Understanding Research Variables: Types & Tips for Formulating Them

Research variables – When compiling research, you will not be able to escape the problem to be scientifically investigated. To do this, Sinaumed’s needs to determine the research variables so that the research remains in accordance with its goals and objectives.

Variable, in short, is something that becomes the focus of the study and to formulate it requires a clear theory, concept or proposition. Because if not, you may find it difficult to formulate research problems and collect the data needed for a study.

In other words, this variable will answer questions such as ” what do you want to find through this research?” ”, “ What must be determined first by the researcher so that the research objectives are achieved? ”, and “ What is the basis for the determination? ”.

Unfortunately, there are still many people who do not understand research variables well. As a result, it takes a long time to finish the research being done or even repeat the writing process from scratch.

Therefore, in this article we will discuss the meaning of research variables, their types, and how to formulate them. Listen carefully, okay!

Definition of Research Variables

In general, variables are objects or concepts to be studied that can be abstract or real . In the process of writing research, the formulation of variables must be carried out systematically and in accordance with scientific principles. That way, the truth of the results of observations in research can be accounted for.

S. Margono (1997) states that a variable is a concept that has a variation in value. That is, this variable certainly has diverse properties and refers to characteristics that differ from one variable to another.

For example, let’s say you are going to do a research study entitled ” The Impact of Extracurriculars on Student Achievement at SMA Negeri 1 Bandung “. In this study, the variables are Extracurriculars and Student Achievement. The types of extracurricular activities and student achievement will certainly vary between students.

Besides being varied, a variable must also be measurable. Especially for quantitative research that requires research results that are objective, measurable, and open to retesting. Elements or the term “research variable” itself is more widely used in this type of research.

Asroi et al explained more fully about what variables and research instruments are in the book Understanding Variables and Research Instruments . Make sure Sinaumed’s uses this book as a reference when writing research, OK?

Types of Research Variables

After knowing the meaning of research variables, you can enter the variable selection stage. At this stage, Sinaumed’s must know the types of variables that can be used in research.

The purpose of knowing the types of variables is so that it is not too difficult for you to determine the right variable and in accordance with the research objectives. Therefore, the type of variable itself is divided into several groups, so that it will be easier for you to make research variables that will be used. The first is based on position and function; by nature; based on urgency; based on the type of measurement scale; and based on appearance at measurement.

Types of Variables Based on Position

Independent Variable ( Independent Variable )

Independent variables are variables that can affect changes in other variables. In other words, if there is a change in a variable, the change is caused by this independent variable.

For example, let’s say Sinaumed’s is currently compiling a study entitled ” The Impact of Extracurriculars on Student Achievement at SMA Negeri 1 Bandung “. Now, in this study, the independent variable is “extracurricular” because it can stand alone and can affect changes in “student learning achievement” which is another variable.

Bound Variable ( Dependent Variable )

In contrast to the independent variable, the dependent variable is a variable that can be influenced by other variables. Therefore, its existence is considered as a result of the presence of independent variables.

For example, in the research mentioned earlier, the dependent variable is “student learning achievement”, because it can be influenced by extracurriculars taken by students.

Basically, your research is an attempt to find relationships between various variables. So, the relationship between the independent variable and the dependent variable is the most basic relationship that you must understand.

As for the type of relationship between the two variables, Machfoedz (2007) states that there are three types of relationships that can be used, namely symmetrical, reciprocal, and asymmetrical relationships.

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A symmetrical relationship is a relationship in which a variable is not affected or does not affect other variables.

Reciprocity means that a variable can be a cause and effect for another variable. For example the variable “quality of education” with “economic level of the millennial generation”. Each of these variables can influence each other.

What this means is, suppose you are a millennial generation with a high economic level. So because of this, it’s easier for you to get a high quality education too.

Conversely, if you come from a family with a high level of education, you can also have a high economic level.

An asymmetrical relationship is the opposite of a symmetrical relationship, which means that a variable can affect other variables.

