testable hypothesis quantitative or qualitative research

Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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Qualitative vs. Quantitative Research — Here’s What You Need to Know

Will Mellor, Director of Surveys, GLG

Read Time: 0 Minutes

Qualitative vs. Quantitative — you’ve heard the terms before, but what do they mean? Here’s what you need to know on when to use them and how to apply them in your research projects.

Most research projects you undertake will likely require some combination of qualitative and quantitative data. The magnitude of each will depend on what you need to accomplish. They are opposite in their approach, which makes them balanced in their outcomes.

When Are They Applied?

Qualitative  

Qualitative research is used to formulate a hypothesis . If you need deeper information about a topic you know little about, qualitative research can help you uncover themes. For this reason, qualitative research often comes prior to quantitative. It allows you to get a baseline understanding of the topic and start to formulate hypotheses around correlation and causation.

Quantitative

Quantitative research is used to test or confirm a hypothesis . Qualitative research usually informs quantitative. You need to have enough understanding about a topic in order to develop a hypothesis you can test. Since quantitative research is highly structured, you first need to understand what the parameters are and how variable they are in practice. This allows you to create a research outline that is controlled in all the ways that will produce high-quality data.

In practice, the parameters are the factors you want to test against your hypothesis. If your hypothesis is that COVID is going to transform the way companies think about office space, some of your parameters might include the percent of your workforce working from home pre- and post-COVID, total square footage of office space held, and/or real-estate spend expectations by executive leadership. You would also want to know the variability of those parameters. In the COVID example, you will need to know standard ranges of square footage and real-estate expenditures so that you can create answer options that will capture relevant, high-quality, and easily actionable data.

Methods of Research

Often, qualitative research is conducted with a small sample size and includes many open-ended questions . The goal is to understand “Why?” and the thinking behind the decisions. The best way to facilitate this type of research is through one-on-one interviews, focus groups, and sometimes surveys. A major benefit of the interview and focus group formats is the ability to ask follow-up questions and dig deeper on answers that are particularly insightful.

Conversely, quantitative research is designed for larger sample sizes, which can garner perspectives across a wide spectrum of respondents. While not always necessary, sample sizes can sometimes be large enough to be statistically significant . The best way to facilitate this type of research is through surveys or large-scale experiments.

Unsurprisingly, the two different approaches will generate different types of data that will need to be analyzed differently.

For qualitative data, you’ll end up with data that will be highly textual in nature. You’ll be reading through the data and looking for key themes that emerge over and over. This type of research is also great at producing quotes that can be used in presentations or reports. Quotes are a powerful tool for conveying sentiment and making a poignant point.

For quantitative data, you’ll end up with a data set that can be analyzed, often with statistical software such as Excel, R, or SPSS. You can ask many different types of questions that produce this quantitative data, including rating/ranking questions, single-select, multiselect, and matrix table questions. These question types will produce data that can be analyzed to find averages, ranges, growth rates, percentage changes, minimums/maximums, and even time-series data for longer-term trend analysis.

Mixed Methods Approach

You aren’t limited to just one approach. If you need both quantitative and qualitative data, then collect both. You can even collect both quantitative and qualitative data within one type of research instrument. In a survey, you can ask both open-ended questions about “Why?” as well as closed-ended, data-related questions. Even in an unstructured format, like an interview or focus group, you can ask numerical questions to capture analyzable data.

Just be careful. While qualitative themes can be generalized, it can be dangerous to generalize on such a small sample size of quantitative data. For instance, why companies like a certain software platform may fall into three to five key themes. How much they spend on that platform can be highly variable.

The Takeaway

If you are unfamiliar with the topic you are researching, qualitative research is the best first approach. As you get deeper in your research, certain themes will emerge, and you’ll start to form hypotheses. From there, quantitative research can provide larger-scale data sets that can be analyzed to either confirm or deny the hypotheses you formulated earlier in your research. Most importantly, the two approaches are not mutually exclusive. You can have an eye for both themes and data throughout the research process. You’ll just be leaning more heavily to one or the other depending on where you are in your understanding of the topic.

Ready to get started? Get the actionable insights you need with the help of GLG’s qualitative and quantitative research methods.

About Will Mellor

Will Mellor leads a team of accomplished project managers who serve financial service firms across North America. His team manages end-to-end survey delivery from first draft to final deliverable. Will is an expert on GLG’s internal membership and consumer populations, as well as survey design and research. Before coming to GLG, he was the vice president of an economic consulting group, where he was responsible for designing economic impact models for clients in both the public sector and the private sector. Will has bachelor’s degrees in international business and finance and a master’s degree in applied economics.

For more information, read our articles: Three Ways to Apply Qualitative Research ,   Focusing on Focus Groups: Best Practices,   What Type of Survey Do You Need?, or The 6 Pillars of Successful Survey Design

You can also download our eBooks: GLG’s Guide to Effective Qualitative Research or Strategies for Successful Surveys

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Quantitative Data Analysis

5 Hypothesis Testing in Quantitative Research

Mikaila Mariel Lemonik Arthur

Statistical reasoning is built on the assumption that data are normally distributed , meaning that they will be distributed in the shape of a bell curve as discussed in the chapter on Univariate Analysis . While real life often—perhaps even usually—does not resemble a bell curve, basic statistical analysis assumes that if all possible random samples from a population were drawn and the mean taken from each sample, the distribution of sample means, when plotted on a graph, would be normally distributed (this assumption is called the Central Limit Theorem ). Given this assumption, we can use the mathematical techniques developed for the study of probability to determine the likelihood that the relationships or patterns we observe in our data occurred due to random chance rather than due some actual real-world connection, which we call statistical significance.

Statistical significance is not the same as practical significance. The fact that we have determined that a given result is unlikely to have occurred due to random chance does not mean that this given result is important, that it matters, or that it is useful. Similarly, we might observe a relationship or result that is very important in practical terms, but that we cannot claim is statistically significant—perhaps because our sample size is too small, for instance. Such a result might have occurred by chance, but ignoring it might still be a mistake. Let’s consider some examples to make this a bit clearer. Assume we were interested in the impacts of diet on health outcomes and found the statistically significant result that people who eat a lot of citrus fruit end up having pinky fingernails that are, on average, 1.5 millimeters longer than those who tend not to eat any citrus fruit. Should anyone change their diet due to this finding? Probably not, even those it is statistically significant. On the other hand, if we found that the people who ate the diets highest in processed sugar died on average five years sooner than those who ate the least processed sugar, even in the absence of a statistically significant result we might want to advise that people consider limiting sugar in their diet. This latter result has more practical significance (lifespan matters more than the length of your pinky fingernail) as well as a larger effect size or association (5 years of life as opposed to 1.5 millimeters of length), a factor that will be discussed in the chapter on association .

While people generally use the shorthand of “the likelihood that the results occurred by chance” when talking about statistical significance, it is actually a bit more complicated than that. What statistical significance is really telling us is the likelihood (or probability ) that a result equal to or more “extreme [1] ” is true in the real world, rather than our results having occurred due to random chance or sampling error . Testing for statistical significance, then, requires us to understand something about probability.

A Brief Review of Probability

You might remember having studied probability in a math class, with questions about coin flips or drawing marbles out of a jar. Such exercises can make probability seem very abstract. But in reality, computations of probability are deeply important for a wide variety of activities, ranging from gambling and stock trading to weather forecasts and, yes, statistical significance.

Probability is represented as a proportion (or decimal number) somewhere between 0 and 1. At 0, there is absolutely no likelihood that the event or pattern of interest would occur; at 1, it is absolutely certain that the event or pattern of interest will occur. We indicate that we are talking about probability by using the symbol [latex]p[/latex]. For example, if something has a 50% chance of occurring, we would write [latex]p=0.5[/latex] or [latex]\frac {1}{2}[/latex]. If we want to represent the likelihood of something not occurring, we can write [latex]1-p[/latex].

Check your thinking: Assume you were flipping coins, and you called heads. The probability of getting heads on a coin flip using a fair coin (in other words, a normal coin that has not been weighted to bias the result) is 0.5. Thus, in 50% of coin flips you should get heads. Consider the following probability questions and write down your answers so you can check them against the discussion below.

• Imagine you have flipped the coin 29 times and you have gotten heads each time. What is the probability you will get heads on flip 30?
• What is the probability that you will get heads on all of the first five coin flips?
• What is the probability that you will get heads on at least one of the first five coin flips?

There are a few basic concepts from the mathematical study of probability that are important for beginner data analysts to know, and we will review them here.

Probability over Repeated Trials : The probability of the outcome of interest is the same in each trial or test, regardless of the results of the prior test. So, if we flip a coin 29 times and get heads each time, what happens when we flip it the 29th time? The probability of heads is still 0.5! The belief that “this time it must be tails because it has been heads so many times” or “this coin just wants to come up heads” is simply superstition, and—assuming a fair coin—the results of prior trials do not influence the results of this one.

Probability of Multiple Events : The probability that the outcome of interest will occur repeatedly across multiple trials is the product [2] of the probability of the outcome on each individual trial. This is called the multiplication theorem . Thinking about the multiplication theorem requires that we keep in mind the fact that when we multiply decimal numbers together, those numbers get smaller— thus, the probability that a series of outcomes will occur is smaller than the probability of any one of those outcomes occurring on its own. So, what is the probability that we will get heads on all five of our coin flips? Well, to figure that out, we need to multiply the probability of getting heads on each of our coin flips together. The math looks like this (and produces a very small probability indeed):

[latex]\frac {1}{2} \cdot \frac {1}{2} \cdot \frac {1}{2} \cdot \frac {1}{2} \cdot \frac {1}{2} = 0.03125[/latex]

Probability of One of Many Events : Determining the probability that the outcome of interest will occur on at least one out of a series of events or repeated trials is a little bit more complicated. Mathematicians use the addition theorem to refer to this, because the basic way to calculate it is to calculate the probability of each sequence of events (say, heads-heads-heads, heads-heads-tails, heads-tails-heads, and so on) and add them together. But the greater the number of repeated trials, the more complicated that gets, so there is a simpler way to do it. Consider that the probability of getting  no heads is the same as the probability of getting all tails (which would be the same as the probability of getting all heads that we calculated above). And the only circumstance in which we would not have at least one flip resulting in heads would be a circumstance in which all flips had resulted in tails. Therefore, what we need to do in order to calculate the probability that we get at least one heads is to subtract the probability that we get no heads from 1—and as you can imagine, this procedure shows us that the probability of the outcome of interest occurring at least once over repeated trials is higher than the probability of the occurrence on any given trial. The math would look like this:

[latex]1- (\frac{1}{2})^5=0.9688[/latex]

So why is this digression into the math of probability important? Well, when we test for statistical significance, what we are really doing is determining the probability that the outcome we observed—or one that is more extreme than that which we observed—occurred by chance. We perform this analysis via a procedure called Null Hypothesis Significance Testing.

