Data analysis was performed using the responses from 409 participants who answered all questions on the EEQ, SPAF, and NPSS. Data analyses were conducted using SPSS version 28.0 (IBM, Inc., Armonk, NY, USA) and SPSS AMOS version 26.0 (IBM, Inc.) at a significance level of 0.05. Descriptive analysis was used to demonstrate the demographic characteristics of respondents. Independent t-tests were used to compare BMI, the degree of emotional eating, PMS, and negative perceived stress between the PMDD and non-PMDD groups. In addition, to assess the reliability and validity of each item and their measurements, confirmatory factor analysis using AMOS version 26.0 was performed. Furthermore, mediation analysis was used to test the relationships among emotional eating, PMS, and negative perceived stress using the PROCESS macro for SPSS developed by Andrew F. Hayes.
Spss amos version 26
Manufactured by IBM
Sourced in United States
SPSS Amos Version 26 is a statistical software package used for structural equation modeling, path analysis, and confirmatory factor analysis. It provides a graphical user interface for building and testing complex models.
Automatically generated - may contain errors
Lab products found in correlation
Spss for windows version 24, by IBM (2 mentions)
Spss statistics version 26, by IBM (2 mentions)
Spss version 28, by IBM (1 mentions)
Spss statistics 26, by IBM (1 mentions)
Spss statistics version 27, by IBM (1 mentions)
Spss statistic, by IBM (1 mentions)
Spss for windows version 26, by IBM (1 mentions)
Spss process macro42, by IBM (1 mentions)
19 protocols using spss amos version 26
1
Emotional Eating, PMS, and Stress in PMDD
2
Examining Psychological Factors in Chronic Illness
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Descriptive statistics were used to describe the sociodemographic characteristics of the participants and the main study variables (PSS, IU, and FCR). In addition, tests for normality and homogeneity of variance were performed. One-way ANOVAs and t-tests were used to determine the relationship between participant characteristics and the three variables, and Pearson correlations were used to test for unadjusted associations between variables. These data above were statistically analyzed using IBM SPSS Statistics 26.0 (IBM Corporation, United States). The hypothetical model was tested using SEM with IBM SPSS AMOS version 26.0 (IBM Corporation, United States). The maximum-likelihood estimation of the entire system in a hypothesized model, and enables the assessment of variables with the data (Jöreskog and Sörbom, 1982 (link)). Finally, bootstrap tests were used to measure the direct, indirect and total effects of the model (Hayes, 2009 ). Statistical significance was set at 0.05.
3
Confirmatory Factor Analysis using SPSS Amos
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
CFA on data was conducted using SPSS Amos Version 26.0 (IBM Corp, Armonk, NY, USA). We utilized comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) to assess model fit. CFI is considered optimal with values above 0.95. SRMR and RMSEA indicates a good model fit when less than 0.08 and 0.06, respectively [40 (link)].
4
Validating Metacognitive Experiences in EFL Writing
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Confirmatory factor analysis (CFA) was conducted on a sample of 420 participants to cross-validate the measurement model of metacognitive experiences in EFL writing. CFA with Maximum likelihood (ML) estimation was run with IBM SPSS AMOS Version 26.0 to examine the factorial structure underlying the PWMEQ, DWMEQ, and POWMEQ. The model fit was assessed using six indices: the value of the ratio of χ2 divided by its degree of freedom (χ2/df), comparative fit index (CFI), the goodness of fit index (GFI), Tucker and Lewis coefficient (TLI), root means square’s error of approximation (RMSEA), and standardized root mean square residual (SRMR). According to Hu and Bentler (1999) (link) and Kline (2015) , Table 1 shows the threshold of each model fit index indicating an acceptable model fit.
