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Scale: Nominal and ordinal.

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

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Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

• Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
• Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
• Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
• Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
• Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
• Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
• Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

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Types of Variable

All experiments examine some kind of variable(s). A variable is not only something that we measure, but also something that we can manipulate and something we can control for. To understand the characteristics of variables and how we use them in research, this guide is divided into three main sections. First, we illustrate the role of dependent and independent variables. Second, we discuss the difference between experimental and non-experimental research. Finally, we explain how variables can be characterised as either categorical or continuous.

Dependent and Independent Variables

An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable.

Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. The dependent and independent variables for the study are:

Dependent Variable: Test Mark (measured from 0 to 100)

Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score)

The dependent variable is simply that, a variable that is dependent on an independent variable(s). For example, in our case the test mark that a student achieves is dependent on revision time and intelligence. Whilst revision time and intelligence (the independent variables) may (or may not) cause a change in the test mark (the dependent variable), the reverse is implausible; in other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent (this simply doesn't make sense).

Therefore, the aim of the tutor's investigation is to examine whether these independent variables - revision time and IQ - result in a change in the dependent variable, the students' test scores. However, it is also worth noting that whilst this is the main aim of the experiment, the tutor may also be interested to know if the independent variables - revision time and IQ - are also connected in some way.

In the section on experimental and non-experimental research that follows, we find out a little more about the nature of independent and dependent variables.

Experimental and Non-Experimental Research

• Experimental research : In experimental research, the aim is to manipulate an independent variable(s) and then examine the effect that this change has on a dependent variable(s). Since it is possible to manipulate the independent variable(s), experimental research has the advantage of enabling a researcher to identify a cause and effect between variables. For example, take our example of 100 students completing a maths exam where the dependent variable was the exam mark (measured from 0 to 100), and the independent variables were revision time (measured in hours) and intelligence (measured using IQ score). Here, it would be possible to use an experimental design and manipulate the revision time of the students. The tutor could divide the students into two groups, each made up of 50 students. In "group one", the tutor could ask the students not to do any revision. Alternately, "group two" could be asked to do 20 hours of revision in the two weeks prior to the test. The tutor could then compare the marks that the students achieved.
• Non-experimental research : In non-experimental research, the researcher does not manipulate the independent variable(s). This is not to say that it is impossible to do so, but it will either be impractical or unethical to do so. For example, a researcher may be interested in the effect of illegal, recreational drug use (the independent variable(s)) on certain types of behaviour (the dependent variable(s)). However, whilst possible, it would be unethical to ask individuals to take illegal drugs in order to study what effect this had on certain behaviours. As such, a researcher could ask both drug and non-drug users to complete a questionnaire that had been constructed to indicate the extent to which they exhibited certain behaviours. Whilst it is not possible to identify the cause and effect between the variables, we can still examine the association or relationship between them. In addition to understanding the difference between dependent and independent variables, and experimental and non-experimental research, it is also important to understand the different characteristics amongst variables. This is discussed next.

Categorical and Continuous Variables

Categorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal , ordinal or dichotomous .

• Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable (50 in fact).
• Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable (and also a nominal variable). Another example might be if we asked a person if they owned a mobile phone. Here, we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable.
• Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the Democratic Party and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that "They are OK" is twice as positive as "Not very much" for example.

Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables.

• Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit). So the difference between 20°C and 30°C is the same as 30°C to 40°C. However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable.
• Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable. So, temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0°C does not mean there is no temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often called absolute zero) indicates that there is no temperature whatsoever. Other examples of ratio variables include height, mass, distance and many more. The name "ratio" reflects the fact that you can use the ratio of measurements. So, for example, a distance of ten metres is twice the distance of 5 metres.

Ambiguities in classifying a type of variable

In some cases, the measurement scale for data is ordinal, but the variable is treated as continuous. For example, a Likert scale that contains five values - strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree - is ordinal. However, where a Likert scale contains seven or more value - strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree - the underlying scale is sometimes treated as continuous (although where you should do this is a cause of great dispute).

It is worth noting that how we categorise variables is somewhat of a choice. Whilst we categorised gender as a dichotomous variable (you are either male or female), social scientists may disagree with this, arguing that gender is a more complex variable involving more than two distinctions, but also including measurement levels like genderqueer, intersex and transgender. At the same time, some researchers would argue that a Likert scale, even with seven values, should never be treated as a continuous variable.

Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

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What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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• Indian Dermatol Online J
• v.10(1); Jan-Feb 2019

Types of Variables, Descriptive Statistics, and Sample Size

Feroze kaliyadan.

Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia

Vinay Kulkarni

1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India

This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation.

What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below.

Quantitative vs qualitative

A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type)

A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks).

Quantitative variables can be either discrete or continuous

Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria).

Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense.

Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variables

Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis).

Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching).

Dependent and independent variables

In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment.

Descriptive Statistics

Statistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots.

Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots.

Descriptive statistics can be broadly put under two categories:

• Sorting/grouping and illustration/visual displays
• Summary statistics.

Sorting and grouping

Sorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”).

Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description.

Suppose the weight in kilograms of a group of 10 patients is as follows:

56, 34, 48, 43, 87, 78, 54, 62, 61, 59

The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ].

Stem and leaf plot

Illustration/visual display of data

The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 .

Composite bar chart

A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ].

Scatter diagram

Summary statistics

The main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance).

Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order:

30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86

Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data.

The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45.

The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median.

The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution.

The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases.

Location of mode, median, and mean

Measures of dispersion

The range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56.

A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range.

Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45.

For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.”

The box plot

The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ].

The concept of skewness and kurtosis

Skewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures ​ [Figures5 5 – 8 ].

Positive skew

High kurtosis (positive kurtosis – also called leptokurtic)

Negative skew

Low kurtosis (negative kurtosis – also called “Platykurtic”)

Sample Size

In an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error.

We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample).

An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources.

We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial).

The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not.

Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%).

Effect size and minimal clinically relevant difference

For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows:

In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size.

Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size.

An increase in variance of the outcome leads to an increase in the calculated sample size.

A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates.

Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this.

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Types of Variables in Research – Definition & Examples

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A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes.

Inhaltsverzeichnis

• 1 Types of Variables in Research – In a Nutshell
• 2 Definition: Types of variables in research
• 3 Types of variables in research – Quantitative vs. Categorical
• 4 Types of variables in research – Independent vs. Dependent
• 5 Other useful types of variables in research

Types of Variables in Research – In a Nutshell

• A variable is an attribute of an item of analysis in research.
• The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative .
• The types of variables in research (correlational) can be classified into predictor or outcome variables.
• Other types of variables in research are confounding variables , latent variables , and composite variables.

Definition: Types of variables in research

A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study.

Note that the correct variable will help with your research design , test selection, and result interpretation.

In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.

Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents.

Types of variables in research – Quantitative vs. Categorical

Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes.

Quantitative or numerical data represents amounts, while categorical data represents collections or groupings.

The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better.

Quantitative variables

The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables .

The table below explains the elements that set apart discrete and continuous types of variables in research:

Categorical variables

Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts.

There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary.

It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete.

Data sheet of quantitative and categorical variables

A data sheet is where you record the data on the variables in your experiment.

In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet.

The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process.

Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research.

Types of variables in research – Independent vs. Dependent

The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival.

Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant.

The table below summarizes independent variables, dependent variables , and control variables .

Data sheet of independent and dependent variables

In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent.

Below is a data sheet based on our experiment:

Types of variables in correlational research

The types of variables in research may differ depending on the study.

In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables.

However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable.

Other useful types of variables in research

The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study.

Below are other types of variables in research worth understanding.

What is the definition for independent and dependent variables?

An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study.

What are quantitative and categorical variables?

Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design.

Discrete and continuous variables: What is their difference?

Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight.

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The Different Types Of Variables Used In Research And Statistics

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Scientists and statisticians conduct experiments on a regular basis. Scientists use these experiments to identify cause and effect, while Statisticians use variables to represent the unknown or varied data in their experiments.

Determining which variables to use is vital to the experiment. Also, choosing the right variables will lead to clearer analyses and more accurate results.

What Is a Variable?

A variable is something you can control, manipulate, or measure when conducting research or experiments. They are characteristics, numbers, or quantities and can represent specific items, people, places, or an idea. The variables may be referred to as data items.

The variables in an experiment will vary depending on the desired outcome. All scientific experiments and statistical studies will analyze a variable.

They are referred to as variables because the values can vary. A variable’s value can change within a single experiment. Whether there is a change between the groups being studied or the value changes over time, it may not necessarily be a constant.

Designing Experiments

As noted, all scientific experiments and statistical studies will control, manipulate, or measure a variable. In fact, the experiments are usually designed to determine the effect one variable has on another variable, cause, and effect.

When designing experiments, it is extremely important to choose the right variables. Choosing incorrectly can skew the results and derail the experiment or study completely. Choosing right can help an experiment or study run much more smoothly and produce more accurate results.

It is not just the specific variable within the experiment that needs to be determined, but the variable type as well. Knowing the variable type will allow you to interpret the results of the experiment or study.

It should be noted, though, that categorizing variables is a little subjective. Scientists and statisticians have some wiggle room when they categorize their experiment variables.

Generally, you will need to know what data the variable represents and what part of the experiment the variable represents in order to determine the variable type.

Independent, Dependent, and Control Variables

Typically, there will be an independent variable, dependent variable, and control variable in every experiment or study conducted.

Independent variables. Independent variables are the variables in your experiment that are being manipulated. They are referred to as independent variables due to the fact that their value is independent of other variables, which means that the other variables cannot change the independent variable.

Dependent variables. Dependent variables are the variables in your experiment that rely on other variables and can be changed or manipulated by the other variables being measured.

Control variables. Control variables are the variables in your experiment that are constant. They do not change over the course of the experiment or study and will have no direct effect on the other variables being measured.

Qualitative Versus Quantitative Variables

Every single variable you include in your experiment will need to be categorized as either a qualitative variable or a quantitative variable.

Qualitative variables. Also referred to as categorical variables, qualitative variables are any variables that hold no numerical value. They are nominal labels. For example, eye color would be a qualitative variable. The data being recorded is not a number but a color.

These variables don’t necessarily measure, but they describe a characteristic of the data set. They can be broken down further as either ordinal variables or nominal variables (see below for definitions).

Quantitative variables. Quantitative variables, or numeric variables, are the variables in your experiment that hold a numerical value. Unsurprisingly, they will represent a measurable quantity and will be recorded as a number.

These variables will measure “how many” or “how much” of the data being collected. These can be broken down further as either continuous variables or discrete variables (see below for definitions).

30 Other Variable Types Used in Experiments

This is by no means a comprehensive list, as the list of all variable types would be difficult to document in one place.

Below are many of the common and some less common variable types used in scientific experiments and statistical studies. Included is a brief overview of what that variable type measures.

Active variable. An active variable is a variable that can be manipulated by those running the experiment.

Antecedent variable. Antecedent variables come before the independent and dependent variables. With “antecedent” meaning “preceding in time or order,” this is not surprising.

Attribute variable. An attribute variable also called a passive variable, is not manipulated during the experiment. It may be a fixed variable or simply a variable that is not manipulated for one experiment but could be for another.

Binary variable. Binary variables only have two values. Typically, this will be represented as a zero or one but can be yes/no or another two-value combination.

Categorical variable. Categorical variables are variables that can be divided into larger buckets or categories. Shoe brands, for instance, could include Nike, Reebok, or Adidas.

Composite variable. This variable type is a bit different from others. A composite variable is made up of two or more other variables. The individual variables that make up the composite variable will be closely related either conceptually or statistically.

Confounding variable. Confounding variables are not good. They can affect both independent and dependent variables and invalidate results. Sometimes referred to as a lurking variable, these variables are considered “extra” and were not accounted for during the designing phase.

