Filled questionnaires were checked for completeness, cleaned, coded, and entered into EPI data statistical software version 3.1 and exported to SPSS windows version 23 for further analysis. Frequencies, proportions, and summary statistics were used to describe the study population in relation to relevant variables and presented using narration and tables. The Bivariate analysis was employed to identify candidate variables for multivariable analysis. Then variables found to have p-values of less than 0.25 were entered into the Multivariable Logistic regression for controlling the possible effects of confounders. Finally, variables which had significant associations were identified at a p-value <0.05 and Adjusted OR with 95% CI were determined to see the strength of the associations. Hosmer and Lemeshow test was used to check the fitness of the model.
Epi data statistical software version 3
Manufactured by IBM
Epi-Data statistical software version 3.1 is a data entry and analysis program. It provides tools for creating, validating, and analyzing epidemiological databases. The software supports a range of statistical functions for data management and analysis.
Automatically generated - may contain errors
Lab products found in correlation
Spss version 22, by IBM (2 mentions)
Spss statistical software version 22, by IBM (2 mentions)
Spss software version 22, by IBM (1 mentions)
Spss windows version 23, by IBM (1 mentions)
Spss software version 21, by IBM (1 mentions)
Spss version 26, by IBM (1 mentions)
Spss version 20, by IBM (1 mentions)
Spss window version 22, by IBM (1 mentions)
Spss software version, by IBM (1 mentions)
16 protocols using epi data statistical software version 3
1
Statistical Analysis of Survey Data
2
Nurses' Surgical Site Infection Prevention Practices
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The collected data were coded, entered, and cleaned by using EpiData statistical software Version 3.1 and then exported into SPSS version 20 for analysis. Descriptive statistical analysis was used to describe the characteristics of participants. Bivariable logistic regression analysis was run to determine the association between the independent variables and outcome variables. The p-value and an odds ratio with 95% CI were computed. Then the variables with a p-value ≤ 0.25 were taken into the multivariable model to control for all possible confounders. The multi-co-linearity test was carried out to see the correlation between independent variables by using tolerance and variance inflation factors (VIF). However, there was no independent variable with a tolerance value of less than 0.1 or VIF of greater than 10. For model fitness, Hosmer-Lemeshow's model of fitness was checked which was 0.411 indicating the test fit the model.
The level of statistical significance was declared at a p-value < 0.05. Finally, by using the coefficient of kappa and its interpretation, the agreement between an observational and self-reported nurses’ SSI prevention practice was determined (k = 0.424) indicating moderate agreement.
The level of statistical significance was declared at a p-value < 0.05. Finally, by using the coefficient of kappa and its interpretation, the agreement between an observational and self-reported nurses’ SSI prevention practice was determined (k = 0.424) indicating moderate agreement.
3
Multivariable Analysis of Explanatory Factors
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Data were entered using epi data statistical software version 3.1 and analyzed using SPSS version 20. Descriptive statistics using tables and graphs was presented. Binary logistic analysis with conditional method calculating odds ratios (OR) and 95% confidence intervals (CI) was used to estimate the association between the dependent variable and independent variables. Statistical significance was set at α. ≤ 0.05.
In an attempt to identify the relative effects of explanatory variables on the outcome variable, hierarchical multivariable analyses was applied. Explanatory variables with P-value <0.2 were entered into the final regression model based on the likelihood ratio for further analyses in two different models.
In an attempt to identify the relative effects of explanatory variables on the outcome variable, hierarchical multivariable analyses was applied. Explanatory variables with P-value <0.2 were entered into the final regression model based on the likelihood ratio for further analyses in two different models.
4
Factors Associated with Folate Deficiency
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The data were entered using Epi-Data statistical software version 3.1 and then
exported to SPSS software version 22.0 for analysis. Simple descriptive
statistics, such as mean, standard deviation, median, frequency, percentiles,
and percentage, were used to present socio-demographic characteristics and
magnitude of RBC folate level. Logistic regression was used to analyze
independent variables with folate deficiency, and multivariate logistic
regression was used to identify the independent associated factors of folate
deficiency. A variable with a p-value of ⩽0.5 in bivariable
logistic regression was eligible for multivariable logistic regression analysis
model. A p-value of less than 0.05 was considered statistically
significant in all analyses.
exported to SPSS software version 22.0 for analysis. Simple descriptive
statistics, such as mean, standard deviation, median, frequency, percentiles,
and percentage, were used to present socio-demographic characteristics and
magnitude of RBC folate level. Logistic regression was used to analyze
independent variables with folate deficiency, and multivariate logistic
regression was used to identify the independent associated factors of folate
deficiency. A variable with a p-value of ⩽0.5 in bivariable
logistic regression was eligible for multivariable logistic regression analysis
model. A p-value of less than 0.05 was considered statistically
significant in all analyses.
