limitations of a quantitative research

Limitations and Weaknesses of Quantitative Research

• Post author: Edeh Samuel Chukwuemeka ACMC
• Post published: August 16, 2021
• Post category: Scholarly Articles

Limitations and Weaknesses of Quantitative Research: Research entails the collection of materials for  academic or other purposes. It is a process of gathering information and data to solve or existing problem or prevent future problems. Research works can be done via two methods. Qualitative research or quantitative research.

Qualitative research involves the carrying out of research by gathering non-numerical data. For example, gathering of video evidence, texts or messages for analysis. On the other hand, quantitative research is the process where by numerical data are collected and analyzed. It is effectively used to find patterns and averages as well as generalising a finding or result to a wider population. Quantitative research is mostly used in natural and social sciences such as biology, psychology, economics, among others.

Quantitative research could be carried out using any four methods of researching which are descriptive research, correlational research, experimental research or survey research. In descriptive, one seeks to know the ‘what’ of a thing rather than the ‘why’ of such thing. It tries to describe the various components of an information.

Correlational research involves the research between two variables to ascertain the relationship between the variables. It understudies the impact of one variable on the other. On the other hand, an experimental research is one that uses scientific methods to establish the relationship between groups of variables. That is, it tries to establish a cause-effect relationship between the various variables under study.

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Finally, the survey research which is most widely used involves the preparation of set questionnaires, interviews and polls to which answers are provided by a segment of the target population and then, conclusions are drawn from such answers given. Survey research studies the relationship between various variables in a given research.

One of the major benefit of  quantitative research method is that it makes one arrive at a well considered conclusion since samples are collected from those who are directly affected by the research. The data collected are majorly converted to a numerical form which aids in statistical analysis. Also, quantitative research is more convenient for projects with scientific and social science inclinations.

Also see: Advantages and Disadvantages of quantitative and qualitative Research

Weaknesses of Quantitative Research

Notwithstanding the benefits of quantitative research, the research method has its own weaknesses and limitations. This is because the method is not applicable and convenient in all cases of research. Thus, using a quantitative research method in a research where qualitative research method should be used will not produce the needed result.

To this end, some of the weaknesses and limitations of quantitative research are highlighted below.

1. It Requires a Large Number of Respondents: In the course of carrying out a quantitative research, recourse has to be made to a large number of respondents. This is because you are sampling a section of a population to get their views, which views will be seen as that of the general population. In doing this, a huge number of respondents have to be consulted so as to get a fair view or percentage of the target population.

For example, if one wishes to carry out a quantitative research in Nigeria as to her acceptance of a policy of the government, one will need to consult wider. This is because Nigeria has a population of over 200 million people and the opinions of a few thousands cannot pass out as that of 200 million people. In the light of this, more respondents will be required to be interviewed so as to enable one get a fair view of the population.

Large number of respondents is thus, one of the weaknesses or limitations of quantitative research as a small sampling of a section of the target population might not be of much help to the research.

2. It is time consuming: Unlike qualitative research which has to do with analysis of already prepared data, quantitative research demands that you source for and collate the data yourself while converting such data collected into a numerical form for proper analysis. This process is time consuming. Again, the task of sending out questionnaires to respondents and waiting for answers to such questionnaires might be time consuming as most respondents will reply late or may not even reply at all.

Great patience is therefore needed in carrying out a quantitative research. It is therefore not always a good method of research in cases of urgencies as the time to get responses might take too long.

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3. It requires huge resources: Quantitative research requires huge investment of time, money and energy. It is time consuming just as it also involve huge financial commitments.

In carrying out quantitative research, one needs to get your questions prepared, sent out and also followed up to ensure that such is answered. Also, some respondents might demand to be paid before giving their inputs to such a research. An example is the trending online surveys in which the target respondents are paid for every survey they carry out for a researcher.

4. Difficulty in Analyzing the Data Collected: Data are collected from respondents and then converted into statistics. This usually poses as a limitation to a researcher who is not an expert in statistics. Analysis of collected data is also demanding and time consuming. A researcher needs to make such information collected into numerical data and correlate them with the larger population. Where this is not properly done, it means that the outcome might be false or misleading.

Also, due to the fact that a researcher might not have control over the environment he is researching in, as any such environment is susceptible to change at any point in time, the outcome of his research might be inconsistent.

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5. Outcomes of quantitative research is usually limited: In quantitative research, the outcomes are usually limited. This is because the outcome is usually based on what the researcher wants. This limited outcome is due to the structured pattern of the questionnaires. Questionnaires usually have close ended questions which gives a respondent little or no opportunity of explanations. Thus, the answers provided are limited to the questions asked and nothing more.

6. Data outcomes are usually generalised : As noted earlier, quantitative research is usually conducted on a section of a target population and not on the whole population. The outcome of this research is then generalised as the view of the entire population. What this portends is that the views of  few respondents in that research is seen as that of the general populace. Such views from them might be biased or insincere, yet they are seen as that of the entire population.

In the light of this, the fallacy of hasty generalisation is prone to be committed in a quantitative research. Generalisation of the views of a section of the population might not be the best as their views may be biased.

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In conclusion, quantitative research is a veritable means of conducting research especially in the fields of natural sciences and social sciences. This is because it mostly has a one on one interaction between the researcher and the various respondents as it majorly studies behavior. This advantage notwithstanding, the research method has its own  limitations and weaknesses. These limitations and weaknesses often times affect the quality of a research which is done using the quantitative method of research.

Edeh Samuel Chukwuemeka, ACMC, is a lawyer and a certified mediator/conciliator in Nigeria. He is also a developer with knowledge in various programming languages. Samuel is determined to leverage his skills in technology, SEO, and legal practice to revolutionize the legal profession worldwide by creating web and mobile applications that simplify legal research. Sam is also passionate about educating and providing valuable information to people.

13 Pros and Cons of Quantitative Research Methods

Quantitative research utilizes mathematical, statistical, and computational tools to derive results. This structure creates a conclusiveness to the purposes being studied as it quantifies problems to understand how prevalent they are.

It is through this process that the research creates a projectable result which applies to the larger general population.

Instead of providing a subjective overview like qualitative research offers, quantitative research identifies structured cause-and-effect relationships. Once the problem is identified by those involved in the study, the factors associated with the issue become possible to identify as well. Experiments and surveys are the primary tools of this research method to create specific results, even when independent or interdependent factors are present.

These are the quantitative research pros and cons to consider.

List of the Pros of Quantitative Research

1. Data collection occurs rapidly with quantitative research. Because the data points of quantitative research involve surveys, experiments, and real-time gathering, there are few delays in the collection of materials to examine. That means the information under study can be analyzed very quickly when compared to other research methods. The need to separate systems or identify variables is not as prevalent with this option either.

2. The samples of quantitative research are randomized. Quantitative research uses a randomized process to collect information, preventing bias from entering into the data. This randomness creates an additional advantage in the fact that the information supplied through this research can then be statistically applied to the rest of the population group which is under study. Although there is the possibility that some demographics could be left out despite randomization to create errors when the research is applied to all, the results of this research type make it possible to glean relevant data in a fraction of the time that other methods require.

3. It offers reliable and repeatable information. Quantitative research validates itself by offering consistent results when the same data points are examined under randomized conditions. Although you may receive different percentages or slight variances in other results, repetitive information creates the foundation for certainty in future planning processes. Businesses can tailor their messages or programs based on these results to meet specific needs in their community. The statistics become a reliable resource which offer confidence to the decision-making process.

4. You can generalize your findings with quantitative research. The issue with other research types is that there is no generalization effect possible with the data points they gather. Quantitative information may offer an overview instead of specificity when looking at target groups, but that also makes it possible to identify core subjects, needs, or wants. Every finding developed through this method can go beyond the participant group to the overall demographic being looked at with this work. That makes it possible to identify trouble areas before difficulties have a chance to start.

5. The research is anonymous. Researchers often use quantitative data when looking at sensitive topics because of the anonymity involved. People are not required to identify themselves with specificity in the data collected. Even if surveys or interviews are distributed to each individual, their personal information does not make it to the form. This setup reduces the risk of false results because some research participants are ashamed or disturbed about the subject discussions which involve them.

6. You can perform the research remotely. Quantitative research does not require the participants to report to a specific location to collect the data. You can speak with individuals on the phone, conduct surveys online, or use other remote methods that allow for information to move from one party to the other. Although the number of questions you ask or their difficulty can influence how many people choose to participate, the only real cost factor to the participants involves their time. That can make this option a lot cheaper than other methods.

7. Information from a larger sample is used with quantitative research. Qualitative research must use small sample sizes because it requires in-depth data points to be collected by the researchers. This creates a time-consuming resource, reducing the number of people involved. The structure of quantitative research allows for broader studies to take place, which enables better accuracy when attempting to create generalizations about the subject matter involved. There are fewer variables which can skew the results too because you’re dealing with close-ended information instead of open-ended questions.

List of the Cons of Quantitative Research

1. You cannot follow-up on any answers in quantitative research. Quantitative research offers an important limit: you cannot go back to participants after they’ve filled out a survey if there are more questions to ask. There is a limited chance to probe the answers offered in the research, which creates fewer data points to examine when compared to other methods. There is still the advantage of anonymity, but if a survey offers inconclusive or questionable results, there is no way to verify the validity of the data. If enough participants turn in similar answers, it could skew the data in a way that does not apply to the general population.

2. The characteristics of the participants may not apply to the general population. There is always a risk that the research collected using the quantitative method may not apply to the general population. It is easy to draw false correlations because the information seems to come from random sources. Despite the efforts to prevent bias, the characteristics of any randomized sample are not guaranteed to apply to everyone. That means the only certainty offered using this method is that the data applies to those who choose to participate.

3. You cannot determine if answers are true or not. Researchers using the quantitative method must operate on the assumption that all the answers provided to them through surveys, testing, and experimentation are based on a foundation of truth. There are no face-to-face contacts with this method, which means interviewers or researchers are unable to gauge the truthfulness or authenticity of each result.

A 2011 study published by Psychology Today looked at how often people lie in their daily lives. Participants were asked to talk about the number of lies they told in the past 24 hours. 40% of the sample group reported telling a lie, with the median being 1.65 lies told per day. Over 22% of the lies were told by just 1% of the sample. What would happen if the random sampling came from this 1% group?

4. There is a cost factor to consider with quantitative research. All research involves cost. There’s no getting around this fact. When looking at the price of experiments and research within the quantitative method, a single result mist cost more than $100,000. Even conducting a focus group is costly, with just four groups of government or business participants requiring up to $60,000 for the work to be done. Most of the cost involves the target audiences you want to survey, what the objects happen to be, and if you can do the work online or over the phone.

