They are variables that can take quantitative - as opposed to qualitative values. For example, the colour of peoples' eyes is a qualitative variable, but their age or shoe size are quantitative variables.
For qualitative variables, appropriate descriptive statistics include frequencies and proportions, as they help summarize categorical data and show the distribution of different categories. For quantitative variables, measures such as mean, median, mode, range, variance, and standard deviation are suitable because they provide insights into the central tendency, spread, and overall distribution of numerical data. The choice of statistics depends on the nature of the data: qualitative data is categorical and non-numeric, while quantitative data is numeric and can be measured.
This kind of data is qualitative, meaning it is an observation of a particular facet of the observed thing. Quantitative date is numerically-based.
Quantitative means it can be measured. Qualitative is something that is subjective meaning there is no way to really measure it. Example: Quantitative: 2=2=4 This is always true. Qualitative: Puppies are cute. (this is only an opinion. No facts)
The strength of the linear relationship between two quantitative variables is measured by the correlation coefficient. The correlation coefficient, denoted by "r," ranges from -1 to 1. A value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. The closer the absolute value of the correlation coefficient is to 1, the stronger the linear relationship between the variables.
It is quantitative.
They are variables that can take quantitative - as opposed to qualitative values. For example, the colour of peoples' eyes is a qualitative variable, but their age or shoe size are quantitative variables.
nominal and ordinal is wrong; those are the two types of qualitative variables. Ratio and interval are the two types of quantitative variables.
No, a crosstabulation does not have to include both categorical and quantitative variables. It is primarily used to summarize the relationship between two categorical variables. However, quantitative variables can be categorized into groups or bins to create a crosstabulation, but it's not a requirement.
No, it is quantitative.
The answer depends on the nature of the variables: for a start, whether they are qualitative or quantitative.
Interval and ratio
A scatter diagram.
In qualitative research, researchers do not typically control variables in the same way as in quantitative research. Instead, they aim to explore and understand the complexities and nuances of a phenomenon without manipulating variables. The focus is on gaining in-depth insights and understanding the context in which the research is conducted.
A quantitative variable is numeric and therefore can be counted discretely or continuously. The other side of the spectrum is qualitative variables.
When forming a hypothesis for quantitative research, a declarative hypothesis states the expected relation between variables, whereas a null hypothesis states that there is no significant relation.
! ANOVA is generally computed for two or more QUANTITATIVE variables. If the quantitative variables are two or less in number, people prefer the t test (one sample t, paired t, or independent samples t) The Independent variable however is qualitative( for example, Girls and boys or Names of Schools.) It is the dependent variable that is Quantitative (for example, the ages - 2, 5 , 70, etc or weight or number of somethings). If you have 2 independent variables, you go for the two way ANOVA. Else, it's the one way ANOVA. !
Some times. At other times it uses mutually dependent variables (changes in each variable affect the other).