Spss 22.0 j for windows

Manufactured by IBM

Sourced in Japan, United States

SPSS 22.0 J for Windows is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including data management, statistical modeling, and visualization. The software is designed to handle a wide range of data types and supports a variety of statistical techniques, such as regression analysis, ANOVA, and clustering.

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SPSS Version 22.0 J for Windows SPSS 22.0.0.0 for Windows SPSS 22.0 for Windows SPSS 22 for Windows SPSS 22.0 Windows 22.0 for Windows SPSS 22.0 IBM SPSS for Window SPSS 22.0 SPSS Windows

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Hemodynamic Changes After Intervention

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At least five samples for each experimental condition were analyzed. All values are presented as mean ± standard deviation (SD). Analyses were performed with SPSS 22.0 J for Windows. We have carried out multicomparison analysis using one-way analysis of covariance (ANCOVA) for ROM. And PS, ED, RI, PI were compared among all groups using the one-way analysis of variance (ANOVA) test. It was utilized to analyze the data followed by the t-test. An alpha less than 0.05 was chosen as the significance level for these statistical analyses Figs. 4, 5, 6, 7.
Fig. 4

Changes of PS in each group before and after intervention. *Significant contribution to compared with the group 2, p < 0.05

Fig. 5

Changes of ED in each group before and after intervention. *Significant contribution to compared with the group 2, p < 0.05

Fig. 6

Changes of RI in each group before and after intervention. *Significant contribution to compared with the group 2, p < 0.05

Fig. 7

Changes of PI in each group before and after intervention. *Significant contribution to compared with the group 2, p < 0.05

Maimaiti P., Aisilahong G., Shu J.J., Rozi P., Keqike N., Miao T, & Wang N. (2023). Multidisciplinary rehabilitation intervention on mobility and hemodynamics of joint contracture animal model. Journal of Orthopaedic Surgery and Research, 18, 300.

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Job Crafting Impact on Work Engagement

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We analyzed the data from 894 respondents after excluding those with non-regular employment and re-employment in a temporary position. The Internet survey system did not allow missing values; therefore, respondents had to answer all of the questions. A hierarchical multiple regression analysis, which can show the associations between an independent and a dependent variable, controlled for all other predictors included in the analysis, was carried out on work engagement and psychological distress. The independent variables were entered into the equation in two steps. Demographic characteristics (age, gender, job position) were entered in Step 1 and the four factors of job crafting were entered in Step 2 simultaneously. Statistical analyses were conducted by SPSS 22.0J for Windows.

Sakuraya A., Shimazu A., Eguchi H., Kamiyama K., Hara Y., Namba K, & Kawakami N. (2017). Job crafting, work engagement, and psychological distress among Japanese employees: a cross-sectional study. Biopsychosocial Medicine, 11, 6.

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Evaluating Fear Conditioning and Extinction

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We used the data from the average of percentage freezing during the initial 6 min on day 3 and on day 28 as TEST and RE, respectively. Further, we used the data from the average of percentage freezing at 2-min intervals in each extinction session to measure and calculate fear extinction. For fear conditioning and extinction, we used one-way (group) and multiple (bin, day and group) analysis of variance (ANOVA) to detect significant differences, respectively.
In the OFT we used the data from the average of moving distance at 2-min intervals and the total duration time in the center area to measure the total distance and center time, respectively. For the center time and total distance, we used a one-way (group) ANOVA or Student's t-test and a two-way (time and group) repeated ANOVA, respectively, for significant differences. We used a post-hoc Bonferroni test or Dunnett's test for multiple comparisons. For all analyses, the level of statistical significance was set at p<0.05. All statistical were performed using SPSS 22.0 J for Windows (SPSS Japan, Tokyo). The data are presented at mean ± SEM.

Mizuno I., Matsuda S., Tohyama S, & Mizutani A. (2022). The role of the cannabinoid system in fear memory and extinction in male and female mice. Psychoneuroendocrinology, 138.

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Predictive Factors Affecting RECIST Outcomes

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Descriptive statistics were applied for the patient sociodemographic characteristics and clinical information. The sociodemographic and clinical indicators potentially associated with predictor variables were examined separately in the univariate logistic regression model. To confirm statistical significance, multivariate logistic regression analysis was used to determine the relative contribution of the various factors affecting RECIST outcomes. We considered a p value less than 0.05 to be statistically significant. Statistical analyses were performed with SPSS 22.0 J for Windows.

Ito S., Ito H., Sato N., Hirayama Y., Kusakabe T., Terui T, & Ishitani K. (2019). Clinical factors associated with the therapeutic outcome of chemotherapy in very elderly cancer patients. International Journal of Clinical Oncology, 24(5), 596-601.

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Gene Expression Comparison Across Time

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The expression of each gene is presented as mean ± standard deviation (SD). When a significant difference was observed in a one-way analysis of variance (ANOVA) with day as the factor, multiple comparisons with the Bonferroni method were performed. P < 0.05 was considered as statistically significant. SPSS 22.0 J for Windows (SPSS Inc., Chicago, IL, USA) was the statistical software used for this analysis.

Okamura K., Inagaki Y., Matsui T.K., Matsubayashi M., Komeda T., Ogawa M., Mori E, & Tanaka Y. (2020). RT-qPCR analyses on the osteogenic differentiation from human iPS cells: an investigation of reference genes. Scientific Reports, 10, 11748.

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