Null Hypothesis Significance Testing

Null hypothesis significance testing , or NHST , is a method of testing for statistical significance by comparing observed data to the data we would expect to see if there were no relationship between the variables or phenomena in question. NHST can take a little while to wrap one’s head around, especially because it relies on a logic of double negatives: first, we state a hypothesis we believe not to be true (there is no relationship between the variables in question) and then, we look for evidence that disconfirms this hypothesis. In other words, we are assuming that there is no relationship between the variables—even though our research hypothesis states that we think there is a relationship—and then looking to see if there is any evidence to suggest there is not no relationship. Confusing, right?

So why do we use the null hypothesis significance testing approach?

• The null hypothesis—that there is no relationship between the variables we are exploring—would be what we would generally accept as true in the absence of other information,
• It means we are assuming that differences or patterns occur due to chance unless there is strong evidence to suggest otherwise,
• It provides a benchmark for comparing observed outcomes, and
• It means we are searching for evidence that disconforms our hypothesis, making it less likely that we will accept a conclusion that turns out to be untrue.

Thus, NHST helps us avoid making errors in our interpretation of the result. In particular, it helps us avoid Type 2 error , as discussed in the chapter on Bivariate Analyses . As a reminder, Type 2 error is error where you accept a hypothesis as true when in fact it was false (while Type 1 error is error where you reject the hypothesis when in fact it was true). For example, you are making a Type 1 error if you decide not to study for a test because you assume you are so bad at the subject that studying simply cannot help you, when in fact we know from research that studying does lead to higher grades. And you are making a Type 2 error if your boss tells you that she is going to promote you if you do enough overtime and you then work lots of overtime in response, when actually your boss is just trying to make you work more hours and already had someone else in mind to promote.

We can never remove all sources of error from our analyses, though larger sample sizes help reduce error. Looking at the formula for computing standard error , we can see that the standard error ([latex]SE[/latex]) would get smaller as the sample size ([latex]N[/latex]) gets larger. Note: σ is the symbol we use to represent standard deviation.

[latex]SE = \frac{\sigma}{\sqrt N}[/latex]

Besides making our samples larger, another thing that we can do is that we can choose whether we are more willing to accept Type 1 error or Type 2 error and adjust our strategies accordingly. In most research, we would prefer to accept more Type 1 error, because we are more willing to miss out on a finding than we are to make a finding that turns out later to be inaccurate (though, of course, lots of research does eventually turn out to be inaccurate).

Performing NHST

Performing NHST requires that our data meet several assumptions:

• Our sample must be a random sample—statistical significance testing and other inferential and explanatory statistical methods are generally not appropriate for non-random samples [3] —as well as representative and of a sufficient size (see the Central Limit Theorem above).
• Observations must be independent of other observations, or else additional statistical manipulation must be performed. For instance, a dataset of data about siblings would need to be handled differently due to the fact that siblings affect one another, so data on each person in the dataset is not truly independent.
• You must determine the rules for your significance test, including the level of uncertainty you are willing to accept (significance level) and whether or not you are interested in the direction of the result (one-tailed versus two-tailed tests, to be discussed below), in advance of performing any analysis.
• The number of significance tests you run should be limited, because the more tests you run, the greater the likelihood that one of your tests will result in an error. To make this more clear, if you are willing to accept a 5% probability that you will make the error of accepting a hypothesis as true when it is really false, and you run 20 tests, one of those tests (5% of them!) is pretty likely to have produced an incorrect result.

If our data has met these assumptions, we can move forward with the process of conducting an NHST. This requires us to make three decisions: determining our null hypothesis , our confidence level (or acceptable significance level), and whether we will conduct a one-tailed or a two-tailed test. In keeping with Assumption 3 above, we must make these decisions before performing our analysis. The null hypothesis is the hypothesis that there is no relationship between the variables in question. So, for example, if our research hypothesis was that people who spend more time with their friends are happier, our null hypothesis would be that there is no relationship between how much time people spend with their friends and their happiness.

Our confidence level is the level of risk we are willing to accept that our results could have occurred by chance. Typically, in social science research, researchers use p<0.05 (we are willing to accept up to a 5% risk that our results occurred by chance), p<0.01 (we are willing to accept up to a 1% risk that our results occurred by chance), and/or p<0.001 (we are willing to accept up to a 0.1% risk that our results occurred by chance). P, as was noted above, is the mathematical notation for probability, and that’s why we use a p-value to indicate the probability that our results may have occurred by chance. A higher p-value increases the likelihood that we will accept as accurate a result that really occurred by chance; a lower p-value increases the likelihood that we will assume a result occurred by chance when actually it was real. Remember, what the p-value tells us is not the probability that our own research hypothesis is true, but rather this: assuming that the null hypothesis is correct, what is the probability that the data we observed—or data more extreme than the data we observed—would have occurred by chance.

Whether we choose a one-tailed or a two-tailed test tells us what we mean when we say “data more extreme than.” Remember that normal curve? A two-tailed test is agnostic as to the direction of our results—and many of the most common tests for statistical significance that we perform, like the Chi square, are two-tailed by default. However, if you are only interested in a result that occurs in a particular direction, you might choose a one-tailed test. For instance, if you were testing a new blood pressure medication, you might only care if the blood pressure of those taking the medication is significantly lower than those not taking the medication—having blood pressure significantly higher would not be a good or helpful result, so you might not want to test for that.

Having determined the parameters for our analysis, we then compute our test of statistical significance. There are different tests of statistical significance for different variables (for example, the Chi square discussed in the chapter on bivariate analyses ), as you will see in other chapters of this text, but all of them produce results in a similar format. We then compare this result to the p value we already selected. If the p value produced by our analysis is lower than the confidence level we selected, we can reject the null hypothesis, as the probability that our result occurred by chance is very low. If, on the other hand, the p value produced by our analysis is higher than the confidence level we selected, we fail to reject the null hypothesis, as the probability that our result occurred by chance is too high to accept. Keep in mind this is what we do even when the p value produced by our analysis is quite close to the threshold we have selected. So, for instance, if we have selected the confidence level of p<0.05 and the p value produced by our analysis is p=0.0501, we still fail to reject the null hypothesis and proceed as if there is not any support for our research hypothesis.

Thus, the process of null hypothesis significance testing proceeds according to the following steps:

• Determine the null hypothesis
• Set the confidence level and whether this will be a one-tailed or two-tailed test
• Compute the test value for the appropriate significance test
• Compare the test value to the critical value of that test statistic for the confidence level you selected
• Determine whether or not to reject the null hypothesis

Your statistical analysis software will perform steps 3 and 4 for you (before there was computer software to do this, researchers had to do the calculations by hand and compare their results to figures on published tables of critical values). But you as the researcher must perform steps 1, 2, and 5 yourself.

Confidence Intervals & Margins of Error

When talking about statistical significance, some researchers also use the terms confidence intervals and margins of error . Confidence intervals are ranges of probabilities within which we can assume the true population parameter lies. Most typically, analysts aim for 95% confidence intervals, meaning that in 95 out of 100 cases, the population parameter will lie within the upper and lower levels specified by your confidence interval. These are calculated by your statistics software as well. The margin of error, then, is the range of values within the confidence interval. So, for instance, a 2021 survey of Americans conducted by the Robert Wood Johnson Foundation and the Harvard T.H. Chan School of Public Health found that 71% of respondents favor substantially increasing federal spending on public health programs. This poll had a 95% confidence interval with a +/- 3.6 margin of error. What this tells us is that there is a 95% probability (19 in 20) that between 67.4% (71-3.6) and 74.6% (71+3.6) of Americans favored increasing federal public health spending at the time the poll was conducted. When a figure reflects an overwhelming majority, such as this one, the margin of error may seem of little relevance. But consider a similar poll with the same margin of error that sought to predict support for a political candidate and found that 51.5% of people said they would vote for that candidate. In that case, we would have found that there was a 95% probability that between 47.9% and 55.1% of people intended to vote for the candidate—which means the race is total tossup and we really would have no idea what to expect. For some people, thinking in terms of confidence intervals and margins of error is easier to understand than thinking in terms of p values; confidence intervals and margins of error are more frequently used in analyses of polls while p values are found more often in academic research. But basically, both approaches are doing the same fundamental analysis—they are determining the likelihood that the results we observed or a similarly-meaningful result would have occurred by chance.

What Does Significance Testing Tell Us?

One of the most important things to remember about significance testing is that, while the word “significance” is used in ordinary speech to mean importance, significance testing does not tell us whether our results are important—or even whether they are interesting. A full understanding of the relationship between a given set of variables requires looking at statistical significance as well as association and the theoretical importance of the findings. Table 1 provides a perspective on using the combination of significance and association to determine how important the results of statistical analysis are—but even using Table 1 as a guide, evaluating findings based on theoretical importance remains key. So: make sure that when you are conducting analyses, you avoid being misled into assuming that significant results are sufficient for making broad claims about the importance and meaning of results. And remember as well that significance only tells us the likelihood that the pattern of relationships we observe occurred by chance—not whether that pattern is causal. For, after all, quantitative research can never eliminate all plausible alternative explanations for the phenomenon in question (one of the three elements of causation, along with association and temporal order).

• Getting 7 heads on 7 coin flips
• Getting 5 heads on 7 coin flips
• Getting 1 head on 10 coin flips

Then check your work using the Coin Flip Probability Calculator .

• As the advertised hourly pay for a job goes up, the number of job applicants increases.
• Teenagers who watch more hours of makeup tutorial videos on TikTok have, on average, lower self-esteem.
• Couples who share hobbies in common are less likely to get divorced.
• Assume a research conducted a study that found that people wearing green socks type on average one word per minute faster than people who are not wearing green socks, and that this study found a p value of p<0.01. Is this result statistically significant? Is this result practically significant? Explain your answers.
• If we conduct a political poll and have a 95% confidence interval and a margin of error of +/- 2.3%, what can we conclude about support for Candidate X if 49.3% of respondents tell us they will vote for Candidate X? If 24.7% do? If 52.1% do? If 83.7% do?
• One way to think about this is to imagine that your result has been plotted on a bell curve. Statistical significance tells us the probability that the "real" result—the thing that is true in the real world and not due to random chance—is at the same point as or further along the skinny tails of the bell curve than the result we have plotted. ↵
• In other words, what you get when you multiply. ↵
• They also are not appropriate for censuses—but you do not need inferential statistics in a census because you are looking at the entire population rather than a sample, so you can simply describe the relationships that do exist. ↵

A distribution of values that is symmetrical and bell-shaped.

A graph showing a normal distribution—one that is symmetrical with a rounded top that then falls away towards the extremes in the shape of a bell

The sum of all the values in a list divided by the number of such values.

