Spss for windows version 24

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SPSS for Windows, version 24.0 is a software application for data analysis and statistical computing. It provides a range of tools and features for managing, analyzing, and visualizing data. The software is designed to run on the Microsoft Windows operating system.

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Lab products found in correlation

Spss amos version 26, by IBM (2 mentions) Stata se 11, by StataCorp (1 mentions) R version 4, by R Project for Statistical Computing (1 mentions) Spss software version 24, by IBM (1 mentions) Sas version 9, by SAS Institute (1 mentions) Prism 7.0 for windows, by GraphPad (1 mentions) Prism 8, by GraphPad (1 mentions) R software version 3, by R Project for Statistical Computing (1 mentions) Spss statistical software for windows version 24, by IBM (1 mentions) Origin software version 9, by OriginLab (1 mentions) Epidata version 3, by IBM (1 mentions) Statistical package for social science, by IBM (1 mentions) Prism 7, by GraphPad (1 mentions)

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SPSS Statistics for Window, version 24.0 SPSS statistical software for Windows, version 24.0 SPSS for Windows statistical software package version 24 SPSS version 24.0 for Windows Statistical Package for the Social Sciences, Windows version 24 (SPSS; IMB SPSS Statistic for Windows, version 24.0 SPSS for Windows software package, version 24 SPSS-Statistics for Windows statistical package version 24 SPSS program for Windows (Version 24.0; SPSS version 24.0 statistical software for Windows

408 protocols using spss for windows version 24

Combination Procedure for Lymph Node Identification

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The aim was to report on experiences with the combination procedure and not the accuracy of the combination procedure, as completion ALND was not undertaken in all patients. The focus was therefore on identification rate and detection of axillary residual disease for the MLN, SLNB and the combination procedure. In terms of identification rate, the combination procedure was considered successful if at least one lymph node (MLN and/or SLN) was identified. Regarding the detection of axillary residual disease, it was determined whether the MLN and the SLN were one and the same node based on data from the surgery and/or pathology report.
Either the χ² test or Fisher's exact test was used to compare unpaired data. The McNemar exact test was used for analysis of paired assessments of the proportion of patients with cN+ disease in whom residual axillary disease was detected by the MLN, SLN(s) or by the combination of MLN with SLN(s). Statistical analysis was performed using SPSS® for Windows® version 24 (IBM, Armonk, New York, USA).

Simons J.M., van Pelt M.L., Marinelli A.W., Straver M.E., Zeillemaker A.M., Pereira Arias‐Bouda L.M., van Nijnatten T.J., Koppert L.B., Hunt K.K., Smidt M.L., Luiten E.J, & van der Pol C.C. (2019). Excision of both pretreatment marked positive nodes and sentinel nodes improves axillary staging after neoadjuvant systemic therapy in breast cancer. The British Journal of Surgery, 106(12), 1632-1639.

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Mapping School Food Advertising Landscape

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Data was imported into IBM SPSS for Windows version 24 (SPSS Inc., Chicago, IL.). The schools were divided into tertiles of school fee, as an indicator of the school’s socio-economic status. Additionally, the schools were classified into government-funded schools and private schools to serve as another proxy of socio-economic status. The rationale behind this was that private schools have fee structures that some families from low-income groups may not fully afford, while government-funded schools have lower school fees [47 ].
The density of ads within a radius of 250 m (school zone) was calculated (ads per 100 m²). For each site, descriptive analyses were conducted to determine the frequency, median, and density of food ads by product type and content, setting and size, school areas (divisions), school types (primary and secondary), school fees (low, medium, and high) and school categories (government-funded and private). Non-parametric tests were used since the data was not normally distributed [48 ]. Mann-Whitney U test was used to compare two independent groups, and Kruskal-Wallis test to compare three independent groups. The statistical significance level was set at α < 0.05 for all analyses.
Henceforth, the term “food” in this paper, is used to refer to foods and beverages.

Dia O.E., Løvhaug A.L., Rukundo P.M, & Torheim L.E. (2021). Mapping of outdoor food and beverage advertising around primary and secondary schools in Kampala city, Uganda. BMC Public Health, 21, 707.