Continuous variable. Continuous variables have an infinite number of values between the highest point and lowest point. Distance is a continuous variable.

Covariate variable. A covariate variable can affect the dependent variable in addition to the independent variable. It will not be of interest in the results of the experiment, though.

Criterion variable. This is a statistical variable only. It is another name for the dependent variable.

Dichotomous variable. This is another name for a binary variable. Dichotomous variables will have two values only.

Discrete variable. Discrete variables are the opposite of continuous variables. Where continuous variables have an infinite number of possible values, discrete variables have a finite number.

Endogenous variable. Endogenous variables are dependent on other variables and are used only in statistical studies, in econometrics specifically. The value of these variables is determined by the model.

Exogenous variable. An exogenous variable is the opposite of an endogenous variable. The value of this type of variable is determined outside of the model and will have an impact on other variables within the model.

Explanatory variable. This is a commonly used name for the independent variable or the variable that is being manipulated by those running the experiment.

Grouping variable. A grouping variable is used to sort, or split up, the data set into groups or categories.

Interval variable. Interval variables show the meaningful difference between the two values.

Intervening variable. Intervening variables, or mediator variables, explains the cause, connection, or relationship between two other variables being measured.

Manifest variable. A manifest variable is a variable that can be directly observed or measured within the experiment.

Moderating variable. A moderating variable can affect the relationship between the independent variable and dependent variable. It can either strengthen, diminish, or negate the relationship.

Nominal variable. This is another way of saying categorical value. Nominal values will have two or more categories.

Observed variable. Observed variables are variables that are being measured during the experiment.

Ordinal variable. Ordinal variables are similar to categorical or nominal variables but have a clear ordering of categories. Examples such as High to low and like to dislike would both be ordinal variables.

Polychotomous variable. Polychotomous variables have more than two possible categories or values. These can be either nominal or ordinal.

Ranked variable. Ranked variables are ordinal variables. The researcher may not know the exact value, but they will know the order in which the data points should fall.

Ratio variable. Ratio variables are similar to interval variables but have a clear definition of zero.

Responding variable. Responding variables are the effect or outcome of the experiment. Similar to dependent variables, responding variables will “respond” to changes being made in the experiment.

Scale variable. A scale variable is a variable that has a numeric value that can be ordered with a meaningful metric. It will be the amount or number of something.

Study variable. Often referred to as a research variable, a study variable is any variable used that has some kind of cause and effect relationship.

Test variable. A test variable also referred to as the dependent variable, is a variable that represents the outcome of the experiment.

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Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC.

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Fundamentals of Research Data and Variables: The Devil Is in the Details

Vetter, Thomas R. MD, MPH

From the Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, Texas.

Accepted for publication June 26, 2017.

Published ahead of print August 4, 2017.

Funding: None.

The author declares no conflicts of interest.

Reprints will not be available from the author.

Address correspondence to Thomas R. Vetter, MD, MPH, Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Dell Pediatric Research Institute, Suite 1.114, 1400 Barbara Jordan Blvd, Austin, TX 78723. Address e-mail to [email protected] .

Designing, conducting, analyzing, reporting, and interpreting the findings of a research study require an understanding of the types and characteristics of data and variables. Descriptive statistics are typically used simply to calculate, describe, and summarize the collected research data in a logical, meaningful, and efficient way. Inferential statistics allow researchers to make a valid estimate of the association between an intervention and the treatment effect in a specific population, based upon their randomly collected, representative sample data. Categorical data can be either dichotomous or polytomous. Dichotomous data have only 2 categories, and thus are considered binary. Polytomous data have more than 2 categories. Unlike dichotomous and polytomous data, ordinal data are rank ordered, typically based on a numerical scale that is comprised of a small set of discrete classes or integers. Continuous data are measured on a continuum and can have any numeric value over this continuous range. Continuous data can be meaningfully divided into smaller and smaller or finer and finer increments, depending upon the precision of the measurement instrument. Interval data are a form of continuous data in which equal intervals represent equal differences in the property being measured. Ratio data are another form of continuous data, which have the same properties as interval data, plus a true definition of an absolute zero point, and the ratios of the values on the measurement scale make sense. The normal (Gaussian) distribution (“bell-shaped curve”) is of the most common statistical distributions. Many applied inferential statistical tests are predicated on the assumption that the analyzed data follow a normal distribution. The histogram and the Q–Q plot are 2 graphical methods to assess if a set of data have a normal distribution (display “normality”). The Shapiro-Wilk test and the Kolmogorov-Smirnov test are 2 well-known and historically widely applied quantitative methods to assess for data normality. Parametric statistical tests make certain assumptions about the characteristics and/or parameters of the underlying population distribution upon which the test is based, whereas nonparametric tests make fewer or less rigorous assumptions. If the normality test concludes that the study data deviate significantly from a Gaussian distribution, rather than applying a less robust nonparametric test, the problem can potentially be remedied by judiciously and openly: (1) performing a data transformation of all the data values; or (2) eliminating any obvious data outlier(s).

Der teufel steckt im detail [The devil is in the details]

Friedrich Wilhelm Nietzsche (1844–1900)

Designing, conducting, analyzing, reporting, and interpreting the findings of a research study require an understanding of the types and characteristics of data and variables. This basic statistical tutorial discusses the following fundamental concepts about research data and variables:

• Population parameter versus sample variable;
• Types of research variables;
• Descriptive statistics versus inferential statistics;
• Primary data versus secondary data and analyses;
• Measurement scales and types of data;
• Normal versus non-normal data distribution;
• Assessing for normality of data;
• Parametric versus nonparametric statistical tests; and
• Data transformation to achieve normality.

