We examined the data distribution first, and then AMOS 26.0 and SPSS 25.0 were used to conduct descriptive statistical analysis, common method bias tests, confirmatory factor analysis, reliability analysis, and correlation analysis of the collected data. On this basis, Model 4 in the SPSS PROCESS macro was further used to test the mediation effect model by estimating the 95% confidence interval of the mediation effect via 5000 sample samples.
Amos 26
Manufactured by IBM
Sourced in United States
AMOS 26.0 is a high-performance laboratory equipment designed for advanced scientific research and analysis. It offers a comprehensive set of features and capabilities to support a wide range of laboratory applications. The core function of AMOS 26.0 is to provide precise and reliable data acquisition, processing, and analysis capabilities to researchers and scientists across various fields of study.
Automatically generated - may contain errors
Lab products found in correlation
Spss 26, by IBM (6 mentions)
Spss 23, by IBM (3 mentions)
Spss 19, by IBM (2 mentions)
Spss 25, by IBM (2 mentions)
Spss v19, by IBM (1 mentions)
Spss 27, by IBM (1 mentions)
Spss 24, by IBM (1 mentions)
Spss version 23, by IBM (1 mentions)
Spss version 26, by IBM (1 mentions)
Origin 8, by OriginLab (1 mentions)
33 protocols using amos 26
1
Examining Mediation Effect through Statistical Analysis
2
Exploring Suicide Ideation and Resilience
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Amos 26.0, SPSS26.0 and SPSS PROCESS plug-ins (Hayes, 2018 ) were used for data statistics and analysis. Firstly, Harman single factor test was used to test the common method deviation. Secondly, mean and standard deviation were used for descriptive statistics, and Pearson Correlation Coefficient was used to test the correlation between variables. Thirdly, We use Amos 26.0 to calculate fit indicators. Finally, taking exercise adherence as the independent variable, gender and grade as the control variable, suicide ideation as the dependent variable, meaning in life and internet addiction as the intermediary variable, the independent intermediary effect test was conducted using the Non-parametric Percentile Bootstrap Method (sample size 5,000, 95% confidence interval) and Model 4 in process, and the chain intermediary effect test was conducted with Model 6.
3
Fatigue and Quality of Life in PA
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AMOS 26.0 and SPSS 24.0 were employed to process the data. The Kolmogorov–Smirnov test was utilized to examine the distribution of the data. The mean ± SD was adopted to present a normally distributed data set. Skewed distributions were described utilizing descriptive statistics, which were represented as medians (25th and 75th percentiles) or percentages. Statistical analysis was performed using the Pearson correlation if the two continuous variables of interest had a normal distribution and the Spearman rank correlation coefficient otherwise. The relationship between fatigue and QoL was investigated using the Spearman correlation analysis. Univariate factors with a P value < 0.05 were subjected to a multiple stepwise linear regression analysis to determine their predictive value for fatigue in PA patients. P < 0.05 (two-sided) was the significance threshold. Furthermore, we conducted a simple path analysis to determine the extent to which sleep mediates between depression and fatigue. In total, 2,000 bootstrap resamples were done to evaluate whether sleep quality was a mediator of the relationship between depression and fatigue. The confidence interval of the mediating effect was calculated by the bootstrapping method with bias correction. The figures and graphs were plotted using GraphPad Prism and RStudio.
4
Examining Brain Diffusivity and Depression
5
Perceived Social Support, Coping Styles, and Depression
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SPSS 25.0 and Amos 26.0 software (SPSS Inc., Chicago, IL, USA) were used for statistical analysis. Descriptive statistics were used to summarize the basic characteristics of the participants and the average PSSS, SCSQ and EPDS scores. Pearson correlation analysis was performed to determine the correlations among perceived social support, coping styles and depression. Scores on the EPDS and the two coping style dimensions were used as dependent variables and background variables in all three analyses, PSSS and SCSQ in the AD analysis, and PSSS in the positive/negative coping analyses were used as independent variables to perform multiple regression analysis. The inclusion level of the equation was 0.05, and the elimination level was 0.10. Structural equation modelling (SEM) was performed to analyse the path relationships between the measured variables. The goodness-of-fit index (GFI), comparative fit index (CFI), normed fit index (NFI), and incremental fix index (IFI) were used to evaluate the optimum of the model. Standardized path coefficients were used to analyse the effects of perceived social support and coping styles on AD. P < 0.05 was statistically significant (two-tailed test).