5. You do not gain access to specific feedback details. Let’s say that you wanted to conduct quantitative research on a new toothpaste that you want to take to the market. This method allows you to explore a specific hypothesis (i.e., this toothpaste does a better job of cleaning teeth than this other product). You can use the statistics to create generalizations (i.e., 70% of people say this toothpaste cleans better, which means that is your potential customer base). What you don’t receive are specific feedback details that can help you refine the product. If no one likes the toothpaste because it tastes like how a skunk smells, that 70% who say it cleans better still won’t purchase the product.

6. It creates the potential for an unnatural environment. When carrying out quantitative research, the efforts are sometimes carried out in environments which are unnatural to the group. When this disadvantage occurs, the results will often differ when compared to what would be discovered with real-world examples. That means researchers can still manipulate the results, even with randomized participants, because of the work within an environment which is conducive to the answers which they want to receive through this method.

These quantitative research pros and cons take a look at the value of the information collected vs. its authenticity and cost to collect. It is cheaper than other research methods, but with its limitations, this option is not always the best choice to make when looking for specific data points before making a critical decision.

Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Limitations and weakness of quantitative research methods

According to Saunders et al. (2009), research methodology serves as the backbone of a research study. Quantitative research’s main purpose is the quantification of the data. It allows generalisations of the results by measuring the views and responses of the sample population. Every research methodology consists of two broad phases namely planning and execution (Younus 2014). Therefore, it is evident that within these two phases, there are likely to have limitations which are beyond our control (Simon 2011). 

Improper representation of the target population

As mentioned in the article , improper representation of the target population might hinder the researcher from achieving its desired aims and objectives. Despite applying an appropriate sampling plan representation of the subjects is dependent on the probability distribution of observed data. This may lead to a miscalculation of probability distribution and lead to falsity in the proposition.

A study purports to check the proportion of females aged between 20-30 years who are applying make-up ranges of international brands. The target population, in this case, is the women belonging to the said age group, with both professional and non-professional backgrounds, residing in Delhi. The sampled population based on the probability distribution has to be calculated against the total females residing in the city (e.g. 400 sampled out of 7,800,615 female populations). However, there is a scope of getting partial information about the range of makeup products from the sampled, owing to its meagre form against the total population. Hence, the results of the study cannot be generalised in context to a larger population, but rather be suggested.

Lack of resources for data collection

Quantitative research methodology usually requires a large sample size. However, due to the lack of resources, this large-scale research becomes impossible. In many developing countries, interested parties (e.g., government or non-government organisations, public service providers, educational institutions, etc.) may lack knowledge and especially the resources needed to conduct thorough quantitative research (Science 2001).

Inability to control the environment

Sometimes researchers face problems to control the environment where the respondents provide answers to the questions in the survey (Baxter 2008). Responses often depend on a particular time which again is dependent on the conditions occurring during that particular time frame.

For example, if data for a study is collected on residents’ perception of development works conducted by the municipality, the results presented for a specific year (say, 2009), will be held redundant or of limited value in 2015. The reasons are, that either the officials have changed or the development scenario has changed (from too effective to minimal effective or vice versa).

Limited outcomes in a quantitative research

The quantitative research method involves a structured questionnaire with close-ended questions. It leads to limited outcomes outlined in the research proposal. So the results cannot always represent the actual occurrence, in a generalised form. Also, the respondents have limited options for responses, based on the selection made by the researcher.

“Does your manager motivates you to take up challenges”

The answer can be Yes / No / Can’t say or Strongly Agree to Strongly disagree. But to know what strategies are applied by the manager to motivate the employee or on what parameters the employee does not feel motivated (if responded no), the researcher has to ask broader questions which somewhat has limited scope in close-ended questionnaires

Expensive and time-consuming

Quantitative research is difficult, expensive and requires a lot of time to perform the analysis. This type of research is planned carefully in order to ensure complete randomization and correct designation of control groups (Morgan 1980). A large proportion of respondents is appropriate for the representation of the target population. So, to achieve in-depth responses on an issue, data collection in quantitative research methodology is often too expensive than the qualitative approach.

To understand the influence of advertising on the propensity of purchase decision of baby foods parents of 5-year old and below in Bangalore, the researcher needs to collect data from 200 respondents. This is time-consuming and expensive, given the approach needed for each of these parents to explain the study’s purpose.

Difficulty in data analysis

The quantitative study requires extensive statistical analysis , which can be difficult to perform for researchers from non-statistical backgrounds. Statistical analysis is based on scientific discipline and hence is difficult for non-mathematicians to perform.

Quantitative research is a lot more complex for social sciences, education, anthropology and psychology. The effective response should depend on the research problem rather than just a simple yes or no response.

To understand the level of motivation perceived by Grade 5 students from the teaching approach taken by their class teachers, mere yes and no might lead to ambiguity in data collection and hence improper results. Instead, a detailed interview or focus group technique might develop in-depth views and perspectives of both the teachers and children.

Requirement of extra resources to analyse the results

The requirements for the successful statistical confirmation of the result are very tough in quantitative research. A hypothesis is proven with few experiments due to which there is ambiguity in the results. Results are retested and refined several times for an unambiguous conclusion (Ong 2003). So it requires extra time, investment and resources to refine the results.

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10 Advantages & Disadvantages of Quantitative Research

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis.

Quantitative Research

When researchers look at gathering data, there are two types of testing methods they can use: quantitative research, or qualitative research. Quantitative research looks to capture real, measurable data in the form of numbers and figures; whereas qualitative research is concerned with recording opinion data, customer characteristics, and other non-numerical information.

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis. An integral component of quantitative research - and truly, all research - is the careful and considered analysis of the resulting data points.

There are several key advantages and disadvantages to conducting quantitative research that should be considered when deciding which type of testing best fits the occasion.

5 Advantages of Quantitative Research

• Quantitative research is concerned with facts & verifiable information.

Quantitative research is primarily designed to capture numerical data - often for the purpose of studying a fact or phenomenon in their population. This kind of research activity is very helpful for producing data points when looking at a particular group - like a customer demographic. All of this helps us to better identify the key roots of certain customer behaviors. 

Businesses who research their customers intimately often outperform their competitors. Knowing the reasons why a customer makes a particular purchasing decision makes it easier for companies to address issues in their audiences. Data analysis of this kind can be used for a wide range of applications, even outside the world of commerce. 

• Quantitative research can be done anonymously. 

Unlike qualitative research questions - which often ask participants to divulge personal and sometimes sensitive information - quantitative research does not require participants to be named or identified. As long as those conducting the testing are able to independently verify that the participants fit the necessary profile for the test, then more identifying information is unnecessary. 

• Quantitative research processes don't need to be directly observed.

Whereas qualitative research demands close attention be paid to the process of data collection, quantitative research data can be collected passively. Surveys, polls, and other forms of asynchronous data collection generate data points over a defined period of time, freeing up researchers to focus on more important activities. 

• Quantitative research is faster than other methods.

Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects. Less delays and a larger sample size ensures you will have a far easier go of managing your data collection process.

• Quantitative research is verifiable and can be used to duplicate results.

The careful and exact way in which quantitative tests must be designed enables other researchers to duplicate the methodology. In order to verify the integrity of any experimental conclusion, others must be able to replicate the study on their own. Independently verifying data is how the scientific community creates precedent and establishes trust in their findings.

5 Disadvantages of Quantitative Research

• Limited to numbers and figures.

Quantitative research is an incredibly precise tool in the way that it only gathers cold hard figures. This double edged sword leaves the quantitative method unable to deal with questions that require specific feedback, and often lacks a human element. For questions like, “What sorts of emotions does our advertisement evoke in our test audiences?” or “Why do customers prefer our product over the competing brand?”, using the quantitative research method will not derive a meaningful answer.

• Testing models are more difficult to create.

Creating a quantitative research model requires careful attention to be paid to your design. From the hypothesis to the testing methods and the analysis that comes after, there are several moving parts that must be brought into alignment in order for your test to succeed. Even one unintentional error can invalidate your results, and send your team back to the drawing board to start all over again.

• Tests can be intentionally manipulative.  

Bad actors looking to push an agenda can sometimes create qualitative tests that are faulty, and designed to support a particular end result. Apolitical facts and figures can be turned political when given a limited context. You can imagine an example in which a politician devises a poll with answers that are designed to give him a favorable outcome - no matter what respondents pick.

• Results are open to subjective interpretation.

Whether due to researchers' bias or simple accident, research data can be manipulated in order to give a subjective result. When numbers are not given their full context, or were gathered in an incorrect or misleading way, the results that follow can not be correctly interpreted. Bias, opinion, and simple mistakes all work to inhibit the experimental process - and must be taken into account when designing your tests. 

• More expensive than other forms of testing. 

Quantitative research often seeks to gather large quantities of data points. While this is beneficial for the purposes of testing, the research does not come free. The grander the scope of your test and the more thorough you are in it’s methodology, the more likely it is that you will be spending a sizable portion of your marketing expenses on research alone. Polling and surveying, while affordable means of gathering quantitative data, can not always generate the kind of quality results a research project necessitates. 

Key Takeaways 

Numerical data is a vital component of almost any research project. Quantitative data can provide meaningful insight into qualitative concerns. Focusing on the facts and figures enables researchers to duplicate tests later on, and create their own data sets.

To streamline your quantitative research process:

Have a plan. Tackling your research project with a clear and focused strategy will allow you to better address any errors or hiccups that might otherwise inhibit your testing. 

Define your audience. Create a clear picture of your target audience before you design your test. Understanding who you want to test beforehand gives you the ability to choose which methodology is going to be the right fit for them. 

Test, test, and test again. Verifying your results through repeated and thorough testing builds confidence in your decision making. It’s not only smart research practice - it’s good business.

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Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Advantages and Disadvantages of Quantitative Research

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Quantitative research is the process of gathering observable data to answer a research question using statistical , computational, or mathematical techniques. It is often seen as more accurate or valuable than qualitative research, which focuses on gathering non-numerical data.

Qualitative research looks at opinions, concepts, characteristics, and descriptions. Quantitative research looks at measurable, numerical relationships. Both kinds of research have their advantages and disadvantages .

How Can Businesses Use Quantitative Research?

Research benefits small businesses by helping you make informed decisions. Conducting market research should be a regular part of any business plan, allowing you to grow efficiently and make good use of your available resources.

Businesses can use research to:

• Learn more about customer opinions and buying patterns .
• Test new products and services before launching them.
• Make decisions about product packaging, branding, and other visual elements.
• Understand patterns in your market or industry.
• Analyze the behavior of your competitors.
• Identify the best use of your marketing resources.
• Compare how successful different promotions will be before scaling up.
• Decide on where new locations or stores should be.

When deciding what type of research will benefit your business, it is important to consider the advantages and disadvantages of quantitative research.

Advantages of Quantitative Research

The use of statistical analysis and hard numbers found in quantitative research has distinct advantages in the research process.

• Can be tested and checked. Quantitative research requires careful experimental design and the ability for anyone to replicate both the test and the results. This makes the data you gather more reliable and less open to argument.
• Straightforward analysis. When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.
• Prestige. Research that involves complex statistics and data analysis is considered valuable and impressive because many people don't understand the mathematics involved. Quantitative research is associated with technical advancements like computer modeling, stock selection, portfolio evaluation, and other data-based business decisions. The association of prestige and value with quantitative research can reflect well on your small business.

