PMC:7543267 / 29990-44198 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7543267","sourcedb":"PMC","sourceid":"7543267","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7543267","text":"Results\n\nParticipants\nAttrition across the two data collection occasions resulted in final sample sizes of 365 (M age = 49.78, standard deviation [SD] = 16.89; 50.1% female; retention rate 73.73%) and 440 (M age = 51.77, SD = 16.26; 46.6% female; retention rate = 62.77%) participants in the Australian and U.S. samples, respectively. Sample characteristics at follow-up are presented in Table 1. Attrition analyses in the Australian sample revealed that participants lost to attrition were younger and were more likely to be non-White. However, there were no differences in proportion of gender, income, and education level. A MANOVA with the social cognition constructs and past behavior as dependent variables and attrition status (lost to attrition vs. included at follow-up) revealed no differences (Wilks’ Lambda = 0.969, F(1,9) = 1.70, p = .077, partial η 2 = .031). Attrition analyses in the U.S. sample also indicated that participants lost to attrition were younger, and more likely to be male, non-White, and lower educated and have low income, than those remaining in the study at follow-up. The MANOVA testing for differences on social cognition constructs and past behavior among participants lost to attrition and those included at follow-up revealed statistically significant differences (Wilks’ Lambda = 0.969, F(1,9) = 2.40, p = .010, partial η 2 = .031). Follow-up tests revealed that mean values for past behavior, attitude, subjective norm, intention, moral norm, and habit with respect to social distancing were significantly lower among participants lost to attrition compared to those retained at follow-up. However, effect sizes for these differences were small (ds \u003c .25). Details of attrition analyses are presented in Supplementary Appendix B.\n\nPreliminary Analyses\nFactor loadings and AVE values exceeded recommended .700 and .500 cutoff values in all cases. Omega reliability coefficients, interitem correlations (for two-item scales), and composite reliabilities indicated good internal consistency of scales used. Latent variable correlations among social cognition constructs were all statistically significant. Correlations among the majority of constructs in the Australia sample were small-to-medium in size (r range = .161 to .564), with some smaller correlations involving the subjective and moral norms constructs and habit (r range = .094 to .118). Correlations were small-to-medium in size in the U.S. sample (r range = .266 to .620). Square roots of the AVE for each latent variable exceeded the correlation of that variable with all other latent variables supporting discriminant validity. Skewness and kurtosis estimates indicated many of the variables were not normally distributed, justifying the use of the variance-based structural equation modeling, which is a “distribution-free” analytic method. Factor loadings, reliability coefficients, and distribution statistics are presented in Supplementary Appendix C, and latent variable correlations for model variables in both are presented in Supplementary Appendix D.\n\nStructural Equation Models\n\nSingle-sample analyses\nGoF and quality indices of the structural equation models are presented in Table 2. The models that excluded (Model 1) and included (Model 2) past behavior exhibited adequate fit and quality indices in both the Australian and U.S. samples. Standardized parameter estimates for the proposed direct effects for each model in the Australian and U.S. samples are presented in Fig. 1. Full parameter estimates for models in both samples are presented in Supplementary Appendix E. Parameter estimates, CIs, and effect sizes for the indirect effects of the models in both samples are summarized in Table 3.\nTable 2. Model quality and GoF statistics for the structural equation models of the integrated model in the Australian and U.S. samples and multigroup model\nSample Model APC AR2 AVIF AFVIF GoF SPR R2CR SSR NLBCDR\nAustralia 1 .104** .177*** 1.177 1.561 .391 .841 .977 .889 .873\n2 .116*** .338*** 1.222 1.904 .543 .819 .991 .931 .785\nUSA 1 .098** .192*** 1.187 1.823 .410 .889 .995 .825 .754\n2 .116*** .338** 1.222 1.904 .543 .819 .991 .931 .785\nMS 1 .100*** .182*** 1.159 1.704 .398 .905 .995 .794 .817\n2 .113*** .300*** 1.186 1.760 .511 .931 .997 .917 .840\nModel 1 = model excluding past behavior; Model 2 = model including past behavior.\nAFVIF average full collinearity variance inflation factor; APC average path coefficient; AR2 average R2; AVIF average block variance inflation factor; GoF Tenenhaus’s goodness-of-fit index; MS multiple sample analysis; NLBCDR nonlinear bivariate causality direction ratio; R2CR R2 contribution ratio; SPR Sympson’s paradox ratio; SSR statistical suppression ratio.