The theorem that states that if you take a series of sufficiently large random samples from the population (replacing people back into the population so they can be reselected each time you draw a new sample), the distribution of the sample means will be approximately normally distributed.

A statistical measure that suggests that sample results can be generalized to the larger population, based on a low probability of having made a Type 1 error.

How likely something is to happen; also, a branch of mathematics concerned with investigating the likelihood of occurrences.

Measurement error created due to the fact that even properly-constructed random samples are do not have precisely the same characteristics as the larger population from which they were drawn.

The theorem in probability about the likelihood of a given outcome occurring repeatedly over multiple trials; this is determined by multiplying the probabilities together.

The theorem addressing the determination of the probability of a given outcome occurring at least once across a series of trials; it is determined by adding the probability of each possible series of outcomes together.

A method of testing for statistical significance in which an observed relationship, pattern, or figure is tested against a hypothesis that there is no relationship or pattern among the variables being tested

Null hypothesis significance testing.

The error you make when you do not infer a relationship exists in the larger population when it actually does exist; in other words, a false negative conclusion.

The error made if one infers that a relationship exists in a larger population when it does not really exist; in other words, a false positive error.

A measure of accuracy of sample statistics computed using the standard deviation of the sampling distribution.

The hypothesis that there is no relationship between the variables in question.

The probability that the sample statistics we observe holds true for the larger population.

A measure of statistical significance used in crosstabulation to determine the generalizability of results.

A range of estimates into which it is highly probable that an unknown population parameter falls.

A suggestion of how far away from the actual population parameter a sample statistic is likely to be.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Quantitative data collection and analysis

• Testing hypotheses
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• Samples and population
• Statistical tests - parametric
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• Analysing relationships
• Useful Books

Testing Hypotheses

• What is a hypothesis?
• Significance testing
• One-tailed or two-tailed?
• Degrees of freedom

A hypothesis is a statement that we are trying to prove or disprove. It is used to express the relationship between variables  and whether this relationship is significant. It is specific and offers a prediction on the results of your research question.

Your research question  will lead you to developing a hypothesis, this is why your research question needs to be specific and clear.

The hypothesis will then guide you to the most appropriate techniques you should use to answer the question. They reflect the literature and theories on which you basing them. They need to be testable (i.e. measurable and practical).

Null hypothesis  (H 0 ) is the proposition that there will not be a relationship between the variables you are looking at. i.e. any differences are due to chance). They always refer to the population. (Usually we don't believe this to be true.)

e.g. There is  no difference in instances of illegal drug use by teenagers who are members of a gang and those who are not..

Alternative hypothesis  (H A ) or ( H 1 ):  this is sometimes called the research hypothesis or experimental hypothesis. It is the proposition that there will be a relationship. It is a statement of inequality between the variables you are interested in. They always refer to the sample. It is usually a declaration rather than a question and is clear, to the point and specific.

e.g. The instances of illegal drug use of teenagers who are members of a gang  is different than the instances of illegal drug use of teenagers who are not gang members.

A non-directional research hypothesis - reflects an expected difference between groups but does not specify the direction of this difference (see two-tailed test).

A directional research hypothesis - reflects an expected difference between groups but does specify the direction of this difference. (see one-tailed test)

e.g. The instances of illegal drug use by teenagers who are members of a gang will be higher t han the instances of illegal drug use of teenagers who are not gang members.

Then the process of testing is to ascertain which hypothesis to believe. 

It is usually easier to prove something as untrue rather than true, so looking at the null hypothesis is the usual starting point.

The process of examining the null hypothesis in light of evidence from the sample is called significance testing . It is a way of establishing a range of values in which we can establish whether the null hypothesis is true or false.

The debate over hypothesis testing

There has been discussion over whether the scientific method employed in traditional hypothesis testing is appropriate.  

See below for some articles that discuss this:

• Gill, J. (1999) 'The insignificance of null hypothesis testing',  Politics Research Quarterly , 52(3), pp. 647-674 .
• Wainer, H. and Robinson, D.H. (2003) 'Shaping up the practice of null hypothesis significance testing',  Educational Researcher, 32(7), pp.22-30 .
• Ferguson, C.J. and Heener, M. (2012) ' A vast graveyard of undead theories: publication bias and psychological science's aversion to the null' ,  Perspectives on Psychological Science, 7(6), pp.555-561 .

Taken from: Salkind, N.J. (2017)  Statistics for people who (think they) hate statistics. 6th edn. London: SAGE pp. 144-145.

• Null hypothesis - a simple introduction (SPSS)

A significance level defines the level when your sample evidence contradicts your null hypothesis so that your can then reject it. It is the probability of rejecting the null hypothesis when it is really true.

e.g. a significance level of 0.05 indicates that there is a 5% (or 1 in 20) risk of deciding that there is an effect when in fact there is none.

The lower the significance level that you set,  then the evidence from the sample has to be stronger to be able to reject the null hypothesis.

N.B.  - it is important that you set the significance level before you carry out your study and analysis.

Using Confidence Intervals

I t is possible to test the significance of your null hypothesis using Confidence Interval (see under samples and populations tab).

- if the range lies outside our predicted null hypothesis value we can reject it and accept the alternative hypothesis  

The test statistic

This is another commonly used statistic

• Write down your null and alternative hypothesis
• Find the sample statistic (e.g.the mean of your sample)
• Calculate the test statistic Z score (see under Measures of spread or dispersion and Statistical tests - parametric). In this case the sample mean is compared to the population mean (assumed from the null hypothesis) and the standard error (see under Samples and population) is used rather than the standard deviation.
• Compare the test statistic with the critical values (e.g. plus or minus 1.96 for 5% significance)
• Draw a conclusion about the hypotheses - does the calculated z value lies in this critical range i.e. above 1.96 or below -1.96? If it does we can reject the null hypothesis. This would indicate that the results are significant (or an effect has been detected) - which means that if there were no difference in the population then getting a result that you have observed would be highly unlikely therefore you can reject the null hypothesis.

Type I error  - this is the chance of wrongly rejecting the null hypothesis even though it is actually true, e.g. by using a 5% p  level you would expect the null hypothesis to be rejected about 5% of the time when the null hypothesis is true. You could set a more stringent p  level such as 1% (or 1 in 100) to be more certain of not seeing a Type I error. This, however, makes more likely another type of error (Type II) occurring.

Type II error  - this is where there is an effect, but the  p  value you obtain is non-significant hence you don’t detect this effect.

• Statistical significance - what does it really mean?
• Statistical tables

One-tailed tests - where we know in which direction (e.g. larger or smaller) the difference between sample and population will be. It is a directional hypothesis.

Two-tailed tests - where we are looking at whether there is a difference between sample and population. This difference could be larger or smaller. This is a non-directional hypothesis.

If the difference is in the direction you have predicted (i.e. a one-tailed test) it is easier to get a significant result. Though there are arguments against using a one-tailed test (Wright and London, 2009, p. 98-99)*

*Wright, D. B. & London, K. (2009)  First (and second) steps in statistics . 2nd edn. London: SAGE.

N.B. - think of the ‘tails’ as the regions at the far-end of a normal distribution. For a two-tailed test with significance level of 0.05% then 0.025% of the values would be at one end of the distribution and the other 0.025% would be at the other end of the distribution. It is the values in these ‘critical’ extreme regions where we can think about rejecting the null hypothesis and claim that there has been an effect.

Degrees of freedom ( df)  is a rather difficult mathematical concept, but is needed to calculate the signifcance of certain statistical tests, such as the t-test, ANOVA and Chi-squared test.

It is broadly defined as the number of "observations" (pieces of information) in the data that are free to vary when estimating statistical parameters. (Taken from Minitab Blog ).

The higher the degrees of freedom are the more powerful and precise your estimates of the parameter (population) will be.

Typically, for a 1-sample t-test it is considered as the number of values in your sample minus 1.

For chi-squared tests with a table of rows and columns the rule is:

(Number of rows minus 1) times (number of columns minus 1)

Any accessible example to illustrate the principle of degrees of freedom using chocolates.

• You have seven chocolates in a box, each being a different type, e.g. truffle, coffee cream, caramel cluster, fudge, strawberry dream, hazelnut whirl, toffee. 
• You are being good and intend to eat only one chocolate each day of the week.
• On the first day, you can choose to eat any one of the 7 chocolate types  - you have a choice from all 7.
• On the second day, you can choose from the 6 remaining chocolates, on day 3 you can choose from 5 chocolates, and so on.
• On the sixth day you have a choice of the remaining 2 chocolates you haven't ate that week.
• However on the seventh day - you haven't really got any choice of chocolate - it has got to be the one you have left in your box.
• You had 7-1 = 6 days of “chocolate” freedom—in which the chocolate you ate could vary!
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• Last Updated: Jan 9, 2024 11:01 AM
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Quantitative Research Methods

• Introduction
• Descriptive and Inferential Statistics
• Hypothesis Testing
• Regression and Correlation
• Time Series
• Meta-Analysis
• Mixed Methods
• Additional Resources
• Get Research Help

Hypothesis Tests

A hypothesis test is exactly what it sounds like: You make a hypothesis about the parameters of a population, and the test determines whether your hypothesis is consistent with your sample data.

• Hypothesis Testing Penn State University tutorial
• Hypothesis Testing Wolfram MathWorld overview
• Hypothesis Testing Minitab Blog entry
• List of Statistical Tests A list of commonly used hypothesis tests and the circumstances under which they're used.

The p-value of a hypothesis test is the probability that your sample data would have occurred if you hypothesis were not correct. Traditionally, researchers have used a p-value of 0.05 (a 5% probability that your sample data would have occurred if your hypothesis was wrong) as the threshold for declaring that a hypothesis is true. But there is a long history of debate and controversy over p-values and significance levels.

Nonparametric Tests

Many of the most commonly used hypothesis tests rely on assumptions about your sample data—for instance, that it is continuous, and that its parameters follow a Normal distribution. Nonparametric hypothesis tests don't make any assumptions about the distribution of the data, and many can be used on categorical data.

