Spss for windows version 22

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SPSS for Windows version 22.0 is a statistical software package designed to analyze and manage data. It provides a range of tools for data manipulation, statistical analysis, and visualization. The software is compatible with the Windows operating system.

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Lab products found in correlation

Prism 7, by GraphPad (7 mentions) Prism 8, by GraphPad (4 mentions) Prism 5, by GraphPad (3 mentions) R version 3, by R Project for Statistical Computing (3 mentions) Amos version 22, by IBM (3 mentions) Originpro 9, by OriginLab (2 mentions) Spss statistics for windows version 22, by IBM (2 mentions) Sas version 9, by SAS Institute (2 mentions) Grubbs test, by GraphPad (1 mentions) Prism 6, by GraphPad (1 mentions) Ncss 10, by NCSS Statistical Software (1 mentions) Minitab version 16, by IBM (1 mentions) Stata se 10, by StataCorp (1 mentions) Stata version 13, by StataCorp (1 mentions) Stata version 15 for windows, by StataCorp (1 mentions) Pass 11, by NCSS Statistical Software (1 mentions) Statistical software version 16, by MedCalc (1 mentions) Microsoft excel 2013, by IBM (1 mentions) Stata version 14, by StataCorp (1 mentions) R statistics software, by R Project for Statistical Computing (1 mentions)

1 028 protocols using spss for windows version 22

Evaluating Multivariate Normality and Model Fit

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The collected data were analyzed using SPSS for Windows version 22.0 and AMOS version 22.0 (IBM Corp., Armonk, NY, USA). The variables related to participants’ general characteristics were analyzed in terms of frequency, percentage, mean, and standard deviation as descriptive statistics.
The multivariate normality of the sample was verified by mean values, standard deviation, skewness, and kurtosis using SPSS for Windows version 22.0. The model fit was validated using AMOS version 22.0. Model fit was tested using the χ² test (CMIN), normed χ² test (CMIN/df), goodness-offit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), a non-normed fit index (Tucker-Lewis index; TLI), normed fit index (NFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). The significance of the estimated coefficient for each path in the hypothetical model was analyzed through the critical ratio and p-value (p<.050). To verify the statistical significance of the direct, indirect, and total effects of the hypothetical model, the bootstrapping method was used.

Lee K.E, & Kim Y. (2020). The Structural Equation Model of Intention to Discontinue Drinking Highly Caffeinated Beverage of Undergraduate Students. Child Health Nursing Research, 26(1), 35-46.

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Statistical Analysis of Experimental Data

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The statistical analysis (analysis of variance/ANOVA) was performed using SPSS for Windows Version 22.0. If the mean differences existed, multiple comparisons were performed using the Duncan's Multiple Range Test (DMRT). All analysis was conducted using SPSS for Windows Version 22.0.

Amrinola W., Sitanggang A.B., Kusnandar F., & Budijanto S. (2021). Characterization of three cultivars of Indonesian glutinous rice: a basis for developing rice-based functional food. The Annals of the University Dunarea de Jos of Galati Fascicle VI – Food Technology, 45(1), 141-156.

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Predictors of Herbal Medicine Use

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Data from filled questionnaires were coded and analyzed using Statistical Package for Social Sciences (SPSS) for Windows, Version 22 (Chicago Inc.). Categorical and continuous variables were represented in frequency, percentages, mean, and standard deviation respectively. We determined the prevalence of herbal medicine use as the proportion of mothers who used herbal medicine using the target population as the denominator. Bivariate analysis using Chi square or fisher exact tests were used to establish an association between socio-demographic and other related characteristics and herbal medicine use. We used a logistic regression model to determine possible socio-demographic and other related predictors of herbal medicine use. Independent variables in the bivariate analysis with p-value ≤0.2 were entered into initial univariate analysis (model1) to calculate crude ORs with 95% confidence interval. Demographic and health related characteristics whose p-values were less than 0.05 in the univariate analysis where entered into the multivariable analysis (model 2) to determine adjusted odd ratios.. We considered covariates in the multivariate model as an independent predictor(s) of herbal medicine use if its p-value was less than 0.05.

James P.B., Kaikai A.I., Bah A.J., Steel A, & Wardle J. (2019). Herbal medicine use during breastfeeding: a cross-sectional study among mothers visiting public health facilities in the Western area of Sierra Leone. BMC Complementary and Alternative Medicine, 19, 66.

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Statistical Analysis of Experimental Data

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Statistical analyses were accomplished using a statistical SPSS for Windows version 22 (SPSS Ltd., IL, USA). Parametric variables were analyzed using the general linear models procedure for analysis of variance. One-way ANOVA accompanied with Duncan’s multiple range tests were used to detect the differences among means; data are shown as mean ± SD. The nonparametric one-way test was applied to detect the difference in scores throughout the time points. Therefore, Friedman’s chi squared estimated by Cochran–Mantel–Haenszel analysis, within repeated measurements was applied. The signed rank test (Wilcoxon two-sample test for paired observations) within univariate analysis was used for post hoc estimation of pair wise differences between two time points. Values of p < 0.05 were considered significant. Data are presented as median (range).

