Economic Journal of Development Issues Vol. 21 & 22 No.

1-2 (2016) Combined Issue

Effectiveness of Teaching Economics in Higher Secondary

School Level, Nepal
Chakra Bahadur Khadka, PhD.
Faculty of Economics, Tribhuvan University, Patan Multiple Campus, Nepal

Abstract
Teaching economics to students in a clear and unbiased manner supports beginner students,
master the essential principles of understanding the economizing problem, specific economic
issues, help the student to understand and apply economics in a precise and empirical
manner on economic issues and promote a lasting student interest in issue of economics.The
objective of this paper is to analyze the effectiveness of teaching economics in higher secondary
school level. Two hundred four teachers and equal students’ number have been selected for
questionnaire survey. The survey data were collected from different training centers of the
training and workshop interval. Psychometric scale, was designed for data collection. For the
data analysis, SEM is used, including simultaneously complete tests of model fit, together with
simultaneously overall tests of model fit, specific parameter estimates, compare simultaneously,
OLS coefficients, Means and Variances. The finding is based on the assumption that is; default
model is correct, the probability of getting a discrepancy as significant as 73.59 is 0.00 of
students' understanding of economics in their classroom. Maximum likelihood estimates at
all the parameter estimates are highly significant. If EFET positive change by 1, T_EFET_2
also positively change by 0.88. The regression weights to estimate, 0.88, has a standard error of
about 0.06. Dividing the regression weight estimate by the estimate of its standard error gives
z = 0.88/0.06 = 14.91. The variables of student understanding are significantly different from 0
except S_QOAT_4. As ATME positive changes by 1, S_ATME_2 also positively change 0.57.
The regression weights to estimate, 0.57, has a standard error of about 0.04. Students they
agreed with 8A and 9A statement. This is recommended that teach the teachers as a workshop
style and training in improving economics instruction in Higher Secondary Schools Level.
The experimental program helps teachers to gain an understanding of economic concepts and
improve pedagogy. Improved classroom environment, the latest text materials might be the
encouraging to economics subject to the student.

Key words: Teaching economics, Deductive, Training, Text Materials, Structural Equation
Modeling.

1. INTRODUCTION
Economics is one of a precise subject taught in the higher secondary school level. It is
important to both students and the civilization as great for the reason that it wounds
transversely all compasses of human effort as it can be understood in its simplest

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definition by Robbins (1935), “…thus economics as a science which studies human

behavior as a relationship between ends and scarce means which have alternative
uses….” By this definition, Robbins (1935) lay emphasis on economics as a science and
that economic investigation would be based on positive and logical method rather
than normative with vague judgments. The important ideas of teaching and learning
economics in classroom are to help the basic stage of student masters the principles
vital for understanding the financial problem, precise economic issues. The policy
alternatives, help to the students understanding, then apply the economic perspective
also reason accurately and empirically with economic matters. This promotes a lasting
student interest in economics and the economy (McConnell, Brue, & Flynn, 2009).
British philosopher of positive science andeconomists John Neville Keynes (1890) who
was the father of renowned economists J. M. Keynes; salvaged that “learning about
economics, both inductive and deductive logical were required to understand how
the economy functions” (p. 44, 100). Moreover, John  Neville  Keynes (1890) divided
economics into three parts; positive economics, normative economics, and applied
economics. The science and art of economics relating the lessons learned in positive
economics to the normative goals determined in normative economics. In generally
means that the objective of applied economics is to find how to come from positive
science to normative economics.

According to the National Council of Educational Research and Training, India

(NCERT, 2005) “the answer to the question; why economics is taught in schools is
not only essential in answering students but also for the teachers when they teach
economics in schools. Teachers are likely to understand why economics is taught so
that they can plan the classroom activities effectively”. The details of opinions would
also help in understanding the content topics and subtopics and why they are included
in the curricular contents. The aims at teaching economics at the higher secondary
stage are: making students understand some basic economic concepts and developing
economic reasoning and thus learners can apply to their daily life as citizens, workers
and consumers; enable learners to realize their role in country building and sensitize
them to the economic issues that the nation is facing today, to equip learners with the
basic tools of economics basic tools of economics and statistics to analyze economic
issues. This is pertinent to even those who may not pursue this course, this course of
the higher secondary stage; and to develop an understanding among students that
there can be more than one view on any economic issue and to develop the skills to
argue logically with reasoning (NCERT, 2005).

Additionally, NCERT, (2005) emphasize “if all competitors in the global economy
are to achieve a better quality of life for their populations, there must be economic
cooperation between all countries. This does not mean that developed countries must

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control the purposes of less-developed countries. Instead, it means sharing concepts

across cultures, against a context of economic theories, ideas awareness.” To achieve
this understanding, students must be taught to consider economic theories, ideas and
activities from the points of view of changed individuals, nations and cultures in the
world economy. Although broad knowledge is impossible, students can exploration
for understanding through a wide range of different aspects of the global economy.
Their exploration may inspire a lifelong interest in the promotion of international level
understanding.

Capable teacher prepares a perspective planfor the entire academic year, where the
entire syllabus is looking into and a term wise plan of different units is prepared. This
can clear confusion created when the concerned teacher is absent and another one takes
over. Also, it leads to transparency and coordination among the group of teachers,
teaching different sections. Besides the overall plan, each unit and content area need
to be structured with regard to the objectives, content coverage, methodology, specific
learning activities and so on, as laid down in the basic components of a Teaching Unit.
Let us briefly discuss each component of a teaching unit (Robertson & Acklam, 2000;
Chibueze, 2014).

In the word of O’Sullivan and Sheffrin (2003), “when we set ‘’out to write an economics
text, we were driven by the vision of the sleeping student.” The book, Macroeconomics
Principles and Tools written by O’Sullivan and Sheffrin (2003) they wrote in preface
… “A few years before, one of the authors was in the internal of a fascinating lecture
on monopoly pricing when he heard snoring. It wasn’t the first time a student had
fallen asleep in one of his classes, but this was the loudest snoring, he had ever heard
it sounded like a sputtering chainsaw. The instructor turned to Bill, who was sitting
next to the sleeping student and asked…. Could you wake him up?” “Bill looked at the
sleeping student and the gazed theoretically around the room at the other students.”
He finally looked back at the professor and said, “well professor, I think you should
wake him up. After all, you put him to sleep….” The occurrence altered the economics
teacher of teaching economics. It highlighted for basic truth about many students,
economics isn’t precisely exciting. The teacher assumed the challenge to get first-time
economics students to see the relevance to economics to their lives, their careers, and
their futures (O’Sullivan & Sheffrin, 2003).

