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Amos 17

Manufactured by IBM
Sourced in United States

AMOS 17.0 is a statistical software package developed by IBM for data analysis and modeling. It provides a comprehensive set of tools for structural equation modeling, path analysis, and multivariate analysis. AMOS 17.0 offers a user-friendly interface and a wide range of advanced features to support researchers and analysts in various fields.

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9 protocols using amos 17

1

Validation of Psychometric Instrument

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Data were entered and managed using EpiData 3.0 (EpiData Association, Odense, Denmark). Continuous variables are expressed as means ± standard deviations, and were analysed using Student’s t-test. Categorical variables are expressed as frequencies and percentages and were analysed using the chi-squared test. Statistical analyses were conducted using SPSS 17.0 (SPSS Inc., Chicago, IL, USA). Reliability and validity were tested using Cronbach’s alpha and confirmatory factor analysis (CFA). The goodness-of-fit indices used were the chi-squared test, the root mean square error of approximation (RMSEA) and the comparative fit index (CFI). Reliability measured as internal consistency was evaluated using Cronbach’s alpha, which was judged as sufficient when ≥0.70.22 ,23 (link) CFA was used to assess construct validity using Amos 17.0 (SPSS Inc., Chicago, IL, USA). For model judgment, a chi-squared test result of P > 0.05 indicated that the covariance matrix of the data matched the covariance matrix of the model. A root mean square residual <0.05 indicated that the sample variance and covariance differed from their estimates under the assumption that the model was correct. A RMSEA <0.05 indicated fair agreement and RMSEA <0.08 indicated moderate agreement. A goodness-of-fit index >0.90 indicated fair agreement. An adjusted goodness-of-fit >0.90 also indicated fair agreement.
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2

Structural Equation Modeling in Social Sciences

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First of all, Pearson's correlations were carried out among the variables under study with the aim to observe the degree of relation and the relational tendency among them, as well as to establish possible collinear problems among them. Second, MANOVA was carried out to examine whether there were differences in levels of the different variables under study. Finally, the fit of the theoretical method designed through Structural Equation Models (SEM) has been tested in AMOS 17.0 (SPSS Inc, 2007 ). The following robust statistics have been used to determine the goodness of fit: the chi-squared compared with the degrees of freedom (χ2/gl), the robust comparative fit index (CFI robust comparative fit index) the goodness fit index (GFI), the adjusted goodness fit index (AGFI) and the Root mean residual (RMR) (Bollen, 1989 ).
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3

Parenting Styles, Peer Relations, and Academic Performance

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First of all, SPSS 22 was used to analyse means and standard deviations. Pearson correlation analysis was carried out to test the relationships among variables. Finally, structural equations modeling (SEM) in AMOS 17.0 (SPSS Inc., 2007) was employed to explore the proposed models, using maximum likelihood method. The following goodness-of-fit indices were used: chi-square, chi-square divided by degrees of freedom (χ2/df), goodness-of-fit index (GFI), and Bentler comparative fit index (CFI). Root mean residual (RMR) and root mean square error of approximation (RMSEA) were used to measure error. We analyzed two mediators (peer relations: attachment, victimization or aggression, and academic self-efficacy) between two independent variables (parent authoritative and parent permissive) and one dependent variable (academic performance).
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4

Confirmatory Factor Analysis and Structural Equation Modeling Protocol

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This study used confirmatory factor analysis (CFA) by structural equation modelling (SEM). SEM is a method to establish, estimate, and test a causality model, which is widely used in social science research. First, the measurement model was analysed and the model’s validity and reliability were assessed. Second, SEM was used to identify relationships among latent constructs. In this study, the SPSS18.0 statistical software (SPSS Inc., Chicago, IL, USA) was used to process sample data, and both CFA and SEM were conducted using AMOS 17.0 (SPSS Inc., Chicago, IL, USA). When using a structural equation model, one should test whether the model’s parameters have the inverse estimation hypotheses and examine whether the theoretical model has a common method bias.
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5