Nahas A.E., Almubarak A.I, & Hagag U. (2023). Epidural lidocaine, butorphanol, and butorphanol – lidocaine combination in dromedary camels. BMC Veterinary Research, 19, 51.

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Anxiety Prevalence and Associated Factors

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Statistical analyses were performed with SPSS for Windows Version 22 (Statistical package for the social sciences, version 22.0, SPSS Inc). Descriptive statistics were used to determine participants' demographic characteristics and mean anxiety scores. The relationship between variables was evaluated by chi‐squared tests, independent t‐tests, paired t‐tests, and analysis of variance (ANOVA). A P value of .05 or less was considered significant.

Zendehdel M., Elyasi F., Jahanfar S, & Emami‐Sahebi A. (2022). Effectiveness of progressive muscle relaxation technique on anxiety caused by Covid‐19 in pregnant women: A randomized clinical trial. Neuropsychopharmacology Reports, 42(2), 158-165.

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Evaluation of Therapeutic Interventions

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Descriptives were calculated [mean (sd) for continuous and n (%) for categorical variables, respectively]. Comparisons between characteristics and outcome of relatives assigned to the two groups (TAU and EXP) were performed by using Fisher's exact tests for categorical items and independent samples t-tests for continuous scores. Changes between baseline and follow-up within each group were evaluated by using repeated measures t-tests and the difference between groups was explored by independent t-tests. All tests were bilateral at p < 0.05. No correction for multiple comparisons was performed due to the explorative nature of the study. All analyses were executed by SPSS for Windows (version 22).

Ruggeri M., Lasalvia A., Santonastaso P., Pileggi F., Leuci E., Miceli M., Scarone S., Torresani S., Tosato S., De Santi K., Cristofalo D., Comacchio C., Tomassi S., Cremonese C., Fioritti A., Patelli G, & Bonetto C. (2017). Family Burden, Emotional Distress and Service Satisfaction in First Episode Psychosis. Data from the GET UP Trial. Frontiers in Psychology, 8, 721.

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Analyzing Water Quality Treatments

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By assuming that there is no precise difference in each concrete pond, samples collected from each treatment per month can be considered as dependent samples. Therefore, for the three independent treatments (control, organic, and inorganic), one way ANOVA and the Duncan multiple range test [71 (link)] were used to test whether differences among treatments and time were significant at p ≤ 0.05. Prior to ANOVA, data were tested for homogeneity using the Levene’s test. All the considered variables showed homogeneity. Specific post hoc comparisons were performed using LSD. Correlation coefficients and Pearson correlations (r) between the different parameters were computed. Correlations and all statistical analyses were conducted using SPSS for windows, version 22 (SPSS, Richmond).

Azab E, & Soror A.F. (2020). Physiological Behavior of the Aquatic Plant Azolla sp. in Response to Organic and Inorganic Fertilizers. Plants, 9(7), 924.

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Dichotomized Survey Data Analysis

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The data were exported from unipark.de to SPSS for Windows Version 22. Only completed data sets were included in the analysis.
We dichotomized the possible answers for reading purposes. “Fully agree” and “agree” were recoded as “Agree” and “Neither nor”, “disagree” and “fully disagree” were recoded as “Disagree”. Rates of agreement are displayed in numbers and percentages.
Thematic analysis was applied to the qualitative written responses.

Ziehm J., Farin E., Schäfer J., Woitha K., Becker G, & Köberich S. (2016). Palliative care for patients with heart failure: facilitators and barriers - a cross sectional survey of German health care professionals. BMC Health Services Research, 16, 361.

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Statistical Analysis of Patient Groups

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Data were analysed using SPSS for Windows (version 22). Differences between groups were analysed using ANOVA tests for continuous and ordinal variables and chi-square tests for categorical variables. Univariable and multivariable logistic regression analyses were performed to examine differences between the two patient groups. For the multivariable logistic regression analysis, the independent variables that were significant in the univariable analyses were entered simultaneously as predictors of the dependent variable.

Victor M., Lau B, & Ruud T. (2016). Patient characteristics in a return to work programme for common mental disorders: a cross-sectional study. BMC Public Health, 16, 745.

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PCSK9 Mutation Analysis in CV Disease

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All statistical analyses were performed using SPSS for Windows, Version 22 (SPSS Inc., USA). Data were presented as percentages for categorical variables and as mean±SD for continuous variables. The study population was divided into two groups according to the presence of any of the GOF mutations (F216L, R496W, S127R, or D374Y) in the PCSK9 mutation-positive and PCSK9 mutation-negative groups. Demographic characteristics, CV disease risk factors, lipid profiles and other laboratory findings, ultrasound and echocardiography findings, and treatment responses were compared between the groups. As all the data were distributed normally (Kolmogorov–Smirnov test, p>0.05), statistical comparison were performed using Student’s t-tests. Categorical variables were compared using Fisher’s exact test or chi-square test, as appropriate. A p-value of <0.05 (two-sided) was considered as statistically significant.

Kaya E., Kayıkçıoğlu M., Vardarlı A.T., Eroğlu Z., Payzın S, & Can L. (2017). PCSK 9 gain-of-function mutations (R496W and D374Y) and clinical cardiovascular characteristics in a cohort of Turkish patients with familial hypercholesterolemia. Anatolian Journal of Cardiology, 18(4), 266-272.

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