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\title{Multivariate Analysis of Factors Influencing Achievement of Students in Selected Subjects at Secondary School Level: A Case Study of Grade 10 Students at Hawassa City, Ethiopia}
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             \author[1]{Endris Assen  Ebrahim}

             \affil[1]{  Debre Tabor University, Ethiopia}

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\date{\small \em Received: 9 December 2015 Accepted: 3 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


This study has been an attempt to determine factors influencing academic achievements of grade 10 students (normally under age 18 years) on specific subjects. A crosssectional survey was conducted on a total of 719 sample students of grade 10 from 11 different government and nongovernment secondary schools using multistage sampling technique. A designed questionnaire was used to obtain data from the respondents. The secondary data on students EGSECE scores were obtained from the Education Department as achievements of students in the five selected subjects: Mathematics, Biology, Physics, Chemistry and English. Descriptive analysis, factor analysis and multivariate multiple linear regression analyses were used to analyze the data. From the descriptive results both governmental and nongovernmental school students were achieved poorest in physics and best in English. However, on average, nongovernmental school students? achievements were better than governmental school students. In factor analysis, self-concept, motivation to the subjects and teaching-learning process explained most of the variations. Multivariate regression results revealed that, the factors, sex, school type, school facilities, family status, school volume, interest to the subject, motivation to the subject, self-concept, safe reading and trouble (anxiety) to the subjects, had significant influence on achievements of students with respect to most of the subjects. Factors like sex, school facility, family status, motivation to the subject, interest to the subject had a significance positive impact on achievements. However, trouble of the subject and school volume had a significant negative influence on students? achievements on Biology, Physics and English subjects. It is suggested that academic facilities and managements at schools, beside home and students? personal efforts need to be promoted for better academic achievements of students in subjects.

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\keywords{factor analysis; multivariate multiple linear regression analysis; school subjects; achievement; grade ten.}

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\let\tabcellsep& 	 	 		 \par
Introduction he current educational system in Ethiopia is organized in cycles or levels of formal schooling that includes ten years of general education. General education is completed at the end of the first cycle of general secondary school education (Grade 9 and 10). Moreover, this cycle is intended to enable students to identify area of interest (Natural Sciences Stream and Social Sciences Stream) for further training in the second cycle of general secondary education (Grades 11and 12) to prepare students for continuing their studies at higher education level (University or collages) or selecting their own vocations. Students appear for the New National Examination at the end of grade 10 (normally under 18 years old) which is known us the Ethiopian General Secondary Education of Certificate Examination (EGSECE). This is after the students have successfully achieved school examinations in all school subjects. However, students should score a minimum of 2.00 on a scale of 4.00 in EGSECE or a minimum of 50 out of 100 in standard school exams at least in five or seven subjects: English, Mathematics (both compulsory) and any other three or five science (Natural or Social) subjects in order to appear in EGSECE \hyperref[b16]{[14]}.\par
Girls' education is one of the fundamental pillars for ensuring sustainable economic development, democratic participation and poverty reduction. As a result, gender discrimination affects not only women but also the overall growth of the economy. In this connection, the Ethiopian government has given more attention to girl's education.  {\ref In 2003} {\ref In -2004}, due to the favorable policy environment, the gross enrollment of female students at general secondary first cycle (9-10) was about 37.0\% and at the preparatory level \hyperref[b11]{(11)}\hyperref[b14]{(12)}, it was 29.0\%. Moreover, in technical, vocational and training institutions (colleges), it was 49.0\%, whereas it was 25.2\% in higher education/Universities. Nevertheless, there was a great variation of students' achievement at different school type (non-governmental and governmental) based on their gender. Without controlling for student background differences, nongovernmental schools scored higher than government (public) schools ( \hyperref[b4]{[4]}; \hyperref[b15]{[13]}).\par
It is obvious that students at schools can be classified as clever (high achievers), medium (average achievers) and lazy (low achievers) with respect to individual's achievements in specific school subjects based on exam scores or general test results of subjects. The general belief is that, if the student is intelligent or clever, he/she is expected to perform well at school in compulsory and science school subjects and is well fitted for national and regional exams. But intelligence is not the only influential factor of academic achievement in school subjects. In addition to intelligence, there are various factors influencing academic achievement of students at school in each school subjects ( \hyperref[b1]{[2]};  {\ref [19]}).\par
This study has been undertaken to investigate multivariate evaluation of the impacts of family with student and school characteristics variables on academic achievement of students on five selected subjects at secondary schools, specifically in grade 10.\par
The presence of all or some of the factors identified above may have resulted in the poor academic achievement of students on each school subjects in some areas of our country. However, evidence of the availability of these factors as well as other factors need to be obtained or checked. The purpose of this study, therefore, is to obtain the factors that are responsible for the poor academic achievement of students with school type and gender gap on school subjects among secondary schools of grade 10 students at Hawassa city, in SNNPR state. 
\section[{Statement of the Problem}]{Statement of the Problem}\par
In 2007/08 the number of students who sat for grade 10 national exams, at SNNPR state, was 92,836 (male 61,742 and female 31,094). Out of these who get CGPA of 2.00 and above out of 4.00 were 33,211 (25,085 males and 8,126 females). The percent of promoted students in a successive three years, 2005/06, 2006/07 and 2007/08, were 45.8\%, 44.2\% and 35.8\%. Specifically, the percentage of promoters (scored 2.00 and above) at Hawassa City Administration in 2007/08 were 46.7 \%. \hyperref[b14]{[12]} Reported that the test items (exam questions) of the EGSECE for English were not relatively content valid. Hence, test items did not match with the syllabus contents.\par
Students might pass from one class level to the other as they evaluated on CGPA result of all subjects. But due to achievement variation with respect to each school subject, students get difficulty and being unsuccessful in higher level education which leads directionless. The current education system of Ethiopia gives a great attention, about 70\%, on natural sciences subjects, to enhance sciences and technology. Therefore, it is better to find solutions to the problems and factors one faced in his/her academic achievements in selected subjects: Mathematics, Biology, Physics, Chemistry and English at secondary schools in grade 10 distinctly but dependably.\par
Many reasons have been attributed for the high failure rate and poor academic achievements in secondary schools. Some researchers traced that the high failure rate of students was due to student's inability to comprehend and balanced the principles of some subjects such as Mathematics, Physics and others. Others are of the view that the abysmal school achievement is due to loaded curriculum (there is too much to be taught within a short time) ( \hyperref[b8]{[8]}; \hyperref[b14]{[12]}).\par
Again some people suggest it on lack of proper supervision on the part of school administration and family control in student's self-carelessness ( \hyperref[b7]{[7]}; \hyperref[b10]{[10]}).\par
Likewise, \hyperref[b15]{[13]} claimed that gender stereotype and student's interest to the subjects have also great influential effect. Peculiar nature of some factors and the students low and unbalanced success rate have led to this study on the multivariate analysis of the determinants of students' academic achievement measured in five selected subjects at general secondary school completion level, first cycle, grade 10.\par
The following research questions have been developed to guide this study:  
\section[{b) Objectives of the Study}]{b) Objectives of the Study}\par
The general objective of the study has been to determine the key factors influencing academic achievements of students measured in exam scores of five subjects in grade 10 (Mathematics, Biology, Physics, Chemistry and English), and to assess the variations accounted at school and individual (student) level for each response (school subjects). The Specific Objectives are ? To identify the most important factors (covariates) influencing academic achievements of student's in each component of selected subjects in grade 10. ? To determine the relationship among the school subjects at both school and student level; and whether there is gender and school type differences in this relationship. ? To quantify and determine the within and between schools variation for each components of selected\par
? To determine the groups or clusters of interrelated observed variables or items as component factors that explain the variation of achievement indictor variables. 
