The IBM SPSS Statistics 26 was used to record and process data. The statistics were stratified by the FOF category. A one-way analysis of variance (ANOVA) test for continuous variables (height, mass, BMI, FES-I, FRQ, FSBT, GAD-7, GDS-15, IPAQ-LF) and chi-squared tests for categorical variables (all other variables) were conducted to compare the low, moderate and high FOF groups with each other on the variables. A P-value of < 0.05 was considered significant. IBM SPSS Amos 26 Graphics was employed to conduct structural equation modeling. We used the maximum likelihood estimation method to test the model. Cutoff points for the root mean square error of approximation (RMSEA ≤ 0.08 with a confidence interval of 95%), the comparative fit index (CFI ≥ 0.90), as well as the Goodness of fit index (GFI ≥ 0.90) were used to assess proper fitness [48 (link)].
Spss amos 26 graphics
Manufactured by IBM
SPSS Amos 26 Graphics is a software tool designed for structural equation modeling and path analysis. It provides a graphical user interface for creating, estimating, and validating models. The software is used for quantitative analysis of data and hypothesis testing.
Automatically generated - may contain errors
3 protocols using spss amos 26 graphics
1
Fear of Falling Among Older Adults
2
COVID-19 Preventive Behaviors and Factors
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Data were exported from the online questionnaire platform and imported into IBM
SPSS statistics 22. Descriptive statistics were applied to demonstrate the
demographic characteristics, COVID-19 preventive behaviors, and IDSHL. One-way
ANOVA (analysis of variance) was adopted to compare the differences among
demographic groups, and the Student-Newman-Keuls method was applied to compare
the differences among subgroups if a significant difference was found. The
Pearson correlation coefficient was used to examine the relationships among
IDSHL, COVID-19 preventive behaviors, and their predictors, which are continuous
variables.
Path analysis was implemented by IBM SPSS Amos 26 Graphics. Maximum likelihood
estimation was conducted to evaluate the parameters with the covariance matrix.
The path model was modified according to the P regression
weight, modification indicators, and goodness-of-fit indexes (GFIs). Only
variables with significant P values (less than .05) remained in
the model. A comparative fit index ≥0.90 and root mean square error of
approximation <0.06 means that the fit was acceptable.19
SPSS statistics 22. Descriptive statistics were applied to demonstrate the
demographic characteristics, COVID-19 preventive behaviors, and IDSHL. One-way
ANOVA (analysis of variance) was adopted to compare the differences among
demographic groups, and the Student-Newman-Keuls method was applied to compare
the differences among subgroups if a significant difference was found. The
Pearson correlation coefficient was used to examine the relationships among
IDSHL, COVID-19 preventive behaviors, and their predictors, which are continuous
variables.
Path analysis was implemented by IBM SPSS Amos 26 Graphics. Maximum likelihood
estimation was conducted to evaluate the parameters with the covariance matrix.
The path model was modified according to the P regression
weight, modification indicators, and goodness-of-fit indexes (GFIs). Only
variables with significant P values (less than .05) remained in
the model. A comparative fit index ≥0.90 and root mean square error of
approximation <0.06 means that the fit was acceptable.19
3
Comprehensive Water Quality and Ecosystem Analysis
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The results of each sampling area in the experiment were calculated as the average of three parallel samples. SPSS 26.0 (IBM, Armonk, NY, USA) and Origin 2018 (OriginLab, Northampton, MA, USA) were used for calculations, statistical analysis, and plotting of relevant data. Redundancy analysis (RDA) was used to analyze the relationship between chemical indicators of water quality and SR and plant biomass, and was performed with Canoco 5 (Microcomputer Power). Structural equation modeling (SEM) was employed to further determine the relationship between chemical indicators, SR, plant biomass, and C flux, and was conducted using IBM SPSS Amos 26 Graphics.
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