# Determining Appropriate Statistics Methodology Chapter

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¶ … Kolmogorov-Smirnof test

Factor analysis

Linear regression

Goldfeld-Quandt test

Kaiser-Meiyer-Oklin (KMO)

Multivariate regression

Correlation -- Pearson's r

Cronbach's ?

Durbin-Watson statistic

See descriptions and justifications below.

Correlation importance and justifications

Correlation measures the strengths of association between two variables and, as such, enables the performance of bivariate analysis ("Statistics Solutions, 2012"). The value range of the correlation coefficient extends between +1 and -1. A correlation coefficient of ± 1 indicates a perfect degree of association between the two variables. Assumptions for the Pearson r correlation include normal distribution, linearity, and homoscedasticity ("Statistics Solutions, 2012"). Linearity assumes a straight line relationship between each of the variables in the analysis and homoscedasticity assumes that data is normally distributed about the regression line ("Statistics Solutions, 2012").

3. Reasoning (justifications) to use parametric or non-parametric statistics

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for only $8.97. Parametric statistics are used when the data is expected to show a type of probability distribution from which inferences can be drawn based on the parameters of that distribution (Geisser & Johnson, 2006). More assumptions are made when using parametric methods than non-parametric methods, which can generate more precise and accurate estimates if the assumptions are correct; this is known as statistical power (Geisser & Johnson, 2006).

4. Selection of statistical method suitable for the selected model(s) And 5. Justification of the selected statistical method

Multivariate analysis is used because there are so many independent variables. This is already discussed in the draft of the paper.

6. Assumption of the selected statistical method(s) AND 7.Discuss the need of Normality assumption

## Methodology Chapter on

With parametric statistics, there is an assumption that the data will be based on normal probability distributions that have the same shape and are characterized (parameterized) by a mean and standard deviations ("Statistics Solutions, 2012"). That is to say, if the researcher knows the mean and standard deviation -- and if the distribution is, in fact, normal -- then the probability of any future observations can be known ("Statistics Solutions, 2012"). To verify data normality, a goodness of fit test may be used; in this study, the Kolmogorov-Smirnof test will be used ("Statistics Solutions, 2012").

7. Multicollinearity assumption & implication to student work

Multiple linear regression assumes little to no multicollinearity in the data. When independent variables are not independent from each other, multicollinearity exists ("Statistics Solutions, 2012").. There is also an assumption of independence regarding the error of the mean ("Statistics Solutions, 2012"). That is to say that the standard mean error of the dependent variable is independent from the independent variables ("Statistics Solutions, 2012").

8. Discuss ways to overcome the Multicollinearity

When multicollinearity occurs in… [END OF PREVIEW] . . . READ MORE

Factor analysis

Linear regression

Goldfeld-Quandt test

Kaiser-Meiyer-Oklin (KMO)

Multivariate regression

Correlation -- Pearson's r

Cronbach's ?

Durbin-Watson statistic

See descriptions and justifications below.

Correlation importance and justifications

Correlation measures the strengths of association between two variables and, as such, enables the performance of bivariate analysis ("Statistics Solutions, 2012"). The value range of the correlation coefficient extends between +1 and -1. A correlation coefficient of ± 1 indicates a perfect degree of association between the two variables. Assumptions for the Pearson r correlation include normal distribution, linearity, and homoscedasticity ("Statistics Solutions, 2012"). Linearity assumes a straight line relationship between each of the variables in the analysis and homoscedasticity assumes that data is normally distributed about the regression line ("Statistics Solutions, 2012").

3. Reasoning (justifications) to use parametric or non-parametric statistics

Get full access

for only $8.97. Parametric statistics are used when the data is expected to show a type of probability distribution from which inferences can be drawn based on the parameters of that distribution (Geisser & Johnson, 2006). More assumptions are made when using parametric methods than non-parametric methods, which can generate more precise and accurate estimates if the assumptions are correct; this is known as statistical power (Geisser & Johnson, 2006).

4. Selection of statistical method suitable for the selected model(s) And 5. Justification of the selected statistical method

Multivariate analysis is used because there are so many independent variables. This is already discussed in the draft of the paper.

6. Assumption of the selected statistical method(s) AND 7.Discuss the need of Normality assumption

## Methodology Chapter on *Determining Appropriate Statistics* Assignment

With parametric statistics, there is an assumption that the data will be based on normal probability distributions that have the same shape and are characterized (parameterized) by a mean and standard deviations ("Statistics Solutions, 2012"). That is to say, if the researcher knows the mean and standard deviation -- and if the distribution is, in fact, normal -- then the probability of any future observations can be known ("Statistics Solutions, 2012"). To verify data normality, a goodness of fit test may be used; in this study, the Kolmogorov-Smirnof test will be used ("Statistics Solutions, 2012").7. Multicollinearity assumption & implication to student work

Multiple linear regression assumes little to no multicollinearity in the data. When independent variables are not independent from each other, multicollinearity exists ("Statistics Solutions, 2012").. There is also an assumption of independence regarding the error of the mean ("Statistics Solutions, 2012"). That is to say that the standard mean error of the dependent variable is independent from the independent variables ("Statistics Solutions, 2012").

8. Discuss ways to overcome the Multicollinearity

When multicollinearity occurs in… [END OF PREVIEW] . . . READ MORE

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