Types of Data and Variables PDF
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This document provides a comprehensive overview of different types of data and variables commonly used in research and statistics. It covers various categories like qualitative and quantitative data, along with subcategories like nominal, ordinal, interval, and ratio variables. Different examples and explanations are provided for each type.
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Types of data and Variables A variable A variable is any kind of attribute or characteristic that you are trying to measure, manipulate and control in statistics and research. All studies analyze a variable, which can describe a person, place, thing or idea. A variable's value can change...
Types of data and Variables A variable A variable is any kind of attribute or characteristic that you are trying to measure, manipulate and control in statistics and research. All studies analyze a variable, which can describe a person, place, thing or idea. A variable's value can change between groups or over time. Researchers organize variables into a variety of categories, the most common of which include: Independent variables Dependent variables Quantitative variables Qualitative variables Qualitative variables is Non-numerical values or groupings and A measure that describes or characterizes an attribute. Examples: Eye color or dog breed Types: Binary, nominal and ordinal Categories of qualitative variables Binary: Variables with only two categories, such as male or female, red or blue. Nominal: Variables you can organize in more than two categories that do not follow a particular order. Take, for example, housing types: Single-family home, condominium, tiny home. Ordinal: Variables you can organize in more than two categories that follow a particular order. Take, for example, level of satisfaction: Unsatisfied, neutral, satisfied. Nominal data Ordinal data Quantitative variables is any data sets that involve numbers or amounts Examples: Height, distance or number of items Types: Discrete and continuous Categories of quantitative variables Discrete: Any numerical variables you can realistically count, such as the coins in your wallet or the money in your savings account. Continuous: Numerical variables that you could never finish counting, such as time. Interval Data A type of quantitative data where the differences between values are meaningful. However, it does not have a true zero point. Can measure the difference between values. No absolute zero means you can't make meaningful statements about how many times greater one value is compared to another. Examples: Temperature in Celsius or Fahrenheit (e.g., 20°C is not "twice as hot" as 10°C). Calendar years (e.g., the difference between 2000 and 2010 is the same as between 1990 and 2000). Interval scales are nice because the realm of statistical analysis on these data sets opens up. For example, central tendency can be measured by mode, median, or mean; standard deviation can also be calculated. Ratio Data Ratio data is also quantitative data, but it has all the properties of interval data, with the addition of a true zero point, allowing for meaningful comparisons of magnitude. Differences between values are meaningful. A true zero point allows for statements about how many times greater one value is than another. Examples: Height (e.g., 0 cm means no height).Weight (e.g., 0 kg means no weight). Age (e.g., a person who is 40 years old is twice as old as someone who is 20). This Device Provides Two Examples of Ratio Scales (height and weight) Independent variables is A variable that stands alone and isn't changed by the other variables or factored that are measured A variable measured or controlled by the experimenter, the variable that is thought to affect the out come. Example: Age: Other variables such as where someone lives, what they eat or how much they exercise are not going to change their age. Dependent variables A variable that relies on and can be changed by other factors that are measured ‘ The outcome variable or final result.’ Example A grade someone gets on an exam depends on factors such as how much sleep they got and how long they studied. Test 1 The most basic distinction between types of data is 1-Numbers that some data are quantitative while other data are 2- open ended response :qualitative. Quantitative data generally consists of 3- any data can consider quantitative 4- Equations 2 Only written material that respondents provide in 1-Any sort of data that can response to open-ended items. be summarized with numbers. 2- Text, pictures, videos, sound recording 3- Survey and questionnaire data. 4-- Equations