Statistical package for the social sciences 18

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Statistical Package for the Social Sciences (SPSS) 18.0 is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and presentation. The core function of SPSS 18.0 is to enable users to perform a wide range of statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing, among others. The software is designed to assist researchers, analysts, and professionals in the social sciences and related fields.

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27 protocols using statistical package for the social sciences 18

Comparative Analysis of Demographic Factors

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All other statistical analyses were performed using Statistical Package for the Social Sciences 18.0 (SPSS, SPSS Inc., Chicago, IL, USA). For continuous measures, differences between groups were assessed using one-way ANOVA with post hoc Bonferroni tests to correct for multiple comparisons. Fisher’s exact test was used to compare frequency distributions of gender.

Yi D., Choe Y.M., Byun M.S., Sohn B.K., Seo E.H., Han J., Park J., Woo J.I, & Lee D.Y. (2015). Differences in Functional Brain Connectivity Alterations Associated with Cerebral Amyloid Deposition in Amnestic Mild Cognitive Impairment. Frontiers in Aging Neuroscience, 7, 15.

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Factors Influencing Recent HIV Testing

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Participants who answered the question of “HIV testing history in the past year” were included in the final analysis (n = 565; 91.9%). All study variables were double-checked for missing data, outliers, and suspected errors. The relationship between demographic information, knowing someone infected with HIV, sexual behaviors, STI experiences, and self-reported HIV testing history were examined by Wilcoxon rank sum test, chi-square test, and analysis of variance, which were conducted with the Statistical Package for the Social Sciences 18.0 (SPSS, Inc., Chicago), with statistical significance defined as p < .05. The distributions of continuous variables were described using means and standard deviations, and categorical variables were described using frequencies and percentages.
The primary outcome of HIV testing behavior was transformed into a binary variable as never tested in the last year and ever tested in the last year. A logistic regression analysis was performed to examine factors independently associated with recent HIV testing. All significant variables in the bivariate analysis were included in the regression model. Adjusted odds ratios were calculated and presented with 95% confidence intervals.

ZHOU J., CHEN J., Goldsamt L., WANG H., ZHANG C, & LI X. (2018). HIV testing and associated factors among men who have sex with men in Changsha, China. The Journal of the Association of Nurses in AIDS Care : JANAC, 29(6), 932-941.

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Survival Analysis of Clinical Outcomes

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Quantitative variables were expressed as mean ± SD or median and interquartile range (25th-75th). Categorical variables were described as frequencies and percentages. The Kaplan-Meier curve was applied to estimate survival time, and the log-rank test was used to calculate survival probability. A P ≤ 0.05 was considered statistically significant. Data were stored and processed using the Statistical Package for the Social Sciences 18.0 (SPSS Inc., Chicago, IL, United States).

de Freitas L.B., Longo L., Santos D., Grivicich I, & Álvares-da-Silva M.R. (2019). Hepatocellular carcinoma staging systems: Hong Kong liver cancer vs Barcelona clinic liver cancer in a Western population. World Journal of Hepatology, 11(9), 678-688.

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Genetic Polymorphisms and Metastasis Risk

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Results are expressed as mean ± S.D. or median (IQ 25–75) unless otherwise specified. Hardy–Weinberg equilibrium for each polymorphism was assessed by χ² tests. Baseline characteristics were compared using χ² tests or Fisher’s exact test for qualitative variables. Quantitative variables were compared between groups using Student’s t-test or Mann-Whitney test. The differences in cumulative lymph node and/or distant metastasis between groups were tested by Kaplan–Meier curves; comparisons between curves were performed using the log rank test. The Statistical Package for the Social Sciences 18.0 (SPSS Inc., Chicago, IL, USA) was used, and P<0.05 was considered as statistically significant.

Ceolin L., Romitti M., Rodrigues Siqueira D., Vaz Ferreira C., Oliboni Scapineli J., Assis-Brazil B., Vieira Maximiano R., Dias Amarante T., de Souza Nunes M.C., Weber G, & Maia A.L. (2016). Effect of 3′UTR RET Variants on RET mRNA Secondary Structure and Disease Presentation in Medullary Thyroid Carcinoma. PLoS ONE, 11(2), e0147840.

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Statistical Analysis of Experimental Data

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The data were analyzed using the Statistical Package for the Social Sciences 18.0 (SPSS, USA) with a p < 0.05 taken as statistically significant. The measurement data were expressed as mean ± SD of at least three experiments. Rank sum test and chi-square test were used for categorical data, and the one-way analysis of variance (ANOVA) test was used for continuous data. The power analysis for the final sample size was calculated with Graph Pad Stat Mate (Graph Pad Software, CA).

Zhang J., Liu Z., Lian Z., Liao R., Chen Y., Qin Y., Wang J., Jiang Q., Wang X, & Gong J. (2016). Monoacylglycerol Lipase: A Novel Potential Therapeutic Target and Prognostic Indicator for Hepatocellular Carcinoma. Scientific Reports, 6, 35784.