Economics is a subject that involves observation and collection of data and in such
a subject the role of the teacher becomes even more important. Teaching economics
with charts, diagrams, equations from as an integral part of teaching and these things
can be used properly only under the guidance of a teacher. In the Nepalese scenario,
economics teachers of higher secondary school level have to act as the major source of

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knowledge of the subject matter as a role model to the students and facilitator to solve
various other raised by the students.For the teaching of economics, it is necessary to
have direct observation of the environment and physical conditions. Students have
to be encouraged for observing things by them and to have a proper assessment and
knowledge of the subject matter. Only a good teacher of economics can provide such
type of encouragement. An economics teacher can accomplish this task successfully if
s/he can guide the student in a scientific and thorough manner.

Training com workshops were organized by the Higher Secondary Education Board
(HSEB), Nepal for economics teachers. The HSEB was the authorized body to plan,
implement and evaluate programs related to higher secondary level. Authority was
also accountable for giving training for the subject teachers. Contents of training
included, curriculum framework, teaching, learning materials, classroom pedagogy
and testing principle, and comprises the fundamentals of pedagogy, the latest concepts
of classroom realities, learner-centered class, planning, materials adaptation and use,
test items’ construction and assessment and many other issues. The objectives of the
study are to examine the effectiveness of teaching economics in higher secondary
school level factors that influences teaching economics to the teachers, and evaluate
the degree of interest and attitudes of students which influences learning economics
in higher secondary level.

2. REVIEW OF LITERATURE
A study report submitted to national teachers’ institute Ebonyi State University study
center by Chibueze in (2014) set the objective of identifying the factors influencing the
effectiveness of teaching and learning of economics in higher secondary schools in the
Izzi local government zone. The investigative design of the research was descriptive
and questionnaire survey. Total population of the study was ten thousand, nine
hundred students. Likewise, seventy-five teachers in the senior secondary schools
have been used. One hundred and fifty teachers and students were sampled in five
selected schools. The descriptive statistics were used to analyze the data. The findings
showed that teaching and learning of economics in our secondary schools are affected
by unqualified economics teachers, poor method of teaching, inadequate instructional
materials and attitudes and interest of the teachers and students. Based on the findings
some recommendations were made thus Employment of economics teachers by the
government through the ministry of education should be strictly based on merit so as
to make it possible for only those who studied the course to be appointed.

A research paper was published by Adu, Galloway and Olaoye (2014) regarding the
teachers’ characteristics and students’ attitude towards economics in secondary schools.
The study samples involved in six hundred and forty students selected through cluster

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sampling and simple random sampling techniques. To test the hypotheses of the study
Pearson product moment correlation and t-statistics were used. The finding of the
study shows that, students perceive their teachers’ in terms of knowledge of contents
of economics, communication ability, teaching methods and classroom management
skills has a significant relationship with the students’ attitude towards economics.
When the students’ perception of their teachers’ characteristics is low, hence the
students’ attitude to economics tends to be negative.

Likewise, a research was completed by Idoko and Emmanuel (2015) about teachers’
effectiveness in teaching economics. Teachers, as the pillars of an education system
are expected to be resourceful as a strategy for effecting teaching in Nigerian schools
and colleges. Structured questionnaire made up of ten items was constructed in an
Ankpa local government area of Kogi State and administered the questionnaire to one
hundred students and ten teachers in fifty secondary schools. A Likert weighted mean
average of four-point rating scale was employed for the analysis of the data. The result
shows that teacher’s strategies and methods of teaching economics in the secondary
schools in the study area was inadequate due to lower educational qualification, lack of
motivation in terms of remuneration and fringe benefit, the lack of teacher’s recognition
and cognitive experience. Employment of teachers, especially in economics should be
based on assessment through written test and classroom teaching to guide against
the influx of quacks into the teaching profession, and government interventions to
ensure that right method of teaching employed should supervise teachers regularly
and make sure that right method should be adopted in teaching and learning process
were the recommendations made by author.

A survey was conducted by Blazar (2015) into education production function that
moved away from narrative teacher inputs, such as education, certification, and
salary, directing as a replacement of on observational measures of teaching practice.
Build on this conversation by exploiting within-school, between grade, and cross-
cohort variation in scores from two observation instruments; further, the condition
with a uniquely rich set of teacher characteristics, practices, and skills. The findings
of the study indicated that inquiry-oriented instruction positively predicts student
achievement. Content errors and imprecision were negatively related, though the
estimates and were sensitive to the set of ‘covariates’ included in the model. Two other
dimensions of instruction, classroom emotional support and classroom organization,
were not related to this outcome. Findings recommended that recruitment and
development efforts aimed at improve the quality of the teacher workforce.

A study by Izci (2016) was concerned about supporting learning assessment forms, an
important part of instruction in internal and external factors affecting teachers’ adoption of

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formative assessment. The objective was to support learning is known as formative assessment
and itcontributes student’s learning gain and motivation. Thestudy, was completed byusinga
teacher’s change environment framework, reviews literature on formative assessment and
presents atentative model that illustrates the factors impacting the teachers’ adoption of
formative assessment in their teaching. There were four main factors consisting personal,
contextual, resource-related and external factors that in fluenced teachers’ practices of
formative assessment were the significant conclusions of the study.

Research articles in economics across the curriculum to the integration of economic concepts
into various disciplines were surveyed by Smirnova in (2016). The main objective of the study
was, to help high school teachers gain a deeper understanding of various economic concepts,
and demonstrate active engagement as well as other collaborative instructional strategies. Five-
day in-residence training com workshop covered three topics was included, the topic was money
and inflation, business cycles and unemployment, and government and the economy. Twenty-
two teachers attended the program in 2014, and seventeen teachers attended the program in
2015. The research contributes to the literature on economic education by describing the
development of a multi-day program of the American Institute for Economic Research that uses
the Economics-Across-the-Curriculum approach. The program focused on economics teachers
and give importance of English language, arts, social studies, math, and foreign languages. The
participants’ diversity created cross-pollination of ideas, dynamism, and an interdisciplinary
method of teaching. The integration of economic concepts into various subjects helps students
develop critical thinking, information of text analysis, real-world application, and other
skills that are transferable to various fields of study, academia, and the workplace. The paper
showcases several lessons that were field-tested by participants in their classrooms after the
completion of the program. The idea might serve as catalysts for other innovative ideas about
integration of economics across the high school curriculum.

A research was accomplished by Vasiliki, Panagiota, and Maria (2016) about a new teaching
method for teaching economics in secondary education. The aim of the study was to find out
the attitudes and perceptions of students, when implementing this teaching process and to
explore the extent to which this method can contribute to the improvement of teaching and
learning. The authors evaluated an interdisciplinary approach to teaching economics through
an innovative teaching method, in the context of the Greek Senior High School. The important
findings of the study were, the use of art and especially the use of a movie, helped students
understand the basic concepts of the Stock Market. Furthermore, the use of audiovisual material
facilitated the active participation in students and made the course more interesting. As a result,
the class climate was friendlier enhancing the freedom of expression. The role-playing was a
significant factor of formatting this climate and it created positive experiences of students. The
new teaching methodology contributed to the enforcement of knowledge results which helped

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students to shape their own views of the economic issues related to the Stock Market and to
develop an overall view of economic science in relation to real life.

