context variables in research

Research Design Review

A discussion of qualitative & quantitative research design, contextual analysis: a fundamental attribute of qualitative research.

One of the 10 unique or distinctive attributes of qualitative research is contextual, multilayered analysis. This is a fundamental aspect of qualitative research and, in fact, plays a central role in the unique attributes associated with data generation, i.e., the importance of context, the importance of meaning, the participant-researcher relationship , and researcher as instrument —

“…the interconnections, inconsistencies, and sometimes seemingly illogical input reaped in qualitative research demand that researchers embrace the tangles of their data from many sources. There is no single source of analysis in qualitative research because any one research event consists of multiple variables that need consideration in the analysis phase. The analyzable data from an in-depth interview, for example, are more than just what was said in the interview; they also include a variety of other considerations, such as the context in which certain information was revealed and the interviewee–interviewer relationship.” (Roller & Lavrakas, pp. 7-8)

The ability — the opportunity — to contextually analyze qualitative data is also associated with basic components of research design, such as sample size and the risk of relying on saturation which “misguides the researcher towards prioritizing manifest content over the pursuit of contextual understanding derived from latent, less obvious data.” And the defining differentiator between a qualitative and quantitative approach, such as qualitative content analysis in which it is “the inductive strategy in search of latent content, the use of context, the back-and-forth flexibility throughout the analytical process, and the continual questioning of preliminary interpretations that set qualitative content analysis apart from the quantitative method.”

There are many ways that context is integrated into the qualitative data analysis process to ensure quality analytical outcomes and interpretations . Various articles in Research Design Review have discussed contextually grounded aspects of the process, such as the following (each header links to the corresponding RDR article).

Unit of Analysis

“Although there is no perfect prescription for every study, it is generally understood that researchers should strive for a unit of analysis that retains the context necessary to derive meaning from the data. For this reason, and if all other things are equal, the qualitative researcher should probably err on the side of using a broader, more contextually based unit of analysis rather than a narrowly focused level of analysis (e.g., sentences).”

Meaning of Words

“How we use our words provides the context that shapes what the receiver hears and the perceptions others associate with our words. Context pertains to apparent as well as unapparent influences that take the meaning of our words beyond their proximity to other words [or] their use in recognized terms or phrases…”

Categorical Buckets

“No one said that qualitative data analysis is simple or straightforward. A reason for this lies in the fact that an important ingredient to the process is maintaining participants’ context and potential multiple meanings of the data. By identifying and analyzing categorical buckets, the researcher respects this multi-faceted reality and ultimately reaps the reward of useful interpretations of the data.”

Use of Transcripts

“Although serving a utilitarian purpose, transcripts effectively convert the all-too-human research experience that defines qualitative inquiry to the relatively emotionless drab confines of black-on-white text. Gone is the profound mood swing that descended over the participant when the interviewer asked about his elderly mother. Yes, there is text in the transcript that conveys some aspect of this mood but only to the extent that the participant is able to articulate it.”

Use of Recordings

“Unlike the transcript, the recording reminds the researcher of how and when the atmosphere in the [focus] group environment shifted from being open and friendly to quiet and inhibited; and how the particular seating arrangement, coupled with incompatible personality types, inflamed the atmosphere and seriously colored participants’ words, engagement, and way of thinking.”

Roller, M. R., & Lavrakas, P. J. (2015). Applied qualitative research design: A total quality framework approach . New York: Guilford Press.

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Contexts for writing, writing in context

• Writing in Context, Contexts of Writing
• Published: 01 September 2012
• Volume 6 , pages 167–174, ( 1991 )

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• Pietro Boscolo 1  

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The role of context variables is emphasized in recent research on writing, from which a great variety of meanings of the word ‘context’ emerges. The aim of this paper is to investigate some aspects of the identification of context variables in writing research by focusing on three main functions of context:

context as a condition for communication, i.e. the ground the writer creates in order to communicate with the reader

context as task environment, i.e. the situational variables (task objectives, motivational aspects, media, etc.) which can influence the writing process and/or product

context as an interactive framework, i.e. context as constituted by what people are doing, as well as by when and where they are doing it.

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The preparation of this paper has been supported by a grant from the National Council of Research. Grant n. CT 88.00964.08.

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Boscolo, P. Contexts for writing, writing in context. Eur J Psychol Educ 6 , 167–174 (1991). https://doi.org/10.1007/BF03191935

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Received : 15 October 1990

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DOI : https://doi.org/10.1007/BF03191935

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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 .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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• Types of Variables in Research & Statistics | Examples

Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts
• Categorical data represents groupings

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables .

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

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You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

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Understanding the role of personal experiences and contextual variables in shaping risk reduction preferences

Manuel barrientos.

a Durham University Business School, Durham University, Mill Hill Lane, Durham, United Kingdom

Felipe Vásquez-Lavin

b School of Business and Economics, Universidad del Desarrollo, Concepción, Chile

d Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile

Constanza Rosales

Luna bratti.

c Observatorio del Contexto Económico, Universidad Diego Portales, Santiago, Chile

Associated Data

Data will be made available on request.

