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Advances in Social Sciences Research Journal – Vol.7, No.8
Publication Date: August 25, 2020
DOI:10.14738/assrj.77.8854.
Effiyaldi, & Murniarti, E. (2020). Analysis Of Confirmatory Factors To Measure Public Trust In The Population Document Service Of
Population Department Of Jambi City. Advances in Social Sciences Research Journal, 7(8) 287-301.
Analysis Of Confirmatory Factors To Measure Public Trust In The
Population Document Service Of Population Department Of Jambi
City
Effiyaldi
STIKOM Dinamika Bangsa-Jambi
Erni Murniarti
Universitas Kristen Indonesia
ABSTRACT
This study aims to identify indicator variables that can measure
organizational transparency variables, work discipline and service
quality to public trust in administering residence documents of the
Department of Population of Jambi City. The research used
Confirmation Factor Analysis method. This study found that Chi-square,
RMSEA, GFI, NFI, CFI, IFI, RMR models have moderate fit. This means that
organizational transparency, work discipline and service quality affect
public confidence in the demographic document service of Jambi
residence. The results of this study can be used as a consideration and
evaluation of demographic document services for the community for
improvement and increased public confidence in the future.
Keywords: CFA, Chi-square, Factor, Public Trust.
INTRODUCTION
Jambi City as the center of Jambi provincial government is a gathering place for citizens with various
backgrounds in life. The city of Jambi, the majority of whom are migrants, mostly work in
government, private sector and trade. This causes the mobility of Jambi residents. High mobility will
result in interrelation between one part of the work with other work, between one institution and
another institution in order to expedite the objectives to be achieved from the work. Most of the
work involved is related to administrative activities, especially population administration. This
conditions makes the documents of population a necessity for the citizens of the city of Jambi. So
the availability of population documents will facilitate their activities. This has an impact on the
increasing number of Jambi residents who are taking care of obtaining population documents.
However, ironically, people feel reluctant to take care of the population documents.
There is an imbalance in population document services in the city of Jambi, including; people who
want to take care of population documents after feeling urged about their interests in connection
with the obligation to attach population documents as administrative requirements. People tend to
use the services of the officer to take care of population documents. There is an impression that the
cost of obtaining a population document is unclear and not transparent, there is no certainty when
the settlement of the population documents is administered, the work process is convoluted
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(Survey: 2011). Ironically, on the one hand, the community demands fast, transparent, and easy
services. Even the government has tried to improve services, but public service affairs are still felt
as unpleasant. Service users are often faced with so much uncertainty when dealing with
bureaucracy. It is uncertain when the output of a service can be obtained/completed.
Based on data from BAPPEDA [Development Planning Agency at Sub-National Level] Kota Jambi,
the data is processed from BPS, BKBKS, Religion Office (BPS Jambi City: 2010), that the total
population of the city of Jambi is 529,118 people spread over eight sub-districts, namely sub- districts; Kota Baru, Jambi South, Jelutung, Jambi Market, Telanaipura, Lake Teluk, Pelayangan, and
Jambi East. However, when more and more residents of the city of Jambi took care of obtaining
population documents, problems arose; starting from the attitude of officers who are indifferent,
less friendly, look down on the community, how to work is too mechanical, too long waiting times,
too strict on procedures and attitude of throwing responsibilities (Effiyaldi; 2015: 7).
This study aims to identify indicator variables that can measure the variables of organizational
transparency, work discipline, and service quality towards public trust in managing population
documents of the City of Jambi Population Office.
THEORITICAL REVIEW
Confirmatory factor analysis is a factor analysis technique which is a priori based on theories and
concepts that are known to be understood or predetermined (Hidayat: 2014), Confirmatory Factor
Analysis (CFA) is one of the multivariate analysis methods that can be used to confirm whether the
measurement model is built in accordance with what was hypothesized (Maiyanti; et all: 2008).
