Ibmspss statistics 22

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IBMSPSS Statistics 22 is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including data management, statistical modeling, visualization, and reporting. The software is designed to help users analyze and interpret complex data sets, supporting a wide range of statistical techniques and methods.

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Spss amos 26 graphics, by IBM (1 mentions)

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Statistics 22.0 (84 citations) IBMSPSS 22.0 (3 citations)

2 protocols using ibmspss statistics 22

COVID-19 Preventive Behaviors and Factors

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Data were exported from the online questionnaire platform and imported into IBM
SPSS statistics 22. Descriptive statistics were applied to demonstrate the
demographic characteristics, COVID-19 preventive behaviors, and IDSHL. One-way
ANOVA (analysis of variance) was adopted to compare the differences among
demographic groups, and the Student-Newman-Keuls method was applied to compare
the differences among subgroups if a significant difference was found. The
Pearson correlation coefficient was used to examine the relationships among
IDSHL, COVID-19 preventive behaviors, and their predictors, which are continuous
variables.
Path analysis was implemented by IBM SPSS Amos 26 Graphics. Maximum likelihood
estimation was conducted to evaluate the parameters with the covariance matrix.
The path model was modified according to the P regression
weight, modification indicators, and goodness-of-fit indexes (GFIs). Only
variables with significant P values (less than .05) remained in
the model. A comparative fit index ≥0.90 and root mean square error of
approximation <0.06 means that the fit was acceptable.¹⁹

Wang H., Cheong P.L., Wu J, & Van I.K. (2021). Health Literacy Regarding Infectious Disease Predicts COVID-19 Preventive Behaviors: A Pathway Analysis. Asia-Pacific Journal of Public Health, 33(5), 523-529.

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Factors Influencing Therapeutic Drug Monitoring

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Categorical variables were expressed as frequencies and percentages, and continuous variables were expressed as medians and interquartile range (IQR). Variables associated with the use of TDM and proactive TDM were assessed using a univariate logistic regression analysis reporting the corresponding P value and odds ratio (OR) as well as 95% confidence interval (CI) for statistically significant variables. To determine the independent effects of variables associated to the use of TDM and proactive TDM, a multiple binary logistic regression analysis was then performed including variables with a P value of <0.1 from univariate analysis, based on the Backward Wald selection method. The results were considered statistically significant when P < 0.05. GraphPad Prism version 5.03 for Windows (GraphPad Software, San Diego, California) and IBM SPSS Statistics 22 (IBM SPSS, Costa Mesa, California) were used for statistical analyses.

Grossberg L.B., Papamichael K., Feuerstein J.D., Siegel C.A., Ullman T.A, & Cheifetz A.S. (2017). A Survey Study of Gastroenterologists' Attitudes and Barriers Toward Therapeutic Drug Monitoring of Anti-TNF Therapy in Inflammatory Bowel Disease. Inflammatory bowel diseases, 24(1).

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