This article explores how preferences for risk reduction during the COVID-19 pandemic are influenced by personal experiences and contextual variables such as having a close friend or relative who has been infected by the virus ( closeness ), the severity of the illness ( severity ), people’s own perceptions of being in a risky group ( risk group ), change in employment status due to the pandemic ( employment situation ), and vaccination status ( vaccination status and altruistic vaccination ). We conducted a choice experiment (CE) in Chile, Colombia, and Costa Rica. The attributes of the experiment were risk reduction, latency, and cost. Then, we estimated a mixed logit model to capture preference heterogeneity across the countries. The attributes presented in the CE were statistically significant, with the expected sign in each country. The variables closeness and employment situation presented homogeneous behavior in each country; however, severity , risk group, and vaccination status showed mixed results. We found that preferences were more heterogeneous for the attributes of the CE than for the personal experiences and contextual variables. Understanding the impact of these variables is essential for generating more effective risk reduction policies. For instance, methodologies such as the value of statistical life base their calculations on society's valuation of risk reduction. We provide evidence that the preferences for risk reduction vary due to the everyday situations that individuals face in the context of the pandemic. The latter may cause distortions in the values used to evaluate policies aimed at mitigating the outbreak.

1. Introduction

The SARS-CoV-2 virus began to spread globally in December 2019. This outbreak triggered the adoption of preventive measures that included social distancing [29] , hand hygiene [48] , the usage of face masks [34] , and even the development and supply of vaccines worldwide [62] . Commitment to these preventive measures and a willingness to be vaccinated are two mechanisms through which individuals can reduce the COVID-19 risks of morbidity and mortality. Participation in strategies to reduce mortality risks depends on sociodemographic, psychographic, and behavioral variables and personal experiences. This study explores how personal experiences and contextual variables during the COVID-19 outbreak impacted preferences for risk reduction programs. We use a stated preference survey conducted in three Latin American countries. These personal experiences and contextual variables include: (1) whether a respondent’s close relative/friend had COVID-19 ( closeness ), (2) the severity of their disease ( severity ), (3) whether a respondent considered themselves as part of the high-risk group in terms of COVID-19 infection ( risk group ), (4) change in employment status because of the outbreak ( employment situation ), and (5–6) vaccination ( vaccination status and altruistic vaccination ). We believe this type of preference drivers for risk reduction has not been sufficiently studied in the past and is essential for promoting more effective risk reduction policies.

Enhancing our understanding of preferences for risk reduction programs is relevant for policy evaluation and to improve social acceptance of such policies. For instance, the value of statistical life (VSL), one of the most widely used methodologies to determine monetary values for cost-benefit analysis, depends on people’s willingness to pay (WTP) for risk reductions. Besides and the focus of this article, the probability that individuals support a policy aimed to invest in research and development of new vaccines can also be linked to risk reduction preferences. Nevertheless, many studies claim that risk preferences are neither perfectly stable [52] nor homogeneous [61] . While sociodemographic variables that explain risk preferences, such as age [18] , [41] , [43] , [53] , gender [18] , [41] , [50] , [56] , education [21] , [56] , and marital status [15] , have been extensively studied, other psychological factors such as feelings, perceptions, and attitudes have received less attention, although they are relevant but more challenging to assess. Lerner and Keltner [33] show that anger and fear are negatively and positively related to perceived risk, respectively. Additionally, Rudisill [47] , in the context of the 2009 H1N1 virus, shows that optimism does not impact individuals' intention to be vaccinated.

People's risk preferences are also affected by personal experiences. Bucciol and Zarri [9] demonstrate that people who have suffered adverse experiences, such as being victims of a physical attack or losing a child, are willing to take fewer financial risks even though those negative experiences are not related to financial issues. Similarly, He and Hong [25] conclude that subjects previously exposed to risky environments are more risk-averse than those without that experience. Wang and Yan [64] show that people who have suffered personal shocks have a greater aversion to risk in medium and large bets than do those who have not experienced shocks in their personal lives. They also conclude that social shocks do not affect risk attitudes. If we considered the virus a social shock, it would not significantly affect risk preferences unless it personally affected individuals.

Although preferences for risk reduction have been studied through different methodologies, we are interested in applications that use stated preferences (SP). SP methods include contingent valuation (CV) and choice experiments (CE), which have been used extensively to study risk preferences due to their flexibility and ability to obtain preferences from hypothetical situations [26] . Due to the vast literature on the subject, several studies conduct meta -analyses or systematic reviews to summarize results [6] , [16] , [28] , [59] , [60] . Some findings in this research area have associated the WTP for risk reduction with population characteristics such as household income, risk reference level, and cultural and demographic variables [1] , [2] , [3] , [4] , [46] , [54] .

