The Usage of Emergency Contraceptive Methods of Female Students in Hawassa University: A Case Study on Natural and Computational Science

American Journal of Theoretical and Alied Statistics 207; 6(): 6-7 htt://www.scienceublishinggrou.com/j/ajtas doi: 0.648/j.ajtas.207060.8 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online) The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science Gezahegn Mekonnen, Nebro Wendmagegn, Demisew Gebiru School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethioia Email address: gm775043@gmail.com (G. Mekonnen), nebrowend@gmail.com (N. Wendmagegn), gebrudemisew206@gmail.com (D. Gebru) To cite this article: Gezahegn Mekonnen, Nebro Wendmagegn, Demisew Gebiru. The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science. American Journal of Theoretical and Alied Statistics. Vol. 6, No., 207,. 6-7. doi: 0.648/j.ajtas.207060.8 Received: December 7, 206; Acceted: January 3, 207; Published: February 24, 207 Abstract: Students in the higher education are found in adolescent eriod. Because of this, they are exosed to unwanted regnancy that leads them to attemt illegal abortion, which results in death or illness or loss of future fertility and on the other hand, they are obliged to leave their education. The objective of the study is to assess the factors that affect female studentsemergency contracetive usagein HU in case of Colleges of Natural and Comutational Sciences. The significance of the study is to create some insight on the female students about contracetive methods and to save female students from illegal abortion, unwanted regnancy and other related roblems. The source of data is rimary data and it was collected using questionnaires. The statistical model under consideration was logistic regression and chi-square test of association. In descritive art, the majority of student (77.6%) isless than 22 years old and42.4%are third year students. Sixty ercent of the resondents come from urban area and the arental educational level of 36.8% of the resondents is higher education level. Most of the female students accessedinformation of emergency contracetive from health rofessional, media and from their friends. 5.2% of Natural and Comutational Sciences female students are familiar with ECM, but the rest 48.8% of female students not use emergency contracetive at all. The usage of emergency contracetive method are associated with where the female students heard and get the ECM, their marital status, arent s educational level and batch of students. The female students usage of ECM affected by source and information about emergency contracetive method (ECM), their marital status and batch of students. Hence HU should reare anel discussion, ost some oster which talk about ECM in some areas like in clinic, invite some guests from the family lanning bureau to teach the students when and how to use the ECM. Keywords: Emergency Contracetive, Higher Education, Logistic Regression. Introduction Emergency contracetion is contracetion administered after unrotected sexual intercourse. Emergency contracetion (EC) is the only method women can use to revent regnancy after they have unrotected sexual intercourse, have exerienced a contracetive failure, have remember too late that they have forgotten to take their birth control ills and have been forced to have sex against their well. Emergency contracetion is sometimes referred to as morning after or ost-coital contracetion. Emergency contracetion (EC) is intended for occasional or emergency use only and not as a regular means of contracetion. Formerly, EC was thought to be effective only with 72 hours, but recent studies have confirmed it is effective for u to 20 hours. Emergency contracetion method includes taking secial doses of ordinary birth control ills as well as inserting an intrauterine device (IUD). Emergency contracetive ills (ECPs) are oral contracetive ills that women can take within 72 hours after unrotected intercourse to reduce her risk of becoming regnant. The resent research suggested the ills are effective if taken within 20 hours of unrotected intercourse. They intended for use after sexual intercourse when no contracetion methods used, when a regular contracetive method does not use roerly a daily oral contracetive

62 Gezahegn Mekonnen et al.: The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science method missed. There are two tyes of ECPs have been rigorously studied during the ast of 30 years. The more effective regimen is a rogestin only ill. Several manufactures now ackage and brand an effective dosage as a dedicated emergency contracetive ills roduct [22] The ills sometimes cause nausea headache craming breast tenders the ills also may cause irregular bleeding until woman menstruated again and menstruated may begin early or later. In the world health organization study cited reviously about 20% at women taking the combined Emergency contracetive ills exerienced to vomiting and 50% hand nausea comared to only 6% with vomiting and 20% with nausea among those taking the rogestin only ill. The awareness of emergency contracetive method of ill is increased but access to it still be roblematic for adolescents. In a study of women having abortions in 2000 200, only 46% reorted that they had not used any form of contracetion during the month in which they conceived []. Further, inconsistent use of contracetion (49% of condom users and 76% of birth-control ill users) was the most commonly cited reason for method failure. Many women reorted not using contracetion because they did not feel that they were at high risk of regnancy, and 32% failed to use contracetion because they were concerned about otential side effects. The women who reorted condom use as their rimary method of birth control, condom breakage or sliage was cited as the reason for unintended regnancy []. The demograhic and health survey from 2000 show that fewer than 2% of youth ages 5 to 24 have ever used emergency contracetive method in America, Cambodia, Malawi, Turkey and Uganda. In survey sexually active youth in 2000 to 200,0 ercent of Jamaican university female students had used emergency contracetive ills. Emergency contracetion adds an imortant otion for heling sexually active adolescents avoid unintended regnancy. Many adolescent