What are Examples of Variables in Research?

Looking for examples of variables in research? Discover clear definitions, independent and dependent types, and 6 real-world phenomena to guide your thesis.

Watch the video above to get a grasp of the key contents of this article or you may go read the article below if that suits you best. You may tap on the interactive card below to see specific examples of variables.

Examples of Variables in Research

Examples of Variables in Research

Click any card to reveal its real-world research example

Independent The Cause

The variable that the researcher deliberately changes, manipulates, or selects to observe its direct effects.

💡 Real-World Example:

Hours of Study: The exact amount of time a student spends studying for an exam.

Dependent The Effect

The variable being measured or tested. It changes in direct response to the modifications made to the independent variable.

📊 Real-World Example:

Exam Scores: The final grade percentage earned by the student on the test.

Control The Constant

Outside factors kept strictly constant throughout the experiment to ensure the testing environment remains completely fair.

🔒 Real-World Example:

Test Conditions: Keeping the difficulty level of the test, the subject material, and the room temperature identical for everyone.

Extraneous The Wildcard

Uncontrolled external variables that could accidentally influence the outcome or introduce bias into the results.

⚠️ Real-World Example:

Natural Variables: The amount of sleep a student got the night before, or their baseline natural intelligence.

Table of Contents

Introduction

Variables are the building blocks of any research study, yet they are often one of the trickiest concepts for students and researchers to master—especially when dealing with quantitative data analysis.

Failing to grasp what variables mean—or confusing independent and dependent variables as you proceed into statistics—can stall a thesis proposal and complicate your conceptual framework.

To make this concept concrete, the guide below breaks down how abstract phenomena translate into measurable, data-driven variables using clear, real-world examples.

What are Variables?

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.

By breaking down abstract concepts into tangible metrics, researchers can isolate specific elements to observe how they interact in the real world. Ultimately, this operationalization turns vague observations into rigorous data that form the backbone of sound empirical investigation.

The next section provides examples of variables related to climate change, academic performance, crime, fish kill, crop growth, and how content goes viral. Note that the variables in these phenomena can be measured, except the last one, where a bit more work is required.


Examples of Variables in Research: 6 Phenomena

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

Phenomenon 1: 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

Here is an explanation of why each of these climate change factors functions as a variable in research, along with the specific units and methods used to measure them:

1. Sea Level

  • Why it is a variable: Sea level changes dynamically over time due to factors like thermal expansion (warming oceans) and the melting of land-based ice sheets and glaciers. Because it fluctuates across different coastal regions and decades, it serves as a measurable indicator of climate shifts.
  • How it is measured: Measured in millimeters or centimeters relative to a fixed land-based vertical datum, often using satellite radar altimetry (from space) and tide gauge stations deployed along coastlines.

2. Temperature

  • Why it is a variable: Atmospheric and ocean temperatures vary constantly across different geographical locations, seasons, and time periods, making them critical for tracking global warming trends.
  • How it is measured: Measured in degrees Celsius (°C), Fahrenheit (°F), or Kelvin (K). In research settings, it is recorded using standardized weather station thermometers, oceanographic buoys, and satellite infrared sensors.

3. The Amount of Carbon Emission

  • Why it is a variable: Carbon emissions (such as carbon dioxide released from fossil fuel combustion and industrial processes) fluctuate significantly depending on geographic region, economic activity, and policy interventions.
  • How it is measured: Quantified in metric tons or gigatons of CO2 equivalent (GtCO2e) per year or region. Researchers calculate this using fuel consumption data, industrial output metrics, and atmospheric gas concentration measurements taken via greenhouse gas monitoring towers and satellites.

4. The Amount of Rainfall

  • Why it is a variable: Precipitation levels vary drastically by region, season, and year, impacting ecosystems, agriculture, and hydrological cycles.
  • How it is measured: Measured in millimeters or inches of liquid water depth over a specific time period (e.g., daily, monthly, or annually). This is captured using rain gauges, weather radar systems, and satellite precipitation tracking.