Fewer analyses have been performed regarding preferences for risk reduction in pandemic outbreaks using SP methods before the COVID-19 outbreak. During the SARS outbreak, Liu et al. [35] conducted a CV study in Taiwan to estimate the WTP to reduce the risk of infection and death. Another example is Determann et al. [17] , who investigated how vaccine characteristics could impact the social acceptance of vaccination programs during pandemic outbreaks. Other articles have investigated preferences for vaccines for the human papillomavirus vaccine [14] , [36] , dengue [31] , [42] , hepatitis B [44] , monkeypox [23] , among others. Presently, during the COVID-19 outbreak, several studies have used SP methods to estimate preferences (and the WTP) for vaccination in countries such as Australia [8] , Chile [13] , [20] , China [19] , [32] , [63] , Ecuador [51] , Indonesia [24] , the United States [10] , [12] , [57] , and the United Kingdom [40] , as well as in groups of countries [58] .

We contribute to this literature by focusing on the impact of personal experiences and contextual variables on the probability of participation in risk reduction programs. Additionally, we analyze the relevance of altruistic reasons for being vaccinated. To evaluate whether preferences varied across countries, we conducted three surveys in Latin America: Chile, Colombia, and Costa Rica.

2.1. Surveys

To understand the impact of different personal experiences and contextual variables on preferences for a risk reduction program during the COVID-19 outbreak, we conducted online surveys in Chile, Colombia, and Costa Rica (henceforth, CH, CO, and CR, respectively). We recruited the respondents through an online panel provided by Offerwise 1 , a leading online research company in Latin America. Offerwise recruits panel members and compensates respondents for remaining on the panel and completing the online surveys. We performed multiple checks on each respondent to avoid potential fraud (e.g., multiple answers from the same IP or delays in answering the questionnaire).

The surveys were conducted between June and August of 2021; our sample comprised people over 18 years old. In CH and CO, we recruited people from cities with severe air pollution problems (Santiago, Concepción, Temuco, and Aysén in CH, and Bogotá, Cali, and Medellín in CO). In the case of CR, we only focused on its capital, San José. The respondents were invited to participate through emails, following quota sampling. We attempted to fill demographic (mainly age and gender) quotas in a manner representative of each country's population.

The survey contained three sections: The first presented the survey and gathered sociodemographic information. Next, we included questions about health status and experiences (those of relatives/friends) with cardiorespiratory diseases and COVID-19. Last, we asked how their employment situation had changed from the beginning of the outbreak and the reasons for their vaccination status. We included a video with training in probabilities and ratios in the second section. We contextualized the current COVID-19 situations in their respective countries (consequences of COVID-19 and cumulative deaths) and presented the CE, which comprised a program aimed at developing and acquiring vaccines to reduce the effects of the disease (more details in the following subsection). The last section included questions to capture the respondents’ psychographic characteristics. The survey structure was tested in 376 pilot surveys, divided into 178 surveys in CH, 124 in CO, and 74 in CR.

2.2. Experimental design

The CE analyzes the determinants of risk reduction preferences. We provided a reduced set of attributes to keep the CE simple: i) reduction of the risk of death due to COVID-19. This quantitative variable (number of deaths avoided compared to the baseline risk) had three levels (low, medium, and high). The baseline risks vary across age groups, then using a constant risk baseline would have been unrealistic since people of different ages face significantly different risks. To avoid this, we built separate baseline risks for respondents between 18 and 59 years old and those older than 60. We generated the baseline risk scenarios based on each country’s official COVID-19 mortality data. ii) Latency of the program results (one, two, or five years) and iii) a monthly contribution to the program (prices varied across the countries; however, on average, they were approximately US$ 6, US$ 12, US$ 18, US$ 24, and US$ 30). An explanatory video presented the baseline risks for each age group, while a second video provided the instructions for the CE. An example of the choice sets shown is presented in Fig. 1 , and the full Bayesian optimal design used is shown in Appendix B .

Choice set example.

The suggested program would finance the research and development of vaccines in the country, the negotiation with foreign firms to ensure the supply of future vaccines, and a long-term research program on vaccines. Each interviewee answered four choice sets (similar to those in Fig. 1 ). Two alternatives corresponded to the variations of the program described, and a third alternative represented the current situation or status quo (no additional risk reduction, latency of program effects, or monthly payment). In each step of the survey, we followed the guidelines proposed by Mariel et al. [38] , Johnston et al. [27] , and Hensher et al. [26] .

2.3. Epidemiological context during surveys

The surveys were conducted between June and August of 2021. During this period, the countries’ pandemic statistics were similar. In panel A of Fig. 2 , we note that weekly COVID-19 infection cases decreased in CH and CR at the beginning of the surveys, while CO’s cases began to decline at the end of June. The decrease ratios in CH and CO were stable during the survey timeline; however, the weekly cases in CR began to increase again during the last week of July. The weekly deaths related to COVID-19 (panel B) in CO and CR showed similar behavior; however, a decrease in weekly deaths in CH started in July. Therefore, we argue that the surveys were conducted in an improving epidemiological scenario, which may have caused positive expectations for the following months and a relaxation in individuals’ concerns about COVID-19.