5
Emotion Regulation and Sexual Desire
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical analyses were performed using IBM SPSS Statistics version 27 and IBM SPSS Amos version 26 (SPSS Inc., Chicago, IL, USA). A path analysis model was used to evaluate the relationship between sexual shame, expressive suppression, cognitive reappraisal and sexual desire. Sexual shame, expressive suppression, and cognitive reappraisal were modeled as exogenous variables. Structural pathways were specified to sexual desire, which was modeled as an endogenous variable. Multiple regression analysis and multivariate analysis of variance (MANOVA) was used to investigate potential relationships between emotion regulation, sexual desire, and sexual shame and to investigate gender differences. Prior to analyses, assumptions of normality and homogeneity of variance were evaluated. The assumption of homogeneity of variance-covariances matrices was shown to be violated (p < 0.001), Pillai’s Trace was therefore used instead of Wilk’s Lambda as it is a more robust test statistic for non-normal and unbalanced samples28 (link). A power calculation revealed that the sample size had a statistical power > 95% to detect medium effect sizes (d = 0.50) with α = 0.05, calculated with GPower version 3.1.9.7.
6
Multivariate Psychometric Validation Study
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data were collected with three instruments accompanied by the required demographics. All the items were rated on the same Likert-type scale ranging from 1 (“Does not describe you at all”) to 5 (“Very characteristic of you”). Before describing study variables, the measurement instruments were assessed for validity via confirmatory factor analyses (CFA), using SPSS AMOS version 26. Shortcomings in factor structure robustness were assessed and addressed as explained below, originating final factor structures which were then used to compute descriptive statistics, correlations, regressions and moderations.
7
Construct Validation Methodology for Scales
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data analysis was performed using SPSS Statistics and SPSS Amos (version 26). Categorical data were described by number and percentage. Continuous data were described by mean ± standard deviation and median. CFA was computed using AMOS to test the measurement models. The model's fit was evaluated using the standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the Tucker–Lewis fit index (TLI). The model's fit is ideal when the SRMR is less than 0.08, and the model's fit is acceptable when the SRMR is less than 0.1. The model's fit is ideal when the RMSEA is less than 0.05. The model's fit is acceptable when the RMSEA is less than 0.08. And when the CFI and TLI are more significant than 0.9, the model's fit is ideal. Cronbach's alpha and composite reliability (CR) were used to assess construct reliability. The recommended cut-off of Cronbach's alpha and CR are both 0.70. Construct validity is established through two forms of validity: convergent validity and discriminant validity. The average variance extracted (AVE) method was used to estimate the convergence validity of scale items. Discriminant validity in the study was assessed for subscales using the Fornell and Larcker Criterion and Heterotrait-Monotrait (HTMT) Ratio.
8
Post-Positivist Structural Equation Modeling
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The plinth of this study permeated on post-positivist research philosophy. The phenomenon of this study was mind-dependent, and the only way to probe the truth was the scientific way of investigation. The empirical observations were used in the cross-sectional deductive settings (Krauss, 2015 ). Ontologically, the reality of this study was singular; epistemologically, it was objective. It has value-free axiology and quantitative survey methods used to conduct it (Bryman, 2016 ). Anderson and Gerbing (1988 (link)) elaborated a two-step approach to structural equation modeling (SEM) application. The application of SEM on parametric data through the general linear regression model (GLM) presented excellent strength of theory testing and development in business studies (Tabachnick et al., 2007 ). The data were analyzed through IBM SPSS AMOS version 26 (Byrne, 2016 (link)).
9
Statistical Tests for Correlation Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical tests (Spearman rho’s and Pearson’s correlation) were performed using IBM SPSS Amos version 26. Different parameters were checked for normal distribution. Correlations were calculated based on distribution of the compared parameters via Spearman’s rho and Pearson’s correlation, respectively. In the manuscript, non-corrected p values were used to describe specific trends; however, Bonferroni corrected p values can be found in Supplementary Table 6 .
10
Age, GM-BHQ, FA-BHQ, and SHS Associations
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Path analysis was performed to investigate the association between age, GM-BHQ, FA-BHQ, and SHS based on the hypothesis that GM-BHQ and FA-BHQ mediate the association between age and SHS. The level of statistical significance was set at p < 0.05. All statistical analyses were performed using SPSS/AMOS version 26 (IBM Corporation, Armonk, NY, USA).
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!