THE CONFIRMATORY FACTOR ANALYSIS
Based on the Schumacker and Lomaz (2004), Confirmatory factor analysis Passing KMO and Bartlett’s test is a pre-requisite to factor analysis. The study’s investigations disclosed precise decent results, where the KMO score is above the .80 value which it is indicates that a significant Barlett’s Test. This figure however describes that the collected data are suitable for conducting factor analysis.
The rotated component matrix above displayed eight separate components with their particular items. From this analysis, there are new forming of endogenous variable which is Attitudinal Loyalty and also new formed of exogenous variables which are Belief and Consumer Ethics. As we can see, the items in CA and CS been
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The minimum, or smallest, value of the variable is 1 for AL and CE. Mean is the arithmetic mean across the observations. It is the most broadly applied measure of central tendency. It is generally so-called the average. The mean is sensitive to tremendously large or small values. The maximum mean from the table is product acceptance associated to the other variable. Standard deviation is the square root of the variance. It measures the spread of a set of observations. The greater the standard deviation is, the more spread out the observations are. In this study, the highest value of standard deviation is consumer ethics .82572. Therefore, skewness measures the degree and direction of asymmetry. A symmetric spreading such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left. In this study the skewness is negative value. Kurtosis is a measure of the heaviness of the tails of a distribution. In SAS, a normal distribution has kurtosis 0. Extremely non-normal distributions may have high positive or negative kurtosis values, while approximately normal distributions will have kurtosis values close to 0. Kurtosis is positive if the tails are "heavier" than for a normal distribution and negative if the tails are "lighter" than for a normal distribution. From this study, the kurtosis showed a positive kurtosis values which indicates that the nearly non-normal …show more content…
Each factor is measured by a minimum of two to a maximum of four observed variables, the reliability of which is influenced by random measurement error, as specified by the connected error term. Each of these observed variables is regressed into its respective factor. Lastly all the seven factors are revealed to be inter-correlated.
The results shown in table above arrange for a rapid summary of the model fit, which includes the x² value (334.687), together with its degrees of freedom (164) and probability value (0.000).
In the table NPAR stands for Number of parameters, and CMIN (x²) is the minimum discrepancy and represents the discrepancy between the unrestricted sample covariance matrix S and the restricted covariance matrix. Df stands for degrees of freedom and P is the probability value.
In SEM a relatively small chi-square value supports the proposed theoretical model being tested. In this model the x² value is 334.687 and is small compared to the value of the independence model (3971.586). Hence the x² value is good. The model is over identified, where the DF value exceeded zero (DF> 0).
The other different common model-fit measures used to assess the models overall goodness of fit as explained
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
In comparing the two models there was hope "to validate a general personality structure within an offender sample to determine if there are any required adaptations of the FFM, and to ascertain if the FFM can offer some validation of Eysenck 's personality theory." With this study, the researchers had a few predictions that they hoped to prove. Researchers predicted that participants would show high levels of extraversion in their personalities and low levels of emotional stability in their scores which would prove high levels of neuroticism according to the PEN model. They also predicted that participants who had experienced a lot of time in prison would show low levels of extraversion. They predicted that younger prisoners would report higher levels of extraversion than the older prisoners and lastly they predicted that the FFM fits the data better than a three-factor PEN
A model developed for using factor analysis to try to determine the key traits in human personality. Although trait theories were well established by the 1960s, there was no consensus concerning the number or nature of the traits that make up personality. Since then, further research has confirmed a basic five factor model of personality or ‘Big Five’ (Tomas 2007). This five factor structure has been replicated by Norma (1963), Borgatta (1964) and Digman and Takemoto-Chock (1981) in list derived from Cattle’s 35 variables (Lawrence &Oliver 2000).
Discussion of inconsistencies in the data, and potential biases in reference to the methods are included
The procedures described here presume that the association between the independent and dependent variables is linear. With some modifications, regression analysis can also be used to estimate associations that go after another practical form (e.g., curvilinear, quadratic). Here we consider associations between one independent variable and one continuous dependent variable. The regression analysis is called simple linear regression - simple in this case refers to the fact that there is a single independent
five factor theory is a fairly recent proposal and has its basis in earlier work,
(1997). McRae et al. (1997) attempted to find if the Five-Factor Model was a universal constant in all cultures. They also attempted to see if cultural views would change how the five traits were viewed. The researchers collected data from 6 different translations of the Revised NEO Personality Inventory, this inventory looks for universal trait dimensions in a variety of languages. These 6 translations were then compared to the American counterpart. It was found that while some cultures did differ slightly in their view of the five traits found in the Five-Factor Model in comparison to Americans, the traits were still universal. All six translations found the big five traits of extraversion, agreeableness, conscientiousness, neuroticism and opens to experience to be
The five-factor model includes five broad domains or dimensions of personality that are used to describe human personality. The five factors are openness, conscientiousness, extraversion, agreeableness, and neuroticism. While these five traits should be sufficient on their own to describe all facets of a personality, there also should be no correlation between the main factors. The Five Factor Model is now perhaps the most widely use trait theory of personality and has achieved the closest thing to a consensus in personality research. The advantage of this theory is that there have been multiple research studies conducted on this theory. Results suggest that this theory is effective in describing and determining personality. However, this theory is very categorical and does not allow for much flexibility. It also looks at the person personality at that time and now how it developed.
Although the Fama and French three-factor model operates slightly better than CAPM, it does not indicate that CAPM is impractical to use (Hibbert and Lawrence 2010).
Mccrae, R. R. and Costa, P. T. 1989. Reinterpreting the Myers-Briggs Type Indicator From the Perspective of the Five-Factor Model of Personality. Journal of personality, 57 (1), pp. 17--40.
Gignac, G. E., Jang, K. L., & Bates, T. C. (2009). Construct redundancy within the Five-Factor Model as measured by the NEO PI-R:. Implications for emotional intelligence and incremental coherence,51(1), 76-86.
The theories for this research suggest that this study will yield similar results as the results found when they were s...
The regression is estimated in Eviews (2014). The results are used to test the following hypotheses deduced from literature review:
Hair, J. F. Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate
R-squared is always between 0 and 100%. 0% means the model explains none of the variability of the response data around its mean. 100% indicates that the model explains all the variability of the response data around its mean.