• Nonparametric Tests at Boston University A lesson covering four common nonparametric tests.
• Nonparametric Tests at Penn State Tutorial covering the theory behind nonparametric tests as well as several commonly used tests.
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• Next: Regression and Correlation >>
• Last Updated: Aug 18, 2023 11:55 AM
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Investigating the relationships of free will belief, presence of meaning in life, and self-consciousness with authenticity: a mixed-methods study

• Open access
• Published: 30 April 2024

Cite this article

You have full access to this open access article

• Yunus Bayram   ORCID: orcid.org/0000-0002-0375-556X 1 &
• Taner Artan   ORCID: orcid.org/0000-0002-8716-2090 2  

Authenticity refers to personality and situational characteristics that enhance the psychological well-being of individuals. Authenticity develops under the influence of various factors. Drawing on the Theory of Existential Psychotherapy (EPT) framework, the present study aimed to investigate the extent to which free will belief, self-consciousness, and presence of meaning in life predict authenticity. The researchers used a mixed-method design to examine the relationship between these predictors and authenticity. The sample consisted of 505 participants, with 455 in the quantitative design and 50 in the qualitative design. The quantitative analyses revealed a positive association between presence of meaning in life and self-consciousness with authenticity. The findings suggest that self-consciousness mediates the relationship between presence of meaning in life and authenticity, and between free will belief and authenticity, with a full mediating effect for free will belief and a partial mediating effect for presence of meaning in life. The qualitative analyses elucidated the relationships between free will belief-authenticity ,  life meaning-authenticity ,  self-consciousness-authenticity , and free will belief-presence of meaning in life-self-consciousness-authenticity . Overall, the findings indicate that the factors investigated, in line with the theoretical framework proposed in EPT, can enhance authenticity.

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Introduction

The idea of authenticity has a long history in Western and Eastern thought (Hoffman et al., 2009 ; Kernis & Goldman, 2006 ). In the realm of Western philosophy, the expression "Know Thyself," engraved in golden letters at the entrance of the Temple of Apollo in Delphi (Yalom, 1980 ), Socrates' urging for individuals to engage in self-reflection, and Aristotle's notion of "eudaimonia" represent pivotal concepts underpinning the notion of authenticity (Kernis & Goldman, 2006 ). In Eastern thought, the idea of self-creation through one's choices, as prominently expounded in Vedanta, Taoism, and Confucianism, the fundamental Buddhist practice of achieving Nirvana by relinquishing desires and attachments that obstruct one's true self (Hoffman et al., 2009 ), Rumi's portrayal of the Sufi quest for self-knowledge as a means to attain truth, and Yunus Emre's belief that links the core of all knowledge to self-awareness encompass the fundamental elements that compose the concept of authenticity (Ernst, 2011 ).

Scientific interest in authenticity has increased in recent years with the empirical identification of its effect on psychological well-being (Smallenbroek et al., 2017 ; Sutton, 2020 ). Many schools of psychology, including positive, developmental, existential, and clinical psychology, view authenticity as contributing to individual well-being and mental health (Rivera et al., 2019 ; Sutton, 2020 ). Authenticity is also central to Deci and Ryan's Self Determination Theory (SDT) and Rogers' Person-centered Therapy (PCT) (Sutton, 2020 ). Empirical studies have associated authenticity with positive psychological themes such as well-being (Sutton, 2020 ), social support, relationship well-being (Rivera et al., 2019 ), life satisfaction (Lutz et al., 2022 ), purpose seeking (Wood et al., 2008 ), autonomy (Rivera et al., 2019 ), and meaning search (Moisseron-Baudé et al., 2022 ). Additionally, empirical studies have associated authenticity with negative psychological themes such as psychological disorders, substance abuse, anxiety, depression, low well-being (Rivera et al., 2019 ), low life satisfaction (Lutz et al., 2022 ; Moisseron-Baudé et al., 2022 ), influence from the external environment, objectification, and self-alienation (Wood et al., 2008 ; Xia et al., 2021 ).

Existential Psychotherapy Theory (EPT) proposes that to achieve authenticity, individuals must confront free will, meaning in life, isolation, death anxiety, and self-consciousness (May, 1961 ). Each of these existential themes can cause anxiety, but according to EPT, embracing and transcending them is necessary for authenticity (Yalom, 1980 ). Despite the broad field of study in EPT, there has been limited empirical research on the factors that promote authenticity. Researchers pointed out the surprising paucity of empirical studies on the factors that foster authenticity despite its potential benefits (Rivera et al., 2019 ; Zhang et al., 2019 ). In addition, our review of empirical research indicates that few have examined the relationship between authenticity and its predictors.

Most studies on authenticity focus on its relationship with positive and negative psychological themes, with the distinction between state and trait authenticity being a frequently studied topic (Lutz et al., 2022 ). However, current research on the main factors influencing authenticity has not included multiple regressions of the themes proposed in the EPT, which are critical theoretical factors in determining authenticity. Our study addresses this gap by investigating the association between free will belief, presence of meaning in life, self-consciousness, and authenticity, important theoretical concepts in the EPT (Yalom, 1980 ). As a result, our research contributes to the literature by testing a robust theoretical foundation that has been addressed only in limited studies before. Our study aims to investigate how free will belief, presence of meaning in life, and self-consciousness relate to authenticity among young adults in Turkey and the correlations between these factors. In the following parts of the research, we will introduce the theoretical framework and develop our hypotheses. Later, we will share our research methodology and our findings. Finally, we will present our results by discussing our findings.

Theoretical framework and hypothesis development

• Authenticity

Authenticity is that the individual is a being compatible with his true self (Bugental, 1965 ). A person becomes authentic when expressing himself autonomously, meeting his psychological needs, becoming aware of his values and thoughts, and creating his true self through behaviors appropriate to them (May, 1961 ; Yalom, 1980 ). Researchers view authenticity in two different ways: trait authenticity and state authenticity (Lutz et al., 2022 ). Trait authenticity is a group of personality attributes that are effective at the interpersonal level and are viewed as permanent characteristics of authenticity (Wood et al., 2008 ). State authenticity refers to the feelings experienced by individuals in different contexts and events (Zhang et al., 2019 ). Our research focuses on trait authenticity and examines authenticity from this perspective.

Empirical studies have shown that authenticity is related to various positive outcomes such as low depression and anxiety (Kernis & Goldman, 2006 ), meaning-seeking (Lutz et al., 2022 ), individual vitality, happiness, after-life satisfaction, morality, life experience, reducing tension at work and increasing well-being, high job performance, job satisfaction, the satisfaction of the need for autonomy, and personal dynamism (Sutton, 2020 ). Authenticity has also been a protective factor for individuals facing challenges such as motherhood difficulties, LGB-related issues, immigrants living in a stigmatized socio-cultural environment, and individuals experiencing the negative impact of interpersonal conflict (Sutton, 2020 ). However, despite these benefits, limited studies examine the factors influencing authenticity (Zhang et al., 2019 ). These factors include nostalgia (Kelley et al., 2022 ), relationship goals, relationship outcomes, interpersonal trust (Wickham, 2013 ), acting on values (Smallenbroek et al., 2017 ), self-reflection, self-compassion (Zhang et al., 2019 ), will, and personal meaning (Ekşi et al., 2022 ). Unlike other studies, we used EPT to determine the factors that foster authenticity in our current study.

EPT suggests that to be authentic, individuals must focus on self-consciousness and turn to their existence (Yalom, 1980 ). This act involves accepting the anxiety that comes with freedom and meaning and taking responsibility for one's choices (May, 1994 ). Authentic individuals design their existence and life meanings through their free will without relying on external help (May, 1961 ). Psychopathologies arise from individuals escaping this responsibility and fleeing societal expectations. By confronting the primary sources of anxiety in their existence, individuals can become authentic and experience therapeutic effects (Bugental, 1965 ). Based on this theoretical background, our study identifies three existential themes that foster authenticity. We propose the following hypothesis :  Free will belief, presence of meaning in life, and self-consciousness significantly and positively predict authenticity in young adults (H1).

Free will belief and authenticity

Definitions of "free will" vary at the philosophical and theoretical levels. Empirical studies often lean towards more understandable definitions of free will (Stillman et al., 2011 ). According to these definitions, free will refers to a person's assessment of their capacity to control their ideas and actions and make decisions independently of external and internal pressures (Paulhus & Carey, 2011 ). Sub-factors commonly used to measure and define free will include inner versus extrinsic orientation, choice, responsibility, self-control, and planned action (Alper & Sümer, 2017 ). Research on laypersons' perspectives on free will has shown that individuals perceive free will as the conscious control of acts and choices that fulfill one's desires and are free from internal or external constraints (Stillman et al., 2011 ). The main reason for this diversity is that free will forms the basis of many theories, such as EPT (Yalom, 1980 ), SDT, and PCT (Sutton, 2020 ).

Empirical research suggests that believing in free will leads to increased presence of meaning in life, greater life satisfaction, more gratitude, and higher relationship commitment (Crescioni et al., 2016 ). On the other hand, disbelief in free will is associated with feelings of alienation (Seto & Hicks, 2016 ), perceived life stress, reduced perceived meaningfulness, and lower sense of self-efficacy (Crescioni et al., 2016 ). Despite extensive research on free will belief, only some studies have examined its relationship with authenticity, with existing research indicating a positive correlation between free will belief and authenticity (Ekşi et al., 2022 ; Seto & Hicks, 2016 ). In the EPT framework, free will belief is critical to fostering authenticity. According to May ( 1994 ), authentic individuals must recreate themselves and their lives. Free will belief is essential for individuals to design their lives and become authentic by connecting with their pure existence. Sartre ( 2003 ) argues that when individuals confront their pure existence, they inevitably encounter free will. Yalom ( 1980 ) suggests that this encounter can positively contribute to an individual's therapeutic well-being. According to EPT, rejecting free will belief that arises from existence can cause alienation and pathology (Bugental, 1965 ). Based on these theoretical and empirical insights, we propose the following hypothesis: Free will belief significantly and positively predicts authenticity in young adults (H2).

Presence of meaning in life and authenticity

There are multiple definitions of the meaning in life in literature, such as the value judgments an individual places on their life, the pursuit of a specific purpose, life's value in the world, and psychological well-being (Wilt et al., 2021 ). Victor Frankl ( 1992 ), a prominent figure in EPT, viewed the meaning in life as a clinical framework consisting of basic judgments that shape an individual's goals, values, and ideas. However, there have been debates regarding the clinical conceptualization of the meaning in life, resulting in a consensus on three fundamental structures for its clinical treatment: consistency, purpose, and importance (Moisseron-Baudé et al., 2022 ; Wilt et al., 2021 ).

In empirical studies, the presence of meaning in life has a positive relationship with psychological well-being, a sense of community (Moisseron-Baudé et al., 2022 ), free will belief (Ekşi et al., 2022 ), life satisfaction and mindfulness (Allan et al., 2015 ). On the other hand, it has a negative relationship with anxiety, depression, and general psychological stress (Wilt et al., 2021 ). Studies evaluating the relationship between the presence of meaning in life and authenticity reveal positive relationships between these variables (Allan et al., 2015 ; Borawski, 2021 ; Ekşi et al., 2022 ; Lutz et al., 2022 ; Moisseron-Baudé et al., 2022 ; Wilt et al., 2021 ). With our research, we wanted to expand the scientific literature on the nature of this relationship. Empirical studies generally support the arguments put forward in the EPT. According to the EPT, the meaning in life represents an important area of anxiety that should be examined in the authentication process (Yalom, 1980 ). Heidegger ( 1962 ) states that the authentic individual is a being who constructs his meaning in life solely based on himself. Frankl ( 1992 ) says that a semantic gap should be filled only with the meanings that the individual discovers. These unique meanings authenticate an individual's life, situation, and character. One can have the power to discover unique meanings that provide authenticity by confronting meaninglessness (May, 1994 ). Based on this theoretical background and empirical study findings, we propose the following hypothesis: Presence of meaning in life significantly and positively predicts authenticity in young adults (H3).