Panel A and B: Covid-19 weekly cases and deaths per million inhabitants. Panel C and D: Covid-19 weekly vaccinations per hundred inhabitants.

The apparently improving epidemiological context could be partly explained by the progress of vaccination campaigns in the surveyed countries. In panels C and D of Fig. 2 , we present the number of vaccinated (one dose and two doses in panels C and D, respectively) inhabitants per hundred. The vaccination campaign in CH was one of the most effective worldwide in 2021 [11] , setting it apart from the campaigns in CO and CR. Despite this difference, the vaccination rate increased consistently in all the countries during the surveys 2 .

2.4. Econometrics and model specification

In our experiment, each respondent, n , faces three alternatives. Following the theory of random utility [30] , [39] , we expect that they will choose an alternative, i , that maximizes their utility. The utility obtained by an individual, n , when choosing alternative i is given by

where V ni represents the deterministic component of the utility function, and ε ni is the random term that represents the unobserved component by the researcher. Depending on the researcher's interests and assumptions, we can use different econometric models to identify the components of V ni . Since we hypothesize that preferences for the proposed programs are highly heterogeneous, we estimate a mixed logit model (ML). In this model, the probability that an individual, n , chooses alternative i is given by

where x nit are a set of explanatory variables, including the attribute levels, respondents' sociodemographic characteristics, and other observable variables; α ni is an alternative specific constant (ASC); and β n is a vector of parameters to be estimated. To capture preference heterogeneity, β n is a random parameter defined as β n = b + σ β η n , where b is the mean, σ β the standard deviation, and η n a normally distributed random term.

To investigate the effect of personal experiences and contextual variables on risk reduction preferences, we asked if the respondents knew someone who was infected with COVID-19. After that, we asked about the degree of closeness (close or not close relative/friend) and the severity of the disease (severe or mild consequences). The respondents could indicate several known individuals who suffered from the disease, although we focused on the first mentioned. Fig. 3 depicts the flow of these personal experiences and contextual questions.

COVID-19-related cases question flow.

Furthermore, we asked if they considered themselves among the high-risk group regarding COVID- infection and why. Additionally, we asked about their employment situations before and after (current) the outbreak. Finally, we included some questions about the respondents' vaccination status and reasons for being (or intending to be) vaccinated. We classified the reasons for vaccination into altruistic (those to do with keeping family or other people safe as the first reason) and non-altruistic. Therefore, we included six personal experiences and contextual variables: severity , closeness , risk group (me) , employment situation, vaccination status , and altruistic vaccination .

To incorporate the personal experiences and contextual variables into the models, it was necessary to interact them with an ASC or attribute. In this case, we interacted each variable with the ASC of the status quo ( α 3 ), which allowed us to identify how the different variables impacted the probability of participation in the program. In particular, a positive (negative) sign for the coefficient implies a higher (lower) probability of choosing the status quo and not participating in the program. Hence, the model specification is as follows:

where reduction i , latency i , and cost i are the CE attributes and contextual n groups the personal experiences and contextual variables.

3.1. Surveys data

We collected 1,629 completed surveys in CH; 1,300 in CO; and 1,301 in CR. Following data cleaning (potential fraud, protest answers, incomplete questionnaires, and respondents with a low certainty in their answers), we obtained a final sample of 1,199; 959; and 887 respondents in CH, CO, and CR, respectively 3 . Since individuals provide four responses in the CE, we have 4,796 observations for CH, 3,836 for CO and 3,548 for CR. Table 1 presents a statistical description of our samples and compares them with national surveys. Considering the difficulties caused by the COVID-19 outbreak and the inherent limitations of online surveys [5] , [7] , we acknowledge that our samples are more representative of the population with access to the internet than of the whole countries’ populations. This means that variables such as education and household income are higher in our sample than the values one should find in a population-representative survey.

Sample descriptive statistics.

We present mean values in each category. We used national surveys CASEN 2020, ANDA 2020, and INEC 2020 as the data source for descriptive statistics for Chile, Colombia, and Costa Rica. The conversion rate were US$ 1 = 792.2 CLP = 3,693 COP = 581.7 CRC.

Generally, most of the socioeconomic variables in the three countries have similar statistical descriptions. Thus, we do not elaborate on these descriptions except for household income. To gather information about household income, we presented ten income ranges to each respondent, adjusted by each country’s purchasing power parity. We used the medium value in these ranges to generate the income variable. The highest household income was observed in CR, followed by CO and CH.

Regarding the personal experiences and contextual variables, most individuals ensured that a close relative or friend was affected by COVID-19 (69 %) in CR, while the lowest percentage (55 %) was observed in CH. The latter finding is interesting because CH had the highest percentage of respondents who had met someone who had suffered severe consequences of COVID-19. Moreover, in CH, 27 % of the respondents considered themselves in the high-risk group regarding COVID-19 infection; this proportion was lower in CR (24 %) and higher in CO (32 %).