Factor analysis is used to reduce data, by finding relationships between variables that are mutually
independent (Stapleton, 1997 in Anonymous; 2008: 5), which are then collected in fewer variables
to determine the structure of latent dimensions (Garson; 2007 in Anonymous; 2008: 5), called the
factors. This factor is a new variable, also called a latent variable, a constructed variable, and has an
unobservable nature.
In the analysis of conformational factors, a researcher has a concept in advance of a hypothesis
based on the concept of structural factors. Then made a number of factors that will be formed, as
well as what variables are included in each factor that is formed and it is definitely the goal. The
formation of a confirmatory factor (CFA) intentionally based on theory and concepts, in an effort to
obtain new variables or factors that represent several items or sub-variables, which is observable
variables (Hidayat: 2014).
Factor Analysis of a latent variable is measured based on several indicators that can be measured
directly. The difference between CFA First Order and Second-Order CFA is that the Second Order
CFA latent variable is not measured directly through the assessment indicators but through other
latent variables (Fernanda, J.H: 2009 in Sari and Trijoyo: 2011; 1). Generally, there are 3 categories
of identification in a simultaneous equation that is Unidentified where the estimated number of
parameters (t) is greater than the amount of known data (s / 2), the data is the variance and
covariance of the observed variables. Identification Just identified by the criteria t = s / 2. And Over
Identified identification is with the criteria t ≤ s / 2 (Fernanda, J.H: 2009 in Sari and Trijoyo: 2011;
2). So the purpose of confirmatory factor analysis is to statistically confirm the model that the
researcher has built (Education Statistics: 2009). Confirmatory factor analysis uses invariant to
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Effiyaldi, & Murniarti, E. (2020). Analysis Of Confirmatory Factors To Measure Public Trust In The Population Document Service Of Population Department Of
Jambi City. Advances in Social Sciences Research Journal, 7(8) 287-301.
scale and correlation or covariance matrix in estimating the structural factors. However, in theory,
the factor estimation uses the maximum likelihood procedure (Widhiarso: 2004). Therefore,
planning analysis is driven by the theory of the relationship between observed variables and
unobserved variables (Schreiber, et al: 2006). Furthermore, Schreiber, et al (2006) said, that when
Confirmatory Factor Analysis (CFA) was carried out, researchers used a hypothetical model to
estimate population covariance matrices compared with observed covariance matrices.
Technically, the researcher wants to minimize the difference between the estimated matrix and the
observed matrix.
In the confirmatory factor analysis, there is no need to convert the data into standardized data. The
data preparation is only when setting variables, some data are observable/manifest variables, some
are latent variables. The purpose of this factor analysis is to explain and illustrate by reducing the
number of parameters available. For the variable reduction stage to a further stage, in Confirmatory
factor analysis is known as second-order factor analysis. This factor analysis not only reduces
observations to latent extracts but also reduces the resulting latent extracts to other latent extracts
(Widhiarso: 2004). In the Confirmatory Factor Analysis, latent variables are considered as causal
variables (independent variables) that underlie indicator variables (Ghozali, 2003).
The very basic objectives of confirmatory factor analysis are: first to identify the relationship
between variables by conducting a correlation test. The second objective is to test the validity and
reliability of the instrument. In testing the validity and reliability of instruments or questionnaires
to obtain valid and reliable research data with confirmatory factor analysis. So the purpose of
confirmatory factor analysis is to statistically confirm the model that the researcher has built
(Education Statistics: 2009). In general, the steps to do a factor analysis are; 1. Model specifications.