Disadvantages of Quantitative Research

However, the focus on numbers found in quantitative research can also be limiting, leading to several disadvantages.

• False focus on numbers. Quantitative research can be limited in its pursuit of concrete, statistical relationships, which can lead to researchers overlooking broader themes and relationships. By focusing solely on numbers, you run the risk of missing surprising or big-picture information that can benefit your business.
• Difficulty setting up a research model. When you conduct quantitative research, you need to carefully develop a hypothesis and set up a model for collecting and analyzing data. Any errors in your set up, bias on the part of the researcher, or mistakes in execution can invalidate all your results. Even coming up with a hypothesis can be subjective, especially if you have a specific question that you already know you want to prove or disprove.
• Can be misleading. Many people assume that because quantitative research is based on statistics it is more credible or scientific than observational, qualitative research. However, both kinds of research can be subjective and misleading. The opinions and biases of a researcher are just as likely to impact quantitative approaches to information gathering. In fact, the impact of this bias occurs earlier in the process of quantitative research than it does in qualitative research.

Tips for Conducting Quantitative Research

If you decide to conduct quantitative research for your small business,

• Work with a professional. Professional market researchers and data analysts are trained in how to conduct survey research and run statistical models. To ensure that your research is well-designed and your results are accurate, work with a professional. If you can't afford to hire researchers for the length of the project, look for someone who can help just with set-up or analysis.
• Have a clear research question. To save time and resources, have a clear idea of what question you want answered before you begin researching. You can find areas that need research by looking at your marketing plan and identifying where you struggle to make an informed decision.
• Don't be afraid to change your model. Research is a process, and needing to change direction or start over doesn't mean you have failed or done something wrong. Often, successful research will raise new questions. Keep track of those new questions so that you can continue answering them as you move forward.
• Combine quantitative and qualitative research. Successfully running a small business relies on understanding people, and the behavior of your customers and competitors cannot be reduced to numbers. As you conduct quantitative research, try to collect qualitative data as well. This can take the form of open-ended questions on surveys, panel discussions, or even just keeping track of opinions or concerns that customers share. By combining the two types of research, you'll end up with the best possible picture of how your business can grow and succeed within its market.
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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write a Research Methodology for a Research Paper

Ever had a tough time with quantitative research? You're not alone! 

Quantitative research is the process of collecting and analyzing numerical data to understand and study various phenomena using statistical methods. Many find this tedious process tricky. 

But don't worry! 

Our complete guide is here to guide you through the important steps and tricks to handle this challenge with confidence. We've even added some examples to make it easier. 

So, let's dive in and learn together!

• 1. Quantitative Research Definition - What is Quantitative Research?
• 2. Data Collection in Quantitative Research
• 3. Data Analysis in Quantitative Research
• 4. Types of Quantitative Research Methods for Students and Researchers
• 5. Types of Data Collection Methodologies in Quantitative Research
• 6. Quantitative vs. Qualitative Research
• 7. Advantages and Strengths of Quantitative Research
• 8. Disadvantages and Weaknesses of Quantitative Research

Quantitative Research Definition - What is Quantitative Research?

Quantitative research involves gathering and studying numerical data. Its applications include identifying trends, making forecasts, testing cause-and-effect links, and drawing broader conclusions applicable to larger groups.

In this method, researchers employ tools such as surveys, experiments, and observations to gather data. Whereas in qualitative research, you deal with non-numeric data, such as text, video, or audio.

Quantitative research is extensively applied in natural and social sciences, including biology, chemistry, psychology, economics, sociology, and marketing, among others.

Characteristics of Quantitative Research

Here are some distinct quantitative research characteristics:

• Large Sample Sizes: Quantitative studies often involve larger sample sizes, allowing for more robust statistical analyses and generalizability of findings.
• Statistical Analysis: Statistical techniques and tools are extensively used to analyze data, unveiling patterns, relationships, and significance.
• Objective and Replicable: Quantitative research aims for objectivity and replicability. Other researchers should be able to conduct the same study and obtain similar results.
• Closed-Ended Questions: Surveys and questionnaires typically use closed-ended questions with predefined response options, making data analysis more straightforward.
• Quantifiable Variables: Researchers identify and measure variables that can be quantified, such as age, income, or test scores, for precise analysis.
• Hypothesis Testing: It often involves testing hypotheses and making inferences about populations based on sample data.
• Cross-Sectional or Longitudinal: Studies can be cross-sectional (data collected at a single point in time) or longitudinal (data collected over an extended period).
• Generalizability: Quantitative research seeks to generalize findings from a sample to a larger population, provided the sample is representative.

These characteristics make quantitative research different from qualitative research.

Data Collection in Quantitative Research

Data collection is the systematic process of gathering information for research purposes. It is a critical starting point, ensuring that the information gathered is relevant, accurate, and comprehensive.

• Structured Instruments - Quantitative research typically employs structured instruments like surveys and questionnaires. These tools ensure consistency in data gathering by posing the same set of questions to each participant.
• Sampling Methods - Researchers use various sampling techniques, such as random sampling, stratified sampling, or convenience sampling, to select a representative group from the target population.
• Objective Observation - Data collection often involves objective observations of phenomena. This may include recording numerical data, such as counting occurrences or measuring attributes.
• Experimental Control - In experimental research, control over variables is essential. Researchers manipulate one or more variables to observe their impact on the outcome, maintaining control over external factors.

Data Analysis in Quantitative Research

Data analysis is the second important aspect of quantitative research. After collecting the data, the data is analyzed with statistical methods. When analyzing, it is important that the results are relevant and related to the objective and aim of the research.

Below are some common statistical analysis methods that are used to analyze the collected data.

• SWOT Analysis - It stands for Strengths, Weaknesses, Opportunities, and Threats. Businesses use this kind of analysis to evaluate their performance and develop appropriate strategies.
• Conjoint Analysis - This kind of analysis helps businesses to identify how customers make difficult purchasing decisions. The businesses involved in direct sales and purchases know this and use the analysis to make the decisions.
• Cross-tabulation - A preliminary statistical analysis helps understand patterns, trends, and relationships between the various factors of the research.
• TURF Analysis - It stands for Totally Unduplicated Reach and Frequency Analysis. It is conducted to collect and analyze the data and responses of a chosen or favored target group.

Afterward, other methods like inferential statistics could be used to gather the results. 

Types of Quantitative Research Methods for Students and Researchers

‘What are the four types of quantitative research?’

Quantitative research has four distinct types, and all four of them are regarded as primary research methods. Primary quantitative research is more common and useful than secondary research methods. 

It is mainly because, in them, the researcher collects the data directly. He does not depend on previous research and collects the data from scratch. 

Below are the four types of quantitative research methods.

Survey Research 

This type of research is conducted through means of online surveys, online polls, and questionnaires. A group of people is chosen for the survey, and the method is used by big and small organizations and companies. They use it to understand their customers better.

Ideally, the survey is done through face-to-face meetings and interviews. Now, it is conducted through various online methodologies. Below are the common types of surveys.

• Cross-Sectional Survey - This research is conducted on a selected group of people at a certain point in time. The researcher evaluates several things. The selected group of people has similarities in all aspects except the ones chosen by the researcher. This kind of research is used for industries like retail, small-scale businesses, and healthcare industries.
• Longitudinal Survey - This research is based on observing a specific group of people for a set duration. The duration could be days, months, or even years. The researcher observes the change in behavior of the selected group of people.

This kind of research is used in the fields of applied sciences, medicine, and marketing.

Correlational Research 

Correlational research is conducted to identify the relationship between two entities. These entities must be closely related and have a significant impact on each other.

This research is conducted to identify, evaluate, and understand the correlation between the variables and how they depend on each other.

The researchers use mathematical and statistical methods to understand this correlation. Some factors that they consider include relationships, trends, and patterns between these variables.

Sometimes, the researchers make changes in one of the variables to notice the effect on the other one.

Causal-comparative Research 

This research is also known as quasi-experimental research. It is based on the cause and effect relationship between the two variables. Here, one of the variables is dependent on the other one, but the other one is independent. The researcher does not change the independent variable.

The research is not limited to statistical analysis only but includes other groups and variables also. The research could be conducted on the variables, no matter the kind of relationship they have. The statistical analysis method is used to acquire the results.

Experimental Research

This kind of research is based on proving or contradicting a theory or statement. It is also known as true experimentation and is usually focused on single or multiple theories.

The respective theory is not proven yet, and the research method is commonly used in natural sciences.

There could be some theories involved in this research. Due to this, it is more common in social sciences.

Types of Data Collection Methodologies in Quantitative Research

After determining the kind of research, finding the right data collection method is the most important step. Data could be collected through both the sampling and surveys and polls method.

Sampling Data Collection Method

In quantitative research, two types of sampling methods are used: probability and non-probability sampling.

1. Probability Sampling 

The data is collected by sifting some individuals from the general population and creating samples. The individuals, data samples are chosen randomly and without any particular selection criteria.

Probability sampling is further divided into the following kinds.

• Simple Random Sampling - This kind of data selection is the simplest one, and the participants are chosen randomly. This kind of sampling is conducted on a large population.
• Stratified Random Sampling - In this sampling, the population is divided into several groups and strata. The participants for the research are chosen randomly from those groups.
• Cluster Sampling - In cluster sampling, the population is divided into several clusters based on geography and demography.
• Systematic Sampling - In this, the samples from the population are chosen at regular intervals. These intervals are predefined, and usually, they are calculated based on the population or size of the target sample.

2. Non-Probability Sampling 

In this kind of data collection, the researcher uses his knowledge and experience to choose the samples. The researcher is involved and has a set of criteria. Due to this, not all individuals have the chance to be selected for the research.

Below are the main types of non-probability sampling frameworks.

• Convenience Sampling - These kinds of samples are probably the easiest to obtain. They are chosen only because they are the easiest ones to obtain. They are usually closer to the researcher, and these samples are easy to work with because there are no rigid parameters.
• Consecutive Sampling - This is similar to convenience sampling, but the researcher could choose a specific group of people for his research. The researcher could repeat the process with other groups of samples.
• Quota Sampling - The researchers select some specific elements based on the researcher’s target personalities and traits. Based on this, different individuals in the groups have equal chances of getting selected.
• Snowball Sampling - This kind of sampling is done on a target audience or a chosen group that is difficult to contact. In this, the chosen group is difficult to put together.
• Judgemental Sampling - This kind of sample is built based on the researcher’s skills, experience, and preferences.

Survey and Polls Data Collection Method

After the sample or group is chosen, the researcher could use polls or surveys to collect the required research data.

In this kind of research, the data is collected from a selected group of people. The data is used to identify new trends and collect information about different things and topics. Through the survey, the researcher could reach a wider population.

Based on the time allocated for the research, it could be used to collect more information and data.