A training manual for economics teachers, was published by Higher Secondary Education Board
(HSEB) Nepal (2006). The main objective of the training manual in the Nepalese context of
higher secondary level economics teacher was to make it as the major source of knowledge
of the subject and assist instructors to act as a role model to the students and a facilitator to
solve various other issues raised by the students. Whereas teaching economics, it is necessary
to have direct observation of the environment and physical conditions. Students have to be
encouraged for observing things by them and to have a proper assessment and knowledge of
the subject matter. Only a good teacher of Economics can provide such type of encouragement.
An economics teacher can accomplish this task successfully if he can guide the students in a
scientific and systematic manner.

3. DATA AND METHODOLOGY

a) Statistical Framework

i) Structural Equation Modeling

Structural equation modeling (SEM) is a very general, predominantly linear, mostly cross-
sectional statistical modeling technique. Factor analysis, path analysis and regression represent
special cases of SEM. SEM is a largely confirmatory, rather than exploratory, technique,
that is, a researcher is more likely to use SEM to determine whether a certain model is valid,
rather than using SEM to find a suitable model although. SEM is  a quantitative research
technique that can also incorporate qualitative methods. The model is used to show the causal
relationships between variables. The relationships shown in SEM represent the hypotheses of
the researchers.  SEM produces data onto a visual display and this is part of its appeal. SEM is
designed to look at the complex relationships between variables, and to reduce the relationships
to visual representations. A research design can be described in terms of the design structure
and the measurements that are conducted in the research. These structural and measurement
relationships are the basis for a hypothesis for this study. SEM is a cross-sectional statistical
modeling technique that has its origins in econometric analysis (Byrne, 2001).

SEM is a combination of factor analysis and  multiple regression.  The term  factor and
variable referred to the same concept in statistics. Path analysis is a variation of SEM, which
is a type of multivariate procedure that allows a researcher to examine the  independent
variables and dependent variables in a research design. Variables can be continuous or discrete.
SEM also works with measured variables and latent variables. Path analysis uses measured
values only. Measured variables can be observed and are measurable. Latent variables cannot

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be observed directly, but their values can be implied by their relationships to observed variables
(Loehlin, 1992; Kline, 2005).

Likewise, other two famous measures model known as the Non-Normed Fit Index (NNFI) or
Tucker-Lewis Index (TLI), (Tucker & Lewis, 1973) and Normed Fit Index (NFI) introduced
by Bentler and Bonett (1980). NFI, proportion in the improvement of the overall fit of the
hypothesized model compared to the independence model, in theory 0 measure poor fit and 1
measures perfect fit, measured acceptable when the statistical value of NFI is greater than .90.
NNFI, also similar to NFI but adjusts for model complexity, theoretically 0 means poor fit and
1 is perfect fit, considered satisfactory when it is greater than .90. Nevertheless, these are fairly
rales of thumbing (Bollen& Joreskog, 1985).

A relative modem approach to model fit is to accept that models are only approximations,
and that perfect fit may be too much to ask for. Instead, the problem is to assess how well a
given model approximates the true model. This view led to the development of an index called
for Root Mean Square Error of Approximation (RMSEA). If the approximation is good, the
RMSEA should be small. Typically, an RMSEA of less than 0.00 is required, and statistical
tests or confidence intervals can be computed to test if the RMSEA is significantly larger than
this lower bound (Hox & Bechger, 2011).

Structural equation modeling incorporates several approaches or frameworks to representing

these models. In one well-known framework popularized by Joreskog and Sorbom (1982)
in University of Uppsala. The general structural equation model can be represented by three
matrix equations:

h(m # 1) = B(m # m) # h(m # 1) + C(m # n) # p(n # 1) + p(m # 1)

Y(p # 1) = K y(p # m) # h(m # 1) + f(p # 1)
X(q # 1) = K X(q # n) # p(n # 1) + Cd(q # 1)

Where, B (m × m) and Γ(m × n) are coefficient and parameters and ζ is a random vector of effects of
residuals. y= p ×1 column vector of endogenous observed variables (y’s); x = q ×1 column
vector of exogenous observed variables (x’s). Error of the vector measurement in x and y
denoted by ε = p × 1 and δ = q × 1. η is a latent, endogenous variable. The regression matrix of y
on η is Λ (x × y). Λy = p × m weight matrix representing paths from endogenous latent variables
(η) to observed y variables Λx= q × n weight matrix representing paths from exogenous latent
variables (ξ) to observed x variables, η = m × 1 vector of endogenous latent variables, ξ = n ×
1 vector of exogenous latent variables.

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In applied work, structural equation models are most often represented graphically. Figure 1
shows the interconnections among variables of a structural equation model.

Figure 1: Graphical Example of a Structural Equation Model

Graphical example of a structural equation model is presented In Figure 1. In structural equation

modeling, the key variables of interest are usually latent constructs. All variables are indicated
by the Greek character. Exogenous constructs are ξ. Endogenous constructs are indicated η.
the structural model parameters representing regression relations between latent constructs γ
regression of an endogenous construct on an exogenous construct, or with the β. Parameters
labeled with the φ represent these covariances. This covariance comes from common predictors
of the exogenous constructs which lie outside the model under consideration. Structural error
term, labeled with the ζ. Manifest variables associated with exogenous constructs are labeled
X, while those associated with endogenous constructs are labeled Y. The loadings linking
constructs to measures are labeled with the λ. Structural equation models can include two
separate λ matrices, one on the X side and one on the Y side. Measurement error terms associated
with X measures are labeled with the δ while terms associated with Y measures are labeled with
ε.  Theoretically, almost every measure has an associated error term.

ii) Cronbach Coefficient Alpha

The Cronbach Coefficient Alpha (Cronbach, 1951) is the truly familiar estimate of internal
consistency of items in a model or survey Reliability and its Item Analysis. C-alpha is not
a statistical test, but a coefficient of reliability based on the correlation between individual
indicators. That is, if the correlation is high, then there is evidence that the individual indicators
are measuring the same underlying construct. Therefore, a high c-alpha, or equivalently a
high “reliability”, indicates that the individual indicators measure the latent innovation well
(European Commission, 2008). Cronbach’s Coefficient Alpha can be defined as:

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Q / i = j COV (x i, x j Q / Var(x j p =
ac = c m c m1 - f j c 1,..,M,.i,j = 1,..,Q (i)
Q-1 Var (x 0) Q-1 Var (x 0)
where, M indicates the number of countries considered, Q the number of individual indicators
available, and x 0 = /q = 1 xj is the sum of all individual indicators. C-alpha measures the portion
Q

of the total variability of the sample of individual indicators due to the correlation between
indicators. It increases to the number of individual indicators and with the covariance of each
pair. If no correlation exists and individual indicators are independent, then C-alpha is equal to
zero, while if individual indicators are perfectly correlated, C-alpha is equal to one.

b) Data Collection
The training for economics teachers was organized by HSEB in Gajuri, Dhading training center.
At the center, 32 teachers were participated in different schools of the central development
region. Likewise, Surkhet, training center, and 32 teachers were participated from different
schools of from Midwest development region. Another was Palpa training center, and 42
teachers from different school of Midwest development region were participated, likewise in
Damauli, training center, participant teachers were 35 from different schools of the western
development region. In the Dhulikhel training center, participants were 33 from different
schools of the central development region, lastly; Bardibas training center, participant were 30
teachers from different schools of the eastern development region.