\section[{II.}]{II.} 
\section[{Materials and Methods}]{Materials and Methods} 
\section[{a) Description of the Study Area and Population}]{a) Description of the Study Area and Population}\par
The study was conducted in Hawassa, the capital city for SNNPR state, which was established in 1960. It is located at about 275 km South West of Addis Ababa, and near to Hawassa Lake. Geographically it lies between 07 0 05 ' Latitude North and 38 0 29 ' Longitude East. According to the report of \hyperref[b6]{[6]}, the estimated population size of the city (urban) in 2007 was 159,013 out of which 81,984 were males and 77,029 females. There are 4 governmental colleges and one university, 8 non-government (private) colleges, 5 governmental high (secondary) schools, about 15 nongovernmental high (secondary) schools. The gross enrollment rate of secondary school students at Hawassa Town Administration has been 62.1\%.\par
The target population for this study was grade 10 students of both government and non-government schools registered in 2010-2011 academic year at Hawassa City secondary schools. The total population of students in all high schools of the city was 6,384 in 2010-2011 academic year.\par
Exclusion criteria were made on the students who were transferred to other schools or those dropped out, only completed enrolment procedures at the school but did not yet attend the national exam or left the school or had been absent for more than four continuous weeks (excluding school vacations) and had no examination results in 2 of the most targeted school subjects (compulsory subjects). This was because full information about those students was not available. 
\section[{b) Sampling Design and Procedure}]{b) Sampling Design and Procedure}\par
A cross-sectional study with stratification sampling designed to take independent samples for different sub-populations was conducted. The stratums were governmental and non-governmental secondary schools as school type.\par
Sampling methods are scientific procedures of selecting those sampling units which would provide the required estimator with associated margins of uncertainty arising from examining only a part not the whole of the population. The main purpose of stratification is to reduce sampling error. Moreover, stratified sampling is a technique which uses any relevant information that might be available in order to increase efficiency. It involves the division or stratification of a population by partitioning the sampling frame in to non-overlapping and relatively homogeneous groups \hyperref[b5]{[5]}.\par
A list of grade 10 students was obtained from Hawassa City Administration Education and Capacity Building Department. The population of grade 10 students was stratified into governmental and nongovernmental school and the required sample size for the study was determined from each stratum. The multistage sampling procedure was employed as: The selection of a simple random sample was usually carried out according to a set of mechanical instructions which guarantees the random nature of the selection procedure. This is an equal probability of selecting individual units for all elements in the population of the school.\par
Stage three: simple random sampling of students from class Taking a list of students with their registration number in each school, then refer to a table of random numbers; the required sample students were selected. In simple random sampling, the selection of one individual was independent of the selection of another individual.\par
i 
\section[{. Sample Size Determination}]{. Sample Size Determination}\par
In the planning of a sample survey or researches, a stage at which a decision must be made about the size of the sample is always required. However, too large a sample implies wastage of resources, and too small a sample diminishes the utility of the results. Therefore the decision should be made with a minimum cost but the estimate will explain the population characteristics with a high probability. However, several formulas developed for sample size calculations that conform to different research situations \hyperref[b5]{[5]}.\par
The sample size for this study was determined based on stratified sampling with proportional allocation at 95\% confidence level using the general formula for sample size determination adopted as: The known methods of estimating for calculating sample size of any survey were by taking the sample in two steps; one by the results of a pilot survey and another by previous studies sampling of the same or similar population and guesswork about the structure of the population \hyperref[b5]{[5]}.\par
But for the present study, and the margin of (absolute) error were determined from the results of previous studies of similar population. The sample variance 2 1 s = 0.20885 and mean = 2.62 were taken for government school from the study which assessed the determinants of students' academic performance in government schools of grade 10 at Hawassa town taking a sample of 920 students \hyperref[b8]{(Hanna;}.\par
Then, for this study was calculated as: = On the other hand, the sample variance 2 2 s = 0.13421 was taken for non-government schools from the previous study at the same area \hyperref[b11]{[11]}. The total population was (number of students in 11 selected secondary schools of grade 10) 5006 = N from 5 governmental and 6 non-governmental selected secondary schools, which contained total number of grade 10 students in governmental schools  
\section[{c) Methods of Data Collection}]{c) Methods of Data Collection}\par
In assessing the academic achievement of students' measured by exam results scored in school subjects, Mathematics, Biology, Physics, Chemistry and English at both government and non-government sample secondary schools, both primary and secondary data were used. The primary data was collected using questionnaire method. The questionnaire consisted the student's, family background and school characteristic variables on the student's academic achievements evaluated in selected 5 subjects. Individuals sampled for this study were asked to complete the determinants of students' outcome (in five school subjects) study questionnaire. The secondary data on academic achievements of respondents was measured by their? ? = = + = 2 1 2 2 1 2 2 1 h h h h h h h S W N V W S W n N N W h h = h n 2 2 ? ? ? ? ? ? ? ? ? ? = ? V ? ? ? ? ? ? ? ? str Var h h h str y W ? = ? ? = ? 2 1 h n i hi h n y y h ? = ? = 1 = = ? ? = ? h N i hi h N y h 1 ( ) 2 1 2 1 ? ? = ? = h N i h hi h N Y y S h 1 y 2 y 96 . 1 025 . 0 2 = = z z ? 2 s 2 s ? 1 y ? n S 2 2 ? ? = ? 0295 . 920 208849 . 0 96 . 1 = ? ? ? ? ? ? ? ? N N W h h = 2 h S n 0 n ? = ? ? ? ? ? ? ? ? ? ? 2 1 2 2 2 h h h S W ? N n n n 0 0 1 + = n N N n h h ? ? ? ? ? ? = 2 , 1 = h Year 2016\par
where h = stratum the EGSECE results (scores) in each of the five selected subject (Mathematics, Biology, Physics, Chemistry and English). Besides, school records with regard to students' exam registration number and some profiles of teachers and schools were taken from record offices of the schools. Sampled grade 10 students were taken with their exam scores of all five school subjects and the student's results were standardized and scaled to be 4.00. 
\section[{d) Variables of Interest in the Research}]{d) Variables of Interest in the Research}\par
The outcome variables used in this study were the five selected school subjects as individual's achievement measures using EGSCEE results or scores on the five school subjects (Mathematics, Biology, Physics, Chemistry and English). All achievement scores were taken as standardized and transformed to assure that all scores were scaled in the same metric. This also allowed us to interpret the between school variances as the percentage of variation in student achievement accounted for by schools in PCFA, MVML and multivariate multiple linear regression analysis with respect to each response. The set of explanatory variables included were the composite common factors of students, family, teachers and schools characteristic variables.\par
i. Students and Family Characteristic Variables These were: Age, gender, religion of student, parents' employment status, natural talent, students' job aspiration, time spent on study, peer(group) effect, student class attendance(absence), skipped class, student's satisfaction with school administration, satisfaction with school rules and regulations, academic confidence, preferred study time, preferred study place, distance of the school from students' home, availability of text and reference books at home, home location, parental involvement, fathers'/guardians' level of education, comfort of study place at home, mothers' education level, average family expenditure, other expenses related to education, satisfactions in food type available in home, pervious grade scores, students attitude and perception on school subjects (difficulty, boringness, preference, etc.).\par
ii. School Characteristic Variables These were: teachers average workload, average year of experience, teachers average educational level, teacher preparation, class size, teaching method, standard of examination, parent to teacher communication, teacher absence, teacher late, average size of school, school fee, completion of the syllabus, school type, student-teacher ratio, teacher efficient and skills, school location/environment, current curriculum, human resources (teachers per subjects, principals, supervisors), infrastructure (buildings, classrooms, sport facilities), library facility, equipment (desks, blackboard, telephone, duplicating computers), amentias (toilets, electricity, water), and availability instructional materials (text and reference books, maps and charts), laboratory facilities, academic counseling service, health service (first aids). 