POPULATION PARAMETER VERSUS SAMPLE VARIABLE

In conducting a research study, one ideally would obtain the pertinent data from all the members of the specific, targeted population, which defines the population parameter. However, this is seldom feasible, unless the entire targeted population is relatively small, and all its members are easily and readily accessible. 1–4

Pertinent data instead are typically collected on a random, representative subset or sample chosen from the members of the overall specific population, which defines the sample variable. The unknown population parameter, representing the characteristic or association of interest, is then estimated from this chosen study sample, with a varying degree of accuracy or precision. One essentially extrapolates from this sample to make conclusions about the population. 1–4

TYPES OF RESEARCH VARIABLES

When undertaking research, there are 4 basic types of variables to consider and define: 1 , 5–7

• Independent variable: A variable that is believed to be the cause of some of the observed effect or association and one that is directly manipulated by the researcher during the study or experiment.
• Dependent variable: A variable that is believed to be directly affected by changes in an independent variable and one that is directly measured by the researcher during the study or experiment.
• Predictor variable: A variable that is believed to predict another variable and one that is identified, determined, and/or controlled by the researcher during the study or experiment (essentially synonymous with an independent variable).
• Outcome variable: A variable that is believed to change as a result of a change in a predictor variable and one that is directly measured by the researcher during the study or experiment (essentially synonymous with a dependent variable).

DESCRIPTIVE STATISTICS VERSUS INFERENTIAL STATISTICS

Descriptive statistics are specific methods used simply to calculate, describe, and summarize the collected research data in a logical, meaningful, and efficient way. Descriptive statistics are reported numerically in the text and tables or in graphical forms. 1 , 8 Descriptive statistics will be the topic of the next basic tutorial in this series.

Researchers often pose a hypothesis (“if this is done, then this occurs” or “if this occurs, then this happens”) and seek to describe and to compare the quantitative or qualitative characteristics of 2 or more populations: 1 with and 1 without a specific intervention, or before and after the intervention in the same group. Purely descriptive statistics alone do not allow a conclusion to be made about association or effect and thus cannot answer a research hypothesis. 1 , 8

Inferential statistics involves using available data about a sample variable to make a valid inference (estimate) about its corresponding, underlying, but unknown population parameter. Inferential statistics also allow researchers to make a valid estimate of the association between an intervention and the treatment effect (causal-effect) in a specific population, based upon their randomly collected, representative sample data. 1 , 3 , 8

For example, Castro-Alves et al 9 recently reported on their prospective, randomized, placebo-controlled, double-blinded trial, in which the perioperative administration of duloxetine improved postoperative quality of recovery after abdominal hysterectomy. Based upon their study sample, these researchers made the valid inference that duloxetine appears to be an effective medication to improve postoperative quality of recovery in all similar patients undergoing abdominal hysterectomy. 9

PRIMARY DATA VERSUS SECONDARY DATA AND ANALYSIS

Frequently, there is confusion about the terms primary data and primary data analysis versus secondary data and secondary data analysis. 10

Primary data are intentionally and originally collected for the purposes of a specific research study, and it is a priori planned primary data analysis. 10–12 Primary data are usually collected prospectively but can be collected retrospectively. 12 Valid and reliable primary clinical data collection tends to be time consuming, labor intensive, and costly, especially if undertaken on a large scale and/or at multiple, independent, care-delivery locations or sites. 13

For example, a large-scale randomized study is being undertaken in 40 centers in 5 countries over 3 years to determine whether a stronger association (and thus more likely causality) exists between relatively deep anesthesia, as guided by the bispectral index, and increased postoperative mortality. 14

Likewise, the General Anesthesia compared to Spinal anesthesia study is an ongoing prospective randomized, controlled, multisite, trial designed to assess the influence of general anesthesia on neurodevelopment at 5 years of age. 15

Secondary data are initially collected for other purposes, and these existing data are subsequently used for a research study and its secondary data analyses. 11 , 16 Examples include the myriad of bedside clinical data recorded for routine patient care and administrative claims data utilized for billing and third-party payer purposes. 13

Such hospital administrative data (health care claims data) represent an important alternative data source that can be used to answer a broad range of research questions, including perioperative and critical care medicine, which would be difficult to study with a prospective randomized controlled trial. 17 , 18

Secondary clinical data can also be gathered and coalesced into a large-scale research data repository or warehouse, which is intentionally created for quality assurance, performance improvement, health services, or clinical outcomes research purposes. Data on study-specific variables are then extracted (“abstracted”) from one of these already existing secondary data sources. 16 , 19 , 20 An example is the National Anesthesia Clinical Outcomes Registry, developed by the Anesthesia Quality Institute of the American Society of Anesthesiologists. 21

Despite the resources needed for their creation, maintenance, and extraction, secondary data are typically less time consuming, labor intensive, and costly than primary data, especially if needed on a large scale (eg, health services and outcomes research questions in perioperative and critical care medicine). 22 However, the possible study variables are limited to those that already exist. 16 , 20 , 22 Furthermore, the validity of the findings of the research study can be adversely affected by a poorly constructed or executed secondary data collection or extraction process (“garbage in—garbage out”). 16 , 22

The term “secondary analysis of existing data” is generally preferred to the traditional term “secondary data analysis” because the former avoids the need to decide whether the data used in an analysis are primary or secondary. 10 An example is the predefined secondary analysis of existing data, prospectively collected in the Vascular Events in Non-Cardiac Surgery Patients Cohort Evaluation study, which assessed the association between preoperative heart rate and myocardial injury after noncardiac surgery. 23

MEASUREMENT SCALES AND TYPES OF DATA

Categorical data.

Some demographic and clinical characteristics can be parsed into and described using separate, discrete categories. The key distinction is the lack of rank order to these discrete categories. Categorical data can also be called nominal data (from the Latin word, nomen, for “name”), implying that there is no ordering to the categories, but rather simply names. Categorical data can be either dichotomous (2 categories) or polytomous (more than 2 categories). 1 , 5 , 24 , 25

Dichotomous data have only 2 categories, and thus are considered binary (yes or no; positive or negative). 1 , 5 , 24 , 25 Many clinical outcomes (eg, postoperative nausea/vomiting, myocardial infarction, stroke, sepsis, and mortality) can be recorded and reported as dichotomous data.

