Statistical analysis was performed using SPSS for Windows (version 26.0; SPSS, Chicago, Illinois, United States) and reported as means ± standard deviation (SD) or medians. The Gaussian distribution of the parameters was tested using the Shapiro–Wilk test. To assess statistical differences, we used the Student t-test and Mann–Whitney U-test for continuous variables, and the Fisher exact test and chi-square test for categorical variables. A Bonferroni corrected post hoc test was conducted to adjust the observed significant level for multiple comparisons. The association between variables was examined by applying the Spearman rank correlation test and was expressed as the Spearman correlation coefficient. We used multinomial linear regression analysis to investigate the risk factors for the development of anxiety and depression. The statistical significance was defined as a two-tailed p-value < 0.05.
Spss for windows version
Manufactured by IBM
Sourced in United States, Japan
SPSS for Windows is a statistical software package that provides a comprehensive set of tools for data analysis, data management, and data visualization. It is designed to handle a wide range of data types and offers a user-friendly interface for performing statistical analysis. The core function of SPSS for Windows is to enable users to analyze and interpret data, identify patterns and trends, and make informed decisions.
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Lab products found in correlation
Sas 9, by SAS Institute (2 mentions)
Spss statistics for window, by IBM (2 mentions)
Prism 5, by GraphPad (1 mentions)
Microcal origin software, by Malvern Panalytical (1 mentions)
Ez test, by Shimadzu (1 mentions)
Spss statistical software version 22 for windows, by IBM (1 mentions)
Excel 2016, by IBM (1 mentions)
Prism 6, by GraphPad (1 mentions)
Spss statistic, by IBM (1 mentions)
Spss version, by IBM (1 mentions)
251 protocols using spss for windows version
1
Statistical Analysis of Anxiety and Depression
2
Cardiovascular Disease Risk Factors Analysis
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Kolmogorov–Smirnov test was used to analyze continuous variables for normal distribution. Normally distributed variables were expressed as means ± standard deviations, and Student’s t-test (two-tailed) was used to compare the patients. Non-normally distributed variables were expressed as medians and interquartile ranges (IQRs), and patients were compared using Mann–Whitney U-test. Categorical variables were expressed as patient numbers and percentages and were analyzed using chi-square test. For further analysis, patients were sub-grouped into four groups as follows: no MetS + no AS, MetS + no AS, no MetS + AS, and MetS + AS. Comparisons among the groups were performed using Kruskal–Wallis analysis or one-way analysis of variance for parameters that presented without or with normal distribution, respectively. Multivariate logistic regression analysis was used to analyze variables associated with the presence of high AS in CAD patients. Kaplan–Meier survival curves with a log rank test were used for comparing the cumulative proportion of CAD patients free from first hospitalization or mortality among these four groups. Multivariate Cox regression models were used to analyze variables associated with the risk of first hospitalization. Data were analyzed using SPSS for Windows (version 19.0; SPSS Inc., Chicago, IL, USA). P-values < 0.05 were considered significant.
3
Diagnostic Potential of lncRNAs in CAD
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Appropriate sample size and power calculations were carried out using Quanto
software package version 1.2.4 (http://biostats.usc.edu/software). Chi-square,
Fisher’s exact, Student’s t, and Mann-Whitney U tests were used
when appropriate. Logistic regression was performed to adjust for smoking
status, lipid profile, platelet count, and white blood cell parameters (as
MIAT was reported to be positively associated with the
percentage of lymphocytes and negatively associated with neutrophils and
platelets) (Sun et al.,
2018 ). Receiver operating characteristic (ROC) curves were generated
to obtain the best cutoff values of both lncRNAs for discriminating CAD patients
from controls. A two-tailed p<0.05 was considered
statistically significant. The fold change of lncRNAs expression was calculated
using the Livak and Schmittgen (2001) (link)
method based on the quantitative cycle (Cq) values with the following equation:
relative quantity = 2-DDCq, where ΔΔCq = (Cq LncRNA – Cq GAPDH) CAD - (Cq LncRNA – Cq GAPDH) Controls. Data were managed using the Statistical
Package for the Social Sciences (SPSS) for Windows (version 20.0).
software package version 1.2.4 (http://biostats.usc.edu/software). Chi-square,
Fisher’s exact, Student’s t, and Mann-Whitney U tests were used
when appropriate. Logistic regression was performed to adjust for smoking
status, lipid profile, platelet count, and white blood cell parameters (as
MIAT was reported to be positively associated with the
percentage of lymphocytes and negatively associated with neutrophils and
platelets) (
2018
to obtain the best cutoff values of both lncRNAs for discriminating CAD patients
from controls. A two-tailed p<0.05 was considered
statistically significant. The fold change of lncRNAs expression was calculated
using the Livak and Schmittgen (2001) (link)
method based on the quantitative cycle (Cq) values with the following equation:
relative quantity = 2-DDCq, where ΔΔCq = (Cq LncRNA – Cq GAPDH) CAD - (Cq LncRNA – Cq GAPDH) Controls. Data were managed using the Statistical
Package for the Social Sciences (SPSS) for Windows (version 20.0).
