Data Analysis Methods Used in Quantitative Research.
DEFINITION:
Quantitative methods are research techniques that are used to gather quantitative data — information dealing with numbers and anything that is measurable e.g. Statistics, tables and graphs, are often used to present the results of these methods.
Quantitative research methods were originally developed in the natural sciences to study natural phenomena. However examples of quantitative methods now well accepted in the social sciences and education.
Differences between parametric and non parametric methods
Parametric method Non parametric
Assumes data comes from a type of probability distributions ( they are not distribution-free) Distribution free methods which do not rely on assumptions that the data are drawn from a given probability distribution
Makes inferences about the parameters of the distribution Non parametric statistics can refer to a statistic( a function on a sample) whose interpretation does not depend on the population fitting any parameterized distributions.
Makes more assumptions Makes fewer assumptions, their applicability are much wider than the corresponding parametric methods
If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates. However if those assumptions are incorrect, parametric methods can be very misleading. Hence have more statistical power Non parametric tests have less power.
They are often not considered robust They are more robust
Parametric formulae are often simpler to write down and faster to compute
PARAMETRIC METHODS
The parametric methods include the following;
• t-test
• z- test
• Analysis of variance (ANOVA)
• MNOVA
• Analysis of co-variance (ANCOVA)
T-TEST
The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental
Quantitative methods are research techniques that are used to gather quantitative data — information dealing with numbers and anything that is measurable e.g. Statistics, tables and graphs, are often used to present the results of these methods.
Quantitative research methods were originally developed in the natural sciences to study natural phenomena. However examples of quantitative methods now well accepted in the social sciences and education.
Differences between parametric and non parametric methods
Parametric method Non parametric
Assumes data comes from a type of probability distributions ( they are not distribution-free) Distribution free methods which do not rely on assumptions that the data are drawn from a given probability distribution
Makes inferences about the parameters of the distribution Non parametric statistics can refer to a statistic( a function on a sample) whose interpretation does not depend on the population fitting any parameterized distributions.
Makes more assumptions Makes fewer assumptions, their applicability are much wider than the corresponding parametric methods
If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates. However if those assumptions are incorrect, parametric methods can be very misleading. Hence have more statistical power Non parametric tests have less power.
They are often not considered robust They are more robust
Parametric formulae are often simpler to write down and faster to compute
PARAMETRIC METHODS
The parametric methods include the following;
• t-test
• z- test
• Analysis of variance (ANOVA)
• MNOVA
• Analysis of co-variance (ANCOVA)
T-TEST
The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental