Research

• Methodology discusses and explains the data collection and analysis methods used in a research project.
• Research methodology should include the type of research conducted, how data was collected and analyzed, tools or materials used, and methods to mitigate or avoid research biases.
• Quantitative method aims to produce generalizable knowledge about the causes of a phenomenon and requires a carefully designed study under controlled conditions that can be replicated.
• Qualitative method produces contextual, real-world knowledge about the behaviors, social structures, or shared beliefs of a specific group of people and is less controlled and more interpretive.
• Quantitative data is a type of data that measures values or counts and is expressed as numbers.
• Qualitative data is information that cannot be counted, measured or easily expressed using numbers and is collected from text, audio and images and shared through data visualization tools.
• Primary data is a type of data that is firsthand or you collected it yourself.
• Experimental data is a type of data that is gathered by controlling and manipulating variables.
• The Mann-Whitney U test works on specific types of data: Continuous data are numbers that can take any value within a range.
• Secondary data is a type of data that is collected by someone else.
• If the U statistic is smaller than the critical value, you reject the null hypothesis.
• Ordinal data are data that can be ranked in order.
• Henry Mann later refined and popularized the Mann-Whitney U test in 1947.
• Mann-Whitney U test is a nonparametric test of the null hypothesis that compares two groups of data, often used when the data are non-normally distributed or a skewed distribution.
• The U statistic depends on the sample sizes (n1 and n2) and the ranks.
• The Mann-Whitney U test was developed by Frank Wilcoxon in 1945.
• The formula for getting the U statistic for the Mann-Whitney U test is: For the group with the smaller sample size (n1), For the group with the larger sample size (n2).
• Descriptive data is a type of data that is gathered via observations.
• Quantitative method data collection includes surveys, experiments, and existing data.
• Types of chi square test include Chi - square goodness of fit test and Chi - square test of independence.
• Qualitative method data collection includes interviews or focus groups, participant observation, and existing data.
• Regression Analysis is a technique of studying dependent variable, on one or more variables, with a view to estimate or predict the average value of the dependent variables.
• Pearson Correlation Coefficient is the most common way of measuring a linear correlation, a number between –1 and 1 that measures the strength and direction of the relationship between two variables.
• ANOVA is a statistical tool that compares three or more groups.
• Types of ANOVA include One way ANOVA, Two way ANOVA, and Repeated Measures ANOVA.
• Types of Regression Analysis include Simple Linear Regression, Multiple Linear Regression, and Nonlinear Regression.
• Nominal Variables have categories with no natural ordering, for example, the preferred mode of transportation.
• Chi square test is a statistical test for categorical data used to determine whether data are significantly different from expected frequencies.
• Ordinal Variables allow the categories to be sorted or have a natural rank order, for example, the variable “frequency of physical exercise”.
• Post hoc testing is performed when you need to find out how the treatment levels differ from one another.
• Categorical Variable belongs to a subset of variables that can be divided into discrete categories, also known as qualitative variables.
• Alternative Hypothesis is the true difference is different from zero.
• T-test is a parametric test of difference, meaning T-test assumes your data; are independent, are normally distributed, have a similar amount of variance within each group being compared (a.k.a homogeneity of variance).
• One tailed t-test is performed if two populations are different from one another.
• Two tailed t-test is performed if one population mean is greater than or less than the other.
• The most important values in a T-test when reporting are the T-value, P-value and the degrees of freedom.