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Spss software

Manufactured by SAS Institute
Sourced in United States, Japan

SPSS is a statistical analysis software developed by IBM. It provides tools for data management, analysis, and visualization. SPSS offers a wide range of statistical procedures for various research and business applications.

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41 protocols using spss software

1

Genetic Analysis of Bruxism and TMD

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Patient characteristics were analyzed using the usual rules for descriptive statistics: frequencies and percentages for categorical variables, and mean and standard deviation for quantitative variables. After ensuring compliance with Hardy–Weinberg equilibrium, allelic frequencies between bruxers and non-bruxers were compared using the χ2 test or Fisher’s exact test in a dominant model. One-way analysis of variance (ANOVA) was used for comparisons of the phenotypic characteristics of participants (bruxism status, TMD subtypes, or muscle phenotypes) in various ACTN3 genotypes. The Bonferroni correction method was used to correct the p-values in multiple testing, with a p < 0.017 (p = 0.05/3, where 3 is the number of SNPs included in this study) considered to indicate statistical significance. To evaluate the effect of ACTN3 genotypes on phenotype, multinomial logistic regression was used to adjust for the association of these SNPs with age, sex and biometrics, and the related odds ratio (OR) values were determined. Muscle phenotype quantitative variables between bruxers and non-bruxers were analyzed using Student’s t-test. When the distribution of the variable was not normal, the nonparametric Wilcoxon test was performed. All statistical analysis was performed using SPSS software (SAS Institute), and p values < 0.05 were considered to indicate statistical significance.
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2

Statistical Analysis of Experimental Data

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SPSS Software (Version 8.2; SAS Institute, Cary, NC, USA) was used to conduct statistical analyses. These data were analyzed by one‐way analysis of variance (ANOVA). Comparison of means was performed by Duncan's multiple range tests and a < .05 was considered statistically significant.
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3

Photographic Measurements Reliability Analysis

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Statistical analysis was carried out using SPSS software (version 20, SAS Institute Inc., Cary, NC, USA). Normality of the variables was tested using Kolmogorov–Smirnov test. The distribution of quantitative variables was given as mean and standard deviation. To determine observer variability of the photographic measures, inter-observer (measuring reproducibility) and intra-observer (measuring repeatability) variability of the measurements were analyzed using intraclass correlation coefficients (ICCs) with a 2-way mixed random model and a 95% confidence interval. To calculate reproducibility, the first round of measurements was used. ICC results were interpreted applying published criteria (0.5–0.75 moderate variability, 0.75–0.9 good, and >0.9 excellent) [25 (link)]. Reliability was not a measure of precision; it was the error of measurement not intrinsic to the method but the population. Thus, to assess the reliability of the tool, the standard error of measurement was calculated with the first round of values using the formula SEM = SD×1ICC [26 (link)]. Univariate analysis comparing researchers’ vs. surgeons’ performances was performed using the Student’s t-test. The significance threshold was set at 5% (p < 0.05).
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4

Tumor Regrowth Prediction Analysis

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Tumor characteristics, size, and volume changes were analyzed and compared between the no growth and regrowth group using Mann–Whitney U test. A Fischer’s exact test was used to compare categorical variables. Univariate logistic regression analysis was conducted to determine significant predictors of regrowth. A Cox proportional hazards model was used to report hazard ratios of significant factors with 95% confidence intervals. A stepwise multivariate logistic regression analysis was also performed to seek any independent predictors of regrowth. Regrowth-free survival was calculated using the Kaplan–Meier method. Data analyses were performed using SPSS software (version 18.0, SAS Institute Inc., Chicago, IL, USA). Graphs depicting long-term tumor volume changes and facial nerve outcome at last follow-up were plotted using Microsoft Excel software (version 16.0, Microsoft Inc., Redmond, WA, USA). Two-tailed and p values of ≤ 0.05 were considered statistically significant.
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5

Statistical Analysis of Experimental Data

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Data were subjected to one-way analysis of variance (one-way ANOVA) using SPSS software (SAS Institute, Cary, NC, USA). Results are reported as significant or non-significant in Tukey’s t Test (Tukey HSD) Test (P < 0.05).
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6

Serum Analysis and Histopathology Scoring

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Results are expressed as mean ± SD. For the analysis of serum data, one-way analysis of variance (ANOVA), followed by Tukey’s post hoc test, was used. In cases of a failed normality test, Kruskal–Wallis ANOVA was performed followed by Dunn’s post hoc test. For histopathological scoring, non-parametric Mann–Whitney U-test was performed. Statistical analyses were conducted with SPSS software (version 16.0, SAS Institute Inc., Cary, NC, USA). P < 0.05 was considered significant.
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7

Comparative Analysis of SLN Metastasis Detection

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The SLN metastases detection rate of OSNA assay, CK-IHC assay, or in combination was compared using the χ2 test. The concordance rates in detection of SLN metastases and measurement of disease burden in involved SLNs between OSNA and CK-IHC assay were evaluated. Statistical analyses were carried out using SPSS software (version 11 for Windows; SAS Institute, Tokyo, Japan). A p value of < 0.05 was considered as statistically significant.
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8

Evaluating Experimental Treatment Effects

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Data are expressed as mean ± SD. Differences between groups were assessed by analysis of variance and the independent samples test using SPSS software (SAS Institute, Cary, NC, USA). P<0.05 was considered statistically significant.
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9

Statistical Analysis of Experimental Data

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Statistical analysis was performed with SPSS software (SAS Inc., Chicago, IL). One-way ANOVA and Tukey’s multiple comparison analysis were applied to determine the statistical significance. Data were represented by means of SEM. The statistical significance level was considered significant at P < 0.05.
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10

Behavioral Analysis Using SPSS

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Data were analyzed using SPSS software (SAS Institute, 1990) 23.0 for Windows. All data were examined to determine if the assumptions of parametric statistical tests were met. When assumptions were violated, data were square root transformed (number of chamber entries and grooming duration in exp. 1 and time spent pushing door open in exp. 2) or cube root transformed (frequency of flank marks in exp. 1). All tests were two-tailed, and results considered statistically significant if p ≤ 0.05. All data are presented as mean ± standard error of the mean.
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