Spss amos software

To analyze the data, the researchers used several methods. Firstly, EFA was used to analyze the dimensionality and the factor loading for the items under each dimension by using IBM SPSS software. Afterward, the researchers conducted CFA for analyzing the field research data using IBM SPSS AMOS software. Prior to performing CFA, researchers conducted data screening and managed missing values, only 370 respondents were included in the CFA analysis.

Path analysis was used to investigate the direct and indirect effect of environmental variables on GPP and LUE. It is a multiple regression model, which can deal with the casual relationships among correlated variables (Shipley, 2004 ):
where i is different independent variables, r_i,y is the correlation coefficient between the independent variable i and the dependent variable y, r_i,n is the correlation coefficient between different independent variables, P_i,y is the direct effect of the independent variable i on the dependent variable y (standardized regression coefficient), and r_i,n×P_n,y (i≠n) is the indirect effect of independent variable i affecting another independent variable n which in turn affects the dependent variable y.
We used DF, PAR, T_a, VPD and SWC in path analysis. Path analysis was performed by SPSS AMOS software (version 24.0, IBM Inc., USA). All input variables initially need to be standardized. Maximum likelihood method is applied in the calculation. Output results included direct impact (SDE, [-1,1]), indirect impact (SIE, [-1,1]) and total impact (STE, [-2,2]). Positive and negative values indicated positive and negative effects, respectively, and the absolute value of coefficient represented relative effect among variables.

Statistical analyses were performed using SPSS version 27 and SPSS AMOS software. The test-retest reliability of the OBQ11-A between the first and second evaluations was assessed using the intraclass correlation coefficient (ICC). For a research instrument, a reliability of 0.70, was considered acceptable [27 ]. ICC values were calculated for each item and the total OBQ11-A score. The percentage of agreement on the validity question was used to demonstrate participant-level content validity. Construct validity was determined using an explanatory factor analysis. The Kaiser–Meyer–Olkin (KMO) value and Bartlett’s test of sphericity were used to examine the suitability of the factor analysis. Cronbach’s alpha was calculated to check the internal consistency and a value of 0.8 or above was deemed acceptable [29 ]. For convergent validity, a moderate association between OB and quality of life was hypothesized, as reported in previous research [2 (link), 3 (link), 5 (link)]. Spearman’s correlation coefficient was calculated for associations between OBQ11-A total score and the single item scores on overall perception of quality of life and overall perception of health, and each of the four domains of WHOQOL-BREF. A two-sided pvalue of 0.05 was considered significant for all statistical tests.

The structural model reflecting the assumed linear, causal relationships among the constructs. The test of structural model was performed using the following steps: (1) estimating the path coefficients, or the strengths of the relationships between the dependent variables and independent variables, and (2) estimate the R-square value which is the amount of explained variance of the independent variables. SPSS AMOS software will be used to determine The path coefficients in the SEM model obtaining standardized path coefficients (β), standard error, p-value and hypotheses result. The hypotheses will be supported or accepted for values of p-value with a level of significance of 0.05