2. Identification of the model. 3. Estimated model. 4. Testing the model. 5. Modification of the model
(Lewis: 2017)
Several measures of model suitability are often used to assess the feasibility of a model (Bollen,
1989 in Maiyanti; et al: 2008); Test χ2; the model is good if the χ2 test is not real at any particular
level. GFI (Goodness of Fit Index); that based on common practice, the feasibility of a model is that
the GFI value is greater than 0.90 and the maximum value is 1 (Sharma, 1996). AGFI (Adjusted
Goodness of Fit Index); that a model can be said to be good if the AGFI value is greater than 0.80 and
the maximum value is 1 (Sharma, 1996). RMSEA (Root Mean Square of Error Approval); if RMSEA
≤ 0.08, in general, the model is already representing the actual data (Sharma, 1996).
Some previous studies include; Efendi and Purnomo's research (2013: 106), confirmatory factor
analysis is used to find out the indicators that contribute greatly to the traffic awareness survey
with the parameter estimation method is the maximum likelihood estimation (MLE) method.
Ersalora Research (2013) with the title Confirmatory Factor Analysis on the Tourism Attraction of
Muara Jaya Curug in Majalengka Regency. Based on the analysis of the Muara Jaya waterfall tourist
attraction divided into two criteria, there are good and moderate. Natural factors, religious factors,
recreational facilities, and health facilities are good criteria. While the criteria are being included
factors from socio-culture, history, shopping facilities, infrastructure, and food and accommodation
facilities. Research Rachmawati, et al (2014: 74) which examines Confirmatory Factor Analysis of
the Indonesian Intermediate Collective Intelligence Test (TIKI-M), that each TIKI-M sub-test
measures aspects that should be measured according to the construct when viewed from
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standardized loading and t-value of each sub-test for the intelligence factor measured. Research
Seok, et al (2016), a study that tested employees' trust in their supervisors. In this study using
confirmatory factor analysis to examine the dimensions of Employee Confidence. This analysis is
carried out with Structural Equation Modeling to assess the suitability of the model. Besides, the
model's reliability and validity was also measured by involving 514 randomly selected employees
from the public and private sector organizations in Kota Kinabalu, Sabah, Malaysia. The findings of
this study can help improve productivity in an organization by increasing trust and building
relationships between employees and employers.
Research by Naveed, et al (2017) who identified and used nine dimensions to measure
organizational change, and 380 bank managers. To adjust the dimensions and their contribution to
the main construction of the first order and the second confirmatory factor analysis uses. The
results show that processes, strategies, attitudes, structures, culture and technology are the main
predictors of organizational change.
METHOD
This research uses descriptive research method. Data in this study were analyzed using
Confirmatory Factor Analysis (CFA) using LISREL 8.80. The general models used in confirmatory
factor analysis are as follows 1; (Bollen, 1989):
x = ΛXξ + δ (1) (1)
With:
x = is a vector for q x 1 indicator variables
ΛX = is a matrix for the loading factor (λ)
coefficient which shows the relationship of X with ξ size q x n
ξ (ksi) = is a vector for latent variables of size n x 1
δ = vector for measurement error measuring q x 1
If the data are ordinal scale, the polychoric correlation matrix is more suitable for estimating model
parameters. To get the polychoric correlation there are adjustments to the linear variables (Wirda:
2002). For example, the C and D categories are considered to be related to the continuous variables
that are X and Y, by: C = ci if γi-1 ≤ X <γi, i = 1, 2, 3, L, r D = dj if τj-1 ≤ Y <τj, j = 1, 2, 3, L, s Where γi
and τj are threshold parameters with γo = τo = - ∞ and γi = τj = ∞. The threshold parameters and
ordinal variable values are taken monotonously increasing γ1 <γ2 <L <γr-1 and c1 <c2 <L <cr. With
the same analogue it applies to τj and dj. To calculate the polychoric correlation matrix, you can use
the Data Prelis 2.30 program. Whereas the Data Prelis 2.30 program is in LISREL version 8.30 or
version 8.50. LISREL (Linear Structural Relationship) is a computer software package used to
operate structural equation modeling methods (Jöreskog and Sär bom, 1996).
