| Literature DB >> 33836715 |
Min Zhang1, Bei Zhu2, Chunlan Yuan2, Chao Zhao3, Jiaofeng Wang1, Qingwei Ruan1, Chao Han1, Zhijun Bao1,3, Jie Chen4, Kevin Vin Arceneaux5, Ryan Vander Wielen5, Greg J Siegle6.
Abstract
BACKGROUND: Cultural differences in affective and cognitive intrinsic motivation could pose challenges for global public health campaigns, which use cognitive or affective goals to evoke desired attitudes and proactive health-promoting actions. This study aimed to identify cross-cultural differences in affective and cognitive intrinsic motivation and discuss the potential value of this information for public health promotion.Entities:
Keywords: Cultural differences; Global public health campaign; Intrinsic motivation; Need for affect; Need for cognition
Year: 2021 PMID: 33836715 PMCID: PMC8034077 DOI: 10.1186/s12889-021-10689-w
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Fig. 1The one-level two-factor model of the 10-item Need for Affect scale. Items within each latent factor were allowed to correlate. Cross-loading was constrained to zero
Demographics of the 4 subsamples split for the purpose of internal verification and replication of the model fit testing
| Chinese sample (1166) | American sample (980) | |||
|---|---|---|---|---|
| 18–90 | 17–87 | 20–84 | 19–88 | |
| 45.62 (21.55) | 45.29 (21.95) | 48.86 (14.32) | 50.04 (13.21) | |
| 63.3% | 67.2% | 69.0% | 66.9% | |
Invariance tests of the basic CFA model between the Chinese group and American group
| Model | Description | λ | df | p | CFI | TLI | NFI | RMSEA | AIC | ECVI | Test | ΔCFI | ΔRMSEA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| M0 | Baseline model, no constraints on model parameters | 263.124 | 26 | <.001 | 0.977 | 0.941 | 0.966 | 0.030 | 607.124 | 0.283 | – | – | – |
| M1 | All factor loadings constrained to be equal | 408.270 | 35 | <.001 | 0.959 | 0.922 | 0.947 | 0.047 | 704.270 | 0.329 | M1-M0 | −0.018 | 0.017 |
| M2 | “Approach” factor loadings constrained to be equal | 281.146 | 31 | <.001 | 0.971 | 0.949 | 0.963 | 0.032 | 654.146 | 0.296 | M2-M0 | −0.006 | 0.002 |
| M3 | “Avoidance” factor loadings constrained to be equal | 367.272 | 31 | <.001 | 0.962 | 0.943 | 0.948 | 0.042 | 720.272 | 0.655 | M3-M0 | −0.015 | 0.012 |
| M4 | All factor loadings constrained to be equal except item NFA_10 | 292.361 | 34 | <.001 | 0.969 | 0.945 | 0.951 | 0.039 | 679.025 | 0.309 | M4-M0 | −0.008 | 0.009 |
| M5 | All factor loadings and the vectors of item intercepts constrained to be equal | 335.565 | 73 | <.001 | 0.953 | 0.929 | 0.942 | 0.049 | 762.515 | 0.683 | M5-M1 | −0.006 | 0.002 |
| M6 | All factor loadings and the variances of constructs constrained to be equal | 320.461 | 41 | <.001 | 0.951 | 0.931 | 0.944 | 0.045 | 738.461 | 0.398 | M6-M1 | −0.008 | −0.002 |
| M7 | All factor loadings and the factor covariance constrained to be equal | 518.586 | 45 | <.001 | 0.946 | 0.908 | 0.939 | 0.061 | 822.586 | 0.703 | M7-M1 | −0.013 | 0.014 |
Criteria for accepting the null hypothesis of invariance (Chen et al [43])
≥ − .010
≤.015
ΔCFI Change in CFI, ΔRMSEA Change in RMSEA
M0: Testing configural invariance between groups, i.e., H0 = Both groups associate the same subsets of items with the same constructs;