Additionally, we included questions about the employment situations before the outbreak and at the time of the survey. A quarter of the respondents in each sample stated that they were worse off than before the COVID-19 outbreak. Finally, when we asked if the respondents were vaccinated or willing to be vaccinated when their turns came, over 90 % of them in each sample answered positively. Lastly, between 55 % and 57 % of the respondents in all the samples provided reasons that were considered altruistic. A classification of the reasons for being vaccinated (or not) is presented in Table 2 .

Distribution of main reasons for being willing/unwilling to receive the COVID-19 vaccine.

Respondents faced the question: Have you received, or are planning to receive when your turn comes, some of the available vaccines against COVID-19? If the said yes to this question, we asked: Why did you decide to get vaccinated (or are you planning to do it)? [Select the main reason], and if they said no: Why not? [Select the main reason].

The first two reasons for being willing to be vaccinated were altruistic (to protect my family and friends) and individualistic (to protect myself). The rest of the reasons constituted less than 10 % of the preferences.

3.2. Econometric results

The results are summarized in Table 3 ; as we are interested in the unobserved heterogeneity in the preferences, the estimations are divided into two panels. The upper panel presents the mean value of each parameter, while the lower panel shows each parameter’s standard deviation. A statistically significant standard deviation implies heterogeneity in the coefficient. Note that the cost parameter was considered fixed; thus, we do not estimate its standard deviation.

ML estimation results.

The personal experience and contextual variables were interacted with the alternative specific constant for the status quo. t statistics in parentheses. + p  < 0.10, * p  < 0.05, ** p  < 0.01, *** p  < 0.001. WTP confidence intervals were calculated by Krinsky Robb method. The conversion rate to 2020 US dollars were US$ 1 = 792.2 CLP = 3,693 COP = 581.7 CRC.

In each estimated model, we incorporated the variable ASC status quo , representing the ASC of the status quo alternative. This variable is always statistically significant, with a negative sign, which means that the respondents have a negative preference for staying in the current situation (without the program proposed in the CE). Regarding the attributes presented in the CE, all were statistically significant and with the expected sign. Risk reductions have a positive sign, which means that the higher the potential deaths from COVID-19 that the program helps reduce, the higher the probability that the respondents will be willing to participate. The latency of the program reflects how much time it will take to benefit society. The estimated parameter has a negative sign, implying that the respondents prefer program effects in the short run. Next, the cost parameter has a negative sign, which means that the higher the program cost, the lower the probability of participating.

Among the personal experiences and contextual variables, closeness and severity refer to how COVID-19 could directly impact the respondents' or their relatives' lives. The closeness variable is statistically significant, at 90 % in each country. Its impact is negative, and since it has been interacted with the status quo, it implies that having some close relative, friend or themselves infected by COVID-19 will decrease the probability of staying in the status quo. In other words, people with some close relatives or friends affected by COVID-19 are more likely to participate in the proposed risk reduction program. Meanwhile, the mean severity variable is only statistically significant in Colombia. This means that knowing someone who was hospitalized or passed away because of COVID-19 increases the probability of choosing the proposed program in CO but not in CH or CR.

When we asked the respondents about their self-perception of vulnerability in terms of COVID-19 infection, we found this variable statistically significant, with a negative sign in CO and CR but not in CH. This variable implies that individuals who believe they are vulnerable are more willing to participate in the program. Another personal experience variable is whether the outbreak has affected the respondents’ employment situation. In this regard, we used different variables for testing (if their employment situation was the same, if they had a different job than before the outbreak, or if they had the same job but were now working from home). None of them were statistically significant.

Additionally, we included a variable about the respondent's vaccination status (yes or no) and another dichotomous variable for when the respondents mentioned altruistic motivations to be vaccinated. The altruistic vaccination variable was statistically significant in CH and CO but not in CR. Meanwhile, the vaccination variable was only statistically significant in CR. All these parameters negatively affect the probability of preferring the status quo alternative. In other words, in CO and CH, the respondents with altruistic reasons for being vaccinated are always more interested in participating in the project. In CR, merely being (or willing to be) vaccinated is sufficient to increase the probability of accepting the proposed program.

Considering the standard deviation parameters, we observe that the status quo (ASC), risk reduction, and latency all have statistically significant standard deviations in all the countries. This means that there is substantial unobserved preference heterogeneity among the respondents. Regarding personal experiences and contextual variables, we note differences between the countries. Closeness shows consistently unobserved heterogeneity across the countries. By contrast, severity shows heterogeneous preferences only in CO. The effects of the perception of belonging to the high-risk group in terms of COVID-19 infection are heterogeneous in CH; however, they are not statistically significant for CO and CR. Similarly, the standard deviation for having a worse employment situation than before the outbreak is only statistically significant in CH. Besides, the impact of vaccination status is highly heterogeneous across the countries, whereas the altruistic reasons for being vaccinated are not. Note that although vaccination has a non-statistically significant mean in CH and CO, this variable presents high and statistically significant preference heterogeneity in all three countries. Lastly, we estimated the WTP for risk reductions in each country. The values ranged between US$ 0.1401 in CO to US$ 0.6671 in CR. These WTPs represent the monthly contributions to finance the research and development of vaccines in the country, the negotiation with foreign firms to ensure the supply of future vaccines, and a long-term research program on vaccines.

