The data was coded and entered into SPSS windows version 20.0 statistical software by investigator. Then, the entered data was cleaned for errors prior to data analysis. Frequencies were used to check for missed values and outliers during analysis. Any errors identified were corrected after revision of the original data using the code numbers given to each questionnaire. The descriptive analyses such as percentages, measures of central tendency were conducted. Statistical association between dependent variable and covariates was done using logistic regression. Significance was determined using crude and adjusted odds ratios with 95% confidence intervals. To assess the association between the different factors variables of skilled birth attendant utilization with the dependant variable (Use of SBA), first relationships between each independent variable and dependent variable was investigated using a binary logistic regression model. Those independent variables that have association with use of SBA at the bivariate level at P < 0.05 were included in a multivariate logistic regression model, after controlling the possible effects of confounders and variable that had significant association were identified by calculating odds ratio with 95% confidence interval and those independent variables with P- value < 0.05 were declared statistically significant.
Spss windows version 20
Manufactured by IBM
Sourced in United States
SPSS Windows version 20 is a statistical software package developed by IBM. It is designed for data analysis, data management, and data visualization. The software provides a range of statistical techniques, including regression analysis, correlation analysis, and hypothesis testing.
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Lab products found in correlation
41 protocols using spss windows version 20
1
Factors Affecting Skilled Birth Attendant Utilization
2
Menstrual Hygiene Knowledge and Practices
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Each completed questionnaires was coded on pre-arranged coding sheet by the principal investigator to minimize errors. Data were cleaned and entered into a computer using Epi-info Window version 3.5.1 statistical program. Then the data were exported to SPSS Windows version 20.0 for analysis. The descriptive analysis including proportions, percentages, frequency distribution and measures of central tendency was done.
Initially, bivariate analysis was performed between dependent variable (Knowledge and practice of menstrual hygiene) and each of the independent variables (Socio-demographic variables), one at a time. Their odds ratios (OR) at 95 % confidence intervals (CI) and P-values were obtained, to identify important candidate variables for multivariate analysis. All variables found to be significant at bivariate level (at P-value < 0.05) were entered in to multivariate analysis using a logistic regression model in order to control for confounding factors.
Initially, bivariate analysis was performed between dependent variable (Knowledge and practice of menstrual hygiene) and each of the independent variables (Socio-demographic variables), one at a time. Their odds ratios (OR) at 95 % confidence intervals (CI) and P-values were obtained, to identify important candidate variables for multivariate analysis. All variables found to be significant at bivariate level (at P-value < 0.05) were entered in to multivariate analysis using a logistic regression model in order to control for confounding factors.
3
Multivariate Analysis of Health Factors
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Data was cleaned and entered into a computer using Epi-info Window version 3.5.1 statistical program. Then the data was exported to SPSS Windows version 20.0 for analysis. The descriptive analysis including proportions, percentages, frequency distribution and measures of central tendency was done. Initially, bivariate analysis was performed between dependent variable and each of the independent variables, one at a time. Their odds ratios (OR) at 95% confidence intervals (CI) and p-values were obtained, to identify important candidate variables for multivariate analysis. All variables found to be significant at bivariate level (at p-value
4
Prevalence and Predictors of Hematological Abnormalities
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The collected data were categorized, coded, entered onto a computer, cleaned (verified) and analyzed using SPSS Windows version 20.0 software packages for statistical analysis. Descriptive analysis was done to determine the prevalence of anemia, leucopenia, neutropenia, lymphopenia, and thrombocytopenia; and patterns of anemia; and was presented using tables, diagrams and summary measures as appropriate. Before analysis, continuous variables were checked on whether they were normally distributed or not using normal graph curves. Normally distributed Continuous variables were compared with dependent variables using T- test and ANOVA; when ANOVA revealed significant difference further post hoc-multivariate comparison were done. Categorical variables were compared with dependent variables using chi- square test and fisher exact test when appropriate. When association was found the odds ratio was used to measure the strength of association. Finally, for variables which had statistically significant association with dependent variable multiple logistic regression analysis were done to come up with the independent predictors of outcome variables (dependent variable). All tests were two tailed and statistical significance was considered at p < 0.05 with 95%CI.
5
Bivariate and Multivariate Analysis of Influential Factors
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Data were cleaned and entered into a computer using Epi-Info window version 6.5 statistical programs. The data were then exported to SPSS windows version 20.0 for further analysis. The descriptive analyses such as proportions, percentages, frequency distribution and measures of central tendency were conducted.
Initially, bivariate analysis was performed between dependent variable and each of the independent variables, one at a time. Their odds ratios (OR) at 95 % CI and p-values were obtained. The findings at this stage helped us to identify important associations. Then all variables found to be significant at bivariate level (at p < 0.05) were entered into multivariate analysis using the logistic regression model to test the significance of the association.