Phenomenon 2: Crime and violence in the streets

Examples of variables related to crime and violence:

  1. number of robberies
  2. number of attempted murders
  3. head count of incarcerated individuals
  4. number of crime victims
  5. active-duty sworn police
  6. formal guilty verdicts
  7. number of reported vehicle thefts

Here is an explanation of why each of these crime and violence metrics functions as a variable in research, along with the specific units and methods used to measure them:

1. Number of Robberies

  • Why it is a variable: The frequency of robberies fluctuates across different neighborhoods, cities, and time periods due to socioeconomic shifts, policing strategies, and urban development. Because it varies from place to place, it serves as a key indicator of public safety.
  • How it is measured: Quantified as a count or frequency (e.g., total number of reported robbery incidents per month or year) and often standardized as a rate per 100,000 residents to allow fair comparisons between different geographic regions using official police department records and crime reporting databases.

2. Number of Attempted Murders

  • Why it is a variable: Attempted murder rates vary based on localized crime trends, community interventions, and interpersonal violence factors, making it a critical metric for measuring the severity of violent crime in an area.
  • How it is measured: Measured as a discrete count of recorded criminal cases where intent to kill was established but the victim survived, typically tracked annually through criminal justice and law enforcement databases.

3. Head Count of Incarcerated Individuals

  • Why it is a variable: Prison populations shift dynamically depending on crime rates, changes in sentencing laws, judicial processing speeds, and parole policies.
  • How it is measured: Counted as the total head count of incarcerated individuals within a specific correctional facility, regional jail, or national prison system at a given point in time (often reported as an incarceration rate per 100,000 people).

4. Number of Crime Victims

  • Why it is a variable: Victimization rates vary significantly depending on demographics, geographic hotspots, and vulnerability factors, serving as a vital measure of the direct human impact of crime.
  • How it is measured: Quantified through individual counts or survey-based estimates (such as national crime victimization surveys that capture both reported and unreported crimes) expressed as a frequency or proportion of a population.

5. Active-Duty Sworn Police

  • Why it is a variable: Police staffing levels and resource allocations differ widely across municipalities and change over time in response to municipal budgets, policy changes, and recruitment initiatives.
  • How it is measured: Measured as a direct count of active-duty sworn police personnel or as a ratio representing the number of law enforcement officers per 1,000 or 100,000 residents in a given jurisdiction.

6. Formal Guilty Verdicts

  • Why it is a variable: Conviction numbers fluctuate based on prosecutorial efficiency, court backlogs, the strength of evidence in submitted cases, and judicial outcomes.
  • How it is measured: Counted as the total number of formal guilty verdicts or plea bargains secured within a specific judicial district over a set period, sourced directly from court records and judicial administration reports.

7. Number of Reported Vehicle Thefts

  • Why it is a variable: Motor vehicle theft rates vary due to factors like vehicle security technology, urban patrol presence, and organized auto-theft rings operating in specific regions.
  • How it is measured: Quantified as the total number of reported vehicle thefts per month or year, typically normalized as a rate per 100,000 registered vehicles or residents using law enforcement and insurance industry data.

Phenomenon 3: 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

Here is an explanation of why each of these academic performance metrics functions as a variable in research, along with the specific units and methods used to measure them:

1. Entrance Exam Score

  • Why it is a variable: Entrance exam scores fluctuate across individual students and schools, reflecting varying levels of academic preparation, aptitude, and prior educational quality. Because it measures academic capability outcomes, it serves as a primary indicator of student readiness.
  • How it is measured: Quantified as a numerical point score or percentage (e.g., total score out of a maximum possible points) obtained from standardized assessment tests.

2. Number of Hours Devoted to Studying

  • Why it is a variable: Study time varies widely from student to student depending on personal discipline, academic demands, and time management skills, directly influencing learning outcomes.
  • How it is measured: Measured in hours per week or day, typically gathered through self-reported student questionnaires, time-use logs, or academic tracking surveys.

