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322 protocols using spss statistics version

1

Quantitative Analysis of Dendritic Cells in Tissue Microarrays

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From each case we performed manual tissue multiarray (TMA) blocks. Tissue was extracted from the paraffin blocks and then embedded into recipient blocks, each including 8 samples from 8 patients. From each TMA block 3 µm sections were performed using a semi-automatic Leica RM2245 rotary microtome. One slide was used for routine stain (H&E) and 10 immunohistochemical slides for CD1a, CD11c and langerin (Table I). As a detection system, we used Polymer Novolink (Leica Biosystems Nussloch GmbH, Nussloch, Germany). Immunohistochemical staining was analyzed using a microscope Olympus CX41 (Olympus, Tokyo, Japan).
Density and distribution of DCs were evaluated by two independent pathologists with expertise in dermatopathology. They estimated both the number of DCs and their pattern of distribution.
All data were registered in a database and statistically analyzed using Microsoft Excel (Microsoft, Redmond, Washington, USA) and IBM SPSS Statistics (version 22.0; IBM Corp., Armonk, NY, USA).
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2

Salmonella Persistence in Laying Hens

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The data for average log10 CFU/qPCR, corticosterone level, and serum and egg yolk was analyzed using a two way analysis of variance (ANOVA) followed by a Tukey’s multiple comparison of the mean. Significance between bacterial titres in organs was tested using a Mann–Whitney test. The correlation between MPN/g fecal count and Salmonella positive eggshell wash, average log10 CFU/qPCR and corticosterone concentration was determined by Pearson correlation test (r2 value). All data was analysed using either by GraphPad Prism version 6 software or IBM®SPSS Statistics® version 21. p values <0.05 were considered statistically significant. A D’Agostino-Pearson omnibus normality test was conducted for all data. Serum and egg yolk antibody titres were normally distributed. MPN data were not normally distributed. MPN data was analysed by a Kruskal–Wallis with a Dunn’s comparison of the means.
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3

Validity and Reliability of an Educational Technology Instrument

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The judges were characterized through descriptive analysis (frequency distribution and measures of central tendency). The instrument data were analyzed using Cronbach’s alpha (α) and McDonald’s omega (Ω). Validity data were obtained from judges’ answers on a Likert scale and were analyzed for inter-judge reliability by the Intraclass Correlation Coefficient (ICC), using the bilateral combined randomization model.
To measure the agreement among judges on the educational technology attributes, the Level Content Validity (CVI - Content Validity Index) was used, calculated in two procedures: Level Content Validity Index (I-CVI), which assessed the level of agreement among expert judges regarding each item, through the number of judges who rated the item as “totally agree” and “partially agree”, divided by the total number of judges; and Scale-Level Content Validity Index/Average Calculation Method (S-CVI/Ave), which calculated the mean I-CVI of the 22 assessed items as well as the mean I-CVI of items in each domain. Items with a level greater than 0.90 were considered valid(19 (link)).
Data come from a master’s thesis in nursing and were tabulated in Microsoft Excel®, analyzed using IBM SPSS® Statistics, version 26.0 (International Business Machines Corporation, Armonk, New York, USA), and Jamovi®, version 1.8. 4.0, adopting a significance level of 5% (p-value <0.05).
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4

Intra-Amniotic Administration Procedure

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Experimental treatments for the intra-amniotic administration procedure were arranged in a completely randomized design. All the results were expressed as means ± standard error deviation (SED) from 7 to 9 biological samples per treatment group (according to hatching). Differences were considered significant when p < 0.05.
The Shapiro–Wilk normality test was used to evaluate values for normal distribution and variance homogeneity. Normally distributed results were analyzed using a one-way analysis of variance (ANOVA). For a significant “p-value,” the post hoc Duncan test was used for those with a normal distribution. The means without normal distribution were analyzed using Kruskal–Wallis and a post hoc Dunn’s test. The statistical analyses were performed using the statistical software IBM SPSS Statistics®, version 25.
The correlation between the biomarkers of intestinal health, the bacterial population, and histological parameters was analyzed using Spearman’s rank correlation coefficient. GraphPad Prism® version 9.0 software packages (GraphPad Software Inc., San Diego, CA, USA) were used for graphics.
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5

Statistical Analysis of Experimental Data

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Statistical analysis was conducted using Graph Pad Prism (version 6; Graph Pad Software Inc, USA) and IBM SPSS Statistics version 26.0 (IBM Corporation, USA). Data are expressed as mean ± standard deviation (SD) or standard error of the mean (SEM). Nonparametric data were compared using the Kruskal-Wallis test followed by Dunn’s post hoc test. A comparison of the two groups was analyzed using Student´s t-test. The significance level was set at p < 0.05 for all comparisons.
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6