\section[{III.}]{III.} 
\section[{Methods of Data Analysis a) Factor Analysis Model}]{Methods of Data Analysis a) Factor Analysis Model}\par
This analysis describes the covariance relationships among many variables (items) in terms of a few underlying and unobservable random quantities.\par
The observable random vector X with P components has mean ? and covariance ?. The factor model postulates that X is linearly dependent upon a few unobservable random variables The factor analysis model is given by: X = LF+ ?, where is a matrix of unknown constants called factor loadings. 
\section[{L pxm = F= and =}]{L pxm = F= and =}\par
The coefficient is the loading of the variable on the factor.\par
i. Assumptions of Factor Model1. E (F) = 0 = (0, 0, ?,0 ) T 2. cov (F)= E (FF T )=I m 3. E (?) = 0= (0, 0,?, 0) 4. Cov ( ? )= E(? ? T )= ? pxp , ? is a diagonal matrix 5. Cov (? ,F) = E(? ,F T ) = 0= (0,0,?,0) T ii. Covariance Structure for Orthogonal Factor Model m p PxM L ? ij l th i th j ? .... ????. ? 11 l 12 l m l 1 21 l 22 l m l 2 1 p l 2 p l pm l ? 2 f 1 f m f p ? ? ? ..... 2 1 Year 2016 1. Cov(X) =LL T + 2. Var = , where is the specific factor. ? ( ) i X i im i i l l l ? + + + + 2 2 2 2 1 ... i ? th i 3. 4. ( ) ij j i l F X Cov = , 5.\par
, loading matrix.\par
Communality is defined by:\par
The factor model assumes that variables and covariance for X can be reproduced from pm factor loadings and p specific variables .\par
The factor model provides a simple explanation of the covariation in X with parameters which are fewer than parameters in ?. 
\section[{iii. Methods of Estimation of Loading}]{iii. Methods of Estimation of Loading}\par
If the off diagonal elements of sample covariance S are small or those of the sample correlation matrix R essentially zero (identity matrix), the variables are not related. This implies that a factor analysis will not prove useful and in these circumstances, the specific factor plays a dominant role. If covariance matrix appears to deviate significantly from a diagonal matrix, then a factor model can be entertained and the initial problem is one of estimating the factor loading and specific variance . There are two popular methods of parameter estimation, Maximum Likelihood (ML) Method and Principal Component Method. However, for this study, the principal component method was used. 
\section[{iv. The Principal Component Method}]{iv. The Principal Component Method}\par
The spectral decomposition of covariance ? having eigenvalues-eigenvector pairs with is given as . From the above equation, we can obtain the loading, . I T T TT = =\par
was a matrix of 'rotated' loadings, where is the identity matrix. This shows that the estimated covariance (correlations) matrix remains unchanged since? ? ? ? ? ? ? ? ? ? + = ? + = ? + * ' * ' ' ' L L L TT L L L .\par
A useful byproduct of factor analysis was factor scores. Factor scores were composite measures that can be computed for each individual on each common ( )km im k i k i k i l l l l l l X X E + + + = ... , 2 2 1 1 ( ) L F X Cov = , 2 2 2 2 1 2 ... im i i i l l l h + + + = ( ) ( ) 2 1 2 1 + = ? + p p p p p ij l i ? (\par
)  pm p + ( ) 2 1 + p p ij l i ? ( ) i i e , ? 0 ...+ + + = ? ... 2 2 2 1 1 1 = L ( ) S tr S S S pp = + + + ... 22 11 ? ? ? ? = + + + ? ? ? p ... 2 1 ? i ? p i ,....., 2 , 1 = 0 ... 2 1 ? ? ? ? ? ? ? m ? ? ? ? L pxmpi p i i i ik x l x l x l x l f ? ? ? ? ? + + + + = ... 3 3 2 2 1 1\par
, where  
\section[{b) Multivariate Multiple Linear Regression Model}]{b) Multivariate Multiple Linear Regression Model}\par
The multivariate extension of multiple linear regression was used to model the relationship between responses and a single set of predictor variables . Each of the response was assumed to follow its own regression model, so that   ?i r ri i i i i i i z z z ? ? ? ? ? + + + + + = ? ...( ) = ? ×m n = ( ) ( ) m r × +1 ? = = ( ) m n× ? = = m M Y Y Y ,..., , 2 1 r r z z z ,..., , 2 1 m 0 .... 2 1 = ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? m E ? ? ? ( ) ? = ? Var th j 10 ? 11 ? ? r 1 ? 20 ? 21 ? ? r 2 ? ???? 0 n ? 1 n ? ? nr ? 11 ? 12 ? ? m 1 ? 21 ? ? m 2 ? ??? 1 ? 2 ? ? ? 22 ? ( ) | 1 ? ( ) | 2 ? ? ( ) m | ? 01 ? 02 ? ? m 0 ? 11 ? 12 ? ? m 1 ? ??? 1 r ? 2 r ? ? rm ? ( ) ( ) | | 2 1 ? ? ?| ( ) m ? 11 ? 12 ? ? m 1 ? 21 ? 22 ? ? m 2 ? ?? 1 n ? 2 n ? ? nm ? ( ) | 1 ? ( ) | 2 ? ? ( ) m ? |\par
The multivariate linear regression model is:with 0 = ? ? ? ? ? ? ? ? and ( ) ? × = ik Cov ? ? ? k i , for . ,..., 2 , 1 , m k i =\par
The 'm' observed responses on the j th trial (student) have covariance matrix ( ) i ' 1 ' i ? ? ? ? ? ? ? ? ? ? = ? ? ?\par
. Then collecting the uni-variate least squares estimates yields:? ? = ' ' ? ? ? ? ? ? ? ? ? ?1 = ? ? ? ? ? ? ? ? ? ? ? ? ? ' 1 '\par
. Using a matrix ? ?, one can easily ascertain that the matrices of predicted values:? ? ? ? ? ? ? ? ? ? ? = ? = ? ? ? ? ' '\textbf{1}\par
? and residuals:\par
.' ' ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? = ? ? ? 1 ?\par
If the model is of full rank, rank (Z) = r + 1 < n; and ? and ? are also uncorrelated. Furthermore, because? ? ? + = ? ? , then one have ? ? ' = ? ? ? ? ' + ? ? ? ? ' Residual SSCP= ? ? ? ? ? ? ? ? ? ? ? = ? ? ' ' ' and the unbiased estimator of ? is 1 r n ' ? ? ? ? = ? ? ? ? . ii. 
\section[{Test of Hypothesis}]{Test of Hypothesis}\par
The hypotheses of all explanatory have no effect on academic achievements of students jointly on the responses, i.e. the th i school subject doesn't depend on the 'r' explanatory variables: ( ) ( ) ? . S t i , s i , s cal ? ? ? ? ? ? ? ? = ? ?\par
, where( ) ( ) ? ? ? ? ? ? ? = ? ? ? ? ? ? ? ? ? i s i s E S , , 
\section[{var .}]{var .}\par
, .\par
Decision Rule: if or p-value less than 05 . 0 = ? , we reject the null hypothesis. On the other hand, the confidence ellipsoid for ? can be easily contracted with the one-at-a-time t value ( ) 2 1 ? ? ?r n t and using intervals ( ) ? ? ? ? ? ? ? × ? ± ? ? ? ? ? i 1 r n i SE 2 t . Here if ( ) ( ) ( ) ( ) ( ) ( ) nxm xm r r nx nxm ? ? + ? = ? + + 1 1 k i, ? k i, ? B m i ,..., 3 , 2 , 1 = ( ) ( ) 1 + ? r n t ( ) ? ? ? ? ? ? ? i Var ? ( ) 1 ' , ? ? ? ? = i i ? ( ) ( ) ( ) ( ) ? ? ? ? ? ? = ? ? ? ? i r i i i Var Var Var Var Diag , 2 1 0 ,..., , , ? ? ? ? ( ) ( ) 1 ' , ? ? = ? ? ? r n i i i i ? ? ? ( ) ( ) 
\section[{iii. Checking the Goodness of Fit of the Model}]{iii. Checking the Goodness of Fit of the Model}\par
It is imperative to examine the adequacy of the model before the estimated function becomes a permanent part of the decision making apparatus \hyperref[b9]{[9]}. All the sample information on lack of fit is contained in the residuals.\par
. 
