Spss statistics for windows version

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SPSS Statistics for Windows is a statistical software package developed by IBM for data analysis, visualization, and reporting. It provides a wide range of analytical tools and techniques for researchers, analysts, and decision-makers. The software is designed to handle a variety of data types and can be used for tasks such as descriptive statistics, hypothesis testing, regression analysis, and more.

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511 protocols using spss statistics for windows version

Randomized Comparative Analysis of Patient Data

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Data from all patients were analyzed according to the group to which they were randomly assigned on a per protocol basis.
Normal distributions of the variables were assessed via Kolmogorov-Smirnov test, Q-Q plots, and histograms. For continuous variables, mean ± standard deviation and for ordinal and categorical variables median and ranges were reported. Baseline characteristics of the study and control groups were compared using the t-test for normally distributed continuous variables and Mann-Whitney U test for continuous variables without normal distribution and ordinal variables. Categorical variables were compared using the chi-square test. All statistical tests that were used to compare 2 groups were 2-sided with a significance level of 0.05; analyses were exploratory, and did not adjust for multiple testing. Data analysis and management were performed using IBM SPSS^® 21 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp).

Basaran B., Basaran A., Kozanhan B., Kasdogan E., Eryilmaz M.A, & Ozmen S. (2015). Analgesia and Respiratory Function after Laparoscopic Cholecystectomy in Patients receiving Ultrasound-Guided Bilateral Oblique Subcostal Transversus Abdominis Plane Block: A Randomized Double-Blind Study. Medical Science Monitor : International Medical Journal of Experimental and Clinical Research, 21, 1304-1312.

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Diagnostic Biomarker Evaluation for Parathyroid Disorders

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The distribution of continuous variables was determined using Shapiro–Wilk’s test and normality graphs. Continuous and categorical variables were presented as median (min–max) and number (%), respectively.
Kruskal–Wallis and chi-square tests were used to compare continuous and categorical variables between groups, respectively. Dunn–Bonferroni correction was applied in post hoc tests. The discriminative ability of PTH, Ca and SI was determined using receiver operating characteristic (ROC) curve analysis, whereas that for MIBI and texture was determined using McNemar’s test. The area under curve (AUC), cut-off point, sensitivity, specificity and their 95% CIs were reported. Wilson’s score method was used to calculate the CIs for sensitivity and specificity. A P value less than 0.05 was considered statistically significant.
Wilson’s score CIs were obtained using the ‘scoreci’ function of the ‘PropCI’ library in R ver. 3.5.1 and RStudio ver. 1.2.1335. All other statistical analyses were performed using IBM SPSS Statistics 22.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, version 22.0.: IBM Corp.).

Cakir B., Cuhaci Seyrek F.N., Topaloglu O., Ozdemir D., Dirikoc A., Aydin C., Polat S.B., Ogmen B.E., Tam A.A., Baser H., Kilic Yazgan A., Kilic M., Alkan A, & Ersoy R. (2019). Ultrasound elastography score and strain index in different parathyroid lesions. Endocrine Connections, 8(12), 1579-1590.

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Evaluating Central Sensitization in Pain Treatment

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Descriptive analysis was carried out to evaluate the cohort. Dichotomous or categorical variables were expressed as absolute values and percentages; continuous variables were expressed as the mean and median. The Chi-square test was used to estimate the relationship between variables and pain. The relative risk (RR) was calculated. Differences in distributions of dichotomous variables were analyzed using Fisher’s exact test. Differences in distributions of continuous variables were analyzed using the Kruskal–Wallis test. A value of p ≤ 0.05 was considered statistically significant.
Kaplan–Meier curves were used to compare treatment efficacy and treatment time, to determine the number needed to treat, number of patients who must be treated to obtain cure of their disease, and to evaluate the response to neuromodulator treatment between patients with central sensitization and patients without central sensitization [16 (link)]. Subsequent to the univariate study, all variables with p < 0.1 entered a multivariate study by performing a Cox logistic regression to determine if central sensitization was an independent prognostic factor [21 ]. The statistical analysis was performed using IBM SPSS Statistics (IBM Corp. Released 2022. IBM SPSS Statistics for Windows, Version 29.0. Armonk, NY, USA: IBM Corp.).

Rubal C., Pereira A., Sastre L.C., Pérez-Cejuela B.A., Gámiz S.H., Chaves P, & Medina T.P. (2023). Managing Vulvodynia with Central Sensitization: Challenges and Strategies. Journal of Clinical Medicine, 12(11), 3851.

