For this trial, all eligible patients were recruited. All statistical analyses were performed using the SPSS for Windows ver. 16.0 (SPSS Inc., Chicago, IL, USA). The independent t-test was used to compare the results between the groups; and the chi-square test or Fisher’s exact test was used to compare the proportions. The Mann-Whitney U-test was used to compare the parametric data without a normal distribution between the two study groups. The data are reported as means±standard deviations or proportions as appropriate. Two-sided p-values <0.05 were considered to indicate statistical significance.
Spss for windows ver 16
Manufactured by IBM
Sourced in United States
SPSS for Windows ver. 16.0 is a software package for statistical analysis. It provides tools for data management, analysis, and presentation. The software offers a wide range of statistical techniques, including regression, correlation, and multivariate analysis.
Automatically generated - may contain errors
Lab products found in correlation
30 protocols using spss for windows ver 16
1
Comparative Statistical Analysis of Outcomes
2
Survival Analysis of Patient Cohort
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All data were analyzed using the SPSS for Windows (ver. 16.0; SPSS Inc.). Overall survival (OS) was measured from the date of diagnosis until the last follow-up or death. P≤0.05 was considered to be statistically significant.
3
Predictors of Sleep Quality
Check if the same lab product or an alternative is used in the 5 most similar protocols
Data were analyzed with SPSS® for Windows® ver 16.0 (SPSS Inc, Chicago, USA). Univariate analysis was done to find the association of sleep quality with various factors using χ2 test. Variables with p<0.25 were included in the final logistic regression model (backward likelihood ratio) to find the independent predictors of sleep quality. The criterion for entering and removing the independent variables from the backward stepwise model was p<0.05.
+ Expand
Data were analyzed with SPSS® for Windows® ver 16.0 (SPSS Inc, Chicago, USA). Univariate analysis was done to find the association of sleep quality with various factors using χ2 test. Variables with p<0.25 were included in the final logistic regression model (backward likelihood ratio) to find the independent predictors of sleep quality. The criterion for entering and removing the independent variables from the backward stepwise model was p<0.05.
4
Factors Influencing Marriage Age and Residency
Check if the same lab product or an alternative is used in the 5 most similar protocols
The age of participants was categorized into <30 and ≥30 years. Age at marriage was categorized as <25 and ≥25 years. The residence of the participants was categorized as “urban” and “rural.”
SPSS® for Windows® ver 16.0 (Chicago, IL, USA) was used for data analyses. Categorical variables were analyzed with χ2 test. Logistic regression analysis was used to determine the independent risk factors. A p value <0.05 was considered statistically significant.
+ Expand
The age of participants was categorized into <30 and ≥30 years. Age at marriage was categorized as <25 and ≥25 years. The residence of the participants was categorized as “urban” and “rural.”
SPSS® for Windows® ver 16.0 (Chicago, IL, USA) was used for data analyses. Categorical variables were analyzed with χ2 test. Logistic regression analysis was used to determine the independent risk factors. A p value <0.05 was considered statistically significant.
5
Multivariate Analysis of Behavioral Factors
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The collected data were analyzed using SPSS for Windows (Ver. 16.0, SPSS Inc., Chicago, IL). The SE data were compared between groups, and multiple comparisons were calculated. The GEE regression model was used to examine the influence of different factors on the magnitude of the absolute change in SE. The results with P <0.05 were considered statistically significant.
6
Statistical Analysis of Qualitative and Quantitative Variables
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The chi-square test was used for the comparison of qualitative variables, and Student t-test (mean±standard deviation [SD]), the MannWhitney U-test, and theWilcoxon test (median [interquartile range]) were used to compare quantitative variables between groups before and after the intervention. All statistical analyses were performed using SPSS for Windows ver. 16.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was set at P<0.05.
7
Statistical Analysis Methods for Experimental Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical analyses were conducted using SPSS for Windows ver. 16.0 (SPSS Inc., Chicago, IL, USA). Descriptive data were demonstrated by mean±standard deviation for numerical variables or numbers with percentages for categorical variables. The Kolmogorov-Smirnov test was used to test the normality of the distribution of the numerical variables. A comparison of mean values between the two study groups was performed using an independent t-test or its nonparametric counterpart (Mann-Whitney U test). A comparison of mean values between more than two groups was performed using a one-way analysis of variance test or its nonparametric counterparts (Kruskal-Wallis H test). Categorical variables were compared using the chi-square test or Fisher’s exact test. A p-value <0.05 was considered statistically significant.
8
Prevalence of Antibiotic-Resistant Salmonella in Chicken
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The MDR pattern is described as having resistance to antibiotics from a minimum of three antimicrobial classes. First, positive Salmonella isolates from chicken cloacal swabs or intestinal contents, chicken breast meat, and phenotypic/genotypic AMR profiles were reported in percentages. Then, the Chi-square test was used to analyze the correlation between the diverse prevalence of Salmonella isolates in chicken cloacal swabs or intestinal contents and chicken breast meat samples and their phenotypic/genotypic AMR profiles. Finally, statistical analyses were performed using statistical package for the social sciences (SPSS) for Windows (ver. 16.0; SPSS Inc., Chicago, IL, USA), and p < 0.05 was considered statistically significant.
9
Statistical Analysis of Social Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The Statistical package for social sciences (SPSS) for Windows ver. 16.0 (SPSS Inc., Chicago, IL) was used for statistical analyzes and all graphs were provided by the same software program. The independent samples t-tests were employed to compare continuous data, and the One-way ANOVA analyzes of variance were also used for comparisons among groups. Statistical analyzes including ROC curves were performed; statistically significant p was accepted as p<0.05.
10
Statistical Analysis of Experimental Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The results are presented as the mean ± standard deviation. Statistical analyses were performed using one-way analysis of variance. Post hoc comparisons were performed with the Duncan or Mann-Whitney nonparametric tests. Data were analysed using SPSS for Windows (ver. 16.0; SPSS Inc., Chicago, IL, USA). P ≤ 0.05 was considered to indicate a statistically significant difference.
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!