOfTi |          1        4.17       95.83
      soFTI |          1        4.17      100.00
------------+-----------------------------------
      Total |         24      100.00

If I understand the fuzzy bestfit command correctly, I should be getting a table with a count of 65 cases, correct? I do not understand why the bestfit table is only displaying 24 cases.

I am aware that Longest & Vaisey mention that cases scoring 0.5 on all individual predictors sets will not appear in the bestfit table because they belong equally to all configurations. But given that only two of my conditions are calibrated to allow a score of 0.5, there can't be a case in which all predictors have a score of 0.5.

Furthermore, I am also aware that a similar question was previously posted and left unanswered ten years ago. However, I was hoping for better luck at this moment in time.