Research Statistics

Four Statistical Scales of Measurement

To measure appropriately the research variables identified and reflected in the conceptual framework, a budding researcher must be very familiar with the four statistical scales of measurement. What are the four statistical scales of measurement and what variables do these measure? The following article enumerates and describes the four statistical scales of measurement and provides examples with exercises.

In the course of gathering your data, you should be very well familiar with the different statistical scales of measurement. This knowledge will help you adequately and appropriately measure the variables that you have identified in your conceptual framework. Further, once you make the variables quantifiable, application of the appropriate statistical test is possible.

I previously discussed the role that variables play in the conduct of research, i. e., it primarily serves as the focal points of the whole research process because the phenomenon is abstract in nature. It takes some skill to isolate such research variables, but with constant practice and familiarity, the identification of these variables becomes easy.

How can you say that the factors studied are variables?

One of the primary attributes of variables is that these lend themselves to statistical scales of measurement. Research variables must be measurable. Statisticians devised four statistical scales of measurement. These are nominal or categorical, ordinal, interval and ratio statistical scales.

The Four Major Statistical Scales of Measurement

1. Nominal or categorical

The nominal or categorical statistical scale of measurement is used to measure those variables that can be broken down into groups. Each group has attributes distinctly different from the other. The most commonly used nominal or categorical variables measured using this research scale of measurement are gender, civil status, nationality, or religion. These variables and their corresponding categories are as follows:

  • gender – male or female
  • civil status – single or married
  • nationality – Filipino, Chinese, Singaporean, Malaysian, Indonesian, Vietnamese
  • religion – Muslim, Christian, Buddhist, Shinto

Notice that the categories of each nominal variable do not indicate that one is superior or greater than the other. These are mainly classifications that separate one group from the other.

The nominal scale of measurement is referred to by statisticians as the crudest statistical scale of measurement. While this may be the crudest, this is a powerful statistical scale of measurement when correlating two nominal variables like gender and reproductive health bill position.

The statistical question in this instance is “Is there a correlation between gender and reproductive health position?” Chi-square is the appropriate statistical test for this question.

2. Ordinal

The ordinal statistical scale of measurement applies to variables that signify, as the root word suggests, “order” of the different groups. It is possible to rank order the different groups because each group shows attributes that are convincingly superior or greater than the other or vice-versa.

To illustrate this statistical scale simply and clearly, examples of variables that are measured using this scale of measurement are the following:

  • order of child in the family – eldest, second eldest … youngest
  • socioeconomic status of families – upper, middle, lower
  • educational attainment – elementary, high school, college, graduate
  • size – small, medium, large

Notice that while the different groups follow an order of magnitude, there is no discernible distance between them or that the distances could vary between each group. Say, the eldest child may be older by two years to the next eldest child, but the second eldest child may be three years older than the next child, and so on. No specific income difference describes the socioeconomic status, and so on. The number of years spent in the elementary is not the same as the number years in high school or the graduate school. The size difference between small, medium and large can vary widely.

3. Interval

The interval scale of measurement measures variables better than the rank order mode of the ordinal scale of measurement. There is now an equal spacing between the different groups that composes the variable. Examples of variables that can be measured using this statistical scale of measurement are the following:

  • household income in PhP5,000 brackets – 1st group: earns up to PhP5,000, 2nd group: PhP10,000, 3rd group: PhP15,000
  • temperature in 5 degree intervals – 5, 10, 15, 20
  • number of student absences in one week – week 1 absence, week 2 absence, week 3 absence
  • water volume in 5 milliliter increments – 5 ml, 10 ml, 15 ml, 20 ml

4. Ratio

The ratio scale of measurement works similarly with the interval scale. In fact, in using statistical tests, these two statistical scales of measurement are not treated differently from the other. The only difference between the ratio and the interval scale is that the former (i.e., the ratio scale) has an absolute zero point.

