AIMs data (expressed as medians + median absolute difference; M.A.D.) were all analyzed by non-parametric statistics. For between-subjects designs, the Kruskal-Wallis ANOVA with Mann-Whitney U post-hoc tests were employed. FAS, HPLC, and PCR data (expressed as means + S.E.M) were analyzed with ANOVAs and LSD post-hoc t-tests when appropriate. Mauchley’s tests of sphericity were conducted to ensure homogeneity of variance prior to proceeding with ANOVA analyses. In cases where assumptions of homogeneity of variance were violated, data were transformed via z-scores, homogeneity of variance was confirmed, and ANOVAs were executed as planned on transformed data. Mixed-design ANOVAs were used to assess the influences of age and lesion status on extracellular cytokine levels across all 9 time points. Extracellular cytokine release in response to acute L-DOPA was also expressed as a percent of baseline and analyzed with repeated-measures ANOVA (Dupre et al., 2011 (link); Lindenbach et al., 2015 (link)). In parametric analyses, age, lesion, and side (if applicable) were used as independent variables. Analyses for all experiments were performed with SPSS software (Chicago, IL, USA) or Statistica with alpha set at p < 0.05.
Statistica
Manufactured by IBM
Sourced in United States
Statistica is a statistical analysis software developed by IBM. It provides a range of tools for data analysis, modeling, and visualization. Statistica offers capabilities for descriptive statistics, regression analysis, time series analysis, and more.
Automatically generated - may contain errors
Lab products found in correlation
10 protocols using statistica
1
Neuroinflammatory Cytokine Regulation in Parkinson's Disease
2
Psychometric Evaluation of PREM-C9 Questionnaire
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All calculations were performed using the Microsoft Excel spreadsheet questionnaire, the StatSoft Inc. 14.0 Statistical package Statistica, and the SPSS program, version 21. In the Statistical description of the quantitative data, classical measures of position such as the arithmetic mean, median, and standard deviation were used as measures of the variability. To evaluate the psychometric properties of the PREM-C9 questionnaire, Cronbach’s alpha, Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin coefficient, and Spearman’s correlation coefficient were used. In all Statistical tests, p < 0.05 was taken as the level of Statistical significance for differences.
The validity of the questionnaire was assessed using a PCA (principal component analysis) for the extracted principal components. The validity of the factor analysis was demonstrated using Bartlett’s sphericity test and the Kaiser–Meyer–Olkin (KMO) coefficient, with p < 0.05 and KMO > 0.6 as the cut-off levels, respectively. A factor analysis was performed using the Oblimin and Varimax rotation method, assuming 0.4 as the threshold value. The reliability of the questionnaire was assessed using Cronbach’s alpha index, assuming 0.70 as the cut-off value.
The validity of the questionnaire was assessed using a PCA (principal component analysis) for the extracted principal components. The validity of the factor analysis was demonstrated using Bartlett’s sphericity test and the Kaiser–Meyer–Olkin (KMO) coefficient, with p < 0.05 and KMO > 0.6 as the cut-off levels, respectively. A factor analysis was performed using the Oblimin and Varimax rotation method, assuming 0.4 as the threshold value. The reliability of the questionnaire was assessed using Cronbach’s alpha index, assuming 0.70 as the cut-off value.
3
Whitefish Metal Concentration Analysis
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All Statistical analyses were performed using Statistica. Independent samples were compared using t-tests to assess differences in the average metal concentrations in whitefish organs and tissues. All graphs were generated using SPSS, Statistica.
4
One-way ANOVA for Research Protocols
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One-way analysis of variance (ANOVA) was performed using Statistica® (v.8 software SPSS Inc., Chicago, IL, USA). Results were expressed as mean ± standard deviation. A p-value of less than 0.05 was considered significant. All tests were conducted in triplicate.