3. Student-Teacher Ratio

  • Why it is a variable: This ratio differs significantly across schools, grade levels, and educational districts, reflecting institutional resources and class sizing policies that affect individual student attention.
  • How it is measured: Calculated as a numerical ratio representing the total number of students enrolled divided by the total number of full-time equivalent teachers in a given classroom, school, or district.

4. Number of Students in the Class

  • Why it is a variable: Class sizes vary from small seminar groups to large lecture halls, impacting classroom dynamics, student engagement, and instructional delivery.
  • How it is measured: Measured as a discrete count of the total number of students officially enrolled and attending a specific classroom session.

5. Educational Attainment of Teachers

  • Why it is a variable: Teachers hold different levels of academic qualifications and professional credentials, which can vary across school districts and directly influence teaching competency and student performance.
  • How it is measured: Categorized using an ordinal scale or discrete categories (e.g., Bachelor’s Degree, Master’s Degree, Doctorate, or years of formal higher education completed).

6. Teaching Style

  • Why it is a variable: Pedagogical approaches vary by instructor and institution (e.g., lecture-based, inquiry-based, collaborative, or flipped classrooms), altering how students absorb and interact with course material.
  • How it is measured: Assessed using categorical scales or standardized observation rubrics (e.g., nominal classifications like authoritarian vs. facilitative, or scored indices from classroom observation checklists).

7. The Distance of School from Home

  • Why it is a variable: Travel distance and commute times vary based on geographic location, urban vs. rural settings, and residential proximity, affecting student fatigue, punctuality, and attendance rates.
  • How it is measured: Quantified in kilometers, miles, or travel time in minutes from a student’s residence to the school campus using mapping data or student surveys.

8. Number of Hours Devoted by Parents in Providing Tutorial Support

  • Why it is a variable: Parental involvement and home academic support fluctuate widely based on parents’ work schedules, educational backgrounds, and availability, serving as an external support variable for student success.
  • How it is measured: Measured in hours per week spent by parents actively helping with homework or providing tutoring, typically collected via parental or student self-reporting surveys.

Phenomenon 4: Fish kill

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

Here is an explanation of why each of these fish kill and aquatic environmental factors functions as a variable in research, along with the specific units and methods used to measure them:

1. Dissolved Oxygen

  • Why it is a variable: Dissolved oxygen (DO) levels fluctuate dynamically in water bodies based on temperature, organic pollution, aquatic plant respiration, and aeration. Because fish and other aquatic organisms rely on oxygen to breathe, sudden drops in DO are the leading cause of fish kills, making it a critical environmental variable.
  • How it is measured: Measured in milligrams per liter (mg/L) or parts per million (ppm) of oxygen dissolved in water, typically recorded using calibrated electronic DO meters, optical probes, or chemical titration kits (such as the Winkler test).

2. Water Salinity

  • Why it is a variable: Salinity—the concentration of dissolved salts in water—varies significantly due to freshwater runoff, evaporation rates, tidal mixing, and pollution. Sudden salinity spikes or drops can shock aquatic species that have narrow salt-tolerance thresholds.
  • How it is measured: Measured in Practical Salinity Units (PSU) or parts per thousand (ppt), and commonly quantified using digital refractometers, conductivity meters, or salinity sensors.

3. Temperature

  • Why it is a variable: Water temperature changes daily and seasonally based on weather conditions, industrial thermal discharges, and geographic depth. It directly affects the metabolic rates of fish and simultaneously dictates how much oxygen water can hold (warmer water holds less oxygen).
  • How it is measured: Measured in degrees Celsius (°C) or Fahrenheit (°F) using submerged data loggers, digital thermometers, or multiparameter water quality sondes.

4. Age of Fish

  • Why it is a variable: Fish susceptibility to environmental stressors (such as low oxygen or toxins) varies across different life stages—from sensitive fry and fingerlings to resilient adults—making age a key biological variable in mortality studies.
  • How it is measured: Measured in days, months, or years, often determined using aquaculture batch records, length-weight growth curves, or by analyzing hard anatomical structures like scales, otoliths (ear bones), and fin rays.