Examples of ratio variables are the following:

  • weight in kilograms or pounds
  • height in meters or feet
  • distance of school from home
  • amount of money spent during vacation


To test your skill at this point, identify which statistical scale of measurement applies for the following variables. Compare your answer with your classmates to confirm.

  1. beauty of contestants
  2. light intensity
  3. water turbidity
  4. environmental awareness
  5. emotional intelligence
  6. number of accidents
  7. vehicle speed
  8. allowance of students
  9. brand of cellphone
  10. softdrink preference


© 2012 December 16 P. A. Regoniel

Cite this article as: Regoniel, Patrick (December 16, 2012). Four Statistical Scales of Measurement [Blog Post]. In SimplyEducate.Me. Retrieved from
Research Statistics

The Importance of Data Accuracy and Integrity for Data Analysis

Data analysis is only as good as the quality of data obtained during the data collection process. How can you ensure data accuracy and integrity? Here are three pointers.

Data analysis is a very important part of the research process. Before performing data analysis, researchers must make sure that numbers in their data are as accurate as possible. Clicking the menus and buttons of statistical software applications like SPSS, Microstat, Statistica, Statview among others is easy, but if the data used in such automated data  analysis is faulty, the results are nothing more than just plain rubbish. Garbage in, garbage out (GIGO).

For many students who just want to comply with their thesis requirement, rigorous and critical data analysis are almost always given much less attention than the other parts of the thesis. At other times, data accuracy is deliberately compromised because of the apparent inconsistency of findings with expected results.

Data should be as accurate, truthful or reliable as possible for if there are doubts about their collection, data analysis is compromised. Interpretation of results will be faulty that will lead to wrong conclusions.

How can you make sure that your data is ready or suitable for data analysis? Here are three pointers to remember to ensure data integrity and accuracy. The following points focus on data collection during interviews.

3 Points to Remember to Ensure Data Integrity and Accuracy

1. Review data entries

Be meticulous about overlooked items in data collection. When dealing with numbers, ensure that the results are within sensible limits. Omitting a zero here or adding a number there can compromise the accuracy of your data.

Watch out for outliers, or those data that seems to be out-of-bounds or at the extremes of the scale of measurement. Verify if the outlier is truly an original record of data collected during the interview. Outliers may be just typographical errors.

2. Verify the manner of data collection

Cross-examine the data collector. If you asked somebody to gather data for you, throw him some questions to find out if the data was collected systematically or truthfully. For paid enumerators, there is a tendency to administer questionnaires in a hurry. In the process, many things will be missed and they will just have to fill-out missing items. To filter out this possibility, the information gathered should be cross-checked.


The following questions may be asked to ensure data quality:

  • How much time did you spend in interviewing the respondent of the study?
  • Is the respondent alone or with a group of people when you did the interview?

To reduce cheating in doing the interview, it will help if you tell your enumerators to have the interviewees sign the interview schedule right after they were interviewed. Ask the enumerators to write the duration of the interview, taking note of the start and end time of the interview.

3. Avoid biased results

Watch out for the so-called ‘wildfire effect’ in data gathering. This happens when you are dealing with sensitive issues like fisherfolk’s compliance to ordinances, rules and regulations or laws of the land. Rumors on the issues raised by the interviewer during the interview will prevent other people from answering the questionnaire. Respondents may become apprehensive if answers to questions intrude into their privacy or threaten them in some way.

Thus, questionnaire administration must be done simultaneously within, say, a day in a given group of interviewees in a particular place. If some of the respondents were interviewed the next day, chances are they have already gossiped among themselves and become wary of someone asking them about sensitive issues that may incriminate them.

Wildfire effect is analogous to a small spark of a match that can ignite dry grass leaves and cause an uncontrollable forest fire. This is the power of the tongue. Hence, the term wildfire effect.

There are many other sources of bias that impact negatively on data quality. These are described in greater detail in another post titled How to Reduce Researcher Bias in Social Research.

Data analysis may then be employed once data accuracy and integrity are ensured.

