netrashetty
Netra Shetty
Amerigroup (NYSE: AGP) is a health insurance company headquartered in Virginia Beach, Virginia. It is the largest publicly-traded company focused exclusively on meeting the needs of those enrolled in publicly-funded healthcare programs such as Medicaid and Medicare. In 1994, Amerigroup was founded to respond to the growing need for private-sector partners in states managing healthcare programs for lower-income families and people with disabilities.
To gather pertinent data, this study will be using survey questionnaires. Particularly, the study arranges to distribute the questionnaires to the mobile phone service companies. In addition, the researcher will also consider the previous studies and contrast it to its existing data in order to give conclusions and proficient recommendations. In accordance to this, the use of IPO model will be considered to give study direction. A process is versioned as a sequence of boxes (processing elements) linked by inputs and outputs. Information or material objects flow in the course of a sequence of activities based on a set of rules or pronouncement points (Harris & Taylor, 1997). Harris & Taylor, (1997) pointed out that flow charts and process diagrams are often used to signify the process. What goes in is the input; what causes the change is the process; what comes out is the output. (Armstrong, 2001) Figure 1.1 illustrates the basic IPO model:
Figure 1
Input – Process – Output Model
The IPO model will give the common structure and guide for the direction of the study. Replacing the variables of this study on the IPO model, the researcher came up with the following:
Figure 2
Conceptual Framework
INPUT PROCESS OUTPUT
With respect to this model, the data gathered will be assessed through the help of Microsoft excel and a statistical software called SPSS. Basically, the SPSS software will be used to validate the hypothesis. Thus, descriptive statistics, correlation and Chi-square will be run in SPSS software.
Descriptive Statistics
In the descriptive statistics, it is likely that the study will be inexpensive and swift. It can also propose unexpected hypotheses. Nevertheless, this statistics will be very firm to rule out different clarifications and principally deduce causations. This descriptive statistics utilizes observations in the study. In descriptive statistics measures of central tendency (e.g. mean, median, and mode) and measures of dispersion (e.g. standard deviation, range, variance) will be computed.
Correlation[1]
According to Guilford & Fruchter (1973), the strength of the linear association between two variables is quantified by the correlation coefficient. Since this paper is in quantitative approach which is also mainly limited to counting, coming up with frequency and cumulative distributions, and computations of percentages, then these methods of analysis yield facts and data, the uses are quite limited. Facts in and of themselves do not speech much, for instance, of achievement or performance are related to other factors that such phenomena are better understood, predicted and to some extent even controlled.
The basic purposes of sciences are description, explanation, prediction and control. Differences in a performance, for example, are better explained if other phenomena, events or even other performance are used to account for each difference. This is achieved through a process called correlation. In a sense t-tests and F-tests are special cases of correlation. Sometimes such relationship show cause-effect but sometimes it is just plain relationship.
Correlations may either be bivariate (at least) or multivariate. Actually, in this paper, the use of Pearson Product moment correlation is considered. The Pearson Product moment correlation is used if the purpose is to determine the relationship or co-variation between two variables that are usually of the interval type of data. Basically, there are two types of correlation depending on the nature of correlation. Correlation may either be positive or negative. Correlation is positive if the objects, items or cases who got high in one variable are also those who got high in another variable, and those who got low in one variable also got low in the other variable.
Correlation is negative if the reverse seems to be the pattern. That is, those who got high in one factor are generally the ones who got low in the other factor; those who got low in one factor got high in the other factor. Correlation, or r for short in the case of a Product Moment Correlation ranges from r = -1.00 to r = +1.00 as limiting values. If r = +1.00 nor r = -1.00. If the general pattern of scores indicates positive correlation or negative correlation, there are usually stray cases which do not fit the mold. These cases cause the correlation to be less than perfect, that is the r may range between, say r = .01 to r = .99 in the case of positive correlation; r =-.01 to r = -.99 in the case of negative correlation. The formula used in this type of statistic is:
Where:
= Correlation between X and Y
= Sum of Variable X
= Sum of Variable Y
= Sum of the product X and Y
N= Number of Cases
= Sum of squared X score
= Sum of squared Y score
The previous formula used in computing correlation coefficient standardizes the values. Therefore no matter what changes in scale or units of measurement are given it will not affect its value. For this cause, the correlation coefficient is frequently more helpful than a graphical representation in evaluating the strength of the relationship between two variables.