Control Variables

Control variables are variables that you can control with the aim that the influence of the independent variable on the dependent variable is not disturbed by other factors that you don’t examine.

For example,
let’s say you have a hypothesis that someone from the upper middle class can tolerate mixed marriages more than someone from the lower middle class.

So, to find out the truth of the hypothesis, you can use “education” or “income” or both as control variables. This means that the respondents from your research will be taken from different social classes but have the same education and income.

With a method like this, you can avoid biased calculation results or conclusions.

Types of Variables Based on Nature

Dynamic Variables

Just as the name suggests, this one variable can change. Starting from up or down to its characteristics. For example, such as student learning interest, employee performance, employee motivation, learning achievement, and others. Some of these examples will always change and usually keep up with the times.

Static Variables

In contrast to dynamic variables, static variables are more fixed and cannot change or are very difficult to change. For example, such as region of origin, gender, social status, and others.

Types of Variables Based on Urgency

The next type of research variable is based on the urgency or interest of the instrument used to collect research data. Within this group of variables, there are two types that you should know about:

Conceptual Variables

Conceptual variable is a type of variable that is hidden and cannot be seen through existing facts. However, it can still be seen using existing indicators. Examples include talent, interest in reading, motivation to work, and so forth.

Factual Variables

Factual variables are the opposite of conceptual variables. That is, this variable can be seen through the facts that already exist. For example, such as age, education, gender, ethnicity, religion, and others.

Therefore, with its factual nature, it is very rare to find errors or confusion in this variable. Even if there is, it is usually caused by dishonest respondents during the interview process.

Types of Variables Based on the Type of Measuring Scale

Nominal Variables

Nominal variables are also called categorical variables or discrete variables. In short, this variable is a variable that can be grouped into several relatively few categories.

For example:

1. Religion:

  • Islam
  • Catholic
  • Hindu
  • Protestant
  • Buddha

2. Gender:

  • Man
  • Woman

3. Quality:

  • Very good
  • Good
  • Not good
  • Bad

Nominal variables will produce ordinal data and nominal data when examined. Ordinal data is data that has attributes (names) and ranks or sequences. In addition, the numbers in this data have levels and function to sort objects from the lowest to the highest, and vice versa. In variables of this type, there is no absolute value in this data.

Judging from the example above, the Quality category is ordinal data because there are differences between the attributes. “Very good” means better than just “good”, and “good” means better than poor.

Meanwhile, nominal data is the simplest variable measure, so the numbers in this data only function as labels or attributes, not as any level.

From the previous example, the categories Religion and Gender are examples of nominal data because they are only based on classification.

Continuous Variable

Continuous variables in short are variables that have levels or levels. Within this variable, there are several other types of variables, namely:

Ordinal variables or variables that have a certain level or order. For example, sports match scores or world championship rankings.

Interval variables or variables that have a certain distance or scale. For example the rating scale obtained by military training participants.

Ratio variables or variables that have a comparison. For example, the weight of two athletes, one weighing 40 kilograms and the other 80 kilograms. This data can also be translated into “the first athlete weighs half of the second athlete”.

Continuous variables are often found in quantitative research because they “play” more with numbers. As stated in the Quantitative Research Methodology book written by Pu;us Insap Santosa.

Types of Variables Based on Appearance at the Time of Measurement

In this group of variables, it is divided into two types, namely maximalist variables and typicalist variables.

Maximal Variable

Maximal variable is a variable that during the data collection process, you give encouragement to respondents to bring out their best performance. Such as achievement, creativity, talent, skill , and others.

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Typical Variables

Conversely, a typical variable is a variable that during the process of collecting data, you don’t give any encouragement to the respondent to show maximum performance. This variable places more emphasis on respondents answering questions or choosing options honestly. Examples include interests, personality, ways of shopping, attitudes towards certain issues and others.

How Many Variables Can You Use in Your Research?

When compiling a research, actually you are free to determine the number of variables to be used, it can be two, three, four, or if you want five it doesn’t matter. It’s just that, the more variables, the process of writing research also becomes more complicated and longer, it can even be that the discussion will become less focused.

