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Spss statistical software program version

Manufactured by IBM
Sourced in United States

SPSS is a comprehensive statistical software program developed by IBM. It provides a wide range of statistical analysis tools, including data management, data analysis, and data visualization capabilities. SPSS is designed to help organizations and researchers analyze and interpret complex data sets, making it a valuable tool for a variety of applications.

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Lab products found in correlation

5 protocols using spss statistical software program version

1

Correlating miR-3978 and Legumain

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All analyses were done by using the SPSS statistical software program version 18.0 (IBM Corporation, NY). Quantitative variables are presented as the mean ± standard deviation. Data analysis was performed using one-way ANOVA. Tukey's post-hoc test was used to determine the statistical significance in all pairwise comparisons of interest. Pearson correlation coefficients were computed to assess the association between ordinal miR-3978 and legumain. P<0.05 was considered to represent a statistically significant difference.
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2

Statistical Analysis Techniques in Research

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Statistical analyses were performed using the SPSS statistical software program version 22 (IBM, IL, USA). Two-tailed Student’s t-test was applied to compare the differences between two groups, and one-way analysis of variance (ANOVA) was used to compare differences among multiple groups. Survival analysis was performed using the Kaplan–Meier method, and differences between the survival curves were assessed using log-rank tests. Statistical significance was set at P < 0.05.
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3

Late-Life Disability and Socioeconomic Status

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All data were analyzed using the IBM SPSS statistical software program version 20.0. First, the characteristics of the sample were compared by gender and described using means (standard deviations, SD) for continuous variables and frequencies (percentages) for categorical variables. The chi-square test (χ2) and Student’s were used to compare categorical variables and continuous variables between groups, respectively. Second, Student’s t test and analysis of variance (ANOVA) were used to compare the means of frequency and limitation between men and women with each independent variable. Finally, multiple linear regression models were used to examine gender-specific associations between late-life disability (frequency and limitation subscales) and SES. We adjusted multiple linear regression models for the following variables based on precedents suggesting that they were risk factors for late-life disability while also being related to SES measures: age, number of chronic diseases, self-rated health and depressive symptoms [23 (link),34 (link),35 (link)]. A 5% level of statistical significance was used for all analyses and estimates were reported with their 95% confidence intervals (CI).
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4

Survival Analysis of BMI1 Expression

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The χ2 test and a linear-by-linear association test were used to compare clinicopathological parameters between the high and low BMI1 expression groups. Student’s t-test and/or Pearson correlation analysis were used to examine differences between continuous variables. The Breslow test and multistep Cox regression analysis were performed to analyze the relationships between the BMI1 expression level and the following survival rates: disease-free survival (DFS), defined as survival from the date of diagnosis to recurrence/new distant metastasis; and disease-specific survival (DSS), defined as survival from the date of diagnosis to cancer-related death. Statistical significance was considered when p-values were ≤ 0.05. Statistical analyses were conducted using the R packages and SPSS statistical software program (version 25.0; SPSS Inc., Chicago, IL, USA).
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5

Psychometric Evaluation of Research Instrument

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Statistical analyses were performed using the SPSS statistical software program version 22. Data were assessed for non-normality and missing values. Missing data were found to be minimal, less than 2% for any item. Regarding non-normality, skewness (1.4, SE = 0.23) and kurtosis (1.3, SE = 0.46) were found to be in the acceptable range for tests assuming a normal distribution. Kaiser-Meyer-Olkin measure of sampling adequacy was 0.849, above the recommended threshold of 0.6 (Dziuban and Shirkey, 1974 ), and Bartlett’s Test of Sphericity was significant at p < 0.001. An EFA was conducted, using oblique (Oblimin) rotation as factors were expected to be intercorrelated. The choice of the number of factors for the eventual solution was based on assessing the eigenvalues of factors generated, and whether one or more factors had significantly higher eigenvalues than subsequent factors–this was confirmed visually through inspection of a scree plot. Internal consistency was calculated using Cronbach’s alpha, and each individual item was assessed for its effect on internal consistency when removed from analysis, as well as its correlation with the total score.
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