Data were collected during the training period with economics teachers. Altogether 204
economics teachers were participating in different training/workshop center of different region
of Nepal. The data also collected from higher secondary school level students in different
region, area of training centers in different point of time. Two hundred four students were
selected for questionnaire survey. A quota sampling technique has been used for data collection
process, and students were from different public and private higher secondary schools. The
questionnaire was designed into psychometric scale, and respondents specify their seven-point
level of agreement or disagreement on a symmetric agree-disagree scale for some sequences of
statements. Thus, the range captures the intensity of their feelings for a given item.

c) Model Specification and Estimation of Parameters

SEM is used as a statistical tool for data analyzed. The significance of SEM in this research
is to examine the structural relationships between endogenous and exogenous variables, and
the measurement model showing the relations between latent variables and their indicators.  The
statistics similarly used to identify the combination of factor analysis and multiple regression
analysis, and to  analyze the  structural  relationship between measured variables and latent
constructs. Employing SEM to Specify pathways in the model, assuming the relationships
of free pathways, in which hypothesized causal relationships between variables are tested. The
parameter estimation is finalized by comparing the actual covariance matrices representing the
relationships between variables and the estimated covariance matrices of the best fitting model.
This is obtained through numerical maximization of a fit criterion as provided by maximum
likelihood estimation, estimates mean, variances and squared multiple correlations, parameters,

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and general least squares methods. This is often undertaken by using a specialized SEM
analysis program, of which various occur. A generalized least squares estimation and maximum
likelihood estimation was developed Kullback and Leibler (1951). Following estimation
equation is ‘scale-free’ least squares estimation (SLs) used for data analysis:

f SLS ^/ ; S h = 12 tr[D (S - / )]
(g) - 1
(g) (g) (g) (g) 2
(ii)

Where, D(g) = diag(S(g))

Maximum likelihood estimation is the additional estimation equation presented as:

f(/ ;S ) = 12 tr[K (S - / )]
(g) (g) (g) - 1 (g) (g) 2
(iii)

with, K = (y| ML )
(g)

where ŶML is the maximum likelihood estimation of Y.

Likewise, a distribution-free method also used in this research. The expectations of

using this method, is likely optimal results of the discrepancy function measured correctly
without any assumption of the distribution of variables (Joreskog & Sorbom, 1982). This is the
ideal situation introduced into covariance structure analysis by the asymptotically distribution-
free (ADF) method of Browne (1984) and the minimum distance method of Chamberlain
(1982), which is identical:

C(a,a) = [N - r];
/ Gg = 1 N(g) f(n(g),/(g), Xr (g),S (g) E
[N - r]F (a,a) (iv)
N
4. RESULT AND DISCUSSION
a) Reliability and Validity Test
Cronbach’s Alpha (α) used to measure the model exceeded 0.90, indicating excellent
level of internal consistency. The value of Cronbach’s Alpha Based on Standardized
Items respective structures on this research model both cases (first case is 0.93
and, second case is 0.92) exceeded 0.92, thus the value indicating excellent internal
consistency, which specifies that about 92.30 percent data are reliable and valid,
therefore only 7.70 data are error.

b) Model Test
With regard to the goodness of fit issue, Measures of Minimum Discrepancy for Chi
Square-Based is presented in Table 1.

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Table 1: Model Fits of the Cross-Validation

Internal & Model NPAR CMIN DF P CMIN/DF
External Factors
of Economics Default model 21 1086.502 34 .00 31.96
Teachers Saturated model 55 .00 0
Independence model 10 2761.62 45 .00 61.36
Understanding of Default model 21 673.59 34 .00 19.81
Economics
Saturated model 55 .00 0
Independence model 10 2233.49 45 .00 49.63

The presented value of Chi Square-Based Measures of Minimum Discrepancy

assumption is that the default model is correct, the probability of getting a discrepancy
as large as 1086.50 is 0.00 in first set of internal and external factors of teachers teaching
economics. And another assumption is that the default model is correct, the probability
of getting a discrepancy as large as 673.54 is 0.00 of students’ understanding of
economics in their classroom.

Table 2: Model Test

Internal & Model NFI RFI IFI TLI CFI GFI RMR PCLOSE RMSEA
External
Factors of
Economics Default model .82 .74 .82 .76 .82 .80 .27 .00 0.03
Teachers Saturated 1.00 1.00 1.00 1.00 .00 .00
Independence .00 .00 .00 .00 .00 .22 .78 0.04
Understanding Default model .92 .83 .92 .75 .92 .87 .10 .00 0.02
of Economics
in Classroom Saturated 1.00 1.00 1.00 1.00 .00 .00

Independence .00 .00 .00 .000 .00 .25 .50 0.03

These measures attempt to contrast some baselines models (not always a null
hypothesis model) after another measurement model. The Baseline Comparisons of
model are presented in Table 2 with different measurement values. The values of NFI
influence of understanding level is 0.92 which indicates acceptable model fit and the
value of NFI Internal & External Factors of Teachers is 0.82 this value is less than 0.9
but more liberal cutoff of 0.80.  The value of RFI in both conditions close to 1 which is
0.74 and 0.83, and the value indicates a good fit of the model. Likewise, IFI value is 0.82
and.92 and it is equal to or greater than 0.90 that indicates accept the model IFI value

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close to 1 indicates a good fit. IFI can be greater than 1 under certain circumstances.
IFI is not recommended for routine use. TLI value is 0.76 and 0.75 in both conditions,
and this value is close to 0.90, this indicates an acceptable level of model fit. The value
of CFI > 0.90 or close to 0.95 indicates good fit, by convention, the CFI should be equal
to or greater than 0.90 to accept the model. The result shows the value of CFI in both
conditions is 0.82 and 0.92, the value indicates a good fit of the model. GFI value is 0.80
and 0.87 in two conditions. The value is close to 1, this means that it is a good fit of the
model. The RMR standard model is 0.00 in both observation and this value indicated
exact fit. The output data onto PCLOSE and RMSEA in both observations are 0.00, the
figure indicates exact fit of the model.

