Variance Is A Statistical Method Used For Test Differences Between Two Or More Independent Groups?

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

Testing the difference between the respondents demographic groups As mentioned in chapter 4: table 4-3, there was a very different between the number of respondents within some demographic groups, e.g. the gender groups compromised 185 males and 25 females and similarly, the academic background groups compromised 3 high school, 185 graduate and 85 postgraduate holders. Therefore, generalising the results of testing the difference between the means of these groups may be invalid and meaningfulness

ANALYSIS OF VARIANCE (MANOVA) Multivariate analysis of variance (MANOVA) is a statistical analysis used when a researcher wants to examine the effects of one or more independent variables on multiple dependent variables. This method is an extension of the analysis of variance (ANOVA) model and is the most commonly used multivariate analysis in the social sciences. MANOVA tests whether there are statistically significant, or not due to chance, mean differences among levels of the independent variable(s)

of appropriate statistical tests and evaluating statistical results. The intent is to discuss the application of the elements in analyzing and making decisions about data. Our book states that there are many kinds of descriptive statistics. Calculating measures of central tendency and measures of variability are two we focused on. As their names suggest, measures of central tendency indicate what is most typical in a data set. Measures of variability gauge how much difference there is in a set

samples (Gravetter & Wallnau, 2006). Fortunately, there is a statistical test employed when making comparison between two independent groups that have no requirement for large and normally distributes samples; the Mann-Whitney U test. This paper provides a summary of the test, an explanation of the logic that underlies the test and its application, and the forces and weaknesses of the test. For instance, one of the major limits of this test is the type I error which is rather amplified in a heteroscedasticity

Correlation Analysis using Excel   The correlation coefficient allows researchers to determine if there is a possible linear relationship between two variables measured on the same subject (or entity). When these two variables are of a continuous nature (they are measurements such as weight, height, length, etc.) the measure of association most often used is Pearson’s correlation coefficient.   This association may be expressed as a number (the correlation coefficient) that ranges from –1 to

all fields and topics evolve, techniques on how to improve research endeavors, such as statistical modeling techniques, become important to utilize. Hierarchical linear modeling, similarly known as multilevel modeling, has been a statistical approach that has gained attention and improved the analysis and interpretation of research data (Osborne, 2000). Hierarchical linear modeling is a regression-based statistical analysis that considers the hierarchical (i.e., multiple levels; nested) nature of variables

Background The t‑test for differences between two means has been a standard tool for more than a century, since William Gosset introduced the method in 1908 (Gosset (Student) 1908). We use the t‑test to infer from a sample a range of values for the true mean of the population from which the sample was drawn. When comparing two independent samples we use a t‑test to decide whether the averages of the two groups are probably different by testing whether the difference in means is sufficiently different

hypothesis, to compare the means of two or more groups, and to calculate the correlation between two variables. Learning Team D’s members have reflected on each of these issues and share their insights on these objectives. Testing a Research Hypothesis The purpose of testing a research hypothesis is to prove or disprove the

Part 1 Demographics The age is firstly included. Though the target respondents are Chinese young consumers, the researcher still want to have a further look at the difference between different age intervals of the young group. The gender is expected to be recorded because male consumers may have different attitudes towards luxury digital branding with female consumers. Also, the region of living is included since the economy conditions as well as the engagement of digital technologies are different