The data carefully checked daily by collectors were further examined by the primary investigator and entered into Epi-Info version 3.5.1 and transferred into SPSS version 21 for analysis. Data cleaning, error checking, and analysis were conducted by using SPSS, version 21. The confidence interval for the overall proportion of anomalies was calculated manually and the rest of the proportions and statistical analysis were conducted by using SPSS version 21. Descriptive frequency and proportion (with its corresponding 95% CI) were used to describe the results.
Spss version 21
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SPSS version 21 is a statistical software package developed by IBM. It is designed for data analysis and statistical modeling. The software provides tools for data management, data analysis, and the generation of reports and visualizations.
Automatically generated - may contain errors
Lab products found in correlation
Epidata version 3, by IBM (48 mentions)
Sas version 9, by SAS Institute (46 mentions)
Prism 5, by GraphPad (42 mentions)
Prism 8, by GraphPad (34 mentions)
Prism 6, by GraphPad (25 mentions)
Epi info version 7, by IBM (20 mentions)
Sas 9, by SAS Institute (19 mentions)
Prism 7, by GraphPad (18 mentions)
Prism version 5, by GraphPad (17 mentions)
Spss statistics for window, by IBM (14 mentions)
Epi info version 3, by IBM (14 mentions)
Stata version 13, by StataCorp (11 mentions)
Prism version, by GraphPad (10 mentions)
Spss statistics for windows version 21, by IBM (10 mentions)
Epidata software version 3, by IBM (10 mentions)
R version 3, by R Project for Statistical Computing (10 mentions)
Stata version 14, by StataCorp (9 mentions)
Excel 2010, by IBM (9 mentions)
Prism 9, by GraphPad (8 mentions)
Epidata version 4, by IBM (8 mentions)
6 859 protocols using spss version 21
1
Statistical Analysis of Anomaly Data
2
Malaria Diagnosis by Microscopy
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After processing of the blood smears according to standard procedures [28 ], a sample was determined as negative for malaria parasites by microscopy if no parasites were observed in 200 fields, otherwise, the sample was classified as microscopy positive.
The demographic and haematological data of all the participants from the three sites were compared using Kruskal–Wallis analysis and Dunn’s multiple comparison test was used as post hoc test to assess any observed differences between groups in GraphPad Prism version 5. Independent students t test was used to compare continuous numerical data from pregnant women and non-pregnant women in GraphPad Prism version 5. Linear correlation analysis was also used to assess possible associations between parasite density, age, haemoglobin levels and axillary temperature.
The sensitivity and specificity as well as the positive and negative predictive values were determined using IBM SPSS version 21. IBM SPSS version 21 was also used to determine the level of agreement between the different tests were determined using the Cohen’s kappa inter-rater reliability test.
The demographic and haematological data of all the participants from the three sites were compared using Kruskal–Wallis analysis and Dunn’s multiple comparison test was used as post hoc test to assess any observed differences between groups in GraphPad Prism version 5. Independent students t test was used to compare continuous numerical data from pregnant women and non-pregnant women in GraphPad Prism version 5. Linear correlation analysis was also used to assess possible associations between parasite density, age, haemoglobin levels and axillary temperature.
The sensitivity and specificity as well as the positive and negative predictive values were determined using IBM SPSS version 21. IBM SPSS version 21 was also used to determine the level of agreement between the different tests were determined using the Cohen’s kappa inter-rater reliability test.
3
Neurobehavioral and gene expression analysis
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Obtained data were expressed as mean ± standard error of the mean. Behavioral (n = 8 per group), electrophysiological (n = 8 per group) and Western blot (n = 5 per group) results were analyzed statistically using 1-way analysis of variance followed by Tukey multiple comparison tests using SPSS version 21 software (IBM Corp, Armonk, NY). Gene expression data (n = 5 per group) were analyzed using Relative Expression Software Tool (REST)-XL version 2. 43 This software determines differences in relative gene expression level of the samples compared with the control group. The ED 50 value for each group was determined using linear regression, with SPSS version 21 software (IBM Corp; n = 8 per group). The ED 50 values were measured as means attended by 95% confidence limits.
4
Reference Interval Determination for Hematological Parameters
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All the data were coded and checked for completeness, then entered into Epidata, and analyzed using SPSS version 21 statistical software for Windows. The data were tested for normality of distribution using the Kolmogorov–Smirnov test; therefore, the non-parametric methods for the determination of RI were used as recommended by CLSI. Median, central 95 percentile, and 90% confidence interval (CI) were calculated. The 97.5 percentile and 2.5 percentile were the upper and lower reference limit for the population. The data for the hematological parameters were collected and analyzed using SPSS version 21 software. The point estimate of the mean and the median with an interval estimate of 2.5 percentile and 97.5 percentile were provided as the reference values. The significant difference between sex groups was determined using the Wilcoxon rank-sum test (Mann–Whitney U test). p value <0.05 was considered statistically significant.
5
Statistical Analysis of Research Data
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The obtained values were recorded, tabulated in Microsoft Excel 2021, and subjected to statistical analysis. The analysis was done using SPSS version 21 software (SPSS Inc., Chicago, IL, USA). Unpaired "t" and analysis of variance was used to find the significance of study parameters between the groups and paired t-test was used to find the significance of study parameters within the groups. Welch's t-test was also applied to find out the equality between the mean of all groups. Descriptive statistical tests will be computed using Excel statistical operations. Inferential statistics will be done using SPSS version 21.
