Sample’s demographic and behavioral characteristics were described as numbers and proportions by SPSS v.22. The Chi-square test and binary logistic regression proceeded in SPSS v. 22 were used to select potential factors that may influence the association between entrapment and depression. The reliability coefficients (Cronbach’s α and Spearman-Brown r) were calculated through SPSS v.22. The Exploratory Factor Analysis (EFA) consisted of Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity as well as the Confirmatory Factor Analysis (CFA) aiming to test the model fit indices and convergent validity were conducted through SPSS v. 22 and AMOS v. 24. The ratio of chi-square and degrees of freedom (X2/df) between 2 and 5, Root Mean Square Error of Approximation (RMSEA) below 0.08, Goodness of Fit Index (GFI) and Comparative Fit Index (CFI) greater than 0.9 indicate a good model fitness [42 ]. After proving the reliability and constructive validity of the Chinese ES, we ran the binary regression to analyze the relationship between entrapment and depressive symptoms. The scores of the Chinese ES were demonstrated in the form of median (Inter-Quartile Range, IQR). Finally, the Receiver Operator Characteristic (ROC) was performed by R × 64 3.6.2 for illustrating the sensitivity and specificity of ES for predicting depression.
Spss v22
Manufactured by IBM
Sourced in United States, Japan, United Kingdom, China, Belgium, Austria, France
SPSS v22 is a statistical software package developed by IBM. It provides tools for data analysis, data management, and data visualization. The software is primarily used for conducting advanced statistical analysis on a variety of data types.
Automatically generated - may contain errors
Lab products found in correlation
Prism v7, by GraphPad (30 mentions)
Sas v9, by SAS Institute (29 mentions)
Prism 8, by GraphPad (24 mentions)
Prism 6, by GraphPad (22 mentions)
Prism 7, by GraphPad (22 mentions)
Prism v6, by GraphPad (18 mentions)
Prism v5, by GraphPad (17 mentions)
Prism v8, by GraphPad (15 mentions)
Prism v9, by GraphPad (7 mentions)
Prism 5, by GraphPad (6 mentions)
Spss statistics for window, by IBM (6 mentions)
Stata v 13, by StataCorp (4 mentions)
Stata v14, by StataCorp (4 mentions)
Stata 13, by StataCorp (4 mentions)
Amos v 22, by IBM (4 mentions)
Spss statistics for windows version 22, by IBM (4 mentions)
Prism v 8, by IBM (3 mentions)
Matlab, by MathWorks (3 mentions)
Stata v 11, by StataCorp (2 mentions)
Stata v 16, by StataCorp (2 mentions)
3 348 protocols using spss v22
1
Evaluating Entrapment and Depression Relationship
2
Predictive Factors Identification via Nomogram
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
We performed statistical analyses by using SPSS V.22.0. Clinical data were represented as mean (SD) and median (P25, P75). According to the type of data, Student’s t-test, Mann-Whitney U test, or chi-square test was performed to compare the differences between the two groups. The level of significance was set at p < 0.05. Subsequently, univariate and multivariate logistic regression analysis was performed to identify the independent predictive factors. The results of logistic regression were compared, and a nomogram was constructed. The performance of the nomogram and the independent predictive factors was assessed by using receiver operating characteristic (ROC) curves (SPSS V.22.0) and C-index calibration (“RMS” package). A p value of <0.05 was considered to be a statistically significant difference.
3
Comparative Analysis of Biomarkers and Microbial Profiles
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Normally distributed continuous variables will be compared by t-tests and non-normally distributed continuous variables by Mann-Whitney rank-sum tests. Categorical variables will be compared by Χ2 tests. SPSS V.22.0 will be used to generate a normal probability graph and perform a hypothesis test to check whether the observed values obeyed a normal distribution. Individual data points will be superimposed on a box-line plot for calculations. The results of anxiety correlation scales and ELISA will be compared by t-tests or Mann-Whitney rank-sum tests according to whether the normal distribution is met or not. The results of 16sRNA will be classified by the RDP reference database (http://www.mothur.org/wiki/RDP_reference_files ) to calculate the relative abundance of microbial communities at different levels. Then, the differences between samples (groups) will be calculated by Principal Component Analysis, Principal Coordinates Analysis, Non-Metric Multi-Dimensional Scaling, Unweighted Pair-group Method with Arithmetic Means and Beta Diversity Index Inter-group Difference Analysis. All statistical analyses will be performed by SPSS V.22.0 software, with p<0.05 considered statistically significant. Patients who used other drugs or therapies on cancer will be stratified in statistical analysis.
4
Survival Analysis of Treatment Outcomes
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Continuous data are given as the mean ±SD. Categorical data are shown as frequencies. Unless specified otherwise, the number of patients with available data (n) was used for the calculation of summary statistics. The chi-squared test was used to compare categorical variables. The Kolmogorov-Smirnov test was employed to compare continuous variables. Survival was evaluated using survival analyses provided by SPSS tables (SPSS v22.0, IBM, Armonk, NY, USA) and the log-rank test was used to determine significance. Kaplan-Meier analysis was undertaken to estimate cumulative survival. Differences in the hazard ratio (HR) for death were evaluated using Cox proportional hazards regression. Unadjusted and adjusted Cox proportional hazards regression were undertaken, and HRs are presented along with 95% confidence intervals. All statistical analyses were done using SPSS v22.0, and P<0.05 was considered significant.