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Validating Nutrition Risk Screening Tools

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A receiver operating characteristic (ROC) curve analysis was used to create adjusted nutrition‐risk cutoffs and to assess both tools ability to identify nutrition risk with original and adjusted cutoffs. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were derived from 2 × 2 crosstab tables and used to compare the screening tools’ original and alternate cutoff points to the SGNA for concurrent validity. κ Analysis was also used to determine the overall agreement of each tool to the reference standard of the SGNA. Prospective validity was assessed by determining the difference in LOS between the nutrition‐risk categories for each screening tool using the Mann‐Whitney U test and the independent‐sample Kruskal‐Wallis test. Demographics were compared with the SGNA and screening tools using χ² analysis and the Mann‐Whitney U test. All statistics were performed by SPSS for Windows version 24 (IBM Corp, 2016, Armonk, NY, USA: IBM Corp). A value of P < 0.05 was considered statistically significant.

Carter L.E., Shoyele G., Southon S., Farmer A., Persad R., Mazurak V.C, & BrunetWood M.K. (2019). Screening for Pediatric Malnutrition at Hospital Admission: Which Screening Tool Is Best?. Nutrition in Clinical Practice, 35(5), 951-958.

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Maternal Iodine Status and Dietary Intake

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Data were analysed using IBM SPSS for Windows, version 24 (IBM Corp., Armonk, N.Y., USA). Normally distributed data were presented as means (standard deviation), but other data were reported as medians (25th–75th percentile) or numbers (%). UIC and BMIC are presented as medians (25th–75th percentile). Spearman's rho was used to assess possible correlation between UIC, BMIC, and maternal intake of dietary sources of iodine (dairy and fish). Student's t test was used to compare means and Mann–Whitney U test was used to test for differences between groups when data was non‐parametric. Pearson Chi‐square test and Fisher's exact test were used on categorical data.

Petersen E., Thorisdottir B., Thorsdottir I., Gunnlaugsson G., Arohonka P., Erlund I, & Gunnarsdottir I. (2020). Iodine status of breastfed infants and their mothers' breast milk iodine concentration. Maternal & Child Nutrition, 16(3), e12993.

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Statistical Analysis of Demographic and Clinical Data

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Demographic and clinical data were statistically analyzed using the Statistical Package for the Social Sciences (SPSS) for Windows, version 24 (IBM Corp., Armonk, NY, USA). Categorical variables were analyzed using Pearson’s χ2 and Fisher’s exact tests. The Mann-Whitney U test was used to compare continuous variables between the two groups. P < 0.05 was considered statistically significant.

Heo J.Y., Choi Y.W., Kim E.J., Lee S.H., Lim S.K., Hwang S.D., Lee J.Y, & Jeong H.W. (2019). Clinical characteristics of acute Q fever patients in South Korea and time from symptom onset to serologic diagnosis. BMC Infectious Diseases, 19, 903.

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Sedentary Behavior and Cardiometabolic Risk

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All analyses were performed using SPSS for Windows, version 24 (IBM Corp., Armonk, NY). Descriptive statistical data for all participants were summarized and reported as mean ± SD and proportions of participants for continuous and categorical variables, respectively. The normality analyses were applied to all dependent variables. Associations between objectively measured total ST, prolonged sedentary bouts, and sedentary breaks with cardiometabolic biomarkers were assessed using generalized linear models. Specifically, a gamma distribution and log link were used for leptin, CRP, and VCAM-1, whereas a normal distribution and identity link were used for other dependent variables. Model 1 was adjusted for age, BMI, activPAL wear time, family history of diabetes, family history of CVD, and ST (sedentary breaks only). Model 2 was additionally adjusted for MVPA. Associations between total ST and levels of target mRNA expression were also determined using generalized linear models. The significance level was set at 0.05.

Zheng C., Tian X.Y., Sun F.H., Huang W.Y., Sheridan S., Wu Y, & Wong S.H. (2020). Associations of Sedentary Patterns with Cardiometabolic Biomarkers in Physically Active Young Males. Medicine and Science in Sports and Exercise, 53(4), 838-844.

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Combination Therapy Outcomes for Liver Tumors

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In this study, technical success, safety, LTP, RFS, and OS after combination therapy were evaluated. Technical success was defined as completion of the treatment protocol. Safety was evaluated on a session basis using the clinical practice guideline of the Society of Interventional Radiology [26] . LTP was defined as the appearance of enlarged nodules around the ablated tumor on follow-up CT [27 , 28] . RFS was defined as the time from the achievement of the tumor-free condition to the date of LTP or new metastasis development or the last follow-up. OS was defined as the time between the initial combination therapy and the date of death or the last follow-up. LTP was evaluated on a tumor basis and RFS and OS on a patient basis.
Cumulative LTP, RFS, and OS curves were generated using the Kaplan-Meier method. The LTP and OS rates were compared via univariable analysis using the log-rank test among subgroups categorized by patient or tumor background. A P value of <0.05 was considered significant. Statistical analyses were conducted using commercially available software (SPSS for Windows, version 24; IBM, Armonk, NY).