5. Presence or Absence of Parasites

  • Why it is a variable: The parasitic load or infection status varies across fish populations depending on water quality, crowding, and exposure to vector species. High parasite loads weaken fish immunity and directly contribute to stress-induced mortality events.
  • How it is measured: Tracked as a binary (presence/absence) categorical variable or quantified numerically as mean intensity/abundance (counting the number of parasites per host fish) via microscopic examination of gill, skin, and internal organ tissue samples.

6. Presence or Absence of Heavy Metals

  • Why it is a variable: Contaminants like lead, mercury, cadmium, and copper enter aquatic ecosystems through industrial runoff and mining waste. Their presence or concentration fluctuates by location and severely poisons aquatic life by accumulating in tissues.
  • How it is measured: Initially recorded as a binary category (presence or absence) in screening tests, and precisely quantified in parts per billion (ppb) or parts per million (ppm) using advanced laboratory techniques like Atomic Absorption Spectroscopy (AAS) or Inductively Coupled Plasma Mass Spectrometry (ICP-MS) on water and tissue samples.

7. Stocking Density

  • Why it is a variable: In aquaculture and managed fisheries, stocking density (how many fish are raised per unit of volume or area) varies widely depending on farm management practices. High stocking densities accelerate the depletion of dissolved oxygen and increase waste accumulation, directly driving stress and fish kills.
  • How it is measured: Measured as the number of fish per cubic meter (fish/m³) or biomass weight per unit area (kg/m²) in ponds, tanks, or cages.

Phenomenon 5: 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

Here is an explanation of why each of these agricultural and soil factors functions as a variable in research, along with the specific units and methods used to measure them:

1. The Amount of Nitrogen in the Soil

  • Why it is a variable: Nitrogen is a primary macronutrient essential for plant leaf growth and chlorophyll production. Its concentration fluctuates across different fields, depths, and management practices (such as fertilizer application), making it a key soil health variable for crop yield studies.
  • How it is measured: Quantified in milligrams per kilogram (mg/kg), parts per million (ppm), or kilograms per hectare (kg/ha) of available nitrogen (as nitrate or ammonium), typically measured using standard laboratory soil testing kits, colorimetric analysis, or automated soil analyzers.

2. The Amount of Phosphorous in the Soil

  • Why it is a variable: Phosphorus supports root development, flowering, and energy transfer in plants. Its availability varies widely depending on soil pH, parent material, and fertilizer history, directly influencing plant growth rates.
  • How it is measured: Measured in milligrams per kilogram (mg/kg) or parts per million (ppm) using standardized extraction methods such as the Bray-1, Mehlich-3, or Olsen tests performed on collected soil samples.

3. The Amount of Potassium in the Ground

  • Why it is a variable: Potassium helps regulate water uptake, enzyme activation, and overall plant disease resistance. Soil potassium levels vary based on mineral weathering, crop uptake, and soil management.
  • How it is measured: Quantified in parts per million (ppm) or meq/100g of exchangeable potassium, usually determined via ammonium acetate extraction and measured using atomic absorption spectrometry or flame photometry.

4. The Amount of Rainfall

  • Why it is a variable: Rainfall determines the natural moisture available for crop root systems and nutrient uptake. Precipitation levels vary drastically across growing seasons, geographic locations, and years.
  • How it is measured: Measured in millimeters or inches of precipitation over a specific timeframe (daily, weekly, or seasonal) using on-site rain gauges, weather stations, or agrometeorological tracking databases.

5. Frequency of Weeding

  • Why it is a variable: Weed management practices vary from farm to farm (e.g., manual pulling, mechanical tilling, or herbicide application). The frequency of weeding dictates how much competition crops face for light, water, and soil nutrients.
  • How it is measured: Measured as a discrete count or rate (e.g., number of weeding operations performed per growing season or weeks elapsed between weed control events) recorded through farmer logs or field observation records.

6. Type of Soil

  • Why it is a variable: Soil texture and classification (such as sandy loam, clay, silt loam, or organic peat) vary by region and field. Soil type dictates water retention capacity, aeration, and nutrient-holding ability, which heavily impact plant health.
  • How it is measured: Categorized using a nominal classification scale based on USDA soil textural triangles (percentages of sand, silt, and clay) determined through laboratory hydrometer or sieve analysis.

