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.
Quantitative variables are those that can be measured and expressed numerically, allowing for mathematical operations. They can be further categorized into discrete variables, which take on specific values (like the number of students in a class), and continuous variables, which can take any value within a range (like height or temperature). Examples of quantitative variables include age, income, test scores, and distances.
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.
The month of birth is a qualitative variable because it categorizes individuals into distinct groups based on the month they were born. Unlike quantitative variables, which represent numerical values that can be measured or ordered, the months are labels that do not have inherent numerical significance.
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)
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.
Quantitative variables are those that can be measured and expressed numerically, allowing for mathematical operations. They can be further categorized into discrete variables, which take on specific values (like the number of students in a class), and continuous variables, which can take any value within a range (like height or temperature). Examples of quantitative variables include age, income, test scores, and distances.
The answer depends on the nature of the variables: for a start, whether they are qualitative or quantitative.
Interval and ratio
Variables are characteristics or attributes that can take on different values or categories. They can be classified as qualitative (categorical) or quantitative (numerical). Qualitative variables describe qualities or characteristics, such as color or type, while quantitative variables represent measurable quantities, such as height or age. Additionally, variables can be independent or dependent, depending on whether they influence or are influenced by other variables in a study or experiment.
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.
In quantitative research, the most relevant aspect is typically the manipulation of independent variables to observe their effects on dependent variables. This approach allows researchers to establish causal relationships and analyze data statistically. By controlling and measuring these variables, quantitative research aims to produce reliable, objective findings that can be generalized to larger populations. Observational data can also be collected, but manipulation is key for testing hypotheses.