The Psychological Inventory of Financial Scarcity (PIFS): A psychometric evaluation
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Having fewer financial resources than needed
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These affective and cognitive effects of financial scarcity depend not only on the financial situation per se. They are also elicited, at least in part, by the subjective perception of the situation.
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we introduce the Psychological Inventory of Financial Scarcity (PIFS), a self-rating scale of subjective perceptions of one’s financial situation and affective and cognitive responses to these appraisals.
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psychological stress framework theory of scarcity , the focus lies on perceived threats and demands without adequate resources to cope, as well as on affective and cognitive responses to this stress. , the focus lies on perceived threats and demands without adequate resources to cope, as well as on affective and cognitive responses to this stress
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theory of scarcity posits that when resources are scarce, (potential) problems loom larger and seize attention, and because of the greater engagement in trying to solve these problems, scarcity leads to neglect of (potential) other problems
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But the PIFS is the first measure to combine stress appraisals with responses to these appraisals in a financial context, thus providing a more encompassing assessment of the subjective experience of financial scarcity
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Respondents were 4,901 students (67% female; 33% male; 0.2% indicated other; and 0.8% preferred not to indicate their gender) of different Dutch universities of applied sciences students Data were collected by the University of Applied Sciences Utrecht espondents were 1,129 self-employed Dutch entrepreneurs (48% female self-employed Dutch entrepreneurs Data were collected by the National Institute for Family Finance Information (Nibud). Respondents were 1,559 Dutch members (51% female) of the Survey Sampling International (SSI) online panel. Data were collected by Nibud Respondents were 1,122 members (55% female) of the Longitudinal Internet Studies for the Social Sciences (LISS) panel. Data were collected by CentERdata.
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n Studies 1-3, results of the PCA yielded a first component with an eigenvalue that ranged from 6.28 to 6.65, and a second component with an eigenvalue that ranged from 1.11 to 1.47. Examination of the KaiserMeyer Olkin measure of sampling adequacy suggested that the three datasets were factorable (KMOstudy1 = .929; KMOstudy2 = .948; KMOstudy3 = .936). In each study, results of the EFA indicated a twofactor solution, whereby the two factors were correlated (Study 1: r =.64; Study 2: r = .61; Study 3: r = .26). The first factor explained more than 50% of the variance, whereas the second factor explained about 10%
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A reliability analysis indicated that the PIFS has a good internal consistency. Corrected item-total correlations ranged from .09 to .81, and from .58 to .81 when both reverse-scored items were excluded. Cronbach’s α was .92 in all three studies
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we examined the factor structure of the PIFS in which the two adjusted items were included. The PCA yielded only one component with an eigenvalue greater than 1 (eigenvalue = 6.83). Examination of the Kaiser-Meyer Olkin measure of sampling adequacy in the EFA suggested that the dataset was factorable, KMO = .941. Results of the EFA indicated a one-factor solution, which explained 56.9% of the variance (see Table 1). Corrected Item-total correlations ranged from .46 to .80, and Cronbach’s α was .93.
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Results of the regression analyses showed that the PIFS was negatively associated with age and education but was not related with income
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Respondents were 2,567 Dutch members (51.1% female) of the Survey Sampling International (SSI) online panel.6
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A Confirmatory Factor Analysis (CFA) was conducted with the Lavaan package (version 0.6-8; Rosseel, 2012) of Rsoftware (version 4.03) and using a covariance matrix with ML estimation. Results of the two models were compared; the one-factor model included all 12 PIFS items (PIFSTotal); and the four-factor model included items 1-3 (Factor 1: Shortage of Money; PIFSSoM), items 4-6 (Factor 2: Lack of Control; PIFSLoC), items 7-9 (Factor 3: Rumination and Worry; PIFSRW), and items 10-12 (Factor 4: Short-Term Focus; PIFSSTF). The four-factor model allowed for intercorrelations
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eene et al., 2011). The one-factor model (χ2[54] = 2395, p <.001) had a CFI of .91, and an SRMR of .05. Factor loadings ranged from 0.66 to 0.96. The statistics for the four-factor model indicated the best fit (χ2[48] = 1093, p < .001), with a CFI of .96, and an SRMR of .03, and factor loadings that ranged from 0.75 to 1.04. An additional ANOVA Chi-Squared Difference Test confirmed that the four-factor model had a statistically significant better fit than the one-factor model (χ2 dif [1] = 327.00, p < .001).
Note: <
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we examined the temporal stability of the PIFS by calculating test-retest reliabilities for six measurements over an eightmonth period (July 2016 to February 2017) Respondents were 470 participants (79.8% female) in a longitudinal study on savings conducted by Nibud. Results indicated that in most cases the test-retest reliability of the PIFS was good, and that it was somewhat higher for the total PIFS than for each of its four sub-components.
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Results indicated that the 12-item PIFS – as one-factor scale and as four-factor scale – is a temporally stable measure, and thereby support the reliability of the PIFS.
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we test the concurrent validity of the PIFS by examining its relationship with executive functions Respondents of Study 7 were 300 US members (51.9% female) of the Prolific participant pool.10 Respondents of Study 8 were 201 UK members (49.8% female) of the Prolific participant pool.
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In Study 9, we provide additional (unique and concurrent) validity tests of the PIFS by examining its relations with financial problems, personality traits, and psychological well-being.
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Respondents were 1,122 LISS panel members.14 These members complete online questionnaires every month of about 15 to 30 min in total.
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Financial problems were operationalized by respondents’ answers (yes or no) to the question which of six financial issues they were confronted with at that moment. The financial issues concerned: (1) having trouble making ends meet; (2) being unable to quickly replace things that break; (3) having to borrow money for necessary expenditures; (4) running behind in paying rent/mortgage or general utilities; (5) having debt collector/bailiff at the door in the last month; and (6) having received financial support from family or friends in the last month. The number of yes-responses (0-6) were used as an index score for financial problems.
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n the third and final line of investigation, we test whether the PIFS mediates the relationship between financial problems and psychological well-being.
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Hierarchical Regressions of Assessments of Demographics, Personality Traits, and the PIFS on Mental Health (n = 707)
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Hierarchical Regressions of Assessments of Demographics, Personality Traits, and the PIFS on Self-esteem (n = 918)
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Hierarchical Regressions of Assessments of Demographics, Personality Traits, and the PIFS on Life Satisfaction (n = 866)