Overall, we can assert that all three countries have similar preferences (and heterogeneity) for risk reduction, latency, cost, closeness, and vaccination. Nevertheless, in CH, the respondents are more sensitive to altruism and less affected by risk group variable than in CO and CR. To complement these results, we include Appendix D , where we present the estimation of models including sociodemographic variables such as age, household size, secondary and tertiary education, gender, and income, and also models stratifying the sample depending on the respondent’s reported certainty about their responses. We show that most key results are robust to these analyses.

4. Discussion

Our main attributes are not new in the literature. For instance, the extent of risk reduction and costs are essential attributes for any CE study used to calculate the VSL. Primarily, both parameters must be statistically significant to calculate the VSL. Regarding latency, the duration of the COVID-19 vaccine effects and the time until vaccine availability have also been relevant for populations in other studies [8] , [19] .

However, the evidence provided by the literature about personal experiences and contextual variables in a pandemic context is scarce. C loseness was statistically significant in its negative impact on the probability of choosing the status quo alternative. Meanwhile, the employment situation was not statistically significant. Moreover, risk group (me), severity, vaccination status, and altruistic vaccination had mixed results among the countries. By contrast, Sadique et al. [49] found that the severity of the adverse events associated with vaccination increased a mother's probability of vaccinating her children; we found the same result in CO but no impact in CH and CR. Similar to our results, in the COVID-19 context, Cerda and García [13] found that individuals who had recovered from COVID-19 had a lower WTP for COVID-19 vaccines. Conversely, in the dengue context, Palanca-Tan [42] found that knowing someone who had dengue does not impact the WTP for a dengue vaccine. Still, in a multi-country study, Lee et al. [31] showed that this effect varies across countries. Furthermore, Machida et al. [37] found a link between the willingness to be vaccinated to protect others and vaccine acceptance.

Regarding heterogeneity, we found that preferences were highly heterogeneous in attributes but less heterogeneous in personal experiences and contextual variables. We discovered that CH presented the highest heterogeneity in the personal experiences and contextual variables (four out of seven were statistically significant) and CR the lowest (two out of seven were statistically significant). These results could indicate a more complex pattern in how the experiences during the COVID-19 outbreak affected the risk perceptions in the population. These differences could be partially due to the cultural aspects of risk perceptions, which is an exciting topic for future research. A relevant factor explaining our results is the mortality and vaccination context related to COVID-19 in each country. For instance, the willingness to vaccination was only statistically significant in CR, the country with the lower vaccination rate when the survey was conducted. At the same time, the altruistic reasons to be willing to vaccinate were significant in CH and CR, countries with a much higher vaccination rate (see Fig. 2 ). Then, when a country is in the early stages of the vaccination campaign, the vaccination itself is a good determinant for increasing the probability of contributing to a vaccine research and development program. However, when the vaccination rate increases, governments should focus their communication efforts on expanding the sense of altruism to ensure support for research and development programs for future pandemics.

Although some evidence shows that past events may have eroded trust in the vaccines [8] , the respondents in this study present a high commitment to vaccination campaigns. Our samples present a willingness to be vaccinated similar to the acceptance rate in CH (90.6 %) and CR (86.1 %) found by García and Cerda [20] and Guzmán et al. [22] , respectively. However, our acceptance rate is higher than the one found by Solís Arce et al. [55] in CO (75 %). Moreover, we asked the respondents about their reasons for being willing (or unwilling) to be vaccinated. We found that, generally, the reasons were homogeneous between the countries.

5. Conclusion

As the pandemic evolves, the percentage of individuals showing personal experiences and contextual variables presented in this study will rise. Therefore, our results imply a systematic increase in the social acceptance of programs with similar characteristics to that employed in this study. Nevertheless, this phenomenon could also generate a normalization of the pandemic situation, which could have the opposite effects. We did not find evidence for this except for high heterogeneity in peoplés preferences. Future studies should analyze how social acceptance evolves through different stages of an outbreak and its relationship to preference heterogeneity since characteristics such as novelty or level of public concern could generate changes in the social valuation of the risk reduction [35] .

This study presents evidence of solid consistency in preferences for attributes such as cost, risk reduction, and latency in three Latin American countries. Although the literature has largely discussed the implications of sociodemographic variables in the preferences for risk reduction programs (mainly in VSL studies), more evidence of how different personal experiences and contextual variables impact it is needed. We find that some personal experiences and contextual variables influence the probability of choosing a risk reduction program, and these effects are highly heterogeneous among individuals and countries. This last result is important for enhancing our understanding of preferences for risk reduction programs since it has implications for policy evaluation. In the context of a pandemic, risk preferences may suffer changes due to changes in individual’s personal experiences and contextual variables, which in turn affect the values used to evaluate policies aimed at mitigating the outbreak. Undoubtedly, there is a need for further research on how these variables impact risk preferences. For instance, understanding differences between countries and their heterogeneity regarding how personal experiences and contextual variables affect decisions is relevant for future research.

