The results were expressed in the form of COI. The chi-square test was deployed when testing the association between the proportion of SARS-CoV-2 positive cases and gender, and between individuals of 50 years of age or younger and > 50 years old at the time of testing. The Mann-Whitney U test was used to compare the full distribution of ranks of anti-SARS-CoV-2 Igs levels between men and women who tested positive, and between individuals of 50 years of age or younger and > 50 years old at the time of testing, all of whom tested positive. The two-sample t-test was employed to compare mean ages between men and women who tested positive. Binary logistic regression analysis was performed to assess independent predictors of SARS-CoV-2 positive cases. The variables tested as covariates were age and gender. Data analysis was performed in MS Excel, as described by McDonald in 2014 [21 ], and IBM SPSS Statistics for Windows v27 (IBM, Armonk, NY).
Spss statistics for windows v27
Manufactured by IBM
Sourced in United States
SPSS Statistics for Windows v27 is a statistical software package developed by IBM. It is designed to perform a wide range of data analysis and statistical procedures. The software provides tools for data management, analysis, and presentation, catering to the needs of researchers, analysts, and decision-makers across various industries.
Automatically generated - may contain errors
Lab products found in correlation
11 protocols using spss statistics for windows v27
1
Epidemiological Analysis of SARS-CoV-2 Seropositivity
2
Predictors of Purpose in Life Among Adults
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Sociodemographic information was descriptively analyzed.
After invoking the Central Limit Theorem (large N), Pearson’s correlation coefficient was the measure of association used to characterize the strength of linear associations between the studied variables. Multilinear linear regression (using the ENTER method and PIL as the dependent variable) was used to obtain a parsimonious model to identify predictors of the purpose in life total score according to two scenarios: (i) working adults; (ii) retirees. The model’s assumptions were analyzed: normal distribution, homogeneity, and error independence. The first two assumptions were validated graphically, and the assumption of independence of errors was validated using the Durbin–Watson statistic (d = 1.59) [44 ]. The variance inflation factor (VIF) was used to identify multicollinearity among the variables. Outliers were examined (data with a studentized residual, in absolute value, more significant than 1.96). Although some outlier cases were found, they were kept in the models, since they did not significantly affect their goodness of fit.
All statistical analyses were performed with the software IBM SPSS Statistics for Windows v.27 (IBM Corp., Armonk, NY, USA). Type I error probabilities (α) of 0.05 and 0.10 were considered for all analyses.
After invoking the Central Limit Theorem (large N), Pearson’s correlation coefficient was the measure of association used to characterize the strength of linear associations between the studied variables. Multilinear linear regression (using the ENTER method and PIL as the dependent variable) was used to obtain a parsimonious model to identify predictors of the purpose in life total score according to two scenarios: (i) working adults; (ii) retirees. The model’s assumptions were analyzed: normal distribution, homogeneity, and error independence. The first two assumptions were validated graphically, and the assumption of independence of errors was validated using the Durbin–Watson statistic (d = 1.59) [44 ]. The variance inflation factor (VIF) was used to identify multicollinearity among the variables. Outliers were examined (data with a studentized residual, in absolute value, more significant than 1.96). Although some outlier cases were found, they were kept in the models, since they did not significantly affect their goodness of fit.
All statistical analyses were performed with the software IBM SPSS Statistics for Windows v.27 (IBM Corp., Armonk, NY, USA). Type I error probabilities (α) of 0.05 and 0.10 were considered for all analyses.
3
Fertilizer and Lime Effects on Nitrogen Dynamics
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Dry weights, growth parameters, specific N absorption rates, specific N utilization rates, %NDFA, atmospheric-derived N, soil-derived N and were compared among the fertilizer and lime treatments using analysis of variance (ANOVA) in IBM SPSS Statistics for Windows v. 27 (IBM Corp. 2017 ). Where the ANOVA results showed significant differences (P < 0.05) among treatments, a Bonferroni post hoc test was used to separate the means.