Polytomous data have more than 2 categories. Examples of such data include sex (man, woman, or transgender), race/ethnicity (American Indian or Alaska Native, Asian, black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and white or Caucasian), body habitus (ectomorph, mesomorph, or endomorph), hair color (black, brown, blond, or red), blood type (A, B, AB, or O), and diet (carnivore, omnivore, vegetarian, or vegan). 1 , 5 , 6 , 24 , 25

Ordinal Data

Unlike nominal or categorical data, ordinal data follow a logical order. Ordinal data are rank ordered, typically based on a numerical scale that is comprised of a small set of discrete classes or integers. 1 , 5 , 24 , 25 A key characteristic is that the response categories have a rank order, but the intervals between the values cannot be presumed to be equal. 26 The numeric Likert scale (1 = strongly disagree to 5 = strongly agree), which is commonly used to measure respondent attitude, generates ordinal data. 26 Other examples of ordinal data include socioeconomic status (low, medium, or high), highest educational level completed (elementary, middle school, high school, college, or postcollege graduate), the American Society of Anesthesiologists Physical Status Score (I, II, III, IV, or V), and the 11-point numerical rating scale (0–10) for pain intensity.

Discrete Data.

Categorical data that are counts or integers (eg, the number of episodes of intraoperative bradycardia or hypotension experienced by a patient) are typically called discrete data. Discrete data may be more appropriately analyzed using different statistical methods than ordinal data 27 ; however, in practice, the same methods are often used for these 2 variable types. In general, “discrete data variables” refer to those which can only take on certain specific values and are thus distinguished from continuous data, which are discussed next.

Continuous (Interval or Ratio) Data

Continuous data are measured on a continuum and can have or occupy any numeric value over this continuous range. Continuous data can be meaningfully divided into smaller and smaller or finer and finer increments, depending upon the sensitivity or precision of the measurement instrument. 25

Interval data are a form of continuous data in which equal intervals represent equal differences in the property being measured. 1 , 5 , 6 , 28 For example, the 1° difference between a temperature of 37° and 36° is the same 1° difference as between a temperature of 36° and 35°. However, when using the Fahrenheit or Celsius scale, a temperature of 100° is not twice as hot as 50° because a temperature of 0° on either scale does not mean “no heat” (but this would be true for Kelvin temperature). 28 This leads us naturally to a definition of ratio data.

Ratio data are another form of continuous data, which have the same properties as interval data, plus a true definition of an absolute zero point, and the ratios of the values on the measurement scale must make sense. 1 , 5 , 6 , 28 Age, height, weight, heart rate, and blood pressure are also ratio data. For example, a weight of 4 g is twice the weight of 2 g. 28 The visual analog scale (VAS) pain intensity tool generates ratio data. 29 A VAS score of 0 represents no pain, and a VAS score of 60 actually represents twice as much pain as a VAS score of 30.

NORMAL VERSUS NON-NORMAL DATA DISTRIBUTION

A statistical distribution is a graph of the possible specific values or the intervals of values of a variable (on the x-axis) and how often the observed values occur (on the y-axis). There are multiple types of data distribution, including the normal (Gaussian) distribution, binomial distribution, and Poisson distribution. 30 , 31 The so-inclined reader is referred to a more in-depth discussion of the various types or patterns of data distribution. 32

The normal (Gaussian) distribution (the “bell-shaped curve”) ( Figure 1 ) is one of the most common statistical distributions. 33 , 34 Many applied inferential statistical tests are predicated on the assumption that the analyzed data follow a normal distribution. Therefore, the normal distribution is also one of the most relevant to basic inferential statistics. 30 , 31 , 33–35

METHODS FOR ASSESSING DATA NORMALITY

The histogram and the Q–Q plot are 2 graphical methods to visually assess if a set of data have a normal distribution (display “normality”). The Shapiro-Wilk test and Kolmogorov-Smirnov test are 2 well-known and historically widely applied quantitative methods to assess for data normality. 36 Graphical methods and quantitative testing can complement one another; therefore, it is preferable that data normality be assessed both visually and with a statistical test. 30 , 37 , 38 However, if one is uncertain about how to correctly interpret the more subjective histogram or the Q–Q plot, it is better to rely instead on a numerical test statistic. 37 See the study by Kuhn et al 39 for an example.

The histogram or frequency distribution of the study data can be used to graphically assess for normality. If the study data are normally distributed, the histogram or frequency distribution of these data will fall within the shape of a bell curve ( Figure 2A ), whereas if the study data are not normally distributed, the histogram or frequency distribution of these data will fall outside the shape of a bell curve ( Figure 2B ). 35 When applicable, authors state in their manuscript their use of a histogram to assess the normality of their primary outcome data, but they do not reproduce this graph. See the study by Blitz et al 40 for an example.

One can also use the output of a quantile–quantile or Q–Q plot to graphically assess if a set of data plausibly came from a normal distribution. The Q–Q plot is a scatterplot of the quantiles of a theoretical normal data set (on x-axis) and the quantiles of the actual sample data set (on y-axis). If the data are normally distributed, the data points on the Q–Q plot will be closely aligned with the 45°, reference diagonal line ( Figure 3A ). If the individual data points stray from the reference diagonal line in an obviously nonlinear fashion, the data are not normally distributed ( Figure 3B ). When applicable, authors state in their manuscript their use of a Q–Q plot to assess the normality of their primary outcome data, but they do not reproduce this graph. See the study by Jæger et al 41 for an example.

Shapiro-Wilk Test and Kolmogorov-Smirnov Test

Both the Shapiro-Wilk and the Kolmogorov-Smirnov tests compare the scores in the study sample with a normally distributed set of scores with the same mean and SD; their null hypothesis is that sample distribution is normal. Therefore, if the test is significant ( P < .05), the sample data distribution is non-normal. 30 , 36 When applicable, authors should state in their manuscript which test was used to assess the normality of their primary outcome data and report its corresponding P value.

