Structural Equation Modeling for Academic Performance Confidence Affecting Factor

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Sunil Kumar Tiwari*, Archi Dubey**

Referred Journal of CMR College of Engineering & Technology January-March 2019, Volume 8, No. 1, pp 58-68

ISSN: 2277-6753 (Print) ISSN: 2322-0449 (Online) http://cmrcetmba.in/sumedha/

Structural Equation Modeling for Academic Performance

Confidence Affecting Factor

* (Research Scholar, MSMSR Dep't, MATS University, Raipur Chhattisgarh. [email protected]) **(Research Supervisor, MSBS Dep't MATS University Raipur Chhattisgarh. [email protected])

Abstract

Institution provide better educational effort to nurture student studying in their institution. But institution never think what actually student has opinion about that institution because institution is an abstract person. Number of person and material make institution a living object who is accountable for providing good education. Number of studies are conducted in different discipline and find factors responsible for academic performance. Researchers use structural equation modeling (SEM) to build complex model for structural relationship among latent factors or construct. Structural equation modeling used in this study examines student responses on certain factor which are responsible for building confidence of academic performance in student.

Keywords: Keywords: Education, General, Analysis of Education, Returns to Education, Other.

JEL Classification: I2, I20, I2, I26, I29.

PUBLISHING CHRONOLOGY PAPER SUBMISSION DATE : OCTOBER 13, 2018 ;

PAPERSENTBACKFOR REVISION : NOVEMBER 9, 2018;

PAPER ACCEPTANCE DATE : DECEMBER 10, 2018

Reference to this paper should be made as follows:

Sunil Kumar Tiwari, Archi Dubey (2019),

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I

NTRODUCTION

Any nation's development is directly related with education. In this scenario dropping out is one of major concern in the education. Dropout in education is defined in literature as "when student withdraw before completing a course of program. Latif A et.al. (2015) conducted a comparative study on economic effects of student dropout.

Educational institutions are meant for providing education to students. Performing confidence is very important for any student. It gives enthusiasm to engage student in academic activity. Academic performance confidence is one of the most important drive to continue the education in the institution. Several studies organized on prediction of student performance using data mining techniques. These studies also explored factors responsible for poor performance of student at the time of performance. But what is relationship among these factors are not answered.

Behavioral science use statistical modeling techniques to develop model named structural equation modeling (SEM). SEM often work on theoretical constructs knows as latent factor represented by the path showing relationship between these constructs. SEM include multivariate procedures for factor analysis, discriminant analysis, regression analysis etc. The path diagram of model is visualization of statistical model.

In this study impact of school physical environment, guardian interest, study satisfaction and teacher student relationship on academic performance confidence is studied using structural equation modeling.

L

ITERATUREREVIEW

Structural equation modeling (SEM) is famous for causal model data analysis. Educational data analysis also using

In a research Lee H W (2011) concluded that traditional approach of data study finds effectiveness of interventions but not psychological construct which affect learners. Thus, latent variable structural equation modeling has advantage over traditional statistical tool. Camgoz-Akdag H et.al. (2012) "introduced a conceptual model of student satisfaction with higher education experience based on the identification of the variable determinants of student perceived quality and the impact of those variables on student satisfaction and/or dissatisfaction with the overall student experience". The model used exploratory and confirmatory factor analysis for strengthens of model.

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Kocakaya S et.al. (2014) conducted study "to propose a model for elementary school student from science and mathematics achievements in their school and in Level Determination Exam (SBS) depend on the number of teacher and expert teacher in their school". In the model they used three construct school achievement, SBS achievement and Teacher factor.

Dubey Archi (2018) did a study on higher secondary school teachers morale. In her research she examined affecting factor for morale of the higher secondary which will help government and policymaker for defining the teaching policies construct.

Pawar I A and Lavuri R (2018) conducted a study to understand the relationship between Customer-based brand equity and demographic variable, identifying the determinants of customer-based brand equity and influence of brand in context of banking industry. In their study they concluded that "multiple regression results showed that brand verdict, brand feeling and brand performance have significant influence on the banking customers".

M

EASURESOF

F

IT

Structural equation modeling literature mention variety of viewpoints and recommendation for a good model. Some of them are:

• Chi Square or CMIN is related with discrepancy function. It is, differ measure of implied moments and sample moments. Higher chi square value indicates poor fit.

• DF is degree of freedom obtained by subtracting sample moments and distinct parameter

• CMIN/DF is ratio of chi square and degree of freedom. Value of CMIN/DF near to 1 indicate correct model. Variety of literature accept different values for acceptable level. Hair [8] mention the threshold of CMIN/DF less than 3 as ideal, 3 to 5 acceptable, above it is not good.

• NFI or normed fit index represents discrepancy between discrepancy value of hypothesized and null model. NFI greater than 0.95 indicates good fit [9].

