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Pasw spss version 22

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PASW SPSS Version 22.0 is a software application for statistical analysis. It provides a range of tools for data management, analysis, and reporting.

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SPSS version 22.0 (9849 citations) SPSS 22.0 (7633 citations) SPSS version (3082 citations) PASW 22.0 (11 citations) SPSSTM (8 citations) PASW version 22.0 (7 citations) PASW Statistics SPSS version 22.0 (4 citations) SPSS-PASW statistics software version 22.0 (2 citations)

4 protocols using pasw spss version 22

1

Radiographic Factors Predict Lateral Humeral Fractures

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The Kolmogorov-Smirnov test was used to assess distribution. Patient characteristics and radiologic parameters were compared between patients with versus without LHF using paired Student t tests. Descriptive statistics were calculated as means with standard deviations and ranges. Univariate and multivariate logistic regression analysis with a forward elimination method was performed to evaluate factors related to LHF with a 95% confidence interval. For categorical variables, we recoded them to dummy variables and performed regression analysis. In addition, the Fisher exact test was performed to evaluate the relationship of the LHF and sufficient osteotomy (sufficient group [type A] vs insufficient groups [types B-D]). Intra- and interobserver reliability were evaluated by calculating the intraclass correlation coefficient and Cohen kappa coefficient between the measurements by the 2 surgeons. The data were analyzed using PASW SPSS Version 22.0 (IBM Corp). P values <.05 were defined as significant. Given the retrospective nature of the study, we could not perform a priori sample size analysis. Instead, a post hoc power analysis was done using G*Power Version 3.0.10 (Heinrich-Heine-University).
Lee S.J., Kim J.H., Baek E., Ryu H.S., Han D, & Choi W. (2021). Incidence and Factors Affecting the Occurrence of Lateral Hinge Fracture After Medial Opening-Wedge High Tibial Osteotomy. Orthopaedic Journal of Sports Medicine, 9(10), 23259671211035372.
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2

Age-Related Changes in Surgical Outcomes

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The data were analyzed using PASW SPSS, version 22.0 (IBM Inc., Chicago, IL, USA). Descriptive statistics are presented as numbers and percentages according to age groups. The Kolmogorov–Smirnov test was used to assess normal distribution. ANOVA was performed to evaluate the significant difference between all age groups. The differences in parameters between age groups were evaluated via post hoc analysis. The results were reported as means, standard deviations, and 95% CI. p values < 0.05 were defined as significant. Intra- and interobserver reliability were evaluated by three surgeons based on the interclass correlation coefficient between measurements.
Choi W., Lee S.J., Oh J., Baek H., Yang J., Shin J., Jung B, & Lee S. (2021). Magnetic Resonance Imaging of Patellofemoral Morphometry Reveals Age and Gender Variations in the Knees of Children and Adolescents. Diagnostics, 11(11), 1985.
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3

Predicting Visual Hallucinations in Patients

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We performed statistical analyses using SPSS (PASW) version 22 (SPSS, Chicago, IL). We used univariate logistic regression analyses to compare demographic and clinical characteristics between VH and non-VH groups. We did not correct for multiple comparisons because of the exploratory nature of these univariate analyses. Multivariate logistic regression analysis was used as the main analysis to identify independent predictors of VH. Demographic and clinical variables that were statistically significant in univariate comparisons with a P< 0.05 were included in the regression model. As our sample was comprised of both inpatients and outpatients, we also carried out a multivariate regression model including inpatient status. For all analyses, we checked modeling assumptions, including normality of measures, non-constant variance, and influential points. We assessed for multicollinearity to avoid redundancy in variables, including using variance inflation factors (VIF).
Chouinard V.A., Shinn A.K., Valeri L., Chouinard P.A., Gardner M.E., Asan A.E., Cohen B.M, & Öngür D. (2019). Visual hallucinations associated with multimodal hallucinations, suicide attempts and morbidity of illness in psychotic disorders.. Schizophrenia research, 208, 196-201.
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4

Metabolic Profiles in Schizophrenia Siblings

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We performed statistical analyses using SPSS (PASW) version 22 (SPSS, Chicago, IL). We used chi-square tests for categorical variables and ANOVAs for continuous variables to compare demographic and clinical characteristics among patient, sibling and control groups. We used mixed model ANCOVA analyses with contrasts to examine 31P MRS measures with diagnosis as a between-subjects factor, age and gender as covariates, and patient-sibling pairs as a random factor. Age was included as a covariate given the age dependence of NAD metabolite measures previously reported in the literature (Zhu et al., 2015 (link)). Our primary outcome measures were pH, PCr/β-ATP and Pi/β-ATP ratios, and the NAD+/NADH ratio. We also examined phospholipid metabolite ratios, NAD+, and NADH. We carried out exploratory bivariate correlation analyses to examine the relationship of Wisconsin Schizotypy positive and negative factor scores, and SCL-90-R total scores to our primary outcome measures in siblings. All hypothesis tests were two-sided and conducted with a significance level of alpha=0.05.
Chouinard V.A., Kim S.Y., Valeri L., Yuksel C., Ryan K.P., Chouinard G., Cohen B.M., Du F, & Öngür D. (2017). Brain bioenergetics and redox state measured by 31P magnetic resonance spectroscopy in unaffected siblings of patients with psychotic disorders. Schizophrenia research, 187, 11-16.
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