4
Comparing Arterial and V-TAC PaCO2 Levels
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Prior studies exploring the agreement between absolute arterial and v-TAC PaCO2 values indicated a requirement for 41 matched samples at 80% power (β-level 0.20) and 5% significance (α-level 0.05).8–15 (link) We assumed a proportionate variability for the change in PaCO2 in the absence of existing data, though aimed to recruit at least 80 patients. In addition to the lack of data, this was because some recruited patients would not require NIV; a review of admissions to our unit in the preceding year showed that 67% of patients assessed were treated with NIV. We elected to include patients prior to NIV because (1) this enabled the agreement analysis to include a wider range of baseline CO2 values, (2) we wished to minimise sampling burden for patients and (3) all matched sample episodes meeting prespecified technical criteria would be available for the agreement analysis for ABG vs v-TAC, CBG and VBG. Bland-Altman analysis of mean and limits of agreement, and analysis of response to NIV, were performed for time-matched samples for values of pH, PaCO2 and PaO2. Pain scores were compared via Friedman’s two-way analysis of variance by ranks (IBM SPSS Statistics for Windows, V.27) and significant values adjusted for by the Bonferroni correction.
5
Detailed Cardiac Surgery Protocol
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Normal distribution and homogeneity of variances were tested by Shapiro–Wilk and Levene’s tests, respectively. Multiple groups were compared by Welch analysis of variance (ANOVA) followed by Games–Howell’s post-hoc test or Kruskal–Wallis one-way ANOVA followed by Bonferroni’s post-hoc test as appropriate. Two groups were compared by Mann–Whitney U-test or t-test as appropriate. In study A, animals in groups Bup-O1 and Bup-O2 were exposed to the same surgery, handling and analgesia procedures from one and a half hours before to 10 hours after MI surgery, and mean serum corticosterone and serum Bup values were not significantly different at any of these time points. Serum corticosterone and Bup data from Bup-O1 and Bup-O2 were hence combined into a pooled Bup-O12 group for statistical comparison with Bup-SC at these time points. Composite scores were used for statistical analysis of behaviour-based pain assessment. IBM SPSS Statistics for Windows v27 (IBM Corp., Armonk, NY) was used for statistical analysis, and p-values <0.05 were considered statistically significant.
6
Temporal Trends in Childhood Psychological and Emotional Violence
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data were analysed using IBM SPSS Statistics for Windows v27 (IBM Corp., Armonk,
NY). Weighted data were applied for descriptive statistics in order to adjust
for under-represented groups in the responding population. Frequencies of the
outcome variables were calculated for each birth year and transformed into time
series, which were then subjected to T4253H smoothing for graphical
presentation.
One-way analysis of variance (ANOVA) was performed using the time series data to
study associations between the respondents’ birth year and the proportion of
respondents exposed to CPV or CEV in each respective birth year. Visually
apparent increases or decreases in outcome variable frequencies were further
analysed using logistic regression, with exposure to CPV or CEV as the dependent
variable and birth year as the independent variable. For this purpose, a dummy
variable was created by dividing the respondents into six 10-year groups
according to birth year. The logistic regressions were performed separately for
women and men using non-weighted data. A significance level of <0.05 and
two-tailed analyses were applied throughout.
NY). Weighted data were applied for descriptive statistics in order to adjust
for under-represented groups in the responding population. Frequencies of the
outcome variables were calculated for each birth year and transformed into time
series, which were then subjected to T4253H smoothing for graphical
presentation.
One-way analysis of variance (ANOVA) was performed using the time series data to
study associations between the respondents’ birth year and the proportion of
respondents exposed to CPV or CEV in each respective birth year. Visually
apparent increases or decreases in outcome variable frequencies were further
analysed using logistic regression, with exposure to CPV or CEV as the dependent
variable and birth year as the independent variable. For this purpose, a dummy
variable was created by dividing the respondents into six 10-year groups
according to birth year. The logistic regressions were performed separately for
women and men using non-weighted data. A significance level of <0.05 and
two-tailed analyses were applied throughout.