When creating questions and options for the survey, the researchers use four measurement scales or criteria. These four parameters include nominal, interval, ordinal, and ratio measurement scales. Without them, no multiple-choice questions could be created.

The questions used for the survey must be close-ended. These could be a mix of different kinds of questions, and the responses could be analyzed through different rating scales.

After creating the survey, the next thing is to distribute it. Below are some of the commonly used survey distribution methods.

• Email - The most common method of distributing the survey is email management software to dispense the survey to your selected participants.
• Buying the Respondents - This is also a quite famous and widely used survey distribution method. Select the respondent and have him respond to the survey. Since the respondents would be knowledgeable, they will help in maximizing the results.
• Embedding the Survey on a Website - This is a great way of getting more responses and targeted results. Embedding the survey on a website works because the researcher is at the right place and close to the brand.
• Social Distribution - Distributing the survey through a social media platform helps collect more responses from the right audience.
• QR Code - The survey is stored in the QR code, and it is printed in magazines or on business cards.
• SMS Survey - It is the most convenient way of collecting more responses and data.

Like surveys, polls are also used to collect the data. It also has close-ended quantitative research questions, and election and exit polls are commonly used in this survey.

Quantitative vs. Qualitative Research

Quantitative and qualitative research are major kinds of research. They are mainly used in the subjects that follow detailed research patterns. How does it differ from quantitative research? 

Below is a detailed comparison of the two kinds of research.

Want to know more about the differences between these types of research? Check out this extensive read on qualitative vs. quantitative research to get more insights!

Advantages and Strengths of Quantitative Research

Quantitative research offers several advantages to researchers. Some of the main reasons why researchers use this kind of research are discussed below: 

• The Data Can Be Replicated - The research and study could be replicated. The data collection methods and definitions of the concepts are clear and easy to understand.
• The Results Can Be Compared Easily - The same study could be conducted in different cultural settings and sample groups. The results could also be compared statistically.
• Usage of Large Samples - Data and information from large samples could be processed and analyzed using different research procedures.
• Hypothesis Could be Tested - The researcher could use formal hypothesis testing. He could report the data collection, research variables, research predictions, and testing techniques before forecasting and establishing any conclusion.
• The Data Collection is Quick - The data could be collected easily and from a wider population. The usage of statistical methods and conducting and analyzing results is also easy and to the point.
• The Data Analysis is Inclusive - Quantitative data and research offer a wider population for sampling. They could be analyzed through research and analysis procedures.

Due to all of these advantages, researchers prefer using this kind of research method. It is easy to sample, collect, and analyze data and repeat the procedure easily.

Disadvantages and Weaknesses of Quantitative Research

Despite the benefits for the researchers, quantitative research design has some limitations. It may not be suitable for more complex and detailed kinds of topics.

Below are some common quantitative research limitations.

• Superficial - since the research includes limited and precise research samples. In quantitative research, the research is presented in numbers. They could be explained in detail through qualitative data and research.
• Limited Focus - the focus is narrow and limited, and the researcher would have to ignore other relevant and important variables.
• Biased Structure - structural biases could exist and affect sampling methods, data collection, and measurement results.
• Lack of Proper Conditions - sometimes, quantitative research may not include other important factors to collect the data.

Due to these reasons, quantitative research is not an ideal choice for detailed kinds of research. For them, qualitative research works better.

To help you further, we have added some useful examples of quantitative research here.

Quantitative Research Examples

Below are some helpful quantitative research examples to help you understand it better.

Sample Quantitative Research

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Open Access

Peer-reviewed

Research Article

Re-use of research data in the social sciences. Use and users of digital data archive

Contributed equally to this work with: Elina Late, Michael Ochsner

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Swiss Centre of Expertise in the Social Sciences, University of Lausanne, Lausanne, Switzerland

• Elina Late, 
• Michael Ochsner

• Published: May 10, 2024
• https://doi.org/10.1371/journal.pone.0303190
• Reader Comments

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Citation: Late E, Ochsner M (2024) Re-use of research data in the social sciences. Use and users of digital data archive. PLoS ONE 19(5): e0303190. https://doi.org/10.1371/journal.pone.0303190

Editor: Hong Qin, University of Tennessee at Chattanooga, UNITED STATES

Received: April 19, 2023; Accepted: April 19, 2024; Published: May 10, 2024

Copyright: © 2024 Late, Ochsner. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This research was partially funded by Academy of Finland ( https://www.aka.fi/en/ ) grant 351247 (EL) and benefitted from a Short Term Scientific Mission of the COST Action CA 15137 ‘European Network for Research Evaluation in the SSH (ENRESSH)’, supported by European Cooperation in Science and Technology ( https://www.cost.eu/ ) (EL, MO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In the context of the Open Science agenda and the Responsible Research and Innovation movement, nations and organizations have put a lot of effort in building research infrastructures for supporting scholars in open science practice. Research data archives (also referred as data repositories) are part of the infrastructure and their aim is to capture and share digital research datasets. Archiving digital research data aims for improving the quality of research, and for economical savings assuming that data once archived will be useful and used by others [ 1 ]. Interest in facilitating data sharing and re-use is high, which is evident in funding agencies’, research organizations’, publishers’ and archives’ efforts in drafting policies regulating data sharing and management [ 2 ]. Also, data openness and sharing are increasingly important factors in the evaluation of impact, concerning both research infrastructures and scholars [ 3 , 4 ].

In the social sciences there is a long tradition in re-using time-series datasets such as those by the World Bank or OECD. However, in the era of open science, data sharing has widened its use to individual scholars uploading their data, which most likely form most of the contents in the digital data archives. Yet, despite the massive financial and intellectual investments, it is still unclear how extensively, by whom and for what purposes research datasets are downloaded from the archives [ 5 ]. The proposed benefits of open data will be materialised only fully if the available data are used or re-used by others [ 5 ]. Also, the importance of creating quantitative metrics for evaluating the impact of research infrastructures is widely recognized [ 6 ].

Will it be possible to realise the optimistic promises of open responsible science when the social sciences go digital? While open research data and data infrastructures have drawn a lot of attention, is there a demand for open data, do differences in re-use exist across types of data, how broad is the base of potential users and where is potential to develop and what service portfolios to be developed? Answering to these questions is vital to understand the evolving knowledge creating practices, the impact of open data and the development of open science and its implementation in research practice. Additionally, this information is important for the archives to better understand the potential needs of their user base. Most of the earlier work has based on self-reported data re-use and focused especially on the experiences and needs of scholars [e.g. 7 – 12 ]. However, before data citation practices are fully formalized in social sciences, log data and number of downloads are useful to measure the frequency of data re-use [ 5 , 13 , 14 ]. Also, Khan, Thelwall and Kousha [ 12 ] call for more comprehensive disciplinary information about repository uptake for enhancing sustainable data sharing.

By now, only very few studies relying on user log data gathered from the social science archives exist. For example, Borgman and colleagues analysed user log data to identify data re-use in the Dutch interdisciplinary data archive DANS [ 5 ] using number of downloads and users. Focusing on data re-use in the social sciences, Late and Kekäläinen [ 15 ] studied the use of the Finnish research data archive in more detail based on enriched log data. Applying their methodology using enriched log data we study the use of Swiss data repository, FORSbase, that archives both qualitative and quantitative social science research data. Our study supplements the findings by Late and Kekäläinen [ 15 ] by providing comparative evidence from another context. We investigate whether there is a demand for open data in the social sciences and address the following research questions:

• How many times and by how many users are datasets downloaded from the FORSbase?
• What type of datasets are downloaded from the archive most often?
• What roles do the users of the archive represent?
• For what purposes are datasets downloaded?

The article is structured as follows. First, we present related literature concerning open data, data archives and data re-use in the social sciences. We will then describe the research setting and present the results, which will be discussed before being put into the policy and research practice context to draw conclusions.

Research data and data archives in the social sciences

The European Commission [ 16 ] defines research infrastructures as “facilities that provide resources and services for research communities to conduct research and foster innovation” (p. 1). Research data archives are thus part of the infrastructure supporting and enabling open science by storing, managing, and disseminating research data by public (or private) funding without a fee for the users. Although, studies have shown that many scholars rely on their personal data storage for sharing data [ 12 , 17 ], there is a long-standing tradition of using and providing open research data and having large data repositories in the social sciences [ 18 ]. International organisations like the World Bank, International Monetary Fund, Freedom House, the OECD or EUROSTAT have provided valuable data for social scientists for decades just as well as national public statistical offices [see e.g., 19 – 21 ]. Furthermore, for more specific data, national and international data infrastructures, such as the General Social Survey in the US since 1972, the World Values Survey, the European Values Study, the International Social Survey Programme, or Inter-university Consortium of Political and Social (ICPSR), have been offering rich datasets in open access to social scientists [see, e.g., 22 , 23 ]. Also, individual scholars or teams generated and shared data, such as the Democracy Index [ 24 ], the Polity Project [ 25 ], or the World Inequality Database [ 26 ]. Social science data archives providing a hub for sources for secondary analyses have been established in the 1960ies in the US as well as in Europe [ 18 , 27 ] and for example, the CESSDA, Consortium of European social science data archives, exists since 1976 [ 27 ]. Established data archives, provide support and curation for long-term data preservation for the entire data life cycle and tools for data search [ 28 , 29 ].

However, while especially international comparative quantitative social science has this long-standing tradition, in other sub-fields like psychology it has been usual that data and measurement instruments were part of a business model and available in closed access. Qualitative social science does not look back on a similar tradition of sharing data even though in 1994, the Qualitative Data Archival Resource Center has been established at the University of Exeter to foster re-use of qualitative data [ 30 ]. However, this policy-based request has resulted in a heated debate whether it is ethical to share qualitative data because data are potentially sensitive [ 30 , 31 ]. The shift to open science in the STEM fields has changed the attention of policy makers and put pressures on those sub-fields in the social sciences where open data sharing has not yet been part of the tradition and, at the same time, opened new opportunities and increased reputation of the shared existing data infrastructures.

Research data has thus been seen as theory-laden concept with a long history [ 5 ]. Data can take different forms in different disciplines and a particular combination of interests, abilities and accessibility determine what is identified as data in each instance [ 32 ]. Borgman [ 33 ] defines data as “entities used as evidence of phenomena for the purposes of research or scholarship” (p. 25). Data are not seen only as by-products of research but as research outputs, valuable commodities, and public objects [ 1 ]. Data in the social sciences can remain relevant for analysis for a long time as societal developments and historical perspectives can offer new opportunities of, and approaches to, analysis of historical data to researchers.