\section[{iv. Residuals}]{iv. Residuals}\par
The residuals are defined as:? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? ? ? = ? ? ? ? ' 1 '\par
Since a residual may be viewed as the deviation between the data and the fit, it is also a measure of the variability in the response variable not explained by the regression model. Plotting residuals is a very effective way to investigate how well the regression model fits the data and to check the assumptions.\par
v 
\section[{. Normal Probability Plot}]{. Normal Probability Plot}\par
The most commonly used methods of checking normality of an individual variable are the Quantile-Quantile plot (Q-Q plot), P-P plot and Normal Curve Histogram. The P-P plotted as expected cumulated probability against observed cumulated probability of standardized residuals -line should be at 45 degrees. The variable is normality distributed if this plot illustrates a linear relationship. In case of the assumption that says the combinations of variables follow a multivariate normal distribution, one can generally test each variable individually and assume that they are multivariate normal if they are individually normal \hyperref[b2]{[3]}; \hyperref[b0]{[1]}).\par
vi. Ethical Issue/ Considerations Ethical approval was obtained from research ethics committee of Hawassa University, Postgraduate school of Computional sciences. Following the endorsement by the research ethics committee and acceptance of the postgraduate school and statistics department, Hawassa City Administration Education and Capacity Building Department was informed about the study through a support letter from Hawassa University research Postgraduate research office. Then verbal permission had been obtained from respective department of the city administration.\par
Following the endorsement by Hawassa City Administration Education and Capacity Building Department, the selected schools were informed about the objective of the study through a support letter from Hawassa City Administration Education and Capacity Building Department and oral permission and supports were obtained from the respected school principals, teachers and students. As the study was conducted through review of academic records, the individual person was not subjected to any harm as far as the confidentiality is kept. Consent was obtained from individual person or student who was selected to fill the study questionnaire. To preserve the confidentiality, data recorders or file keepers, in the City Administration Education and Capacity Building Department extracted the data from the academic records. Moreover, no personal identifiers were used on data collection form. The recorded data was never accessed by a third person except the principal investigator, and was kept with a firm confidentiality in a secured place.\par
IV. 
\section[{Results}]{Results} 
\section[{a) Descriptive Results}]{a) Descriptive Results}\par
From the results in Table \hyperref[tab_4]{3}.1, the average academic achievements of students measured in Mathematics, Biology, Physics, Chemistry and English subjects for non-government school students were, respectively, 2.99, 2.97, 2.50, 2.88, and 3.14 with standard deviations 0.822, 0.899, 0.942, 0.806 and 0.805, respectively, and that of government schools were 2.61, 2.73, 2.24, 2.74 and 2.77 with standard deviations 0.838, 0.866, 0.964, 0.872 and 0.802, respectively.  Table \hyperref[tab_4]{3}.2 shows the mean academic achievements and the coefficient of variations for the five subjects. In terms of coefficient of variation, the variability was the lowest for English and highest for Physics subjects. This may indicate that students' achievements were most consistent for the English subject and least consistent for Physics subject. Physics was considered as difficult subject for many students. 
\section[{b) Results of Factor Analysis}]{b) Results of Factor Analysis}\par
Before conducting the central MVML and multivariate multiple regression analyses it is important first to establish the psychometric properties of the instrument used. Principal Component Factor Analysis was done in two steps. The first one was a general PCFA that considered the socioeconomic and demographic variables with general school characteristic variables and the second was a separate PCFA relative to each achievement measures of the five subjects. This provided component factors for each of the five school subjects each based on the subject related observed items as students' responses on their personal, school and teacher characteristic variables relative to school subjects. The overall reliability was computed to be Cronbach's alpha=0.724 indicating that the questionnaire items were consistent. The KMO statistic values test if sufficient items (by partial correlation among variables) are available for each factor component in the factor analysis. KMO statistic for the separate PCFA with respect to the school subjects Mathematics, Biology, Physics, Chemistry and English were 0.81, 0.77, 0.82, 0.78 and 0.84, respectively; with the general PCFA of 0.79. These were all greater than 0.5 indicating that the sampling was adequate for factor analysis and there were significant relationships among the perceived factors of achievements in the school subjects.\par
The data were also checked for Bartlett's test of Sphericity to see that the original variables were sufficiently (bi-variate) correlated and these met the criteria with 0 . 11170 These indicated that the original observed variables were sufficiently correlated (the variables were not completely uncorrelated) and factor analysis was possibly appropriate in each case. The output matrixes contained the loading of each variable onto each factor. All loadings less than 0.5 were suppressed in the output and so were blank spaces for many of the loadings. Thus, the loadings were acceptable and easy for interpretation.\par
The results of separate factor analysis (with factor loadings greater than 0.5) are presented in Tables \hyperref[tab_13]{4, 5}, 6, 7 and 8 of Appendix-1 and Figures 1 in Appendix-2 of the Scree plots. The criteria that the required amount of explained variation accounted for being large, logical interpretability of factors and Scree plot tests were considered with Kaiser Criteria. Kaiser criteria is accurate when there are less than 30 variables with lager sample and communalities after extraction being greater than 0.6. Depending on the correlation matrix and communalities, some observed variables were rejected. Of all 140 observed items, using principal component extraction and Varimax rotation, the study found factor solution of the 28-variables for each subject. Then, six underlying common factors were obtained for each separate factor analysis of Biology, Physics, Chemistry and English related items that constituted or explained 76.67\%, 78.80\%, 68.64\% and 73.43\% of the total variability in the corresponding original observed variables, respectively. There were four common factors for Mathematics related items which constituted or explained 77.38\% of the total variability in the original observed variables related to Mathematics.\par
Factor scores of each component factor for each of the 719 individual respondents were computed and these scores were used as data for further analysis. The common factors obtained from the general and separate PCFAs which were used as covariates,\par
? School facilities (SF), Interest (InterstS) to the subjects, ? Family status (FS), Motivation (MotivS) to the subjects, ? School volume (SV), Trouble (TroubS) to the subjects and ? Safe reading (SafR), Self-concept (SelfC) to the subjects. ? Loadings Less than 0.5 were suppressed. 