It is important to note some limitations of this research. Firstly, our online survey may not accurately represent the population of the studied countries. Our findings indicate that our sample group is highly educated and has a higher household income. Secondly, personal experiences and contextual variables can differ throughout the pandemic, so the results reflect only a specific outbreak stage. Thirdly, our research showed high unobserved preference heterogeneity in the CE attributes, personal experiences, and contextual variables. This heterogeneity could be explored further using different questionnaires and statistical methods, such as a latent class model. Finally, in the data-wrangling stage, we discarded many observations because they were potential frauds, protest responses, low-certainty responses, or suffered of missing information. Although this strategy is straightforward, it could be enhanced in future studies. For instance, investigating the reasons behind protest responses in a pandemic context could provide valuable insights.

Project EfD MS-526 provided funding for this study. Felipe Vásquez also acknowledges partial financial support from ANID FONDECYT Regular N°1210421, and the project ANID PIA/BASAL FB0002 (CAPES). Finally, we state that the publication of study results does not depend on the approval or censorship of the manuscript by the sponsor.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

1 https://www.offerwise.com/ .

2 This section was elaborated using data provided by ourworldindata: https://ourworldindata.org/coronavirus - coronavirus-country-profiles.

3 Further information of the data cleaning is presented in the appendix C.

Appendix A. Questions used to measure personal experience and contextual variables

Appendix B. Optimal design output

Table B1 shows the different attributes and levels combinations presented to the individuals. For instance, in block 1, choice situation 2, individuals faced one alternative with low risk reductions (that varies along the countries and age group), low latency, and the third level of the cost attribute (that varies along the countries), while in the second alternative they faced the high level of risk reductions, the high level of latency and the second level of the cost attribute. Finally, they also faced a status quo alternative that represents do not support the proposed program.

Optimal design used in the Choice experiment.

We performed a Bayesian optimal design using NGENE choosing the design in terms of the smallest d−error.

Appendix C. Data wrangling

This appendix summarizes the data-wrangling procedures followed in this article. In particular, Table C1 shows the observations discarded as potential frauds, protests, low-certainty responses, and missing information.

Data cleaning description and analysis of opt-in and opt-out behavior.

The data cleaning followed the same order as the variables exhibited in the table.

We analyzed the time it took for participants to finish the survey and verified if they were on the same internet connection. On average, the survey took around 24 min to complete. To ensure the validity of the responses, we excluded any surveys finished in less than five minutes, which could indicate rushed answers. Moreover, we used IP addresses to detect multiple survey responses from the same household and removed any IP addresses with three or more responses from our sample. After that, we conducted a protest analysis following the guidelines discussed by Mariel et al. [38] . We included an open follow-up question when respondents chose three or four times the status quo alternative. After that, we identified the responses that did not reflect an economic reason for rejecting the alternatives. For instance, protest responses such as “the government should use our taxes to finance the project” were widespread. Other interesting protest responses were linked to individuals who did not believe COVID-19 was relevant (or real).

Next, we included a question ranging between 1 and 5 to capture how certain the respondents were about their responses in the choice experiment. We discarded observations with a very low certainty (certainty = 1). We know that many other analyses can be conducted based on uncertainty of responses, but that is not the focus of our article. Additionally, we have variables such as vaccination or employment, where respondents had the alternative “I prefer not answer” which were considered missing information. We could try to impute some of these variables, but we preferred to discard them as they represented a low sample percentage.

Lastly, in the lower panel of Table C1 , we summarized the opt-in and opt-out behavior in each sample. In general, we found that the data cleaning process reduced the percentage of individuals choosing the status quo. Still, the ratio between opt-in and opt-out behavior is similar between countries.

Appendix D. Additional analyses

In this appendix, we conducted two additional analyses. First, we evaluated the role of different sociodemographic variables on the probability of participation in risk reduction programs. Second, we evaluated how individuals’ responses varied depending on how certain they were about their responses.

In Table D1 , we estimate models that include sociodemographic variables that interacted with the ASC status quo constant. The variables included were age, household size, secondary and tertiary education, gender, and income. Noticeably, few of these variables are statistically significant.

ML estimation results with sociodemographic variables.

Socio = sociodemographic variables, contextual = personal experience, and contextual variables. All the sociodemographic and personal experience and contextual variables interacted with the alternative specific constant (ASC) for the status quo. t statistics in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

In general, the attributes are statistically significant and with the expected sign. A notorious change is the reduced statistical significance of the ASC status quo, which can be explained by the inclusion of many variables interacting with this ASC. Most sociodemographic variables are not affecting the probability of choosing the status quo, with the exceptions being age in the three countries and income in Chile. Regarding unobserved heterogeneity, preferences for risk reductions and the latency of these reductions kept being highly heterogenous, but the heterogeneity of personal experience and contextual variables is reduced when sociodemographic variables are included. The heterogeneity of sociodemographic variables varies along the countries. Only the variable age is always heterogeneous.