7. Temperature

  • Why it is a variable: Ambient air and soil temperatures fluctuate throughout the day and across seasons, governing the biological rates of seed germination, root respiration, photosynthesis, and microbial activity in the soil.
  • How it is measured: Measured in degrees Celsius (°C) or Fahrenheit (°F) using digital data loggers, soil thermometers, or local meteorological station records.
examplesofvariablespic
Poor crop growth can be measured by the number of healthy seeds in a cob of maize.

Phenomenon 6: How Content Goes Viral

  1. positive,
  2. interesting,
  3. prominent,
  4. useful,
  5. surprising, and
  6. causing physiological arousal.

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

While they start as subjective feelings or traits, researchers convert them into quantifiable data using specific measurement scales and research instruments.

Here is how each one is typically measured:

1. Positive

  • Why it is a variable: Content that evokes positive emotions (such as awe, joy, or inspiration) tends to be shared more widely than purely negative content. The emotional valence fluctuates from heavily negative to strongly positive depending on the choice of words, imagery, and framing.
  • How it is measured: Measured using ordinal scales (such as Likert scales rating text tone from 1 = “Very Negative” to 5 = “Very Positive”) or through nominal categorization via automated sentiment analysis tools that classify media or text as positive, neutral, or negative.

2. Interesting

  • Why it is a variable: The degree to which a topic captures and holds a reader’s attention varies significantly based on novelty, relevance, and presentation style.
  • How it is measured: Quantified through behavioral engagement metrics (such as average time-spent-reading, scroll depth, and interaction rates) or subjective rating scales where respondents evaluate interest levels on a 1 to 7 scale.

3. Prominent

  • Why it is a variable: Prominence refers to how visibly or centrally a piece of information, headline, or brand is displayed. Highly prominent content catches the eye faster and influences click-through rates.
  • How it is measured: Assessed by evaluating visual hierarchy and placement. Researchers measure this by counting keyword frequencies, tracking visual dominance (such as font size and header placement), or using ordinal ranking scales.

4. Useful

  • Why it is a variable: Content that provides practical value, actionable advice, or solves a specific problem is frequently shared by users looking to help their peers. The level of perceived utility varies by target audience.
  • How it is measured: Assessed using perceived usefulness survey scales (asking users to rate utility from “Not at all useful” to “Extremely useful”) or tracked via practical digital outcomes like total bookmark frequencies, share counts, and download metrics.

5. Surprising

  • Why it is a variable: Content that defies expectations or challenges conventional wisdom triggers curiosity and social sharing. The element of surprise varies based on how counter-intuitive or novel the information is.
  • How it is measured: Quantified via self-reported emotional impact scales (rating the degree of unexpectedness from 1 to 5) or by observing immediate cognitive and facial expression markers in controlled testing environments.

6. Causing Physiological Arousal

  • Why it is a variable: High-arousal emotions—whether positive (like excitement and awe) or negative (like anger and anxiety)—activate the autonomic nervous system and drive people to share content. Low-arousal emotions (like sadness) typically suppress sharing.
  • How it is measured: Measured objectively using biological and physiological indicators. Researchers use tools like Galvanic Skin Response (GSR) to measure sweat gland activity, heart rate variability (HRV), or facial electromyography to track micro-muscle tension associated with high-energy states.

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis. Variable descriptions, correlations, or differences are then determined.

Examples of Independent and Dependent Variables

Which of the above examples of variables are the independent and the dependent variables?

Independent Variables

The independent variables are those variables that may influence or affect the other variable, i.e., the dependent variable.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

  1. number of robberies,
  2. number of attempted murders,
  3. number of prisoners, 
  4. number of crime victims, and
  5. the number of carnapping incidents.

The five variables listed under crime and violence in the streets as the theme of a study are all dependent variables.

Dependent Variables

The dependent variable, as previously mentioned, is the variable affected or influenced by the independent variable.

