What’s In

You have learned from the previous lessons that quantitative research is concerned about numerical or measurable values that we can analyze statistically. How do we measure such values? Is it measurable at all times? Do these values change? Are these values applicable for descriptive, correlational, ex post facto, quasi-experimental and experimental research? In this lesson, you will learn about the different classifications of data used in quantitative research and their examples.

What I Need to Know

Variables play a significant role in quantitative research. When you intend to accomplish something through research, the boundaries of your goal must be defined first to direct your focus into a specific characteristic or condition through identifying the variables of your research study. Doing such eliminates complexities and elaborate work especially for a senior high school student like you. Knowing the different kinds of research variables also aids in smooth data collection and analysis.

What’s New

Activity 1: Let’s review!

Directions: Read and analyze the following questions. Choose the letter of the correct answer.

What Is It

To get an answer to an inquiry that they are investigating, researchers will observe and measure the quality or quantity of the object of the study. It is therefore imperative for the researcher to identify the variables significant in explaining observed effects or behavior.

A Variable is anything that has a quantity or quality that varies. For instance, during the quarantine period, your mother planted tomato seedlings in pots. Now common understanding from science tells you that several factors are affecting the growth of tomatoes: sunlight, water, kind of soil, and nutrients in soil. How fast the tomato seedlings will grow and bear fruits will depend on these factors. The growth of tomatoes and the number of fruits produced are examples of the Dependent Variables. The amount of sunlight, water, and nutrients in the soil are the Independent Variables. If there is an existing relationship between the independent and dependent variables, then the value of the dependent variable varies in response to the manipulation done on the independent variable. The independent variable is also identified as the presumed cause while the dependent variable is the presumed effect. In an experimental quantitative design, the independent variable is pre-defined and manipulated by the researcher while the dependent variable is observed and measured. For descriptive, correlational, and ex post facto quantitative research designs, independent and dependent variables simply do not apply.

It is important to note other factors that may influence the outcome (dependent variable) not manipulated or pre-defined by the researcher. These factors are called Extraneous Variables. In our example above, the presence of pests and environmental stressors (e.g. pets, extreme weather) are the extraneous variables. Since extraneous variables may affect the result of the experiment, it is crucial for the researcher to identify them prior to conducting the experiment and control them in such a way that they do not threaten the internal validity (i.e. accurate conclusion) of the result. Controlling the extraneous variable can be done by holding it constant or distribute its effect across the treatment. When the researcher fails to control the extraneous variable that it caused considerable effect to the outcome, the extraneous variable becomes a Confounding Variable. For example, if the tomato had been infested by pests (confounding variable) then you cannot conclude that manipulations in sunlight, water, and soil nutrients (independent variable) are the only contributing factors for the stunted growth and poor yield (dependent variable) of the plant or is it the result of both the independent variables and the confounding variable.

​The variables can also be classified according to their nature. The diagram below shows the different classifications:

I. Quantitative Variables, also called numerical variables, are the type of variables used in quantitative research because they are numeric and can be measured. Under this category are discrete and continuous variables.

A. Discrete variables are countable whole numbers. It does not take negative values or values between fixed points. For example: number of students in a class, group size and frequency.

B. Continuous variables take fractional (non-whole number) values that can either be a positive or a negative. Example: height, temperature.

Numerical data have two levels of measurement, namely:

A. Intervals are quantitative variables where the interval or differences between consecutive values are equal and meaningful, but the numbers are arbitrary. For example, the difference between 36 degrees and 37 degrees is the same as between 100 degrees and 101 degrees. The zero point does not suggest the absence of a property being measured. Temperature at 0 degree Celsius is assigned as the melting point of ice. Other examples of interval data would be year and IQ score.
B. Ratio type of data is similar to interval. The only difference is the presence of a true zero value. The zero point in this scale indicates the absence of the quantity being measured. Examples are age, height, weight, and distance.

II. Qualitative Variables also referred to as Categorical Variables are not expressed in numbers but are descriptions or categories. It can be further divided into dichotomous, nominal or ordinal.

A. Dichotomous variable consists of only two distinct categories or values, for example, a response to a question either be a yes or no.

B. Nominal variable simply defines groups of subjects. In here, you may have more than 2 categories of equivalent magnitude. For example, a basketball player’s number is used to distinguish him from other players. It certainly does not follow that player 10 is better than player 8. Other examples are blood type, hair color and mode of transportation.

C. Ordinal variable, from the name itself, denotes that a variable is ranked in a certain order. This variable can have a qualitative or quantitative attribute. For example, a survey questionnaire may have a numerical rating as choices like 1, 2, 3, 4, 5ranked accordingly (5=highest, 1=lowest) or categorical rating like strongly agree, agree, neutral, disagree and strongly disagree. Other examples or ordinal variable: cancer stage (Stage I, Stage II, Stage III), Spotify Top 20 hits, academic honors (with highest, with high, with honors).

What’s More

Activity 2: Identifying variables

Directions: Identify the Independent, Dependent and Extraneous variable/s in each of the following situations.

Activity 3: Classifying variables

Directions: Identify the following variable as either qualitative or quantitative. Then, classify which specific category they belong.

Activity 4: Let’s Go Online

Go to the link below and practice what you’ve learned from this lesson:

https://bit.ly/2TEw2o4

https://bit.ly/2X3TdtL

True/False

What I Have Learned

Directions: Explain briefly what is being asked for.

What I Can Do

Make a quick survey to your friends, family members and professors who have previously conducted a quantitative research study. Evaluate the title of their papers and identify the independent, dependent, and other variables.