© 2012 December 6 P. A. Regoniel


The Role of Statistics in Decision Making

At some point in your life, you might be encountering stressful situations that require you to make a choice. How will you be able to make informed and objective decisions? This article describes how statistics can help you out of your misery.

Do you have difficulty in making decisions about personal issues and concerns? Chances are, you are one of those who have decision-making woes especially on personal matters that involve your emotions. How can this be resolved?

The use of statistical tools may help you in this situation. It will help you reduce the uncertainty associated with decision-making that can affect your way of life. It reduces the guesswork related to decision making.

Below is an example of a personal decision-making scenario that demonstrates the role of statistics in decision-making.

The Role of Statistics in Decision Making

As a practicing statistician for many years, I find the experience of using some tools of statistics like the t-test rather satisfying, especially if I can use it to aid me in decision making. A simple addition of points given for the advantages and disadvantages of a choice may be sufficient in some circumstances, but in some in some instances, more rigorous analysis of statistical data can provide useful information. Statistics can also verify whether the decision made was, after all, a good one.

Example Decision-Making Situation Aided by t-test

One concrete, personal experience that demonstrates the role of statistics in decision making happened several years ago. That decision dilemma occurred in 2005. I decided to buy a vehicle to meet a personal and professional need.

driving a car

I was then very much concerned about the fuel consumption of my second, probably more accurately, third-hand customized owner-type Toyota jeep I bought from a colleague. The jeep guzzles up about 1 liter of gasoline for barely 4 or 5 kilometers of road covered! I thought that this is something that need immediate attention, so I decided to bring the jeep to the automotive repair shop.

I requested the mechanic for a major engine overhaul, where the engine block has to be re-bored to make way for a cylindrical metal sleeve. The metal sleeve narrows the opening where the piston is fired up and down by the series of explosions that occur in the combustion chamber.

However, this engine-related jargon can confound many people unfamiliar with these terms. The whole tune up process aimed to eliminate loose compression of fuel in the engine that leads to a minuscule distance to fuel ratio.

Curiosity struck me, whether my decision to spend for tune-up mattered. Did the number of kilometers covered by the old jeep significantly improve after the tune up?

Armed with a knowledge of the t-test, I sought to find out the answer to this question using the monthly monitoring data I gathered on the total number of kilometers traveled for one gas up. I religiously recorded the number of liters of gasoline in the receipt each time I visit the gasoline station.

I encoded these data in MS Excel, anticipating the t-test computation I will make. I intend to compare the gasoline consumption of my jeepney before and after the tune-up.

Here’s how the simple table where I logged the data on gas consumption looked.

Gas Fill-up NumberNo. of km/liter (Before Tune-Up)No. of km/liter (After Tune-Up)

The km/liter is computed by simply dividing the total number of kilometers traveled by the number of liters for one gas fill-up.


100 km/10 liters = 10 km/li

I logged the gasoline consumption of my jeep for several months after the tune up. I then used this data in comparing the jeep’s performance, before and after the tune-up, waiting until I have gathered data for at least ten gas fill ups.

Result of the t-test Analysis

So, did I find a significant difference in gasoline consumption before and after the tune-up? The answer is “Yes”. Indeed, the kilometer covered per liter significantly increased almost twice the previous gasoline consumption. It can now run at 8 or 9 kilometers per liter of petrol. That is definitely a better performance than the last 4 kilometers of the road traveled per liter of gasoline. My investment somehow paid off.

This information helped me decide whether I should keep the jeep after having it tuned up in the automotive repair shop. I expected that the jeep should cover more than 8 or 9 kilometers because new vehicles run this distance with the air conditioner on. Moreover, the jeep does not have any air conditioning in it. When the climate is hot, it is hot inside the jeep and when the surrounding environment is cold, well, of course, the jeep is cool too.

My Decision After the t-test Analysis

I decided to give the jeep up, sold it and bought a newer, diesel-powered Mitsubishi pickup truck that runs at 11 kilometers per liter of diesel with the air conditioning on. That was five years ago, and my pick up truck still runs like new as I make sure the engine oil is regularly changed to keep the parts within at its best working condition, thus efficiently running.

