‘Lock them up and throw away the key’: an evaluation of the structure of punitive attitudes

Figures & data

Table 1. Antecedents in the psychometric evaluation of punitive attitudes.

Reference	Sample	Sample characteristics	Method	Dimensions	Results
Ortet-Fabregat and Pérez (1992)	Study 1 (n = 382) Spain Study 2 (n = 357) Spain	Study 1: Second-year university students at the Autonomous University of Barcelona. 72% females. Mage = 20 Study 2: Different groups of professionals in the criminal justice system from Catalonia, Spain, together with the student sample of Study 1. 46% females. Mage = 26.4	Alpha + EFA	Prevention: coercive + social intervention Treatment: assistance vs. punishment	Prevention: multidimensional Treatment: unidimensional
Mascini and Houtman (2006)	n = 1982 Netherlands	Representative sample of Dutch Population. 48.4% females. Mage not reported.	Alpha + EFA	Support for prevention Support for rehabilitation Support for decriminalisation	Multidimensional
Piquero et al. (2010)	n = 1502 USA	Representative sample of Pennsylvania households. 59.7% females. Mage = 50.2	Corr. + Alpha  + EFA	Global (juvenile rehabilitation)	Unidimensional
Maguire and Johnson (2015)	n = 11,155 Antigua and Barbuda, Barbados, Guyana, Jamaica, Saint Lucia, Suriname, and Trinidad and Tobago	Stratified representative sample of the seven countries. Mean age or gender not reported.	Corr. + EFA  + CFA	Punitive Extra-judicial Progressive	Multidimensional
Aizpurúa (2015)	n = 1000 Spain	Representative sample of the Spanish population. 51.1% females. Mage = 46.3.	CFA	Punitive youth Punitive adults	Unidimensional (differentiated for each population)
Ramirez (2015)	n = 515 USA	Representative sample on the black population in the United States. Mean age or gender not reported.	Alpha + CFA	Punitive Preventive	Multidimensional
Armborst (2017)	n = 1272 Germany	Representative sample of the adult population in Germany. No mean age or gender reported.	CFA	Global (severity, death penalty, prison conditions, age of criminal responsibility, sex offenders)	Unidimensional
Silver and Silver (2017)	Study 1 (n = 1464) USA Study 2 (n = 1025). USA	Study 1: Undergraduate students at the Pennsylvania State University. 58% females. Mage = 19.8. Study 2: Amazon Mechanical Turk paid survey. 54% of females. Mage = 37.0.	Alpha + EFA + CFA	Study 1: harsher sanctions for adults and juveniles + death penalty + harsher treatment during incarceration Study 2: Global (adult offenders, juvenile offenders, and sex offenders).	Unidimensional (mixed evidence)

Note: EFA = exploratory factor analysis; CFA = confirmatory factor analysis; Alpha = Cronbach’s alpha; Corr. = Correlation analysis.

Table 2. Wording and distribution of the punitive items.

