The largest database of trusted experimental protocols

Spss v 21.0 for windows

Manufactured by IBM
Sourced in United States

SPSS V.21.0 for Windows is a statistical software package developed by IBM. It is designed for data analysis, data management, and data visualization. The software provides a range of tools for statistical modeling, including regression analysis, factor analysis, and cluster analysis.

Automatically generated - may contain errors

60 protocols using spss v 21.0 for windows

1

Nurses' Duty to Care: Ethical Challenges

Check if the same lab product or an alternative is used in the 5 most similar protocols
The descriptive analysis involved frequencies and percentages for categorical
variables, and means and standard deviations for continuous variables.
Chi-square and Fisher’s exact tests were used to compare ethical statements. The
Fisher’s exact test was used when the data contained only a few observations in
one of the cells (e.g. less than five). The primary outcome was measured with
the statement ‘Doctors and nurses have a duty to care for the sick, even when
there are high risks for themselves or their families’. This was calculated as
the percentage reporting agreement and disagreement with the statement.
To evaluate the associations between well-being and agreement with the duty to
care, the level of well-being was also coded as a dichotomous answer: low or
high well-being. Differences within and between groups of nurses from the two
countries were investigated, using chi-square tests, with a p< 0.05 being accepted as statistically significant. Statistical analyses were
performed using SPSS v. 21.0 for Windows (IBM, Armonk, NY, USA).
+ Open protocol
+ Expand
2

Statistical Analysis of Experimental Data

Check if the same lab product or an alternative is used in the 5 most similar protocols
All statistical analyses were carried out using the Statistical Package for the Social Sciences (SPSS) v.21.0 for Windows (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY, United States: IBM Corp). Categorical variables were evaluated using frequencies and percentages and quantitative variables with means and standard deviations that included maximum and minimum values (range). Distribution of the data was evaluated using Shapiro-Wilk test. Comparisons at various time intervals within each group were analyzed using Friedman’s test and, if statistical significance was detected, multiple comparisons were carried out using Wilcoxon’s sign rank test. Categorical variables were analyzed using the Pearson’s chi-square (χ2) test. Groups were compared with Kruskal-Wallis test complemented by the Bonferroni correction. Cohen’s d was calculated to evaluate effect sizes. P values < 0.05 were considered statistically significant.
+ Open protocol
+ Expand
3

Analyzing Plant Growth with Statistical Methods

Check if the same lab product or an alternative is used in the 5 most similar protocols
The effects of the treatments on the measured variables were evaluated by one-way analysis of variance (P < 0.05) and differences between mean values by Tukey’s multiple-range test (P < 0.05) using the SPSS v. 21.0 for Windows software package (IBM Corp., Armonk, NY). Pearson’s correlation analysis and the structural equation model (SEM) were used to test the effects of DSE on plant growth using the SPSS software package (version 19.0, SPSS, Chicago, IL) and AMOS software (version 21.0, Amos Development Corp., Meadville, PA).
+ Open protocol
+ Expand
4

Hyperuricemia Prevalence and Predictors

Check if the same lab product or an alternative is used in the 5 most similar protocols
Kolmogorov–Smirnov test was used to check the normality of continuous variables. Continuous variables were expressed as the mean±SD, and categorical variables were presented as frequencies and percentages. We compared characteristics of different GGT quintile categories using analysis of variance or the Kruskal-Wallis rank-sum test, and the χ2 test or Fisher’s exact test for categorical variables. The prevalence of hyperuricaemia differing by age or gender or ethnic groups was tested by χ2 test. Logistic regression models were used to calculated the ORs. The effect estimates were expressed as ORs and 95% CIs. Model 1 was unadjusted. Model 2 was adjusted for age, sex, BMI, ethnicity, education, smoking status, drinking habit, hypertension, hyperglycaemia, TG, LDL-c, HDL-c and serum creatinine. Model 3 was adjusted for age, sex, BMI, ethnicity, education, smoking status, drinking habit, hypertension, hyperglycaemia, TG, LDL-c, HDL-c, creatinine, daytime napping duration, therapy for hypertension or other CVD and ALT, AST. Sensitivity analysis was conducted by defining hyperuricaemia as SUA above 5.6 mg/dL. The statistical analyses were performed using SPSS V.21.0 for Windows (IBM Corporation), and the significance level was set as p<0.05.
+ Open protocol
+ Expand
5

Analyzing Demographic Characteristics and Pre-Post Changes

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analyses were performed utilizing IBM® SPSS v. 21.0 for Windows (Armonk, New York), with an a priori level of statistical significance α = 0.05. Frequencies were calculated for demographic items. Pre-post changes were assessed using paired t-tests since the data were normally distributed.
+ Open protocol
+ Expand
6