In the Confirmatory Factor Analysis with the maximum likelihood method, in the process of
estimating the parameters of the model using a variety structure, which basically removes the
charter matrix Σ (matriks) with the sample matrix S or polyphoric correlation matrix (Σ). Suppose
the fitting function is stated with F (S, Σ), which is a function that depends on S and Σ. If the
parameter parameter θ is substituted in Σ, then Σ (θ) is obtained. The value of the fitting function
on θˆ is F (S, Σ (θˆ)) (Bollen; 1989 in Maiyanti; et al: 2008).
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Effiyaldi, & Murniarti, E. (2020). Analysis Of Confirmatory Factors To Measure Public Trust In The Population Document Service Of Population Department Of
Jambi City. Advances in Social Sciences Research Journal, 7(8) 287-301.
After testing the validity, the first step in interpreting the resulting Confirmatory Factor Analysis
model is to conduct a feasibility assessment of the model itself, whether the model is feasible or not.
In assessing a model so far there is no single measure to assess the feasibility of a model (Maiyanti;
et al: 2008). At least three authors were found who suggested using at least three model feasibility
tests (Kline: 1998), (Sharma: 1996 in Maiyanti; et al: 2008).
The resulting model must be tested through structural model equation testing using Lisrel 8.30
software (Joreskog and Sorbom, 1989). The model analysis method used is the maximum likelihood
extraction method with Oblimin rotation to confirm the dimensions of the originating instruments
(Nimako: 2012). In the first analysis, the model obtained did not meet the model accuracy index. By
using the modification indices recommendations, it is found that to get a fit model, the researcher
must link the measurement error that is realized in the error covariance measurement between the
openess factor and the extrovert.
After modification, the model that meets the accuracy is found. The results are presented with a
factor loading value which is considered strong (Garson 2007; Kline 2005 in Nordin; 2012). GFI
(goodness fit index) is an index of the accuracy of the model in explaining the model being compiled.
CFA analysis is done by summing the scores of each as observed variables. But it can be found to
have negative values that are not acceptable. According to Kline (2005) in Nordin (2012),
estimation of negative variance or unacceptable values, the revised model, and a suitable index
show better results.
In this study, the population is all households that are domiciled in the city of Jambi, in this case,
each household is represented by a household member (husband/wife/adult child). The number of
households/heads of households in the city of Jambi based on the results of the 2010 population
census is 126,829 households/heads of households spread across eight districts. Distribution of
Households / Households can be seen in the following table;
Table 1. Average Members of District Households in Jambi City
Sub- distric
the number of family
heads
The number of
population
Average
Household
Member
Kota Baru 33.245 137.856 4,15
Jambi Selatan 29.678 123.201 4,15
Jelutung 14.578 60.141 4,13
PasarJambi 3.286 12.988 3,95
Telanaipura 22.823 92.603 4,06
DanauTeluk 2.310 11.803 5,11
Pelayangan 2.483 12.895 5,19
Jambi Timur 18.426 77.631 4,21
Total 126.829 529.118 4,18
Source: 2010 City of Jambi Population Census, Central Statistics Agency of Jambi
City.
Determination of the size of the sample used refers to the determination of sampling by Joreskog,
K.G (1999), that the size of the sample used is at least five times the number of indicators. In this
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study the number of indicators used was 64 indicators (18 + 16 + 14 + 16), then the sample size
used was 64 people. This number is very adequate because a minimum sample of 30 is considered
a large sample size for statistical analysis (Cooper and Schindler, 2006). Weedaman & Thompson
(2003) argue that the RMSEA value is relatively independent of the sample size. Fan & Sivo (2007)
also stated that NFI, GFI, and AGFI fit values have high sensitivity to sample size. Similarly, Marsh
(1988) in their study found that RMR, GFI, and AGFI values were positively influenced by sample
size. As a result, questions in a single-factor structure are accepted (Evrekl, et al: 2010).