The Shapiro-Wilk test is more appropriate for small sample sizes (N ≤ 50), but it can also be validly applied with large sample sizes. The Shapiro-Wilk test provides greater power than the Kolmogorov-Smirnov test (even with its Lilliefors correction). For these reasons, the Shapiro-Wilk test has been recommended as the numerical means for assessing data normality. 30 , 36 , 37

PARAMETRIC VERSUS NONPARAMETRIC STATISTICAL TESTS

The details and appropriate use of the wide array of available inferential statistical tests will be the topics of several future tutorials in this current series.

These statistical tests are commonly classified as parametric versus nonparametric. This distinction is generally predicated on the number and rigor of the assumptions (requirements) regarding the underlying study population. 42 Parametric statistical tests make certain assumptions about the characteristics and/or parameters of the underlying population distribution upon which the test is based, whereas nonparametric tests make fewer or less rigorous assumptions. 42

Specifically, parametric statistical tests assume that the data have been sampled from a specific probability distribution (a normal distribution); nonparametric statistical tests make no such distribution assumption. 43 , 44 In general, parametric tests are more powerful (“robust”) than nonparametric tests, and so if possible, a parametric test should be applied. 43

DATA TRANSFORMATION TO ACHIEVE NORMALITY

Researchers may find that their available study data are not normally distributed, ostensibly calling into question the validity of using a more robust parametric statistical test.

While the results of the above tests of normality are typically reported (including in Anesthesia & Analgesia ), they are not a panacea. With small sample sizes, these normality tests do not have much power to detect a non-Gaussian distribution. With large sample sizes, minor deviations from the Gaussian “ideal” might be deemed “statistically significant” by a normality test; however, the commonly applied parametric t test and analysis of variance are then fairly tolerant of a violation of the normality assumption. 36 , 45 The decision to apply a parametric test versus a nonparametric test is thus sometimes a difficult one, requiring thought and perspective, and should not be simply automated. 36

If the normality test concludes that study data deviate significantly from a Gaussian distribution, rather than applying a less robust nonparametric test, the problem can potentially be remedied by judiciously and openly: (1) performing a data transformation of all the data values; or (2) eliminating any obvious data outlier(s). 36 , 46 Most commonly, logarithmic, square root, or reciprocal data transformation are applied to achieve data normality. 47 See the studies by Law et al 48 and Maquoi et al 49 for examples.

CONCLUSIONS

A basic understanding of data and variables is required to design, conduct, analyze, report, and interpret, as well as to understand and apply, the findings of a research study. The assumption of study data demonstrating a normal (Gaussian) distribution, and the corresponding choice of a parametric versus nonparametric statistical test, can be a complex and vexing issue. As will be discussed in detail in future tutorials, the type and characteristics of study data and variables essentially determine the appropriate descriptive statistics and inferential statistical tests to apply.

DISCLOSURES

Name: Thomas R. Vetter, MD, MPH.

Contribution: This author wrote and revised the manuscript.

This manuscript was handled by: Jean-Francois Pittet, MD.

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Research Variables: Types, Uses and Definition of Terms

The purpose of research is to describe and explain variance in the world, that is, variance that occurs naturally in the world or change that we create due to manipulation. Variables are therefore the names that are given to the variance we wish to explain and it is very critical to the research because the way the researcher uses or handles them in the research process could determine the nature and direction of the research (Nwankwo and Emunemu, 2014). Closely related to the understanding of what a variable is, is the idea of definition of terms. This chapter explores the use of variables in research, types of variables and the definition of terms, so as to help some of the students who have a problem identifying and clarifying the variables they are working on in their project work.

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• → What is descriptive research? Definit...

What is descriptive research? Definition, examples, and use cases

Descriptive research is a research methodology that focuses on understanding the particular characteristics of a group, phenomenon, or experience.

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Descriptive research is critical in nearly every business—from e-commerce to SaaS to everything in between. Whether you’re selling luxury quilted comforters or an advanced market research automation tool, you need to know who your customers are, what their preferences are, and how to analyze the competitive landscape. 

While you can scrape some of this information from third-party data, there’s nothing like zero-party data for the most accurate information about your customers. (After all, why not go straight to the source?) That’s why research methods like surveys, observational studies, case studies, and other descriptive types of research are necessary: They all provide that sweet, sweet zero-party data for your team. 

Today, we’ll explore the nature of descriptive research and what differentiates it from other research types—plus look at how you can put these strategies to work for your business. 

What is descriptive research?

If you want to understand your customers better, descriptive research is a powerful tool for determining what users want. This approach is typically used to discover more information about a specific segment or demographic or to further segment an existing group.

It can be helpful to think of descriptive research as the opposite of experimental research —if you’re doing experiments, you’re changing variables in your target group. (Think of famous experiments like Newton’s discovery of light!) If you’re doing descriptive research, however, you want to understand the characteristics of your target group without changing any variables. 

In business, the data from research like this is invaluable, as it can help you better understand (and segment) your customers. 

Descriptive research characteristics

Now that we’ve learned about the definition of descriptive research, let’s look at some common characteristics of research like this. (Spoiler: It’s a lot of surveys .) Because we’re not looking to answer any “why” questions, this type of research will analyze data without impacting or altering it.

If your research contains the following elements, it’s probably descriptive: 

Measuring data trends with statistical outcomes: This method analyzes data using statistical tools and techniques to identify patterns and changes over time. 

Example: A retail business might analyze sales data from 2013-2023 to identify seasonal trends, then use that data to predict future sales peaks.

Quantitative research: This method analyzes numerical data to uncover patterns and relationships—frequently utilizing the forms or surveys we know and love. 

Example: A SaaS company might survey users to discover usage rates and patterns per feature to optimize their product better. 

Designed for further research: If your research has different phases and starts with a general study to pave the way for a more detailed study, that’s descriptive research.