Re-use of research data in social sciences

Open access to research data is an essential aspect in open science because, among others, it facilitates the verification of given results and enhances the effectiveness of research by the re-use of data. However, also negative aspects of data re-use have been identified, such as narrowing the scope and increasing the bias of research [ 34 , 35 ] and leading to injustice in work division, i.e., when data collectors document and share their data, others may take just advantage of the work accomplished by others, as data stewardship is not acknowledged yet [ 36 ]. Furthermore, not all kinds of data can be opened due to data protection and ethical principles [ 37 ]. This is a frequent issue in the social sciences and earlier studies have claimed relatively low levels of data sharing and re-use [ 38 – 41 ]. However, some data are frequently re-used in the social sciences as for example the open data published by the European Social Survey led to at least 5000 scientific English language publications between 2003 and 2020 [ 42 ].

The whole concept of data re-use needs to be understood far more deeply. Re-use of data can mean for example re-using data to reproduce research, to re-use data independently or to integrate data with other data [ 12 , 33 ]. Re-using a dataset in its original form can be difficult, even if adequate documentation and tools are available, since much must be understood about why the data were collected and why various decisions about data collection, cleaning, and analysis were made [ 33 , 43 , 44 ]. Combining datasets is far more challenging, as extensive information must be known about each dataset if they are to be interpreted and trusted sufficiently to draw conclusions [ 45 ].

By now several studies have analysed scholars needs, experiences and perceptions of data re-use relying on surveys and interviews [ 7 – 12 , 46 , 47 ]. In a recent survey [ 12 ] almost half of the respondents representing social sciences reported re-using data. However, there was some variation between research fields. Data re-use was more frequent by experienced scholars and by those sharing their data. When selecting data for re-use, scholars consider proper documentation, openness, information on usability of data, availability of data in a universal standard format and evidence that the dataset has an associated publication as important factors [ 12 ].

Social scientists re-using data value data that are comprehensive, easy to obtain, easy to manipulate, and credible [ 46 ]. Identified obstacles for data re-use are, for example, barriers to access, lacking interoperability and lack of support [ 47 ]. Faniel, Frank and Yakel [ 9 ] identified ICPSR’s data users’ information needs in 12 contexts relating to how data was originally produced, about the repository it has been archived and about the previous re-use of data. They argue that scholars representing different disciplines have distinct differences in the types of information desires, that should be considered in service development. For example, information about missing data was important for the social scientists. Studies focusing on data re-use by novice scholars emphasize the importance of details about the data collection and coding procedures and peer support for data use [ 8 ]. Re-using data may contribute to the knowledge creating skills of junior scholars and foster them to socialize to their disciplinary communities [ 48 ].

Studies have also focused on how data is searched [ 49 , 50 ] and witnessed scholars struggling with finding datasets to re-use [ 12 , 51 ]. Most typically, data is found from relevant papers, conducting web searches, and searching from disciplinary and interdisciplinary data archives [ 12 ]. Recently, Lafia, Million and Hemphill [ 52 ] studied data search basing their analysis on usage data from ICPSR website. They identified three user paths for navigating the website: direct, orienting, and scenic. Direct and scenic paths targeted dataset retrieval, as orienting paths aimed gathering contextual information. They argue that data archives should support both direct and serendipitous data discovery.

Only a few studies have investigated the use of data archives in the social sciences relying on log data. Borgman and colleagues studied the use of the Danish Data Archiving and Networked Services (DANS) using transaction logs, documentation, and interviews, and showed that communities of data infrastructures can be amorphous and evolve considerably over time [ 5 ]. They argue that trust plays an important role in the re-use of a dataset collected by someone else and the reputation of the hosting archive and organizations responsible for the curation process are important elements in trust creation.

Late and Kekäläinen [ 15 ] studied the use and users of the Finnish research data archive for social sciences by analysing user log data between 2015 and 2018. According to their study, most datasets were downloaded at least once during the time frame and a clear majority of the downloaded data were quantitative. They discovered that the datasets from the archive were downloaded most often for the purposes of studying or master’s or bachelor’s theses. One fifth of the downloading’s were made for research purposes. Similarly, Bishop’s and Kuula-Luumi’s study [ 53 ] about the re-use of qualitative datasets showed that data was downloaded for studying, master’s theses, teaching and research, indicating that data re-use is even less prevalent for qualitative studies. According to Late and Kekäläinen [ 15 ] the most typical downloaded dataset was survey data. The Finnish research data archive was most often used by social scientists from Finnish universities. However, there were users from other European countries and even from outside Europe and other organizations. Borgman and colleagues [ 5 ] argue that user behaviour tends to correlate with existing data practices in a field, and archives tend to be tailored accordingly. However, the results by Late and Kekäläinen [ 15 ] showed that users of the archive for social sciences data represented all major disciplines. Thus, data practices in several fields must be considered when developing the services.

Research setting

The context: forsbase.

The research data archive investigated in this study is FORSbase. FORS is the Swiss Centre of Expertise in the Social Sciences that offers data and consulting services in social sciences, conducts national and international surveys, and offers data and research information services to researchers and academic institutions [ 54 ]. FORSbase was the archive for research projects and research data in the social sciences in Switzerland managed by FORS. It was established in 1992 and was replaced in December 2021 by SWISSUbase ( https://www.swissubase.ch/ ) based at the same institution and issued in collaboration with several partners that includes the functions of FORSbase but serves as the national data repository across disciplines in Switzerland.

Research data from FORSbase and SWISSUbase can be accessed from the online catalogue ( https://forsbase.unil.ch/ and https://www.swissubase.ch/ ). The catalogue is available in English, German and French. Datasets are downloadable free of charge, but users are required to register before downloading datasets. The database has a special structure: It is centred around research projects. Each project can have several datasets and each dataset can have different versions, while only the latest available version is downloadable.

The FORSbase and SWISSUbase data services follow FAIR data principles [ 1 ] and have obtained the official certification of CoreTrustSeal. The CoreTrustSeal is a community based non-profit organization promoting sustainable and trustworthy data infrastructures. FORSbase is a member of CESSDA. The change from FORSbase to SWISSUbase does not have any impact on our analysis and its conclusions because the FORSbase service is integrated in SWISSUbase. The main difference is that the services have been upscaled to accommodate research data and projects from other disciplines (and transdisciplinary research).

Data collection and analyses

The study is based on quantitative user log data that was collected from FORSbase for the time window from 29.2.2016 to 9.2.2020. This time window represents the full user data available for FORSbase since its rebuild in 2016 until the time of the start of our project. The log data contains information about the number of downloads and downloaded datasets. The data is enriched with a) project information data collected from the database and b) data coming from the registration survey that users have to fill in when downloading data. The project information entails information about the archived datasets such as the dataset type. Registration survey data entails information about the users including their role and purpose of data use. Each time a person downloads data from FORSbase, this information is collected.

The data is structured as follows (see Fig 1 ): the main unit is a download; downloads are cross-nested across datasets and users (a user downloading a dataset creates thus a unique download). Each download also points to the version of the dataset that has been downloaded. The raw number of observations (downloads) in the data was 6661. Removing downloads from the dataset made for testing purposes by the FORSbase team resulted in 6656 observations.

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https://doi.org/10.1371/journal.pone.0303190.g001

The process continued with variable selection and coding. Nine variables analysed in this study are presented in Table 1 along with the research question(s) the variable is used to address. Information for variables 1 to 4 is collected automatically whereas information for variable 5 is constructed in two steps, the name being drawn automatically from the database and then assigned a type of dataset manually from the project information data in the FORSbase online catalogue. Information for variables 6 to 9 is asked from the users in a survey format during registration and when downloading the data.

https://doi.org/10.1371/journal.pone.0303190.t001

To identify how many times and by how many users are datasets downloaded from FORSbase (RQ1), we analyse the download date, user id, and dataset id ( Table 1 , variables 1–3). Concerning the number of downloads, we analyse the full number and share of downloaded datasets and unique user-dataset downloads ( Table 1 , variable 3) to control for downloading dataset updates and to exclude duplicate downloads. By analysing the unique dataset downloads we can identify whether the same user downloaded the same dataset twice or two versions of it. Concerning the number of users, we analyse the average number of downloads for the registered users and the active users ( Table 1 , variable 2). Registered users are those who have registered to FORSbase for archiving and downloading data. The number of registered users was asked from the archive personnel in time of the data collection in 2020. Active users are those who downloaded data during the time window of the data collection. Each user is identified in the data with a unique user ID number automatically provided by the system during registration ( Table 1 , variable 2).

To identify what type of datasets are downloaded from the archive most often (RQ2) we use the id of the dataset , the type of dataset (quantitative or qualitative data) and the name of the dataset ( Table 1 , variables 3, 4, 5). The name of the downloaded dataset ( Table 1 , variable 5) was also used to study the 10 most downloaded datasets in more detail. For these datasets, information (i.e., descriptive details) were traced from the FORSbase online catalogue.

To analyse what roles do the users of the archive represent (RQ3), we use the role of the downloading user ( Table 1 ). Originally, users were provided a list of 11 roles from which they selected the most suitable one. For the analyses, some categories were combined to form a shorter list of seven different roles (i.e., student, doctoral student, lecturer/post doc, professor, other research/project manager, teacher, and non-academic).

Finally, to identify for what purposes datasets are downloaded (RQ4), we use information on the use purpose of the data , the research description and whether a publication is expected ( Table 1 , variables 7,8, 9). When users were downloading datasets from FORSbase, they were asked whether the dataset was downloaded either for research or for teaching purposes ( Table 1 , variable 7). Although these categories did not serve well for the students downloading datasets for their course work, they were forced to choose between the two options. Therefore, for the means of this study, a new use purpose type “studying” was constructed manually in two steps. First, all the users that identified themselves as students were identified from the data ( Table 1 , variable 6). In the second step, the coding was assigned by thoroughly reading the research descriptions ( Table 1 , variable 8) written by the students to find out the purpose of the download. Based on these descriptions we also categorised the sub-type of studying purpose if possible (e.g., bachelor theses, master’s theses). However, the research description was asked only for those downloads where the users were indicating research ( Table 1 , variable 7) as the purpose for the download. Consequently, this information is missing for the downloads where users indicated teaching as purpose. Obviously, this applies also to students who had selected teaching as use purpose. These were categorised as studying as we assume that students do not teach yet but chose teaching as there was no option for studying. Downloading data for doctoral dissertation were categorized as “research” purpose.

Variable nine ( Table 1 ) was used to study the purpose of research use of the dataset by asking whether the user was expecting a publication resulting from the downloaded dataset. This information was asked only for those downloading data for research purposes. Thus, this information is missing for the downloads the users indicated teaching as the purpose.

For the analyses step the data were gathered into one dataset and analysed with Stata 16. Given that we analyse full data, we do not apply inferential statistics. Whenever we are interested in differences between groups, we apply bootstrapped 95 per cent stability intervals to indicate the precision of the estimates. Differences were then tested also using bootstrapping procedures either with regression models (numbers of downloads per user group) or tests on the equality of proportions [ 55 ] for the intention to publish across user groups.