In this study, the questionnaire was developed using a Likert scale. This scale is used to measure
the level of agreement or disagreement of respondents to a series of statements that measure an
object (Istijanto: 2010: 87). Before the questionnaire is used to collect data, the questionnaire is
first tested for validity and reliability. In the Confirmatory Factor Analysis the hypothesized model
must be valid which refers to the ability of an indicator to measure what is actually wanted to be
measured (Supranto, J: 2004). Validation is a process carried out by the composer or user of the
instrument to collect data empirically to support the conclusions generated by the instrument score.
Validity is the ability of a measuring instrument to measure its measurement goals (Ahiri; 2009).
The validity of indicators in measuring latent variables is assessed by testing whether all loading
(λi) is real by using t-test for a certain level of confidence α. For this reason, further confirmation is
needed, namely checking its validity and reliability. This can be done with Factor Analysis, so it is
called the Confirmatory Factor Analysis. So in principle we will only confirm based on existing
theories or concepts on the accuracy (valid and reliable) of the instruments made (Arisanti: 2010).
While reliability is the consistency of an instrument measuring something to be measured reliability
indicates the extent to which the results of measurements with the tool can be trusted. Or reliability
is the proportion of the diversity of test scores caused by systematic diversity in the test taker
population (Ahiri: 2009: 17). The reliability test of the instrument aims to find out and guarantee
that an instrument/questionnaire is indeed reliable to be used in collecting data. Therefore,
reliability is an index that shows the extent to which a measuring tool can be trusted or reliable
(Wiersma: 1986 in Margono: 2013)
In this study, the total population was 25,366 people/family, while the number of respondents was
320 people spread across eight sub-districts within the city of Jambi. The following is general
information about the characteristics of the respondents;
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Effiyaldi, & Murniarti, E. (2020). Analysis Of Confirmatory Factors To Measure Public Trust In The Population Document Service Of Population Department Of
Jambi City. Advances in Social Sciences Research Journal, 7(8) 287-301.
Tabel 2. CHarateristics and Distribution of Respondents
No Category Total No Category Total
1
Level of Education
3.
Agama
Senior high school 107 Moslem 235
Diploma 90 Catholic/Protestant 49
S1 97 Hindu 28
S2 26 Budha 18
Total 320 Total 320
2
Profession
4
Gender
Civil Servant 57 Male 185
Private/ 66 Female 135
Teacher/ Lecturer 59 Amount 320
Farmer 44
5
Age
House wife 43 18 – 25 years 65
College student 51 26 – 40 165
> 40 90
Total 320 Total 320
Source; Data Processed.
The following is the operationalization of research variables in this study;
Table 3. Description of Public Trust Variables in this study
Indikator Item
Internal factor (personal) Z.1.1, Z.1.2
Eksternal factor (institution characteristik) Z.2.1, Z.2.2, Z.2.3, Z.2.4
Integrity Z.3.1, Z.3.2
Competence Z.4.1, Z.4.2, Z.4.3, Z.4.4
Consistensy Z.5.1, Z.5.2, Z.5.3, Z.5.4
Loyalty Z.6.1
Openness Z.7.1
Table 4. Description of Variable Data on Public Service Quality (Y)
Indikator Item
Tangibles (penampilan) Y.1.1, Y.1.2, Y.1.3, Y.1.4, Y.1.5
Empathy (kemauan memberi layanan) Y.2.1, Y.2.2
Reliability (Kehandalan) Y.3.1, Y.3.1, Y.3.1
Responsiveness (kesediaan membantu) Y.4.1, Y.4.2
Assurance (jaminan) Y.5.1, Y.5.2, Y.5.3, Y.5.4
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Table 5. Description of Employee Disciplinary Variable Data (X2)
Indicator Item
Preventive X2.1.1, X2.1.2, X2.1.3
Corrective X2.2.1, X2.2.2, X2.2.3
Job goals and job abilities X2.3.1, X2.3.2
As an example X2.4.1, X2.4.2
Without ulterior motive X2.5.1
Justice X2.6.1
Firmness X2.7.1
Human relations X2.8.1
Table 6. Description of Data on Organizational Transparency Variables (X1)