females are at high risk of unintended regnancy. They have limited knowledge of contracetion and generally lack access to services or do not feel comfortable using these services. According to the Demograhic and Health Surveys (DHS) in Ghana, Kenya, Namibia, and Nigeria, the roortion of currently regnant women under age 20 who reorted that their regnancies were mistimed or unwanted was 46 ercent, 50 ercent, 55 ercent, and 58 ercent, resectively. The steadily decreasing age of menarche and increasing age of marriage have created a widening window of time for remarital sexual intercourse and regnancies. Even in countries where age at first intercourse has risen, the increase in age of marriage is usually greater, resulting in a widening ga. [2]. Studies have found a delay of about one year on average between starting sexual activities and first use of modern contracetives. Many unlanned regnancies occur within a year after first sexual intercourse Deending on the method used, Emergency contracetion (EC) can reduce women s risk of becoming regnant from a single act of intercourse by between 75 and 99 ercent. Reasons for such huge number of unintended regnancies include a low rate of contracetive used, method failure, and high unmet need for contracetive. Each year about 20 million women around the world become regnant among them, about 75 million regnancies 36% are unlanned or unwanted regnancy is one the leading cause of maternal mortality and morbidity. Each year worldwide, more than 20 million women exerience ill health because of regnancy. It is estimated that between 8 and 30 million regnancies each year result from contracetive failure either due to inconsistent or incorrect use of contracetive methods. Research studies conducted in the USA have reorted that higher rates of unintended regnancy occur among collegeage women, with 60% of regnancies among 20-24 years old being unintended. The 79% of unintended regnancy is even higher among 8-9-year-old females [4]. In Neal, the data suggest that more than a third (35%) of all regnancies and 4% of the current regnancy among currently regnant women are unintended. The revalence of remarital sex has been reorted as 39% among college males and 2% among college females. A considerable roortion of both males (0%) and females (22%) reorted that their first sexual intercourse haened without their consent. These students are at the greatest risk of unintended regnancy. A study has also found that a large roortion of college students who were studying in Kathmandu (43% of males and 55% of females) did not use a condom during their first sexual intercourse [6]. In develoing country, WHO estimates that one woman dies every eight minutes due to unsafe abortion. In Africa, such as Gahanna, Emergency contracetive ills have now become an integral art of contracetive services to revent concetion following unrotected and unlanned exosure or contracetive accidents like sliage of diahragm and forgotten ills. Also Emergency contracetive ills are used by victims of rae cases. There has been very extensive camaign and dissemination of knowledge of any contracetive method is almost universal in Gahanna, with 98% of all women and 99% of all men knowing at least one method of contracetion. However, 47.4% of Gahanna women have a history of contracetive use only 20% are current user [7]. In case of our country Ethioia, reroductive health need assessment reort showed that there is little knowledge and information available about emergency contracetive. Unwanted regnancy is one of the most commonly observed reroductive health roblems in Ethioia. The [23] reorted that 35% regnancies among women in reroductive age were unintended. The major factor limiting the use of Emergency contracetion was inadequate information about their effectiveness and availability or unfavorable oinions about their safety due to misinformation. The limited studies conducted on the issue of Emergency contracetion in the country, most of them focused on university students. Since most of college students are from rural areas, where the chance of getting information on sexual and reroductive

American Journal of Theoretical and Alied Statistics 207; 6(): 6-7 63 health is limited and most go outside camus and without arents suervision, their vulnerability to unintended sex and unwanted regnancy is high [3]. In case of south Ethioia, in Hawassa University, female students had been exeriencing high rate of unintended sexual ractice and regnancy. The resent study shows that 4.9% of female students ever had sexual intercourse and from this, 2.2% are exerienced forced sex. Oral contracetive ills were the most widely used form of all emergency contracetive [6]. Statement of the Problem The roortion of adolescents in the oulation is increasing and so the extent of their involvement in sexual activity. Students of the higher education are found in adolescent eriod. Because of this, they are able to have unwanted regnancy that leads them to attemt illegal abortion, which results in death or illness or loss of future fertility, and on the other hand, they are obliged to leave their education. Therefore, one can need to have meant on how to revent regnancy by way of contracetion, to save the life of these eole and increase the socio-economic develoment of the country. Since contracetion rovide as safe and effective way to regulate fertility and imroves women s healthy by reventing unwanted and high-risk regnancies and reducing the need for unsafe abortion [3] The study was attemts to addresses the following research questions:. What are the major barriers in using ECM? 2. Are most of the students of the university having awareness about usage of ECM? 3. Where the students get information about ECM? Objective of the Study The general objective of this study would be to assess the usage of emergency contracetive method of female students in Hawassa University in case of Natural and Comutational Science. Secific Objective. To see the major factors that affect female students on the usage of emergency contracetive method. 2. To identify the reference sources of information to female students among emergency contracetive method. 3. To see where the female students get information about the usage of emergency contracetive method. 