All analyses were performed using IBM Statistical Package for Social Sciences (SPSS) for Mac version 24[23 ]. The level of significance was set at 5%. Sociodemographic characteristics and clinical features were reported as means and standard deviations (SD) for continuous measures such as age and as frequency and percentage for categorical measures such as education level. To compare the scores on the PSST items by MINI-U responses (Yes vs No), we reported the median and interquartile range (IQR), and we used the Wilcoxon-Mann-Whitney test to determine if the PSST severity measures are valid to differentiate between those who answered Yes vs No on MINI-U. Bonferroni correction (an option in SPSS) was used to correct for the multiple comparisons. The comparisons were followed by receiver operating characteristics (ROC) analyses using the MINI-U answers as the gold standard to determine the cut-off scores on the PSST, in addition to their sensitivity and specificity measures. Finally, we used the highest Youden indices (J) to determine the best cut-off scores on each item in PSST and the corresponding sensitivity and specificity[24 (link)].
Statistical package for social sciences spss for mac version 24
Manufactured by IBM
SPSS for Mac version 24 is a statistical software package designed for data analysis. It provides a wide range of statistical techniques for data manipulation, analysis, and visualization. The software is primarily used for social science research, market research, and data-driven decision making.
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Lab products found in correlation
2 protocols using statistical package for social sciences spss for mac version 24
1
Validating PSST Severity Measures
2
Analyzing Suicide Attempts and Accidental Self-Harm
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The analyzed data were divided into three groups: suicidal attempt/admitted to Psychiatry (Suicide/Adm), suicidal attempt/not admitted to Psychiatry (Suicide/NAdm), and accidental self-harm with no suicidal intent (parasuicide). Continuous variables (like age) were analyzed as the mean ± standard deviation (SD) while the categorical ones as frequency and percentage. All the variables analyzed were categorized and thus we used the Chi-square test to compare the data between the above groups. We used IBM Statistical Package for Social Sciences (SPSS) for Mac version 24 (IBM Corp, Armonk, NY, 2015) to analyze the data. P value was set at 0.05 for level of significance. All analyses were done with Bonferroni corrections for multiple comparisons across subgroups. This type of correction is automated in SPSS and usually based on the significance set level divided by the number of comparisons in each set. Regarding missing data, we calculated the percentage for each variable and category in the study. For the nominal variables, the missing data was added and analyzed as a separate category. When the latter was more than 25%, we did not proceed with the interpretation of the results. Furthermore, if the missing data category was showing significance, then the results were considered invalid.
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