Spss 22

Manufactured by StataCorp

Sourced in United States

SPSS 22.0 is a statistical software package designed for data analysis, visualization, and reporting. It provides a comprehensive set of tools for managing and analyzing data, including capabilities for data manipulation, descriptive and inferential statistics, and advanced modeling techniques.

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Lab products found in correlation

Stata 13, by StataCorp (6 mentions) Stata 14, by StataCorp (4 mentions) Stata 15, by StataCorp (3 mentions) Stata 16, by StataCorp (2 mentions) Stata 12, by StataCorp (2 mentions) Stata mp 13, by StataCorp (1 mentions) Stata 8, by StataCorp (1 mentions) Stata, by StataCorp (1 mentions) Prism 7.0a, by GraphPad (1 mentions) Stata se 14, by StataCorp (1 mentions) R software, by GraphPad (1 mentions)

26 protocols using spss 22

Retinal Layer Thickness Analysis

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Statistical analyses were performed using SPSS 22.0 (StataCorp LP, College Station, TX, USA). Normality in measurements was tested graphically and using Kolmogorov–Smirnov test. Nonparametric tests were used for non-normal or skewed data and parametric tests for normally distributed data. Respectively, median (interquartile range) and mean (± standard deviation) are shown. Differences between groups were analyzed using the chi-squared test for categorical variables, the 2-tailed t test for parametric continuous variables, and the Mann–Whitney test for nonparametric continuous variables.
Differences for segmented retinal layer thickness or volume data between groups were analyzed using generalized estimating equations (GEE) as recommended [12 (link)]; these were adjusted for intrasubject intereye correlations and repeated measurements, and employed an exchangeable correlation structure.
A p value of 0.05 was accepted as statistically significant.

Miscioscia A., Puthenparampil M., Miante S., Pengo M., Rinaldi F., Perini P, & Gallo P. (2022). Retinal inner nuclear layer thinning is decreased and associates with the clinical outcome in ocrelizumab-treated primary progressive multiple sclerosis. Journal of Neurology, 269(10), 5436-5442.

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Visual Field Eccentricity Analysis

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SPSS 22.0 and STATA16.0 software were used for the statistical analyses. Normality was tested with the Shapiro–Wilk normality test. Continuous variables were expressed as means with standard deviation (SD), or medians with the interquartile range (IQR), as appropriate. Single-factor repeated-measures analysis of variance (ANOVA) was used to compare the measured data at each time point. Paired t-test and Wilcoxon signed-rank test were used to observe the difference of RPR at each eccentricity. Non-parametric data were analyzed by Friedman test followed by Dunn's multiple comparisons test. Correlations were analyzed by using Spearman's (non-normality) or Pearson (normality) correlation. All values were rounded to three decimal digits. P < 0.05 represented a statistically significant difference.

Chen X., Xiong Y., Qi X, & Liu L. (2023). Nasal-temporal asymmetric changes in retinal peripheral refractive error in myopic adolescents induced by overnight orthokeratology lenses. Frontiers in Neurology, 13, 1006112.

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Prognostic Significance of PNI in Cancer

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The relationship between PNI and clinicopathological characteristics was tested using the chi-square test. The Kaplan–Meier method was used to calculate disease-free survival (DFS) and overall survival (OS), and the difference in survival rates between the groups was tested using the log-rank test. All statistically significant prognostic factors identified in the univariate analysis were included in the multivariate Cox regression analysis, and stepwise regression and collinearity tests were used in our analysis. The chi-square test was used to assess the fit of the Cox proportional hazards model. A two-tailed test with a P value of < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS 22.0 and Stata 15.0.

Ma Y., Chen J., Yao X., Li Z., Li W., Wang H, & Zhu J. (2022). Patterns and prognostic predictive value of perineural invasion in esophageal squamous cell carcinoma. BMC Cancer, 22, 1287.

