The association between material-psychological-behavioral framework of financial hardship and markers of inflammation: a cross-sectional study of the Midlife in the United States (MIDUS) Refresher cohort
Thoughts
Connects with: @sturgeon2016 @tucker-seeley2019
Annotations
surachman2023 - p. 3
We used data from the Midlife Development in the United States (MIDUS), a national study of health and well-being
surachman2023 - p. 3
Thus, only participants who completed SAQs were included in the analytic sample (N = 3,106). Participants who completed SAQs but did not complete the biomarker data collection were included in the exploratory factor analysis or EFA (N = 2,243). The remaining participants who completed SAQs (N = 863) completed the biomarker study and were included in the confirmatory factor analysis (CFA).
surachman2023 - p. 3
The material domain includes indicators related to the lack of financial resources [24]. Three indicators were used as the measures of the material domain of financial hardship: 1) income to poverty line ratio, adjusted for the total household size (3 = < 300%, 2 = ≥ 300% but less than 600%, 1 = ≥ 600%), 2) health insurance coverage (no = 1 or yes = 0), and 3) public/government financial assistance, based on whether the household received public or government assistance in the last calendar year (yes = 1, or no = 0)
surachman2023 - p. 3
As part of the psychological domain, we included two indicators related to perceived financial satisfaction and two measures related to perceived financial stress or worry. Measures of perceived financial satisfaction include current financial situation and financial control. Participants reported their current financial situations on a 0 (“the worst possible financial situation”) to a 10 (“the best possible financial situation”) scale.
surachman2023 - p. 5
financial control, we used the item that asked participants: “How would you rate the amount of control you have over your financial situation these days?” on a 0 (“no control at all”) to 10 (“very much control”) scale. The original scores for the current financial situation and financial control were reverse-coded. Thus, higher scores represent higher financial hardship. Measures of perceived financial stress or worry include: 1) perceived availability of money to meet needs and 2) perceived difficulty paying bills. To operationalize the availability of money to meet needs, we used the item that asked participants: “In general, would you say you (and your family living with you) have more money than you need, just enough for your needs, or not enough to meet your needs?” on a 1–3 scale (3 = not enough money, 1 = just enough money, 1 = more money than you need). To operationalize the difficulty level of paying bills, we used the item that asked participants to rate their difficulty level in paying monthly bills on a 1–4 scale (4 = very difficult, 3 = somewhat difficult, 2 = not very difficult, 1 = not at all difficult)
surachman2023 - p. 5
Measures for the behavioral domain were taken from a scale used in the National Survey of Unemployed Adults conducted by the Heidrich Center for Workforce Development that included job-, home-, and financial-related hardships [40]. For this analysis, we included four items related to behavioral actions associated with dealing with financial hardship [39]. Participants responded to whether they ever experienced the following (1 = Yes, 0 = No): 1) missed a credit card payment, 2) missed other debt payment (e.g., car or student loans), 3) sold possessions to make ends meet, and 4) cut back on spending.
surachman2023 - p. 5
Domains of financial hardship were hypothesized to be correlated with each other. Thus, we utilized an oblique rotation method. Given the strong theoretical foundation of the three-factor solution for financial hardship, we used CF- FACPARSIM rotation to minimize factor complexity by spreading variances evenly across all rotated factors
surachman2023 - p. 6
Our data showed a meritorious Kaiser–Meyer–Olkin (KMO) value of 0.89. Bartlett’s test of sphericity was significant (χ2 [55] = 13,215.03, p < 0.001), indicating that the data are suitable for factor analysis. Results from EFA suggested that the 3-factor model was the best-fitting solution (χ2 = 125.88, df = 25, p < 0.001; RMSEA = 0.04; CFI = 0.99; TLI = 0.98; SRMR = 0.03; see Table 2). Furthermore, compared to the 2-factor model, the 3-factor model showed significantly better model fit (χ2 [df = 9] = 181.328, p < 0.001; Table 2), and the 3-factor solution fit the hypothesized financial hardship domains. While the four-factor solution showed a significantly better fit than the 3-factor solution (χ2 [df = 8] = 77.24, p < 0.001), the additional factor was theoretically uninterpretable.
surachman2023 - p. 7
Finally, the alpha and omega scores indicate sufficient reliabilit
surachman2023 - p. 8
The final threefactor measurement model fulfilled overall goodness-offit criteria (χ2 = 102.06, df = 40; RMSEA = 0.04; CFI = 0.99; TLI = 0.98; SRMR = 0.04; Table 2).
surachman2023 - p. 8
As expected, the model fit of the secondorder measurement model was identical to the threefactor measurement model
surachman2023 - p. 14
file