Exploring unnecessary invasive procedures in the United States: a retrospective mixed-methods analysis of cases from 2008-2016

Table 6 Cluster analysis results (N = 79)

Clustering Variables	Clusters					V^a	n
Clustering Variables	1 (n = 21)	2 (n = 9)	3 (n = 17)	4 (n = 11)	5 (n = 21)	V^a	n
Traits/Motives
Financial gain	100%(21)	66.7%(6)	100%(17)	81.8%(9)	95.2%(20)	.42	73
Personality disorder	100%(21)	22.2%(2)	11.8%(2)	45.5%(5)	38.1%(8)	.67	38
Poor problem solving	0%	100%(9)	0%	0%	0%	.99	9
Ambition	0%	0%	5.9%(1)	0%	9.5%(2)	.22	3
Environmental factors
Oversight deficits	100%(21)	77.8%(7)	82.4%(14)	0%	100%(21)	.83	63
Corrupt moral climate	0%	0%	0%	0%	100%(21)	.99	21
Financial COI	0%	0%	47.1%(8)	0%	14.3%(3)	.53	11
Ambiguous norms	0%	0%	17.6%(3)	9.1%(1)	0%	.33	4
Summary of Clusters	1	2	3	4	5
Financial gain	H	M	H	M	H
Suspected antisocial	H	M	L	M	M
Poor problem-solving	–	H	–	–	–
Ambition	–	–	L	–	L
Oversight deficits	H	M	M	–	H
Corrupt moral climate	–	–	–	–	H
Financial COI	–	–	M	–	L
Ambiguous norms	–	–	M	L	–
Seriousness rating	54.4 (7.9)	51.9(8.2)	45.6(11.7)	50.9(7.9)	47.9(10.8)

^aCramer’s V was used to test the association of these nominal variables. Values above .35 are particularly important in discriminating among clusters, which is reinforced by p-values of < .05 for those Vs; values < .35 indicate weak relationships that did not discriminate significantly
Percentage is percent of cases within the cluster. The raw number of cases representing a variable appears in parentheses
H High (84–100%), M Medium (17–83%), L Low (1–16%); − = Absent

ISSN: 1754-9493