Example: A payroll management software company might conduct a study to gauge customer satisfaction levels, which could then lead to a study further analyzing specific parts of the tool. 

Uncontrolled variables: In descriptive research, none of the variables are impacted by the team doing the research in any way. (Doing so could introduce bias and impact the validity of the research.)

Example: In a study examining internal employee satisfaction, you might be unable to account for individual health or family concerns. 

Cross-sectional studies: These studies examine data from a single point in time, like taking a picture of your audience at a specific moment. 

Example: An online retailer looks at customer satisfaction in December to optimize customer experience during the holiday season.

What is descriptive research used for?

Now that we better understand what descriptive research looks like, you might recognize this research type in work your business is doing already. If so, congratulations, you’re ahead of the game! If not, you may wonder why one might go through all the trouble of doing this in-depth analysis. 

Here are a few ways we’ve seen companies successfully leverage descriptive research: 

Customer satisfaction surveys: A company might conduct a customer satisfaction survey to gauge customers' feelings about their products or services. By asking customers to rate their experience with product quality, customer service, and even pricing, the business can identify strengths and areas for improvement.

Market segmentation research: A company might use descriptive research to segment its market based on demographic, geographic, and behavioral characteristics. This helps the marketing team target specific groups more effectively. 

Trend analysis: Analyzing historical survey data to identify trends and patterns can help businesses forecast future sales, surface key insights, and even benchmark for future performance. 

Competitor benchmarking: A company might use descriptive research methods to benchmark performance against competitors. (Yes, you can!) A simple customer research survey can arm your team with information on competitors' pricing, product offerings, and market share.

Employee satisfaction research: A company might conduct research to assess employee satisfaction and engagement. An employee satisfaction survey can help businesses understand their workforce and identify factors contributing to job satisfaction or dissatisfaction. 

Descriptive research methods

Now that we’ve covered some examples of descriptive research in the wild, you may be itching to start your own. Here are the four descriptive research methods and how to utilize them.

Observational research

The observational research method is perhaps the simplest (and arguably the most effective) of the descriptive research methods we’ll examine today. In observational research, the researcher simply records behavior as it occurs without manipulating the variables. This can look like qualitative or quantitative research —and yes, both can be observational!

In qualitative observation , the researcher simply documents what they see and hear. They may not even need to interact directly with the study subjects. This can include social media research, focus group interviews, forum discussion analysis, or even surveys with open-ended questions. 

In quantitative observation , the researcher takes a much more structured approach to collecting hard data. For example, they may perform detailed data analysis on survey results containing information about age, race, gender, position, or industry. They can then splice and dice the results to reveal numerical insights about the group in question. 

When utilizing either of these methods, you’ll want to be careful not to skew the data as you work. (For example, don't accidentally exclude any customer segments!)

Survey research

Survey research is fairly simple conceptually—it does what it says on the tin. (They’re probably also the first thing you think of when you think of market research.) A researcher using this method sends surveys or questionnaires to the selected groups and uses the data gleaned from this research to inform business decisions. Surveys are a very popular research method due to their accessibility and straightforward nature, as users can access them online and from any location. 

Case studies

Case studies are another popular method of performing descriptive research. They’re a great way to dive deep into the experiences of a particular individual or group and really understand that specific experience with your product or service. You can do this using multiple interviews with multiple parties involved. 

The downside is that data gleaned from these studies may not be particularly quantitative—but you will likely get a very strong understanding of how your customers feel about the topic of your study.  

Finally, a method of descriptive research design that’s gaining popularity in businesses is the interview method. This is distinct from the case study method in that interviews focus on gathering in-depth information from individuals , while case studies comprehensively analyze a particular experience within a context. All case studies should contain interviews—but not all interviews must be part of case studies. It’s sort of a squares-and-rectangles situation.

Descriptive research pros and cons

All that said, there’s no one-size-fits-all solution for learning about your customer in a practical, actionable way you can accomplish in a reasonable amount of time. Next, we’ll cover the pros and cons of this type of research—and how we see research teams working with (and around) those elements. 

Detailed data collection: Descriptive research provides rich and highly detailed data about the studied demographics. You can analyze this data and use it for various market research purposes. 

Cost efficiency: With the power of online surveys, research is easy and cost-efficient. 

Highly accurate: Descriptive research captures a highly accurate picture of the subjects, meaning any data you glean will be valuable to your business. 

Versatile: This method can be applied across various fields and disciplines and used for business research of almost any variety.

Easy to build on: Once you’ve begun a descriptive research program, it’s easy to build on year after year—making each compounding round of research more valuable. 

Time-consuming analysis: While collecting large swathes of data may be easy—especially with surveys—analyzing that data can take time and resources. 

No causality data: Since you’re only looking at a snapshot of data, you won’t know why certain things are true, only that they are true. Additional research may be necessary to discover more. 

Static: Again, since you’re only getting a snapshot of data, it will not remain accurate over time, and you may need to do another study to keep your information up-to-date. 

Here are some examples of descriptive research in practice. 

Example 1: Customer satisfaction in the hospitality industry  

A cruise line conducts a comprehensive survey of guests who have booked travel with them in the last year. The survey includes questions about their stay, including ease of booking, room cleanliness, staff service, check-in and check-out, food and beverage experiences, entertainment options, and overall satisfaction. 

The company can then analyze this data to identify patterns, such as the most common complaints about food options. The data is then shared with hospitality management to improve the quality of the food on the cruise. 

Example 2: Market segmentation for a SaaS platform  

A company that developed a SaaS platform for developers conducts a cross-sectional market research study to understand its users' demographics and usage patterns. They collect data on users’ location, industry, number of employees at the company, frequency of use, and more. 

By analyzing this data, the company identifies distinct market segments, such as learning that a large percentage of its users serve the automotive industry. This allows the company to develop new features explicitly targeted to these users. 

Example 3: Employee engagement at a dental office

A dental practice conducts an annual employee engagement survey to measure employee satisfaction at the company. The survey covers topics such as work-life balance, management support, career development opportunities, and company culture. 