Maybe I should subject it again to t-test in the coming months to see if I still have to keep it or consider buying a new one. Such is the role of statistics in decision-making.

See how simply adding numbers can help you decide in my Ezinearticles post titled How to Improve Your Decision Making Skills.

© 2012 December 2 P. A. Regoniel

Data Analysis Research Statistics

Example of a Research Using Multiple Regression Analysis

Data analysis using multiple regression analysis is a fairly common tool used in statistics. Many people find this too complicated to understand. In reality, however, this is not that difficult to do especially with the use of computers.

How is multiple regression analysis done? This article explains this very useful statistical test when dealing with multiple variables then provides an example to demonstrate how it works.

Multiple regression analysis is a powerful statistical test used in finding the relationship between a given dependent variable and a set of independent variables. The use of multiple regression analysis requires a dedicated statistical software like the popular Statistical Package for the Social Sciences (SPSS), Statistica, Microstat, among other sophisticated statistical packages. It will be near impossible to do the calculations manually.

However, a common spreadsheet application like Microsoft Excel can help you compute and model the relationship between the dependent variable and a set of predictor or independent variables. But you cannot do this without activating first the set of statistical tools that ship with MS Excel. To activate the add-in for multiple regression analysis in MS Excel, view the Youtube tutorial below.

Example of a Research Using Multiple Regression Analysis

I will illustrate the use of multiple regression by citing the actual research activity that my graduate students undertook two years ago. The study pertains to the identification of the factors predicting a current problem among high school students, that is, the long hours they spend online for a variety of reasons. The purpose is to address the concern of many parents on their difficulty of weaning their children away from the lures of online gaming, social networking, and other interesting virtual activities.

Upon reviewing the literature, the graduate students discovered that there were very few studies conducted on the subject matter. Studies on problems associated with internet use are still in its infancy.

The brief study using multiple regression is a broad study or analysis of the reasons or underlying factors that significantly relate to the number of hours devoted by high school students in using the Internet. The regression analysis is broad in the sense that it only focuses on the total number of hours devoted by high school students to activities online. The time they spent online was correlated with their personal profile. The students’ profile consisted of more than two independent variables; hence the term “multiple”. The independent variables are age, gender, relationship with the mother, and relationship with the father.

The statement of the problem in this study is:

“Is there a significant relationship between the total number of hours spent online and the students’ age, gender, relationship with their mother, and relationship with their father?”

The relationship with their parents was gauged using a scale of 1 to 10; 1 being a poor relationship, and 10 being the best experience with parents. The figure below shows the paradigm of the study.

multiple regression conceptual framework
Research paradigm of the multiple regression study showing the relationship between the independent and the dependent variables.

Notice that in multiple regression studies such as this, there is only one dependent variable involved. That is the total number of hours spent by high school students online. Although many studies have identified factors that influence the use of the internet, it is standard practice to include the profile of the respondents among the set of predictor or independent variables.

Hence, the common variables age and gender are included in the multiple regression analysis. Also, among the set of variables that may influence internet use, only the relationship between children and their parents were tested. The intention is to find out if parents spend quality time to establish strong emotional bonds between them and their children.

Findings of the Study

What are the findings of this exploratory study? The multiple regression analysis revealed an interesting finding.

The number of hours spent online relates significantly to the number of hours spent by a parent, specifically the mother, with her child. These two factors are inversely or negatively correlated. The relationship means that the greater the number of hours spent by the mother with her child to establish a closer emotional bond, the lesser the number of hours spent by her child in using the internet. The number of hours spent online relates significantly to the number of hours spent by the mother with her child

The number of hours spent online relates significantly to the number of hours spent by the mother with her child

While this may be a significant finding, the mother-child bond accounts for only a small percentage of the variance in total hours spent by the child online. This observation means that there are other factors that need to be addressed to resolve the problem of long waking hours and abandonment of serious study of lessons by children. But establishing a close bond between mother and child is a good start.