\section[{c) Results of Multivariate Multiple Linear Regression Analysis}]{c) Results of Multivariate Multiple Linear Regression Analysis}\par
Multivariate multiple linear regression analysis was used to examine the effect of independent variables or factors on the outcome variables, i.e. academic achievement in selected subjects. Most of the explanatory variables were the common factors obtained from the general PCFA and some were the regularly appeared component factors in each separate PCFA. The results are shown in Table \hyperref[tab_4]{3}.6. In this analysis the overall determinants of academic achievement were assessed in terms of the five school subjects to identify the basic determinant factors for both government and non-government schools taken together. The factors sex, school type and school facilities (SF) were found to be jointly statistically significant for achievements in all the five selected school subjects. Family status (FS) was significant for achievements in the four school subjects (Biology, Physics, Chemistry and English) but statistically insignificant for achievement in Mathematics. School volume (SV) has a significant influence on achievements in the two school subjects Biology and Chemistry. Interest to the subjects (InterstS) has a significant influence on academic achievements of students in Biology and Physics. Moreover, the factors trouble to the subject (TroubS) and motivation (MotivS) to the subject in terms of Mathematics, self-concept (SelfC) in terms of Physics and students future aspiration (FutureAspira) in terms of Physics and Chemistry had significant impact on student's academic achievement as observed in overall combined data of government and nongovernment schools.\par
Moreover, the factors such as sex, interest to the subject (InterstS), motivation to the subject (MotivS), self-concept (SelfC), family status (FS), school facilities (SF) and future aspiration (Future Aspira) had positive impacts on students' academic achievements of the school subjects. However, trouble (TroubS) of the subjects and school volume (SV) showed significant negative impact on students' achievements of all the five subjects.  
\section[{Volume XVI Issue}]{Volume XVI Issue} 
\section[{Discussions and Conclusions}]{Discussions and Conclusions}\par
The PCFA technique was used as separate PCFA of items with respect to the each five responses and the general PCFA incorporated other general student with family and school with teacher characteristics variables in the data reduction. The multivariate single level multiple linear regression was applied on overall schools data. The results obtained are discussed as follows:\par
On an average, students, in non-government secondary schools, performed better than those in government secondary schools in almost all the achievement measures of the five school subjects. This might be because of higher availability of school and home educational supply and facilities, better study positions and higher parental involvement with teachers and students at the schools as compared to that at government schools. Moreover, on overall average, male students achieved better in almost all school subjects than female students. This implied that the school and family might treat gender differently and the variation in students' personal factors such as trouble to the subjects, self-concept, interest and motivation to the subjects showed significant impact on students' achievement ( \hyperref[b4]{[4]}; \hyperref[b6]{[6]};  {\ref [20]}).\par
The results obtained from the separate PCFA in each achievement measuring response indicated that about four factors related to Mathematics and six factors related to Biology, Physics, Chemistry and English were sufficient to explain the total achievement variability. Thus, factors self-concept to the subjects, motivation to the subjects, interest to the subjects, trouble (anxiety) to the subjects, teaching-learning process and absenteeism explaining most of the achievement variations in five school subjects. Moreover, the result of general PCFA indicated that the factor named as family status (FS) that encompasses parentstudent communication, parent-teacher communication, availability of book at home, satisfaction in food available at home, mother educational level and father education level explained the higher variability for the overall achievement. This finding is in consistent with other studies ( \hyperref[b15]{[13]}; \hyperref[b20]{[18]}).\par
The result of the multivariate multiple linear (single-level) regression analysis point to several interesting overall findings. The result indicated that the factors sex, school type, school facility (SF) which encompassed availability and satisfactoriness of school amenity, human resources, library, laboratory, equipment and academic counseling have significant impacts on achievements of the students in terms of all the selected five subjects. School volume (SV) that encompassed school size, class size, teacher workload and experience had a significant negative impact on academic achievements in terms of Biology and Chemistry. This may be due to the negative effect of school size, class size and teacher work load on academic achievement of students at school, as reported earlier ( \hyperref[b18]{[16]}; \hyperref[b19]{[17]}).\par
The factor school facility (SF) that deals availability and satisfactoriness of the school instructional materials, school library, laboratories, amenities, academic counseling services and other school characteristics had significant positive impact in all five school subjects used as a measures of academic achievement. Family status (FS) which encompass parent-student communication, parentteacher communication, availability of book at home, satisfaction in food available at home, mother educational level and father education level had a significant positive impact on academic achievements in terms of Biology, Physics, Chemistry and English subjects as observed earlier ( \hyperref[b20]{[18]};  {\ref [19]}).\par
This study was intended to identify some factors influencing the academic achievements of students' measured by five selected subjects (Mathematics, Biology, Physics, Chemistry and English) at secondary school level based on primary and secondary data. Accordingly, factor analysis, multivariate multiple linear regression and MVML multiple linear regression techniques on the five school subjects were employed.\par
The factor analyses conducted in this study indicated that 4 or 6 factors (instead of twenty eight original observed variables or items) were sufficient to explain 77.4\%, 76.7\%, 78.8\%, 68.6\% and 73.4\% the total variation in achievement for each separate PCFA of observed items related to Mathematics, Biology, Physics, Chemistry and English subjects, respectively. The factors self-concept, motivation, interest and trouble to the subject were the common factors explaining most of the variability of achievements in terms of each five subject, since these factors were appeared regularly in each separate PCFA. Moreover, six common factors were enough to explain about 64\% of the variation using 34 originally observed variables in the generalized PCFA.\par
The study revealed that the factors sex, school type, family status (FS) holding parents-student communication, parent-teacher communication, satisfaction in food available at home, availability of books at home, mother educational level and father education level, and school facility (SF) enclosing school instructional materials, amenities, library and laboratory facilities had statistically significant influence on achievements of students for the selected subjects. Moreover, school volume (SV) that covers school size, class size, teacher work load and teacher experience in teaching; interests to the subject, motivation to the subject, trouble to the subject and self-concept in school subjects have been significant factors      \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}(}\end{figure}
