101 
 

 American Scientific Research Journal for Engineering, Technology,  and Sciences  (ASRJETS) 

ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 

© Global Society of Scientific Research and Researchers  

http://asrjetsjournal.org/  

 

Multivariate Analysis of Psychometric Variables Affecting 

Job Satisfaction Among School Teachers: A Case Study at 

Primary and Secondary Schools in Shashemene Town, 

Ethiopia 

Sano Kedir Mohammed
*
 

Department of Statistics, Madda Walabu University, Robe, Ethiopia 

Email: sanokedir@yahoo.com 

 

Abstract 

This study has dealt with factors affecting the level of job satisfaction among school teachers at Shashemene 

town in 2010/11 academic year. Data were obtained from primary and secondary sources. A cross-sectional 

survey was conducted on a total of 405 teachers from different government and non-government schools using 

stratified random sampling technique. In the present study, the relationships between psychometric items (55-

items) were examined by the combination of factor analysis and Bayesian logistic regression analysis. Firstly, 

factor analysis was used to group the items of the same characters as factors or explanatory variables of job 

satisfaction among teachers. Then, ten factors, having eigen-values greater than 1, were selected as independent 

variables and the factor scores coefficients were used in the Bayesian logistic regression analysis. It was found 

that eight unobserved variables had significant effects on job satisfaction. 

Key words: Psychometric variables; Factor analysis; job satisfaction; eigen-values; teachers. 

1. Introduction 

The teachers’ level of satisfaction has been identified as the critical factor in determining their performance and 

professional development. The subject has received increasing attention of researchers and scholars around the 

world since 1950s [1]. Many studies of job satisfaction have been carried out across the economy, education, etc 

[2]. 

------------------------------------------------------------------------ 

* Corresponding author. 

 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

102 
 

have defined work satisfaction as an effective response or reaction to a wide range of conditions or aspects of 

one’s work such as pay, supervision, working conditions and the work itself. Others have defined it as an 

effective orientation towards anticipated outcome [3] or an employee’s effective reactions to a job based on 

comparing actual outcomes with desired outcomes [4].  It has been generally recognized as multifaceted 

construct that includes employees feeling about a variety of both intrinsic and extrinsic job elements [5].  

Intrinsic determinants pertain to the nature of activities inherent to, a position or set of tasks such as intellectual 

stimulation or feeling of accomplishment. Extrinsic determinants focus on external factors such as relation with 

co-workers or job security. So, job satisfaction is a subjective variable which does not lead itself readily to 

quantification. It is experienced, when employees have fulfilled, whatever needs or considerations, they deem 

important in their work.   Most people in employment consciously experience a degree of satisfaction or 

dissatisfaction with their job. Moreover, they tend to be more satisfied with some aspects of their job than 

others. The relevance of job satisfaction is crucial to the long term growth of any work industry all over the 

world. This has to do with needs satisfaction which is essential in the lives of workers because it forms the 

fundamental reason for working [6]. People work because they expect returns or salary which will help to meet 

their needs in life. The present study is an attempt to find out which facet or dimension affects the job 

satisfaction of primary and secondary school teachers significantly. The study has taken into account intrinsic 

and extrinsic factors to find out the level of job satisfaction and to see the effect of age, gender, marital status, 

educational level, occupational level and length of employment which are called demographic factors on the job 

satisfaction of academicians. 

1.2 Statement of the Problem  

Teachers’ job satisfaction is a major and current issue of the international communities. Specially, in sub-

Saharan countries, education is not up to the standard qualitatively and quantitatively, hence to improve these 

many factors has to be considered, but from these issues of teachers need to be studied. The questions that this 

study seeks to probe are as follows:  

 What relationships (correlations) are there among the items of psychometric properties which shows 

feeling of teachers in many aspects? 

 What are the most significant factors that influence overall job satisfaction of teachers with respect to 

psychometric variables? 

The general objective of the study has been to determine or asses factors affecting overall job satisfaction among 

teachers at Shashemene town. 

2. Data and Methodology 

The study was conducted in Shashemene town, a rapidly growing town in Oromia region of Ethiopia. 

Shashemene is centrally located, which is 250 km far from the capital city of Ethiopia, i.e. Addis Ababa. 