The above example of multiple regression analysis demonstrates that the statistical tool is useful in predicting the behavior of dependent variables. In the above case, this is the number of hours spent by students online.

The identification of significant predictors can help determine the correct intervention resolve the problem. The use of multiple regression approaches prevents unnecessary costs for remedies that do not address an issue or a problem.

Thus, in general, research employing multiple regression analysis streamlines solutions and brings into focus those influential factors that must be given attention.

©2012 November 11 Patrick Regoniel

Cite this article as: Regoniel, Patrick (November 11, 2012). Example of a Research Using Multiple Regression Analysis [Blog Post]. In SimplyEducate.Me. Retrieved from
Research Statistics

How to Write a Concept Paper

What is a concept paper? Why is there a need to write a concept paper? How do you write it? This article explains the reasons why a concept paper is important before writing a full-blown research paper. It also provides a step-by-step approach on how to write it.

I once browsed the internet to look for information on how to write a concept paper. It took me some time to find the information I want. However, I am not quite satisfied with those explanations because the discussion is either too short or it vaguely explains what a concept paper is.

Preparing a concept paper entails different approaches but I somehow drew out some principles from these readings. I wrote a concept paper in compliance with a request to come up with one. Nobody complained about the output that I prepared.

I was reminded once again when a colleague asked me the other day to explain what is a concept paper and how to write it. He needs this information because students have been asking him on how to go about writing the stuff.

To him and his students, I dedicate this article.

What is a Concept Paper and Why Do You Need It?

First, before going into the details on how to prepare a concept paper, let me explain what a concept paper is and why do you need it.

A concept paper serves as a prelude to a full paper. What is the full paper all about? The full paper may be a thesis, a program, a project, or anything that will require a longer time to prepare.

In essence, a concept paper is an embodiment of your ideas on a certain topic or item of interest. The concept paper saves time because it is possible that your thesis or review panel may say that your idea is not worth pursuing.

One expects that the concept paper should consist only of 1 or 2 pages. Alternatively, if you want to resolve some matters, it can go up to 5 pages.

For example, as a student you may be asked to prepare your concept paper for your thesis proposal (see 4 steps in preparing the thesis proposal). This means that you will have to develop an idea and express it for others to understand. You may glean from either your experience or from the literature that you have read. Of course, your topic should be within your respective area of specialization.

If you are a student of computer science, you might want to study the behavior of wi-fi signals bounced to different kinds of material. Alternatively, maybe you wish to create a simple gadget to concentrate signals for a portable USB wi-fi connection to improve its performance. Or maybe you would like to find out the optimum cache size for greatest browsing experience on the internet. The list could go on.

How Do You Write a Concept Paper?

As I mentioned a while ago, there is no hard and fast rule on how to write a concept paper. It is not desirable to have a format as your ideas may be limited by placing your ideas in a box. You may miss some important points that may not be in the format given to you. The point is that you can express to others what you intend to do.

What then are the things that the concept paper as a prelude to a thesis should be able to address or contain? To systematize your approach, a concept paper must have at least the following elements and in the following order:


Image Source

1. A Rationale

You explain here the reasons why you need to undertake that thesis proposal of yours. You can ask yourself the following questions:

What prompted you to prepare the concept paper?
Why is the issue of such importance?
What should you be able to produce out of your intended study?

2. A Conceptual Framework

A conceptual framework is simply your guide in working on your idea. It is like a map that you need to follow to arrive at your destination. An excellent way to come up with one is to do a mind mapping exercise.

That brings up another thing, what is mind mapping anyhow?

A mind map is simply a list of keywords that you can connect to make clear an individual issue. It is our subconscious way of analyzing things. We tend to associate a thing with another thing. This relates to how we recall past experiences. In computers, we have the so-called “links” that connect commands in a computer module to make an application program work.