Functional Pathways within Default Mode Network

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We specified a DCM model with full connectivity consisting of three modules from DMN to estimate pairwise EC among the DMN modules and constructed a directed and weighted graph (representing an EC network) for each subject. We applied serial multiple mediation analysis model in AMOS 17.0 (SPSS Inc., Chicago, IL, USA) to uncover underlying functional pathways within DMN. Specifically, we first estimated the direct relationships between dependent variable (EC from RSC-HIPP module to Association module) and independent variable (FC within Frontal module). Then, in a mediation model, the FC between Association and Frontal module was added as a mediator. In this context, full mediation occurs when the relationship between the independent variable and the dependent variable is no longer significant with the inclusion of a mediator variable. Detailed data analysis is further described in Supplementary Methods.
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6

Neurocognitive Mediation in Older Adults with HIV

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Descriptive statistics were presented. Between-group comparisons (depressive symptoms, physical activity level, and potential) were performed using the chi-square test or independent samples 2-tailed t test as appropriate. Potential confounders were controlled when comparing the differences in raw scores of neurocogitive tests and global- or domain-specific z-scores between older people living with HIV and HIV-negative controls using multivariable linear regression. Crude P values and adjusted P values were presented.
Path analysis was conducted to test the mediation model (Figure 1). HIV status was used as independent variable, while raw score of a neurocognitive test, or global or a domain z-score was included as a dependent variable in each mediation model. Standardized path coefficients (β) and unstandardized path coefficients (B) were reported. Bootstrapping analyses tested the mediation hypotheses. The 95% CIs of the indirect effects would be obtained from 5000 bootstrap samples. A statistically significant mediation effect would be observed when the CI did not include zero. SPSS 21.0 for Windows and AMOS 17.0 (IBM Corp) were used for data analysis; the level of significance was set to P<.05.
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7

Exploring Childhood Abuse's Impact on Depression

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We took three steps to verify our hypothesis that childhood abuse affects depressive symptoms through neuroticism, social support, and coping style in turn. Firstly, we examined the differences in PHQ-9 score between different demographic characteristics using the Mann–Whitney test. Next, Spearman’s rank correlation coefficient and multiple regression analysis for CTQ-SF, EPQR-S, SSRS, SCSQ, and PHQ-9 were computed in SAS9.4 with two-tailed probability value of <0.05 considered to be statistically significant. Finally, we used an SEM approach by AMOS17.0 (IBM Corp., Chicago, IL, United States) to test the theoretical model (Figure 1) relating childhood abuse, neuroticism, social support, coping style, and depressive symptoms.
An SEM was designed based on the hypothesis. In this path analysis, the direct and indirect effects were analyzed using maximum-likelihood covariance estimation. We calculated the indices of goodness of fit to assess the statistical evaluation of the SEM, and a GFI value above 0.90 indicates a good fit. The standardized coefficients were shown.
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8

Statistical Analysis of Research Data

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Statistical and factor analysis were carried out using SPSS 17.0 (IBM Corp., Armonk, NY, USA) and AMOS 17.0 (IBM Corp.), respectively.
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9

Structural Equation Modeling Analysis of Constructs

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This study used SPSS 22.0 (IBM Corp, Armonk, NY, USA) and AMOS 17.0 (IBM Corp, Armonk, NY, USA) to analyze the data. Structural equation modeling (SEM) was used to examine the relationships between the constructs of interest. The whole test process followed the two-step procedure. First, confirmatory factor analysis (CFA) was conducted to examine the construct validity. Then, the descriptive statistics and correlations were calculated by SPSS 22.0. Second, the method of structural equation modeling (SEM) and mediation analysis were conducted using AMOS 17.0. The model fit was assessed using the χ² statistics, and the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root-mean-square error of approximation (RMSEA) were the indices used to assess the goodness-of-fit. The acceptable and excellent model fit requires, respectively, the CFI and TLI values to be no less than 0.90 and 0.95, and the RMSEA value to be less than 0.06 and 0.08 [47 (link)]. Moreover, bootstrapping, one of the most powerful and sensible methods for detecting indirect effect [48 (link)], was employed to examine the mediated effects.
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