Number of dataset downloads

In February 2020, at the time of our data collection, FORSbase had 6628 registered users. The archive contained 725 datasets, the majority of which were quantitative. Within the time window that covers 49 months, a total of 6656 downloads were made from FORSbase ( Table 2 ). This results in an average 136 downloads per month or 5 downloads per day. When excluding incomplete months from 2016 and 2020 in our dataset, we cover a total of 6593 downloads over 47 months, leading to a mean of 140 downloads per month ( range = 40–286, median = 122). From 2017 to 2018, the number of downloads increased by 18 per cent and from 2018 to 2019 by 16 per cent. The downloads per months show a high volatility as can be seen from Fig 2 that shows the downloads per month for fully covered months, i.e., March 2016 to January 2020, and a smoothed moving average. The figure makes visible an increase of downloads over time with a tendency to stabilise. Note that March, April and October, November show the highest downloads while July and August show the lowest downloads, reflecting semester beginnings for highs and semester break for lows.

Smoothed moving average is calculated using weights as suggested in [ 56 ].

https://doi.org/10.1371/journal.pone.0303190.g002

https://doi.org/10.1371/journal.pone.0303190.t002

Of the 725 datasets archived in FORSbase, 470 datasets were downloaded at least once representing 65 per cent of all archived datasets. One fifth of the downloaded datasets were downloaded once and 13 per cent twice. Consequently, 67 per cent were downloaded three times or more (see Table 3 ). Datasets, however, can be updated and new versions are released. Users are informed so that they can download the new version. This leads to the fact that some datasets are downloaded more often than others. Additionally, users can download the same dataset twice (e.g., on two different workstations). To control for updates and to have a measure that reflects better the number of times a dataset is used (as opposed to downloaded), we identified duplicates, i.e., if the same user downloaded the same dataset twice or two versions of it. This was counted as one unique user-dataset download (see Table 3 , columns on the right). Both measures are somewhat imperfect because, on the one hand, regarding the full count measure, a dataset that is published quickly and corrected afterwards will score more downloads than one that is not updated. On the other hand, regarding the corrected measure, it might be that a same user downloads the same data multiple times for different persons, e.g., as teacher and student (a situation that is not compliant to the user agreement) or for different uses. Additionally, it is not clearly defined by the database what a “version” is. It is usually an update of the same dataset, but it could also be used to have a dataset updated with new waves while another dataset would create a new dataset for each new wave added. We did our best to control for the later and try to treat a study (and each wave) as a dataset if archived separately.

https://doi.org/10.1371/journal.pone.0303190.t003

Table 4 shows that the main download statistics between the two measures differ only slightly. The mean amounts to 9 downloads per data (8 if only unique user-dataset downloads are counted), but the distribution is highly skewed with a first quartile of 0 downloads, a median of 2 downloads and a third quartile of 6 downloads irrespective of how to count dataset-downloads.

https://doi.org/10.1371/journal.pone.0303190.t004

Type of most downloaded datasets

FORSbase allows the archiving of both quantitative and qualitative data. Qualitative data can be archived since 2017 only. From the 725 datasets, only 15 datasets were archived as qualitative datasets, which corresponds to 2 per cent. Of the 470 datasets that were downloaded at least once, 5 were qualitative datasets (1%). On the level of downloads, the vast majority (98%) of the downloads concerned quantitative datasets. Qualitative datasets were downloaded only 15 times (13 times if we consider only unique user-dataset downloads). Two of which were downloaded once, two twice and one nine times (7 times if only unique user-dataset downloads are counted).

Ten datasets were downloaded more than 100 times (see Table 5 ). Downloads for these 10 datasets represent almost 40 per cent of all downloads from FORSbase in the given time window. FORS was the collector of eight out of the ten most downloaded datasets. The other two datasets were collected by Swiss universities. The most downloaded datasets were all quantitative and either cumulative datasets or single year issues of longitudinal (cross-sectional or panel) surveys collected at regular intervals. Those surveys can be considered social sciences data infrastructures of national or even international importance and are designed for secondary data analysis.

https://doi.org/10.1371/journal.pone.0303190.t005

The most downloaded dataset, SHP Data Waves 1–19, is the Swiss annual household panel study based on a random sample of private households in Switzerland, interviewing all household members mainly by telephone. SHP is provided free of charge from FORSbase for the scientific community [ 57 ]. Other datasets are related with Swiss elections or popular votes (datasets 2, 3, 4, 5, 6, 9) or with education and civil society (datasets 7, 10).

The fact that the share of the ten most downloaded datasets decreases slightly if duplicates and versions of the same dataset are excluded ( Table 5 “Percentage of total downloads” vs. “Percentage of unique user-dataset downloads”) shows that the most downloaded datasets are updated more often than the other datasets. However, the ranking of the most downloaded datasets does not change substantially showing that duplicates and versions spread quite evenly across those highly downloaded datasets. The bootstrapped 95%-stability intervals (see Table 5 , column 3 in brackets) show that the ranking consists of four parts: A clear leader (dataset 1) and a clear second place (dataset 2) followed by a middle part (datasets 3 to 8) and studies 9 and 10 form the fourth group.

Users of the archive

During the examined time window, 2281 unique users downloaded data from FORSbase. These users are called as “active users” in Table 6 . In February 2020, there were 6628 registered users in FORSbase. Thus, only a third of the registered users downloaded a dataset during the time window (note that to upload data, one needs to register as a user). One half of the active users downloaded only one dataset during the given time period ( Table 6 , column on the righthand side). One fifth downloaded two datasets and 28 per cent downloaded three or more datasets. There was a group of heavy users downloading more than 5 datasets (5% of the registered users and 13% of the active users). At the end of the scale, one user downloaded 149 datasets during the time window. The group of 306 users downloading at least five datasets combined more than half (51.7%) of all the downloads during the time window. On average, considering all registered users, one user downloaded one dataset, while considering only active users, a user downloaded 2.9 datasets.

https://doi.org/10.1371/journal.pone.0303190.t006

Looking at unique user-dataset downloads ( Table 7 ), 58 per cent of the active users downloaded only one unique dataset whereas 21 per cent downloaded two and 22 per cent three or more. The group of heavy users (5+ downloaded datasets) amounts to 4 per cent of all registered users and 11 per cent of the active users. The person who downloaded most datasets downloaded 140 unique datasets. If only unique user-dataset downloads are considered, the average is 0.9 downloads per registered user and 2.6 downloads per active user.

https://doi.org/10.1371/journal.pone.0303190.t007

A clear majority of users downloaded only quantitative datasets (99%), 8 users downloaded both quantitative and qualitative data and 4 users only qualitative data.

Regarding the role of users, the majority of the downloads were made by users registered as students, while doctoral students, lecturers/postdocs and professors and other researchers were downloading less, and teachers and non-academics the least ( Table 8 ).

https://doi.org/10.1371/journal.pone.0303190.t008

Regarding download frequency across user groups, students were more likely to download many datasets compared to scholars, teachers, and non-academics (see Fig 3 ). Note that using bootstrapped regression, only the difference between students and scholars, teachers and non-academics were significant. If one takes only unique user-dataset downloads into account, students downloaded significantly more unique datasets than all other groups except for non-academics (as the latter have a large variability). However, the user roles are not clear-cut entities as the same person can indicate a different role for each download. This means that for unique user-dataset downloads only the first role is retained.

Average number of Downloads per user group with bootstrapped 95% stability intervals using 1000 resamples on the basis of a) all downloads and b) only unique user-dataset downloads.

https://doi.org/10.1371/journal.pone.0303190.g003

Purpose of the downloads

The majority of the downloads were made for studying purposes (see Table 9 ). Of those downloading data for study purposes, at least 13 per cent (n = 497) downloaded the dataset for a bachelor’s thesis and at least 12 per cent (n = 452) for master’s thesis (combining 14.3% of all downloads used for a BA or MA thesis). However, these numbers represent minima because not all users did describe their purpose of download in such detail and the users not describing the purpose in detail might have used the data for a thesis as well.

https://doi.org/10.1371/journal.pone.0303190.t009

Almost 40 per cent of the downloads served research purposes. Out of downloads used for research, at least 5 per cent download data for doctoral thesis (2% of the total downloads). However, the real share of downloading data for doctoral theses is probably much higher since more than 14 per cent of the users were registered as doctoral students.

Finally, only 3 per cent of the downloads served teaching purposes. This is surprising given that the biggest user group are students, and one would expect that it is the teachers who inform students about the dataset(s) used in the courses. However, users can only indicate one purpose for the download but can of course use it for many purposes after download. Also, it might mean that some teachers invite students to download the data themselves, while others download it and distribute the data to the students–which would mean that even more users would be students as the data covers only those students who downloaded the data themselves.

Users downloading datasets were also asked if they expect to write publications using the downloaded dataset. This was asked only if they were indicating that they were using the data for research and not teaching. Also, the question has a high share of non-response (463 or 7% of those who indicated research as the use of the download). Of those who replied to the question, a large majority (77.4%) did not expect to publish and just over one fifth expected to do so. Those downloading the dataset for research purposes were most likely to expect to write a publication (43%). Expectedly, professors, lecturers/postdoctoral researchers, and doctoral students expected publication more often compared to students ( Table 10 ). Indeed, professors, lecturers/post-docs and, more unexpectedly, non-academics have a similar percentage intending to publish as the bootstrapped differences are not significant. All other groups do differ significantly from these three groups and between each other. The relationship between role and intention to publish is quite strong with a Cramér’s V of 0.43.

https://doi.org/10.1371/journal.pone.0303190.t010

This study investigated whether there is a demand for open data in the social sciences by examining the use and users of a research data archive. It continued a discussion started by Late and Kekäläinen [ 15 ] studying the use of social science research data archives based on user log data. The results show that there is a demand for research data as datasets have been downloaded frequently from the FORSbase, i.e., on average 145 downloads per month. As in Finland [ 15 ], the number of downloads has increased in Switzerland from 2016 to 2019. During the time window of the study, a large majority (65%) of the datasets archived in FORSbase were downloaded at least for once. The share of downloaded datasets was similar with the Finnish results (70%) [ 15 ].

An overwhelming majority of the downloaded datasets are quantitative. The number of archived qualitative datasets in FORSbase is very low, which explains the low numbers in the downloads. Earlier studies have discussed the obstacles of data sharing and re-use in social sciences [ 38 – 40 , 58 ]. Our results suggest that there might be strong differences in the habit of downloading open data from repositories across different specialisations: in qualitative social sciences, data sharing seems to be far less prominent than in quantitative social sciences. There is little evidence about the re-use of qualitative datasets and further studies are needed to understand the potential and pitfalls of open data policies for qualitative studies [ 53 , 58 ]. The lack of data sharing, and re-use has certainly several reasons but ethical issues play an important role [ 59 ].

