Question

share how you can use Statistics in real life. come up with concrete example(s) to clarify your idea. Then, connect the variable(s) of interest with types of data and levels of measurement. Use at least 100 words to elaborate your input.

Answer 1

The Variables of the interest -

A List of Common and Uncommon Types of Variables

While a “variable” in algebra really just means one thing–an unknown value–you’ll come across dozens of types of variables in statistics. Some are used more than others. For example, you’ll be much more likely to come across continuous variables than you would dummy variables.

Click on any bold variable name to learn more about that particular type.

Common Types of Variables

• Categorical variable: variables than can be put into categories. For example, the category “Toothpaste Brands” might contain the variables Colgate and Aquafresh.
• Confounding variable: extra variables that have a hidden effect on your experimental results.
• Continuous variable: a variable with infinite number of values, like “time” or “weight”.
• Control variable: a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plants grow faster, you would have to control for soil quality and water.
• Dependent variable: the outcome of an experiment. As you change the independent variable, you watch what happens to the dependent variable.
• Discrete variable: a variable that can only take on a certain number of values. For example, “number of cars in a parking lot” is discrete because a car park can only hold so many cars.
• Independent variable: a variable that is not affected by anything that you, the researcher, does. Usually plotted on the x-axis.
• A measurement variable has a number associated with it. It’s an “amount” of something, or a”number” of something.
• Nominal variable: another name for categorical variable.
• Ordinal variable: similar to a categorical variable, but there is a clear order. For example, income levels of low, middle, and high could be considered ordinal.
• Qualitative variable: a broad category for any variable that can’t be counted (i.e. has no numerical value). Nominal and ordinal variables fall under this umbrella term.
• Quantitative variable: A broad category that includes any variable that can be counted, or has a numerical value associated with it. Examples of variables that fall into this category include discrete variables and ratio variables.
• Random variables are associated with random processes and give numbers to outcomes of random events.
• A ranked variable is an ordinal variable; a variable where every data point can be put in order (1st, 2nd, 3rd, etc.).
• Ratio variables: similar to interval variables, but has a meaningful zero.

Less Common Types of Variables

• Active Variable: a variable that is manipulated by the researcher.
• Attribute variable: another name for a categorical variable (in statistical software) or a variable that isn’t manipulated (indesign of experiments).
• Binary variable: a variable that can only take on two values, usually 0/1. Could also be yes/no, tall/short or some other two-variable combination.
• Collider Variable: a variable represented by a node on a causal graph that has paths pointing in as well as out.
• Covariate variable: similar to an independent variable, it has an effect on the dependent variable but is usually not the variable of interest.
• Criterion variable: another name for a dependent variable, when the variable is used in non-experimental situations.
• Dichotomous variable: Another name for a binary variable.
• Dummy Variables: used in regression analysis when you want to assign relationships to unconnected categorical variables. For example, if you had the categories “has dogs” and “owns a car” you might assign a 1 to mean “has dogs” and 0 to mean “owns a car.”
• Endogenous variable: similar to dependent variables, they are affected by other variables in the system. Used almost exclusively in econometrics.
• Exogenous variable: variables that affect others in the system.
• Explanatory Variable: a type of independent variable. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’s an explanatory variable.
• Extraneous variables are any variables that you are not intentionally studying in your experiment or test.
• A grouping variable (also called a coding variable, group variable or by variable) sorts data within data files into categories or groups.
• Identifier Variables: variables used to uniquely identify situations.
• Indicator variable: another name for a dummy variable.
• Interval variable: a meaningful measurement between two variables. Also sometimes used as another name for a continuous variable.
• Intervening variable: a variable that is used to explain the relationship between variables.
• Latent Variable: a hidden variable that can’t be measured or observed directly.
• Manifest variable: a variable that can be directly observed or measured.
• Manipulated variable: another name for independent variable.
• Mediating variable: variables that explain how the relationship between variables happens. For example, it could explain the difference between the predictor and criterion.
• Moderating variable: changes the strength of an effect between independent and dependent variables. For example, psychotherapy may reduce stress levels for women more than men, so sex moderates the effect between psychotherapy and stress levels.
• Nuisance Variable: an extraneous variablethat increases variability overall.
• Observed Variable: a measured variable (usually used in SEM).
• Outcome variable: similar in meaning to a dependent variable, but used in a non-experimental study.
• Polychotomous variables: variables that can have more than two values.
• Predictor variable: similar in meaning to the independent variable, but used in regression and in non-experimental studies.
• Responding variable: an informal term for dependent variable, usually used in science fairs.
• Scale Variable: basically, another name for a measurement variable.
• Test Variable: another name for theDependent Variable.
• Treatment variable: another name for independent variable.

Data Levels of Measurement

A variable has one of four different levels of measurement: Nominal, Ordinal, Interval, or Ratio. (Interval and Ratio levels of measurement are sometimes called Continuous or Scale). It is important for the researcher to understand the different levels of measurement, as these levels of measurement, together with how the research question is phrased, dictate what statistical analysis is appropriate. In fact, the Free download below conveniently ties a variable’s levels to different statistical analyses.

In descending order of precision, the four different levels of measurement are:

• Nominal–Latin for name only (Republican, Democrat, Green, Libertarian)
• Ordinal–Think ordered levels or ranks (small–8oz, medium–12oz, large–32oz)
• Interval–Equal intervals among levels (1 dollar to 2 dollars is the same interval as 88 dollars to 89 dollars)
• Ratio–Let the “o” in ratio remind you of a zero in the scale (Day 0, day 1, day 2, day 3, …)

The first level of measurement is nominal level of measurement. In this level of measurement, the numbers in the variable are used only to classify the data. In this level of measurement, words, letters, and alpha-numeric symbols can be used. Suppose there are data about people belonging to three different gender categories. In this case, the person belonging to the female gender could be classified as F, the person belonging to the male gender could be classified as M, and transgendered classified as T. This type of assigning classification is nominal level of measurement.

The second level of measurement is the ordinal level of measurement. This level of measurement depicts some ordered relationship among the variable’s observations. Suppose a student scores the highest grade of 100 in the class. In this case, he would be assigned the first rank. Then, another classmate scores the second highest grade of an 92; she would be assigned the second rank. A third student scores a 81 and he would be assigned the third rank, and so on. The ordinal level of measurement indicates an ordering of the measurements.

The third level of measurement is the interval level of measurement. The interval level of measurement not only classifies and orders the measurements, but it also specifies that the distances between each interval on the scale are equivalent along the scale from low interval to high interval. For example, an interval level of measurement could be the measurement of anxiety in a student between the score of 10 and 11, this interval is the same as that of a student who scores between 40 and 41. A popular example of this level of measurement is temperature in centigrade, where, for example, the distance between 940C and 960C is the same as the distance between 1000C and 1020C.

The fourth level of measurement is the ratio level of measurement. In this level of measurement, the observations, in addition to having equal intervals, can have a value of zero as well. The zero in the scale makes this type of measurement unlike the other types of measurement, although the properties are similar to that of the interval level of measurement. In the ratio level of measurement, the divisions between the points on the scale have an equivalent distance between them.

The researcher should note that among these levels of measurement, the nominal level is simply used to classify data, whereas the levels of measurement described by the interval level and the ratio level are much more exact.