Geographically, it lies between Latitude of 7, 2000 (712'0.000"N) and Longitude of 38, 6000 (3836'0.000"E). 

The altitude of this town ranges from 1500metres to 2300meters above sea level. According to CSA (2007) 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

103 
 

report, the estimated population size of the town was 102,062, of which 51,477 were males and 50,585 were 

females. Educational institutes in the town, include one governmental TVET college, two non-governmental 

university college and four private colleges, three governmental high schools and two governmental preparatory 

schools.  In Shashemene town, there were 48 primary schools of which 10 were governmental and 38 were non-

governmental schools, having 278 and 501 teachers, respectively. There were also 10 high schools of which 

seven were non-governmental and three were governmental and there were six preparatory schools of which 

four were non-governmental and two were governmental. The total numbers of teachers employed in 

preparatory schools and high schools were 69 and 254, respectively [7].  The target populations for the study 

have been teachers employed in primary and secondary schools of governmental and non-governmental during 

2010/11 at Shashemene town. These teachers were full timer paying tax from their salary. A cross- sectional 

survey design with stratified random sampling technique was used in this study. 

2.1 Multivariate Analysis 

Multivariate statistical methods have grown increasingly popular over the past twenty-five years. The continued 

explosion of multivariate statistical procedures can no doubt be attributable to the belief that models of nature 

and human behavior, must often account for multiple, inter-related variables that are conceptualized 

simultaneously or over time [8]. 

2.1.1 Principal Component Analysis (PCA) 

According to [8], principal components are defined as linear combinations of random variables when seen 

analytically. Geometrically, these linear combinations represent the selection of a new coordinate system 

obtained by rotating the original coordinate system in such a way that the new axes represent directions with 

maximum variability and provide simpler description of covariance. The general objectives of principal 

component analysis (PCA) are the reduction of a large number of variables whose inter relationships are 

complex to a much smaller set of new variables whose interrelationships are simple, but which contain most of 

the variation in the original variables and ease of interpretation. PCA is a mathematical technique which does 

not require the user to specify an underlined statistical model to explain the error structure. In particular no 

assumption is made about the probability distribution of the original variables. PCA is done either using the 

theoretical covariance matrix Σ, or theoretical correlation matrix ρ of X, where X is a random vector with p 

dimensions. Usually the covariance matrix (Σ) is used to analyze variables with the same unit of measurement. 

Since the correlation matrix is the covariance of a standardized variable, the correlation matrix (ρ) has been used 

to analyze the variables with different unit of measurement.  As the unit of measurements of the variables, in 

this study is the same, it is possible to use either covariance or correlation matrix. The PCA was based on the 

correlation matrix ρ of X. Algebraically; principal components (PCs) are those uncorrelated linear combinations 

pYYY ,...,, 21
 whose variances are as large as possible. Geometrically; these linear combinations represent the 

selection of a new coordinate system obtained by rotating the original coordinate axes pXXX ,...,, 21 . Let the 

covariance matrix associated with the random vector 
T

pXXXX ),...,,( 21 has the eigenvalue-eigenvector 

pairs (λ1, e1), (λ2, e2), … , (λp, e p) where λ1  λ2 …  λp, then the i
th

 principal component is given by 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

104 
 

             piXeXeXeXeY ipii

T

i ,...,2,1,....21  ……………………….3.3 

With this choices 

pieeXVar i

T

i ,...,2,1,)(    

kieeYYCov T

k

T

iki  ,0),(  

The PCA provides a highly parsimonious summary that might be useful in further analysis. So it is necessary to 

determine the number of components that are needed to provide an adequate summary of a given set of [8].  

The Number of Principal Components 

As a rule of thumb, only those components are retained whose variance λ are greater than unity, or equivalently 

only those components are retained, which individually explain at least a proportion 1/p of the total variance. 

Another useful visual aid determining an appropriate number of PCs is the scree plot. It is a plot of i  versus i, 

with eigenvalues ordered from largest to smallest (the magnitude of eigenvalues versus its number). Then, to 

determine the appropriate number of components, one looks for elbows (bends) in the scree plot. The number of 

components is taken to be the point at which the remaining eigenvalues are relatively small and all are about the 

same size. 