c) Demographics
Fundamental attributes including economics teachers’ experience, and student’s
identity of class eleven and twelve, demographics is presenting: Among the valid
samples (N1 = 204, N2 = 204, Total N = 408). In a survey, the numbers of men and women
were dissimilar in N1 sample number that indicates men were 93%, and women were
7%; in N2 sample number 36.3% students were men and, 63.7% women. In addition,
81% respondents were younger aged 30 and above between 45 years aged, about 13%
respondents were aged 46 years and older. Moreover, 78% respondents had teaching
experiences more than 5 years. In computation, about 6% respondents had 20 years
teaching experiences. Similarly, 92% respondents were working in public school and
had a permanent job, and, 8% respondents were working in private school and they
had no permanent job, they were working as part-time and contract job. Among them,
76% respondents were working more than one school, whereas, 24% respondents were
working their own school only. Similarly, 94% respondents were younger aged 19 to 23
years and 6% respondents were aged above 24 years and older. 50% respondents were
studying class 11 and 50% class 12. Finally, regarding respondent of N2, 86.6% were
enrolled in public school, and 13.4% enrolled in private school.

d) Descriptive Statistics
The internal and external appearances are the influencing factors in the economics.
To identify the influencing factors of the economics teacher psychometric scale, was
designed and descriptive statistics are presented of the respondents specify their level
of agreement or disagreement in Table 3.

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Table 3: Descriptive Statistics of Effectiveness of Teaching Economics

N Variables Mean Mean % Mode Std. Dev.
1I Leading of economics teachers is MA economics 6.20 88.57 7 1.17
degree holders.
2I Teachers’ insufficient qualification influences to 6.30 90.00 7 1.17
poor performance of students in economics.
3I Teachers’ uncaring, attitudes to teaching of 6.11 87.29 7 1.09
economics affect the student’s performance.
4I Lack of good teaching method affects students’ 6.13 87.57 7 1.13
performance in economics
5I The deductive teaching method is chosen than 5.11 73.00 5 1.45
inductive method of teaching economics.
6E Nonexistence of classroom space affects the 5.57 79.57 6 1.17
teaching of economics.
7E Given time affects to the teaching of economics 5.94 84.86 7 1.34
teachers’ preparation.
8E Lack of economics textbooks affect student 5.89 84.14 6 1.25
learning of economics.
9E Some schools do not even have libraries and this 6.76 96.57 7 .74
contributes to ineffective teaching.
10E Training for teacher in economics improving 6.01 85.86 7 1.24
teachers' qualities and better teaching economics in
the classroom.

There are ten statements and statements were divided into two groups internal and
external factor that influencing teaching economics in classroom for higher secondary
school level. From internal factor, the percentage of the mean value of the seven-point
scale are a minimum of 73% and maximum of 90%, and only one mode value is 5 and
all points, mode value is 7. About 85.3 % on average respondents were totally agreed
with the statements and only 14.7 data are error. The average percentage of mean
valve means that teachers’ insufficient qualification uncaring, attitudes to teaching,
lack of good teaching method and chosen of logical methods of teaching influences
to poor performance of students and this affects students’ performance in economics
subject.

The additional set of statement was external factor that influencing teaching economics
in classroom for higher secondary level. According to the descriptive statistics in this
group, the mode value is 7 for three questions and for two questions mode value is
6. Minimum percentage of mean values is 79.57 and maximum of 96.57%. According
to the maximum percentage of the mean value about 97 % teachers agreed with

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the statements. Which indicates some schools does not even have libraries and this
contributes to ineffective teaching economics. The average mean percentage is about
86.2 %, this data indicates 86.2% respondents were totally agreed with all statements
and only 13.8 data are error.

Table 4: Maximum Likelihood Estimates: Regression Weights

Estimate S.E. C.R. P Label Standardized
Regression
Weights:
Estimate
T_EFET_1 <- EFET 1.00 .84
T_EFET_2 <- EFET .88 .059 14.90 *** par_1 .80
T_EFET_3 <- EFET .53 .034 15.69 *** par_2 .82
T_EFET_4 <- EFET .39 .085 4.59 *** par_3 .30
T_EFET_5 <- EFET .39 .062 6.40 *** par_4 .41
T_IFET_1 <- IFET 1.00 .92
T_IFET_2 <- IFET .78 .057 13.74 *** par_5 .73
T_IFET_3 <- IFET .83 .051 16.46 *** par_6 .80
T_IFET_4 <- IFET 1.07 .042 25.38 *** par_7 .93
T_IFET_5 <- IFET .93 .047 20.04 *** par_8 .87

Maximum likelihood estimates of at all the parameter estimates are highly significant.
In other words, all variables are significantly different from 0. The interpretations of
the parameter estimate are straight forward. When EFET goes up by 1, T_EFET_2
goes up by 0.88. The regression weights to estimate, 0.88, has a standard error of about
.059. Dividing the regression weight estimate by the estimate of its standard error
gives z = 0.882/.059 = 14.908. This indicates that, the regression weight estimate is 14.91
standard errors above zero. When IFET goes up by 1, T_IFET_5 goes up by 0.93%. The
regression coefficient of EFET_1 and T_EFET_1 positive and statistically significant
at 99% confidence level. When IFET goes up by 1, T_IFET_4 goes up by 1.071. When
EFET goes up by 1, T_EFET_4 goes up by 0.391. When EFET goes up by 1 standard
deviation, T_EFET_1 goes up by 0.849 standard deviations. When IFET goes up by 1,
standard deviation T_IFET_4 goes up by 0.939 standard deviations. The probability of
getting a critical ratio as large as 14.908 in absolute value is less than 0.001. The value
indicates, the regression weight for EFET in the prediction of T_EFET_2 is significantly
different from zero at the 0.001 level (two-tailed). It is estimated that the predictors
of T_IFET_4 explain 88.2 percent of its variance. In other words, the error variance
between T_IFET_4 is approximately 11.8 percent of the variance between T_IFET_4
itself. Maximum likelihood estimates are also presented in Figure 2.

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Figure 2: SEM Graphs of Effectiveness of Teaching Economics

The standardized regression estimates are comparable, which may assist us to pick up
more important factors and relationships which is presented in Table 5.

The variance between EFET is estimated to be 1.302. The variance estimate, 1.302, has
a standard error of about 0.173. Dividing the variance estimate by the estimate of its
standard error gives z = 1.302/.173 = 7.536. In other words, the variance estimate is
7.536 standard errors above zero. The probability of getting a critical ratio as large as
7.536 in absolute value is less than 0.001. Likewise, the variance estimate for EFET is
significantly different from zero at the 0.001 level (two-tailed).