How does mind mapping work? You just have to come up with a word, for example, that will help you start off. You can begin with an issue on computers and from there, generate other ideas that connect with the previous one. There are a lot of literature on the internet that explains what a mind map is.

Now, after reading an explanation of the mind map, how will you come up with your conceptual framework? Well, I do not need to explain it again here because I wrote about it previously. You may read an easy to understand explanation and example here.

3. Your Hypothesis

Once the idea of the conceptual framework is quite clear to you, then you may write your hypothesis. A hypothesis is just your expected output in the course of conducting your study. The hypothesis arises from the conceptual framework that you have prepared.

Once you have identified the specific variables in the phenomenon that you would like to study, ask yourself the following questions: How are the variables related? Does one variable affect another? Alternatively, are they related at all?

A quick review of relevant and updated literature will help you identify which variables really matter. Nowadays, it’s easy to find full articles on your topic using the internet, that is if you know how. You can start off by going to, a directory of open access journals.

Example of Hypotheses

Considering the issues raised a while ago, the following null hypotheses can be written:

1. There is no significant difference in wi-fi signal behavior between wood and metal.
2. There is no significant difference in browsing speed between a ten MB cache and a 100 MB cache storage setting using Mozilla Firefox.

At this point, you may already have a better idea of how to prepare a concept paper before working on a full thesis proposal. If you find this discussion useful, or you would like to clarify further the discussion above, your feedback is welcome.

© 2012 October 31 P. A. Regoniel

Cite this article as: Regoniel, Patrick (October 31, 2012). How to Write a Concept Paper [Blog Post]. In SimplyEducate.Me. Retrieved from
Research Statistics

Example of a Research Activity Using t-test

Are you a statistics teacher looking for a simple example of a t-test activity that you can use in your class? Or are you a student who wants to have an idea how t-test works? I describe below an example of a situation where the t-test can be applied right after learning the procedures and understanding how it works. Read more to find out.

Teaching students through practical hands-on exercises enable them to appreciate how the different analytical tools used in research can help them address issues and problems that they encounter in their respective disciplines. I applied this approach in one of my classes in the graduate school. My students consisted of more than 44 graduates of different courses namely education, biology, nursing, environmental science, public administration, mathematics, business and tourism.

After giving them an LCD projector presentation about t-test, a statistical tool to test differences between two groups of data, I gave them a simple situation which can be applied right there in the classroom. This is to find out the difference between one’s heartbeat before and after exercise.

The t-test Research Activity

Since some of my students are graduates of nursing, they are the ones who took charge of recording the heartbeats per minute of all 44 students in every 5-member group before they exercise. After recording the heartbeats of each of their classmates, the whole class marched briskly in the classroom for about 5 minutes. It is expected that their heartbeats should be higher after the brisk walk in place.

I just can’t keep myself from getting amused seeing them enjoy the activity. I can see smiles in their faces while those who can’t keep their peace laughed it all the way. I even took a picture and a video to record this momentous occasion.

It was 7 o’clock in the evening as classes in the graduate school are held from 5:30 to 8:30 in the evening. This activity is quite beneficial to employees of the various government and non-government institutions where these students are working. Sleepiness and tiredness of the whole work day is dispelled for the moment as they stretch their leg as well as face muscles.

Right after the exercise, each student recorded their heartbeats and gave them to their group leaders. The group leaders then recorded the numbers on the board for everyone to see. Everyone in the class computed for the t-test value and compared their results with those of their classmates.

t test

Their findings showed, of course, a significant difference between heartbeats before and after exercise. But something intriguing happened. Some of the students have actually lower heartbeats after they exercised. These somehow puzzled us because before the exercise, everyone rested for about 10 minutes or even more.

Discussion of the t-test Results

This finding shows that there are unexpected things that could happen in the course of doing research. And explanation to this phenomenon requires further investigation. Why did the heartbeat decrease after exercise? Is this something worth investigating. Will we get the same results if a greater number of people are involved in the study?