2.1.2 Factor Analysis 

Factor analysis is a statistical technique by which one describes (if possible) the covariance relationship among 

many variables in terms of few underlying unobservable random quantities called factors. The underlying idea 

in factor analysis is grouping variables that are highly correlated together to form a single underlying factor that 

is responsible for the observed correlation. 

The Orthogonal Factor Model  

Let the observed random vector  with p component has mean  and covariance   matrix . The factor model 

postulates that   is linearly dependent upon a few unobservable random variables m21 ,...,F,FF  and p 

additional source of variation p ,...,, 21 called specific errors where m<p. The factor model in matrix 

notation is given as:

4.3................................................................................
(px1)(mx1)(pxm)(px1)(px1)
ε FL  

Where L = ( ijl ) is matrix of loadings ijl is loading of the i
th

 variable on j
th 

factor.  



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

105 
 

     )( pxmL























pmp

m

m







.............

...

...

,....,,

,....,,

1

22221

11211

 , ][]['

p21m21 ,...,ε,εεand,...,F,FFF  ε'
.
 

Assumptions of Factor Model 

The assumptions of orthogonal factor model (equation 3.4) are 

1. 0 )(F
                                               

 

2. )()cov( 'FFIF m   

3. 0)ε(   

4. pxp )()cov( εε' where  is diagonal matrix 

5. pxmFF 0'εε,  )()cov(  

Covariance Structure for Orthogonal Factor Model 

The orthogonal factor model implies the covariance matrix   of   has the form 

1.  'LL  

2. iiimiii  andp1,2,...,i,...)var( 22

2

2

1 lll
 
is i

th
 specific factor. 

3. kmimkikiki ,X llllll  ...)( 2211  

4. m1,2,...,jandp1,2,...,i,)cov(  ijji ,F l  

5. LF  )cov( ,  

The portion of variance of i
th

 variable contributed by the m common factors is called the i
th

 communality and 

that portion of variance of i
th

 variable i  due to the specific factor is called specific error. 

 The i
th 

Communality is defined by 

         p1,2,...,i,... 22

2

2

1

2  imiiih lll
………………………………..3.5

 

The factor model assumes that the 
2

1)p(p
p


 variances and co-variances of  can be reproduced from the 

pm factor loadings lij and p specific variances i . Thus, the factor model explains the covariance matrix   with 

fewer (i.e. p + pm) parameters.   



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

106 
 

Estimation of Factor Loading   

The orthogonal factor model is essential when the covariance matrix   deviates significantly from diagonal 

matrix. The initial problem is estimation of the factor loadings lij and the specific variances i . The two 

popular methods of estimating are principal component method and maximum likelihood method. In this study, 

only the principal component method was used.  

The Principal Component Method 

The spectral decomposition of the covariance matrix provides us one factoring of . Let    have eigenvalue-

eigenvector pairs ),( iei with 0....21  p  then   can be written as: 

)6.3.(.............21

'

pp

'

22

'

11 eeeeee p   

This first prescribed covariance structure for the factor analysis model have as many factors as variables and 

specific variances i
 
= 0 for all i the loading matrix is   ppL eee  ,,, 2211   that is 'LL  ( 

equation 3.6) is exact form and it has as many common factors as there are variables, hence it does not result in 

data reduction. One prefers models that explain the covariance structure in terms of few common factors. One 

approach is to neglect the last p-m eigenvalues that are small. Then one has the approximation:
 

'....222111 LLeeeeee T

mmm

TT         

The Contribution to the Total Sample Variances 

 In applying the principal component to perform factor analysis, one uses the sample covariance matrix S or the 

sample correlation matrix R . 

Trace of the sample covariance matrix = tr ( S ) =
PPSSS  ...2211

and the trace of sample correlation 

matrix is pRtr p   ˆ...ˆˆ)( 21 , where p ˆ,...,ˆ,ˆ
21 are eigen-values of the sample covariance 

matrix S . 

