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Spss advanced statistics version 26

Manufactured by IBM
Sourced in United States

SPSS Advanced Statistics Version 26 is a software product that provides advanced statistical analysis capabilities. It offers a range of statistical procedures and techniques to analyze complex data sets and extract meaningful insights. The core function of this product is to enable users to perform advanced statistical modeling, data exploration, and hypothesis testing.

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Lab products found in correlation

3 protocols using spss advanced statistics version 26

1

Glucose Dynamics and Exercise Intensity

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Questionnaire responses were assessed using descriptive statistics. Moreover, exercise intensity, changes in glucose levels over time, and the IAUC were determined using the Wilcoxon signed‐rank test. The IAUC was calculated using the trapezoidal method. SPSS Advanced Statistics Version 26 (IBM Japan, Ltd.) was used for all analyses. A P value of <.05 was considered statistically significant.
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2

Statistical Analyses for Numerical and Categorical Data

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Statistical analyses were performed using IBM SPSS advanced statistics, version 26 (SPSS Inc., Chicago, Illinois, USA. Numerical data were presented as mean ± SD and median (range), whereas categorical data were presented as number (percent). The Mann-Whitney U-test and the χ2-test are used when appropriate. Statistical significance is considered if P value is less than or equal to 0.05.
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3

Napping Effects on Handball Performance

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Demographic results are expressed as mean±standard deviation (M±SD) and variables of performance and HRV as mean±the standard error of the mean (M±SEM). To compare variables with nap propensities between shorter and longer nap conditions, we used a Wilcoxon signed-rank test. Napping conditions (including the no-nap condition) were compared by using Friedman’s nonparametric analysis of variance. To assess differences of performance across sequential trials between napping conditions, linear mixed effect (LME) models were applied, as these enable effective use of all information, even that from participants with missing data, increasing the overall statistical power. LME allows the influence of factors for which data are extracted randomly from a population, providing more reliable results
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. To examine handball-related performance according to different napping opportunities, a model was built using “participant” as a random effect and “condition” (no nap, short nap, and long nap) as a fixed effect. Bonferroni correction for multiple comparisons was used during post-hoc analysis if the association was deemed significant.
Spearman's rank-order correlations were calculated to examine the association between task performance, PSQI, and the indices obtained by HRV analysis. All calculations were performed using (SPSS Advanced Statistics version 26, IBM Corp., Armonk, NY, USA).
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