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Comparative Gene Expression Analysis

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One-way ANOVA was performed, and in the cases of statistically significance (p<0.05), Fisher Least Significance Test (LSD) was conducted to determine which groups differed [48 ]. P<0.05 was considered statistically significant, but p-values above 0.01 are defined in text. The results are shown as mean ± standard deviation (SD) of 6–8 rats per group. mRNA levels were normalized to the house keeping gene Rplp0 and set as relative to controls. Pearson’s correlation coefficients were used when comparing two independent variables. The statistics was performed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp. Armonk, USA) and graphs were designed with GraphPad Prism for Windows, Version 6.00 (GraphPad Software, La Jolla, CA, USA, www.graphpad.com).

Lindquist C., Bjørndal B., Rossmann C.R., Svardal A., Hallström S, & Berge R.K. (2018). A fatty acid analogue targeting mitochondria exerts a plasma triacylglycerol lowering effect in rats with impaired carnitine biosynthesis. PLoS ONE, 13(3), e0194978.

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White Matter Changes and Driving Ability

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We compared patients with three different degrees of white matter changes on MRI. A χ² test was performed to examine trends in categorical data. Analysis of variance (ANOVA) was performed for continuous variables to compare groups’ scores for demographics and driving status. To evaluate the relative effect of WMH on driving with or without mediation of cognition and motor function, a structured equation model (SEM) was used. We used generalized estimating equation (GEE) to estimate longitudinal effects of WMH on change in driving status. All statistical analyses were performed using SPSS version 22.0 for demographics, AMOS 21.0 for SEM (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. IBM Corp., Armonk, NY, USA), and GEE package of R version 3.1.2 for GEE [A language and environment for statistical computing (Version 3.1.2). Vienna, Austria: R Foundation for Statistical Computing].

Jang M., Hong C.H., Kim H.C., Choi S.H., Seo S.W., Kim S.Y., Na D.L., Lee Y., Chang K.J., Roh H.W, & Son S.J. (2018). Subcortical Ischemic Change as a Predictor of Driving Cessation in the Elderly. Psychiatry Investigation, 15(12), 1162-1167.

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RT-qPCR Data Analysis Protocol

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A standard method 2^−∆∆Ct (formulae are given below) was used to calculate the relative gene expression of the mRNA genes used with each replicate [92 (link),93 (link)]. The RT-qPCR experiment quantifying and counting results were statistically analyzed using IBM Corp. Released 2019 (IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp.) to determine the relationship between groups (reproductive and non-reproductives) using a t-test [29 (link)]. All values were expressed as mean ± standard deviation (p-values < 0.05) and statistical figures constructed with Microsoft Excel (0365) and OriginPro (2018).

Ratio = \frac{{(E_{t gene of interest})}^{Δ Ct}_{t gene of interest}}{{(E_{housekeeping gene})}^{Δ Ct}_{housekeeping gene}}

whereas

{^{Δ Ct}}_{t gene of interest} {=^{Ct}}_{control} {-^{Ct}}_{treatment}

and

{^{Δ Ct}}_{housekeeping gene} {=^{Ct}}_{control} {-^{Ct}}_{treatment}

Ratio = 2^{- Δ Δ Ct}

whereas

^{Δ Δ Ct} {=^{Δ Ct}}_{housekeeping gene} {-^{Δ Ct}}_{t gene of interest}

H.a.r.o.o.n., Li Y.X., Ye C.X., Su J., Nabi G., Su X.H, & Xing L.X. (2022). De Novo Transcriptome Assembly and Analysis of Longevity Genes Using Subterranean Termite (Reticulitermes chinensis) Castes. International Journal of Molecular Sciences, 23(21), 13660.

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Evaluating Construct Validity of Research Measures

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Subsequent to the EFA, we ran the confirmatory factor analysis (CFA) [22 (link)]. The internal consistency of latent dimensions identified in the EFA was checked by running scale reliabilities using the Cronbach’s alpha coefficient [23 ]. The measurement model identified in CFA was checked for convergent and discriminant validity by calculating the Average Variance Extracted (AVE) as suggested [20 , 24 (link)].
Participants’ ratings of GE markers were positively skewed, which was reduced by log transformation prior to running EFA and CFA [22 (link)]. All statistical analyses were undertaken using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp INC: Armonk, NY) except the CFA for which we used the IBM SPSS AMOS for windows, version 26.0 (IBM Corp INC: Armonk, NY).

