The largest database of trusted experimental protocols

Spss for windows version 19

Manufactured by IBM
Sourced in United States, United Kingdom, Japan

SPSS for Windows, version 19.0 is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and presentation.

Automatically generated - may contain errors

504 protocols using spss for windows version 19

1

SPSS Statistical Analysis Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analyses were performed using the statistical package SPSS for Windows (version 19; SPSS, Chicago, IL, USA). Data were expressed as mean ± standard deviation (SD) values. All statistical tests were two-sided, and a p value <0.05 was considered statistically significant.
+ Open protocol
+ Expand
2

Hematological and Coagulation Profiles Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
According to the parametric or non-parametric distribution, the data were expressed as mean ± SD or median and interquartile range (IQR) as pertinent. The groups were compared for haematological profile, coagulation profile, anticoagulant proteins and D-dimer levels by using one-way an ANOVA on ranks (Kruskal-Wallis H test). When statistical differences were indicated, Dunn’s non-parametric comparison for post-hoc tests were obtained. The statistical significance was assumed at a “p” value of less than 0.05. The data were analysed with SPSS for Windows, Version 19 (SPSS Ltd., Surrey, UK).
+ Open protocol
+ Expand
3

Analyzing Trends in Undernutrition Prevalence

Check if the same lab product or an alternative is used in the 5 most similar protocols
SPSS for windows version 19 was used to conduct the analyses. The design effect parameters ‘sampling weight’, ‘sample domain’ and ‘sample cluster’ [32 ] were incorporated using SPSS’ Complex Samples Module. In line with recommendations that emphasize provision of levels of uncertainty in the estimates of undernutrition [33 ], 95% confidence intervals (C.I.) for the prevalence estimates were computed and are presented in Tables 2, 3, 4. Logistic regression was used to test trends. This involved modeling change in undernutrition prevalence regressed on time (the four survey years) with probability values for Wald F tests less than 0.05 considered significant (Tables 2, 3, 4). It is important to note that in the Tables, the 95% C.I. are calculated separately for each prevalence estimate and are not associated with the Wald F statistics that were generated by the logistic regression tests for trends.
+ Open protocol
+ Expand
4

Statistical Analysis of Qualitative and Quantitative Variables

Check if the same lab product or an alternative is used in the 5 most similar protocols
All data was analyzed by the software package SPSS for Windows, version 19. Qualitative variables were expressed as percentages, and quantitative variables as means and standard deviation. Fisher's exact test was used to compare qualitative variables, and Mann–Whitney U-test for quantitative variables. The McNemar test for related samples was used to analyze the evolution of the variables of the 2020 survey with respect to that of 2015. Statistical significance was considered when p < 0.05.
+ Open protocol
+ Expand
5

Smoking Patterns and Mental Health

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analyses were conducted using SPSS for Windows, Version 19.
Descriptive analyses included means and standard deviations for continuous
variables and frequencies for categorical variables. We compared current
smokers, past smokers, and never smokers on demographic, clinical, and cell
phone use characteristics. Women were identified as current smokers if they
endorsed current daily smoking, regular smoking despite having cut down, or
smoking once in a while. Current smokers were asked the average number of
cigarettes that they smoked daily. Due to the low frequency of Caucasian, Asian,
and Latina racial/ethnic groups, we dichotomized race/ethnicity as African
American vs. Non-African American. We used PHQ-9 scores to differentiate Major
Depression, “other depression,”and “no
depression” based on the methods described by Kroenke et al. (20 (link)).
We used a chi-square test to examine discrete variables and analysis of
variance to test group differences on continuous variables, with
Tukey’sHSD testa posteriori. Statistical significance
was set at alpha=0.05.Although we considered using regression analysis to
control the type I error rate, because this was a hypothesis-generating study,
we chose to limit the analyses to univariate tests for ease of
interpretation.
+ Open protocol
+ Expand
6

Statistical Analysis of Experimental Data

Check if the same lab product or an alternative is used in the 5 most similar protocols
All data were expressed as the mean ± standard deviation (SD) and compared using the t-test method. P < 0.05 was considered statistically significant. All statistical analyses were performed using the SPSS for Windows version 19.
+ Open protocol
+ Expand
7

Marital Status Impact on Prostate Cancer Survival

Check if the same lab product or an alternative is used in the 5 most similar protocols
Two-sided χ2 tests were used to compare patient baseline characteristics in different marital status groups. As to follow-up time, one-way analysis of variance and a post hoc test by Dunnett's test were used to compare the difference. Survival curves were generated using Kaplan-Meier estimates and differences in the survival rates were assessed using the log-rank test. The impact of marital status and other clinicopathological parameters on survival outcomes were evaluated by building multivariable Cox regression models. The primary endpoint of the present study was CSS. PCa-associated mortality was treated as an event and mortality from other causes was treated as a censored observation. The secondary endpoint was OS. All statistical analyses were performed using the statistical software package SPSS for Windows, version 19 (SPSS, Inc., Chicago, IL, USA). All P-values were two tailed and P<0.05 was considered to indicate a statistically significant difference.
+ Open protocol
+ Expand
8

Statistical Analysis of Experimental Data

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analysis was performed using SPSS for windows version 19. Data were measured for normal distribution and plotted in the figures as mean ± SEM. The significance of the differences between groups was determined with Student t test (two comparisons) or one-way ANOVA (multiple comparisons). P < 0.05 was considered significant.
+ Open protocol
+ Expand
9

Statistical Analysis of Categorical and Continuous Variables

Check if the same lab product or an alternative is used in the 5 most similar protocols
Statistical analysis was performed using SPSS for Windows version 19 (SPSS, Chicago, IL, USA). Categorical variables were expressed as frequencies, and percentages and continuous variables as the mean ± standard deviation, as appropriate. The data were analysed by ANOVA and, when appropriate, Duncan's test was used. A significance level of p<0.05 was applied.
+ Open protocol
+ Expand
10

Drug Cocktail Usage Patterns

Check if the same lab product or an alternative is used in the 5 most similar protocols
With respect to the consumption of drug mixtures, the study participants were divided into three groups. The category ‘intensive use’ is used for persons who took at least two different kinds of cocktails and at least one of them on a daily basis. The ‘moderate users’ consumed the combination of opioids, benzodiazepines and/or antihistamines on at least 1 day (or more) in the past month without fulfilling the above-mentioned criteria. The ‘non-cocktail users’ did not use these drug combinations during the past 30 days.
The data were transferred from the paper forms to a digital format. Statistical data analysis was performed using the Statistical Package for the Social Sciences (SPSS for Windows, version 19 [14 ]), and any differences in frequencies were analysed using the chi-square test; the ANOVA and t test served to compare the means (level of significance p < .05).
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!