SPSS version 18.0 for Windows (IBM, Chicago, IL, USA) was used to evaluate the data. The Kolmogorov-Smirnov test was used to test the distributions of the parameters. Data with a non-normal distribution were expressed as the median (Q1–Q3). Normally distributed data were expressed as the mean ± standard deviation (SD). The chi-squared (χ2) test was used to compare categorical variables. If the requirements for the chi-squared test were not met, Fisher’s exact test was used. A t-test of two independent samples was used to compare variables that fit a normal distribution. The Mann-Whitney U test was used to compare variables that did not fit a normal distribution. Risk quantification for sexual dysfunction was conducted by using logistic regression models with stepwise variable selection (Wald test). A P-value <0.05 was considered to be statistically significant.
Spss version 18.0 for windows
Manufactured by IBM
Sourced in United States
SPSS version 18.0 for Windows is a statistical software package that provides tools for data analysis, management, and presentation. It offers a wide range of statistical procedures, including descriptive statistics, bivariate analysis, and multivariate analysis. The software is designed to assist users in exploring and understanding data, making informed decisions, and presenting findings effectively.
Automatically generated - may contain errors
Lab products found in correlation
Prism 9, by GraphPad (1 mentions)
Prism 5, by GraphPad (1 mentions)
Statistical software version 19, by MedCalc (1 mentions)
Sas version 9, by SAS Institute (1 mentions)
Origin version 7, by OriginLab (1 mentions)
Sigmaplot version 12, by Grafiti LLC (1 mentions)
Bacterial genomic dna extraction kit, by Takara Bio (1 mentions)
Mastercycle gradient thermocycler, by Eppendorf (1 mentions)
Gelred, by Biotium (1 mentions)
160 protocols using spss version 18.0 for windows
1
Evaluating Sexual Dysfunction Risk Factors
2
Descriptive Statistical Analysis of Research Data
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Categorical variables are expressed as frequencies and percentages and quantitative variables as mean and standard deviation (SD) or median and interquartile range (IQR) (25th–75th percentile). Descriptive statistics are presented. The SPSS (version 18.0 for Windows) (IBM Corp.) was used for data analyses.
3
Correlation of KSR1 and NOTCH1 Expression
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Data from the Gene Expression Omnibus (GEO) of NCBI (http://www.ncbi.nlm.nih.gov/projects/geo; accession no. GSE33630) were subjected to Gene Set Enrichment Analysis (GSEA) (Subramanian et al. 2005) (link). Analysis of the correlation between KSR1 and NOTCH1 was performed using GeneNetwork (a free scientific web resource, http://www.genenetwork.org/). Statistical analysis was carried out using SPSS version 18.0 for Windows (IBM Corporation, Armonk, NY, USA) or GraphPad Prism (GraphPad Software, Inc., San Diego, CA, USA). Comparisons of means were performed by paired t-test or Mann-Whitney U test. Data are expressed as means GS.E.M., *P!0.05, **P!0.01, and ***P!0.001. All reported P values are two sided.
4
Validation of CUCQ-8 Questionnaire for IBD
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The Statistical Package for the Social Sciences (SPSS) version 18.0 for Windows was used for statistical analysis. The CUCQ-8 items were assessed for internal consistency using Cronbach's alpha reliability coefficient [17 (link)]. The convergent validity of the total CUCQ-8 score was tested using Pearson's correlation coefficient with the IBDQ score. Chi-square and t-tests were performed to examine differences in variables between the IBD groups, and multiple linear regression tests were conducted to identify factors affecting the CUCQ-8 score. Exploratory factor analysis was performed to assess the structure of the CUCQ-8 questionnaire. Factors with eigenvalues of >1 were identified by principal component analysis. A value of P < 0.05 was taken to indicate statistical significance.
5
Evaluating Rehabilitation Outcomes using Statistical Analysis
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The normal distribution suitability of the variables was tested using the Kolmogorov–Smirnov test with Lilliefors Significance Correction. Quantitative data were represented as median and interquartile range and categorical data as frequency. The distribution of categorical variables to groups was compared using the Pearson chi-square and likelihood ratio chi-square tests. The two groups were compared using the Mann–Whitney U test. The pre- and postrehabilitation averages were compared using the Wilcoxon test for both groups. The SPSS version 18.0 for Windows was used in the analysis. Post hoc power analysis was performed using G*Power version 3.1.9.2 (Franz Faul Universitat Kiel, Germany).
6
Evaluating Dietary Intervention Outcomes
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Numerical variables were presented as mean ± standard deviation (SD), and the categorical variables were displayed as frequency and proportion. Changes in numerical variables between pre- and post-intervention were analyzed with a paired t-test. A Z-test was used to compare the changes in selected indicators between the intervention group and reference group. A two-sample t-test was used to compare the changes between subgroups. Numerical variables were checked for normality before statistical tests, in which logarithmic transformation was performed with right skewness. The multivariable-adjusted mean was calculated with a general linear model. All analyses were performed using SPSS Version 18.0 for Windows (SPSS Inc, Chicago, IL, USA). A two-sided P value less than .05 was considered statistically significant.
7
Metabolic Pathways and FGF1 Regulation
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All data are shown as the means ± standard errors of the means (s.e.m. s). SPSS version 18.0 for Windows (SPSS Inc) was used for statistical analysis of the data. Comparisons of continuous variables between two groups were carried out using independent sample t-tests. Paired samples t-tests were used to compare clinical data before and after weight loss. Categorical variables were analyzed using the chi-square test. The variables were tested for normality with the Shapiro–Wilk test. Non-normally distributed parameters were logarithmically transformed before correlation analysis in the obese group. Pearson correlations tests were performed for bivariate correlation analyses. Multivariate stepwise linear regression analysis was conducted to explore the association of FGF1 with other metabolic variables. The multivariate regression model included variables significantly associated with FGF1 at the bivariate correlation analyses. Two-tailed P values of less than 0.05 were considered significant.
8
Evaluating APE1/Ref-1 for Bladder Cancer
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Data were expressed as the mean ± standard error of the mean (SEM). A repeated-measures ANOVA was used to compare more than three variables, while Student's t-test was used to evaluate differences between two variables. Receiver operating characteristic (ROC) curves were generated by plotting the sensitivity value against the false-positive rate (1 − specificity). We assessed the predictive value of APE1/Ref-1 for BCa by calculating the area under the curve (AUC), and we estimated the optimal cut-off value (Youden index), based on maximum sensitivity and specificity [20 (link)]. SPSS version 18.0 for Windows was used to analyze the data. Differences were considered statistically significant if the null hypothesis could be rejected with >95% confidence (p < 0.05).
9
Comparing microRNA Expression in Pleura
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Two-tailed independent samples t-test was used to compare the differences in microRNA expression between normal pleura and tumor samples. Statistical calculations were done using SPSS version 18.0 for windows (SPSS, Inc., Chicago, IL, USA). A value of P < 0.05 was considered statistically significant.
10
Statistical Analysis of Experimental Data
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Data were analyzed by one-way ANOVA using the SPSS version 18.0 for Windows (Chicago, IL, United States). The figures and charts were drawn using ORIGIN 2018 (Massachusetts, United States).
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