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Genetic Risk Factors for COPD

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All data are expressed as X ± S. Allele frequencies and genotypes of the four tag‐SNP loci in the case group and the control group were statistically analysed for Hardy–Weinberg equilibrium, odds ratio (OR) value and 95% confidence interval (CI) using SPSS 22.0 and STATA MP13 software. A two‐tailed P < 0.05 was considered statistically significant. In addition, Pearson's χ² and Fisher's exact tests were used to calculate the allele frequencies of cases and controls, and MAF in controls was defined as baseline. After adjusting for age, sex and smoking status, ORs and 95% CIs were calculated using unconditional logistic regression analysis. The relationship between the selected SNPs and the risk of COPD was calculated using genotypic model analysis (codominant, dominant, recessive, overdominant and log additive) by the website software programme SNPStats.²³ Student's t test was used to compare the differences in quantitative data if the data followed a normal distribution; otherwise, the χ² test was used. Comparison of dual fluorescence reporter gene activity was tested by Student's t test.

Wang Y., Zhou Q., Chen L., Dong L., Xiong M., Xie X., Zhao L., Xu J., Zheng Z., Wang J, & Lu W. (2022). Identification of genetic variants of the IL‐22 gene in association with an altered risk of COPD susceptibility. The Clinical Respiratory Journal, 16(8), 537-545.

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Oral Contraceptives and Psychiatric Prognosis

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Descriptive data were presented as means and standard deviations for continuous variables or frequencies and percentages for categorical variables. Categorical variables were compared using the Chi-square test. Continuous variables were compared by t test for independent groups, or by ANOVA. A logistic regression model for the follow-up illness course was estimated in order to explore the possible interaction between OCs and PRS.
All p-values were two-tailed with an accepted significance level of 0.05, with no multiple testing correction applied due to the explorative nature of the study. Analyses were performed using SPSS 22.0 or Stata 15.

Tosato S., Bonetto C., Vassos E., Lasalvia A., De Santi K., Gelmetti M., Cristofalo D., Richards A, & Ruggeri M. (2021). Obstetric Complications and Polygenic Risk Score: Which Role in Predicting a Severe Short-Term Outcome in Psychosis?. Genes, 12(12), 1895.

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Validating Nutrition Literacy Instrument

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We examined the Cronbach's alpha coefficient with SPSS 22.0 to evaluate internal consistency. CFA and EFA were conducted with maximum likelihood estimation using AMOS 23.0 to validate the structural validity on Likert-type items, whilst the tetrachoric correlation of STATA was used for binary items. Model fit was determined by comparative fit index and root mean square error of approximation, the overall goodness-of-fit of the model was used as the evaluation criteria of validity. Frequencies and means were utilized to describe the participant demographic characteristics and nutrition literacy. The percentage correct and standard deviation for each item were also calculated. SPSS 22.0 and STATA 16.0 were used to perform all data analyses.

Wang T., Zeng M., Xie C., Zhu Y., Shi Z., Sharma M, & Zhao Y. (2022). Development of Nutrition Literacy Scale for Middle School Students in Chongqing, China: A Cross-Sectional Study. Frontiers in Nutrition, 9, 888137.

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Predictors of Patient Engagement in Intensive Therapy Program

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We used SPSS 22.0 and Stata 15 for all analyses. We performed descriptive statistics to evaluate ITP demand, practicality, and acceptability. We were then interested in exploring factors predictive of patient flow through the program (i.e., predictors of acceptance to the program and predictors of attendance after acceptance to the program). We first examined bivariate associations between demographic variables of interest and patient status (not accepted, accepted but did not attend, accepted and attended). All variables that were significantly bivariate predictors were included in two separate logistic regression analyses examining ITP acceptance (outcome 1; 0 = not accepted; 1 = accepted) or attendance (outcome 2; 0 = did not attend; 1 = attended). Analyses were re-conducted to examine alternate reference categories for categorical predictors. Goodness of fit for logistic regression models was assessed using McFadden’s pseudo-R² which is based on log likelihood (McFadden, 1974 ). Finally, to examine satisfaction with the program, study authors met to review the qualitative feedback that was provided in response to the open-ended questions in the satisfaction survey. Study authors selected quotes which appeared to accurately represent experiences shared by ITP participants.

