Statistical analysis was executed using the IBM SPSS statistics v.24.0 for Windows statistical package. Student’s t-test was used to find significant differences between samples.
Spss statistics v 24.0 for windows
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SPSS Statistics V.24.0 for Windows is a statistical software package designed for data analysis. It provides tools for data management, analysis, and presentation. The software supports a wide range of statistical procedures, including regression, correlation, and hypothesis testing. SPSS Statistics V.24.0 for Windows is compatible with Windows operating systems.
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Lab products found in correlation
8 protocols using spss statistics v 24.0 for windows
1
Statistical Analysis of Experimental Data
2
Validating Activity-Specific METs in Elderly
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All values are presented as means and SD. Differences were considered to be significant if the p-value was less than 0.05. Elderly and Younger were compared using an unpaired t-test. The relationship between the METs measured using the Douglas bag method (DB_METs) and METs predicted using the ASP (ASP_METs) was evaluated using Pearson’s correlation coefficient (r). DB_METs and ASP_METs within a group were compared using the paired t-test. The validity of ASP was expressed as error (ASP_METs–DB_METs), error rate ((ASP_METs–DB_METs)/DB_METs×100)) and error plots. Stepwise multiple regression analysis was also carried out to evaluate factors related to DB_METs. DB_METs was applied as dependent variables, and age, sex, weight, BMI and ASP_METs were applied as independent variables.
All statistical analyses were performed using IBM SPSS Statistics V.24.0 for Windows.
All statistical analyses were performed using IBM SPSS Statistics V.24.0 for Windows.
3
Comparative Analysis of Transapical and Transfemoral Procedures
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For the data collection, we used Excel (Excel 2013, Microsoft Corporation, Redmond, Washington, USA). We analysed the data with SPSS (IBM SPSS Statistics V.24.0 for Windows, IBM Corporation). Frequency distributions and percentage rates were used for the categorical variables. Data are displayed as the mean±SD deviation or median with range. We used the χ2 test (based on Pearson’s χ2 test), the two-sided t-test and univariate and multivariate logistic regression analyses to compare the transapical (TA) group and the transfemoral (TF) group. P<0.05 was regarded as statistically significant.
4
Statistical Analysis of Questionnaire Responses
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Statistical analyses were performed in IBM SPSS Statistics V.24.0 for Windows. The responses to questionnaires were tabulated to allow for descriptive and inferential analyses. Median, mode and interquartile ranges were calculated for ordinal variables. Differences between baseline and follow-up were assessed using McNemar’s test for dichotomous variables. Statistical significance was set at P value <0.05.
5
Emotional States during COVID-19 Confinement in ASD
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Statistical analyses were performed using IBM SPSS Statistics v 24.0 for Windows (IBM SPSS Corp., Chicago, IL). The means and standard deviations of each sample were obtained from the direct scores provided by the participants for each of the items corresponding to each test.
The effects of interaction between age, gender, and intellectual levels differences in overall comparison on emotional state during COVID-19 confinement between individuals with ASD and healthy controls was primarily analyzed using MANOVA.
The analysis of differences in emotional states between the ASD group and the control group was carried out using the t test. In the same way, the analysis of the differences in autism symptoms between the ASD group during the confinement period and ASD participants in 2018 was carried out using the Student's t test. Those differences with p < .05 were considered significant. To determine if there were statistically significant differences between the proportions found, the corresponding effect size was calculated: .20 ≤ d ≤ .50 was a low effect size, while .51 ≤ d ≤ .79 was moderate and d ≥ .80 was high (Cohen, 1988 )
The effects of interaction between age, gender, and intellectual levels differences in overall comparison on emotional state during COVID-19 confinement between individuals with ASD and healthy controls was primarily analyzed using MANOVA.
The analysis of differences in emotional states between the ASD group and the control group was carried out using the t test. In the same way, the analysis of the differences in autism symptoms between the ASD group during the confinement period and ASD participants in 2018 was carried out using the Student's t test. Those differences with p < .05 were considered significant. To determine if there were statistically significant differences between the proportions found, the corresponding effect size was calculated: .20 ≤ d ≤ .50 was a low effect size, while .51 ≤ d ≤ .79 was moderate and d ≥ .80 was high (Cohen, 1988 )
6
Identifying Factors Associated with Mortality in Liver Injury
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Continuous data are presented as median with IQR. Comparisons between groups are performed using the Mann-Whitney U test. Categorical data are reported as proportions and tested for significance using Pearson’s χ2 test and Fisher’s exact test as appropriate. For all analyses, a p value <0.05 derived from a two-tailed test was considered statistically significant.
A forward stepwise selection of significant covariates including potential confounders was performed to identify variables independently associated with 30-day mortality in patients with liver injury. These core variables were then applied to construct a multiple logistic regression model evaluating mortality. All variables were prespecified and considered clinically important. The fit of the models was measured with the Hosmer-Lemeshow goodness-of-fit test statistic. Calculation of the accuracy of the test was measured by the area under the receiver operating characteristic curve for the prediction of 30-day mortality. All statistical analyses were performed using the IBM SPSS Statistics V.24.0 for Windows.
A forward stepwise selection of significant covariates including potential confounders was performed to identify variables independently associated with 30-day mortality in patients with liver injury. These core variables were then applied to construct a multiple logistic regression model evaluating mortality. All variables were prespecified and considered clinically important. The fit of the models was measured with the Hosmer-Lemeshow goodness-of-fit test statistic. Calculation of the accuracy of the test was measured by the area under the receiver operating characteristic curve for the prediction of 30-day mortality. All statistical analyses were performed using the IBM SPSS Statistics V.24.0 for Windows.
7
Assessing Cardiorespiratory Fitness and Physical Performance
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IBM SPSS Statistics v. 24.0 for Windows (IBM Corp., Armonk, NY, United States) was used for data processing. Normality tests for each gender and age group were applied to identify outliers, which were subsequently excluded. Means, standard deviations and frequencies were calculated using descriptive statistics. Participants were allocated into quintiles according to the result of their endurance shuttle run test. Classification of schoolchildren to cardio-vascular fitness quintiles of “very low” to “very high” fitness was based on previous studies (Blair et al., 1989 (link), 1995 (link); Myers et al., 2002 (link); Catley and Tomkinson, 2013 (link)). For the analysis, data from the three different decades were pooled. A generalized linear model univariate analysis using BMI and decade (the year in which the measurements were taken) as covariates was performed to test for the differences in the performance in other physical fitness tests between the quintiles of cardio-respiratory fitness level. Analyses for boys and girls were performed separately. A Bonferroni post hoc test was used for multiple comparisons, and two-sided p-values of <0.05 were considered statistically significant.
8
Demographic and Clinical Data Analysis
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Standard descriptive statistics were recorded for the baseline demographic and clinical characteristics of each patient and the corresponding pathological data, with quantitative variables expressed as the mean and standard deviation and qualitative variables expressed as the frequency. The association between qualitative variables was determined by a contingency table and χ 2 test. In all cases, a p-value < 0.05 was accepted as statistically significant. All analyses were performed using IBM SPSS Statistics V24.0 for Windows.
The study protocol was approved by the Research Ethics Committee of the Principality of Asturias (Spain) and complied with ethical and data protection standards (approval no. 02/16).
The study protocol was approved by the Research Ethics Committee of the Principality of Asturias (Spain) and complied with ethical and data protection standards (approval no. 02/16).
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