In the present study, analyses were performed using the Statistical Program for Social Sciences (IBM SPSS Statistics for Windows, Armonk, NY, U.S.A.) Version 23 for Windows and SPSS Macro [37 ], applying Model 1 with 1000 bias-corrected bootstrap samples. Demographic characteristics of the sample were examined and Pearson’s correlations between variable were calculated. One-way analyses of variance (ANOVAs) were used to assess whether there were significant differences between the two groups (customer service group and administrative work group). Hierarchical multiple regression analyses were conducted to test whether ego-resiliency interacted with emotional labor to influence salivary cortisol levels and SPSS Macro was used to test the statistical significance of the relationship between emotional labor and cortisol levels at different levels of ego-resiliency in an interaction. We excluded the effect of demographic variables in testing models by controlling for gender, age and education level as covariates based on suggestions of previous studies [38 (link),39 (link)].
Spss macro
Manufactured by IBM
Sourced in United States
SPSS macro is a programming language within the IBM SPSS Statistics software that allows for the automation of repetitive tasks and the creation of custom analyses. It provides a flexible and powerful way to extend the functionality of the SPSS platform.
Automatically generated - may contain errors
19 protocols using spss macro
1
Emotional Labor and Cortisol Levels: The Moderating Role of Ego-Resiliency
2
Maternal Early Adversity and Child Behavior
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Zero-order correlations were performed between sociodemographic variables and child negative emotionality/behavioral dysregulation to identify potential confounding factors (see Table 2 for bivariate correlations between all study variables). Mediation analyses were performed using an IBM SPSS Macro (PROCESS; Andrew F. Hayes, School of Communication, Ohio State University, Release 2.15, 2016), which tests total indirect and specific indirect effects by bootstrapping confidence intervals (CIs; Hayes, 2013) . The model parameters were set to give 95% confidence intervals and to run 10,000 bootstrap resamples. We tested our alternative hypotheses by examining the mediating effects of maternal symptoms of depression and maternal sensitivity on the association between maternal history of early adversity and child negative emotionality/behavioral dysregulation in a parallel and a serial mediation model. We included covariates as identified from the zero-order correlations (Table 2). The comparison of model fit was conducted using R version 3.2.0 (R Core Team, 2015).
3
Detecting Outliers and Normality in Likert Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Records with missing values were pair-wise excluded. Ceiling and floor effects were absent in the three outcome measures. Considering our sample size, an absolute value for standardised Z-score greater than 3.2932 (link) and absolute values greater than 2 and 7 for skewness and kurtosis33 (link) respectively, were considered as non-normal; moreover, a chi-square critical value of <0.001 in Mahalanobis distance was considered a multivariate outlier34 .
The scores of 24 questions and the four scales were distributed normally, as none of them exceeded these cut-off points. However, the normality was violated by Item-E15, in which, absolute skewness and kurtosis were 3.32 and 9.7, respectively (Supplementary TableS1 ), and Z-scores of each of the 16 cases were 3.88 (>3.29); therefore, they were considered as a potential univariate outlier. Necessarily, we tested the multivariate distribution for all 25-items using IBM SPSS macro from DeCarlo35 (link), which revealed asymmetry and significant p-values for both skewness and kurtosis (Mardia’s test). Non-normality is expected in ordinal data such as Likert-items36 (link); consequently, we followed Feng et al.37 (link) and utilised non-parametric tests instead of log-transformation.
The scores of 24 questions and the four scales were distributed normally, as none of them exceeded these cut-off points. However, the normality was violated by Item-E15, in which, absolute skewness and kurtosis were 3.32 and 9.7, respectively (Supplementary Table
4
Mediation Analysis of Sphingolipids and T2D
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Mediation models were performed using the SPSS macro for simple and multiple mediation analysis by Preacher and Hayes (see [26 (link)]). Different paths were produced in the model: Path a represents effects of sphingolipids and/or module eigengenes on mediators, path b represents effects of mediators on T2D, path c represents effects of sphingolipids on T2D not through mediators, and path a*b represents effects of sphingolipids on T2D through mediators. The bootstrapping method was applied, with coefficients estimated from 1,000 bootstrap samples.