Item	Label	Item from	N	M	SD	Mdn
‘Make sentences more severe for all crimes’	sev1	Hogan et al. (2005)	895	5.65	1.90	7
‘A person convicted of murder should receive the death penalty’	sev2	Tyler and Boeckmann (1997)	895	3.72	2.48	4
‘Community alternatives (e.g. probation, community service, electronic monitoring) should be assigned more often’	sev3	Calaway et al. (2016); Melvin et al. (1985)	895	5.29	2.04	6
‘It is all right for the police to break the law in order to better control violent crimes’	sev4	Maguire and Johnson (2015)	895	4.54	2.54	5
‘In general, our courts have been more concerned with the rights of criminals than victims’	sev5	Viney et al. (1982)	895	5.58	1.99	7
‘Universities and military academies should have a policy of automatically dismissing students who are caught cheating on examinations’	sev6	Viney et al. (1982)	895	4.42	2.17	5
‘Juveniles who commit serious crimes should be treated like adults’	sev7	Mascini and Houtman (2006)	895	4.90	2.28	6
‘Make sentences more severe for juveniles who commit crimes’	sev8	Pickett et al. (2013)	895	5.39	2.07	6
‘Punishing criminals more harshly would reduce crime by setting an example and showing others that crime does not pay’	rpun1	Cullen et al. (1988)	895	5.37	2.08	6
‘Criminals deserve to be punished because they have harmed society’	rpun2	Cullen et al. (1985)	895	6.22	1.39	7
‘The amount of punishment that a criminal receives should be equal to the harm that the victim of the crime was forced to suffer’	rpun3	Cullen et al. (1988)	895	5.71	1.88	7
‘We should put criminals in jail so that innocent citizens will be protected from criminals who will victimise them’	rpun4	Cullen et al. (1985, 1988)	895	6.05	1.54	7
‘We could reduce teenage crime if parents would return to an old fashioned “spare the rod spoil the child” attitude’	rpun5	Viney et al. (1982)	895	4.08	2.34	4
‘Since most criminals will commit crimes over and over again, the only way to protect society is to put these criminals in jail and throw away the key’	rpun6	Cullen et al. (1985, 1988)	895	4.40	2.35	5
‘All rehabilitation programmes have done is to allow criminals who deserve to be punished to get off easily’	rpro1	Cullen et al. (1985)	895	5.02	2.12	6
‘Confronting perpetrators with the sufferings of their victims prevents them from relapsing (interpersonal rehabilitation: fostering ties with community)’	rpro2	Mascini and Houtman (2006)	895	4.24	2.23	5
‘The rehabilitation of prisoners has proven to be a failure’	rpro3	Cullen et al. (1985)	895	5.33	1.91	6
‘The only way to reduce crime in our society is to punish criminals, not try to rehabilitate them’	rpro4	Cullen et al. (1985)	895	4.12	2.35	4
‘The main goals of the criminal justice system should be the reparation of harm through a more personalised approach’	rpro5	Created for this survey, based on the concept of restorative justice (Gromet & Darley, 2011)	895	5.83	1.55	6

Note: M = Mean; SD = Standard Deviation; Mdn = Median.

Figure 1. Correlation matrix.

Figure 2. Parallel analysis.

Table 3. Exploratory factor analysis.

Factor	Eigenvalue	Difference	Proportion	Cumulative
Factor 1	6.626	5.392	.349	.349
Factor 2	1.234	0.029	.065	.414
Factor 3	1.204	0.219	.063	.477
Factor 4	0.986	0.069	.052	.529
Factor 5	0.916	0.04	.048	.577
Factor 6	0.877	0.022	.046	.623
Factor 7	0.854	0.08	.045	.668
Factor 8	0.775	0.047	.041	.709
Factor 9	0.728	0.06	.038	.747
Factor 10	0.668	0.05	.035	.783
Factor 11	0.617	0.012	.033	.815
Factor 12	0.605	0.096	.032	.847
Factor 13	0.509	0.013	.027	.874
Factor 14	0.496	0.021	.026	.9
Factor 15	0.475	0.051	.025	.925
Factor 16	0.423	0.043	.022	.947
Factor 17	0.38	0.034	.02	.967
Factor 18	0.347	0.067	.018	.985
Factor 19	0.28		.015	1

Note: Likelihood ratio (LR) test: independent vs. saturated: χ²(55) = 5634.28, Prob > χ² = 0.0000; Kaiser-Meyer-Olkin (KMO) = .925; Determinant > .061. Own elaboration.

Figure 3. Global confirmatory factor analysis (CFA) model.

Table 4. Fit measures.

Models	χ^2	df	CFI	RMSEA	SRMR	GFI	TLI
Global	507.255	77	.984	.079	.059	.989	.981
Three dimensions	473.785	74	.985	.078	.057	.99	.981
Second order	473.785	74	.985	.078	.057	.99	.981
Bifactor	267.197	63	.992	.06	.046	.994	.989

Note: CFI = comparative fit index; RMSEA = root mean square error of approximation; SRMR = standardised root mean square residual; GFI = goodness of fit index; TLI = Tucker–Lewis Index.

Figure 4. Three-dimensions confirmatory factor analysis (CFA) model.

Figure 5. Second-order confirmatory factor analysis (CFA) model.

Figure 6. Bifactor model.

Figure 7. Exploratory bifactor analysis.

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