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

I will leave the classification of the other variables to you. Find out whether those are independent or dependent variables. Note, however, that some variables can be both independent or dependent variables, as the context of the study dictates.

Finding the relationship between variables

How will you know that one variable may cause the other to behave in a certain way?

Finding the relationship between variables requires a thorough literature review. Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire 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 with your peers, who may have difficulty in understanding what the variables are in research.


Frequently Asked Questions (FAQs)

What are examples of variables in quantitative research?

In quantitative research, variables are measurable characteristics that change or vary across subjects or data points. Examples include:
Numeric Continuous Variables: Age, monthly income, temperature, and height (can take any numerical value on a scale).
Numeric Discrete Variables: Number of children, frequency of visits, or number of errors (counted in whole numbers).

What are examples of variables in a research paper?

Common examples found across research papers depend on the specific phenomenon being studied. For instance:
In Educational Research: Student-teacher ratio and hours devoted to studying (independent variables) affecting entrance exam scores (dependent variable).
In Environmental Research: Amount of rainfall and soil nitrogen levels affecting crop growth.

What are examples of variables in a research proposal?

When outlining a research proposal, variables must be explicitly defined so they can be measured. For example, if your proposal investigates workplace productivity:
Independent Variable: Number of hours spent in remote work training.
Dependent Variable: Employee output score per week.
Extraneous Variable: Employee work experience or age (controlled during the study).

What is the importance of variables in research?

Variables are vital because they transform abstract research concepts into measurable, concrete data. They allow researchers to:
Clearly define the scope and focus of a study.
Establish cause-and-effect relationships or correlations.
Build a reliable conceptual framework and run accurate statistical analyses.

What are the main types of variables in quantitative research?

Quantitative research primarily categorizes variables into several key types:
Independent Variables: The presumed cause or predictor that is manipulated or observed to see its effect.
Dependent Variables: The outcome or response measured to see how it is affected by the independent variable.
Mediating (Intervening) Variables: Factors that help explain the mechanism or relationship between the independent and dependent variables.
Moderating Variables: Factors that influence the strength or direction of the relationship between independent and dependent variables.
Control Variables: Extraneous variables that researchers hold constant to prevent them from confounding the study results.

Building Your Thesis?

Variables are just one piece of the puzzle. Discover how to connect them properly by reading our comprehensive guide on building a conceptual framework.