In this study, from the 725 archived datasets, the ten most frequently downloaded ones were investigated in more detail. Each of these datasets was downloaded more than 100 times, the most popular being downloaded more than 600 times. The downloads of these ten datasets amounts to almost 40 per cent of all downloads from the archive, which indicates that, similar to publications [ 60 ], a small share of datasets gains most of the attention. The same phenomenon was observed by Late and Kekäläinen [ 15 ]. The most frequently downloaded datasets share a few properties: all of them are longitudinal or time-series survey data collected not by individual scholars or research groups but by organizations or consortia such as FORS. Also, those datasets are local survey projects and the analysed archive, FORSbase, is the main source for obtaining this data. International longitudinal or time-series datasets were not among the ten most downloaded, even though local versions of these datasets would be available in the archive. Researchers interested in those cross-national datasets are more likely to download the datasets containing data from several countries from the international repository. Again, these results are in line with the study of Late and Kekäläinen [ 15 ]. In Finland, most downloaded datasets were local and national surveys. However, in the Finnish archive, the most downloaded datasets also included large international statistics collected by a single scholar. Qualitative datasets were also more often downloaded from the Finnish archive compared to the Swiss archive.

The fact that the most downloaded datasets were collected by prestigious and well-known organizations is in line with the argument raised in earlier studies [ 5 , 9 ] that scholars’ trust in data is essential for the data re-use. However, what is considered as trustworthy may differ between disciplines. For the social scientists, reputation along with data selection and cleaning process play an important role in trust creation [ 61 ]. Systematic documentation and providing high quality paradata (i.e. data about the data) is valued by the data users [ 8 , 9 , 12 , 62 ]. Other factors influencing the users’ trust in the data archives are recommendations, frequency of use, past experiences, and perceptions of the role of the archive [ 10 ]. However, frequently downloaded datasets are probably more well-known and thus, more visible for the users. Data findability is another critical point for data re-use that should be supported better [ 12 , 52 ]. Furthermore, archives can increase their own visibility and prestige by archiving high quality and well-known datasets by establishing collection strategies and profiling for certain topics and data types to gain competitive advantage and reputation. However, the value of non-used (or non-downloaded) datasets cannot be overlooked, since they may become valuable in the future as needs are difficult to predict (i.e. delayed recognition in science [ 63 ]).

Earlier studies have not investigated the number of users of the data archives although it can be considered as an is important metric for evaluating the impact of archives. Our results show that FORSbase was used by more than 2000 unique users as one third of the registered users downloaded data from FORSbase. Most of them downloaded only one dataset. However, there was a smaller group of heavy users of the archive downloading several datasets and forming a remarkable share of all downloads. This might be an indication of field specific differences; in some fields of social sciences data can and is re-used more often. Also, it might indicate personal differences between the users. Users that have found datasets useful come back for downloading more relevant data or new versions of the datasets. Indeed, other studies have shown that scholars sharing their data are also more active re-users of data shared by others [ 12 ]. Our results show, however, that not all registered users download data which might indicate that some users of FORSbase use it for archiving, not data retrieval. Late and Kekäläinen [ 15 ] showed that users represented several countries, disciplines, and organisations. Our data did not allow for such analyses.

Earlier research has focused mainly on scholars’ data sharing and re-use practices and shown experienced scholars being the most active data re-users [ 12 ]. Yet, our findings confirm the results by Late and Kekäläinen’s [ 15 ] that students form the largest user group for the data archive. Students as a special user group should be taken into special consideration by data archives and service providers since there is a great potential in this user group as future data users and providers. Re-using data is important for developing knowledge creation skills and in socializing into the discipline [ 48 ]. Novice users have specific needs for data re-use and are influenced by experiences of their mentors [ 8 ]. Therefore, data archives need to pay special attention when thinking what services could be offered especially for the students and what guidance students need. More research, for example on the data management skills of students, is certainly needed. This is not only relevant for students who want to become future academics, but data becomes an important part of many professions in a digitalised society and skills in data use, management, archiving, and documenting will be relevant competences students need to learn. Also, scholars wish training for data management skills [ 64 ]. The role of data archives along with data managers and libraries have been identified as central in fostering such skills [ 17 ].

Only three per cent of the downloads served teaching purposes. However, studies by Late and Kekäläinen [ 15 ] and Bishop and Kuula-Luumi [ 53 ] show higher share of downloads for teaching purposes from Finnish and UK archives. There might be several reasons for the difference. However, users of FORSbase can only indicate one use purpose per download, while they could use the data for several purposes. Researchers can download a dataset for a research project and then use this project and the dataset in teaching without re-downloading the data and register it as a purpose for teaching. Also, they may ask the students to download the data, for example, in a research methods seminar. The high share of students among the users suggests that teaching is a frequent use of the datasets downloaded from FORSbase. However, an important question for future research is what data re-use means in teaching. Is it rather to teach research methods or also to replicate studies and foster the idea of responsible research already in teaching? Familiarizing students with the open research infrastructures might be an effective way to promote open science ideals.

More than one third of the downloads were made for research purposes. The share of research use was lower in the study by Late and Kekäläinen [ 15 ] covering only on fifth of the total use. In the Swiss archive, about half of the downloads for research were expected to result in a publication. Professors, lecturers, and post-doctoral scholars were most likely to plan to use the dataset for a publication. However, there is little evidence about how often re-used data are actually utilized in publications and for what purposes data are used for [ 65 ]. Unfortunately, no further information is available from our data that shows other research purposes than publications. Regarding Responsible Research and Innovation, it would be interesting to follow how often data is re-used for validation or replication purposes rather than publication.

Regarding the policy demand for open science and open data, the valorisation of data sharing becomes relevant. Data stewardship is not yet a relevant aspect in academic career development, which might hinder the motivation to share and document data sufficiently [ 36 , 39 ]. However, European guidelines for responsible research assessment have already included data and data sharing as research outputs and activities to be recognized in the evaluation [ 66 ]. Therefore, further efforts should be made to study how (and how often) re-used datasets are cited in publications and how archives guide users to cite data. Data citation practices in social sciences are still evolving since citations are shown to be often incomplete or erroneous [ 15 , 67 – 69 ]. Not all re-used research data are cited, at least not in a formal way [ 15 ]. Developing more formal data citation practices would enable a quantitative evaluation of the impact of data re-use. The challenge is to get scholars to cite data in a systematic way [ 70 ]. This would also serve the need to provide quantitative metrics for evaluating the impact of research infrastructures [ 6 ]. User log data can provide information concerning the number of downloaded data, but for evaluating the impact on research, further studies are needed exploiting, for example, bibliometric methods.

Practical implications and limitations of the study

The results provide several practical implications for utilizing user log data for evaluating digital data archive use and as a source of research data. First, it would be important for the archives to define clearly what a data “version” is and to separate updates from new waves that comprise a new dataset. As new versions and updates of the datasets influence user behaviour and the number of downloads and thus, should be taken into consideration when user log data is used in archive evaluation or in research. The most frequently downloaded datasets are characterised by various versions and are updated more often than datasets provided by individual scholars. In our study we decided to analyse both, the full number of downloads and unique downloads to recognize the share of duplicates. The differences were not significant yet existed. Further, our results provide implications for collecting user log data. For example, information collection should cover all kinds of users and use types. In the case of FORSbase, for example, “studying” as a data re-use purpose was not provided. This underlines the importance of user studies for the service providers to truly know who their clients are. Given the relevance of replicational and open research data in science policy and the lack of knowledge on open research data practices, it is also advisable to archives to collect meaningful log data to be able to supplement ethical considerations with empirical evidence on data re-use.

This study comes with limitations: making conclusions about data re-use based on user log data is somewhat unreliable since it is likely that not all downloaded datasets are used, or some are used for many times or for other purposes than expected. Generalizing findings across organizations may be challenging because download metrics may be contingent on the specific characteristics of the data archive or related organisations [ 4 ]. For example, datasets can be used as course material possibly leading to hundreds of data downloads [ 15 ]. Additionally, log-data cannot provide qualitative insights into the data re-use (e.g., why a dataset was selected and how it was used). Still, user log data can give useful insights into re-use of research data and the users of data archives on the macro level beyond self-reported data re-use and from the point of view of the archive [ 5 ]. Our findings show that data is downloaded for various purposes and by various user groups from the archive. Thus, studying data re-use based for example on citations captures only part of the data re-use. Results of this study will give grounds for future studies in this respect. In addition, we analysed log data only from one archive. However, as our results are in line with a similar study conducted in Finland [ 15 ], we believe the results can be generalised to similar national social science data archives. Future research will show how the frequency of data downloads will develop as open data practices establish in the social sciences.

Conclusions

This study contributes to our understanding of the utilization of digital data archives in the realm of social sciences. The findings indicate the demand for social science data, as evidenced by the increasing number of data downloads from a Swiss data archive. However, it is noteworthy that as majority of the archived datasets were downloaded at least once, a limited set of longitudinal and time-series survey datasets compiled by organizations rather than individual scholars gained substantial share of the downloads. Since the case archive primarily specializes in housing quantitative data, the re-use of qualitative data was marginal. Among the users, students constituted a significant proportion who accessed the archive to acquire data for their educational purposes. Nonetheless, the user base encompassed individuals from diverse roles, including experienced and novice scholars and non-academics. As the findings are in line with previous research [ 15 ] it is likely to find similar patterns across data archives specialised for the social sciences. The increasing availability of digital datasets for the re-use may create new data practices within social sciences.

Enriched log data capturing the use of the digital data archive provide a macro level understanding about the re-use of the data from singular archive. To obtain more comprehensive insights into data re-use and evolving data practices within social sciences, future research applying quantitative and qualitative approaches is needed. A future research agenda on data re-use would include comparative studies of different archives (which would preclude some previous agreement on the collection of meta-data between archives), studies into the (epistemological and empirical) meanings and definitions of re-use of research data in social sciences and into the trade-offs between collecting new data versus re-using existing data. A very important issue is the data citation practices in social sciences. For further developing the research infrastructures, user studies are needed to address how users interact with the infrastructures, what obstacles they face and what support they desire.

Supporting information

https://doi.org/10.1371/journal.pone.0303190.s001

Acknowledgments

We thank Dr. Jaana Kekäläinen for her valuable comments for the manuscript.

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• Published: 10 May 2024

The role of leisure activities in enhancing well-being in Saudi’s retired community: a mixed methods study

• Homoud Mohammed Nawi Alanazi 1  

Humanities and Social Sciences Communications volume  11 , Article number:  604 ( 2024 ) Cite this article

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• Cultural and media studies

This mixed-methods study delves into the impact of leisure activities on the well-being of retirees in Saudi Arabia, focusing on health, emotional balance, social integration, and self-fulfillment. In the quantitative phase, 545 retirees were selected through a snowball sampling, providing a diverse sample of age, gender, socio-economic status, and educational background. Data were collected using a structured questionnaire and analyzed using SPSS. The qualitative phase involved randomly selecting 23 participants from the initial cohort for semi-structured interviews, with the data subjected to thematic analysis for deeper insights. Findings revealed a moderate overall enhancement in well-being attributed to leisure activities, with health benefits being most significantly improved. Emotional and social well-being showed moderate enhancements, while self-fulfillment benefits were less pronounced. Demographic variations were evident, with gender, socio-economic status, and education level influencing the perceived benefits. Qualitatively, the importance of cultural alignment in leisure activities was highlighted, underscoring their role in social connectivity and personal development. The study underscores the need for culturally sensitive and accessible leisure programs tailored to the varied needs of the retired population in Saudi Arabia. It provides crucial insights for policymakers and community planners, emphasizing the importance of demographic considerations in leisure interventions to improve retirees’ quality of life. This research contributes significantly to understanding leisure’s role in enhancing post-retirement well-being, offering a comprehensive perspective for future leisure-related initiatives and policies.