Thus, 

 

































.ofanalysisfactorafor
ˆ

.covariance sampleofanalysisfactorafor
)(

ˆ

factorthjtodue

variancesample

totalofproportion

ncorrelatio
P

Str

j

j





 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

107 
 

The number of common factors retained in the model is increased until a suitable proportion of the total sample 

variance has been explained. As a rule of thumb (or convention), the number of common factors m is set to be 

equal to the number of eigenvalues of the sample correlation matrix R greater than one if the sample correlation 

matrix is factored, or the number of common factors m is set to be equal to the number of positive eigenvalues 

of  S  if the sample covariance matrix is factored.   

Factor Rotation 

Factor rotation is an orthogonal transformation of the factor loading since the original factor loadings may not 

be readily interpretable. The rationale behind is very much kin to sharpening the focus of a microscope in order 

to see the detail more clearly. 

If L̂  is the pxm matrix of estimated factor loadings, then ITTTTwhereTLL  '',ˆ*̂

 
is pxm matrix of 

rotated loadings. The estimated covariance (or correlation) matrix remains unchanged since   

Ψ'LLΨ'LTT'LΨ'LL
** ˆˆˆˆˆˆˆˆˆ  . Moreover, specific variances, communalities and residual 

matrices remain unchanged. Since the original loadings (initial loadings) may not have an easy interpretation, it 

is usually a common practice to rotate them until a simple structure is achieved. The rotation should be such that 

each variable loads high on a single factor and has small to moderate loading on the remaining factor.  

The most widely used factor rotation is Kaiser’s Varimax rotation. If one defines 

i

ij

ij

ĥ

ˆ
~

*

*


  to be the final 

rotated coefficients scaled by the square root of the communalities, then the varimax procedure selects the 

orthogonal transformation T such that: 

                             


 

































m

j

p

i

p

i

ij

ij
pp

V
1 1

2

1

*

4*

~

)
~

(
1



   

is as large as possible. 

3. Results and Discussion 

It can be seen from Table 1, 48.1% governmental school teachers, of which 54.4% were of elementary and 

45.6% were of secondary school teachers, whereas 51.9% were non-governmental school teachers, of which 

80.5% were from elementary schools and 19.5% were from high schools. 

 

 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

108 
 

Table 1: Cross Tabulation of School Type with School Level. 

Type school Vs Level of 

school 

Elementary Secondary Total 

N % N % N % 

Government 106 54.4% 89 45.6% 195 48.1% 

Non-governmental 169 80.5% 41 19.5% 210 51.9% 

Total 275 67.9% 130 32.1% 405 100% 

3.1 Data Reduction 

Performing principal component analysis helps firstly to identify interdependence in the independent variables, 

secondly to derive few numbers of manageable and meaningful constructs with a minimum loss of information 

and thirdly to get reliable measure to those interdependent variables. There were 55-items of psychometric 

character to be analyzed in data reduction stage. Ten principal components were extracted from the 55 variables 

which were retained in data reduction. As a rule of thumb it is checked by the scree plots, in which the 

components were retained whose variance λ were greater than unity. Forty two of the 55 variables were retained 

in data reduction as can be checked by the criterion in the factor analysis.  Prior to analyzing, the reliability of 

the data was checked with Cronbach’s α. Sampling adequacy were checked using Kaiser-Meyer-Olkin (KMO) 

and Bartlett‘s test of sphericity were observed to check the significance of the correlation matrix. To keep the 

reliability and high sampling adequacy, psychometric variables, with cumulative extraction greater than 0.55, 

were retained in the analysis. 

3.2 Factor Analysis 

An exploratory factor analysis was carried out to examine the factor structure of the 55-items scale. The 

principal component method with varimax rotation was conducted to assess the underlying structure for the 55-

items of the psychometric properties so that the final factors were uncorrelated with each other. As a result, one 

can assume that the information explained by one factor is independent of the information in the other factors. 

Before factor analysis, the overall reliability of items was checked and Cronbach’s alpha was 0.898 for all 55-

items and it was 0.885 for the selected 42-items, then the items were consistent. Using SPSS, the factor analysis 

program generates a number of tables depending on the options one chooses. Several assumptions were also 

tested from the tables of the output. The 2
nd 

part of the questionnaire containing 55-items, was used for 

exploratory factor analysis, and the determinant of correlation matrix, was below 0.00001 indicating 

multicollinearity problems. Looking at the correlation matrix and table of communalities, some of the items 

were eliminated and then 42 items were used for factor analysis. The selection was made by observing the 

variables which were correlated highly, rejecting the variables having communalities after extraction less than 

0.55. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.867 which was greater than 0.70, 

implying that enough items were predicted by each factor. The Bartlett’s test of Sphericity showed that

025.98022

861  , p-value<0.001 and it was also significant indicating that the variables were correlated 

highly enough to provide a reasonable basis for factor analysis and indicated that the assumption was met. 