M1-M0: Testing construct-level metric invariance between groups, i.e., H0 = The strength of the relationships between items and their underlying constructs are the same between groups;
M2-M0: Testing the metric invariance of “Approach” between groups, i.e., H0 = The strength of the relationships between 5 approach items and the construct “Approach” are the same between groups;
M3-M0: Testing the metric invariance of “Avoidance” between groups, i.e., H0 = The strength of the relationships between 5 avoidance items and the construct “Avoidance” are the same between groups;
M4-M0: Testing the metric invariance of item NFA_10 between groups, i.e., H0 = The strength of the relationship between NFA_10 and the construct “Avoidance” is the same between groups;
M5-M1: Testing the scalar equivalence of items between groups, i.e., H0 = The values of each item corresponding to the zero value of the underlying constructs are the same between groups;
M6-M1: Testing equivalence of construct variances between groups, i.e., H0 = The variances of “Approach” and “Avoidance” are the same between groups;
M7-M1: Testing the equivalence of construct covariance between groups, i.e., H0 = The covariance between “Approach” and “Avoidance” is the same between groups;
Goodness-of-fit statistics of using Exploratory Structural Equation Modeling (ESEM), a modified Confirmatory Factor Analysis (CFA) model with cross-loadings and a basic CFA model without cross-loadings for the Appel et al.’s 10-item NFA scale in the 4 subsamples
| Model tested | λ | df | CFI | TLI | NFI | RMSEA | AIC | ECVI | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Chinese subsample 1 | 79.325 | 22 | <.001 | 0.975 | 0.950 | 0.967 | 0.067 | 165.325 | 0.285 | |
| Chinese subsample 2 | 90.266 | 22 | <.001 | 0.971 | 0.940 | 0.962 | 0.073 | 176.266 | 0.302 | |
| American subsample 1 | 40.860 | 22 | <.001 | 0.988 | 0.969 | 0.974 | 0.041 | 126.86 | 0.251 | |
| American subsample 2 | 52.681 | 22 | <.001 | 0.976 | 0.940 | 0.960 | 0.054 | 138.681 | 0.294 | |
| Chinese subsample 1 | 19.299 | 14 | <.001 | 0.997 | 0.991 | 0.982 | 0.028 | 125.299 | 0.216 | |
| Chinese subsample 2 | 26.168 | 14 | <.001 | 0.989 | 0.974 | 0.972 | 0.045 | 132.168 | 0.226 | |
| American subsample 1 | 15.033 | 14 | <.001 | 0.999 | 0.997 | 0.989 | 0.013 | 117.033 | 0.248 | |
| American subsample 2 | 24.746 | 14 | <.001 | 0.994 | 0.983 | 0.980 | 0.032 | 126.746 | 0.250 | |
| Chinese subsample 1 | 83.327 | 18 | <.001 | 0.971 | 0.912 | 0.965 | 0.089 | 187.327 | 0.323 | |
| Chinese subsample 2 | 86.597 | 18 | <.001 | 0.969 | 0.908 | 0.964 | 0.090 | 190.597 | 0.326 | |
| American subsample 1 | 28.122 | 17 | <.001 | 0.983 | 0.980 | 0.982 | 0.035 | 124.122 | 0.245 | |
| American subsample 2 | 35.795 | 17 | <.001 | 0.982 | 0.957 | 0.973 | 0.046 | 131.795 | 0.279 | |
| ≥ 0.9 | ≥ 0.9 | ≥ 0.9 | ≤ 0.1 | |||||||
Note: λ Normed chi-square, df Degree of freedom, CFI Comparative fit index, TLI Turker and Lewis’s Index of fit, NFI Normed fit index, RMSEA Root mean square error of approximation, AIC Akaike Information Criterion, ECVI Expected Cross-Validation Index
Factor loadings of the short version NFA scale 10 items in the Chinese and American samples along with the factor loadings in a German adult sample from Appel et al.’s [22]
| Approach | Avoidance | |||||
|---|---|---|---|---|---|---|
| Appel et al. [ | Chinese | American | Appel et al. [ | Chinese | American | |