5
Microbial Diversity and Antibiotic Interactions
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The diversity index and Bray–Curtis similarity were calculated in R v4.2.0 using the “vegan” package [40 ]. Pearson’s correlation coefficients between Chao 1 and antibiotic concentrations were calculated in SPSS v22.0 (IBM Corp, Armonk, NY, USA). Non-metric multidimensional scaling ordination (NMDS) and analysis of similarity (ANOSIM) were performed in PRIMER v7.0 [41 ], based on Bray–Curtis similarity. One-way analysis of variance (ANOVA) was performed in SPSS v22.0, and significant differences (P < 0.05) within groups were calculated by Tukey and Dunnett’s T3 post hoc test based on the homogeneity of the data set.
6
Parental Knowledge and Attitude on Strabismus
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
This was a prospective study conducted in the Outpatient Department of Ophthalmology at a tertiary eye care center where 120 parents of children who were having strabismus were included in the study. Ethical clearance for the study was obtained from the Institutional Ethical Review Board. Written informed consent from all individuals participating in the study was taken.
A questionnaire was formulated on the basis of review of literature and socio-cultural-economic milieu to assess the knowledge and attitude of parents whose children were having strabismus. It was a structured, pilot tested and interview-based test with 16 close-ended questions. A total of 120 parents were interviewed personally during their routine visit to the strabismus clinic from January 1, 2016, to March 31, 2016. Data were collected through questionnaire and tabulated in MS-Excel. SPSS v22.0 (IBM SPSS v22.0) was used to analyze the data. Frequency distributions were drawn to study the pattern of responses of the parents. Chi-square test was used to test the significance of difference in distribution of responses. Pie charts were used to depict the data graphically.
A questionnaire was formulated on the basis of review of literature and socio-cultural-economic milieu to assess the knowledge and attitude of parents whose children were having strabismus. It was a structured, pilot tested and interview-based test with 16 close-ended questions. A total of 120 parents were interviewed personally during their routine visit to the strabismus clinic from January 1, 2016, to March 31, 2016. Data were collected through questionnaire and tabulated in MS-Excel. SPSS v22.0 (IBM SPSS v22.0) was used to analyze the data. Frequency distributions were drawn to study the pattern of responses of the parents. Chi-square test was used to test the significance of difference in distribution of responses. Pie charts were used to depict the data graphically.
7
Reducing Multicollinearity in Species Distribution Modeling
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
A total of 77 environment variables closely related to six large wild herbivores were collected for species distribution models, which were divided into four categories (Table S1 ). The autocorrelations and multiple linear duplications among environment variables might affect the prediction results of the model (Carlos‐Júnior et al., 2015 (link)). To reduce the overlap of information between variables, SPSS v22 software was used to calculate the environmental attribute values of six herbivores and calculate the correlation coefficient to screen the environmental variables. The variables with high correlation (|r| ≥ 0.80) were eliminated, and those with low correlation and more biological implications were introduced into the model operation (Johnson et al., 2016 (link); Kumar et al., 2015 (link)), so as to improve the accuracy of the simulation results of the niche model. SPSS v22 was used for all statistical analysis of environmental variables.
8
Exploratory Factor Analysis Protocol
Check if the same lab product or an alternative is used in the 5 most similar protocols
Questionnaires were coded for data analysis. EFA (Exploratory Factor Analysis) was used to evaluate all the items. In addition, statistical analyses were executed by SPSS v.22, except for CFA (Confirmatory Factor Analysis), which was carried out by LISREL V.8.54. Varimax rotation was used to reduce the items; and Kaiser-Meyer-Olkin (KMO) and Bartlett’s test were computed. Furthermore, content validity and reliability (Cronbach’s α-coefficient) were computed using the Microsoft Excel and SPSS v.22, respectively.
+ Expand
Questionnaires were coded for data analysis. EFA (Exploratory Factor Analysis) was used to evaluate all the items. In addition, statistical analyses were executed by SPSS v.22, except for CFA (Confirmatory Factor Analysis), which was carried out by LISREL V.8.54. Varimax rotation was used to reduce the items; and Kaiser-Meyer-Olkin (KMO) and Bartlett’s test were computed. Furthermore, content validity and reliability (Cronbach’s α-coefficient) were computed using the Microsoft Excel and SPSS v.22, respectively.
9
Statistical Analysis of Research Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data were entered in a spreadsheet using (SPSS v.22, Chicago, Illinois, USA) program. Discrete values were presented in numbers, and the chi-square test made percentages and inferences of significance and the Fisher exact test. Quantitative data were expressed as mean ± standard deviation (SD). Qualitative data were expressed as frequency and percentage. Continuous variables were presented in means ± , standard deviation, the Student t test for normally distributed data, and the Kruskal–Wallis test for non-normal distribution data. The significance level was set at p < 0.05. All data analysis was done by the statistical package for social sciences (SPSS v.22, Chicago, Illinois, USA).
10
Factorial ANOVA and Bonferroni Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Departures from normal distribution were checked using the Kolmogorov–Smirnov (K–S) goodness of fit test. A general linear model of factorial ANOVA (Statistical Package for Social Sciences, SPSS v.22, Inc. Chicago), with genotype, line/genetic background as between subject factors, or repeated measures analysis of variance (RMANOVA) with the type of cell count (total cell and neuronal counts) as within subject factors were used to analyze the data. Eta squared (η2) was used to estimate the effect size, i.e. the proportion of variance associated with each of the main effects and interactions. Bonferroni adjustment of α level (MODLSD Bonferroni t-tests, SPSS v22) was applied in multiple planned comparisons. In the case when data represented discrete category measures on a nominal scale and did not meet the assumption of parametric statistics, a χ2 test of independence was used to test for homogeneity between the groups.
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!