Held P., Klassen B.J., Boley R.A., Wiltsey Stirman S., Smith D.L., Brennan M.B., Van Horn R., Pollack M.H., Karnik N.S, & Zalta A.K. (2019). Feasibility of a Three-Week Intensive Treatment Program for Service Members and Veterans with PTSD. Psychological trauma : theory, research, practice and policy, 12(4), 422-430.

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Factors Influencing Unmet Healthcare Needs

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Binary multivariate logistic regression was used to estimate the association between unmet healthcare needs and universal health insurance coverage, along with other factors. Our analysis included a regression of unmet health services and a regression of the main causes of unmet health services. For the first regression, the dependent variable, unmet healthcare need, was defined by “non-use of health services”, which was dichotomized into 0 = “no reported non-use of health services” and 1 = “reported non-use of health services”. For the second regression, the dependent variable “main reason for unmet healthcare need” was defined as follows: “main reason” = 1 and “other reasons” = 0. Comprehensive analyses were conducted, and all data collation and statistical analyses were performed using SPSS 22.0 and Stata 24.0.

Zhou S., Huang T., Li A, & Wang Z. (2021). Does universal health insurance coverage reduce unmet healthcare needs in China? Evidence from the National Health Service Survey. International Journal for Equity in Health, 20, 43.

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Trends in Knee Arthroplasty Incidence

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We described patient characteristics, categorized into sex and age groups, using descriptive statistics presented as mean and standard deviation (SD). Incidences are presented as the number of operations performed per 104 of population. Age was categorized into 3 groups: < 65 years, 65–74 years and ≥75 years. We analyzed trends in the general incidence of TKAs and UKAs in Denmark, Norway, and Sweden from 1997 to 2012 and in Finland from 2000 to 2012. The incidence was calculated as incidence density, which is defined as the number of new cases in a population during a given time period relative to the sum of the person-time values of the at-risk population. Negative binomial regression was used to estimate the incidence rate ratios (IRRs) and the 95% confidence intervals (CIs) of UKAs and TKAs for each country because of evidence of overdispersion of data. IRR reports the estimated average annual increase of incidence. Analyses were stratified by sex and age group. The statistical analyses were conducted with SPSS 22.0 and Stata 8.2 software.

NiemeläInen M.J., MäKelä K.T., Robertsson O., W-Dahl A., Furnes O., Fenstad A.M., Pedersen A.B., Schrøder H.M., Huhtala H, & Eskelinen A. (2017). Different incidences of knee arthroplasty in the Nordic countries: A population-based study from the Nordic Arthroplasty Register Association. Acta Orthopaedica, 88(2), 173-178.

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Computational Model Validation Methods

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Statistical methods less prominently apply in general to computational models. The results are consistent due to the methodology (every time one tries to solve a case with the same parameters, he will get the same results), unlike experiments in general and clinical studies in particular. The variables considered in the study were all continuous scalar in nature; in order to test and confirm correlations of involved variables, Pearson Correlation Analysis was carried out, showing very significant correlation, either positive or negative depending on which variable was considered. Details are provided in the Supplementary Material. Data entry was carried out in Microsoft Excel, while statistical tests were performed in IBM SPSS 22.0 and Stata (Supplementary Material Figs S1–S3).

Marom G., Weltert L.P., Raanani E., Chirirchilli I., Giebels C., Irace F.G., De Paulis R, & Schäfers H.J. (2024). Systematic adjustment of root dimensions to cusp size in aortic valve repair: a computer simulation. Interdisciplinary Cardiovascular and Thoracic Surgery, 38(2), ivae024.

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