Mediation effect of self-consciousness

Self-consciousness is a phrase utilized in psychotherapy to express an individual's interest, attention, and awareness towards different aspects of themselves, including their interpretation of their innermost realities and orientation toward the deep structures of their being (Mittal & Balasubramanian, 1987 ). It also suggests a person's capability to delve into the depths of their mental structure that are not immediately apparent (Hart et al., 2019 ). Early studies on the construction of self-consciousness have found that certain individuals are more focused on their feelings and thoughts, while others are more sensitive to the external environment and other people being (Mittal & Balasubramanian, 1987 ). Therefore, later definitions of self-consciousness often used two essential substructures: private and public self-consciousness. Individuals with high private self-consciousness focus more on their values, feelings, beliefs, and life goals. In contrast, those with high public self-consciousness are more concerned with social roles, external appearances, and attitudes (Hart et al., 2019 ). The researchers operationally defined self-consciousness by subdividing it into internal state awareness and self-reflectiveness for private self-consciousness and appearance and style consciousness for public self-consciousness (Mittal & Balasubramanian, 1987 ). The concept of self-consciousness in EPT, which contributes to authenticity, is linked to the internal state awareness subscale in empiric literature (Hart et al., 2019 ; Yalom, 1980 ). Previous empirical research has concluded that internal state awareness predicts happiness, positive affect, active coping, cognition, self-esteem, self-actualization, the satisfaction of psychological needs, and vitality (Klussman et al., 2022 ). Few studies evaluating the relationship between authenticity and self-consciousness claim that attention directed toward one's tendencies and experiences can significantly increase authenticity (Allan et al., 2015 ; Klussman et al., 2022 ).

The research findings support the theoretical ideas in EPT, which propose that self-consciousness requires confronting one's existence and that this confrontation is essential to individual authenticity (May, 1961 ). EPT argues that self-consciousness is nourished by free will belief and presence of meaning in life (Yalom, 1980 ). Bugental ( 1965 ) states that when an individual uses his free will, his awareness of himself increases, and he becomes authentic in this way. Frankl ( 1992 ) emphasizes that the meanings of life increase the individual's awareness of himself and make him an authentic being. Empirical studies confirm positive and significant relationships between self-awareness and free will belief (Bell & Showers, 2021 ) and the presence of meaning in life (Allan et al., 2015 ). Research results and theoretical foundations suggest that free will belief and presence of meaning in life can indirectly affect authenticity through self-awareness. We have not found any empirical studies discussing such a mediating effect before. Therefore, based on both theory and empirical studies, we propose the following hypotheses: Self-consciousness has a mediating effect on the free will belief-authenticity relationship (H4), and Self-consciousness has a mediating effect on the relationship between the presence of meaning in life and authenticity (H5).

Present study

Previous studies cited in the theoretical background reveal significant relationships regarding authenticity. These relationship clusters often consist of evaluations between positive psychology variables and authenticity (Rivera et al., 2019 ; Sutton, 2020 ; Xia et al., 2021 ). However, only some studies examine the simultaneous relationships of variables that directly feed authenticity (Ekşi et al., 2022 ; Kelley et al., 2022 ; Moisseron-Baudé et al., 2022 ). It is necessary to discuss the factors that nourish authenticity, which is essential in ensuring the individual's well-being broadly and holistically (Zhang et al., 2019 ). At this point, EPT offers a comprehensive theoretical framework for authenticity. This framework provides valuable contributions to forming scientific hypotheses and successfully determining the field of discussion (May, 1961 ; Yalom, 1980 ). The main aim of our current study was to examine how free will belief, presence of meaning in life, and self-consciousness relate to the authenticity in young adults in Turkey. We also investigated the mediating role of self-consciousness to reveal this set of relationships more clearly. Our proposed model depicts in Fig.  1 .

Theoretical model

Research design

We used convergent parallel mixed methods design in the research. First, we collected qualitative and quantitative data simultaneously. In the next step, we analyzed the data we obtained separately. Finally, we combined the analyses and made our discussions (Creswell & Plano Clark, 2017 ). (see Fig.  2 ).

Flowchart of the convergent parallel design adapted from Creswell and Plano Clark ( 2017 )

Participants and procedure

We conducted this study with the approval of the Istanbul-Cerrahpasa University Social Sciences Ethics Committee. We obtained the data through online surveys and face-to-face interviews with young adult participants in August and September 2021. In collecting qualitative and quantitative data, we used criterion sampling, one of the purposive sampling methods. Accordingly, the inclusion conditions of the participants were to be Turkish citizens living in Turkey and between the ages of 18–45. We used the online questionnaire form created through Microsoft Office Form, an online platform, to collect quantitative data. The online survey link was shared with 600 people who met the inclusion criteria, and 455 participants completed the survey questions online. Volunteers did not request any payment for their participation in the study. There was no need to delete any survey data. 39.6% of the participants in the study were men, and 60.4% were women. We determined the mean age of the participants as 28.3 (SD = 7.6). (see Table  1 ) We performed a post hoc power analysis with G*Power 3.1 to assess the sample size's adequacy in the study's quantitative design. The analysis parameters were a sample size of 455, 3 independent variables, an effect size of 0.26 (F 2 ), and a significance level of 0.05. The analysis resulted in a power value of 1.00, indicating that the study had a sufficient sample size and number of independent variables to achieve adequate statistical power for detecting the influence of predictors on the outcome variable (Faul et al., 2007 ).

We collected qualitative data for our study through face-to-face interviews with 50 young adults who met the inclusion criteria. These participants were different from those in the quantitative data collection process. We used a semi-structured interview format and ensured the interview environment was free from distractions. With the participants' consent, we recorded the interviews and transcribed them. We informed the participants that participation in face-to-face meetings was voluntary and anonymous, and that personal data would not be shared with third parties except under certain conditions. The interviews lasted an average of 34.6 min. Of the participants, 30 were women, and 20 were men, with a mean age of 25.8 (SD = 5.2). (see Table  1 ) The study used the data saturation method to establish the optimal sample size in the qualitative design. This method requires gathering data until the analysis of additional data does not yield any new themes or insights, which signifies that data saturation is attained (Fusch & Ness, 2015 ). This study reached a data saturation point after conducting interviews with 50 participants, beyond which no further themes or insights emerged. Hence, a sample size of 50 was considered adequate for the study objectives.

We adhered to the American Psychological Association Ethical Standards throughout the data collection process. We obtained verbal and written consent from all study participants before participation. All participants received notice about the secrecy of the information, that they could terminate the participation at any time, and that the data obtained is not shared with third parties except under certain conditions. Our research complies with the ethical standards set out in the 2013 revision of the Declaration of Helsinki.

Demographic characteristics

The researchers created a form to collect demographic information, such as age, gender, and marital status, from adults. Both questionnaires and semi-structured interview forms have this measurement tool.

Authenticity scale

The original Authenticity Scale developed by Wood et al. ( 2008 ) consists of 12 items (an example is: "I think it is better to be yourself than to be popular."), assessed on a 7-point Likert scale (1 (does not describe me at all) to 7 (describes me very well)), and comprises three sub-factors ( authentic living, self-alienation, and acceptance of external influence ). The self-alienation sub-factor represents the gap between an individual's true self and conscious awareness, with an internal consistency coefficient of 0.78. The authentic life sub-factor reflects the alignment between an individual's conscious awareness and behaviors, with an internal consistency coefficient of 0.69. Finally, the acceptance of external influence sub-factor denotes an individual's beliefs related to accepting the influence of others and living according to their expectations, with an internal consistency coefficient of 0.78. Subtracting scores of accepting external influences and self-alienation sub-factors from the authentic life sub-factor score yields the authenticity score within the scale. In the original scale, the dimension of authentic life is positively related to happiness. In contrast, the dimensions of self-alienation and acceptance of external influence are positively related to stress and anxiety (Wood et al., 2008 ). The Authenticity Scale was adapted into Turkish by İlhan and Özdemi̇r ( 2013 ). The current study’s Cronbach alpha coefficient for the entire scale was 0.71.

Self-consciousness scale

The original Self-Consciousness Scale created by Mittal and Balasubramanian ( 1987 ) comprises 19 items (an example is: "I am constantly examining my motives.") assessed on a 5-point Likert scale (0 (does not describe me at all) to 4 (describes me very well)) and five sub-factors ( self-reflectiveness, internal state awareness, appearance consciousness, style consciousness, and social anxiety ). The internal state awareness sub-factor indicates an individual's awareness of their inner thoughts, ideas, feelings, and impulses. The original study found acceptable internal consistency for this sub-measurement (Mittal & Balasubramanian, 1987 ). The scale measures the existential aspect of self-consciousness through the internal state awareness subfactor. The Self-consciousness Scale was adapted into Turkish by Akin et al. ( 2007 ). In the present study, Cronbach's alpha coefficient for the sub-dimension of internal state awareness within the scale was 0.72.

Free will and determinism scale

The Free Will and Determinism Scale, developed by Paulhus and Carey ( 2011 ), consists of 27 items (an example item is: "Strength of mind can always overcome the body's desires.") assessed on a 5-point Likert scale (1 (strongly disagree) to 5 (strongly agree)) and four sub-factors ( free will, randomness, fatalistic determinism, and scientific determinism ). The scale measures free will belief through the free will subfactor. In the original measurement instrument, the sub-dimension of free will pertains to the concept that individuals possess full authority over their choices and actions, consequently holding personal accountability. Therefore, this sub-measure considers moral responsibility as a part of free will, and the questions in the scale include values related to free will and ethical responsibility. The internal reliability coefficient for this specific subscale within the original instrument registers at 0.70. Alper and Sümer ( 2017 ) adapted the Free Will and Determinism Scale into Turkish. In the present study, Cronbach's alpha coefficient for the sub-dimension of free will within the scale was 0.73.