Moreover, the detailed results are given in appendix (Table B.1). Hence the data were suitable for factor 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

109 
 

analysis and multivariate analysis of variance.   In varimax rotation of factor analysis solution for the original 42 

items, 70.12% of the total variance was explained by the first ten factors with factor loadings greater than 0.6 as 

shown in Table 1.  The questions or items that loaded highly on factor one were related to motivation of the 

teacher which had eight items {I read more to know the subject in detail, the subject I teach meets with my 

talent, I make enough preparation for all classes, the work of a teacher is very pleasant to me, I am very 

interested in clarifying the subject for students, I find my work mentally stimulating, the social status of the 

teaching profession is encouraging, teaching encourages me to be creative}.  Factor two was renamed as to 

payment condition which included five items {the income is same as my expectation, I am well paid in 

proportion to my ability, the salary is very poor with respect to that of other school, the payment condition is 

improper in our school, my salary adequately meets my needs}.  Factor three was related to school 

administration containing five items {the school administration praises good work, the School administration is 

fair and supportive, our school leader decides satisfying idea, my administrators support my decisions and 

actions, the behavior of our school leader is very good}.  Factor four renamed as student behavior comprised of 

five items {my students are hard-working and initiate me to do more, the students have positive behavior 

towards me, my students have a good communication with me, my students respect me as a teacher, my students 

have high motivation levels for learning}.  Factor five was related to growth opportunity (promotion) which had 

four items {the policy of promotion is satisfying in our school, the promotion policy makes me disappointed, the 

opportunity to advance professionally is good, the promotion in our school depends on ability and performance 

of a person}.  Factor six was related to working conditions containing four items {condition of the job gives me 

a sense of accomplishment, condition of work is challenging and tiresome to me, working condition in the 

school are comfortable, numbers of classes that I have to teach are overloaded}.  Factor seven was related to co-

workers condition which includes four items {my colleagues are very helpful, my co-workers stimulate me to do 

better work, my co-workers are responsible and clever to do, I like the people with whom I work with}. Factor 

eight was related to school facility containing three items {the recreational materials of the school are enough 

for recreation, there are enough books in my subject at the library, there are enough facilities of computer and 

other infra-structures}.  Factor nine was related to personal life with two items {I am very happy in my life, I 

feel secured in my life}. Factor ten was related to curriculum condition with two items {It is possible to finish 

the content of the textbook on time, the way that the curriculum designed is very good and comfortable for a 

teacher}. These factors account for 12.8%, 8.38%, 8.29%, 8.07%, 7.25%, 7.21%, 5.28%, 5.02%, 4.01% and 

3.82% of the variation, respectively. The scree plot in appendix of (Figure A3.1) can be used when the sample 

size is large (around 300 cases and above) to extract the number of factors in the analysis. The communalities of 

each variable before and after extraction, principal component analysis works on the initial assumption that all 

variance is common; therefore, before extraction, the communalities are all 1. The communalities, in the column 

labeled extraction, reflect the common variance in the data structure. So for example, one can say that 78.8% of 

the variance associated with item one is common or shared variance and 76.3% of the variance associated with 

item two is common or shared Another way to look at these communalities is in terms of the proportion of 

variance explained by the underlying factors. After extraction, some of the factors were discarded and so some 

information was lost. The amount of variance in each variable that can be explained by the retained factors is 

represented by the communalities after extraction.   



