Study design and period
This study was questionnaire based cross-sectional study carried out for four months (November up to February 2021).
Injibera was the administrative center of Agew Awi Zone, which is located 447 km from Addis Ababa and 113km Bahir Dar City in Amhara regional state Ethiopia. Formerly the name Injibera was the name of small village towns around Kosober meaning “Koso tree” the name given by Emperor Haile Selassie during the Italian attack on Ethiopia. Today, Injibera was dominated the name of Kosober and Capital City of Agew nations Awi Zone in Amhara regional state. Injibera was the richest in cultural heritage and tourism specially Horse ride in cultural celebration during December to the end of March and the Agew Horse Association in 1933E.C, 81th celebration was conducted in Injibera on January 30 yearly before one month Adwa victory celebration in Ethiopia.
Study Population and sample
All Injibera males and females was investigated in the study. All those who met the inclusion criteria were included.
All Injibera males and females in Injibera during the study period with simple random sampling was included.
All males and females not meeting the inclusion criteria were excluded.
Since, primary source of data collection used to collect raw data from respondents through personal interview and closed ended questionnaires in Injibera.
The dependent variable was challenges in control COVID-19 (low, medium and high).
Low: The challenging knowledge, motivation, capability and controls are in place to prevent virus or at least significantly tangible solutions to prevent COVID-19 from being very good.
Medium: The Average knowledge, motivation and capability were good, but in control in place that may needs successful exercise to prevent COVID-19.
High: The knowledge, motivation and capability were highly poor and in control to prevent the COVID-19 were not enough and no idea about COVID-19.
The predictor variables were Education level (Illiteracy, Student, Diploma and Degree and above), Social media (Facebook, Television, Community, Radio, SMS, Twitter and Others), Racism (In colour, In Language, In Country and In Continent), Job types (Agriculture, Merchant , Civil servant, Teacher, Banker, Driver, Politician and Others), Housing status (rented, owned, others ), Residence (Urban, Rural), Religion (Orthodox, Muslim, Protestant, Others), Influence (Health status, Political, Social, Economical), Households size(Zero, 1-5, 5), Awareness (Poor, low, medium, high, very high) and Access (No, Yes).
Sampling Design and Techniques
The sampling method used in this study was simple random sampling procedure. The study used the cross-sectional sample design to determine the challenges of in control COVID-19 in Injibera.
Sample size determination
According to the 2007 national census conducted by the central statistical agency of Ethiopia, Injibera has an estimated total population of 21,065 of whom 10,596 are males and 10,469 are females. The sample size for this study was determined based on simple random sampling at 95% confidence level. The sample size formula is given by
Data entry and Analysis
After the data collected, the next step is edited, analyzed and summarized the data in appropriate manner and the available data would be transformed in to reliable and useful information with the help of statistical analysis procedure by using statistical package for social science (SPSS) version 25. Descriptive statistics was used information by frequency, percentage and table. Inferential statistics was making inference or conclusion about population, chi-square test and ordinal logistic regression model were used.
Chi-square test of association
The chi-square can’t be negative, then the curve don’t extended to the left of zero. The variables in chi-square distribution must be nominal or ordinal scale. The significance in chi-square test is right tailed areas of the distribution. In this statistical procedure there is relationship between categorical variables or not.
Ordinal logistic regression
The outcome variable can be grouped into ordinal, in this case, ordinal logistic regression models have been used to analyze ordinal response variables. Moreover, when there is a need to consider several factors, special multivariate analysis for ordinal data is the natural alternative. Ordinal logistic regression models have been widely applied in most investigation. The commonly used ordinal logistic regression model is the constrained cumulative logit model.
Cumulative logit model
Ordinal logistic regression refers to the case where the dependent variable has an order. The most common ordinal logistic model is the proportional odds model, also called cumulative probabilities of the response categories. If we pretend that the dependent variable is recorded as ordinal having categories, then the application of ordinal logistic model is the appropriate method. An attempt to extend the logistic regression model for binary responses to allow for ordinal responses has often involved modeling the cumulative logit.