Initially, bivariate analysis was performed between dependent variable and each of the independent variables, one at a time. Their odds ratios (OR) at 95 % CI and p-values were obtained. The findings at this stage helped us to identify important associations. Then all variables found to be significant at bivariate level (at p < 0.05) were entered into multivariate analysis using the logistic regression model to test the significance of the association.
6
Multivariate Analysis of Health Data
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Data were cleaned and entered into a computer using Epi-Info window version 6.5 statistical programs. The data were then exported to SPSS windows version 20.0 for further analysis. The descriptive analyses such as proportions, percentages, frequency distribution and measures of central tendency were conducted.
Initially, bivariate analysis was performed between dependent variable and each of the independent variables, one at a time. Their odds ratios (OR) at 95 % confidence intervals (CI) and p-values were obtained. The findings at this stage helped us to identify important associations. Then all variables found to be significant at bivariate level (at p <0.05) were entered into multivariate analysis using the logistic regression model to test the significance of the association.
Initially, bivariate analysis was performed between dependent variable and each of the independent variables, one at a time. Their odds ratios (OR) at 95 % confidence intervals (CI) and p-values were obtained. The findings at this stage helped us to identify important associations. Then all variables found to be significant at bivariate level (at p <0.05) were entered into multivariate analysis using the logistic regression model to test the significance of the association.
7
Determinants of Mistreatment: Regression Analysis
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Data were cleaned and checked for completeness and consistency before they were entered into Epi-info version 7.2 statistical software and exported to SPSS windows version 20.0 software for analysis.
Descriptive statistics were expressed using summary statistics such as mean, range, frequency, and percentage. Confidence intervals were drawn for each mistreatment proportion using SPSS software. Both bivariable and multivariable logistic regression analyses were computed to identify the determinants. All explanatory variables with a p-value of less than 0.25 in the bivariable logistic regression analysis were included in the final multivariable logistic regression model after checking multicollinearity assumptions. Moreover, the model fitness was checked using Hosmer and Lemeshow’s test (P = 0.135). In the final model, a p-value of less than 0.05 and Adjusted Odds Ratios (AOR) with a 95% confidence interval (CI) were used to identify statistically associated factors.
Descriptive statistics were expressed using summary statistics such as mean, range, frequency, and percentage. Confidence intervals were drawn for each mistreatment proportion using SPSS software. Both bivariable and multivariable logistic regression analyses were computed to identify the determinants. All explanatory variables with a p-value of less than 0.25 in the bivariable logistic regression analysis were included in the final multivariable logistic regression model after checking multicollinearity assumptions. Moreover, the model fitness was checked using Hosmer and Lemeshow’s test (P = 0.135). In the final model, a p-value of less than 0.05 and Adjusted Odds Ratios (AOR) with a 95% confidence interval (CI) were used to identify statistically associated factors.
8
Prevalence of E. coli O157:H7 in Chicken Cloacae
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All the data were coded and entered into Microsoft Excel® 2007. The data were then exported to SPSS windows version 20.0 (SPSS) (IBM, Armonk, USA) for appropriate statistical analysis. The occurrence of the pathogen was determined by using descriptive statistics. Chi square (χ2) and odds ratio were used to measure the association between the different risk factors and occurrence of E. coli O157:H7 in chicken cloacae. Effects were reported as statistically significant if P value is less than 5% (P < 0.05).
9
Statistical Analysis of Survey Data
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Each completed questionnaire was coded on a prepared coding sheet by the principal investigator to minimize errors. Data were entered into a computer using the statistical program Epi Info for Windows version 7.0. Data were cleaned and exported to SPSS Windows version 20.0 for further analysis. Descriptive analyses, such as proportions, percentages, frequency distribution, and measures of central tendency, were carried out. Bivariable and multivariable logistic regression models were used. Initially, bivariable analysis was performed between the dependent variable and each of the independent variables, one at a time. Their odds ratios (ORs), 95% confidence intervals (CIs), and p-values were obtained. Then, all variables found to be significant at the bivariable level (p<0.2) were considered as candidates for the multivariable logistic regression model. Violations of regression model assumption were checked by inspection multicollinearity tests and variance inflation factors. Model goodness of fit was tested by the Hosmer–Lemeshow model goodness-of-fit test. A p-value of <0.05 and 95% confidence level was used as a statistically significant difference for the final models.
10
In Vitro Acaricidal Efficacy of Medicinal Plants
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Collected raw data was stored in a Microsoft Excel database system used for data management. Statistical software package called SPSS windows version 20.0 was used for data analysis. Descriptive statistics, percentages, and 95% confidence intervals were used to summarize the proportion of infected and noninfected animals. The effects of different host risk factors were considered and computed. All statistical significant levels was set at P < 0.05, and analysis of variance (one-way ANOVA-Tukey test) was used to compare the means of different treatments (concentrations) of the extracts and controls in different time used for in vitro acaricidal efficacy studies of medicinal plants.
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