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\caption{\label{fig_3}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_5}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_6}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_8}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{fig_9}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-9.png}
\caption{\label{fig_11}}\end{figure}
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\noindent\textbf{2}\includegraphics[]{image-10.png}
\caption{\label{fig_13}Appendix- 2 :}\end{figure}
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\noindent\textbf{120162}\includegraphics[]{image-11.png}
\caption{\label{fig_14}Figure 1 :- 2016 Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31} \par 
\begin{longtable}{P{0.03152818991097923\textwidth}P{0.05801186943620178\textwidth}P{0.3190652818991098\textwidth}P{0.03909495548961424\textwidth}P{0.03909495548961424\textwidth}P{0.03909495548961424\textwidth}P{0.03909495548961424\textwidth}P{0.08323442136498516\textwidth}P{0.09584569732937685\textwidth}P{0.10593471810089021\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2016\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 33\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XVI Issue II Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep G )\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep (\\
School Type\tabcellsep School Name Comboni SOS\tabcellsep \multicolumn{5}{l}{Selected Subject (Hawassa, 2010) Students' Academic Achievement Maths Biology Physics Chemistry 35 35 35 35 Mean N 3.49 3.63 2.37 3.11 SD. 0.743 0.598 0.877 0.758 N 30 30 30 30 Mean 2.83 2.87 2.23 2.83 SD. 0.647 0.973 0.897 0.791}\tabcellsep English 35 3.68 0.471 30 3.07 0.827\tabcellsep Overall Average 35 3.25 0.689 30 2.77 0.827\tabcellsep Global Journal of Human Social Science -\\
\tabcellsep \tabcellsep N\tabcellsep 30\tabcellsep 30\tabcellsep 30\tabcellsep 30\tabcellsep 30\tabcellsep 30\\
\tabcellsep Adventist\tabcellsep Mean\tabcellsep 2.90\tabcellsep 2.77\tabcellsep 2.63\tabcellsep 2.77\tabcellsep 2.80\tabcellsep 2.77\\
\tabcellsep \tabcellsep SD.\tabcellsep 0.844\tabcellsep 0.817\tabcellsep 0.999\tabcellsep 0.971\tabcellsep 0.805\tabcellsep 0.887\\
\tabcellsep \tabcellsep N\tabcellsep 29\tabcellsep 29\tabcellsep 29\tabcellsep 29\tabcellsep 29\tabcellsep 29\\
\tabcellsep Mount Olive\tabcellsep Mean\tabcellsep 2.72\tabcellsep 2.55\tabcellsep 3.03\tabcellsep 2.97\tabcellsep 3.10\tabcellsep 2.88\\
\tabcellsep \tabcellsep SD.\tabcellsep .702\tabcellsep .783\tabcellsep .778\tabcellsep .778\tabcellsep .772\tabcellsep 0.763\\
Non-\tabcellsep \tabcellsep N\tabcellsep 29\tabcellsep 29\tabcellsep 29\tabcellsep 29\tabcellsep 29\tabcellsep 29\\
government\tabcellsep BNB\tabcellsep Mean\tabcellsep 2.83\tabcellsep 2.72\tabcellsep 2.31\tabcellsep 2.79\tabcellsep 2.93\tabcellsep 2.72\\
\tabcellsep \tabcellsep SD.\tabcellsep .889\tabcellsep .959\tabcellsep .967\tabcellsep .726\tabcellsep .753\tabcellsep 0.859\end{longtable} \par
 
\caption{\label{tab_3}Table 3 . 1 :}\end{figure}
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\noindent\textbf{3} \par 
\begin{longtable}{P{0.85\textwidth}}
Students\end{longtable} \par
  {\small\itshape [Note: 2 : Descriptive Statistics Student's Achievement in Ascending Order for the Overall Sample of]} 
\caption{\label{tab_4}Table 3 .}\end{figure}
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\noindent\textbf{3} \par 
\begin{longtable}{P{0.388150289017341\textwidth}P{0.034393063583815026\textwidth}P{0.10072254335260115\textwidth}P{0.019653179190751442\textwidth}P{0.019653179190751442\textwidth}P{0.019653179190751442\textwidth}P{0.022109826589595375\textwidth}P{0.24566473988439308\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2016\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 35\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XVI Issue II Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep G )\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep (\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Global Journal of Human Social Science -\\
\multicolumn{8}{l}{4 : The Generalized Principal Component Factor Analysis (Hawassa, 2010)}\\
Accounted for 64.28\%\tabcellsep 1\tabcellsep \multicolumn{4}{l}{Common Factors: Component 2 3 4 5}\tabcellsep 6\tabcellsep Communality\\
Eigenvalues\tabcellsep 4.56\tabcellsep 3.50\tabcellsep 2.70\tabcellsep 2.21\tabcellsep 1.65\tabcellsep 1.45\\
Variations accounted for \%\tabcellsep 18.24\tabcellsep 14.0\tabcellsep 10.8\tabcellsep 8.84\tabcellsep 6.60\tabcellsep 5.80\\
Parent student communication\tabcellsep .902\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.841\end{longtable} \par
 
\caption{\label{tab_6}Table 3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{33} \par 
\begin{longtable}{P{0.07727272727272727\textwidth}P{0.4207070707070707\textwidth}P{0.0601010101010101\textwidth}P{0.0601010101010101\textwidth}P{0.07727272727272727\textwidth}P{0.0601010101010101\textwidth}P{0.09444444444444444\textwidth}}
\tabcellsep \multicolumn{5}{l}{Separate Principal Component Factor Analysis}\tabcellsep General\\
Responses\tabcellsep Maths\tabcellsep Biology\tabcellsep Physics\tabcellsep Chemistry\tabcellsep English\tabcellsep PCFA\end{longtable} \par
  {\small\itshape [Note: ?Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.]} 
\caption{\label{tab_7}Table 3 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{35} \par 
\begin{longtable}{P{0.017346938775510204\textwidth}P{0.008673469387755102\textwidth}P{0.026020408163265306\textwidth}P{0.008673469387755102\textwidth}P{0.008673469387755102\textwidth}P{0.16479591836734692\textwidth}P{0.13010204081632654\textwidth}P{0.20816326530612245\textwidth}P{0.14744897959183673\textwidth}P{0.13010204081632654\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Responses\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Mathematics\tabcellsep Biology\tabcellsep Physics\tabcellsep Chemistry\tabcellsep English\\
\multicolumn{3}{l}{R}\tabcellsep 2\tabcellsep \tabcellsep 0.72\tabcellsep 0.74\tabcellsep 0.68\tabcellsep 0.68\tabcellsep 0.71\\
R\tabcellsep 2\tabcellsep \multicolumn{2}{l}{adj}\tabcellsep .\tabcellsep 0.64\tabcellsep 0.67\tabcellsep 0.61\tabcellsep 0.64\tabcellsep 0.65\end{longtable} \par
 
\caption{\label{tab_8}Table 3 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{36} \par 
\begin{longtable}{P{0.7133928571428572\textwidth}P{0.06071428571428571\textwidth}P{0.07589285714285714\textwidth}}
Parent teacher communication\tabcellsep .891\tabcellsep 0.813\\
Satisfaction in food at home\tabcellsep .853\tabcellsep 0.767\\
Availability of books at home\tabcellsep .840\tabcellsep 0.750\\
Year 2016\tabcellsep \tabcellsep \\
36\tabcellsep \tabcellsep \\
G )\tabcellsep \tabcellsep \\
(\tabcellsep \tabcellsep \\
Global Journal of Human Social Science -\tabcellsep \tabcellsep \\
s\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 3 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.5885829207920792\textwidth}P{0.1667388613861386\textwidth}P{0.013675742574257426\textwidth}P{0.013675742574257426\textwidth}P{0.013675742574257426\textwidth}P{0.0536509900990099\textwidth}}
\tabcellsep \multicolumn{5}{l}{fostering problem solving skills, Bahir Dar University,}\\
29 (1):17 -29.\tabcellsep \multicolumn{5}{l}{Ethiopia. Educational Expert, Bureau of Education,}\\
19. Brian, H. and Russell, K. (2009). LANNA Tests and\tabcellsep \multicolumn{5}{l}{Amhara regional State, Ethiopia.}\\
the Prediction of Year 10 English and Mathematics\tabcellsep \multicolumn{5}{l}{21. Alan J. Izenman (2010). Modern Multivariate}\\
Results: Charles Sturt University, Educational\tabcellsep \multicolumn{5}{l}{Statistical Techniques: Regression, Classification,}\\
Research, 19(1).\tabcellsep \multicolumn{3}{l}{and Manifold Learning.}\tabcellsep \tabcellsep \\
20. Alemayehu B., and Assaye A., (2010). An evaluation\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
of grades 9 and 10 mathematics textbooks vis-à-vis\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Appendices}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Appendix-1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Accounted for 77.38\%\tabcellsep \multicolumn{4}{l}{Common Factors: Components}\tabcellsep \\