4. To recommend the concerned body based on the result and discussion art. 2. Methodology 2.. Descrition of Study Area The study was conducted in Hawassa University. Hawassa University is established in 976 and it is located in historic city of SNNPR, Hawassa, Ethioia. It was almost 273 kilometers far from Addis Ababa and one of the biguniversities in Ethioia. Hawassa University formerly called Debub University and with the amalgamation of four indeendently oerating Colleges Hawassa College of Agricultural, Wondogenet College of Forestry and Natural Resource, Dilla College of Teacher Education and Health. Currently Hawassa University has six Branch Camuses. This study was conduct on main camus in case of Natural and Comutational Sciences. It consists six different deartments, Mathematics, Statistics, Physics, Biology, Chemistry and Sort Science. 2.2. Study Design In this study, the cross-sectional study design was used. The data were obtained through self-administered questionnaire. The questionnaire was designed to cature both qualitative and quantitative information related ECM. 2.3. Samling Design and Technique The samling technique used in this study was stratified random samling technique where the oulation is divided in to strata that reresent clearly defined grous of units within the oulation. 2.4. Samle Size Determination The question of how large a samle to take arises early in the lanning of any survey. This is an imortant question that should not be treated lightly. To take a reresentative samle which is needed to achieve the desired results. The aroriate samle size used for this study was obtained using the following formula [5]. where V= n= (), is the secified variance of the estimate and in case of estimating roortion, the stratum variance is given by S = P Q = S = PQ for common. Since we use roortional allocation samle,! " = # " /#= % " /%,! " stands for stratum weight, N=N + N 2 N 3. For this study, = 0.5 is used. This choice is determined from the revious study. The samling error is also called the level of recision in samling contexts. In this study a value of samling error of 4 ercent at 5 ercent significance level will used referring to similar studies. #= "4 )* " % " +, 3 ( *" (% " ) 2 ' 5 + 789 < : ; = 0.2054 0.00097 =24.26804=25 Thus, the required samle size for this study was 25 students from 788who were belongs to College of Natural and Comutational Science, Hawassa University, Next, the researcher carried out samle size allocation to each stratum with roortional allocation. Proortional allocation of total samle size to each batch is erformed.

64 Gezahegn Mekonnen et al.: The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science The oulation of N units was first divided in to sub oulations of N, N2, and N3 units resectively. Therefore, There were three stratums, namely stratum one for st year Table. Proortional allocation of total samle size to each butch (HU,206). students, stratum two for 2 nd year students and stratum three for 3 rd year students. S. N Stratum Total number of studentsin each butch Ni (Ni/N) Proortional Allocation to each Bach ni=n((ni/n) Year one 523 0.663706 82.9632 2 Year two 06 0.3458 6.8472 3 Year three 59 0.20777 25.22208 4 Total 788 25 2.5. Variables of the Study Deendent variable: ractice of emergency contracetive method (contracetive ractice status of female students, by classifying as non-user and user coding as 0 and resectively). Indeendent Variables Table 2. Indeendent variables with their categories (HU, 206). Variables Category Code Variables Category Code Less than 22 0 6) Parent less than500 0 ) Age 22-25 monthly 500-000 More than 25 2 income 00-500 2 2) Religion Orthodox 0 More than500 3 of students Protestant 7) Net Less than 50 0 Muslim 2 monthly 50-300 Catholic 3 income 300-500 2 Others 4 More than 500 3 3) Batch of one year 0 8) Educational Illiterate 0 Students Two year Level of Primary Three year 2 Parent High school 2 >three year 3 high school & above 3 4) Marital Single 0 9) Reason I didn t know use 0 status of Married why not use I couldn t get easily Students Divorced 2 ECM Not allowed in 2 5) Residence Rural 0 Religion 0) Source & Teacher 0 information Media of Student Urban ofecm Health rofession 2 Parent 3 Friends 4 2.6. Method of Data Analysis In this study, both descritive statistics and inferential statistics were used to analyze the given data. 2.6.. Logistic Regression Model Logistic regression analysis extends the techniques of multile regression analysis to research situations in which the outcome variable is categorical. Logistic regression allows to redict a discrete outcome, such as grou membershi, from a set of redictor variables may be continuous, discrete, dichotomous, or mixed [7]. Generally, the resonse variable is binary, such as emergencycontracetive ractice status of a students, by classifying as non-user and user coding as 0 and resectively. The logistic regression is also referred to multile regressions and discriminated analysis as it results in a biologically meaningful interretation, it is mathematically flexible and easily used distribution and it requires fewer assumtions [2]. In the context of this study the deendent variable was emergency contracetive usage status of students, by classifying as non-user and user coding as 0 and resectively. There are two main uses of logistic regression: Firstly, to redict the grou membershi. Since logistic regression calculates the robability of success over the robability of failure, the results of the analysis are in the form of an odds ratio. Secondly, logistic regression also rovides knowledge of the relationshis and strengths among the variables. 2.6.2. Model Descrition Since the resonse variable in logistic regression is usually dichotomous, we were define such a resonse variable as Y, and denote the even y=, when the subject has the characteristic of interest and y=0, when the subject does not have that characteristic of interest. In logistic regression, a single outcome variable Y i (i=... n) follows a Bernoulli robability function that takes the value with robability of success i or the value 0 with robability of failure G. This tye of variable is called a Bernoulli variable. The redictor variables in logistic regression could be discrete, continuous or mix of both.