6
Validating Psychological Constructs: A Rigorous Approach
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In this study, the data were analyzed using AMOS 26.0 and SPSS 26.0. First, to ensure construct reliability and convergence validity of all variables, initial tests for reliability and validity, including Cronbach's α, construct reliability (C.R.), average variance extracted (AVE) and standardized factor loadings were conducted. Second, descriptive analyses involving means and standard deviations as well as Pearson's correlation analysis were performed. Third, confirmatory factor analysis (CFA) was used to test the measurement model. A multicollinearity test and a one-way analysis of variance (ANOVA) were performed to ensure the accuracy of the proposed model. Finally, the hypotheses were tested using hierarchical regression and bootstrap analyses.
7
Odor Identification and Schizotypal Traits
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Data analysis was completed with SPSS 26.0 and AMOS 26.0 (SPSS Inc., Chicago, IL, USA). All statistical tests were 2-sided, and an α = 0.05 was considered to be significant. We first checked the normality of the distribution in all datasets, and all of the variables were normally distributed except for odor identification. Aside from the association between odor identification and these variables, which was examined using Spearman’s correlation analysis, the associations between other components were examined using Pearson’s correlations. A partial correlation analysis was also employed to control for the influence of age and gender. Mediating analysis was conducted to explore the role of odor identification in the relationship between schizotypal traits, especially the interpersonal–affective factor of the SPQ-C, and odor hedonic capacity measured by the U-Sniff and the CPS-C. Finally, chi-square difference tests were performed to examine the gender invariance of children and adolescents in the mediating models. The bootstrap mediation technique was continuously applied to 5000 samples with replacement to determine the statistical significance of the mediating effects.
8
Health-related Quality of Life Factors
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수집된 자료는 IBM SPSS ver. 28.0과 AMOS 26.0 (IBM Corp., Armonk, NY, USA)을 이용하여 다음과 같이 분석하였다.
• 대상자의 특성 및 측정변수의 서술적 통계는 빈도, 평균, 표준편차 및 백분율 등의 기술통계를 사용하여 분석하였다.
• 대상자의 일반적 특성 및 질병관련 특성에 따른 삶의 질은 independent t-test와 분산분석(analysis of variance)으로 분석하였으며, 사후 검정으로는 Scheffé post hoc test로 검증하였다.
• 대상자의 자기효능감, 사회적 지지, 증상, 극복력, 지각된 건강상태, 건강관련 삶의 질 간의 상관관계 분석은 Pearson correlation coefficient로 분석하였다.
• 측정 모형의 타당성을 평가하기 위해 확인적 요인 분석(confirmatory factor analysis)를 시행하였다.
• 건강관련 삶의 질에 영향을 미치는 요인 간의 직, 간접 경로계수를 산출하기 위해 공분산 구조분석으로 하였으며, 다변량 정규성을 가정하는 최대우도법(maximum likelihood)을 사용하였다.
• 건강관련 삶의 질 가설적 모형에 대한 적합도 검정은 chi-square (χ2), χ2/degree of freedom (df), Turker-Lewis index (TLI), comparative fit index (CFI), standardized root mean-squared residual (SRMR), root mean-square error of approximation (RMSEA)을 사용하였다.
• 건강관련 삶의 질 가설적 모형의 경로에 대한 유의성은 표준화 계수(standardized coefficient), standard error, critical ratio, p값으로 확인하였으며, 내생 변수의 설명력은 다중 상관제곱(squared multiple correlation)으로 평가하였다.