Therefore, the most important thing when selecting research variables is that you must ensure that they reflect the characteristics of the population you are studying. Apart from that, also pay attention to the position and relationship between the variables, OK? The reason is, this will affect the research framework that you use.

For example, does variable X determine variable Y, does variable X precede variable R, or are there other variables that interfere with variables X and R.

After determining the research variables that you will use, the next task is to make conceptual and operational definitions. Conceptual definition here means explanation of existing concepts with your own understanding. Usually the conceptual definition is written briefly, clearly, and firmly.

While the operational definition means a more detailed explanation and includes how to measure existing concepts. For example, the variable academic achievement.

So, conceptually, academic achievement is the achievement of student learning outcomes over a certain period of time. Operationally, academic achievement is the value of the report card or ranking given by the teacher.

Tips for Formulating Research Variables

In the process of writing research, many researchers are confused when formulating research variables. Especially undergraduate students who are not very experienced.

Therefore, in this article sinaumedia will provide some tips that can help the process of formulating research variables.

1. Determine the Main Problem

This main problem will later become the Y variable which is the dependent variable and the core of the research that you will do. For example: student learning interest

2. Find the Factors

Next, find the problem factors that will become variable X or the independent variables. Well, generally in a study there are two variables, namely X and Y. As an example of the previous title, “The Impact of Extracurriculars (X) on Student Learning Interests (Y) SMA Negeri 1 Bandung”.

3. Prepare Research Theory

From the variables that you have determined, you should prepare a research theory that can support both. Usually these theories can be taken from journals, theses, and previous studies.

4. Prepare Research Needs

Finally, also prepare the needs for conducting research such as plans, funds, documents, locations, and so on. The goal is so that you can find out whether the research is possible to do or not.

Examples of Research Variables

Based on the type of research, research variables can be divided into two categories. The first is a qualitative variable and the second is a quantitative variable.

Qualitative Variables

Qualitative variables or categorical variables are variables that include quality that cannot be measured by numbers from a group or population. This variable is often associated with the physical attributes of a group of individuals.

These variables can then be further divided into two types, namely nominal and ordinal. Nominal qualitative variables are variables that do not recognize order criteria and do not have a predetermined numerical value.

Meanwhile, qualitative variables are also known as semi-quantitative variables. Because these variables are classified using a value scale, even though they talk about attributes or qualities that do not have a numerical value.

Some examples of qualitative variables are:

  1. Marital status–single, married, divorced, widower/widow
  2. Fear
  3. Hunger
  4. Beauty
  5. feeling happy
  6. Ignorance
  7. Creativity
  8. Exam qualification

You can find other examples of qualitative variables in the book Theory & Practice Qualitative Research Methods compiled by Imam Gunawan.

Quantitative Variables

Quantitative variables are variables that are written using numeric values ​​(numbers). Therefore, this variable requires operations and also mathematical calculations to measure it.

There are two types of quantitative variables that you can use, namely continuous and discrete. A continuous quantitative variable is a variable that can take a number of values ​​that do not always have two digits. This means that what is used is a decimal number.

The accuracy also varies, depending on the measuring instrument you use. However, in essence, a continuous quantitative variable has an unlimited number of decimal places.

A discrete variable is a variable that only takes finite values ​​into account. In other words, a discrete quantitative variable is a variable that only calculates the number of accounts with a value scale that can be separated and shows a specific value.

Some examples of quantitative variables are:

  1. Number of family members
  2. Number of chickens in the coop
  3. The value (price) of an object
  4. Height
  5. Weight
  6. The number of rounds in a sports competition
  7. Vehicle speed
  8. Screen size

Thus the discussion about research variables this time, I hope that all the discussion in this article can be useful for Sinaumed’s, especially in compiling scientific papers or those who want to do research. For Sinaumed’s who want to find books on research methods, they can get them at sinaumedia.com . Together with sinaumedia, you can get #MoreWithReading information