American Journal of Theoretical and Alied Statistics 207; 6(): 6-7 65 In logistic regression, the relationshi between the redictor and resonse variable is not linear. Instead, the logistic regression function is used, which is the logit transformation of arameter and we assume that Y is dichotomous, taking on values of (success) and 0 (failure). Logistic regression model were used in order to address the issues under objectives of this study. And in this case, In the context of this study the deendent variable was contracetive ractice status that reresent Y G =, when female students not use emergency contracetive method and Y G =0, when they are use emergency contracetive method. The logistic model is defined Y I be a dichotomous outcome random variable with categories 0 and code. Let X (I (L)) denote the oint in (k+) dimension sace variables of Y, where X X ) M= (.... ' X I X X L X X L 3.. 2.. X I X IL X is called regression matrix and without the loading column of s is termed as redictor data matrix. Then, the conditional robability that an emloyee is use contracetive method given that X set of redictor variable is denoted by P(Y G = X G =P G ). The exression P G in logistic regression model can be exressed in the form of: P G = PQR STRU V T TR X U VX Y = PUR P QR STRU V T TR X U VX Y P UR (2) Where P(x i ) is the robability of i students can't use contracetive, for x redictors. And, - P (x i ) is the robability of i th student being use contracetive, for x redictors. β ~ (k+) x is a vector of unknown coefficients. However, the relationshi between the redictor and resonse variables is not a linear function in logistic regression; instead, the logarithmic transformation of equation yields the linear relationshi between the redictor and resonse variables. So an alternative form of the logistic regression equation is the logit transformation of P i given as logit_ Pi `=loga Pi Pi b=β d +β X G +β X G + +β L X GL (3) e f gfor every one unit change increase in h i j=,2,,k. 2.6.3. Parameter Estimation In logistic regression, the arameters can be estimated by maximum likelihood estimation method. Let Y be the random binary resonse variable whose value is either zero or one and X ' = x, x2, x3,, x is a vector of redictor variables. The robability P Y = x,, x is given by: ex βjxj j= P Y = x,, x = + ex α + βjxj j= The arameters of the above equation are estimated by maximum likelihood estimation technique. To aly the technique, each observation can be considered as Bernoulli trial and by assumtion that each yiis indeendent, the joint distribution of the observed values can be written as: P ( Y = y, Y2 = y2,, Y n = y n ) = P ( Y = y ) P( Y 2 = y 2 ) P( Y n = y n ). The robability of the is given by we obtain The coefficient can be interreted as the change in the logodds associated with a one unit change in the corresonding indeendent variable or the odd increases multilicatively by ex βjxji n = L ( α, β, β y,, y n ) j = i= + ex βjxji = j th i observation given yi P Y = yi x i, xi = i ( i ), x,, i x i Where, i =,, n and n is the number of cases in the data. The likelihood function for ( α, β, β 2,, β ) given ( y,, y n ) can be exressed as n yi yi L α, β, β y,, y n = i ( i ) i= By substituting = ( y x,..., xi) given above, y i + ex i = j= β x j ji i yj (4)

66 Gezahegn Mekonnen et al.: The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science The log of the likelihood function is given by This is equivalent to l= log ex n j i= + ex l = n i= ex j yi log + ex = = i β jxji β + jxji ex j= β jxji β jxji = j y + ( yi ) j= j= β x n log + ex j ji yj ( β x ) j ji The estimates of arameters can be found by maximizing the equation using iterative techniques such as Newton- Rahson method. 2.6.4. Assessment of Fitting Logistic Regression Model After fitting logistic regression model or a model has been develoed through the various stes in estimating the coefficients, there are several techniques involved in assessing the aroriateness, adequacy and usefulness of the model. First, the imortance of each of the exlanatory variables were be assessed by carrying out statistical tests of the significance of the coefficients. Then, the overall goodness of fit of the model was tested [4]. i. Model Selection With several exlanatory variables, there are many otential models. Model selection for logistic regression faces the same issues as for ordinary regression. The selection rocess becomes difficult as the number of exlanatory variables increases because of the increase in ossible effects and interactions. In