5
Vascular Function and Intima-Media Thickness
6
Reliability and Validity of the CQS
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The software STATISTICA (version 12.00; Site License) and IBM SPSS AMOS (version 25.00) were used for STATISTICAl analysis. The licences were obtained by and granted to Jagiellonian University for research purposes and were used in accordance with the terms of use by the first author only (KB). In the descriptive statistics we used means and standard deviations. To analyse the reliability of the CQS we utilized (a) Cronbach’s alpha [58 ] to assess the internal consistency, (b) correlation coefficients to determine the test-retest stability of the CQS [59 ], and (c) confirmatory factor analysis (CFA; [60 ]) to confirm the postulated four-factor structure of CQS [2 ]. To assess the validity of the CSQ we used (a) an independent sample t-test to examine the theoretical validity [61 ], and (b) correlation coefficients to determine the convergent validity and criterion validity [59 , 62 ]. For all STATISTICAl tests reported below the rejection level was set at 0.05 (unless otherwise specified, see 4.2.3). For all t-tests the effect size was measured by Cohen’s d with small, medium, and large effects defined as 0.2, 0.5, and 0.8, respectively [63 ].
7
Comparative Diagnostic Accuracy Analysis
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Contingency tables were constructed with kappa values and Chi-squared tests. Proportion tests were used to compare the performance results listed in Tables 3 and 4 . Inter-reader agreement per modality was evaluated pairwise for all readers using Cohen’s kappa and collectively for all readers using Fleiss’ kappa. Wilcoxon’s matched pairs test was used to compare the confidence levels. Graph production and Statistical calculations were made using Dell Statistica, version 13, and IBM SPSS, version 23.
8
Multivariate Analysis of COVID-19 Psychological Impact
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Statistical analysis of the study results began with one-way analysis of variance (ANOVA) to verify whether the three groups differed from one another in FCV-19S PL, CDI 2, STAIC and LOI-CV scores. Then, a matrix of correlation coefficients between all variables was computed for each group. In the next step, complex two-factor interactions between groups and questions were verified. Finally, a self-developed multidimensional Statistical model was applied. In addition, the Bartlett sphericity test was calculated, which was less than 0.001, and the Kaiser–Meyer–Olkin coefficient was 0.50. This proves the acceptability of the sample according to Kaiser [57 (link)]. All Statistical tests were performed at the significance level, α = 0.05. Statistical analysis was conducted using IBM SPSS Statistics (Amos) and Statistica.
9
Spatial Distribution of OCPs in Punjab
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Descriptive statistics were applied to all milk samples collected from different districts.
Samples were further analyzed by Cluster analysis and Krushkal Wallis Test to check differences in the OCPs concentration between different districts. P value > 0.05 was considered to be Statistically significant. Microsoft Excel version 2010, Statistica and IBM SPSS were used to calculate the detailed descriptive (mean, 50 th , 90 th percentiles and range). The graphs were prepared by using Origin (Pro 8). Furthermore, Arc GIS (version 10.2) was used to show the spatial distribution of OCPs concentration profile in the different districts of Punjab, Pakistan.
Samples were further analyzed by Cluster analysis and Krushkal Wallis Test to check differences in the OCPs concentration between different districts. P value > 0.05 was considered to be Statistically significant. Microsoft Excel version 2010, Statistica and IBM SPSS were used to calculate the detailed descriptive (mean, 50 th , 90 th percentiles and range). The graphs were prepared by using Origin (Pro 8). Furthermore, Arc GIS (version 10.2) was used to show the spatial distribution of OCPs concentration profile in the different districts of Punjab, Pakistan.
10
Statistical Analysis of Research Data
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For descriptive Statistical analysis, mean and median values, standard deviations, and absolute and relative frequencies were calculated. Qualitative data were compared by the Pearson Chi-square test. Quantitative variables were compared using Mann-Whitney U or Kruskal-Wallis test. Survival was analysed by the Kaplan-Meier method and compared with log-rank test. A p value of less than 0.05 was considered to be significant. Propensity score matching analysis was performed using logistic regression analysis, and then thirds were matched to primers with a 1:1 matching in propensity scores without replacement. The match tolerance was set to 0.1. Statistics were calculated by TIBCO Software Inc. (2018), Statistica (data analysis software system), version 13, and by IBM SPSS version 25.
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