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Factors Associated with Referral Dispositions

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Univariate and bivariate analyses, including one-way Analysis of Variance (ANOVAs) for continuous variables and chi-square tests for categorical variables, were used to identify factors associated with the main outcome variables. Factors with significant associations were retained in multivariate logistic regression analyses. Statistical Package for the Social Sciences 18.0 was used for quantitative analyses.
Sample sizes for analyses varied depending on available data. Specifically, analyses that included parental concerns were only conducted with families who completed the young child screening packet (n = 371) for whom data on parent concerns about child behavior were coded. However, children of all ages (i.e., 9 months-8 years) were included in analyses that did not include parent concerns, including those examining the association between child age or sociodemographic factors on referral dispositions (n = 664) and family attendance at the TA (n = 136). This inclusive data analytic approach was taken to examine a broader age range where possible and to provide a comparison group for very young children. Descriptions of subsamples included in analyses are presented below.

Godoy L., Carter A.S., Silver R.B., Dickstein S, & Seifer R. (2014). Mental Health Screening and Consultation in Primary Care: The Role of Child Age and Parental Concerns. Journal of developmental and behavioral pediatrics : JDBP, 35(5), 334-343.

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Prognostic Factors in Oncology Patients

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All analyses were performed using the Statistical Package for the Social Sciences 18.0 (SPSS Inc., Chicago, IL, USA). Categorical variables were analyzed using a χ² test or Fisher’s exact test. OS and PFS were compared between the 2 groups using the Kaplan-Meier method, and 95% confidence intervals (CI) were presented. Univariate analysis was carried out and variables with P<0.1 (age, performance status, tumor stage, tumor mutation types and abundance) were entered into multivariate Cox regression to evaluate prognostic factors. P<0.05 was considered statistically significant.

Liu Y., Wang H., Yang S., Yang Y., Wu Y., He Z., Ma S., Mo Y., Chen H., Wang Q, & Ge H. (2022). EGFR mutation types and abundance were associated with the overall survival of advanced lung adenocarcinoma patients receiving first-line tyrosine kinase inhibitors. Journal of Thoracic Disease, 14(6), 2254-2267.

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Statistical Analysis of Social Sciences

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Statistical analysis was performed using Statistical Package for the Social Sciences 18.0 (SPSS, Chicago, IL, USA) software. Shapiro-Wilk test was used to test the normality of the data. Descriptive data were expressed as mean ± standard deviation values. Skewed data were shown as median and interquartile range (IQR). Spearman’s correlation method was used in correlation analysis. For all tests, a p-value of less than 0.05 was accepted as statistically significant.

Güven A., Cebeci A.N., Ellard S, & Flanagan S.E. (2016). Clinical and Genetic Characteristics, Management and Long-Term Follow-Up of Turkish Patients with Congenital Hyperinsulinism. Journal of Clinical Research in Pediatric Endocrinology, 8(2), 197-204.

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Nude Mice Survival Analysis

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Kaplan-Meier method was used to generate the survival curve of the remainder of the nude mice (¹²⁵I RPI group [n = 11], the CIK cell group [n = 11], the combination therapy group [n = 12], and the untreated control group [n = 11]). The data were analyzed with log-rank test.
The data were analyzed using the Statistical Package for the Social Sciences 18.0 (SPSS, Inc, Chicago, Illinois), with P < .05 taken as statistically significant. Data are represented as mean (standard deviation [SD]) of at least 3 independent experiments. Repeated-measures analysis of variance (ANOVA) was used for comparison within each group. One-way ANOVA was used for comparison between each group. The LSD (Least-Significant difference)-t method was applied for multiple comparisons.

Zhang J., Wu N., Lian Z., Feng H., Jiang Q., Chen X., Gong J, & Qiao Z. (2017). The Combined Antitumor Effects of 125I Radioactive Particle Implantation and Cytokine-Induced Killer Cell Therapy on Xenograft Hepatocellular Carcinoma in a Mouse Model. Technology in Cancer Research & Treatment, 16(6), 1083-1091.

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Predicting Amyloid Burden from PiB PET

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In experiment 1, to determine whether global or regional PiB SUVR would better predict global pathological Aβ burden, receiver-operating characteristic (ROC) analyses were performed. We also explored area under the ROC curve (AUC), and the optimal cut-offs that optimize sensitivity and specificity. DeLong analyses (DeLong et al., 1988 (link)) were performed to compare ROC curves between global and regional PiB SUVR.
In experiment 2, to investigate the relationship between in vivo imaging and pathological Aβ types in five customized PiB ROIs that correspond to the post-mortem sampling regions, multiple linear regression analyses with the “Enter” method were performed using two models. In Model 1, we entered the period of PET to autopsy and each pathological Aβ type (NPs, DPs, or CAA) as the independent variables and PiB SUVR from the customized ROI as dependent variable. In Model 2, we additionally entered all the resulting statistically significant pathological amyloid aggregates from Model 1 (defined as p < 0.05) as independent variables.
Statistical analyses were performed using the Statistical Package for the Social Sciences 18.0 (SPSS Inc.,Chicago,IL) and MedCalc for Windows, version 9.3 (MedCalc Software, Ostend, Belgium).

Seo S.W., Ayakta N., Grinberg L.T., Villeneuve S., Lehmann M., Reed B., DeCarli C., Miller B.L., Rosen H.J., Boxer A.L., O’Neil J.P., Jin L.W., Seeley W.W., Jagust W.J, & Rabinovici G.D. (2016). Regional correlations between [11C]PIB PET and post-mortem burden of amyloid-beta pathology in a diverse neuropathological cohort. NeuroImage : Clinical, 13, 130-137.

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