• 건강관련 삶의 질 가설적 모형의 총효과와 직접·간접효과의 통계적 유의성 검정을 위해 붓스트레핑(boostrapping)을 사용하였다.
• 대상자의 특성 및 측정변수의 서술적 통계는 빈도, 평균, 표준편차 및 백분율 등의 기술통계를 사용하여 분석하였다.
• 대상자의 일반적 특성 및 질병관련 특성에 따른 삶의 질은 independent t-test와 분산분석(analysis of variance)으로 분석하였으며, 사후 검정으로는 Scheffé post hoc test로 검증하였다.
• 대상자의 자기효능감, 사회적 지지, 증상, 극복력, 지각된 건강상태, 건강관련 삶의 질 간의 상관관계 분석은 Pearson correlation coefficient로 분석하였다.
• 측정 모형의 타당성을 평가하기 위해 확인적 요인 분석(confirmatory factor analysis)를 시행하였다.
• 건강관련 삶의 질에 영향을 미치는 요인 간의 직, 간접 경로계수를 산출하기 위해 공분산 구조분석으로 하였으며, 다변량 정규성을 가정하는 최대우도법(maximum likelihood)을 사용하였다.
• 건강관련 삶의 질 가설적 모형에 대한 적합도 검정은 chi-square (χ2), χ2/degree of freedom (df), Turker-Lewis index (TLI), comparative fit index (CFI), standardized root mean-squared residual (SRMR), root mean-square error of approximation (RMSEA)을 사용하였다.
• 건강관련 삶의 질 가설적 모형의 경로에 대한 유의성은 표준화 계수(standardized coefficient), standard error, critical ratio, p값으로 확인하였으며, 내생 변수의 설명력은 다중 상관제곱(squared multiple correlation)으로 평가하였다.
• 건강관련 삶의 질 가설적 모형의 총효과와 직접·간접효과의 통계적 유의성 검정을 위해 붓스트레핑(boostrapping)을 사용하였다.
9
Personal-Job Fit, Emotional Labor, and Health
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The descriptive statistics, correlations, and the Cronbach's α coefficient of each variable in present study were analyzed using SPSS 26.0 (IBM, New York, NY, USA). Additionally, structural equation modeling (SEM) was executed to analysis the relationship between personal-job fit, emotional labor, burnout and physical and mental health using AMOS 26.0 (IBM, New York, NY, USA). We used means ± standard deviations (SD) to describe the scores of each scale, respectively and independent samples t-tests and one-way ANOVA to compare the differences of the scores of each scale between categorical variables. Moreover, Pearson correlation analysis was utilized for the correlation analysis among continuous variables, such as personal-job fit, emotional labor, burnout, and physical and mental health. To evaluate the significance of the mediating effects model, we used the bootstrapping procedure 5000 resamples and that we consider the effect to be significant when the 95 % confidence interval does not include zero. P values were calculated using two-tailed tests. To verify the model fit, we used multiple fit indices as follows: χ2 test (P < 0.05), χ2/df < 3, comparative fit index (CFI > 0.90), incremental fit index (IFI > 0.90), goodness of fit index (GFI > 0.90), Tucker-Lewis index (TLI > 0.90), and root mean square error of approximation (RMSEA <0.05) (Hu and Bentler, 1999 (link)).
10
Elderly Irrational Consumption Tendencies
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In this study, SPSS V.19.0 (IBM, New York, NY, USA) and AMOS 26.0 (IBM, New York, NY, USA) were used to analyze the survey results. First, the basic characteristics of the respondents were shown by frequency (N) and percentage (%). Second, we calculated the statistical data, including the means (M) and standard deviations (SD) to show the scores of loneliness, social support, and irrational consumption tendencies among the respondents. Finally, structural equation models were employed to verify the path and synthetic relationship between social support, loneliness, and irrational consumption tendencies among older adults. Statistical significance was set at p < 0.05.
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