\n*p \u003c .05, **p \u003c .01, ***p \u003c .001\nTable 3. Standardized parameter estimates for indirect effects for the structural equation model of the integrated model in the Australian and U.S. samples\nEffect Model excluding past behavior Model including past behavior\nβ p 95% CI ES Β p 95% CI ES\nLB UB LB UB\nAustralian sample\n Indirect effects\n  Att.→Int.→Beh. .011 .359 −.052 .074 .003 .004 .444 −.059 .067 .001\n  SN→Int.→Beh. .042 .094 −.021 .105 .016 .016 .312 −.047 .079 .006\n  MN→Int.→Beh. .068 .016 .005 .131 .024 .028 .192 −.035 .091 .010\n  AR→Int.→Beh. .011 .356 −.052 .074 .003 .003 .457 −.060 .066 .001\n  PBC→Int.→Beh. .040 .101 −.023 .103 .011 .016 .307 −.047 .079 .005\n  Int.→AP→Beh. .040 .106 −.023 .103 .014 .011 .365 −.052 .074 .004\n  Hab. (T1).→Hab. (T2)→Beh. .102 \u003c.001 .041 .163 .016 .078 .007 .017 .139 .013\n  PB→Hab.→Beh. – – – – – .021 .214 −.030 .072 .011\n  PB→Beh.a – – – – – .081 .034 −.007 .169 .042\n Total effectsb\n  Int.→Beh. .220 \u003c.001 .134 .306 .081 .090 .022 .004 .176 .033\n  PBC→Beh. .126 \u003c.001 .040 .212 .036 .055 .110 −.033 .143 .016\n  Hab. (T1)→Beh. .096 .016 .010 .182 .015 .076 .044 −.012 .164 .012\n  PB→Beh. – – – – – .494 \u003c.001 .412 .576 .258\n U.S. sample\n Indirect effects\n  Att.→Int.→Beh. \u003c.001 .495 −.052 .054 \u003c.001 .004 .443 −.049 .057 .001\n  SN→Int.→Beh. .072 .003 .019 .125 .029 .023 .190 −.030 .076 .009\n  MN→Int.→Beh. .102 \u003c.001 .051 .153 .044 .040 .067 −.013 .093 .017\n  AR→Int.→Beh. .023 .192 −.030 .076 .011 .001 .478 −.052 .054 .001\n  PBC→Int.→Beh. .088 \u003c.001 .037 .139 .025 .038 .079 −.015 .091 .011\n  Int.→AP→Beh. .061 .011 .008 .114 .029 .004 .441 −.049 .057 .002\n  Hab. (T1).→Hab. (T2)→Beh. .212 \u003c.001 .161 .263 .075 .166 \u003c.001 .115 .217 .059\n  PB→Hab.→Beh. – – – – – .068 \u003c.001 .025 .111 .043\n  PB→Beh.a – – – – – .178 \u003c.001 .105 .251 .112\n Total effectsb\n  Int.→Beh. .377 \u003c.001 .306 .448 .177 .142 \u003c.001 .069 .215 .066\n  PBC→Beh. .146 \u003c.001 .073 .219 .042 .074 .024 .001 .147 .021\n  Hab. (T1)→Beh. .242 \u003c.001 .169 .315 .086 .171 \u003c.001 .098 .244 .061\n  PB→Beh. – – −.052 .074 – .673 \u003c.001 .604 .742 .423\naSum of indirect effects of past behavior on behavior through all model constructs.\nbTotal effect comprising sums of all indirect effects through model constructs plus the direct effect.\nβ standardized parameter estimate; 95% CI 95% confidence interval of standardized parameter estimate; AP action planning; AR anticipated regret; Att. attitude; Beh. behavior; ES effect size of the standardized parameter estimate; Hab. (T1) self-reported habit measured at baseline (T1); Hab. (T2) self-reported habit measured at follow-up (T2); Int. intention; LB lower bound of 95% CI; MN moral norm; PB past behavior; PBC perceived behavioral control; SN subjective norm; UB upper bound of 95% CI. Focusing on the model excluding past behavior (Model 1), intention, action planning, and habit at follow-up were statistically significant direct predictors of social distancing behavior, with effect size for intention and habit generally larger in the U.S. sample. PBC directly predicted behavior in the Australian sample only, also with a small effect size. Intention predicted action planning in both samples with large effect sizes. Subjective norm, moral norm, and PBC predicted intention in both samples, with small-to-medium effect sizes, but effects of attitude were not significant. There was a small effect of anticipated regret on intention in the U.S. sample only. Habit at baseline predicted habit at follow-up in both samples, with large effect sizes. There was also a small-sized effect of habit at baseline on intention in the U.S. sample only. Overall, the model accounted for significant variance in social distancing behavior (Australian sample, R2 = .198; U.S. sample, R2 = .361), intentions (Australian sample, R2 = .571; U.S. sample, R2 = .623), and habit at follow-up (Australian sample, R2 = .416; U.S. sample, R2 = .486). Intentions (Australian sample, R2 = .066; U.S. sample, R2 = .148), action planning (Australian sample, R2 = .029; U.S. sample, R2 = .044), and habit at follow-up (Australian sample R2 = .041; U.S. sample, R2 = .129) each accounted for substantive variance in behavior. Action planning significantly moderated the intention–behavior relationship in the Australian sample only. While the effect was not in the predicted direction, probing the interaction revealed that the intention–behavior relationship increased as the level of planning increased, consistent with theory. However, the intention–behavior relationship is more likely to be smaller at lower levels of planning, and it seems that planning makes less of a difference when the intention–behavior relationship is large. A plot of the interaction effect is presented in Supplementary Appendix F.