• TLI or Tucker Lewis Index or non-normed fit index(NNFI) resolves issues of negative bias. The value greater than 0.95 indicate good fit [9].

• CFI or Comparative Fit Index compare proposed model performance with null or baseline model performance which has no correlation between observed variable. CFI value greater than 0.95 is very good fit

• RMSEA or root mean square error of approximation is population discrepancy function. RMSEA value less than 0.05 indicate good fit and 0.05 to 0.1 moderately fit.

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• AGFI or adjusted goodness of fit index, adjust GFI using degree of freedom with saturated model [10]. The acceptable value for AGFI is greater than 0.9. [11]

• PRATIO or parsimony ratio represents ratio between number of constraints evaluated with fraction of constraint in the independence model.

R

ESEARCH

M

ETHODOLOGY

SURVEY INSTRUMENT

A detailed literature survey studied to find the various factor responsible for academic performance. In this regard faculty, student, parents, educationist and other relevant people's opinion is also taken for the study purpose. Out of the many factor 5 factors are considered for the study. Table shows the list of the variable and description.

Table 1: Construct used for study and their description

Construct Description

SPE Sch ool Physical Environment TSR Teacher student relationship

SS Study Satisfaction

LGI Guardian Interest

AP Academic performance confidence

Source : complied data

To record the responses Likert's scale is used. To measure the all variables 7-point scale is used, where 1 represents "Strongly Disagree" and 7 represents "Strongly Agree".

R

ESEARCH

D

ESIGNAND

H

YPOTHESIS

During the month of Nov 2017, primary data collected from the student studying at higher secondary school from two District Baloda Bazar (Government Higher Secondary School Lawan, Government managed) and Raipur (Disha college of higher secondary studies, Kota, Raipur, Privately managed) of Chhattisgarh. The survey includes 500 student responses in completing the self-administered questionnaire. Out of these 461 questionnaires were found for the research purpose.

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Table 2: Hypothesis for the studied problem

Sr No. Hypothesis

H1 School physical environment has significant relationship with study satisfaction. H2 Teacher student relationships have significant relation with guardian interest. H3 Guardian interest has significant relation with study satisfaction.

H4 Academic performance confidence has significant relation with study satisfaction. H5 Academic performance has significant relation with Guardian interest.

SPE TSR

H1 H2

SS H3

H4 LGI

AP H5

Figure 1: Hypothesized Framework

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NALYTICALTOOLAND

M

EASUREMENTMODEL

Collected data analyzed using AMOS 20.0 software and SPSS 20.0. Measurement tool includes AVE, CR, Cronbach's alpha, Chi square, Degree of Freedom(DF), CMIN/DF, NFI etc.

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R

ESULTANDDISCUSSION

Fornell C.L. (1981) suggested commonly used criterion to test the latent variables of the model. In the studied constructs the value of Cronbach's alpha is greater than 0.7 that indicates the reliability of the scales. Composite reliability of each construct is greater than 0.7 for each construct. Average variance extracted should be more than 0.5, so in the studied construct only SPE construct. Because of composite reliability is greater than 0.6, average variance extracted up to 0.4 is acceptable (C. Fornell et.al., 1981; C Huang et.al. 2013). Hox J J et.al. (1998) mentioned that ". . a value of at least 0.90 is required to accept a model……However these are just rules of thumb". Thus, all construct fulfills the minimum requirement for the model analysis. The values of the Cronbach's alpha, average variance extracted and composite reliability is shown in table 3.

Cronbach's alpha, average variance extracted and composite reliability for the study has significant value. All the values are above the minimum threshold value. AVE value for SS, AP and LGI below 0.5 but composite reliability is above 0.6, so it is also acceptable (C. Fornell et.al., 1981; C Huang et.al. 2013) for further study.

Path diagram is started with initial relation according to hypothesis and modified by defined method of modification. Initial model had very high chi square value, degree of freedom, CMIN/ DF and other values are far from the significant level. By observing the residual covariance moments and modification indices the model is modified by removing the observed variable which are problematic. The final model gave significant values for the developed model. Threshold value, initial model and final model values are shown in the table 4.