Meaning in life scale

The Meaning in Life Scale, consisting of 10 items (an example item is: "I understand my life's meaning.") developed by Steger et al. ( 2006 ), is assessed on a 7-point Likert scale (1 (completely false) to 7 (completely true)) and includes two sub-factors. The presence of meaning sub-measurement assesses an individual's subjective sense that their life is meaningful. In contrast, the search for meaning sub-measurement gauges an individual's drive and orientation toward finding meaning in their life. The scale structure is designed to establish orthogonal factors, allowing for the evaluation of two distinct foundations. The scale measures the meaning in life through the presence of meaning subfactor. Therefore, in the present study, we exclusively employed the " presence of meaning " sub-factor for assessing the construct of meaning in life. Given the scale's orthogonal structure, the " search for meaning " sub-factor was intentionally omitted from the investigation, and a total score encompassing the entire scale was not calculated. The internal consistency coefficient for the presence of meaning sub-factor is 0.86 (Steger et al., 2006 ). The Turkish adaptation of the Meaning in Life Scale was created by Akın and Taş ( 2015 ). In the present study, Cronbach's alpha coefficient for the sub-dimension of presence of meaning within the scale was 0.85.

We collected participant narratives through semi-structured interviews. The researchers designed the interview form based on theoretical and empirical data aligned with the research's objectives. The tool mainly consists of open-ended questions that ask participants to evaluate the factors that affect authenticity.

Data analysis

Quantitative study.

The researchers employed statistical analysis software SPSS 22.0 to calculate the study's variables' means, standard deviations, and correlations. They then used multivariate structural equation modeling (SEM) to investigate the role of self-consciousness as a moderator and the link between observed and latent variables. SEM combines factor analysis (measurement model) and multivariate regression analysis (structural model). Power analysis in SEM commonly applies the likelihood ratio test proposed by Satorra and Saris ( 1985 ) and the RMSEA-based analysis proposed by MacCallum et al. ( 1994 ). In this study, the researchers followed a two-step SEM procedure using AMOS 20.0. First, they analyzed the measurement model and obtained statistically acceptable results. They then performed structural model tests, utilizing the indices proposed by Kline ( 2011 ) to evaluate the model's fit to the data.

Qualitative study

This research uses the MAXQDA 2018 statistical program to analyze data obtained from semi-structured interviews. The analysis follows the Thematic Analysis (TA) procedures outlined by Braun and Clarke ( 2006 ). TA is a method that allows for the expression of comprehensive themes within a large data set, providing researchers with insight into dominant themes within the data. This method is beneficial for researching an unstudied area and understanding participants' opinions on a subject. In this study, the researchers followed a six-stage application model of TA, which included the following:

Deciphering the interview recordings to form initial ideas about the data.

Becoming familiar with the data through multiple readings of the recordings.

Using an inductive approach to estimate participants' views on factors affecting authenticity and coding the data based on the theoretical framework.

Evaluating the similarity relationship between codes and grouping them into different themes.

Conducting a detailed analysis of the new relationship networks.

Reviewing the analysis and revealing the final version of the themes and networks.

Descriptive statistics and zero-order correlations

Table 2 presents the means, standard deviations, and correlations for the variables used in the study. The data supports the assumption of normality of the variables, as the absolute values of skewness and kurtosis are not greater than two, and the results of multiple normality tests were favorable (Kline, 2011 ). The study's scales' reliability scores are higher than the usually accepted threshold value (Nunnally, 1978 ). The correlation analysis indicates that authenticity has a moderately positive relationship with free will belief, presence of meaning in life, and self-consciousness. (see Table  2 ).

Analysis of the measurement model and structural model

The study's measurement model includes 27 observed and six latent variables (free will, presence of meaning in life, self-consciousness, authentic living, self-alienation, and acceptance of external influence). The results of this model demonstrated a strong and appropriate fit to the data: χ2 = 596.24; df = 306; χ2/df = 1.94; RMSEA = 0.04; GFI = 0.90; SRMR = 0.05; CFI = 0.90; and PClose = 0.90. All indicators represented their latent factors. While the commonly used reference points outlined by Kline ( 2011 ) in the literature persist, recent research in SEM has highlighted that fit indices can exhibit variability based on sample size and the number of indicators. Consequently, our present study employed Monte Carlo simulation to establish cut-off values for SRMR, RMSEA, GFI, and CFI specific to our research context. Our analysis revealed the following values: SRMR ≤ 0.08, RMSEA ≤ 0.07, GFI ≥ 0.90, and CFI ≥ 0.89. The fit indices derived from simulations suggest that the collected data align well with an acceptable fit for the proposed model.

Next, we tested the structural model in Fig.  1 . As a result of the tests, the proposed structures showed a significant and strong agreement with the data: χ2 = 272.48, df = 111, χ2/df = 2.45; GFI = 0.93; CFI = 0.90; SRMR = 0.04; RMSEA = 0.05; and PClose = 0.97. The study compared the original structural model with three other mathematically equivalent models to provide a stronger quantitative argument for the relationships in the proposed structural model. The findings in Fig.  3 show that the relationships obtained from the equivalent models are largely consistent with those proposed in the original model. In conclusion, the actual structural model's relationships strongly parallel those presented in the equivalent models. Additionally, three equivalent models exhibit a significant and robust concordance with the collected data. The fit indices of the three equivalent models are parallel to each other. While the fit indices for Models A and B are χ2/df = 2.45; GFI = 0.93; CFI = 0.90; SRMR = 0.05; RMSEA = 0.05, the fit indices for Model C are χ2/df = 2.44; GFI = 0.93; CFI = 0.90; SRMR = 0.05; RMSEA = 0.05. However, the fit indices of the original model, particularly concerning the SRMR value, are at a more acceptable level compared to the equivalent models, albeit with slight differences.

Equivalent models test results. Note: All paths depict the standardized coefficient’s effects. ** p  <.05

Finally, we performed a post-hoc Monte Carlo power analysis to determine the statistical power of the structural model. The results in Table  3 indicate that the examined model exhibits high power levels ranging from 0.97 to 1.00 for various sample sizes. These findings suggest that the analysis possesses a significant degree of statistical reliability. Furthermore, the Monte-Carlo simulation points to a statistical power 1.00 for the given sample size (455). As indicated by the simulation, the effect magnitudes reveal that the impact of the independent variables under scrutiny on the dependent variable across varying sample sizes falls within the medium to high range.

The relationship between presence of meaning in life, free will belief, self- consciousness, and authenticity

Figure  4 displays the results of the structural model test. The findings indicate that the presence of meaning in life moderately and significantly predicts self-consciousness (0.53; p  < 0.01) and authenticity (0.26; p  < 0.01). This supports H3. Additionally, the results reveal that free will belief moderately and significantly predicts self-consciousness (0.36; p  < 0.01). However, free will belief did not significantly predict authenticity (0.01; p  > 0.05). The mediating effect of self-consciousness is the main reason for this finding. Without the effect of self-consciousness, free will belief positively, moderately, and significantly predicts authenticity (0.23; p  < 0.01), supporting H2. Finally, the results show that self-consciousness moderately and significantly predicts authenticity (0.28; p  < 0.01). Therefore, it is possible to think of self-consciousness as a tool for promoting authenticity.

Structural model test results. Note: All paths depict the standardized coefficient’s effects. ** p  <.05

The free will factor encompasses the belief in free will and moral responsibility. To elucidate the specific relational networks embedded within the tested model, we exclusively examined the model using items related solely to free will, excluding those pertaining to moral responsibility. The relationship between free will and authenticity exhibited a negligible change upon removing moral responsibility measures from the model (0.04, p  > 0.05). Notably, all other relationships remained identical with the original model. Our findings indicate that the statistical outcomes of the relationships in the free-will-only model did not significantly deviate from those in the original model.

Self-consciousness as a mediating variable

This study employed the bootstrap technique to evaluate the mediating variable effects of self-consciousness in the model presented in Fig.  1 . Table 4 summarizes the direct and indirect effects obtained from the mediation test. The results show that the self-consciousness variable has a significant partial mediation effect in the relationship between the presence of meaning in life and authenticity (β = 0.15, se = 0.06, p  < 0.05). The indirect mediation effect accounted for 36.4% of the total effect (β = 0.41, se = 0.05, p  < 0.05), thus confirming hypothesis H5. Furthermore, the self-consciousness variable had a significant full mediation effect in the relationship between free will belief and authenticity (β = 0.10, se = 0.05, p  < 0.05). (see Table  4 ) The indirect mediation effect accounted for 90.2% of the total effect (β = 0.11, se = 0.05, p  < 0.05), thus confirming hypothesis H4. In addition, this result supports H2. The findings suggest that self-consciousness is a mediator that indirectly affects the existing meaning-authenticity relationship (partial) and the free will belief-authenticity relationship (full). The results showed that the presence of meaning in life, free will belief, and self-consciousness simultaneously affected the authenticity path coefficient value. This finding supports H1.

Qualitative results

The authenticity top theme consists of the presence of meaning in life– authenticity relationship, free will belief– authenticity relationship, self-consciousness -authenticity relationship and life meaning, free will belief, self-consciousness, and authenticity relationship sub-themes.

The link between presence of meaning in life and authenticity

A large proportion of participants (77.9%) highlighted the significance of the presence of meaning in life in the manifestation of authenticity. The study suggests that the presence of meaning in life is critical in allowing individuals to remain true to their authentic selves, confront fundamental existential anxieties, and continually reinvent themselves through their existence. In this sense, the presence of meaning in life can serve as a driving force to sustain one's authentic being, contributing to a stable and authentic state of existence. This qualitative evidence provides support for hypothesis H3. As an illustration, a 31-year-old female participant (K6) shared the following insights regarding this sub-theme:

"I feel more meaningful when I do scientific research and contribute to humanity. If I did not have such a strong purpose in life, I would probably try to spend my life in a way, like friends, social media, etc. But when a person has a meaning, he lives this life more real. When you choose a meaning for yourself, it is like you live more real. That is why the meaning you choose becomes like a bridge that takes you to more real and true life. Thus, you lead a life more dependent on real and truth by always adhering to that purpose."

The link between free will belief and authenticity

Based on the analysis, most participants (88.9%) stated that freedom is crucial in the emergence of authenticity. The results indicate that freedom helps individuals break away from the false world around them, directs them toward their essential existence, and ultimately assists them in remaining true to themselves. The concept of freedom is critical for individuals to achieve authenticity. Participants noted that individuals who attain freedom are better equipped to confront problematic anxieties and fears of existence and maintain an authentic level of being. Consequently, freedom is essential in developing and sustaining an authentic sense of self. This finding supports H1. Participant K8, a 36-year-old male, shares the following insight regarding how freedom enables an individual to become authentic:

“Let's say things went your way. You have realized that what is considered trustworthy in this world is foolish things. So, what do you have left? I think I left with my true self. Freedom gave me this. The foolish world tells me to consume like there is no death. Freedom says that, remember, death is your truth! The world tells me I will be happy if I spend money. Freedom tells me you create your happiness through your own choices and values. The world tells me to sit back and watch. Freedom says that your existence is not meant for watching; you must get on stage and live."