To gather pertinent data, this study will be using survey questionnaires. Particularly, the study arranges to distribute the questionnaires to the mobile phone service companies. In addition, the researcher will also consider the previous studies and contrast it to its existing data in order to give conclusions and proficient recommendations. In accordance to this, the use of IPO model will be considered to give study direction. A process is versioned as a sequence of boxes (processing elements) linked by inputs and outputs. Information or material objects flow in the course of a sequence of activities based on a set of rules or pronouncement points (Harris & Taylor, 1997). Harris & Taylor, (1997) pointed out that flow charts and process diagrams are often used to signify the process. What goes in is the input; what causes the change is the process; what comes out is the output. (Armstrong, 2001) Figure 1.1 illustrates the basic IPO model:
Figure 1
Input – Process – Output Model
The IPO model will give the common structure and guide for the direction of the study. Replacing the variables of this study on the IPO model, the researcher came up with the following:
Figure 2
Conceptual Framework
INPUT PROCESS OUTPUT
With respect to this model, the data gathered will be assessed through the help of Microsoft excel and a statistical software called SPSS. Basically, the SPSS software will be used to validate the hypothesis. Thus, descriptive statistics, correlation and Chi-square will be run in SPSS software.
Descriptive Statistics
In the descriptive statistics, it is likely that the study will be inexpensive and swift. It can also propose unexpected hypotheses. Nevertheless, this statistics will be very firm to rule out different clarifications and principally deduce causations. This descriptive statistics utilizes observations in the study. In descriptive statistics measures of central tendency (e.g. mean, median, and mode) and measures of dispersion (e.g. standard deviation, range, variance) will be computed.
Correlation[1]
According to Guilford & Fruchter (1973), the strength of the linear association between two variables is quantified by the correlation coefficient. Since this paper is in quantitative approach which is also mainly limited to counting, coming up with frequency and cumulative distributions, and computations of percentages, then these methods of analysis yield facts and data, the uses are quite limited. Facts in and of themselves do not speech much, for instance, of achievement or performance are related to other factors that such phenomena are better understood, predicted and to some extent even controlled.
The basic purposes of sciences are description, explanation, prediction and control. Differences in a performance, for example, are better explained if other phenomena, events or even other performance are used to account for each difference. This is achieved through a process called correlation. In a sense t-tests and F-tests are special cases of correlation. Sometimes such relationship show cause-effect but sometimes it is just plain relationship.
Correlations may either be bivariate (at least) or multivariate. Actually, in this paper, the use of Pearson Product moment correlation is considered. The Pearson Product moment correlation is used if the purpose is to determine the relationship or co-variation between two variables that are usually of the interval type of data. Basically, there are two types of correlation depending on the nature of correlation. Correlation may either be positive or negative. Correlation is positive if the objects, items or cases who got high in one variable are also those who got high in another variable, and those who got low in one variable also got low in the other variable.
Correlation is negative if the reverse seems to be the pattern. That is, those who got high in one factor are generally the ones who got low in the other factor; those who got low in one factor got high in the other factor. Correlation, or r for short in the case of a Product Moment Correlation ranges from r = -1.00 to r = +1.00 as limiting values. If r = +1.00 nor r = -1.00. If the general pattern of scores indicates positive correlation or negative correlation, there are usually stray cases which do not fit the mold. These cases cause the correlation to be less than perfect, that is the r may range between, say r = .01 to r = .99 in the case of positive correlation; r =-.01 to r = -.99 in the case of negative correlation. The formula used in this type of statistic is:
Where:
= Correlation between X and Y
= Sum of Variable X
= Sum of Variable Y
= Sum of the product X and Y
N= Number of Cases
= Sum of squared X score
= Sum of squared Y score
The previous formula used in computing correlation coefficient standardizes the values. Therefore no matter what changes in scale or units of measurement are given it will not affect its value. For this cause, the correlation coefficient is frequently more helpful than a graphical representation in evaluating the strength of the relationship between two variables.
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