Yoshihara T., Hasegawa T., Sato Y., Yamaura H., Murata S., Chatani S., Tsukii R., Nagasawa K., Tsushima Y, & Inaba Y. (2023). Clinical Outcomes of Radiofrequency Ablation Combined with Transarterial Chemoembolization Using Degradable Starch Microsphere Mixed with Mitomycin C for the Treatment of Non-hepatocellular Carcinoma Malignant Liver Tumors. Interventional Radiology, 8(1), 7-13.

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Perinatal factors influence on NfL

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Statistical analyses were performed using SPSS for Windows version 24 (IBM) and included descriptive statistics, Spearman's rank-order correlation analyses and multiple linear regressions (MLR) using NfL as dependent variable. NfL variables were log10 transformed for the correlations and MLR. The independent variables included for MLR were based on significant correlations and significant non-parametric univariate analyses such as the Mann-Whitney U (2 levels) and Kruskal-Wallis tests (>2 levels). For cohort 1 these variables were: BW, 5-min Apgar, delivery mode (3 levels), preeclampsia, sepsis, and oxygen duration. For cohort 2 they were: BW, 5-min Apgar, sex, brain damage, sepsis, amniotic infection, and oxygen duration. Due to collinearity between BW and GA, we used only BW in MLR, where it showed stronger correlation with NfL than GA.

Depoorter A., Neumann R.P., Barro C., Fisch U., Weber P., Kuhle J, & Wellmann S. (2018). Neurofilament Light Chain: Blood Biomarker of Neonatal Neuronal Injury. Frontiers in Neurology, 9, 984.

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COVID-19 Physical Activity, Sedentary Behavior, and Sleep

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Three international guidelines for PA, SB and sleep for adults for health were applied for data analysis: (1) achievement of at least 150 min of moderate-intensity aerobic PA or at least 75 min of vigorous-intensity aerobic PA throughout the week [22 ]; (2) engagement in <9 h of SB per day for adults [23 (link)]; (3) score of sleep quality <5 with sleep duration between 7 and 9 h [24 ]. Descriptive information (all and stratified by sex), including participant characteristics, COVID-19 related issues, and participants’ daily behaviors, were summarized and reported as means ± standard deviation (SD) or median (interquartile range) for continuous variables and as proportions of participants for categorical variables. The normality analysis was applied to all variables. Because VPA, MPA, MVPA, walking, and MET min/week are not a normal distribution, the Wilcoxon Signed-Rank Test was used to assess the differences between males and females for the above variables. For other variables, independent samples t-tests and Chi-Square tests were used for continuous variables and categorical variables, respectively. The change in participants’ daily behaviors (e.g., PA, SB, and sleep) was determined using paired sample t-tests and shown as means ± SD. All statistical tests were performed using SPSS for Windows, version 24 (IBM Corp., Armonk, NY, USA).

Zheng C., Huang W.Y., Sheridan S., Sit C.H., Chen X.K, & Wong S.H. (2020). COVID-19 Pandemic Brings a Sedentary Lifestyle in Young Adults: A Cross-Sectional and Longitudinal Study. International Journal of Environmental Research and Public Health, 17(17), 6035.

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Psychosocial Factors of Khat Chewing in Dental and Medical Students

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The Statistical Package for Social Sciences Software (SPSS) for windows version 24 (IBM Corp, Armonk, New York, USA) was used for data analysis. Descriptive analysis was undertaken and categorical data (e.g. gender) were reported as frequency and percentage (%) and continuous variables as mean/Standard deviation (SD). Chi-square bivariate analyses and two sample t-test were run to identify any statistically significant association between explanatory categorical and continuous variables with the dependent variable khat chewing ‘Yes’ or ‘No’.
The multivariable logistic regression was run to underscore the potential significant (p < 0.05) psychosocial factors (e.g. depression) of khat chewing among male dental and medical students accounting to age and other factors. The variable selection from the Chi-square and t-test into the multivariable regression was based on the statistical significance (p < 0.2). This was based on lax criteria proposed by Altman³⁷ that suggested variables may have contributed to the logistic regression in unforeseen ways that related to complex interrelationships among the variables. The resilience variable was forced into model to be explored further though was in bivariate analysis with a statistical significance p > 0.2.

, & Kassim S. (2020). The impact of protective psychosocial factors on khat chewing among male medical and dental future health-care providers in Yemen. Journal of Dental Sciences, 15(4), 451-456.

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