model selection, there are two cometing goals: on one hand the model should be comlex enough to fit the data well. On the other hand, it should be simle to interret, smoothing rather than over fitting the data [] Therefore, it is imortant to examine several ossible models that allow the inclusion of many redictors and to choose a subset among them on the basis of subject matter and arsimony. Statistical software like SPSS uses either backward elimination or stewise model selection rocedures to come u with the final model. Stewise logistic regression, the forward or backward stewise logistic regression methods 30 utilizes the likelihood ratio test (chisquare differences) to determine automatically which variables to add or dro from the model. ii. Goodness of Fit of the Model The goodness of fit or calibration of a model measures how well the model describes the resonse variable. Assessing goodness of fit involves investigating how close values redicted by the model with that of observed values (Bewick and Jonathan, 2005). The comarison of observed to redicted values using the likelihood function is based on the statistic called deviance. I G4 (5) D= 2 my G lno q g s+( y r G )lno q g st g r g For uroses of assessing the significance of an indeendent variable, the value of Dare comared with and without the indeendent variable in the equation as given below: χ = D (model without the variable) D (model with the variable) The goodness-of-fit χ rocess evaluates redictors that are eliminated from the full model, or redictors (and their interactions) that are added to a smaller model. In general, as redictors are added or deleted, log-likelihood decreases or increases. The question in comaring models is whether the log-likelihood decreases or increases significantly with the addition or deletion of redictor(s) in the model. Likelihood-Ratio Test The likelihood ratio test statistic (G 2 ) is the test statistic commonly used for assessing the overall fit of the logistic regression model. An alternative and widely used aroach to testing the significance of a number of exlanatory variables is to use the likelihood ratio test. This is aroriate for a variety of tyes of statistical models [4] argues that the likelihood ratio test is better, articularly if the samle size is small or the arameters are large. The likelihood-ratio test uses the ratio of the maximized value of the likelihood function for the full model (L ) over the maximized value of the likelihood function for the simler model (L 0 ). The likelihood-ratio test statistic is given: G = 2logo v S v s= 2wlog(L d ) log(l )= 2(L d L )y (6) It is comared with a χ distribution with degree of freedom. This log transformation of the likelihood functions yields a chi-squared statistic. It tests the null hyothesis that all oulation in logistic regression coefficient is zero excet the constant one. And a small -value, for examle, < 0.05 leads to rejection of the null hyotheses that all of the redictor effects are zero. Thus

American Journal of Theoretical and Alied Statistics 207; 6(): 6-7 67 when likelihood test is significant, at least one of the redictors is significantly related to the resonse variable. Hosmer and Lemeshow Test Statistic The final measure of model fit is the Hosmer and Lemeshow goodness-of-fit statistic, which measures the corresondence between the actual and redicted values of the deendent variable. The Hosmer and Lemeshow test is a commonly used test for assessing the goodness of fit of a model and allows for any number of exlanatory variables, which may be continuous or categorical. z{ = a (} ~ } ~ + ~ ) # ~ + ~ ( + ~ ) b ~4 lower Wald statistic, and therefore the exlanatory variable may be incorrectly assumed to be unimortant in the model (Bewick and Jonathan, 2005). The likelihood ratio test is more reliable for small samle sizes than the Wald test []. R 2 Statistic: A number of measures have been roosed in 2 logistic regression as an analog to R in multile linear regressions. The Cox and Snell measure is based loglikelihoods and considers samle size. The maximum value that the Cox and Snell R 2 attain is less than. The Nagelkerke R 2 is an adjusted version of the Cox and Snell R 2 and covers the full range from 0 to, it is often referred. Therefore, in this study R 2 statistic to indicate how useful the exlanatory variables are in redicting the resonse variables were used []. Where g is the number of grous, # ~ is the number of covariate atterns in the k th grou, } ~ = 4ƒ is the number of resonses among the # ~ covariate