\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep \\
Eigenvalues\tabcellsep 4.56\tabcellsep 3.41\tabcellsep 3.28\tabcellsep 2.67\tabcellsep Communality\\
\% Variations accounted for\tabcellsep 25.42\tabcellsep 18.93\tabcellsep 18.19\tabcellsep 14.84\tabcellsep \\
Need to do Maths well to get into the University\tabcellsep .905\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.832\\
The teacher prepares well for Maths daily lessons\tabcellsep .894\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.813\\
Need to do Maths well to get job\tabcellsep .873\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.782\\
Learning Maths helps me in my daily life\tabcellsep .845\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.717\\
Exam questions of Maths are standard\tabcellsep .839\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.726\\
Teaching Maths covers the whole syllabus\tabcellsep .828\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.692\\
Often study Maths in groups\tabcellsep \tabcellsep .944\tabcellsep \tabcellsep \tabcellsep 0. 907\\
Maths is difficult to learn\tabcellsep \tabcellsep .912\tabcellsep \tabcellsep \tabcellsep 0.861\\
No strength in learning Maths\tabcellsep \tabcellsep .895\tabcellsep \tabcellsep \tabcellsep 0.810\\
Need lots of hard work studying Maths to perform well\tabcellsep \tabcellsep .894\tabcellsep \tabcellsep \tabcellsep 0.835\\
Teaching method used by Maths teacher fits with the current curriculum\tabcellsep \tabcellsep \tabcellsep .933\tabcellsep \tabcellsep 0.880\\
I am satisfied with the current curriculum of Maths\tabcellsep \tabcellsep \tabcellsep .921\tabcellsep \tabcellsep 0.857\\
Maths need more time to understand\tabcellsep \tabcellsep \tabcellsep .886\tabcellsep \tabcellsep 0.809\\
Maths is Boring\tabcellsep \tabcellsep \tabcellsep .833\tabcellsep \tabcellsep 0.729\\
I usually do Maths well\tabcellsep \tabcellsep \tabcellsep \tabcellsep .898\tabcellsep 0.819\\
Enjoy learning Maths\tabcellsep \tabcellsep \tabcellsep \tabcellsep .874\tabcellsep 0.784\\
I have natural talent in Maths.\tabcellsep \tabcellsep \tabcellsep \tabcellsep .764\tabcellsep 0.603\\
Understand Maths quickly in class\tabcellsep \tabcellsep \tabcellsep \tabcellsep .625\tabcellsep 0.568\\
\multicolumn{6}{l}{Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.}\end{longtable} \par
 
\caption{\label{tab_10}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.09444444444444444\textwidth}P{0.6532407407407407\textwidth}P{0.03148148148148148\textwidth}P{0.012367724867724866\textwidth}P{0.058465608465608464\textwidth}}
\tabcellsep \multicolumn{2}{l}{International Journal of Educational Development,}\tabcellsep \\
Year 2016\tabcellsep \tabcellsep \tabcellsep \\
40\tabcellsep \tabcellsep \tabcellsep \\
Volume XVI Issue II Version I\tabcellsep \tabcellsep \tabcellsep \\
G )\tabcellsep \tabcellsep \tabcellsep \\
(\tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Human Social Science -\tabcellsep \multicolumn{2}{l}{Students (Cronbach's ? =0.72, Hawassa, 2010) Accounted for 76.65\% Common Factors: Components 1 2 3 4 5 Eigenvalues 4.13 3.52 2.34 2.17 1.63 \%Variations accounted for 20.63 17.57 11.69 10.87 8.14 The teacher prepares well for Biology daily lessons .914}\tabcellsep 6 1.55 7.75\tabcellsep Communality 0.853\\
\tabcellsep Need to do Biology well to get into the Preparatory or University\tabcellsep .907\tabcellsep \tabcellsep 0.847\\
\tabcellsep Need to do Biology well to get job\tabcellsep .902\tabcellsep \tabcellsep 0.820\\
\tabcellsep Learning Biology helps me in my daily life\tabcellsep .897\tabcellsep \tabcellsep 0.833\\
\tabcellsep Teacher is efficient and skilled while teaching Biology\tabcellsep .864\tabcellsep \tabcellsep 0.754\\
\tabcellsep I usually do Biology well\tabcellsep .949\tabcellsep \tabcellsep 0.922\\
\tabcellsep Understand Biology quickly in class\tabcellsep .911\tabcellsep \tabcellsep 0.864\\
\tabcellsep Enjoy learning Biology\tabcellsep .900\tabcellsep \tabcellsep 0.844\\
\tabcellsep s\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_11}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.6603252647503782\textwidth}P{0.01928895612708018\textwidth}P{0.033434190620272315\textwidth}P{0.016717095310136158\textwidth}P{0.014145234493192133\textwidth}P{0.014788199697428138\textwidth}P{0.013502269288956125\textwidth}P{0.07779878971255673\textwidth}}
\multicolumn{5}{l}{Students (Cronbach's ? =0.71, Hawassa, 2010)}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{4}{l}{Common Factors: Components}\tabcellsep \tabcellsep \\
Accounted for 78.80\%\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\tabcellsep 6\tabcellsep \\
Eigenvalues\tabcellsep 3.90\tabcellsep 3.75\tabcellsep 3.64\tabcellsep 2.86\tabcellsep 1.60\tabcellsep 1.58\tabcellsep Communality\\
\% Variations accounted for\tabcellsep 17.75\tabcellsep 17.06\tabcellsep 16.55\tabcellsep 13.01\tabcellsep 7.27\tabcellsep 7.18\tabcellsep \\
Need to do Physics well to get job\tabcellsep .898\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.835\\
Need to do Physics well to get into the Preparatory or University\tabcellsep .871\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.789\\
Teaching Physics covers the whole syllabus\tabcellsep .850\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.784\\
Learning Physics helps me in my daily life\tabcellsep .837\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.738\\
I have natural talent in Physics.\tabcellsep .794\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.676\\
I usually do Physics well\tabcellsep \tabcellsep .967\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.951\\
Understand Physics quickly in class\tabcellsep \tabcellsep .965\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.952\\
Enjoy learning Physics\tabcellsep \tabcellsep .964\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.948\\
Physics need more time to understand\tabcellsep \tabcellsep .956\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.923\\
Teacher is efficient and skilled while teaching Physics\tabcellsep \tabcellsep \tabcellsep .941\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.913\\
I am satisfied with the current curriculum of Physics\tabcellsep \tabcellsep \tabcellsep .940\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.933\\
Often study Physics in groups\tabcellsep \tabcellsep \tabcellsep .929\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.502\\
Need lots of hard work studying Physics to perform well\tabcellsep \tabcellsep \tabcellsep .911\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.888\\
Physics is Boring\tabcellsep \tabcellsep \tabcellsep \tabcellsep .977\tabcellsep \tabcellsep \tabcellsep 0.961\\
Physics is difficult to learn\tabcellsep \tabcellsep \tabcellsep \tabcellsep .973\tabcellsep \tabcellsep \tabcellsep 0.955\\
No strength in learning Physics\tabcellsep \tabcellsep \tabcellsep \tabcellsep .947\tabcellsep \tabcellsep \tabcellsep 0.899\\
Physics teacher is often late for class\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep -.730\tabcellsep \tabcellsep 0.609\\
Student get at least a onetime Physics homework /assignments/ class works per week\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .697\tabcellsep \tabcellsep 0.565\\
Physics teacher often absent from class\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep -.688\tabcellsep \tabcellsep 0.596\\
Exam questions of Physics are standard\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .778\tabcellsep 0.641\\
Teaching method used by Physics teacher fits with the current curriculum\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .648\tabcellsep 0.547\\
The teacher prepares well for Physics daily lessons\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .556\tabcellsep 0.573\end{longtable} \par
 
\caption{\label{tab_12}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.6307539682539682\textwidth}P{0.03485449735449735\textwidth}P{0.08882275132275132\textwidth}P{0.005621693121693121\textwidth}P{0.005621693121693121\textwidth}P{0.004497354497354497\textwidth}P{0.011243386243386243\textwidth}P{0.06858465608465608\textwidth}}