Prognostic Factors for Overall Survival

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OS was estimated using the Kaplan-Meier method, and the statistical significance of differences in survival curves between the groups was assessed using a log-rank test. A prognostic model was established by searching variables that significantly influenced OS with p values of <0.05 in the univariable analysis. For the multivariable analysis, a Cox proportional hazards regression model was used to estimate the adjusted hazard ratio (HR) to determine the significance of specified clinical variables on OS. OS was defined as the interval from the start date of second-line chemotherapy to the date of death or last follow-up. As of October 31, 2013 (the date of data cutoff), a patient who died of any cause or who was lost to follow-up for >1 year without further information on the survival status (i.e., if the last follow-up date was before November 1, 2012) was regarded as an event case. If a patient visited a hospital or clinic at least once within 1 year before October 31, 2013 and the survival status was not reported as dead in the last claim specification, the case was regarded as a censored case. All analyses were conducted using the SAS/STAT software (version 9.4; Cary, NC, USA, SAS Institute Inc.) and IBM SPSS Statistics (version 18; Armonk, NY, USA, IBM Corp.). The statistical significance was set at p values of <0.05.
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7

Statistical Analysis of Prevalence Estimates

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We used descriptive and inferential statistics to calculate prevalence. CIs were calculated using the Wald method. Prevalence for the main outcome was estimated after standardisation for age and sex based on data from the 2011 Portuguese National Census. For hypothesis testing, we used logistic regression (analyses were adjusted for age and sex), Student’s t-tests or nonparametric tests, and Pearson correlations, depending on the variables. The Kolmogorov–Smirnov test was used to check for normal distributions. The significance level was set at 0.05. Data were encoded and registered in a Microsoft Office Excel 2010® database and analysed using IBM SPSS Statistics®, version 21.0 (IBM Corp., Armonk, NY, USA).
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8

Evaluating Bronchial Thermoplasty Outcomes

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Statistical analyses were performed in GraphPad Prism version 5.01 (GraphPad Software Inc) or IBM SPSS Statistics version 25.0. Demographic parameters were provided as mean with SD or median with interquartile ranges. Mann-Whitney U tests were performed to assess the difference in change from baseline between the immediate group, 6 months after BT, and the delayed treatment group, 6 months after standard clinical care. The effect of BT in the total group of patients was calculated with paired t tests or Wilcoxon signed rank test. The Hodges-Lehman estimator (29) with 95% confidence interval is used to calculate median differences to quantify treatment effects (Rstudio Version 1.2.1335). Spearman rank correlation was used to explore associations between patient characteristics and ACQ or AQLQ change. An improvement of .0.5 points on ACQ-6 or AQLQ scores was considered clinically relevant (30, 31) . Two-sided P values were used with a statistical significance at P , 0.05.
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9

Non-parametric Analysis of Intervention Outcomes

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Intention-to-treat analysis was performed a priori. The Kolmogorov-Smirnov test was used to determine the distribution of the data. As non-normal distribution was found, median values and the interquartile range (IQR) of all indices were calculated for each group. Friedman’s repeated-measures ANOVA was used to test differences among Evaluations 1, 2, 3 and 4, as well as to determine whether a specific treatment introduced statistically significant changes (intra-group analysis). The Mann Whitney test was used to evaluate differences between groups at baseline (pre-intervention) and Evaluations 2, 3 and 4 (inter-group analysis). A p-value < 0.05 was considered indicative of statistical significance. The data were organized and tabulated using the IBM® SPSS® Statistics (version 26.0; IBM Corp., Armonk, NY, USA, Chicago, IL, USA) [29 ].
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10

Statistical Analysis of Experimental Data

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Statistical analyses and plotting of graphs were performed using IBM SPSS Statistics version 26.0 for Windows (SPSS Inc.), GraphPad Prism version 8.0.2 (GraphPad Software Inc., San Diego, CA, USA), and Adobe Illustrator version 23.0 (Adobe Inc., CA, USA). Normally distributed continuous variables (OD values, and qPCR data) are presented as mean ± standard deviation (SD), non‐normally distributed continuous variables (Sa values) are presented as median and interquartile range (median (IQR1‐IQR3)), and categorical variables (cell adhesion rate) are converted into continuous variables and expressed as percentage. All data were analyzed by one‐way analysis of variance (ANOVA), followed by LSD‐t test for variance homogeneity and Kruskal‐Wallis H test for variance heterogeneity. A two‐sided P‐value <0.05 was considered statistically significant.
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