4
Comparative Gene Expression Analysis
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The data were analyzed with SPSS for Windows (version 20.0, SPSS Inc., Chicago, IL, USA). Relative gene expression levels in ML and 10 h IIL stages are shown as the mean ± standard deviation (SD), and the differences between two groups were assessed using Student’s t test. P < 0.05 was regarded as statistically significant.
5
Robust Statistical Analysis of Experimental Data
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Statistical analysis was performed by using SPSS for Windows (version 13.0; SPSS Inc., Chicago, USA). The normality of data distribution was analyzed by Shapiro–Wilk test and Kolmogorov–Smirnov test with Lilliefor’s correction. One-way analysis of variance and Tukey HSD multiple comparisons post hoc test were used for data analysis. Values were expressed as mean±SEM. A p value <0.05 was accepted as statistically significant.
6
Statistical Analysis of Dry Eye Biomarkers
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Statistical analyses were performed using SPSS for Windows (version 21.0; SPSS Inc., Chicago, IL). Testing for the normality of distribution was performed by using the Kolmogorov–Smirnov test. For in vivo and in vitro data that was normally distributed, the independent t-test was performed to compare the differences between the two groups and one-way ANOVA test with Bonferroni correction was used to make comparisons among three or more groups. A linear mixed model with Bonferroni correction for repeated measures covariance pattern with unstructured covariance within samples was used for estimating significant differences between groups over time. As the majority of the clinical data including tear cytokine levels did not show a normal distribution, non-parametric tests were adopted. Analyses included the frequency for categorical data and the median with IQR for continuous data. The Mann-Whitney U test was performed to compare the differences between the two groups and the Kruskal-Wallis test with Bonferroni-Dunn’s procedure was used to compare the groups for continuous variables. Correlation between tear cytokine levels and dry eye symptoms was assessed using Spearman’s correlation test. A p value of < 0.05 was considered significant different. Results are presented as mean ± standard errors of means (s.e.m).
7
Facial Measurements in Cleft Lip Patients
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To minimize confounders as much as possible, the following steps were taken;
Data analysis was performed using Statistical Package for Social Sciences (SPSS) for Windows (version 23.0, SPSS Inc., Chicago, IL, USA). Means of each variable measured preoperatively and postoperatively were generated for each group, and a comparison of these, cleft side with noncleft side and the control, was done, using Student's t-test. In addition, the mean values of the measured variables in each group were compared within the three groups using one-way ANOVA. Statistical significance was P ≤ 0.05.
The camera has a stand that was permanently located at the distance, 45 cm, as stated above
Several photographs were taken of the participants out of which the best was chosen by the lead author and imported into the Adobe software, stated above
All participants with microform or incomplete CL phenotype were excluded from the study.
Data analysis was performed using Statistical Package for Social Sciences (SPSS) for Windows (version 23.0, SPSS Inc., Chicago, IL, USA). Means of each variable measured preoperatively and postoperatively were generated for each group, and a comparison of these, cleft side with noncleft side and the control, was done, using Student's t-test. In addition, the mean values of the measured variables in each group were compared within the three groups using one-way ANOVA. Statistical significance was P ≤ 0.05.
8
Statistical Analysis of Categorical and Continuous Data
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All statistical analyses were performed using SPSS software (IBM SPSS for Windows, version 16.0, SPSS, Inc., Chicago, IL) for descriptive analysis. Chi-squared tests were employed for categorical variables while parametric Student's t-tests or Mann–Whitney U tests were used for continuous counterparts, as appropriate. For all comparisons, a p-value < 0.05 was considered to represent a significant difference. All statistical tests were 2-sided.
9
Association of Pediatric BMI and BP
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All data analyses were performed by SPSS for Windows (version 19.0, SPSS Inc., Chicago, IL, USA). Continuous variables were described as mean and standard deviation (s.d.) for, numerical data were described as rates. Chi square tests were used to compare the prevalence of HBP subtypes between boys and girls. Linear regression was used to analyze the associations between BMI and SBP/DBP, and β with standard error (SE) were estimated. Logistic regression was used to analyze the associations between BMI categories and HBP or different HBP subtypes, and odds ratios (ORs) with 95% CIs (confidence intervals) were also estimated.
10
Astigmatism and Visual Acuity Analysis
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All data were collected in an Excel database (Microsoft Office 2003) and analyzed using SPSS for Windows (version 16.0; SPSS Inc., Chicago, IL, USA). Decimal uncorrected distance visual acuity (UDVA) and CDVA were converted into logMAR for the mathematical and statistical calculations. Paired samples t-tests were used to analyze visual acuity and astigmatism parameters within each group. Power vector analysis of astigmatic change (Alpin's method[12 (link)]) was used to quantify the contribution of lens to the residual refractive error. P value less than 0.05 was considered statistically significant.
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