6
Statistical Analysis of Experimental Data
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The results of all experiments were expressed as mean ± standard deviation (SD) of at least three separate tests. Student's t-test or two-way ANOVA was performed with SPSS Version 21.0. P<0.05 was considered statistically significant and P<0.05 indicates a statistically significant difference. All data are presented as the mean and standard deviation from at least three separate experiments. P<0.05 was considered to indicate a statistically significant difference. All data are presented as the mean and standard deviation from at least three separate experiments. And a p value <0.05 was consider statistically significant. The statistical analysis software used was SPSS Version 21.0.
7
Metabolomic and Gut Microbiome Analyses
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Data for growth performance, immune factors, morphological, and VFA analyses are presented as the mean ± SEM and analyzed using SPSS Version 21.0 (SPSS Inc., Chicago, IL). One-way ANOVA and Tukey's multiple comparison tests were performed to evaluate the variation among and between the groups. P < 0.05 indicates statistically significant. Figures were prepared using GraphPad Prism 8.0. Data analysis and figure preparation for the metabolome and microbiome were performed on the Majorbio Cloud Platform as mentioned above.
Correlation assays were conducted between the differential serum metabolites, significant gut microbes (at the genus level), growth indices (BW and ADG), and immune parameters (immunoglobulins and inflammatory cytokines). Data were inputted into SPSS Version 21.0 to calculate the correlation coefficients based on Spearman's correlation distance. Heatmaps were prepared using GraphPad Prism 8.0 to assess bivariate relationships between variables. * P < 0.05, ** P < 0.01, *** P < 0.001.
Correlation assays were conducted between the differential serum metabolites, significant gut microbes (at the genus level), growth indices (BW and ADG), and immune parameters (immunoglobulins and inflammatory cytokines). Data were inputted into SPSS Version 21.0 to calculate the correlation coefficients based on Spearman's correlation distance. Heatmaps were prepared using GraphPad Prism 8.0 to assess bivariate relationships between variables. * P < 0.05, ** P < 0.01, *** P < 0.001.
8
Genetic Variants of CTNNA3 and T2D Risk
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The differences in demographic characteristics (age, gender, BMI, etc.) were tested by SPSS version 21.0 software (SPSS, Chicago, IL, USA) (χ2 test/t-test). After testing whether the four candidate genetic loci of CTNNA3 meet Hardy–Weinberg equilibrium (SPSS version 21.0 software), we used logistic regression model to calculate the odds ratio (OR) and 95% confidence interval (CI). Then, according to the value of OR and CI, the association between CTNNA3 candidate SNPs and T2D risk was estimated (OR value represents relative risk; OR = 1: this factor has no effect on T2D risk; OR < 1: T2D protective factor; OR > 1: T2D risk factor). Using the wild-type allele as a reference, the online tool software plink 1.07 was used to estimate multiple genetic models. The statistical results obtained were adjusted by age and gender, and all tests were two-sided tests. In addition, we conducted a false-positive report probability (FPRP) analysis to detect whether the significant findings were just chance or noteworthy observations [24 (link)]. In this study, haplotype analysis was conducted by plink1.07 and Haploview software and linkage disequilibrium (LD) was calculated. Finally, the interaction of candidate SNPs in T2D risk was evaluated by multi-factor dimensionality reduction (MDR).
9
Evaluating Intervention Fidelity and Outcomes
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There were few missing items on the participants’ questionnaires altogether (18 items = .46% in total). When no more than two items were missing, values were replaced with the mean value of the scale or subscale. To compare services during and after implementation, and to compare clinician- and consumer-rated outcomes pre- and post-implementation, paired samples t tests with bootstrapping were performed in SPSS (version 21). To examine associations between clinician participation and their intention to further use of IMR, path analysis was performed using the lavaan R package [43 (link)]. Multiple regression analyses were performed in SPSS (version 21) to examine whether higher intervention fidelity was associated with better consumer outcomes.
10
Antibiotic Prescribing Practices in PHC Centers
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The sampling technique used was Multistage cluster sampling, to select PHC centers during the first phase of the study, then to select registered physicians in the second phase of the study and then later to select their prescriptions. After obtaining the list of all PHC centers of Makkah from the Ministry of Health, we randomly selected 20% of the PHC centers with the help of Statistical Package for the Social Sciences software (SPSS) version 21. As there are 70 PHC centers in Makkah, a total 14 PHC centers were selected. From each selected PHC center we randomly selected general practitioners and from each physician we randomly selected their prescriptions for ARI. After obtaining their consent, a total of 908 prescriptions were collected by going to clinic daily and collecting the prescriptions. We compared the prescriptions with WHO guidelines recommendations for acute respiratory infections [33 ]. The main variables assessed, beside basic demographic characteristics, included: chief complaints, temperature, pulse rate, respiratory rate, provisional diagnosis and the type of antibiotic prescribed. All variables were entered into the computer and analyzed by SPSS version 21.
The protocol was submitted to IRB of College of Medicine and ethical approval was obtained as HAPO-02-K-012-2016-02-142.
The protocol was submitted to IRB of College of Medicine and ethical approval was obtained as HAPO-02-K-012-2016-02-142.
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