Table 3: Cronbach's Alpha, Average Variance extracted and composite reliability of Latent Variables

Construct Cronbach’s Alpha AVE CR

SPE 0.907 0.701 0.77

TSR 0.829 0.504 0.670

SS 0.806 0.472 0.842

AP 0.724 0.408 0.732

LGI 0.804 0.452 0.765

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Table 4: Initial and final model fit statistics comparison

Parameter of model fit Accepted Threshold Value Initial model Final model

Chi Square Low Chi square 1306.2 328.685

DF - 318 158

CMIN/DF < 3 good and 3 to 5 oderate 4.108 2.08

NFI >0.90 0.783 0.92

TLI >0.90 0.807 0.95

CFI >0.90 0.825 0.96

GFI >0.90 0.806 0.93

RMSEA <0.07 0.082 0.048

AGFI >0.8 0.769 0.91

PRATIO - 0.906 0.83

F

INDINGS

The path analysis result is shown in the table 5. In the study School physical environment and Study satisfaction has path coefficient 0.350, the school physical environment has significant and positive influence on study satisfaction, thus hypothesis H1 is established. In the studied architecture teacher student relationship and guardian interest path coefficient is 0.453, the teacher student relationship has significant and positive influence on guardian interest, thus hypothesis H2 is established.

Guardian interest and study satisfaction has path coefficient 0.236 in the studied model, the guardian interest has significant and positive influence on study satisfaction, Thus, hypothesis H3 is established.

Study satisfaction and academic performance confidence path coefficient is 0.188, the study satisfaction has significant and positive influence on academic performance confidence. Thus, hypothesis H4 is established

Guardian interest and academic performance path coefficient is 0.216, the guardian interest has significant and positive influence on academic performance. Thus, hypothesis H5 is also established in the studied architecture.

Table 5: Path test significant in final model

Relationship Estimate SE CR P Hypothesis

TSRLGI 0.350 0.077 4.554 *** Accepted

SPESS 0.453 0.040 11.359 *** Accepted

LGISS 0.236 0.046 5.114 *** Accepted

SSAP 0.188 0.035 5.440 *** Accepted

LGIAP 0.216 0.041 5.309 *** Accepted

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Table 6 contains the observed variable's mean, standard deviation. Table 7 shows the standardized factor loading of observed variable, Cronbach's alpha value if deleted and status of inclusion in the model. These observed variables are used to construct the latent variable. Observed variables S5, T1, T2, T5 and T6 is removed from the study as their factor loading is not significant for the model.

Table 6: Observed variable mean, Standard deviation

Construct Observed Variable Mean Std Deviation

SPE

S1 4.73 1.025

S2 4.86 0.989

S3 4.74 1.006

S4 4.65 1.044

S5 4.76 1.013

TSR

T1 4.03 0.785

T2 3.95 0.767

T3 3.97 0.737

T4 3.92 0.769

T5 4.10 0.754

T6 4.15 0.729

SS

SS1 4.47 0.861

SS2 4.20 0.752

SS3 4.59 0.908

SS4 4.23 0.706

SS5 4.20 0.752

SS6 4.22 0.809

AP

A1 3.15 0.586

A2 3.18 0.633

A3 3.08 0.678

A4 2.99 0.671

LGI

G1 4.49 1.108

G2 4.44 1.085

G3 4.56 1.050

G4 4.46 1.127

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Table 7: Standardized factor loading and Cronbach's Alpha value if deleted, Latent variable and inclusion status in the model.

Observed Variable

Standardized Factor Loading

Cronbach’s Alpha if item deleted

Included in model

S1 0.81 0.885 Y

S2 0.86 0.881 Y

S3 0.84 0.878 Y

S4 0.83 0.887 Y

S5 0.75 0.899 N

T1 0.68 0.805 N

T2 0.58 0.806 N

T3 0.72 0.785 Y

T4 0.70 0.799 Y

T5 0.74 0.789 N

T6 0.56 0.822 N

SS1 0.71 0.775 Y

SS2 0.73 0.764 Y

SS3 0.70 0.789 Y

SS4 0.78 0.788 Y

SS5 0.59 0.764 Y

SS6 0.58 0.766 Y

A1 0.62 0.671 Y

A2 0.70 0.641 Y

A3 0.70 0.626 Y

A4 0.53 0.710 Y

G1 0.81 0.747 Y

G2 0.68 0.753 Y

G3 0.78 0.765 Y

G4 0.68 0.753 Y

Source: complied data

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IMITATIONSOFTHESTUDY

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C

ONCLUSIONOFTHESTUDY

Path diagram shown in the figure 1 reflects causal connection among variable. In this paper 5 hypothesis are considered for the study using structural equation modeling, in the table 5 path significant values are shown. All paths estimate and p value are significant and model is fit, so all the hypothesizes are accepted.

It is concluded from the above discussion that, on the student academic performance confidence; school physical environment, teacher student relationship, study satisfaction and guardian interest play a significant role. Thus, an institution, which wants to build academic performance confidence among the students, give attention on school physical environment, teacher-student relationship, study satisfaction and guardian interest.

F

UTUREWORK

This study focused on causal relationship among attribute. In this study behavioral intention used. This intention may change when moderating factors (like gender, age group, family education etc. In future these moderating factors will study to make appropriate policy for education.

R

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Surya Narayana Reddy*, Narayana Reddy**, Viswanatha Reddy***

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