| NFA_3_Appr | −.01 | |||||
| NFA_4_Appr | −.19 | |||||
| NFA_6_Appr | .07 | |||||
| NFA_18_Appr | −.13 | |||||
| NFA_19_Appr | −.00 | |||||
| NFA_1 | .26 | |||||
| NFA_8 | −.22 | |||||
| NFA_9 | −.19 | |||||
| NFA_10 | −.05 | |||||
| NFA_11 | .02 | |||||
* < .05
Fig. 2Comparisons of the NFA subscale mean scores across different cultural samples. The r values indicate the correlation between the approach and avoidance scores. * indicates significance at an α level of .05
The mean, standard deviation, sample size of NFA average subscale scores and NFC total scores in multinational samples, and the effect size indices of the scores comparisons across cultures
| Two samples being compared | Mean ± SD (N) | Cohen’s d | Hedges’ g | SEg | 95% CI | 95% CI | |
|---|---|---|---|---|---|---|---|
| Approach | CA vs. AA | .55 ± 1.54 (1186)vs. .91 ± .92 (980) | −0.28 | 0.04 | |||
| CA vs. GAS | .55 ± 1.54 (1186)vs. 1.28 ± .96 (1160) | −0.57 | 0.04 | ||||
| CA vs. GA | .55 ± 1.54 (1186)vs. 1.29 ± .92 (627) | −0.54 | 0.05 | ||||
| CA vs. AC | .55 ± 1.54 (1186)vs. 1.15 ± 1.07 (126) | −0.40 | 0.09 | ||||
| CA vs. UKA | .55 ± 1.54 (1186)vs. 1.02 ± 1.0 (236) | −0.32 | 0.07 | ||||
| AA vs. GAS | .91 ± .92 (980) vs. 1.28 ± .96 (1160) | −0.39 | 0.04 | ||||
| AA vs. GA | .91 ± .92 (980) vs. 1.29 ± .92 (627) | −0.41 | 0.05 | ||||
| AA vs. AC | .91 ± .92 (980) vs. 1.15 ± 1.07 (126) | −0.26 | 0.09 | ||||
| AA vs. UKA | .91 ± .92 (980) vs. 1.02 ± 1.0 (236) | −0.12 | −0.12 | 0.07 | −0.26 | 0.02 | |
| Avoidance | CA vs. AA | −.05 ± 1.29 (1186) vs. -.91 ± 1.15 (980) | 0.70 | 0.04 | |||
| CA vs. GAS | −.05 ± 1.29 (1186) vs. -1.39 ± 1.12 (1160) | 1.11 | 0.04 | ||||
| CA vs. GA | −.05 ± 1.29 (1186) vs. -1.06 ± 1.18 (627) | 0.81 | 0.05 | ||||
| CA vs. AC | −.05 ± 1.29 (1186) vs. -1.5 ± 1.07 (126) | 1.14 | 0.10 | ||||
| CA vs. UKA | −.05 ± 1.29 (1186) vs. -.55 ± 1.2 (236) | 0.39 | 0.07 | ||||
| AA vs. GAS | −.91 ± 1.15 (980) vs. -1.39 ± 1.12 (1160) | 0.42 | 0.04 | ||||
| AA vs. GA | −.91 ± 1.15 (980) vs. -1.06 ± 1.18 (627) | 0.13 | 0.05 | ||||
| AA vs. AC | −.91 ± 1.15 (980) vs. -1.5 ± 1.07 (126) | 0.52 | 0.10 | ||||
| AA vs. UKA | −.91 ± 1.15 (980) vs. -.55 ± 1.2 (236) | −0.31 | 0.07 | ||||
| NFC | CA vs. AA | 44.90 ± 11.7 (1186) vs. 55.28 ± 11.87 (980) | −0.88 | 0.05 |
Note: CA Chinese adults, AA American adults, GAS German/Austria students, GA German adults, AC Austria couples, UKA UK adults
Sample CA and AA were from the current study, other samples were from Appel et al. [22]
Fig. 3Density plot of the NFA approach and avoidance scores of the Chinese and American samples. The fit lines represent the correlation between the approach and avoidance scores in each sample
Fig. 4Comparing NFA approach, NFA avoidance and NFC between Chinese seniors with and without hearing loss. Error bars represent ±1 SE
The mean and SD of NFA and NFC scores in the Chinese senior participants with different hearing status
| Hearing Status | ||||
|---|---|---|---|---|
| Normal hearing | Mild hearing loss | Moderate-to-severe hearing loss | Hearing loss deniers | |
| NFA approach | −0.62 ± 1.02 | −1.08 ± 1.05 | − 0.94 ± .88 | −0.34 ± 1.31 |
| NFA avoidance | −0.01 ± 1.16 | −0.94 ± 1.10 | − 0.93 ± 1.01 | −0.23 ± 1.27 |
| NFC | 54.98 ± 11.94 | 47.49 ± 13.54 | 45.98 ± 13.94 | 52.88 ± 11.60 |