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3.7 Validity, Reliability, and Normality Test
According to Ghozali, 2001 validity test is a tool which is used to measure validation of questioner. Questioner is valid if the range is more than
0.30 question of questioner can describe something that will be measured by questioner. Validity test used SPSS program.
In order to validate a measurement instrument, it must first be subjected to test of both validity and reliability. Since most of the questions in the
questionnaire were self administered, it is felt necessary to examine the validity of the measure. One of the important steps in data analysis is to confirm whether or
not the variables representing responses to questions are uniquely associated to the theoretical dimension of the variables of interest Sekaran, 2000.
According to Ghozali 2001:41 is measuring instrument to measure a questioner which represent indicator of construct variable. The most popular test
of inter item consistency reliability is the Cronbach’s coefficient alpha Cronbach’s alpha; Cronbach, 1946; cited from Sekaran 2003. The way to
calculating of a data reliability level is using Cronbach alpha is between 0.60 –
1.00. Normality test can be used the Kolmogorov Smirnov test, whereby if the
sign value 0.05, then it can be concluded that the data variables were tested with the normally distributed.
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3.8 Data Analysis
This study is intended to test a model that explained the effect of justice perception on job satisfaction and its impact for nurse’s motivation to answer the
hypothesis, the data will be analysed using statistical package for the social sciences SPSS 16.0 and for structural equation model SEM. SPSS is needed to
analyse the respondent characteristic in represent the frequency and percentage of respondent data. Beside that this tools also used to determine the validity,
reliability, and in this research normality measure using AMOS. The data will be analysed using structural equation model SEM by AMOS as software
application. This software provides information about goodness-of-fit model and relationship among the hypothesis. Moreover SEM was commonly used measures
of fit include:
Chi-Square a fundamental measure of fit used in the calculation of many other fit measures. Conceptually it is a function of the sample size and the
difference between the observed covariance matrix and the model covariance matrix.
Akaike information criterion AIC
o
A test of relative model fit: The preferred model is the one with the lowest AIC value.
o o
where k is the number of parameters in the statistical model, and L is the maximized value of the likelihood of the model.
Root Mean Square Error of Approximation RMSEA
42
o
Another test of model fit, good models are considered to have a RMSEA of .05 or less. Models whose RMSEA is .1 or more have a
poor fit.
Standardized Root Mean Residual SRMR
o
The SRMR is a popular absolute fit indicator. A good model should have an SRMR smaller than .05.
Comparative Fit Index CFI
o
In examining baseline comparisons, the CFI depends in large part on the average size of the correlations in the data. If the average
correlation between variables is not high, then the CFI will not be very high
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Table 3.2 Evaluation of SEM with Goodness of fit Measure
Types of Measure Goodness of fit Measures
Recommended Level of
acceptable Fit
Absolute Fit Measure
Goodness of fit index GFI Root
mean square
error of
approximation RMSEA Greater than .90
Under .08 Incremental Fit
Measure Adjusted goodness if fit index AGFI
Turker – Lewis index TLI
Normed fit index NFI Comparative Fit Index
Greater than .90 Greater than .90
Greater than .90 Greater than .90
Parsimonious Fit Measure
Normed chi- square χ2df
AIC Lower limit 1.0
Upper limit 2.03 3.0 or 5.0
Smaller positive value indicate
parsimony Source: Tabachnick and Fidell 2000; Hail et al. 1998; Byrne 2000.
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CHAPTER IV ANALYSIS AND RESULTS
4.1 Survey Results
This chapter will discuss analysis of research result related with job stress, organizational support, job satisfaction and performance of nurses who work at
private hospital in Padang. The result of this survey can be seen from the number of questioner have been distributed to nurses who work at some private hospitals
in Padang. Data was distributed to nurse through their head of installation and chief of room for each installation in private hospital, and take it back about 2
weeks to fill in the questioners. This is the table 4.1 that show about the number of questioner.
Table 4.1 Survey Result Survey
Number of questionairs
Distributed 164
Returned 150
Not Returned 14
Analysed 150
Source: Processed from questionnaire by using SPSS From the table 4.1, 164 questionnaires was distributed to nurses who
work at some private hospital in Padang. Others 14 questionnaires were not returned back to researcher because of some nurses were not in Padang,
absenteeism and vacation, and some nurses unfilled it because they did not have
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