atterns, and + ~ = + /# ~ is the average estimated robability. The test is similar to a χ goodness of fit test and has the advantage of artitioning the observations into grous of aroximately equal size. In this case, better model fit is indicated by a smaller difference in the observed and redicted classification [4]. The Wald Statistic: The Wald statistic is an alternative test, which is commonly used to test the significance of individual logistic regression coefficients for each indeendent variable (that is to test the null hyothesis in logistic regression model that a articular logit coefficient is zero). In logistic regression we have a binary outcome variable and one or more exlanatory variables. For each exlanatory variable in the model there was an associated arameter. The Wald test, described by [] is one of a number of ways of testing whether the arameters associated with a grou of exlanatory variables are zero. If for a articular exlanatory variable, or grou of exlanatory variables, the Wald test is significant, then we would conclude that the arameter associated with these variables are not zero, so that the variables should be included in the model. If the Wald test is not significant, then these exlanatory variables can be omitted from the model. Wald χ2 statistic was used to test the significance of individual coefficients in the model and is calculated as: Z= f Š Œ(f Š ) Each Wald statistic is comared with a χ distribution with degree of freedom. Wald statistic is easy to calculate but their reliability is questionable, articularly for small samles. For data that roduce large estimates of the coefficient, the standard error is often inflated, resulting in a X (7) 3. Results and Discussions Table 3. Characteristics of the racticeof ECM of Female Students (HU, 206). Descritive Statistics Descritive statistics utilizes numerical and grahical methods to look for atterns in a data set, to summarize the information in a data set and to resent the information in a convenient form. As indicated in Table 3 below, 77.6% of female students have age below 22 years, 9.2% ages are between 22 and 25 years and the remaining 3.2% ages are above 25 years. It was observed that among all samled female students, the ercentage of Orthodox, Protestant, Muslim, Catholic and othersfollowers were female students 72.8, 20.8, 3.2, 2.4 and 0.8resectively. About 37% of female students are first year students, 20% are second year students, 42% are third year students and the other % is above third year students. Among the total female students samled, 90.4% of them are single, 7.2% are married and 2.4% are divorced. About 39.2% of female students are coming from rural area and the other 60.8%from urban area. About 3.2%, 24.8%, 3.6% and30.4% female students' arents obtained less than 500 birr, between 500-000 birr, 00-500 and more than 500 birr resectively. Whereas, about 23.2%,24.8%, 33.6% and8.4% of student s net monthly income is below50 birr, between 50-300 birr, between 300 and 500 birr and above 500birr resectively. The result showed that 6.8% of female student s arents are illiterate, 22.4% of them have rimary education, the other 24.0% of them have high school education and the rest 36.8% of them have higher education. The reslt also showed that 36.0% of female students didn t know the use of emergency contracetive method, 23.0% of female students couldn t get the Emergency Contracetive easily and the remaining 4.0% of female students are not allowed by their own religion to use Emergency Contracetive Method. Characteristics Count Percent Less than 22 97 77.6 Age 22-25 24 9.2 More than 25 4 3.2

68 Gezahegn Mekonnen et al.: The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science Characteristics Count Percent Orthodox 9 72.8 Protestant 26 20.8 Religion Muslim 4 3.2 Catholic 3 2.4 Others 0.8 one year 46 36.8 Two year 25 20.0 Batch Three year 53 42.4 Above three year 0.8 Single 3 90.4 Marital status Married 9 7.2 Divorced 3 2.4 Residence Rural 49 39.2 Urban 76 60.8 Less than 500 39 3.2 Parent monthly 500-000 3 24.8 Income 00-500 7 3.6 More than 500 38 30.4 Less than 50 29 23.2 Net monthly 50-300 3 24.8 Income 300-500 42 33.6 More than 500 23 8.4 Illiterate 2 6.8 Parent Primary 28 22.4 Education High school 30 24.0 Level Higher level education 46 36.8 I didn t know the use 22 7.8 Reason why not use I couldn t get easily 4.2 ECM Not allowed in religion 25 20.0 Health rofession 52 4.6 Source of ECM Media 33 26.4 Teacher 8.8 Parent 5 2.0 Friends 4.2 Figure. Bar Grah of ractice of ECM.