7
Drowning Prevention Study Protocols
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Observation and survey data were collected using paper questionnaires with EIA staff oversight. Data were later entered into Qualtrics 17 by EIA and USC GRIT staff, where it was then uploaded to a cloud database managed by a central data manager at USC GRIT lab. Student researchers at USC GRIT lab downloaded, cleaned and analysed the data using Microsoft Excel (2021) and IBM SPSS Statistics for Windows V.27. 18 Frequencies and percentages were calculated for key variables of interest including all landing site observations, general participant demographics, water safety knowledge and experience, including ability to swim and recognise a drowning victim, and access to preventive services such as emergency response systems and drowning prevention education.
Focus group and KII discussions were recorded, transcribed and translated into English by EIA staff. Transcribed transcripts were then analysed separately by two USC GRIT lab researchers using Nvivo software. Each researcher first independently reviewed the text to identify main themes and subthemes. They then met to compare, confirm and code final key themes. 19 (link) This method was used to ensure coding reliability across analysts.
Focus group and KII discussions were recorded, transcribed and translated into English by EIA staff. Transcribed transcripts were then analysed separately by two USC GRIT lab researchers using Nvivo software. Each researcher first independently reviewed the text to identify main themes and subthemes. They then met to compare, confirm and code final key themes. 19 (link) This method was used to ensure coding reliability across analysts.
8
Measurement Accuracy and Reliability Assessment
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data analysis was performed using IBM SPSS Statistics for Windows, v27 (IBM Corp., Armonk, NY, USA). Mean absolute percentage errors (MAPE) were calculated by comparing all measurement values from both participants to ground truth values. Bland–Altman analysis was performed by calculating mean difference (‘bias’) and the limits of agreement (mean difference ±1.96 × standard deviation (SD) of mean difference), along with their 95% confidence intervals (expressed in [brackets]) [12 (link)]. Interobserver and intraobserver variability were assessed using the intraclass correlation coefficient (ICC) and the within-subject coefficient of variance (CV) [13 (link)]. ICC was calculated using the two-way mixed absolute agreement model. Significance levels were set at p < 0.05. CV was defined as the SD of within-subject differences expressed as a percentage of the mean [14 (link)]. All 3 repeated measurements of participant 1 were used to assess intrauser variability, and the first measurement of both participants for interuser variability. ICC values of <0.5, 0.5–0.75, 0.75–0.9 and >0.9 were regarded to reflect poor, moderate, good and excellent correlation, respectively [11 (link)]. CV <5% and 5–10% were regarded to reflect good and acceptable repeatability, respectively.
9
Seroprevalence Trends in COVID-19 Pandemic
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Standard descriptive statistical methods were used in the data analysis. The Chi-square (χ2) test was used to determine a statistically significant difference by age and gender. The exact test of goodness-of-fit was used to compare the seroprevalence by month (September 2020 – May 2021). Data analysis was performed in MS Excel, as described by McDonald in 2014 (18 ) and IBM SPSS Statistics for Windows v27 (IBM, Armonk, NY). The level of statistical significance was P<0.05. The Bonferroni correction for 36 comparison (seroprevalence by month) was used to reduce type I error, where statistical significance was P<0.0014.
10
Syndecan-1 as Biomarker for Active UC
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical analyses were performed using IBM SPSS Statistics for Windows, v. 27 (IBM Corp., Armonk, USA). Values are displayed as mean and standard deviation (SD), or n. To test for significance, unpaired t-tests (to compare means) or Pearson's χ 2 tests (to compare n in contingency tables) were used, and an alpha level of a p < 0.05 was considered significant. To examine the correlation between the complete Mayo score and Sdc1, the Kruskal-Wallis test was used, and a p < 0.05 was considered statistically significant. To examine the correlations of CRP and stool calprotectin with Sdc1, bivariate correlation (Pearson's r) was used. A receiver operator characteristic (ROC) curve analysis was performed to check for the discrimination threshold of Sdc1 (discrimination of colonoscopic Mayo score <2 compared to ≥2). Points on the ROC curve nearest the upper left corner were chosen for cutoff values as this resulted in optimal sensitivity and specificity. Logistic regression analysis was performed to determinate the regression coefficients of CRP, hemoglobin, hematocrit, and Sdc1 since these parameters were proven to be significant in t-test analyses. Based on these data, a model (see results) was created to improve the prediction of active UC. This model was again analyzed using ROC curve analysis.
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
Revolutionizing how scientists
search and build protocols!