Epi info version 3

Data were collected using a structured and pretested questionnaire via face-to-face interview at the participant’s home. The questionnaire was first prepared in English and then translated into local language (Amharic), and back to English to ensure consistency. Five midwifery nurses and one supervisor were involved in the data collection process. Local guiders were also participated in recruiting eligible women. Two days training was given to the data collectors and supervisor.
Data were entered using EPI-INFO version 3.5.3 and exported to SPSS version 20 statistical software for further analysis. Descriptive statistics were carried out to characterize the study population using different variables. Both bivariate and multiple logistic regressions were used to identify associated factors. Variables having p value ≤ 0.2 in the bivariate analyses were fitted into a multiple logistic regression model to control the effects of confounding. Crude and adjusted odds ratio with their 95% CI were calculated to determine the strength and presence of association. P value of 0.05 was considered to declare the level of significance.

Data was cleaned, coded, and entered using EPI-INFO version 3.5.3 and exported to SPSS version 16 for analysis. Bivariate analysis was done to see the association of each independent variable with the outcome variable (anemia status). Those independent variables having P value less than 0.2 in the bivariate analysis were entered into the multivariate analysis to determine the effect of each explanatory variable on outcome variable and to control the possible effect of confounders. Odds ratio with 95% confidence level was used to determine the strength of association. In the multivariate analysis, independent variables with P value ≤ 0.05 were considered as significant.

Complete pre-coded data was double entered into a computer using Epi-Info version 3.5.3, and transferred to SPSS version 16.0 after cross-checking and data cleaning. Tabulation, frequencies, proportion, and summary statistics were used to present the distribution of the study findings and to check missing values. Binary logistic regression was used to determine the association between dependent and independent variables. Variables that have a significant association with delay in treatment-seeking in binary logistic regression were further tested by multivariable model. Multivariable logistic regression was used to determine the relationship between several independent variables and a dependent variable. Odds ratio with 95% CI and P-value <0.05 were considered as cut point to measure the strength and significance of the association.

Data was entered in Epi Info version 3.5.3 and exported to SPSS version 20. Descriptive statistics like frequency and proportion were calculated to explain the characteristics of vendors and the contamination status of vegetables and fruits. Binary logistic regression analysis was done to assess factors associated with fruit and vegetable contamination. Variables with a p-value <0.25 in the binary logistic regression analysis were taken as candidates for multiple logistic regression in order to avoid the effect of confounders. An association with a p-value <0.05 at 95%CI was considered as significant.

Data were cleaned and entered to EPI info version 3.5.3 [26 ] and exported to SPSS version 20 [27 ] for descriptive and logistic regression analysis. We used descriptive data analysis techniques to describe the distribution of factors for adolescent-parent-communication among adolescents. We employed logistic regression to identify associated factors for adolescent-parent communication about sexual and reproductive health issues. We computed odds ratio with 95% CI to show the strength of the association between the adolescent-parent communication and associated factors. All variables which showed statistically significant results (P-value < 0.05) with adolescent-parent communication in bivariate logistic regression were taken to multivariate logistic regression model. Thus, the independent effect of each explanatory variable on an outcome variable was determined while controlled for others.

Data were entered to EPI info version 3.5.3 and then transferred to SPSS version 20 statistical package for analysis. Descriptive and summary statistics were carried out using percentages and mean ± SD and were presented in tables and graphs. Binary logistic regression analysis was conducted to evaluate the difference in anemia prevalence across the relevant variables. Odds ratio, Chi-square, and 95% CI for odds ratio were computed to assess the strength of association and statistical significance in bivariate analysis. Independent variables having P less than or equal to 0.2 in univariate analysis were included in multivariate analysis to control confounders in regression models. Variables having P value less than 0.05 in multivariate binary logistic regression model were considered to be statistically significant.

After thorough check up for completeness, free from any error on daily, the data were coded, cleaned and entered into Epi-info version 3.5.3 and exported to SPSS version 20 for analysis. Then descriptive frequencies were used for checking of outliers and to clean the data. The frequency distribution of dependent and independent variables was worked out. Correlation, Bivariate and Multivariable logistic regression were done. For all statistical significance test, the cut off value set was P < 0.05 as this is considered statistically reliable for the analysis of this study.