Indicator Item
Mechanism X1.1.1, X1.1.2, X1.1.3, X1.1.4
Information access channel X1.2.1, X1.2.2, X1.2.3
Media/Tools/Material/Complaint X1.3.1, X1.3.2, X1.3.3, X1.3.4
Public Right of Information X1.4.1, X1.4.2, X1.4.3, X1.4.4, X1.4.5
RESULT
The goodness of fit test of the model for the confirmatory factor analysis of the public trust variable
is obtained as follows;
Table 7. GOF Model 1 Testing Results Public Trust
Goodness of fit
(GOF) Indeks Cut off value Hasil output Description
Chi-square P ≥ 0.05 372.44 (p=0.0) not fit
RMSEA ≤0,08 0.074 fit
GFI ≥0,9 0.89 not fit
NFI ≥0,9 0.89 not fit
CFI ≥0,9 0.93 good fit
IFI ≥0,9 0.93 good fit
RMR ≤ 0.05 1.13 not fit
From the table above, the chi-square value and its probability (p) <0.05. This shows the model is
not good. Next, an analysis of each of the indicators that constitute public trust is provided in the
standardized estimate value graph and the model's t-value as follows:
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Figure 7 and Figure 8. Standardized Value of Organizational Transparency Analysis and T-Count
Value Confirmatory Analysis of Organizational Transparency
From this figure, it can be seen the results of the estimated parameter relationship between latent
variables and indicator variables.
DISCUSSION
Based on table 7 about the GOF test results of the public trust variable, it is known that the chi- square value and its probability (p) <0.05. This shows the model is not good. However, please note
that the chi-square value is very sensitive to the number of samples so that another fit test is needed.
From several other Goodness of fit index criteria, the model shows that the model is eligible, but
there are also some Goodness of fit index criteria, which shows the model does not meet the
requirements. Based on this, it can be argued that the model has a moderate fit.
Based on Figure 1 and Figure 2. Standardized Value of Confirmatory Analysis of Service Quality and
T-Count Value of Confirmatory Analysis of Service Quality that is above, the chi-square value, and
its probability (p) <0.05. This shows the model is not good. T-count value shows that all indicators
have a t-value greater than t-table of 1.96 (α = 5%). which shows that the indicators jointly present
a unidimensional variable for public trust.
Based on table 8. GOF Model 1 Testing Results of Public Service Quality, from the table above, the
value of chi-square and its probability (p) <0.05. This shows the model is not good. However, please
note that the chi-square value is very sensitive to the number of samples so that another fit test is
needed. From several other Goodness of fit index criteria, the model shows that the model is eligible,
but there are also some Goodness of fit index criteria, which shows the model does not meet the
requirements. Based on this, it can be argued that the model has a moderate fit.
Based on Figure 3 and Figure 4. Standardized Value of Confirmatory Analysis of Service Quality and
T-Calculate Value of Confirmatory Analysis of Service Quality. Based on the t-count value shows that
all indicators (16 indicators) have a t-value greater than the t-table of 1.96 (α = 5%). which shows
that the indicators jointly present a unidimensional variable for public service quality (Y).
Based on table 9. The results of the Gof Model 1 employee work discipline test found that from the
above table, the value of chi-square and its probability (p) <0.05. This shows the model is not good.
However, the chi-square value is very sensitive to the number of samples so it needs another fit test.
From several other Goodness of fit index criteria, the model shows that the model is eligible, but
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Effiyaldi, & Murniarti, E. (2020). Analysis Of Confirmatory Factors To Measure Public Trust In The Population Document Service Of Population Department Of
Jambi City. Advances in Social Sciences Research Journal, 7(8) 287-301.
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