Types of Variables in Research

# Types of Variables in Research ## Introduction - Each person/thing we collect data on is called an **observation** (in our research work these are usually people/subjects). - Observations (participants) possess a variety of **characteristics**. - If a characteristic of an observation (participan...

# Types of Variables in Research ## Introduction - Each person/thing we collect data on is called an **observation** (in our research work these are usually people/subjects). - Observations (participants) possess a variety of **characteristics**. - If a characteristic of an observation (participant) is the same for every member of the group, i.e., it does **not vary**, it is called a **constant.** - If a characteristic of an observation (participant) **differs** for group members, it is called a **variable**. ## Meaning of Variables - A variable is a **concept** or **abstract idea** that can be described in **measurable terms**. In research, this term refers to the **measurable characteristics**, **qualities**, **traits**, or **attributes** of a particular **individual**, **object**, or **situation** being studied. - **Anything** that can vary can be considered a variable. For instance, **age** can be considered a variable because age can take **different values** for different people or for the same person at different times. Similarly, **income** can be considered a variable because a person's income can be assigned a value. ## Types of Variables ### Dependent and Independent Variables - **Independent variables** are variables that are **manipulated or controlled or changed**. It is what the researcher studies to see its **relationship** or **effects.** - **Presumed** or **possible cause** - **Dependent variables** are the **outcome variables** and are the variables for which we calculate statistics. The variable which changes **on account of** the independent variable is known as the **dependent variable**. i.e., it is influenced or affected by the independent variable - **Presumed results** (**Effect**) ## The Relationship between Independent and Dependent Variables A diagram depicting the relationship between independent and dependent variables. - **Left box:** Independent variable(s) (presumed or possible cause) - **Right box:** Dependent variable(s) (presumed results) - An arrow going from left to right marked "Affects" ## Example Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. What are the dependent and independent variables for the study? ## Solution - **Dependent Variable:** Test mark (measured from 0 to 100) - **Independent Variables:** Revision time (measured in hours), intelligence (measured using IQ score) ## Activity - Identify the dependent and independent variables for the following examples: 1. A study of teacher-student classroom interaction at different levels of schooling. 2. A comparative study of the professional attitudes of secondary school teachers by gender. ## Solution 1. **Independent variable:** Level of schooling. Four categories: primary, upper primary, secondary, and junior college - **Dependent variable:** Score on a classroom observation inventory which measures teacher-student interaction. 2. **Independent variable:** Gender of the teacher; male, female. - **Dependent variable:** Score on a professional attitude inventory. ## Moderator Variable - It is a **special type** of independent variable. - The **independent variable's relationship** with the dependent variable may change under different conditions. That condition is the **moderator variable**. - That factor which is **measured**, **manipulated**, or **selected** by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. ## Example A strong relationship has been observed between the **quality of library facilities (X)** and the **performance of the students (Y)**. Although this relationship is **supposed** to be true generally, it is nevertheless contingent on the **interest** and **inclination** of the students. It means that only those students who have the **interest** and **inclination** to use the library will show improved performance in their studies. - In this relationship, **interest** and **inclination** are the moderating variables, i.e., they moderate the strength of the association between X and Y variables. ## Quantitative and Qualitative Variables - **Quantitative variables** are ones that **exist along a continuum** that runs from low to high. Interval and ratio variables are quantitative. - **Quantitative variables** are sometimes called **continuous variables** because they have a **variety (continuum)** of characteristics. - **Height in inches** and **scores on a test** would be examples of quantitative variables. - **Qualitative variables** do not express differences in amount, **only differences**. - They are sometimes referred to as **categorical variables** because they **classify by categories**. Ordinal and Nominal variables are qualitative. - **Nominal variables**, such as **gender**, **religion**, or **eye color**, are categorical variables. Generally speaking, categorical variables ## A diagram of types of variables - A tree diagram showing the types of variables: - **Top:** Variable - **Second level:** Qualitative, Quantitative - **Third level:** Nominal, Ordinal, Interval, Ratio ## Measurement Scales **A black and white photo of Albert Einstein writing on a blackboard. He's writing the following terms on the blackboard:** - Nominal - Ordinal - Interval - Ratio ## Nominal Scale - **Nominal scale**, also called the **categorical variable scale**, is defined as a scale used for labelling variables into distinct **classifications** and does not involve a **quantitative value** or **order**. - This scale is the **simplest** of the four variable measurement scales. ## Nominal Scale Examples - **Gender** - **Political Preferences** - **Place of Residence** A table with 3 columns showing the possible answers to the following questions: | **What is your Gender** | **What is your Political Preference?** | **Where do you live?