Table 5: Variances and Squared Multiple Correlations

Estimate S.E. C.R. P Label Squared Multiple Correlations
EFET 1.302 .173 7.536 *** par_10 Estimate
IFET 1.184 .137 8.671 *** par_11
e1 .504 .052 9.674 *** par_12 T_IFET_5 .758
e2 .565 .056 10.017 *** par_13 T_IFET_4 .882
e3 .173 .018 9.875 *** par_14 T_IFET_3 .642
e4 1.921 .190 10.114 *** par_15 T_IFET_2 .534
e5 .987 .097 10.147 *** par_16 T_IFET_1 .856
e6 .199 .024 8.345 *** par_17 T_EFET_5 .173
e7 .637 .065 9.762 *** par_18 T_EFET_4 .094
e8 .462 .048 9.580 *** par_19 T_EFET_3 .682
e9 .182 .023 7.844 *** par_20 T_EFET_2 .642
e10 .330 .036 9.198 *** par_21 T_EFET_1 .721

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The variance of IFET is estimated to be 1.184. The variance between estimates, 1.184,
has a standard error of about .137. Dividing the variance estimate by the estimate of
its standard error gives z = 1.184/.137 = 8.671. In other words, the variance estimate
is 8.671 standard errors above zero. The probability of getting a critical ratio as large
as 8.671 in absolute value is less than 0.001. In other words, the variance estimate for
IFET is significantly different from zero at the 0.001 level (two-tailed). Likewise, the
variance in e4 is estimated to be 1.921. The variance between e3 is estimated to be
0.173. The variance in e7 is estimated to be .637.

It is estimated that the predictors of T_IFET_5 explain 75.8 percent of its variance.
In other words, the error variance in T_IFET_5 is approximately 24.2 percent of the
variance between T_IFET_5 itself. It is estimated that the predictors of T_IFET_4
explain 88.2 percent of its variance. This means that, the error variance in T_IFET_4
is approximately 11.8 percent of the variance between T_IFET_4 itself. It is estimated
that the predictors of T_IFET_1 explain 85.6 percent of its variance. In other words,
the error variance in T_IFET_1 is approximately 14.4 percent of the variance between
T_IFET_1 itself. It is estimated that the predictors of T_EFET_1 explain 72.1 percent
of its variance. In other words, the error variance in T_EFET_1 is approximately 27.9
percent of the variance between T_EFET_1 itself.

The qualifiers of economics teachers (QOET) and availability of text materials on

economics (ATME) are the influencing factors for learning economics to the higher
secondary level students. To identify the influencing factors of the economics teacher
psychometric  scale, was designed and descriptive statistics are presented of the
respondents specify their level of agreement or disagreement in Table 6.

There are ten statements and statements were divided into two group QOET and
ATME factors that influencing teaching and learning economics in classroom for higher
secondary level. Defined variables were Q_1 to and Q_5 and A_6 to A_10. The first
statement was your teacher has the knowledge of mathematics in teaching economics.
In this statement, the percentage of the mean was 64.57 and mode value was 4. The
statistics show that about only 65 % respondents are agreed with the statement and 35
% data were error. This means that about 35 % economics teachers have not a good
knowledge of mathematics in teaching economics.

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Table 6: Descriptive Statistics of QOET and ATME Economics Learning

N Variables Mean Mean % Mode Std. Dev.
1Q Your teacher has the knowledge of 4.52 64.57 4 1.33
mathematics in teaching economics
2Q The method of teaching used by your 6.14 87.71 7 0.82
teachers affects the learning of economics.
3Q Many of your economics teachers are degree 6.76 96.57 7 0.68
holders.
4Q Teachers do not make use of appropriate 6..03 86.14 7 1.28
teaching materials.
5Q Your teachers use of different teaching 6.09 87.00 7 1.16
method in the teaching of economics affect
your performance.
6A The school libraries do not have current 6.03 86.29 7 1.13
economics textbooks.
7A Poor preparation by teachers makes 6.16 88.00 7 0.99
economics learning uninteresting to students.
8A Some students absent themselves from 6.24 89.14 7 0.77
economics class with hope to copy notes
from others and this affect their performance.
9A Majority of student’s dislike economics 6.14 87.71 7 1.01
because of its mathematical involvement
10A Employment of less qualified economics 6.04 86.29 7 1.61
teachers affects the students’ interest in its
learning.

The minimum percentage of men for all variables were greater than 80 and the average
mean % was 87.64, and mode value for all variables was 7 except first variable. The
average percent of statistics shows that about 88 % students agreed the statement.
That means, a method of teaching used by teachers affects the learning of economics.
Likewise, teachers are highly qualified as higher secondary level, but teachers do
not make use of appropriate teaching materials. Likewise, use of different teaching
method to teach the economics, this affect teachers’ performance. The data shows that,
some school libraries do not have current economics textbooks. Present of teachers in
classroom without preparation, it makes economics learning uninteresting. Students
also agree with 8A and 9A statement, which mean percentage were 89.14 and 87.71,
so some students absent themselves from economics class with hope to copy notes
from others and this affect their performance. Also, it is totally agreed that, majority of
student’s dislike economics because of its mathematical involvement.

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Table 7: Maximum Likelihood Estimates: Regression Weights

Estimate S.E. C.R. P Label Estimate
Standardized
Regression
Weights
S_ATME_1 <- ATME 1.000 .899
S_ATME_2 <- ATME .572 .043 13.407 *** par_1 .730
S_ATME_3 <- ATME .874 .039 22.176 *** par_2 .925
S_ATME_4 <- ATME .480 .043 11.220 *** par_3 .652
S_ATME_5 <- ATME .894 .041 22.054 *** par_4 .923
S_QOAT_1 <- QOAT 1.000 .672
S_QOAT_2 <- QOAT .885 .111 7.966 *** par_5 .516
S_QOAT_3 <- QOAT .606 .059 10.313 *** par_6 .692
S_QOAT_4 <- QOAT .180 .101 1.782 .075 par_7 .109
S_QOAT_5 <- QOAT 1.236 .101 12.254 *** par_8 .853

Maximum likelihood estimates at all the parameter estimates are actual significant.
In other words, all the variables are significantly different from 0 except S_QOAT_4.
The interpretations of the parameter estimate are straight forward. When ATME goes
up by 1, S_ATME_2 goes up by 0.572.The regression weights to estimate, 0.572, has a
standard error of about 0.043. Dividing the regression weight estimate by the estimate
of its standard error gives z = .572/.043 = 13.407. This indicates that, the regression
weight estimate is 13.407 standard errors above zero. The probability of getting a
critical ratio as large as 13.407 in absolute value is less than 0.001. In other words, the
regression weight for ATME in the prediction of S_ATME_2 is significantly different
from zero at the 0.001 level (two-tailed). When ATME goes up by 1 standard deviation,
S_ATME_1 goes up by 0.899 standard deviations.