My hypothesis in this case is that at the end of the day, everyone is quite stressed after work; thus, their rapid heartbeat. While doing the exercise, somehow their muscles relaxed and caused blood flow to be much more efficient, causing their heartbeats to drop.

This may be something that has already been discovered. A review of literature should be done to find out. This could be a groundbreaking study related to stress and exercise. And computing for the t-test value may be applied to find out differences in means before and after exercise.

Pedagogical Approach that Works!

The whole activity transpired within the three-hour duration of classes each week. It consisted of a short lecture followed by application of knowledge gained right there. The activity is quite memorable and found quite effective in getting across the principles of research and statistics and how it is applied in real life.

At the end of the day, the students were able to understand and actuate their learning through practical, hands-on experience. Most of the class were able to compute for the t-test value without a fuss.

© 2012 October 28 P. A. Regoniel

Research Statistics

What are Examples of Research Questions?

To effectively write the statement of the problem of your thesis, you will need to bear in mind certain principles that will guide you in framing those critical questions.  Well-written research questions determine how the whole research process will proceed.

At least three basic research outcomes are expected. These are described below along with examples of research questions for each outcome.

There are already many pieces of literature written on how to write the research questions required in investigating a phenomenon. But how are the research questions framed in actual situations? How do you write the research questions?

You will need to bear in mind certain rules and principles on how to go about writing the research questions. Before you start writing the research questions, you should be able to discern what you intend to arrive at in your research.

What are your aims and what are your expected research outcomes? Do you intend to describe something, determine differences or explain the causes of a phenomenon?

Three Basic Research Outcomes

There are at least three basic research outcomes that will arise in writing the research questions. These are 1) come up with a description, 2) determine differences between variables, and 3) find out correlations between variables.

Research Outcome Number 1. Come up with a description.

The outcome of your research question may be in the form of a description. The description is provided to contextualize the situation, explain something about the subjects or respondents of the study or provide the reader an overview of your study.

Below are examples of common research questions for Research Outcome Number 1 on a research conducted on teachers as respondents in a study.

Example Research Questions

  • What is the demographic profile of the teachers in terms of age, gender, educational attainment, civil status, and number of training attended?
  • How much time do teachers devote in preparing their lessons?
  • What teaching styles are used by teachers in managing their students?

The expected outcomes of the questions above will be a description of the teachers’ demographic profile, a range of time devoted to preparing their lessons, and a description of the teaching styles used by the teachers. These research outcomes can be presented in the form of tables and graphs with accompanying descriptions of the highlights of the findings. Highlights are those interesting trends or dramatic results that need attention such as very few training provided to teachers.

Research Outcome Number 2. Determine differences between variables.


To be able to write research questions that integrate the variables of the study, you should be able to define what is a variable. If this term is already quite familiar to you, and you are confident in your understanding, you may read the rest of this post.

You might want to find out the differences between groups in a selected variable in your study. Say, you would want to know if there is a significant difference in long quiz score (the variable you are interested in) between students who study at night and students who study early in the morning. You may frame your research questions thus:

Example Research Questions

  • Non-directional: Is there a significant difference in long quiz score between students who study early in the morning and students who study at night?
  • Directional: Are the quiz scores of students who study early in the morning higher than those who study at night?

The intention of the first research question is to find out if a difference exists in long quiz scores between students who study at night and those who study early in the morning, hence is non-directional. The second research question aims to find out if indeed students who study in the morning have better quiz scores as what the review of the literature suggests. Thus, the latter is directional.

Research Outcome Number 3. Find out correlations or relationships between variables.

The outcome of research questions in this category will be to explain correlations or causality. Below are examples of research questions that aim to find out correlations or relationships between variables using a combination of the variables mentioned in research outcome numbers 1 and 2.

Example Research Questions

  • Is there a significant relationship between teaching style and long quiz score of students?
  • Is there a significant association between the student’s long quiz score and the teacher’s age, gender, and training attended?
  • Is there a relationship between the long quiz score and the number of hours devoted by students in studying their lessons?