** | |---|---|---| | M - Male | 1 - Independent | 1 - Suburbs | | F - Female | 2 - Democrat | 2 - City | | | 3 - Republican | 3 - Town | ## Ordinal Scale - **Ordinal scale** is defined as a variable measurement scale **used** to simply **depict the order** of variables (what's important and significant) and not the difference between each of the variables. - Differences between each one is **not really known**. - For example, is the **difference** between "OK" and "Unhappy" the same as the difference between "Very happy" and "Happy?" We can't say. ## Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc. - "Ordinal" is easy to remember because it sounds like "order" and that's the key to remember with "ordinal scales:" it is the order that matters. ## Example On a survey, you might code **Educational Attainment** as: - 0 = less than high school - 1 = some high school - 2 = high school degree - 3 = some college - 4 = college degree - 5 = post college In this measure, higher numbers mean more education. But is the **distance** from 0 to 1 the same as 3 to 4? Of course not. ## Interval Scale - **Interval scale** is defined as a numerical scale where the **order of the variables is known**, as well as the **difference** between these variables. - Variables which have **familiar**, **constant**, and **computable** differences are classified using the Interval scale. - **Interval scale contains all the properties of the ordinal scale**, in addition to which, it offers a **calculation** of the difference between variables. - The main characteristic of this scale is the equidistant difference between objects. ## In statistics, interval scale is frequently used as a **numerical value** can not only be **assigned to variables**, but a calculation on the basis of those values can also be carried out. - **Calendar years** and **time** also fall under this category of measurement scales. - **Likert scale** is the most-used interval scale examples. ## Ratio Scale - **Ratio scale** is defined as a variable measurement scale that not only **produces the order of variables**, but also **makes the difference** between variables known **along with information** on the value of **true zero**. - It is calculated by assuming that the variables have an **option for zero**: - The difference between the two variables is the same, and there is a **specific order** between the options. ## In addition to the fact that the ratio scale does **everything** that a nominal, ordinal and interval scale can do, it can also establish the value of **absolute zero**. ## Examples The following questions fall under the **Ratio Scale** category: - **What is your daughter's current height?** - Less than 5 feet - 5 feet 1 inch - 5 feet 5 inches - 5 feet 6 inches - 6 feet - More than 6 feet - **What is your weight in kilograms?** - Less than 50 kilograms - 51 - 70 kilograms - 71 - 90 kilograms - 91 - 110 kilograms - More than 110 kilograms ## Continuous and Discontinuous Variables - If the values of a variable can be **divided into fractions**, then we call it a **continuous variable.** - Such a variable can take **infinite number of values**. **Income**, **temperature**, **age**, or a **test score** are examples of continuous variables. - These variables may take on **values within a given range** or, in some cases, an **infinite set.** - Any variable that has a **limited number of distinct values** and which **cannot be divided into fractions is a discontinuous variable.** - Such a variable is also called as **categorical variable** or **classificatory variable**, or **discrete variable**. - Some variables have only **two values** reflecting the **presence or absence** of a property: **employed-unemployed** or **male-female** have two values. These variables are referred to as **dichotomous**. - There are others than can take **added categories**, such as the **demographic variables** of race, religion. All such variables that **produce data** that fit into categories are said to be **discrete/categorical/classificatory**, since only **certain values are possible.** ## Variables Examples A table showing examples of variables with two, three, or multiple values. | **VARIABLES EXAMPLES** | **Examples** | |---|---| | **Dichotomous** | - Gender: male and female. - Variables Type of property: Commercial and residential - Pregnant and non pregnant. - Alive and dead. - HIV positive and HIV negative. - Education: Literate and illiterate. | | **Trichotomous** | - Residence: Urban, semi urban and rural. *Variables* - Religion: Hindu, muslim and Christianity. | | **Multiple Variables** | - Blood groups: A,B,AB and O | ## Demographic Variables - "Demographic variables are **characteristics or attributes** of subjects that are collected to **describe the sample.** They are also called **sample characteristics**. - It means these **variables describe study samples** and determine **if samples are representative of the population of interest.** - Although **demographic variables cannot be manipulated**, researchers can **explain relationships** between demographic variables and dependent variables. - Some common demographic variables are **age,** **gender,** **occupation,** **marital status,** **income**, etc. ## Extraneous Variable - It happens sometimes that after **completion of the study** we wonder that the actual result is **not what we expected**. In spite of taking all the **possible measures**, the outcome is **unexpected**. It is because of extraneous variables. - Variables that **may affect research outcomes** but have not been adequately **considered in the study** are termed as **extraneous variables**. Extraneous variables **exist in all studies** and can affect the measurement of study variables and the relationship among these variables. ## Extraneous variables that are not recognized until the study is in process, or are recognized before the study is initiated but cannot be controlled are referred to as confounding variables. These variables interfere with the results of the existing activity - **Certain external variables** may influence the **relationship between the research variables**, even though researchers cannot see it. These variables are called **intervening variables**. ## Control Variable - Sometimes certain characteristics of the objects under scrutiny are **deliberately left unchanged**. These are known as **constant** or **controlled variables**. - The variables that are not measured in a particular study must be held **constant**, **neutralized/balanced**, or **eliminated**, so they will not have a biasing effect on the other variables. - In the **ice cube experiment**, one constant or controllable variable could be the **size and shape of the cube**. By keeping the **ice cubes' sizes and shapes** the same, it's easier to **measure the differences** between the cubes as they melt after shifting their positions, as they all started out as the same size.