American Journal of Theoretical and Alied Statistics 207; 6(): 6-7 69 From the above figure, one can observe that, in Hawassa University in case of Natural and Comutational Science (5.2%) of students are using Emergency Contracetive Method and the remaining 48.8% are not using (ECM) out of 25 samled students. Inferential Statistics Chi- Square Test A chi square test of association between usage of emergency contracetive methods of female students and related indeendent variables; as Table 4 result reveals that, the chi-square test of association between usage of Emergency Contracetive Method (ECM) of female students and batch, source and information of ECM, arental educational level as well as marital status of resondents have the Pearson chi-square values 9.909, 9.088, 6.926 and 6.543 resectively and all the -values are less than level of significance (0.05) and one can conclude that there is association between the usage of ECM and those indeendent variables. Table 4. Chi- Square Test (HU, 206). Variables Chi-square df -value Batch 9.909 3 0.09 Residence.60 0.206 Source of ECM 9.088 4 0.049 Parent educational level 6.926 3 0.044 Marital status 6.543 2 0.038 Logistic Regression Analysis Model Adequacy Checking The omnibus tests are measures of how well the model erforms, the chi-square tests measures the difference between the initial model and the regression model in terms of number of correctly classified subjects or it is the change in the -2log-likelihood from the revious ste. Since the omnibus test issignificant, the model in final ste is considered be aroriate. Final Ste Table 5. Omnibus Tests of Model Coefficients (HU, 206). Chi-square Df -value Ste 50.744 25.002 Block 50.744 25.002 Model 50.744 25.002 Cox & Snell R Square and Nagelkerke R- Squareare seudo R-squares. The model summary with -2log-likelihood statistic shows the overall fit of the model as shown in Table 6. Cox and Snell R square is 0.334, indicating that 33.4% of the variation in the variable is exlained by the redictor variables and the nagelkerke R-square shows that aroximately 45% of the variation in thedeendent variable is exlained by the redictor variables. Table 6. Model Summary of Cox & Snell R2 and Nagelkerke R2 (HU, 206). -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 4.774 0.334 0.455 The Hosmer&Lemshow statistics indicated that there is no significant difference between the observed and redicted model values and hence the model good fit tothe data as indicated in Table 7 below. Table 7. Hosmer - Lemeshow Test (HU, 206). Chi-square Df Sig. 0.346 8.242 Table 8. Results of the Final Multile Logistic Regression Model(HU,206). From Table 8, the ositive coefficient.833 for 2 nd year female students indicates that batch of students and emergency contracetive method have ositive association. As the female students increases by one batch the log odds of emergency contracetive users increased by.833. The odds of usage of emergency contracetive method of female students are6.254 times higher for second year students as comared to first year students. Whereas the ositive coefficient 2.864 for third year female students show that batch of students and emergency contracetive method have ositive association. As the female students increases by one batch the log odds of emergency contracetive users increased by 2.864. The odds of usage of emergency contracetive method of female students are 7.532times higher for second year students as comared to first year students. The coefficient of marital Status tomarriedis 0.908 and its odds is 2.478. Therefore, the odds of using emergency contracetive method 2.478 more likely than the corresonding odds for students who wasn't use emergency contracetive method keeing the other indeendent variables constant. The ositive coefficient.407 for female students indicates that arent educational level of students and emergency contracetive methods have ositive relationshi. As the female students arent education level increased the log odds of emergency contracetive users increased by.407. The odds of usage of emergency contracetive method of female students whose arents is high school is.245 times higher which comared from whichilliterate arents. The odds of female students who is getting and hearing the usage of ECM from the health rofessions is 30.08 times higher as comared to those getting and hearing the usage of ECM from the teachers. Variables Ž S.E. Wald df Sig. Ex( ) Age Below 22 (Ref.) 3.377 2.85 22 25 -.629.887 3.377.066.96 Above25 32.796 2.852 2.00.079.75

70 Gezahegn Mekonnen et al.: The Usage of Emergency Contracetive Methods of Female Students in Hawassa University: A Case Study on Natural and Comutational Science Variables Ž S.E. Wald df Sig. Ex( ) Batch first Year.003 3.02 2 nd year.833.854 4.63.032 6.254 3 rd year 2.864.87 0.86.00 7.532 More than 3 rd year -39.33 4.497.070.99.000 Marital Status Single(Ref.).799 2.003 Married.908.27.24.00 2.478 Divorced.820.485 2.858.09 2.27 Residence Rural(Ref) Urban -.29.659 2.933.087.323 Monthly income of arents lessthan 500 3.289 3.349 500-00.867.840.065.302 2.380 00-500.05.946.5.283 2.758 Greater than 500.743.973 3.208.073 5.77 Monthly income of Students less than 50 5.09 3.64 50-300 -.42.853.028.868.868 300-500 -.488.909.288.59.64 More than 500 2.06.369 2.266.32 7.855 Religion Orthodox (Ref) 3.284 4.52 Muslim -.825.720.33.252.438 Protestant -.482.904.606.436.227 Catholic -2.685.83 2.94.39.068 Other 8.863 4.09 6.02.86.556E8 Parent educational level Illiterate (Ref) 3.029 3.387 Primary.444.984.203.652 2.642 High School.407.932 2.277.03.245 above high school.658.023.43.520 3.58 Source of information about EMCTeacher(Ref) 7.543 4.0 Media.44.684.044.833.55 health rofessional 3.402.439 5.587.08 30.08 Parent.605.047 2.348.25 4.977 Friend.723.94.626.429 2.06 Constant.538.757.506.477.73 4. Discussion The urose of this study is to assess the usage of