Final sample did not need removal of any subjects due to missing data. A total of 626 participants answered our online form. From the total, 402 (64,2%) are female, 223 (35,6%) male, with an age of M=34,96 (DP =13,18). Considering location, 85,7% of the participants were from Rio de Janeiro city. Results for dimensionality assessment were obtained from the package nFactors by plotting the non-graphical solutions to scree test. Kaiser criteria, parallel analysis and optimal coordinates resulted in a two factors solution, whereas acceleration factor resulted in a single factor solution. Solution is available at figure 1.

Figure 1

Considering the results problematic considering the original paper’s structural factor, dimensionality was also assessed considering an Exploratory Graphical Analysis (EGA), which also resulted in a two-factors solution, where items 1 to 4 were correlated in a first dimension, while items 5 to 8 loaded in the second dimension. Considering listed items, the first dimension seems to be related to values on the management of resources, while the second dimension has items related to the management of financial resources. The robust solution for the resulted structure achieved a fit of X ²(19)=61,15, CFI=0,99, RMSEA=0,036, GFI=1,00, NFI=0,97. As the chi-square test resulted significant, statistical fit still indicates there’s room for improvement in the model. Solution for the EGA is available in figure 2.

Figure 2

Considering aspects that could be related to a bifactor solution, command omega was executed considering a two-factors solution. That resulted in an alpha coefficient of 0,73 and an omega total of 0,79. Factor loadings in the Schmid Leiman solution were all significant and above 0,20 for the general factor, while just item 2 resulted in a factor loading below 0,30 on that factor. Factor 1, which included items from 5 to 6 had factor loadings above 0,50, while factor 2 has only item 4 with a weak factor loading of 0,23, with the others achieving loadings above 0,30. Structure and factor loadings are available at figure 3.

From the results obtained through the omega command it is possible to include in the the possibility of a bifactor structure. Among possible solutions we tested for a two-factors oblique solution, a second-order solution with two factors and a bifactor structure. An orthogonal solution was also tested just for comparison. Obtained fit results are available in Table 2

Table 2

Model	Robust X²	p-statistics	df	CFI	TLI	RMSEA	SRMR
Orthogonal	179.1	<.001	20	.71	.60	.113	.113
Oblique	61.15	<.001	19	.92	.89	.060	.045
Second order	failed
Bifactor	43.17	<.001	12	.94	.87	.065	.032
Re-specified bifactor	12.09	.439	12	1.00	1.00	.003	.016
Baseline	579.87	<.001	28

Among the obtained solutions, all of the proposed models achieve better fit when compared to the baseline model. The orthogonal structure performed the worse when compared to other modeled structures. A second order factor structure failed converging a solution, while the oblique and bifactor solution performed close comparing other fit indexes than chi-square, where the bifactor model achieved the best performance.

Figure 3

Considering the bifactor solution, there remains a significant chi-square fit and through the items, item 4 did not achieve a significant factor loading on its factor, which was expected as it obtained a low factor loading on the Schmid Leiman solution, while still loading significant at the general factor. After keeping only the factor loading from item 4 into the general factor, a high dependence between item 7 and 8 was detected in the modification indices output. After applying the two mentioned modifications, the model achieved a fit of X ²(12)=12.09, p=0.439, CFI=1.00, TLI=1.00, RMSEA=0.003 and SRMR=0.016. Factor loadings for each item and factor is available at table 3.

Table 3

Factor	Item	Loadings	S.E.	Z	p-value
Resources	FRG1	0.38	0.10	3.83	0.000
	FRG2	0.41	0.10	4.13	0.000
	FRG3	0.39	0.12	3.34	0.001
Finances	FRG6	0.68	0.06	12.12	0.000
	FRG5	0.57	0.06	9.16	0.000
	FRG7	0.63	0.06	10.78	0.000
	FRG8	0.51	0.08	6.68	0.000
Frugality	FRG1	0.33	0.09	3.67	0.000
	FRG2	0.19	0.10	2.01	0.045
	FRG3	0.57	0.09	6.44	0.000
	FRG4	0.55	0.09	6.26	0.000
	FRG6	0.37	0.07	5.00	0.000
	FRG5	0.42	0.08	5.55	0.000
	FRG7	0.41	0.08	5.25	0.000
	FRG8	0.43	0.09	4.97	0.000

The frugality scale has been used extensively as a unidimensional measure since its proposal by Lastovicka et al (1999). Our results represent the value in a Brazilian sample and items were expected to vary considering that across different countries relationship between people and their possessions might vary due to the economic or political moment (Hampson & McGoldrick, 2013).

Different from the original structure, in Brazil the frugality scale seems to be organized through two primary order factors, which can be named as “resource management” and “financial management”, while all of the items also loaded in a general factor. The general factor might represent frugality itself. It is reasonable to think that behaviors related to being conservative with money or possessions might not necessarily be related only to wisely managing resources to its fullest as Lastovicka et al (1999) defines the construct. Some of the listed behaviors and attitudes could also be motivated by buying intentions activated by a desired good or even by economic crisis forcing people to act frugal. That configuration is better represented by a bifactor solution, where a general independent factor accounts for part of the variance of items, while other items can vary also due to other independent primary factors.

Considering the implications for the usage of the scale in Brazil, it is still safe to score the scale with a single score summing the set of items. Still, it is important to take under consideration that one might be actually looking at variance being explained not only by frugality, but by other activated values. It is important to mention as well that the frugality scale is a very brief measure and there’s a risk of underrepresentation issues. Less items also mean less portions of the latent trait being represented. To investigate more thoroughly frugality as a latent trait, we recommend the development of a longer form, with a more varied set of behaviors and attitudes.

Another point is that the scale was applied during the risk of economic recession being communicated through media on the mid 2014. One hypothesis to test in the future could be if the current warning of an incoming economic crisis induced sample respondents to a higher activation of financial-related attitudes and behaviors in a way other than caused by frugality. That way our current sample could be used as a prior for future investigations using the frugality scale.

A major weakness of our research is that we did not use other frugality measures, which could bring more information on the functioning of the other underlying dimensions of the scale that we were able to detect in the current study. Still, in our bibliographic research it was noticeable that frugality measures are scarce or miss a more thoroughly investigation of psychometric qualities. A second weakness is our research design that followed a cross-sectional design, where understanding frugality’s stability through time could also elucidate another psychometric information related to test-retest stability or even comparing the factor structure across time.