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

110 
 

Table 2: Results of Factor Analysis Using Varimax Rotation of 42 Psychometric Variables for a Sample of 405 

Teachers 

Accounted for 70.12% 
Common Factors: Component 

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 

Eigen values 5.4 3.5 3.5 3.4 3.1 3.0 2.2 2.1 1.7 1.6 

I read more to know the subject in detail. .86          

The subject I teaches meets with my talent. .81          

I make enough preparation for classes. .80          

The work of a teacher is very pleasant to me. .76          

I am very interested in clarifying the subject 

for students. 
.76 

         

I find my work mentally stimulating. .74          

The social status of the teaching profession is 

encouraging. 
.72 

         

Teaching encourages me to be creative. .66          

The income is same as my expectation.  .85         

I am well paid in proportion to my ability.  .85         

The salary is very poor with respect to that of 

other school. 

 
-.83  

       

The payment condition is improper in our 

school. 

 
-.82  

       

My salary adequately meets my needs.  .69         

The school administration is fair and 

supportive. 

  
.82  

      

The school administration praises good work.   .81        

Our school leader decides satisfying idea.   .78        

My administrators support my decisions and 

actions. 

  
.77 

  
 

    

The behavior of our school leader is very 

good. 

  
.67 

  
 

    

My students are hard-working and initiate me 

to do more. 

   
.76 

  
  

  

The students have positive behavior towards 

me. 

   
.76 

   
 

  

My students have a good communication 

with me. 

   
.75 

   
  

 

My students respect me as a teacher.    .70       

My students have high motivation levels for 

learning. 

   
.69 

    
  

The policy of promotion is satisfying in our 

school. 

    
.90 

    
  

The promotion policy makes me 

disappointed. 

    
-.83 

    
  

The opportunity to advance professionally is 

good. 

    
.79 

     

The promotion in our school depends on 

ability and performance of a person. 

    
.69 

     

Condition of the job gives me a sense of 

accomplishment. 

     
.87 

    

Condition of work is challenging and 

tiresome to me. 

     
.86 

    

Working conditions in the school are 

comfortable. 

     
.82 

    

Numbers of classes that I have to teach are 

overloaded. 

     
.82 

    



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

111 
 

My colleagues are very helpful.       .82    

My co-workers stimulate me to do better 

work. 

      
.75 

   

My co-workers are responsible and clever to 

do. 

      
.61 

   

I like the people with whom I work with.       .54    

The recreational materials of the school are 

enough for recreation. 

       .79   

There are enough books in my subject at the 

library. 

       .78   

There are enough facilities of computer and 

other infra-structures. 

       .69   

I feel secured in my life.         .87  

I am very happy in my life.         .84  

The way that the curriculum designed is very 

good and comfortable for a teacher. 

         .85 

It is possible to finish the content of the 

textbook on time. 

         .83 

The extraction method was principal component analysis and the rotation method was varimax with Kaiser 

Normalization to obtain orthogonal factors. Varimax rotation convergence was established after seven iterations. 

From the results of bivariate analysis, the predictor variables considered in psychometric properties which were 

grouped in factor analysis like motivation, school administration, payment condition, growth opportunity, 

student behavior, working condition, co-workers (colleagues), school facility and curriculum condition. 

Table 3: Tests of Association between Satisfaction Levels with Factored Psychometric Instruments using Wald 

Statistic 

Variable/Factors Wald df P-value 

Motivation 25.623 1 .000 

School administration 14.660 1 .000 

Payment condition 10.488 1 .001 

Working condition 20.733 1 .000 

Co-workers condition 10.268 1 .001 

Growth opportunity 1.990 1 .158 

Student behavior 6.164 1 .013 

School facility 0.375 1 .540 

Curriculum condition 6.772 1 .009 

Personal life 6.592 1 .010 

3.3 Interpretation and discussions of the Results  

It can be seen from Table 3, that there was an association between psychometric factors and outcome variable 

except growth opportunity and school facility using the Wald statistic. Therefore, motivation, school 

administration, payment condition, working condition, student behavior, co-workers condition, curriculum 

condition and personal life were the candidate variables for the analysis. 



American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 76, No  1, pp 101-112 

112 
 

4. Conclusions 

The factor analyses conducted in this study indicated that ten factors instead of 42-items were sufficient to 

explain the total sample variance in job satisfaction. The psychometric properties were grouped in to ten factors. 

The predictor variables of job satisfaction of teachers were found to be motivation, working conditions, payment 

condition of teacher, student behavior, school administration, co-workers (colleagues) condition, personal life of 

teacher and curriculum condition. 

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