Statistical Analysis of Human and Murine Data

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analysis of human data was performed using the SPSS v.21.0 for Windows (IBM Corporation) program. A Kolmogorov-Smirnov test was performed to test whether the studied cohort was representative of the entire PLIC population in terms of age, gender distribution, and clinical parameters. A Shapiro-Wilk test was performed to verify the normal distribution of linear variables; median and inter-quartile range (IQR) was reported and Mann-Whitney U non-parametric test was performed. Outliers detection (above and below 1.5 × IQR) was performed by use of Grubb’s test. Spearman correlation coefficients (ρ) were reported for univariate correlations between linear variables. ANOVA test was performed to compare clinical parameters and leukocyte subfraction among subjects divided by BMI and then adjusted for age, gender, and therapies by using analysis of covariances (ANCOVA). For all analysis, statistically relevant differences were considered for p values < 0.05.
For murine studies, data are expressed as mean ± SEM. Two-tailed Student’s t test was used to compare two groups with parametric data distribution. For multiple comparison analysis, 1-, 2-, or 3-way ANOVA was used, unless otherwise specified (Figure 1E). In all cases, a p value of less than 5% was considered to be significant.
+ Open protocol
+ Expand
7

Dioxin Exposure and Liver Fibrosis Associations

Check if the same lab product or an alternative is used in the 5 most similar protocols
SPSS v. 21.0 for Windows (IBM Corp., Armonk, NY, USA) was used for the data analysis. The concentrations of TCDD and TEQ-PCDD/Fs and the laboratory indices were logarithmically transformed (base 10) to improve normality. The relationships between the TCDD and TEQ-PCDD/Fs levels and the laboratory indices were analyzed using a regression linear model after adjusting for covariates, including gender, age and smoking status. At this time, variables were selected as covariates if they correlated with at least one biochemical or hematological marker, based on a Pearson’s correlation analysis, or if the groups differed significantly in at least one biochemical or hematological marker (Student’s t-test, p < 0.05). Since the number of the patients who showed the F0 METAVIR grade was small in the present study, we divided the subjects into positive and negative METAVIR scores as follows: positive METAVIR score for those who showed liver fibrosis at stage F2 and negative METAVIR scores for those who showed liver fibrosis at stages F0 or F1. Then, the associations between dioxin exposure, as indicated by the TCDD and TEQ-PCDD/Fs levels, and positive METAVIR scores were analyzed using a binary logistic regression model after adjusting for the same confounding factors as above. A p-value of < 0.05 was considered statistically significant.
+ Open protocol
+ Expand
8

Statistical Analysis of Clinicopathological Characteristics

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analyses were performed using SPSS v21.0 for Windows (IBM, Armonk, NY, USA). Quantification values are presented as mean ± standard deviation (SD). Continuous data were analyzed by one-way ANOVA, and data that were normally distributed were analyzed using the two-tailed Student's t-test. Categorical variables were analyzed by the chi-square tests. Relationships between clinicopathological characteristics were investigated by the chi-square test or Fisher's exact test. Kaplan–Meier curve analysis, the log-rank test, and Cox regression analysis were applied for survival analysis. A P-value < 0.05 was considered statistically significant.
+ Open protocol
+ Expand
9

Genetic Polymorphism Analysis Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols
The statistical analyses were calculated using Statistical Package for the Social Sciences (SPSS), v.21.0 for Windows (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp). The descriptive analysis was assessed in means and standard deviations (SD) for quantitative variables, while qualitative variables were expressed as frequencies and percentages. The Hardy–Weinberg equilibrium (HWE) was tested for each polymorphism using χ2 tests. T test and one-way ANOVA were used for the comparison of the different functional tests between groups, with an F-value estimate. The improvement of intervention test variables was calculated with confidence intervals of 95% (95% CI) for each polymorphism.
p < 0.05 results were considered statistically significant.
+ Open protocol
+ Expand
10

Validation of Dietary Assessment Methods

Check if the same lab product or an alternative is used in the 5 most similar protocols
Participants who completed both SDPQ and FF Quantity surveys were included in the analysis. From the SDPQ survey, consumption points per week (pts/wk) for the 12 food/nutrient categories were calculated for each participant. Intake of the corresponding 12 food/nutrients per day was calculated using the FF Quantity method. Total energy (kcal/day) and salt (NaCl, g/day) intakes obtained using the FF Quantity method were used to compare with the consumption points for high-calorie foods and high-sodium foods in the SDPQ, respectively. Participants were grouped into quintiles according to the 12 SDPQ consumption points and 12 food/nutrient intakes obtained using the FF Quantity survey. For each food/nutrient category, the relationship between quintiles of SDPQ consumption points and food/nutrient intake by FF Quantity was examined using Spearman's correlation coefficients. Percentages of agreement within the same or adjacent quintile from the two surveys were also examined.
Statistical testing was two-tailed, with 0.05 indicating significance. SPSS v.21.0 for Windows (IBM Corporation, Chicago, IL, USA) was used throughout the analyses.
+ Open protocol
+ Expand

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

Sign up now

Revolutionizing how scientists
search and build protocols!