Initial inter-correlations (Pearson’s r) among study variables were calculated in SPSS v. 24 (IBM Corp., Armonk, NY, United States). Following this a path model was estimated in AMOS v. 24 (IBM Corp., Armonk, NY, United States) to test the study hypotheses, featuring the variables that significantly correlated with the outcome variables (i.e., lapses, errors, and violations). The path model included System 1 traits as exogenous predictors, System 2 traits as potential mediators, and the three outcome variables and modeled the regression parameters simultaneously. Error terms for all endogenous variables were permitted to correlate. Given that the outcome variables were correlated, average effects of each of the predictors on the three outcomes were estimated, as well as the differences in the size of these effects using AMOS custom estimands. Bootstrapping (10,000 resamples; Wood, 2005 (link)) was used to estimate the significance of coefficients in the path model (Hayes and Scharkow, 2013 (link)). Bootstrapping is incompatible with missing data, so a complete case analysis was conducted (n = 307).
Amos v 24
Manufactured by IBM
Sourced in United States
AMOS v. 24 is a statistical software package designed for structural equation modeling (SEM) and path analysis. It provides a comprehensive set of tools for data analysis, model building, and hypothesis testing. The software enables researchers and analysts to explore complex relationships between variables and test theoretical models.
Automatically generated - may contain errors
Lab products found in correlation
5 protocols using amos v 24
1
Modeling Cognitive Traits and Driving Behaviors
2
Path Analysis of Autonomy Factors
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
IBM SPSS v.24 and AMOS v.24 (Arbuckle, 2016 ) software package were employed for data analysis. After preliminary analyses (reliability analyses, calculation and the Relative Autonomy Index (RAI), descriptive statistics, Pearson correlations, independent samples t-tests and univariate analysis of variance [ANOVA]), path analyses were performed for each country separately as well as for the full data set. In this study we report the combined model. Significance was set at p < .05.
3
Loneliness, Depression, and Anxiety: Mediating Role of Self-Disgust
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Pearson's (r) correlations were used to assess the associations among the study variables. Bootstrapped path analysis was also used to assess the direct and indirect associations of loneliness with depression and anxiety symptoms and the mediating effects of self-disgust in this relationship, after controlling for demographic factors and cognitive ability (MoCA). As recommended (e.g., Hayes 2009; Hayes & Scharkow 2013) , we used biascorrected and accelerated bootstrapping to obtain confidence intervals (and associated probability values) for all direct and indirect effects in the path model. All data were analysed in SPSS v. 22 (IBM Corp., Armonk, NT, USA), and AMOS v. 24 (IBM Corp., Armonk, NT, USA). In the path analysis, probability values and confidence intervals (CIs) were based on 10,000 bias-corrected and accelerated (BCa) bootstrapped resamples (Mallinckrodt et al., 2006) .
4
Factorial ANCOVA and Path Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
An a priori power analysis on G*Power 3.1.7 for a 3*3 factorial ANCOVA design (with interactions) was conducted to determine the required sample size. In this analysis, we assumed a small effect size of f = .10 and α = .05. A minimum sample of N = 1,199 was required for 80% power, and a sample size of N = 1,204 was required for 9 balanced groups.
We intended to recruit an additional 5% for possible bad data, giving a target sample size of N = 1,267. This number was applicable for both the baseline and covariate models.
Following randomization and manipulation checks, descriptives, planned ANOVAs, and ANCOVAs, for each of the three outcomes, were conducted on R 3.6.1 [46] , using packages car [47] , lsmeans [48] , psych [49] , and sjstats [50] . Following these results, an exploratory path analysis was conducted on AMOS v 24 (IBM Corp., Armonk, NY, US), using bias-corrected and accelerated bootstrapping to test the significance of direct and indirect effects [51] , with 10,000 resamples [52] .
We intended to recruit an additional 5% for possible bad data, giving a target sample size of N = 1,267. This number was applicable for both the baseline and covariate models.
Following randomization and manipulation checks, descriptives, planned ANOVAs, and ANCOVAs, for each of the three outcomes, were conducted on R 3.6.1 [46] , using packages car [47] , lsmeans [48] , psych [49] , and sjstats [50] . Following these results, an exploratory path analysis was conducted on AMOS v 24 (IBM Corp., Armonk, NY, US), using bias-corrected and accelerated bootstrapping to test the significance of direct and indirect effects [51] , with 10,000 resamples [52] .
5
Soil Properties and Microbial Limitations
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Correlations between the soil properties and microbial resource limitations were determined using generalized linear models.
Structural equation modeling (SEM) was performed using Amos v.24 (IBM, Armonk, United States) to identify the possible ways in which variables could influence microbial resource restriction. The importance of the influencing factors for microbial metabolic limitation were identified using a random forest (RF) analysis for classification by Rv. 4.0.4 (R Development Core Team, 2021) with the random forest package (Trivedi et al., 2016) (link). Percentage increases in the mean squared error (MSE) of variables were used to estimate the importance of these indices: a high MSE% implied the importance of the variables (Breiman, 2001) (link). Other statistical analyses were performed using IBM SPSS Statistical software v. 22 (SPSS Inc., Chicago, United States).
Structural equation modeling (SEM) was performed using Amos v.24 (IBM, Armonk, United States) to identify the possible ways in which variables could influence microbial resource restriction. The importance of the influencing factors for microbial metabolic limitation were identified using a random forest (RF) analysis for classification by Rv. 4.0.4 (R Development Core Team, 2021) with the random forest package (Trivedi et al., 2016) (link). Percentage increases in the mean squared error (MSE) of variables were used to estimate the importance of these indices: a high MSE% implied the importance of the variables (Breiman, 2001) (link). Other statistical analyses were performed using IBM SPSS Statistical software v. 22 (SPSS Inc., Chicago, United States).
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!