5
Personality Profiles and Engagement
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
This study employed a cross-sectional descriptive and correlational design. First, to identify the relationships between the individual variables (empathy, affect and personality) and engagement, the Pearson’s correlation coefficient was calculated, in addition to the corresponding descriptive statistics. A two-step cluster analysis was also performed to determine the personality profiles. Once the groups or clusters had been identified, a comparison of means was done to determine the existence of significant differences between the groups with respect to the engagement components, using the Student’s t-test for independent samples, and the Cohen’s d [64 ] to find the effect size of those differences. In addition, to find out how the predictor variables (Empathy: affective and cognitive, Affect: positive and negative) were related to the criterion variable, a stepwise multiple linear regression analysis was carried out. Finally, a multiple mediation model was computed for each of the dimensions of engagement, taking the personality profile as the predictor variable. The SPSS macro [65 ] was used to compute the mediation models. Bootstrapping was applied with the coefficients estimated with 5000 bootstraps.
6
Exploring Insomnia's Psychological Factors
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All statistical analyses were performed using SPSS 21.0 for Mac. Descriptive statistics were computed for all study variables. Univariate normality was examined through skewness and kurtosis.
Correlations between variables were computed using point-biserial correlations between dichotomous and continuous variables and Pearson’s correlation for continuous variables.
The hypothetical mediating role of psychological stress and emotion dysregulation in the relationship between POPU and insomnia was tested using Hayes’ SPSS macro PROCESS.47 Specifically, Model 6 was performed to assess the significance of all the hypothesized relation between the study variables, controlling for both demographic covariates (ie, age and gender) and COVID-19 related variables (ie, someone close infected with SARS-CoV-2, having been in quarantine, being infected-with SARS-CoV-2). The 95% bias-corrected confidence interval (CI) was examined on 5000 bootstrap samples. Bootstrapping is a random resampling method that “makes no assumption about the shape of the distributions of the variables or the sampling distribution of the statistic” (p. 722).48 (link) The indirect effect of POPU on insomnia through psychological stress and emotion dysregulation is considered statistically significant if CI does not contain zero.47 All the results with an α value lower than 0.05 were considered significant.
Correlations between variables were computed using point-biserial correlations between dichotomous and continuous variables and Pearson’s correlation for continuous variables.
The hypothetical mediating role of psychological stress and emotion dysregulation in the relationship between POPU and insomnia was tested using Hayes’ SPSS macro PROCESS.47 Specifically, Model 6 was performed to assess the significance of all the hypothesized relation between the study variables, controlling for both demographic covariates (ie, age and gender) and COVID-19 related variables (ie, someone close infected with SARS-CoV-2, having been in quarantine, being infected-with SARS-CoV-2). The 95% bias-corrected confidence interval (CI) was examined on 5000 bootstrap samples. Bootstrapping is a random resampling method that “makes no assumption about the shape of the distributions of the variables or the sampling distribution of the statistic” (p. 722).48 (link) The indirect effect of POPU on insomnia through psychological stress and emotion dysregulation is considered statistically significant if CI does not contain zero.47 All the results with an α value lower than 0.05 were considered significant.
7
Genetic Risk Factors for Breast Cancer
8
Childhood Maltreatment and PTSD Symptoms
9
Analyzing Factors Influencing Aggressive Driving
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Descriptive statistics were performed using the SPSS24.0 software. The data were expressed as a number, percentage, and sub-group comparisons.The regression analysis on the influencing factors of aggressive driving behavior was undertaken using Jamovi statistical software and the simple slop test of interactions was automatically generated. Second, the SPSS macro program was also used to calculate the simple effect. Two kinds of methodology were used to determine the simple effect of the interaction.
10
Emotional Intelligence and Mobbing Perception
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The study design was quantitative, observational and cross-sectional. First, to identify the relationships between the study variables (emotional intelligence, perceived social support and sensitivity to anxiety) and perceived mobbing, the Pearson’s correlation coefficient was calculated in addition to the corresponding descriptive statistics.
A two-stage cluster analysis was performed to identify the profiles by emotional intelligence factor scores. A comparison of means of the groups or clusters identified was then carried out with the Student’s t test for independent samples, with a significance level of 0.05, and the Cohen’s d [61 ] to find the effect size of the differences.
Finally, a multiple mediation model was computed to analyze the relationships between the emotional intelligence profile and perceived mobbing, including perceived social support and the general sensitivity to anxiety index as potential mediators. The SPSS macro was used to compute the mediation models [62 ] with bootstrapping, using 5000 bootstrap samples.
A two-stage cluster analysis was performed to identify the profiles by emotional intelligence factor scores. A comparison of means of the groups or clusters identified was then carried out with the Student’s t test for independent samples, with a significance level of 0.05, and the Cohen’s d [61 ] to find the effect size of the differences.
Finally, a multiple mediation model was computed to analyze the relationships between the emotional intelligence profile and perceived mobbing, including perceived social support and the general sensitivity to anxiety index as potential mediators. The SPSS macro was used to compute the mediation models [62 ] with bootstrapping, using 5000 bootstrap samples.
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!