emergency contracetive method of female students in Hawassa University in case of Natural and Comutational Science. The study revealed that, out of the samled students, 5.2% of them are using Emergency Contracetive Method. In the logistic regression analysis, the association between the emergency contracetive usage among students and each redictor was tested. Students' family education level was found statistically significant redictor. The odds of emergency contracetive users were higher for student whose family is high school level than un-educated. This finding was consistent with studies by [9], which revealed that the ractice of contracetive methods was more revalent among students' family with rimary education. It is ossible that the higher educated women are more informed about various methods. The frequency of following health rofessional advice was an imortant redictor on emergency contracetive usage among students. This finding indicated that student who follow advise from rofessional had better chance to use emergency contracetive methods. This result was similar with [20]. Concerning source information, students who were getting information about emergency contracetive method from family lanning field workers were less robable to ractice than those who get from teachers. Batch of students had statistically significant effect on the emergency contracetive usage among married students. Those who were 3rd year students are more likely to use emergency contracetion methods. In addition, marital status was one of the significant redictors of emergency contracetive usage. Students who are married were more likely to use emergency contracetive method than students who were single. 5. Conclusion and Recommendation 5.. Conclusion The result obtained above can give us evidence to conclude the following. There is strong association between usage of emergency contracetive method with batch of female students, marital status of female students, educational level of femalestudent and sourceinformation about emergency contracetive method. The binary logistic regression results show that, batch

American Journal of Theoretical and Alied Statistics 207; 6(): 6-7 7 of female students, marital status of female students, source of information that student gathered and student arent educational level were the major factors that affectusage of emergency contracetive method in Hawassa University case of College of Naturaland Comutational Science. 5.2. Recommendation The statistical results of this study indicate that the most factors that facilitate usage of Emergency Contracetive Method (ECM) are marital status of students as well as source and information about Emergency Contracetive Method (ECM). This imlies, some of female students did not use Emergency Contracetive Method. Hence, the researchers recommend that, Hawassa University Shouldreare anel discussion and some brochure which deals with emergency contracetive methods in student s clinic. The University should also invite some guests from the family lanning bureau to teach the students when and how to use emergency contracetive methods. Government and nongovernmental organizations should focus on emergency contracetive method in the Universities. References [] Agresti, A. (2002). An Introduction to Categorical Data Analysis. John Wiley and So, Inc. New York. [2] Aziken, M. E., Okonta, P. I., & Adedao, B. A. (2003). Knowledge and ercetion of emergency contracetion among female Nigerian undergraduates. [3] Berhanu, D. and Nigatu, R. (20) Emergency contracetion among female students of Haramaya University, Ethioia, surviving the level of knowledge and attitude. [4] Bewick, L and Jonathan, B. (2005). Statistics Review 4: Logistic Regression. [5] Cochran, W. G. (977). Samling Techniques. Third Edition, John Wiley and Sons (ASIA) Pte Ltd, Singaore. P: 428. [6] Dkt Ethioia, (205) Safety of emergency contracetion. [7] Gelman, A. and Hill, J. (2007). Data Analysis using Regression and Multilevel or Hierarchical Models, Columbia University and Columbia University [8] Gujarati, D. (2002). basic econometrics (3rd edition). Mcgraw-hill, Inc, New York. [9] Jones (2002) Why do university students use hormonal emergency contracetion. [0] The Euroean Journal of Contracetion and Reroductive Health Care, 8, 39 44. [] Jones, R. K., Dorrach, J. E., &Henshaw, S. K. (2002). Contracetive use among US women having abortions in2000-200. Persectives on Sexual and Reroductive Health, 34 (6), 294 304. [2] Hosmer, D. and Lemeshow, S. (2000). Alied Logistic Regression (2 nd Ed.), New York. [3] Kongnyuy, E. (2007) A survey of knowledge, attitude and ractice of emergency contracetion among university students in Cameroon, BMC Emergency medicine. [4] Kwikmed, B. (204) The emergency contracetive ill: a survey of knowledge and attitudes among students at Princeton University. [5] Mathew s, W. (99). Family lanning survey among Ethioian Domestic Cororation Emloyees. Eth. J Health Dvt. 7(2): 86-9. [6] Ramash, A. (2009) factor affecting awareness of emergency contracetion among college students in Kathmandu, Neal. [7] Victor, M. (204) Adiosity hyertension and weight management behaviors in Ghanaian tye 2 diabetes mellitus atients age 20-70 years. [8] WHO. (999) Exanding otions in reroductive health: An assessment of reroductive health needs. [9] Islam, M. and Muhmud, M. (995). Contracetionamong adolescents in Bangladesh Asian-acific oulation Journal. United Nation: 0 (): 2-38. [20] Kabir, M. and Islam, A. (2000). The Imact of Mass Media Family Planning Programmes on Current Use of Contracetion in Urban Bangladesh. [2] Patriciao and Eskaya, S. (2005). Contracetive use and method choice in Turkey. International journal of Gynecology and Obstetrics. 75 (): 87 98. [22] Central Statistical Agency (CSA), (2006). Summary and Statistical Reort of Poulation and Housing Census Results. 2006. Addis Ababa, Ethioia.