Note that in all the preceding examples of research questions, the variables of the study found in the conceptual framework of the study are integrated. Therefore, research questions must always incorporate the variables in them so that the researcher can describe, find differences, or correlate them with each other.

If you find this helpful, take the time to share this with your peers so that they can likewise discover new, exciting and interesting things along their fields of interest.

© 2012 October 22 P. A. Regoniel

Cite this article as: Regoniel, Patrick (October 22, 2012). What are Examples of Research Questions? [Blog Post]. In SimplyEducate.Me. Retrieved from
Research Statistics

What are Examples of Variables in Research?

In the course of writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing good research. What then are variables and how do you use variables in your study? I explain the concept below with lots of examples on variables commonly used in research.

You may find it difficult to understand just what variables are in the context of research especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this important concept of research as well as statistics.

Understanding what variables mean is crucial in writing your thesis proposal because you will need these in constructing your conceptual framework and in analyzing the data that you have gathered. Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will be able to use your data for statistical analysis.

I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.

Definition of Variables and Examples

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary”, meaning, changing in amount, volume, number, form, nature or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

The following examples of phenomena from a global to a local perspective. The corresponding list of variables is given to provide a clear illustration of how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.

  • Phenomenon: climate change

Examples of variables related to climate change:

  1. sea level
  2. temperature
  3. the amount of carbon emission
  4. the amount of rainfall
  • Phenomenon: Crime and violence in the streets

Examples of variables related to crime and violence:

  1. number of robberies
  2. number of attempted murders
  3. number of prisoners
  4. number of crime victims
  5. number of laws enforcers
  6. number of convictions
  7. number of car napping incidents
  • Phenomenon: poor performance of students in college entrance exams

Examples of variables related to poor academic performance:

  1. entrance exam score
  2. number of hours devoted to studying
  3. student-teacher ratio
  4. number of students in the class
  5. educational attainment of teachers
  6. teaching style
  7. the distance of school from home
  8. number of hours devoted by parents in providing tutorial support
  • Phenomenon: Fishkill

Examples of variables related to fish kill:

  1. dissolved oxygen
  2. water salinity
  3. temperature
  4. age of fish
  5. presence or absence of parasites
  6. presence or absence of heavy metal
  7. stocking density
  • Phenomenon: Poor crop growth

Examples of variables related to poor crop growth:

  1. the amount of nitrogen in the soil
  2. the amount of phosphorous in the soil
  3. the amount of potassium in the ground
  4. the amount of rainfall
  5. frequency of weeding
  6. type of soil
  7. temperature
Arid land
Poor crop growth in the arid soil of a hill in an island.

Notice in the above examples of variables that all of them can be counted or measured using a scale. The expected values derived from these variables will, therefore, be in terms of numbers, amount, category or type. Quantified variables allow statistical analysis. Variable correlations or differences are then determined.

Difference Between Independent and Dependent Variables

Which of the above examples of variables are the independent and the dependent variables? The independent variables are just those variables that may influence or affect the other variable, i.e., the dependent variable.

For example, in the first phenomenon of climate change, temperature (independent variable) may influence sea level (dependent variable). Increased temperature will cause expansion of water in the sea. Thus, sea level rise on a global scale may occur. In the second phenomenon, i.e., crime and violence in the streets, the independent variable may be the number of law enforcers and the dependent variable is the number of robberies.

I will leave to you the other variables so you can figure out how this works.

How will you know that one variable may cause the other to behave in a certain way? Finding the relationship between variables require a thorough review of the literature. Through a review of the relevant and reliable literature, you will be able to find out which variables influence the other variable. You do not just simply guess relationships between variables. The whole process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information to your peers who may have difficulty in understanding what the variables are in research.

©2012 October 22 P. A. Regoniel

Cite this article as: Regoniel, Patrick (October 22, 2012). What are Examples of Variables in Research? [Blog Post]. In SimplyEducate.Me. Retrieved from