LitCovid-sentences  

PMC:7543267 / 23863-29988 JSONTXT 8 Projects

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Id Subject Object Predicate Lexical cue
T187 0-13 Sentence denotes Data Analysis
T188 14-233 Sentence denotes Hypothesized relations among the integrated model constructs were tested in the Australian and U.S. samples separately using variance-based structural equation modeling implemented in the WARP 7.0 analysis package [33].
T189 234-512 Sentence denotes Model parameters and standard errors (SEs) were computed using the “Stable3” estimation method, which has been shown to provide the most precise parameter estimates in complex structural models in smaller samples and outperforms bootstrapping methods in simulation studies [33].
T190 513-702 Sentence denotes Simulation studies have also shown this method to provide more consistent and precise estimates in data containing outliers, which may inflate SEs and lead to abnormally high p-values [33].
T191 703-1003 Sentence denotes Two models were estimated in each sample: a model testing predictions of the proposed integrated model with the binary demographic variables also included as covariates (Model 1; Fig. 1, upper panel) and a model that included effects of past social distancing behavior (Model 2; Fig. 1, lower panel).
T192 1004-1079 Sentence denotes All constructs were latent variables indicated by single or multiple items.
T193 1080-1171 Sentence denotes There were no missing data for the social cognition and self-reported behavioral variables.
T194 1172-1332 Sentence denotes There were a few instances of missing data for the demographic variables ranging from 0.5% to 8.8% in the Australia sample, and 0.9% to 6.4% in the U.S. sample.
T195 1333-1387 Sentence denotes Missing data are reported in Supplementary Appendix B.
T196 1388-1460 Sentence denotes Missing data were imputed using stochastic hierarchical regression [33].
T197 1461-1592 Sentence denotes The analysis afforded a number of analyses to evaluate the adequacy of measures used to indicate the latent variables in the model.
T198 1593-1912 Sentence denotes Construct validity of the latent factors for the social cognition, intention, and behavioral variables was established using the normalized factor pattern loadings after oblique rotation and Kaiser normalization [33] and the average variance extracted (AVE), which should approach or exceed .700 and .500, respectively.
T199 1913-2099 Sentence denotes Internal consistency of the factors was estimated using omega reliability coefficients (ω) and composite reliability coefficients (ρ), which should exceed .700 and ideally approach .900.
T200 2100-2184 Sentence denotes We also conducted tests of the discriminant validity of the constructs in the model.
T201 2185-2331 Sentence denotes Discriminant validity was supported when the square root of the AVE for each latent variable exceeded its correlation with other latent variables.
T202 2332-2531 Sentence denotes Adequacy of the proposed model in describing the data was established using the goodness-of-fit (GoF) index with values of .100, .250, and .360 corresponding to small, medium, and large effect sizes.
T203 2532-2641 Sentence denotes Further information on model quality was provided by the average path coefficient and average R2 coefficient.
T204 2642-2868 Sentence denotes These indices summarize the average parameter estimates of relations in the model and the amount of variance explained in each dependent variable, respectively, and should be statistically significant for a good-quality model.
T205 2869-3111 Sentence denotes In addition, an overall GoF index is provided by the average block variance inflation factor for model parameters and the average full collinearity variance inflation factor, which should be equal to or lower than 3.3 for well-fitting models.
T206 3112-3235 Sentence denotes These indices indicate the extent to which latent variables in the model overlap and contribute to model multicollinearity.
T207 3236-3342 Sentence denotes They, therefore, provide an indication as to the uniqueness of the existing latent variables in the model.
T208 3343-3575 Sentence denotes Four further indices were also used to evaluate model quality: the Simpson’s paradox ratio (SPR), R2 contribution ratio (R2CR), the statistical suppression ratio (SSR), and the nonlinear bivariate causality direction ratio (NLBCDR).
T209 3576-3797 Sentence denotes The SPR indicates whether the model is free from incidences of Simpson’s paradox (i.e., when the path coefficient and the correlation associated with a latent variable have opposite signs), indicating a causality problem.
T210 3798-3852 Sentence denotes The SPR should exceed .700 and ideally approach 1.000.
T211 3853-4000 Sentence denotes The R2CR and SSR provide indication of the extent to which models are free from instances of negative R2 contributions and statistical suppression.
T212 4001-4060 Sentence denotes The R2CR and SSR should exceed .900 and .700, respectively.
T213 4061-4397 Sentence denotes The NLBCDR provides an estimate of the extent to which the proposed “causal” associations in the proposed model are more tenable than those in the opposite direction and provide an initial indicator of support for the hypothesized directions of the causal links in the proposed model compared to if the proposed direction were reversed.
T214 4398-4452 Sentence denotes The NLBCDR should exceed .700 for high-quality models.
T215 4453-4531 Sentence denotes Kock [33] provides further technical details on model fit and quality indices.
T216 4532-4650 Sentence denotes Model effects were estimated using standardized path coefficients with confidence intervals (CIs) and test statistics.
T217 4651-4851 Sentence denotes Effect sizes were estimated using a variant of Cohen’s f-square coefficient and represent the individual contribution of the predictor variable to the R2 coefficients of the criterion latent variable.
T218 4852-4942 Sentence denotes Values of .02, .15, and .35 represent small, medium, and large effect sizes, respectively.
T219 4943-5121 Sentence denotes Differences in the path coefficients in the models across the samples were tested using multiple-group analysis using the Satterthwaite method with two-tailed significance tests.
T220 5122-5329 Sentence denotes We also tested whether the inclusion of participants that were never under a “shelter-in-place” order, or had the “shelter-in-place” order lifted during the study, affected predicted relations in the models.
T221 5330-5621 Sentence denotes The small numbers of participants that were, at some point, not subjected to “shelter-in-place” orders meant we could not conduct a formal moderator analysis, so we conducted a sensitivity analysis testing whether effects in the models differed if data from these participants were excluded.
T222 5622-5884 Sentence denotes Models excluding and including past behavior were estimated 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.
T223 5885-6016 Sentence denotes Formal comparisons of parameter estimates in these models with those from the full sample were made using the Satterthwaite method.
T224 6017-6125 Sentence denotes Data files, analysis scripts, and output files for all analyses are available online: https://osf.io/x9tms/.