Read the Conceptual Framework Guide →

©SimplyEducate.me | Patrick A. Regoniel

128 Comments

  1. Jo November 7, 2013
  2. Randi November 13, 2013
    • Pat Regoniel November 14, 2013
    • Gorjuz Gorjuz November 16, 2016
    • Ella Grande July 7, 2017
    • Regoniel, Patrick A. July 10, 2017
    • Anonymous February 23, 2015
    • Gorjuz Gorjuz November 16, 2016
    • alishbakhawar February 28, 2017
  3. Geof Biriwiri February 18, 2014
  4. Emma July 3, 2014
  5. arvella albay August 12, 2014
  6. Ndapandula Nenghwanya February 27, 2015
  7. D. Akuh Ogah April 15, 2015
    • Hakizimana Prosper March 16, 2017
  8. Patrick Regoniel, PhD April 16, 2015
    • philosopher November 6, 2019
  9. Anonymous May 11, 2015
  10. Gabe May 19, 2015
  11. Patrick Regoniel, PhD May 21, 2015
    • Bard December 27, 2015
    • Regoniel, Patrick A. December 27, 2015
  12. Anonymous June 10, 2015
  13. Smith August 17, 2015
  14. Austin September 10, 2015
  15. Mafumo Frank October 12, 2015
  16. y.y. barry October 15, 2015
  17. Regoniel, Patrick A. October 15, 2015
  18. Issy Oliveira October 20, 2015
  19. Regoniel, Patrick A. October 21, 2015
  20. solomon karoki November 13, 2015
  21. Martin November 29, 2015
  22. Regoniel, Patrick A. November 30, 2015
  23. alvisswe December 29, 2015
  24. Regoniel, Patrick A. December 30, 2015
    • Lovely Roxas August 28, 2017
    • Regoniel, Patrick A. September 22, 2017
  25. Christian Acolatse February 18, 2016
  26. Quad March 13, 2016
  27. Quad March 13, 2016
  28. Regoniel, Patrick A. March 13, 2016
  29. Annabelle Kim March 29, 2016
    • Regoniel, Patrick A. March 30, 2016
  30. Justme April 2, 2016
    • meenakshi choudhury December 12, 2016
  31. abdull May 2, 2016
  32. Eliseu Lopes Tavares May 11, 2016
  33. Regoniel, Patrick A. May 14, 2016
  34. Edward June 3, 2016
  35. Regoniel, Patrick A. June 4, 2016
  36. Dr. Jacob L. Oyugi June 16, 2016
  37. Regoniel, Patrick A. June 16, 2016
  38. axiaka June 21, 2016
  39. Dennis Planteras July 3, 2016
    • Regoniel, Patrick A. July 3, 2016
  40. Marlyn divina gracia ziganay August 31, 2016
    • Regoniel, Patrick A. September 18, 2016
    • Ijeoma January 29, 2019
  41. Nainwa September 5, 2016
  42. Grace September 8, 2016
  43. Paul Increase September 12, 2016
  44. Paul Increase September 12, 2016
  45. ahpz September 14, 2016
  46. ahpz September 14, 2016
  47. Regoniel, Patrick A. September 18, 2016
  48. Eddie September 24, 2016
  49. rahim October 7, 2016
  50. Regoniel, Patrick A. October 8, 2016
  51. Ari October 18, 2016
  52. Ari October 18, 2016
    • Regoniel, Patrick A. October 20, 2016
  53. rashid November 23, 2016
  54. salamarh December 5, 2016
  55. Regoniel, Patrick A. December 8, 2016
  56. Berry January 2, 2017
  57. hameed January 4, 2017
  58. hameed January 4, 2017
  59. Enomen January 15, 2017
  60. Regoniel, Patrick A. January 15, 2017
  61. daudi February 7, 2017
  62. Monwabisi Nkomentaba April 6, 2017
  63. med April 10, 2017
  64. Ela July 19, 2017
  65. Regoniel, Patrick A. July 19, 2017
  66. Jennie August 6, 2017
  67. Lovely Roxas August 28, 2017
  68. Nasreen Zehra September 13, 2017
    • Regoniel, Patrick A. September 22, 2017
  69. carol paguia October 1, 2017
  70. Em Wenzl Sarabillo October 14, 2017
  71. kailigi simon mukambwe October 22, 2017
  72. Isma October 30, 2017
  73. kevwe bae January 2, 2018
  74. Badmos Dare January 4, 2018
  75. LastBlair January 15, 2018
  76. Ngesaye Nkeng March 14, 2018
  77. Akello Caroline April 26, 2018
  78. Peter M. Mwendwa May 26, 2018
  79. Lamech June 10, 2018
  80. Blake July 23, 2018
    • Blake July 23, 2018
  81. Mavelle August 24, 2018
  82. Grace Chioma Baba November 18, 2018
    • Regoniel, Patrick A. December 1, 2018
  83. Alhaji December 9, 2018
    • Regoniel, Patrick A. December 10, 2018
  84. Prisca December 10, 2018
  85. Sophia January 18, 2019
  86. Maur Bwanamaka March 14, 2019
    • Regoniel, Patrick A. April 13, 2019
    • Regoniel, Patrick A. April 20, 2019
  87. Regoniel, Patrick A. April 13, 2019
  88. Regoniel, Patrick A. April 20, 2019
  89. Celia July 15, 2019
  90. obiro william October 13, 2019
  91. Gomna AbdulRahman Ahmed October 17, 2019
  92. Adane Melkamu November 20, 2019
  93. Damas Nangi Masanja April 26, 2020
  94. Calvin Muhereza October 14, 2020
  95. Regoniel, Patrick October 14, 2020
  96. Regoniel, Patrick November 15, 2020