Accounted for 73.43\% Eigenvalues\tabcellsep 1 3.66\tabcellsep \multicolumn{4}{l}{Common Factors: Components 2 3 4 5 3.59 3.50 2.70 1.45}\tabcellsep 6 1.25\tabcellsep Communality\\
\% Variations accounted for\tabcellsep 16.65\tabcellsep 16.31\tabcellsep 15.93\tabcellsep 12.28\tabcellsep 6.59\tabcellsep 5.67\\
Need to do English well to get into the Preparatory or University\tabcellsep .862\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.758\\
Learning English helps me in my daily life\tabcellsep .857\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.751\\
The teacher prepares well for English daily lessons\tabcellsep .833\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.738\\
Need to do English well to get job\tabcellsep .827\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.741\\
Teacher is efficient and skilled while teaching English\tabcellsep .784\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.683\\
Exam questions of English are standard\tabcellsep \tabcellsep .850\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.774\\
Need lots of hard work studying English to perform well\tabcellsep \tabcellsep .845\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.732\\
I am satisfied with the current curriculum of English\tabcellsep \tabcellsep .844\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.783\\
Teaching method used by English teacher fits with the current curriculum\tabcellsep \tabcellsep .843\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.797\\
English need more time to understand\tabcellsep \tabcellsep .743\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.577\end{longtable} \par
 
\caption{\label{tab_13}Table 5 :}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US)} 		 		\backmatter   			 
\subsection[{Acknowledgements}]{Acknowledgements}\par
First, and foremost, I would like to thank my God for giving me the opportunity to allow me learning Statistical and doing researches in every fields of sciences, and for being with me in all aspects 			  			 \par
elsewhere. I would like to gratefully and sincerely thank Dr. Ayele Taye, for his invaluable comments and suggestions that contributed to the successful realization of this study. He devoted his time to provide me insightful feedback on drafts of this manuscript and challenged me to become a better researcher and scholar. I would like to acknowledge the efforts of the School of Natural and Computional sciences and all my colleagues for their scholarly input I got through the discussions.\par
My special thanks also go to the staff members of the Department of Education and Capacity Building at Hawassa City Administration, school principals and home room teachers of all secondary schools in the city for their positive and genuine cooperation provided to me during data collection and those selected grade 10 students who gave me the information and data used in this study.\par
I owe tremendous amount of gratitude to my parents, friends and relatives; specifically, I feel greatly indebted to my brother Awol Assen, my friends Bereket Adem, Hussien Ali and Mebrat Seid who were never failed to give me support, continuous encouragement and appreciation for my studies.\par
At last, my deep appreciation and heart-felt thanks goes to my wife Medina Yimer Muhie who is always there to provide me with continuing motivation, encouragement and constructive advice.  			 			  				\begin{bibitemlist}{1}
\bibitem[Sc]{b13}\label{b13} 	 		\textit{},  		 			M Sc 		.  		Ethiopia.  		 			Thesis. Hawassa University 		 	 
\bibitem[Burdenski et al. ()]{b2}\label{b2} 	 		\textit{},  		 			T Burdenski 		,  		 			A Texas 		,  		 			M University 		.  		2000.  	 
\bibitem[Nigussie ()]{b11}\label{b11} 	 		\textit{},  		 			Y Nigussie 		.  		2010.  	 
\bibitem[Adebola and Ademola ()]{b18}\label{b18} 	 		 			O Adebola 		,  		 			I Ademola 		.  		\textit{School Quality Factors And Secondary School Students' Achievement In Mathematics In South-Western And North-Central Nigeria, the African Symposium: An online journal of the African Educational Research Network},  				2011.  	 
\bibitem[Bivariate, and Multivariate Normality Using Graphical and Statistical Procedures, Multiple Linear Regression View Points]{b3}\label{b3} 	 		\textit{Bivariate, and Multivariate Normality Using Graphical and Statistical Procedures, Multiple Linear Regression View Points},  		 		 			Evaluating Univariate 		 	 
\bibitem[Christopher and Sarah ()]{b4}\label{b4} 	 		 			L Christopher 		,  		 			T Sarah 		.  		\textit{Charter, Private, Public Schools and Academic Achievement: New Evidence from NAEP Mathematics Data},  				 (Columbia)  		2006.  	 
\bibitem[Engin-Demir ()]{b20}\label{b20} 	 		\textit{Factors Affecting the Academic Achievement of Turkish Urban Poor},  		 			C Engin-Demir 		.  		2009.  	 
\bibitem[Anastacia (20011)]{b19}\label{b19} 	 		\textit{Factors Affecting the National Achievement Test Performance Of Selected Second Year High School Students},  		 			N Anastacia 		.  		20011. In Santa Maria, Bulacan.  	 
\bibitem[Kassahun and Zelalem ()]{b10}\label{b10} 	 		 			M Kassahun 		,  		 			T Zelalem 		.  		\textit{Assessment on the Impact of Plasma Television Implementation on the Teaching Learning Process of Mathematics Class: The Case on Selected High Schools},  				 (Jimma, Ethiopia)  		2006.  		 			Mathematics Department, Education Faculty, Jimma University 		 	 
\bibitem[Multilevel Analysis of Determinants of Academic Achievement of Grade Eight Students: A Case Study at Hawassa Town]{b12}\label{b12} 	 		\textit{Multilevel Analysis of Determinants of Academic Achievement of Grade Eight Students: A Case Study at Hawassa Town},  		 	 
\bibitem[Johnson ()]{b9}\label{b9} 	 		\textit{Pearson Prentice Hall, Trademark of Pearson Education},  		 			A Johnson 		.  		2007. Upper Saddle River, New Jersey. p. 7458.  	 	 (Applied Multivariate Statistical Analysis) 
\bibitem[Antonia ()]{b1}\label{b1} 	 		‘Personal, Family and Academic Factors Affecting Low Achievement in Secondary School’.  		 			L Antonia 		.  	 	 		\textit{Journal of Research in Education Psychology and Psycho Pedagogy}  		 1696-2095.  		2002.  	 
\bibitem[Cochran ()]{b5}\label{b5} 	 		\textit{Sampling Techniques},  		 			W G Cochran 		.  		1997. New York: John Wiley and Sons.  	 
\bibitem[Getahun ()]{b7}\label{b7} 	 		\textit{School Based Factors Contributing to Differences in Students' Achievement Between High and Low Passing Rate Scorers of Government Secondary Schools of Addis Ababa at National Examination, Faculty of Education},  		 			W Getahun 		.  		2002. Ethiopia.  		 			Addis Ababa University 		 	 
\bibitem[Zuccaro ()]{b17}\label{b17} 	 		\textit{Statistical Alchemy-The Use of Factor Scores in Linear Regression},  		 			C Zuccaro 		.  		2007.  	 
\bibitem[Hanna ()]{b8}\label{b8} 	 		\textit{Statistical Analysis of Determinants of Academic Performance of Students at Government Schools and University Levels, Thesis},  		 			T Hanna 		.  		2010. Ethiopia.  		 			Department of Statistics, Hawassa University 		 	 
\bibitem[Csa ()]{b6}\label{b6} 	 		\textit{Summary and Statistical Report of 2007 Population and Housing Census},  		 			Csa 		.  		2008. Addis Abeba, Ethiopia.  	 
\bibitem[Nugussie ()]{b14}\label{b14} 	 		\textit{The Content Validity of the Ethiopian General Secondary Education Certificate English Examination, M.Sc Thesis in Curriculum and Instruction},  		 			T Nugussie 		.  		2002. Addis Ababa University.  	 
\bibitem[Tadesse ()]{b15}\label{b15} 	 		\textit{Understanding Female Students' Academic Performance: An Exploration of the Situation in South Nations Nationalities and Peoples Regional State},  		 			S Tadesse 		.  		2009. Ethiopia: International Institute of Social Studies.  	 
\bibitem[Alyee ()]{b0}\label{b0} 	 		‘Ways to Evaluate the Assumption of Normality’.  		 			S A Alyee 		.  	 	 		\textit{Paper Presented at Annual Meeting of the Southwestern Psychological Association},  				1998.  	 
\bibitem[Unesco-Wde ()]{b16}\label{b16} 	 		\textit{World Data on Education},  		 			Unesco-Wde 		.  		2007. Ethiopia.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