Next, in Table D2 we analyzed how preferences varied depending on the reported respondent’s uncertainty about their choices. We asked them about their uncertainty level regarding their responses in the Choice Experiment. We used a scale between 1 and 5, where 1 is low certainty and 5 is high certainty. Then, we stratified the sample into three subgroups, following the strategy used by Regier et al. [45] . We have a certain (certainty = 5), hesitant (certainty = 3 or 4), and uncertain (certainty = 1 or 2) groups. The filter used in the main results was to discard respondents with very low certainty (certainty = 1). The results of this analysis are presented as follows:

ML estimation results of certainty subgroups.

t statistics in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

As expected, we found fewer statistically significant variables in this analysis since we are using smaller subsamples. Nevertheless, the choice experiment attributes were mostly statistically significant and with the expected sign. Regarding the personal experience and context variables, altruistic vaccination remained statistically significant, with the expected sign in most estimations, while the other variables varied along them. Lastly, most variables still showed a relevant unobserved heterogeneity despite the stratification.

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The Independent Variable vs. Dependent Variable in Research

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In any scientific research, there are typically two variables of interest: independent variables and dependent variables. In forming the backbone of scientific experiments , they help scientists understand relationships, predict outcomes and, in general, make sense of the factors that they're investigating.

Understanding the independent variable vs. dependent variable is so fundamental to scientific research that you need to have a good handle on both if you want to design your own research study or interpret others' findings.

To grasp the distinction between the two, let's delve into their definitions and roles.

What Is an Independent Variable?

What is a dependent variable, research study example, predictor variables vs. outcome variables, other variables, the relationship between independent and dependent variables.

The independent variable, often denoted as X, is the variable that is manipulated or controlled by the researcher intentionally. It's the factor that researchers believe may have a causal effect on the dependent variable.

In simpler terms, the independent variable is the variable you change or vary in an experiment so you can observe its impact on the dependent variable.

The dependent variable, often represented as Y, is the variable that is observed and measured to determine the outcome of the experiment.

In other words, the dependent variable is the variable that is affected by the changes in the independent variable. The values of the dependent variable always depend on the independent variable.

Let's consider an example to illustrate these concepts. Imagine you're conducting a research study aiming to investigate the effect of studying techniques on test scores among students.

In this scenario, the independent variable manipulated would be the studying technique, which you could vary by employing different methods, such as spaced repetition, summarization or practice testing.

The dependent variable, in this case, would be the test scores of the students. As the researcher following the scientific method , you would manipulate the independent variable (the studying technique) and then measure its impact on the dependent variable (the test scores).

You can also categorize variables as predictor variables or outcome variables. Sometimes a researcher will refer to the independent variable as the predictor variable since they use it to predict or explain changes in the dependent variable, which is also known as the outcome variable.

When conducting an experiment or study, it's crucial to acknowledge the presence of other variables, or extraneous variables, which may influence the outcome of the experiment but are not the focus of study.

These variables can potentially confound the results if they aren't controlled. In the example from above, other variables might include the students' prior knowledge, level of motivation, time spent studying and preferred learning style.

As a researcher, it would be your goal to control these extraneous variables to ensure you can attribute any observed differences in the dependent variable to changes in the independent variable. In practice, however, it's not always possible to control every variable.

The distinction between independent and dependent variables is essential for designing and conducting research studies and experiments effectively.

By manipulating the independent variable and measuring its impact on the dependent variable while controlling for other factors, researchers can gain insights into the factors that influence outcomes in their respective fields.

Whether investigating the effects of a new drug on blood pressure or studying the relationship between socioeconomic factors and academic performance, understanding the role of independent and dependent variables is essential for advancing knowledge and making informed decisions.

Correlation vs. Causation

Understanding the relationship between independent and dependent variables is essential for making sense of research findings. Depending on the nature of this relationship, researchers may identify correlations or infer causation between the variables.

Correlation implies that changes in one variable are associated with changes in another variable, while causation suggests that changes in the independent variable directly cause changes in the dependent variable.

Control and Intervention

In experimental research, the researcher has control over the independent variable, allowing them to manipulate it to observe its effects on the dependent variable. This controlled manipulation distinguishes experiments from other types of research designs.

For example, in observational studies, researchers merely observe variables without intervention, meaning they don't control or manipulate any variables.

Context and Analysis

Whether it's intentional or unintentional, independent, dependent and other variables can vary in different contexts, and their effects may differ based on various factors, such as age, characteristics of the participants, environmental influences and so on.

Researchers employ statistical analysis techniques to measure and analyze the relationships between these variables, helping them to draw meaningful conclusions from their data.

We created this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor.

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