\nTurning to the indirect effects, there were significant indirect effects of subjective norm, moral norm, and PBC on social distancing behavior mediated by intention in the U.S. sample. By contrast, only the indirect effect of moral norm on behavior through intention was significant in the Australian sample. The smaller indirect effects in the Australian sample is principally due to the significantly smaller effect size for the intention–behavior relationship in this sample compared to the U.S. sample. Habit at baseline predicted behavior through habit at follow-up in both samples. Effect sizes in all cases were small. There were significant total effects of intention, PBC, and habit at baseline on behavior, with effect sizes larger in the U.S. sample than in the Australian sample.\nFor the model including past behavior, significant effects of past behavior on all model constructs were observed in both samples with effect sizes ranging from small to large. The effects of past behavior on social distancing behavior were particularly large. Inclusion of past behavior led to an attenuation of model effects in both samples. Specifically, the effects of intention and habit at follow-up on behavior were reduced but remained statistically significant in both samples with small effect sizes. In addition, effects of subjective norm, moral norm, and PBC on intention, and the effect of intention on action planning, remained statistically significant in both samples, with small-to-medium effect sizes. The effect of habit at baseline on habit at follow-up was statistically significant in both samples, with large effect sizes. Variance explained in social distancing behavior increased substantially with the inclusion of past behavior, with only modest changes in explained variance in intentions (Australian sample R2 = .598; U.S. sample, R2 = .702) and habit at follow-up (Australian sample R2 = .416; U.S. sample, R2 = .486). Specifically, intentions (Australian sample, R2 = .029; U.S. sample, R2 = .065), past behavior (Australian sample, R2 = .216; U.S. sample, R2 = .311), and habit at follow-up (Australian sample R2 = .031; U.S. sample, R2 = .101) each accounted for substantive variance in behavior.\nTurning to indirect effects, we found significant indirect effects of habit at baseline on behavior mediated by habit at follow-up in both samples with small effect sizes. There were also significant total effects of intention and habit at baseline on behavior in both samples, and of PBC on behavior for the U.S. sample, with small effect sizes. There were significant total indirect and total effects of past behavior on behavior in both samples, with large effect sizes. There was a small-sized indirect effect of past behavior on behavior mediated by habit at both time points in the U.S. sample, but the effect was not significant in the Australian sample.\n\nMultisample analyses\nMultisample analyses permitted for tests of difference in parameter estimates for each model across the Australian and U.S. samples. For the model excluding past behavior (Model 1), only effects of intention on habit at baseline, habit at follow-up on social distancing behavior, and intention on action planning differed across samples. These effects were significantly larger in the U.S. sample. Some effects with observed differences across samples, such as the effect of habit at baseline on intention or the moderator effect of planning on the intention–behavior relationship, did not differ significantly across samples. For the model including effects of past behavior (Model 2), multisample analysis revealed no differences in effect size across samples, indicating that the attenuating effect of past behavior on model effects also had the effect of eliminating the few differences in model effects across samples. Full details of the multiple-group analysis are presented in Supplementary Appendix G.\n\nSensitivity analyses\nWe re-estimated both models in samples excluding participants who were never subject to a “shelter-in place” order, and in the sample that were never subject to an order, or who had the order lifted at some stage during the study. Comparisons of parameter estimates in these models with those from the models estimated in the full sample revealed no significant differences in any of the model parameters. Results are reported in Supplementary Appendices H and 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