The link between self-consciousness and authenticity

According to the analysis, most participants (81.4%) identified self-consciousness as essential to achieving authenticity. The findings indicate that self-consciousness is a powerful instrument for individuals to overcome false societal narratives, confront their existence, and live a life that aligns with their real being. A 29-year-old female participant, K45, highlighted this point, stating the following about the relationship between self-consciousness and authenticity:

"During the interview, I talked about the deep truths of human existence, right? Look at the word I used: "Deep." So, we must strive to achieve it. This effort is self-consciousness. Self-consciousness is like a ladder to the depths of one's being. If only the real truths of person were immediately available. But it is not. One must go down those stairs step by step, reflecting on himself for his truths. There are truths deep inside. Where there is no self-consciousness, lies, bragging, rapes, violence, and arrogance exist. One should get rid of them and go deep. It should go as deep as possible."

According to the analysis, participants frequently viewed turning to self-consciousness as a factor that brings individuals closer to reality. By turning to self-consciousness, individuals separate themselves from the false world narratives. Additionally, the findings suggest that moving towards self-consciousness is supportive in enabling individuals to live in a way consistent with their authentic existence. These results support H4 and H5.

The link between presence of meaning in life, free will belief, self-consciousness, and authenticity

Most participants (72.6%) in the study indicated that the presence of meaning in life, free will belief, and self-consciousness have a holistic impact on authenticity. The participants emphasized that all three factors are equally important for an individual's behavior by their essence. To achieve authenticity, one must turn inward, exercise free will belief in decision-making, and sincerely embrace certain meanings of life while accepting the consequences. The absence of any of these components will slow the attainment of authenticity. The participants' holistic perspective supports H1. Participant K17, a 35-year-old male, stated the following regarding this sub-theme:

"Free choices, the meanings we hold on to in life, turning within ourselves… These are all important parts that make us real people. These parts perform the same function. They free us from falsehood and give us strength to face difficulties. So, of course, all of these parts are necessary and important for me to reach my true self."

This study aims to explore the extent to which free will belief, self-consciousness, and presence of meaning in life predict authenticity among young adults. We used qualitative and quantitative methods to investigate these relationships in greater detail. The study's first hypothesis focuses on how free will belief, self-consciousness, and presence of meaning in life predict authenticity (H1). The research findings indicate that all tested model regressions, except for the relationship between free will belief and authenticity, are positively and significantly related. This lack of association is due to the full mediating effect of self-consciousness, demonstrating that free will belief can indirectly affect authenticity (Kline, 2011 ). Without the effect of mediating self-consciousness, free will belief positively and significantly predicts authenticity. In conclusion, results from the SEM analyses conducted in the quantitative design support H1. Moreover, thematic analyses in the qualitative design revealed that the sub-themes of authenticity included free will belief, self-consciousness, and presence of meaning in life, which further corroborated H1. Our findings suggest that these three factors contribute to enhancing authenticity among young adults. Empirical reviews reveal limited studies on the multiple regression relationships of authenticity predictors. These studies indicate that personal meaning and free will belief positively affect authenticity, which aligns with our findings (Ekşi et al., 2022 ). Other regression relationships, such as value orientation (Smallenbroek et al., 2017 ), relationship purpose (Wickham, 2013 ), self-compassion (Zhang et al., 2019 ), and mindfulness (Allan et al., 2015 ), also influence authenticity. When considered collectively, these relationships are consistent with our study's results.

Our research findings aligned well with the theoretical concepts proposed in the Existential Philosophy of Therapy (EPT). EPT posits that self-consciousness, free will belief, and presence of meaning in life encourage individuals to face their true selves, promoting authenticity and providing therapeutic benefits (May, 1961 ; Yalom, 1980 ). This attempt frees him from the world of instructions of Das Man , which clings to the individual, surrounding, ghosting, mechanizing, and objectifying it (Heidegger, 1962 ; Sartre, 2003 ). The world of instructions, full of ready-made meanings, has effects on individuals that falsify, render meaningless, and create psychopathologies (Bugental, 1965 ). Our study's three variables can serve as tools to promote authenticity and protect individuals from falsifying pathologies, helping them reconnect with their genuine selves.

Our second hypothesis (H2) examined the connection between free will belief and authenticity. Both quantitative (path analysis) and qualitative (thematic analyses) tests supported this hypothesis. Our findings indicate that free will belief positively predicts authenticity. This outcome is consistent with prior research on authenticity, which has also found that free will belief increases authenticity (Ekşi et al., 2022 ; Seto & Hicks, 2016 ). Therefore, our results on H2 correspond with previous empirical studies. Our study supports the theoretical arguments proposed in various approaches such as SDT and PCT (Sutton, 2020 ), particularly in EPT (May, 1994 ), regarding the relationship between free will belief and authenticity. One of the main reasons for this link may be that free will is a fundamental presence of a human's pure existence, which is synonymous with freedom (Yalom, 1980 ). Avoiding free will can lead to a false sense of being, reducing the ability to establish meaningful relationships with one's environment (Frankl, 1992 ; Sartre, 2003 ). Therefore, practitioners may encourage individuals to embrace free will and responsibility as therapeutic actions to increase authenticity. Such action may involve confronting individuals with the anxiety and fear associated with making choices that reflect their true selves. These confrontations can lead to an increase in the intensity of an individual's experience of guilt. EPT asserts that guilt is an inevitable human experience. However, instead of being an obstacle, guilt provides an opportunity for individuals to question their actions and values (May, 1994 ). This self-examination creates the possibility of understanding one's authentic existence. Sartre ( 2003 ) argues that the pain elicited by guilt is productive and necessary. On the other hand, Yalom ( 1980 ) considers guilt emerging in the process of personal authenticity as a necessity that enables the revelation of the meaning of life and free will.

We investigated the relationship between the presence of meaning in life and authenticity in our third hypothesis (H3). The quantitative and qualitative data analyses we conducted supported and confirmed our hypothesis. Our results showed that the presence of meaning in life positively predicts authenticity, which is consistent with previous research on authenticity (Borawski, 2021 ; Lutz et al., 2022 ; Moisseron-Baudé et al., 2022 ; Wilt et al., 2021 ). Therefore, our findings support the existing empirical literature on this topic. The findings of our third hypothesis are consistent with the theoretical arguments presented in EPT. Our results suggest that having fundamental meanings and frameworks for one's life can enhance the relationship with one's authentic self. As individuals create and act upon their meanings in life, they can align with their true nature (Yalom, 1980 ). The meanings an individual creates shape their life experiences and contribute to a sense of fulfillment and authenticity (Heidegger, 1962 ). Conversely, lacking personal meaning can lead to an existential vacuum, a pathological state (Frankl, 1992 ). Practitioners can support authenticity by helping individuals develop or strengthen their capacity to create meaning.

Our study evaluated the fourth and fifth hypotheses, which examined the role of self-consciousness as a mediator (H4, H5). Results from quantitative analyses conducted through structural equation modeling (SEM) and thematic analyses of qualitative data supported H4 and H5, indicating that self-consciousness mediates the relationships between free will belief and authenticity and between the presence of meaning in life and authenticity. We did not find previous empirical studies that directly supported our findings. This may be due to a lack of research on the factors contributing to authenticity (Zhang et al., 2019 ). Furthermore, the narrow philosophical framework used to conceptualize self-consciousness may have limited its association with authenticity. The results of our fourth and fifth hypotheses support the arguments put forward in EPT. Our outcomes imply that self-consciousness is essential in creating a sense of responsibility for one's life and adopting vital meanings, which nourish one's perception of self-consciousness. Free choice and meaning creation require a detailed examination of self-consciousness (May, 1961 ). Self-consciousness is the essential factor that confronts the individual with his pure, given existence (Yalom, 1980 ). That is why we can better understand the relationships between authenticity and its predictors by understanding the role of self-consciousness.

Limitations and implications for research

There are a few limitations to the current study that require attention. Firstly, due to the cross-sectional design, no causal inferences can be made among the variables. Future studies could adopt longitudinal or experimental methods to address this limitation. Secondly, relying solely on subjective assessments may lead to biases related to social desirability, posing a threat to internal validity. Future studies could employ multiple evaluation methods, such as peer or parent reports, to mitigate this bias. Thirdly, the study was limited to a young adult sample, and future research should extend the sample to include other populations, such as working professionals. Fourthly, the study did not collect information on any possible mental disorders in the sample.

Lastly, the limitation of the study is the unexplored impact of "death anxiety" on authenticity. According to Heidegger ( 1962 ), confronting the theme of death is an essential component of an individual's process of becoming authentic. Death anxiety, which individuals have been grappling with since childhood and which has the potential to adulterate their authenticity throughout life, is considered one of the most crucial factors that needs to be effectively overcome. Hence, EPT posits that death anxiety, which individuals have been avoiding through unhealthy defense mechanisms since childhood and is at the root of neurotic behaviors, fundamentally underlies the most critical factors. The avoidance of existential death anxiety, which inherently carries the fear of ceasing to exist, necessarily grounds the avoidance of one's authentic structure. Therefore, in EPT, the therapeutic process aimed at authenticating the individual includes the theme of death (Yalom, 1980 ). The primary reason for not incorporating death anxiety into our research stems from this theoretical framework. Existential death anxiety is a concept that individuals persistently avoid, distort, and deceive themselves more about compared to the other themes we included in the research, making it challenging to identify (May, 1961 ; Yalom, 1980 ). Therefore, the cross-sectional patterns of individuals' perspectives on this theme might be less reliable compared to other themes. However, from a theoretical perspective, existential philosophy and EPT reveal a strong relationship between authenticity and death anxiety. Consequently, in future research, the impact of death anxiety on authenticity can be more robustly demonstrated through the comprehensive analysis of multiple research designs.

Implications for practice

The study's findings offer important implications for practitioners working with young adults. The positive and significant relationship between free will belief and authenticity suggests that interventions aimed at strengthening young adults' sense of agency and free will beliefs could be beneficial. Such interventions could encourage individuals to reflect on their choices, set realistic goals, and develop awareness of their values. In this context, existential psychotherapy techniques, existential discussions, and mindfulness exercises could help young adults develop a stronger belief in their capacity to make autonomous choices. Additionally, therapeutic interventions could address the discrepancies and similarities between individuals' internal values and goals and their actions and expressions in life, exploring the presence of free will-based choices in collaboration with the individual.

Considering the significant positive relationship between meaning in life and authenticity, therapeutic interventions that prioritize the search for purpose and values could contribute to psychological well-being in interventions targeting young adults. Clinical approaches such as existential psychotherapy, logotherapy, and narrative therapy can facilitate an individual's authentication through meaning-making. Practitioners can guide young adults in exploring their strengths, potential contributions to the world, and areas of interest. Additionally, incorporating techniques such as referrals to volunteer programs that facilitate meaning-making and goal-setting exercises can be integrated into the therapeutic relationship. Finally, the findings regarding the mediating role of self-consciousness suggest that fostering a deeper understanding of oneself and one's motivations can be beneficial for young adults in becoming authentic. Journaling, meditation, cognitive-behavioral techniques focused on self-reflection, and group interventions that encourage individuals to reflect on their emotions, thoughts, and behaviors can all contribute to becoming authentic through self-consciousness.