Analysis of next variable, if ATME goes up by 1, S_ATME_3 goes up by 0.874. The

regression weight estimate, .874, has a standard error of about 0.039. Dividing the
regression weight estimate by the estimate of its standard error gives z = 0.874/0.039 =
22.176. The value indicates that, the regression weights to estimate is 22.176 standard
errors above zero. The probability of getting a critical ratio as large as 22.176 in
absolute value is less than 0.001. In other words, the regression weight for ATME in
the prediction of S_ATME_3 is significantly different from zero at the 0.001 level (two-
tailed). When ATME goes up by 1 standard deviation, S_ATME_3 goes up by 0.925
standard deviations.

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The analysis of S_QOAT_5 to QOAT variables, When QOAT goes up by 1, S_QOAT_5

goes up by 1.236. The regression weights to estimate, 1.236, has a standard error of
about .101. Dividing the regression weight estimate by the estimate of its standard
error gives z = 1.236/0.101 = 12.254. That indicates, the regression weight estimate is
12.254 standard errors above zero. The probability of getting a critical ratio as large as
12.254 in absolute value is less than 0.001. In other words, the regression weight for
QOAT in the prediction of S_QOAT_5 is significantly different from zero at the 0.001
level (two-tailed). When QOAT goes up by 1 standard deviation, S_QOAT_5 goes up
by 0.853 standard deviations. Maximum likelihood estimates are also presented in
Figure 3.

Figure 3: SEM Graphs of QOAT and ATME of Teachers

The standardized regression estimates are comparable, which may assist us to pick up
more important factors and relationships which is presented in Table 8.

The variance between ATME is estimated to be 1.097. The variance estimate, 1.097, has
a standard error of about 0.133. Dividing the variance estimate by the estimate of its
standard error gives z = 1.097/0.133 = 8.263. In other words, the variance estimate is
8.263 standard errors above zero. The probability of getting a critical ratio as large as
8.263 in absolute value is less than 0.001. Which suggests that, the variance estimate for
ATME is significantly different from zero at the 0.001 level (two-tailed).

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Table 8: Variances and Squared Multiple Correlations

Estimate S.E. C.R. P Label Squared Multiple Correlations
ATME 1.097 .133 8.263 *** par_10
QOAT .606 .108 5.631 *** par_11 Estimate
e1 .262 .028 9.336 *** par_12 S_QOAT_5 .728
e2 .315 .032 9.915 *** par_13 S_QOAT_4 .012
e3 .141 .016 8.907 *** par_14 S_QOAT_3 .479
e4 .342 .034 9.975 *** par_15 S_QOAT_2 .266
e5 .151 .017 8.948 *** par_16 S_QOAT_1 .452
e6 .735 .067 10.942 *** par_17 S_ATME_5 .853
e7 1.309 .122 10.695 *** par_18 S_ATME_4 .425
e8 .242 .022 10.947 *** par_19 S_ATME_3 .856
e9 1.631 .161 10.104 *** par_20 S_ATME_2 .533
e10 .346 .034 10.306 *** par_21 S_ATME_1 .807

The predicated of variance QOAT is to 0.606. The variance estimate, 0.606, has a
standard error of about .108. Dividing the variance estimate by the estimate of its
standard error gives z = 0.606/0.108 = 5.631. In other words, the variance estimate is
5.631 standard errors above zero. The probability of getting a critical ratio as large as
5.631 in absolute value is less than 0.001. Which means that, the variance estimate for
QOAT is significantly different from zero at the 0.001 level (two-tailed).The variance
between e1 is estimated to be 0.262. The variance estimate, 0.262, has a standard error
of about .028. Dividing the variance estimate by the estimate of its standard error gives
z = 0.262/0.028 = 9.336. In other words, the variance estimate is 9.336 standard errors
above zero. The probability of getting a critical ratio as large as 9.336 in absolute value
is less than 0.001. Thus, the variance estimate for e1 is significantly different from zero
at the 0.001 level (two-tailed). It is estimated that the predictors of S_QOAT_1 explain
45.2 percent of its variance. In other words, the error variance between S_QOAT_1 is
approximately 54.8 percent of the variance in S_QOAT_1 itself.

The variance at e6 is estimated to be 0 .735. The variance estimate, 0.735, has a standard
error of about .067. Dividing the variance estimate by the estimate of its standard error
gives z = 0.735/0.067 = 10.942. The viewpoint is that, the variance estimate is 10.942
standard errors above zero. The probability of getting a critical ratio as large as 10.942
in absolute value is less than 0.001. The estimation indicates that, the variance estimate
for e6 is significantly different from zero at the 0.001 level (two-tailed). It is estimated
that the predictors of S_ATME_1 explain 80.7 percent of its variance and the error
variance between S_ATME_1 is approximately 19.3 percent of the variance between
S_ATME_1 itself.

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Maximum likelihood estimates at latent variable, endogenous and exogenous variable

and find out the relation between gender, age and teaching experience and their
effectiveness of economics teaching in classroom for higher secondary school level. In
Table 9, the statistical results of maximum likelihood estimate are presented.

Table 9: Maximum Likelihood Estimates and Standardized Regression Weights of

Latent Endogenousand Exogenous Variables
Estimate S.E. C.R. P Label Estimate
Age <- Gender -.034 .226 -.151 .880 par_1 -.011
Experience <- Gender .002 .214 .008 .994 par_2 .001
S_QOAT_2 <- Experience .076 .084 .911 .362 par_3 .059
S_QOAT_4 <- Experience .250 .084 2.967 .003 par_4 .198
S_QOAT_1 <- Age -.225 .071 -3.176 .001 par_5 -.217
S_QOAT_3 <- Age -.127 .042 -3.008 .003 par_6 -.207
S_QOAT_4 <- Age -.287 .080 -3.591 *** par_7 -.240
S_QOAT_5 <- Age -.234 .070 -3.328 *** par_8 -.227
S_QOAT_5 <- Experience .079 .074 1.068 .286 par_9 .073
S_QOAT_1 <- Experience -.064 .075 -.860 .390 par_10 -.059
S_QOAT_2 <- Age -.456 .080 -5.735 *** par_11 -.373
S_QOAT_3 <- Experience -.012 .044 -.278 .781 par_12 -.019

The regression weights to estimate, -0.034, has a standard error of about 0.226.
Dividing the regression weight estimate by the estimate of its standard error gives z =
-0.034/.226 = -0.151. The regression weights to estimate is 0.151 standard errors below
zero. The probability of getting a critical ratio as large as 0.151 in absolute value is .880.
The regression weight for gender in the prediction of Age is not significantly different
from zero at the 0.05 level (two-tailed). When gender goes up by 1 standard deviation,
age goes down by 0.011 standard deviations.

The analysis of the relation between gender and experience, when gender goes up by 1,
Experience goes up by 0.002. The regression weights to estimate, 0.002, has a standard
error of about .214. Dividing the regression weights to estimate by the estimate of its
standard error gives z = 0.002/.214 = 0.008. In other words, the regression weights to
estimate is 0.008 standard errors above zero. The probability of getting a critical ratio
as large as 0.008 in absolute value is 0.994. The regression weight for Gender in the
prediction of Experience is not significantly different from zero at the 0.05 level (two-
tailed).