Henderson L.R., Shah S.G., Ovseiko P.V., Dam R., Buchan A.M., McShane H, & Kiparoglou V. (2020). Markers of achievement for assessing and monitoring gender equity in a UK National Institute for Health Research Biomedical Research Centre: A two-factor model. PLoS ONE, 15(10), e0239589.

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Genetic Associations with Outcomes

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Hardy–Weinberg equilibrium was assessed for each analyzed SNP using the chi-square test (χ2). Codominant, dominant, and recessive models of inheritance were considered to assess associations with outcome variables whenever appropriate.
Quantitative data were expressed as mean (± SD) for normally distributed variables. Normality was assessed using the Shapiro–Wilks test. Depending on the number of groups compared, the Student’s t-test or ANOVA were used for normally distributed variables. Bivariate association between qualitative dichotomous variables was assessed using χ2 or Fisher’s exact test. The associations between SNPs and qualitative response variables were tested using the χ2 test.
All the tests were two-sided, significance was set to 5% (α = 0.05), and statistical analyses were performed using IBM-SPSS (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp).

Sainz L., Riera P., Moya P., Bernal S., Casademont J., Díaz-Torné C., Millán A.M., Park H.S., Lasa A, & Corominas H. (2022). Clinical Value of IL6R Gene Variants as Predictive Biomarkers for Toxicity to Tocilizumab in Patients with Rheumatoid Arthritis. Journal of Personalized Medicine, 13(1), 61.

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Statistical Analysis of Anesthetic Outcomes

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The Kolmogorov–Smirnov test was applied to determine whether the data distribution was or was not normal. Normally distributed data such as the parturients’ demographic characteristics were presented as means ± SD and were analyzed using a one-way analysis of variance. Non-normally distributed data such as the sensory block level and surgical times were presented as medians and ranges and were analyzed using the Kruskal–Wallis test. Categorical data such as kinds of incidence were presented as numbers (percentages) and were analyzed using chi-square tests. The ED₅₀ for the phenylephrine infusion was calculated using probit regression. A comparison of the differences among groups used the methodology of overlapping confidence intervals, which used a P-value < 0.05 if the 83% CIs were non-overlapping.12 (link) GraphPad Prism version 5.0 (GraphPad Software Inc., San Diego, CA) and IBM SPSS Statistics for Windows version 17.0 (IBM Corp, Armonk, NY) were used to carry out the data analysis. A P-value < 0.05 was considered as statistically significant (two-sided).

Qian J., Liu L., Zheng X, & Xiao F. (2020). Does an Earlier or Late Intravenous Injection of Ondansetron Affect the Dose of Phenylephrine Needed to Prevent Spinal-Anesthesia Induced Hypotension in Cesarean Sections?. Drug Design, Development and Therapy, 14, 2789-2795.

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Predictive Modeling of OS and PFS in Immunotherapy

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Predictive models for OS and PFS were fit using multiple Cox regression. Predictive
factors were chosen based on prior published results or hypothesized associations. Sex,
ECOG PS score (0-1 vs ⩾2), and smoking status (never vs current/former) were modeled as
binary variables. Body mass index (BMI) was modeled both as a binary variable
(⩾ 25 kg/m² vs <25 kg/m²) and as a continuous variable using a
4-knot restricted cubic spline. Splines are flexible parameterizations used to model
smooth but non-linear associations between the predictor and outcome.¹⁰ A hazard ratio (HR) and
confidence interval (CI) can be calculated at any 2 points within the variable’s range;
for convenience, we chose round values corresponding to approximately the first and third
quartiles of the variable’s distribution. Age was similarly modeled using a 3-knot
restricted cubic spline. Immune-related adverse events (irAEs) could occur any time during
the follow-up period and were therefore modeled as a time-varying binary variable. If an
irAE occurs at time t, the patient belongs to the no irAE group before
time t, then moves to the irAE group.¹¹ Wald null hypothesis tests for each
variable are reported. Kaplan-Meier curves were calculated for OS and PFS. Analysis was
performed using R software, version 3.4^{12 ,13} and IBM SPSS Statistics for Windows,
Version 25.0 (IBM Corp, Armonk, NY, USA).

Zhang S., Pease D.F., Kulkarni A.A., Kumar M., Shanley R.M., Xu B., Joshi S.P, & Patel M.R. (2021). Real-World Outcomes and Clinical Predictors of Immune Checkpoint Inhibitor Monotherapy in Advanced Lung Cancer. Clinical Medicine Insights. Oncology, 15, 11795549211004489.

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