5
Multivariable Analysis of Factors Associated
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Data was collected, verified as complete, cleansed, coded, entered into EPI Data statistical software version 3.1, and then exported to SPSS Windows version 23 for additional analysis. The study population was described in relation to relevant variables using frequencies, proportions, and summary statistics, and then presented using text, tables, and figures. The bivariate analysis was employed to identify candidate variables for multivariable analysis. Then, to control potential confounding effects, variables with p-values less than 0.25 were included in the multivariable logistic regression. Finally, variables which had significant associations were identified at p value <0.05 and Adjusted OR with 95% CI was determined to see the strength of the associations.
The Hosmer and Lemeshow test was used to check the fitness of the model. For qualitative data, in-depth interviews were audio-recorded, transcribed, and translated to English. After reading the text several times, codes were given and related codes were categorized. Then, categories were merged to form themes. Finally, the findings were presented in narrations and triangulated with quantitative findings.
The Hosmer and Lemeshow test was used to check the fitness of the model. For qualitative data, in-depth interviews were audio-recorded, transcribed, and translated to English. After reading the text several times, codes were given and related codes were categorized. Then, categories were merged to form themes. Finally, the findings were presented in narrations and triangulated with quantitative findings.
6
Predictors of Modern Contraceptive Utilization
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The completed data was entered using Epi-Data statistical software version 3.1 and then exported to SPSS version 22 for final analysis. Frequencies and percentages were used to present categorical data. The household wealth index was also constructed by using Principal Component Analysis (PCA). Odds ratios (ORs), 95% confidence intervals (CIs), and p value were calculated using a logistic regression model to determine association levels of predictors to the outcome variables. All variables with p value ≤0.15 were taken into the multivariable model to control for all possible confounders, and the variables were selected by all methods. A multivariable logistic regression analysis was used to estimate the adjusted OR of predictors to modern contraceptive utilization by controlling confounding factors. A variable having p < 0.05 was considered a statistically significant variable in all models. Before the inclusion of predictors to the final logistic regression models, the multicollinearity was checked using VIF < 10, tolerance tests >0.1, and standard error. The goodness of fit of the final logistic model was tested using Hosmer and Lemeshow test at a value of >0.05.
7
Statistical Analysis of Blood Samples
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After checking for completeness and cleaning, processing and analysis of the data obtained from laboratory analyses of the blood samples and questionnaires were performed by coding and entering the data into Epi-Data statistical software version 3.1 and then exporting the data to Statistical Package for Social Sciences (SPSS) software version 23 package, and the different variables were tested and analyzed. Simple descriptive statistics were used to present the socio-demographic and clinical characteristics of the study subjects. While chi-square (χ2) tests were used to compare categorical variables, continuous variables were presented as mean ± standard deviation (SD) and were compared using Student’s t-tests for groups. Other associations were performed with Pearson’s correlation coefficient as well as multiple linear regression analysis. A p-value of <0.05 was considered to be statistically significant in all the analyses.
8
Factors Influencing Maternal Health Knowledge
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The completed data was entered using Epi-Data statistical software version 3.1 and then exported to SPSS version 22 for final analysis. Frequencies and percentages were used to present categorical data. The uni-variable analysis was done using aIndependent two-Sample t-testand analysis of variance (ANOVA). The correlation between knowledge and practices was evaluated by the Pearson correlation coefficient test. Multivariable analysis was done using linear regression after multi-collinearity was checking using the variance inflation factor and tolerance test. Finally, p < 0.05 was considered a significant level.
9
Neutrophil-to-Lymphocyte Ratio in Diabetic Nephropathy
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Simple descriptive statistics such as mean, standard deviation, percentiles, and percentages were used to present socio-demographic characteristics, clinical parameters, NLR, and other laboratory tests, and the values were compared between DN and without DN patients. The data were entered using Epi-data statistical software version 3.1, and then exported to SPSS software version 21.0 for analysis. Student’s t-test and a chi-square test were used to test significant differences between the means of dependent quantitative variables and independent categorical variables of the two groups (T2DM with and without DN) and Pearson correlation was applied to analyze the relationship between NLR and other independent and dependent variables. The p values <0.05 were considered to be statistically significant in all analyses.
10
Epidemiological Data Analysis Protocol
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Data were coded, checked, and entered into Epi-data statistical software version 3.1 and then exported to SPSS software version 23 for analysis. Descriptive statistics were presented as frequency and percentage. Pearson's correlation was used to compute the association between dependent and independent variables. P value < 0.05 at 95% confidence level was considered to be statistically significant in all the analysis.
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