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When experience goes up by 1, S_QOAT_4 goes up by 0.25. The regression weight

estimate, 0.250, has a standard error of about 0.084. Dividing the regression weight
estimate by the estimate of its standard error gives z = 0.250/0.084 = 2.967. Further
difference between opinion, the regression weight estimate is 2.967 standard errors
above zero. The probability of getting a critical ratio as large as 2.967 in absolute
value is 0.003. The regression weight for Experience in the prediction of S_QOAT_4 is
significantly different from zero at the 0.01 level (two-tailed). When Experience goes
up by 1 standard deviation, S_QOAT_4 goes up by 0.198 standard deviations.

When age goes up by 1, S_QOAT_4 goes down by 0.287. The regression weights to
estimate, -0.287, has a standard error of about 0.080. Dividing the regression weight
estimate by the estimate of its standard error gives z = -0.287/0.080 = -3.591. The result
of, regression weight estimate is 3.591 standard errors below zero. The probability of
getting a critical ratio as large as 3.591 in absolute value is less than 0.001. In addition,
the regression weight for Age in the prediction of S_QOAT_4 is significantly different
from zero at the 0.001 level (two-tailed).

In Table 10 variance between gender and its statistical results are presented. The
variance between gender is estimated to be 0.118.The variance estimate, 0.118, has
a standard error of about 0.012. Dividing the variance estimate by the estimate of its
standard error gives z = 0.118/0.012 = 10.075. Furthermore, the variance estimate is
10.075 standard errors above zero. The probability of getting a critical ratio as large
as 10.075 in absolute value is less than 0.001. In other words, the variance estimate for
gender is significantly different from zero at the 0.001 level (two-tailed). In figure 3
presenting the graphs of the SEM of age, gender and experience in teachers influence
to students learning economics in higher secondary school level.

Table 10: Variances and Squared Multiple Correlations

Estimate S.E. C.R. P Label Estimate
Gender .118 .012 10.075 *** par_13
e1 1.224 .122 10.075 *** par_14 Experience .000
e2 1.105 .110 10.075 *** par_15 Age .000
e3 1.253 .124 10.075 *** par_16 S_QOAT_5 .057
e4 1.575 .156 10.075 *** par_17 S_QOAT_4 .097
e5 .442 .044 10.075 *** par_18 S_QOAT_3 .043
e6 1.589 .158 10.075 *** par_19 S_QOAT_2 .142
e7 1.227 .122 10.075 *** par_20 S_QOAT_1 .051

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The variance between e1 is estimated to be 1.224. The variance estimate, 1.224, has
a standard error of about .122. Dividing the variance estimate by the estimate of its
standard error gives z = 1.224/0.122 = 10.075. Moreover, the variance estimate is 10.075
standard errors above zero. The probability of getting a critical ratio as large as 10.075
in absolute value is less than 0.001. The variance estimate for e1 is significantly different
from zero at the 0.001 level (two-tailed). The variance between e6 is estimated to be
1.589. The variance estimate, 1.589, has a standard error of about 0.158. Dividing the
variance estimate by the estimate of its standard error gives z = 1.589/0.158 = 10.075.
This indicates that, the variance estimate is 10.075 standard errors above zero. The
probability of getting a critical ratio as large as 10.075 in absolute value is less than
0.001. In additional arguments, the variance estimate for e6 is significantly different
from zero at the 0.001 level (two-tailed).

Figure 4: SEM Graph among Age, Gender and Experience of Teachers relation to
Students

It is estimated that the predictors of experience explain 0 percent of its variance.

This indicates that the error variance in experience is approximately 100 percent of
the variance between experiencing itself. The predictors of age explain 0 percent of
its variance. In the calculation, the error variance between age is approximately 100
percent of the variance in Age itself. The predictors of S_QOAT_5 explain 5.7 percent
of its variance, the error variance between S_QOAT_5 is approximately 94.3 percent
of the variance in S_QOAT_5 itself. The predictors of S_QOAT_4 explain 9.7 percent
of its variance or the error variance between S_QOAT_4 is approximately 90.3 percent
of the variance in S_QOAT_4 itself. Likewise, it is estimated that the predictors of S_

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QOAT_3 explain 4.3 percent of its variance or, the error variance between S_QOAT_3
is approximately 95.7 percent of the variance in S_QOAT_3 itself. The estimation of
the predictors of S_QOAT_2 explains 14.2 percent of its variance or the error variance
between S_QOAT_2 is approximately 85.8 percent of the variance in S_QOAT_2 itself.
The estimated predictors of S_QOAT_1 explain 5.1 percent of its variance and, the
error variance between S_QOAT_1 is approximately 94.9 percent of the variance in
S_QOAT_1 itself.

5. CONCLUSION
The research findings specify that the nonexistence of classroom spaced in the school,
given time to the teaching of economics teachers with a new technology, unavailability
of recent economics textbooks, systematic libraries and computer facilities, influences
the teaching performance. Training with new teaching andragogy with computer
application facilitated to economics teacher improved teachers’ qualities and better
teaching economics in the classroom, which vary the most important factors that affect
teaching economics. Likewise, appoint highly qualified teachers for higher secondary
level, administration to teachers do make use of appropriate teaching materials and
also encourage use of different teaching method in the teaching of economics that
affect teaching performance. According to the age, gender and experiences do not exist
the teaching and learning economics, but knowledge of teacher and preparation for
class lecture and other activities can give the interest in economics class. Application of
mathematics in economics with real data onto microeconomics, an example demand
analysis of the local market, GDP data can be analyzed in macroeconomics. Time and
again teachers’ training to play the important role to better teach economics in higher
secondary school levels in Nepal. This is recommended that teach the teachers as a
workshop style training as improving economics instruction in higher secondary school
level. The experimental program helps teachers to gain an understanding of economic
concepts and to improve andragogy-pedagogy. Improved classroom environment, the
latest text materials might be the encouraging to economics subject to the student. And
also, recommended two types of tanning pre-service training for new teachers, and in-
service training for those teachers who are already teaching. Both are essential ways
for improving the prospects of imbuing economics in other subject areas.

Acknowledgement
The author warmly acknowledges the kind cooperation extended by Mr. Dhal B. Khadka (the
then joint secretary of HSEB) and respected professors Dr. Parthiveshwor P. Timilshina and
Dr. Keshav R. Khadka, along with other resource persons Dr. Chakra P. Luitel, Mr. Binod
Joshi, Ms. Indira Shrestha, Mr. Madhav P. Dahal, Nar B. Bista, and Mr. Tara P. Bhusal who
accompanied with the author in the training cum workshop organized by HSEB(the then) and
shared their ideas and experienceon several issues of teaching economics.

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