In our study, we aimed to understand the concept of authenticity through its multiple relationships with various predictors simultaneously. Our results indicate that free will belief, self-consciousness, and presence of meaning in life can contribute to authenticity. We also found that self-consciousness mediates the relationship between predictors and authenticity. These findings contribute to a better comprehension of the importance of authenticity in well-being, especially in the context of EPT. While some predictors of authenticity were known theoretically, few studies have explored their effects empirically. Our research has expanded our knowledge in this area and uncovered new relationships using multiple regression analysis.

Our research has practical implications for practitioners working towards fostering individual authenticity. They can focus on promoting free will belief, self-consciousness, and presence of meaning in life in their therapeutic interventions with individuals. For researchers, the findings of our study can have three critical implications. First, using different research designs, populations, and samples, empirical research can re-evaluate the relationships between authenticity and its predictors. Second, the researcher can explore the relationship between authenticity and other factors that promote it through multiple regressions. Third, studies can compare the factors contributing to trait authenticity versus state authenticity. These studies can enhance and diversify our scientific understanding of authenticity.

Data availability

While the data cannot be made available online, interested readers may request access by contacting the first author. Requests will be subject to ethical approval before data sharing.

Akin, A., Abacı, R., & Öveç, Ü. (2007). The construct validity and reliability of the Turkish version of self- consciousness scale. Ankara University, Journal of Faculty of Educational Sciences,  2 (40), 257–276. https://doi.org/10.1501/Egifak_0000000178

Akin, A., & Taş, İ. (2015). Meaning in life questionnaire: A study of validity and reliability. Journal of Turkish Studies,  10 (3), 27–27. https://doi.org/10.7827/TurkishStudies.7860

Allan, B. A., Bott, E. M., & Suh, H. (2015). Connecting mindfulness and meaning in life: Exploring the role of authenticity. Mindfulness, 6 (5), 996–1003. https://doi.org/10.1007/s12671-014-0341-z

Article   Google Scholar  

Alper, S., & Sümer, N. (2017). The adaptation of free will and determinism plus (FAD-Plus) scale into Turkish and its psychometric properties. Turkish Psychological Articles, 20 (39), 26–35. https://www.psikolog.org.tr/tr/yayinlar/dergiler/1031828/tpy1301996120170000m000031.pdf

Bell, K. R., & Showers, C. J. (2021). The moral mosaic: A factor structure for predictors of moral behavior. Personality and Individual Differences,  168 , 110340. https://doi.org/10.1016/j.paid.2020.110340

Borawski, D. (2021). Authenticity and rumination mediate the relationship between loneliness and well-being. Current Psychology,  40 (9), 4663–4672. https://doi.org/10.1007/s12144-019-00412-9

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology,  3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Bugental, J. F. T. (1965). The search for authenticity: An existential-analytic approach to psychotherapy . Holt.

Google Scholar  

Crescioni, A. W., Baumeister, R. F., Ainsworth, S. E., Ent, M., & Lambert, N. M. (2016). Subjective correlates and consequences of belief in free will. Philosophical Psychology,  29 , 41–63. https://doi.org/10.1080/09515089.2014.996285

Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.

Ekşi, H., Şahin, Y., Akça Koca, D., Eminoğlu, Z., & Ekşi, F. (2022). A bridge from will to authenticity: The role of meaning. Current Psychology . https://doi.org/10.1007/s12144-022-02744-5

Ernst, C. W. (2011). Sufism: An Introduction to the Mystical Tradition of Islam . Shambhala.

Faul, F., Erdfelder, E., Lang, A., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods,  39 (2), 175–191. https://doi.org/10.3758/bf03193146

Frankl, V. E. (1992). Man’s search for meaning: An introduction to logotherapy (4th ed.). Beacon Press.

Fusch, P. I., & Ness, L. R. (2015). Are we there yet? Data saturation in qualitative research. The Qualitative Report,  20 (9), 1408–1416. https://doi.org/10.46743/2160-3715/2015.2281

Hart, W., Tortoriello, G. K., & Richardson, K. (2019). Profiling public and private self-consciousness on self-presentation tactic use. Personality and Individual Differences,  147 , 53–57. https://doi.org/10.1016/j.paid.2019.04.010

Heidegger, M. (1962). Being and time (J. Macquarrie & E. S. Robinson, Trans.). Harper.

Hoffman, L., Yang, M., Kaklauskas, F. J., & Chan, A. (Eds.). (2009). Existential psychology East-West . University of the Rockies Press.

İlhan, T., & Özdemi̇r, Y. (2013). Adaptation of authenticity scale to Turkish: A validity and reliability study. Turkish Psychological Counseling and Guidance Journal,  4 (40), 142–153. https://doi.org/10.17066/pdrd.15631

Kelley, N. J., Davis, W. E., Dang, J., Liu, L., Wildschut, T., & Sedikides, C. (2022). Nostalgia confers psychological well-being by increasing authenticity. Journal of Experimental Social Psychology,  102 , 1–12. https://doi.org/10.1016/j.jesp.2022.104379

Kernis, M. H., & Goldman, B. M. (2006). A multicomponent conceptualization of authenticity: Theory and research. In Advances in Experimental Social Psychology,  38 , 283–357. https://doi.org/10.1016/S0065-2601(06)38006-9

Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling . Guilford Publications.

Klussman, K., Nichols, A. L., Curtin, N., Langer, J., & Orehek, E. (2022). Self-connection and well-being: Development and validation of a self-connection scale. European Journal of Social Psychology,  52 (1), 18–45. https://doi.org/10.1002/ejsp.2812

Lutz, P. K., Newman, D. B., Schlegel, R. J., & Wirtz, D. (2022). Authenticity, meaning in life, and life satisfaction: A multicomponent investigation of relationships at the trait and state levels. Journal of Personality,  91 , 541–555. https://doi.org/10.1111/jopy.12753

MacCallum, R. C., Roznowski, M., Mar, C. M., & Reith, J. V. (1994). Alternative strategies for cross-validation of covariance structure models. Multivariate Behavioral Research,  29 (1), 1–32. https://doi.org/10.1207/s15327906mbr2901_1

May, R. (1961). Existential psychology . Crown Publishing Group/Random House.

May, R. (1994). The courage to create . Norton.

Mittal, B., & Balasubramanian, S. K. (1987). Testing the dimensionality of the self-consciousness scales. Journal of Personality Assessment,  51 (1), 53–68.

Moisseron-Baudé, M., Bernaud, J.-L., & Sovet, L. (2022). Relationships between sense of community, authenticity, and meaning in life in four social communities in France. Sustainability , 14 (2). https://doi.org/10.3390/su14021018

Nunnally, J. C. (1978). Psychometric Theory . McGraw-Hill.

Paulhus, D. L., & Carey, J. M. (2011). The FAD–Plus: Measuring lay beliefs regarding free will and related constructs. Journal of Personality Assessment, 93 (1), 96–104.

Preacher, K. J. & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods , 40 , 879–891. https://doi.org/10.3758/BRM.40.3.879

Rivera, G. N., Christy, A. G., Kim, J., Vess, M., Hicks, J. A., & Schlegel, R. J. (2019). Understanding the relationship between perceived authenticity and well-being. Review of General Psychology,  23 (1), 113–126. https://doi.org/10.1037/gpr0000161

Sartre, J. P. (2003). Being and nothingness (S. Richmond, Trans.). Routledge.

Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika,  50 , 83–90. https://doi.org/10.1007/BF02294150

Seto, E., & Hicks, J. A. (2016). Disassociating the agent from the self: Undermining belief in free will diminishes true self-knowledge. Social Psychological and Personality Science,  7 (7), 726–734. https://doi.org/10.1177/1948550616653810

Smallenbroek, O., Zelenski, J. M., & Whelan, D. C. (2017). Authenticity as a eudaimonic construct: The relationships among authenticity, values, and valence. The Journal of Positive Psychology,  12 (2), 197–209. https://doi.org/10.1080/17439760.2016.1187198

Steger, M. F., Frazier, P., Oishi, S., & Kaler, M. (2006). The meaning in life questionnaire: Assessing the presence of and search for meaning in life. Journal of Counseling Psychology,  53 (1), 80–93. https://doi.org/10.1037/0022-0167.53.1.80

Stillman, T. F., Baumeister, R. F., & Mele, A. R. (2011). Free will in everyday life: Autobiographical accounts of free and unfree actions. Philosophical Psychology,  24 (3), 381–394. https://doi.org/10.1080/09515089.2011.556607

Sutton, A. (2020). Living the good life: A meta-analysis of authenticity, well-being and engagement. Personality and Individual Differences,  153 , 109645. https://doi.org/10.1016/j.paid.2019.109645

Wickham, R. E. (2013). Perceived authenticity in romantic partners. Journal of Experimental Social Psychology,  49 (5), 878–887. https://doi.org/10.1016/j.jesp.2013.04.001

Wilt, J. A., Grubbs, J. B., Exline, J. J., & Pargament, K. I. (2021). Authenticity, presence of meaning, and struggle with ultimate meaning: Nuanced between-and within-person associations. Journal of Research in Personality,  93 , 104104. https://doi.org/10.1016/j.jrp.2021.104104

Wood, A. M., Linley, P. A., Maltby, J., Baliousis, M., & Joseph, S. (2008). The authentic personality: A theoretical and empirical conceptualization and the development of the Authenticity Scale. Journal of Counseling Psychology,  55 (3), 385–399.

Xia, M., Lv, H., & Xu, X. (2021). Validating the Chinese version authenticity scale: Psychometrics in college and community samples. Current Psychology,  41 , 7301–7313. https://doi.org/10.1007/s12144-020-01326-7

Yalom, I. D. (1980). Existential psychotherapy . Basic Books.

Zhang, J. W., Chen, S., Tomova Shakur, T. K., Bilgin, B., Chai, W. J., Ramis, T., Shaban-Azad, H., Razavi, P., Nutankumar, T., & Manukyan, A. (2019). A compassionate self is a true self? Self-compassion promotes subjective authenticity. Personality and Social Psychology Bulletin,  45 (9), 1323–1337. https://doi.org/10.1177/0146167218820914

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This research was derived from the primary author's doctoral thesis.

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Bayram, Y., Artan, T. Investigating the relationships of free will belief, presence of meaning in life, and self-consciousness with authenticity: a mixed-methods study. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06047-9

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Accepted : 23 April 2024

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DOI : https://doi.org/10.1007/s12144-024-06047-9

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