The survey results show a trend toward employee dissatisfaction with the policies for requesting paid time off, allowing leadership to revisit those policies. By positively addressing these policies, the following year’s employee satisfaction rate increased by 25%. 

Ready to get started? 

Research doesn’t have to be hard. If you’re ready to learn more about your customers, users, or employees, don’t overengineer it. Typeform’s user-friendly form templates make research easier for you (and more fun for everyone else!). Try Typeform for free today.

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These Popular Savings Accounts Could Cost You Hundreds of Dollars per Year

Published on June 28, 2024

By: Kailey Hagen

• Brick-and-mortar savings accounts tend to have low yields and many charge maintenance fees.
• Online banks offer high-yield savings accounts that could put hundreds of dollars in your pocket each year.

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A savings account that only keeps your money safe isn't a very good one. These accounts are supposed to reward you for keeping your cash there through monthly interest payments. But what you get varies a lot from one bank to the next.

Biggest doesn't always mean best

The largest and best-known banks in the U.S. are brick-and-mortar giants like Chase and Bank of America. They have branches in states across the country and a full suite of services, including one or more savings accounts.

Our Picks for the Best High-Yield Savings Accounts of 2024

Many brick-and-mortar savings accounts offer a lackluster 0.01% APY. With one of these accounts, you'd only earn $1 on a $10,000 initial deposit in a year. That's a really poor return on your investment. It's even worse when you consider that you could pay monthly maintenance fees, depending on your account balance and the bank's rules. This could potentially cost you money over time.

And let's not forget that annual inflation rates are almost always higher than 0.01%, so the buying power of your savings might be declining even while its dollar value grows. All in all, it's not a pretty picture.

Put your money where it'll do you the most good

High-yield savings accounts are the best place for most people to put their savings today. The top accounts have APYs near 5.00% right now. That would earn you $500 in a year on a $10,000 initial deposit. Even better, most of these accounts don't have minimum balance requirements or annual fees. They're just there to help you make money.

These banks do have some drawbacks, though. It can be a little more difficult to access cash directly from a high-yield savings account. However, several top online banks now offer ATM cards that allow customers to withdraw cash when needed.

Some of these banks also aren't as full-featured as their brick-and-mortar counterparts. You might be able to find savings accounts and certificates of deposit (CDs). But you might not be able to open a checking account at the same bank, for example.

Even if you prefer to do most of your business with a brick-and-mortar bank where you can get personalized support and access cash easily, the payoff of a high-yield savings account is worth the extra hassle of having to transfer funds back and forth from another bank. Whenever you're ready, just pick a high-yield savings account you like and open an account from the comfort of your own home.

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Two of our top online savings account picks:

Our Research Expert

Kailey Hagen has been covering personal finance topics, including banks, insurance, and retirement since 2013.

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• Knowledge Base

Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• Population health management

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Potential Racial Bias Found in Type 2 Diabetes Risk Prediction Models

New research shows that type 2 diabetes prediction models may contain biases that contribute to over- or under-estimations of risk based on a patient’s race..

• Shania Kennedy, Assistant Editor

Artificial intelligence (AI) algorithms used to screen for and predict type 2 diabetes may be racially biased, which could perpetuate health disparities, according to a study published last week in PLOS Global Public Health .

Risk prediction models for type 2 diabetes have shown promise in bolstering early detection and clinical decision-making, but the researchers pointed out that these models can bias the decision-making process if risk is miscalibrated across patient populations.

Concerns about model miscalibration and bias are especially pertinent as efforts to address health equity and racial disparities in healthcare have grown in recent years.

In this study, the researchers assessed three AI models to determine whether they demonstrate racial bias between non-Hispanic Black and non-Hispanic white populations: the Prediabetes Risk Test (PRT), a screening algorithm for prediabetes and type 2 diabetes, and two prognostic models, the Framingham Offspring Risk Score and the ARIC Model.

The research team utilized data from 9,987 adults without a prior diagnosis of diabetes and with available fasting blood samples from the National Health and Nutrition Examination Survey (NHANES). Patient information was then sampled in two-year batches starting with 1999 and ending with 2010.

From there, the researchers calculated year- and race-specific average predicted risk of type 2 diabetes, and compared these with observed risk across racial groups taken from the US Diabetes Surveillance System.

Doing so revealed that all three algorithms were miscalibrated in terms of race across all survey years evaluated.

The research team found that the Framingham Offspring Risk Score underestimated type 2 diabetes risk for non-Hispanic Black patients, but overestimated risk for their white counterparts.

The ARIC Model and PRT overestimated risk for both groups, but to a greater extent for white patients.

The overestimation of type 2 diabetes risk in non-Hispanic white populations could result in these patients being overdiagnosed or overtreated, in addition to being prioritized for preventive interventions, the researchers noted. This phenomenon may also lead to non-Hispanic Black patients being underprioritized and undertreated.

“Biased prediction models may prioritize individuals of certain racial groups for preventive action at different rates or at different stages in their disease progression. Such unequal predictions would exacerbate the systemic health care inequalities we are currently seeing, which stem from socioeconomic inequalities, differential health literacy and access to health care, and various forms of discrimination between majority and minority populations,” the researchers explained.

Research like this highlights that while data analytics and AI approaches may help find gaps in chronic disease management and care , racial disparities are still a major obstacle to achieving health equity for diabetes patients.

A 2021 study of city-level data revealed significant disparities in diabetes mortality rates across the United States.

The analysis sourced data from the 30 largest cities in the US and demonstrated that mortality rates were higher for Black individuals than for white individuals. Disparities were also found to be up to four times larger in some cities compared to others, with Washington, DC experiencing the highest rates of diabetes mortality inequities.

• Artificial Intelligence Approach Helps Identify Type 2 Diabetes Risk
• City-Level Data Shows Stark Racial Disparities in Diabetes Deaths
• Population Health Management Strategies to Reduce Health Disparities

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