Introduction

The transition to retirement is a significant event that impacts individuals’ daily lives, social dynamics, and personal aspirations. This topic acquires added importance in the Kingdom of Saudi Arabia due to the country’s unique socioeconomic changes and demographic trends toward an ageing population. Leisure activities are crucial for enhancing retirees’ well-being, addressing physical health, and self-actualization needs (Bashatah et al. 2023 ; Morse et al. 2021 ). This research is essential for scholars, policymakers, and practitioners focused on ageing, well-being, and cultural specificity in retirement planning.

Research has increasingly recognized the value of leisure activities in promoting retirees’ health, emotional balance, and social integration (Han et al. 2021 ; Lee et al. 2023 ; Michèle et al. 2019 ; Nielsen et al. 2021 ; Wheatley and Bickerton 2022 ). However, existing studies predominantly reflect Western contexts, leaving a noticeable gap in our understanding of retirement within the socio-cultural landscape of the Middle East, particularly Saudi Arabia. This gap is significant because cultural norms and societal changes profoundly influence retirement experiences, suggesting that findings from Western studies may not fully translate to the Saudi context. The absence of culturally tailored research highlights a critical need for empirical studies to inform culturally appropriate interventions and policies for enhancing retiree well-being in Saudi Arabia.

This study aims to fill the existing gap by examining the impact of leisure activities on the well-being of Saudi retirees through a mixed-methods approach. It will quantify the benefits of leisure activities across different well-being dimensions and explore how these perceptions vary across demographic groups within the retired population. Additionally, it will provide in-depth insights into retirees’ experiences and perceptions regarding leisure activities. This research seeks to fundamentally enhance our understanding of retiree well-being in the Saudi context by offering new theoretical perspectives and empirical evidence. The findings promise to challenge existing paradigms by incorporating the cultural subtleties of the Middle Eastern retirement experience, thus advancing the field and guiding the development of targeted culturally sensitive well-being interventions. Given this, the following questions are put under the lens:

What is the overall level of well-being enhancement attributed to leisure activity engagement among retirees in Saudi Arabia, as measured by comprehensive scores on the Leisure Benefits Scale?

Which domains, among health, emotional well-being, social integration, and self-fulfillment, are most significantly enhanced by leisure activities, according to the retired population in Saudi Arabia?

Are there identifiable differences in the perceived benefits of leisure activities across various demographic groups (age, gender, socio-economic status and educational level) within the retired population of Saudi Arabia?

How do retirees in Saudi Arabia perceive the role of leisure activities in enhancing their well-being, particularly in terms of health, emotional balance, social integration, and self-fulfillment, post-retirement?

Literature review

This literature review serves as the foundational piece of this research undertaking, thoroughly analyzing the intricate relationship between recreational activities and the welfare of the retired population. Amidst a period marked by significant demographic changes, including a growing elderly population, the welfare of retirees has emerged as a central topic of academic investigation and public interest. This academic article systematically examines the various complex aspects of recreational activities and their simultaneous effects on the health of retired individuals, emphasizing the importance of conducting this research in the unique sociocultural context of Saudi Arabia. This section explores the theoretical and empirical aspects of the relationship between leisure and well-being.

Theoretical framework

This study’s underpinning is rooted in an integrated theoretical framework synthesizing insights from Activity Theory, Socioemotional Selectivity Theory, Self-Determination Theory, and cultural adaptation theories. This amalgamation offers a sophisticated lens through which the dynamics of leisure activities and their consequential impact on retirees’ well-being in Saudi Arabia can be examined. The ensuing discourse delineates the contribution of each theoretical perspective to comprehending the relationship between leisure pursuits and the multifaceted construct of well-being in the context of retirement.

Activity theory asserts the imperative of active engagement across various physical, social, and cognitive dimensions for sustaining and augmenting well-being in later life stages (Teles and Ribeiro 2019 ). It posits that a retiree’s involvement in diverse leisure activities is pivotal for achieving enhanced health, life satisfaction, and purpose (Winstead et al. 2014 ). Within the Saudi Arabian milieu, this theory underscores the investigation into how a spectrum of leisure engagements contributes to retirees’ overall well-being, advocating for a proactive and enriched lifestyle during retirement

Socioemotional selectivity theory, propounded by Carstensen, elucidates the evolution of social motivations and preferences toward emotionally meaningful engagements as one advances in age (Carstensen 2021 ). This theoretical approach illuminates the predilections of the Saudi retiree population toward leisure activities that fulfill emotional needs, foster social connections, and enhance a sense of community belonging—critical aspects of well-being in the autumn years of life.

Self-determination theory emphasizes the centrality of satisfying fundamental psychological needs—autonomy, competence, and relatedness—for optimal well-being. It is proposed that leisure activities, which resonate with retirees’ intrinsic motivations and facilitate psychological need satisfaction, significantly uplift their life quality (Ryan and Vansteenkiste 2023 ). This study utilizes SDT to probe into how leisure engagements underpin the psychological well-being of the Saudi retired populace, aligning leisure pursuits with their inherent motivations and the broader social fabric.

Incorporating cultural adaptation theory allows for accommodating Saudi Arabia’s distinct sociocultural nuances. This theory aids in deciphering how cultural values, norms, and the transformative socio-economic landscape shape leisure preferences and practices among retirees (Elliott 2020 ). It facilitates exploring the interaction between traditional and contemporary leisure activities and their role in satisfying psychological needs and enhancing well-being against the backdrop of Saudi Arabia’s evolving society.

Grounded in Activity Theory, Socioemotional Selectivity Theory, Self-Determination Theory, and cultural adaptation insights, this framework lays a solid foundation for exploring how leisure activities influence well-being among Saudi retirees. Considering Saudi Arabia’s unique cultural context, these theoretical constructs collectively provide a nuanced approach to understanding the role of leisure in promoting well-being.

The Importance of well-being in retirement

The concept of “well-being” has been the subject of extensive scholarly discussion, giving rise to many definitions that span various fields of study and paradigms. Fundamentally, well-being encompasses balanced health, contentment, and affluence as an intermediary between the subjective perceptions and the objective facts of human life (Carruthers and Hood 2004 ). In retirement, well-being transforms, assuming a multifaceted nature that interweaves human existence’s material, mental, and interpersonal aspects (Trenberth 2005 ). Beyond the absence of illness or disability, it embodies favorable qualities, including a deep-seated sense of direction, independence, and proactive involvement with the fabric of existence (Morse et al. 2021 ).

The significance of well-being is particularly pronounced in retirement, a phase of life characterized by substantial changes. When individuals are no longer employed, they encounter changes in their daily routines, social responsibilities, and sense of self, making maintaining their health and wellness a critical concern (Michèle et al. 2019 ). There is empirical support for retirees who report greater well-being, are more inclined to lead physically active and satisfying lives and demonstrate superior health outcomes (Mansfield et al. 2020 ).

Well-being among retired individuals is a complex and multifaceted phenomenon. Life satisfaction, the preponderance of positive emotions, and the reduction of negative affective states are all indicators of psychological well-being (Nielsen et al. 2021 ). Social well-being is characterized by strong social networks, enduring support systems, and an ingrained sense of inclusion within the fabric of the community (George 2010 ). Physical well-being’s foundation is preserving health, capability, and independence (Capio et al. 2014 ). Each of these dimensions exerts an intricate influence on the others and is reciprocally impacted by them, creating an intricate mosaic of interdependencies (Anglim et al. 2020 ).

Leisure activities and well-being

Leisure activities—elective, intrinsically motivated engagements conducted during discretionary periods—are critical in enhancing well-being in the retirement landscape (Morse et al. 2021 ). These activities, which range from cultural immersion and artistic endeavors to physical activities and social engagements, provide a respite from necessary work and a setting for self-discovery and personal evolution (Kuykendall et al. 2020 ). The significance of leisure in the post-career phase is multifaceted; it serves as a critical tool for acclimating to the post-occupational epoch, encouraging continuous intellectual stimulation, experiential exploration, and the cultivation of both new and existing social connections (Michèle et al. 2019 ; Verma 2017 ).

Academic discourses advocate leisure as an essential component of a more prosperous existence, especially during the golden years of retirement. Individuals frequently confront an abundance of temporal resources at this juncture, which is juxtaposed with the task of infusing this time with activities that resonate with their transforming identities and proficiencies (Li et al. 2019 ; Wang 2023 ). In this sense, leisure activities provide a conduit for retirees to re-calibrate their sense of purpose and contact with their surroundings. They make it easier to pursue dormant hobbies, rekindle old passions, and learn new skills and avocations (Hakman et al. 2019 ).

Furthermore, leisure activities help to maintain physical and cognitive vigor, reducing the potential attrition associated with the ageing process. They also act as a hub for social integration, allowing retirees to form new associations, strengthen existing relationships, and contribute to their societal spheres (Chul-Ho et al. 2020 ). Fundamentally, leisure is necessary in the alchemy of a satisfying and vibrant retirement, endowing it with exhilaration, intentionality, and a deep sense of community.

Leisure benefits

The variety of benefits derived from leisure activities generates a complex mosaic that significantly impacts several well-being elements. Engaging in such activities offers retirees more than just a diversion; it serves as a springboard for all-encompassing personal growth and renewal. Empirical evidence suggests that retirees who are heavily involved in leisure activities generally experience significant increases in their physical endurance, mental clarity, emotional equilibrium, and social involvement (Eskiler et al. 2019 ). Trekking and swimming workouts improve cardiac health and muscle strength, and intellectually stimulating activities like strategic games and artistic creativity sharpen mental abilities and help prevent cognitive decline (Lee et al. 2023 ). On the emotional front, recreational activities provide a therapeutic refuge from daily tensions, generating a sense of peace and well-being (Ertekin 2021 ). Furthermore, these activities frequently serve as focal areas for social interaction, fostering strong and long-lasting bonds within social circles and communities. These aspects of leisure activity work together to improve seniors’ physical strength, cognitive capacities, emotional resilience, and social fiber, thus improving their overall quality of life.

Health benefits

Leisure activities provide many health advantages, from soothing, meditative practices to more active and demanding workouts. Tranquil exercises, such as tai chi and strolling, have various health benefits, including lower blood pressure, increased joint mobility, and improved mental tranquillity (Fancourt et al. 2021 ). Aerobics and trekking, on the other hand, not only energize the body but also provide considerable health benefits. These exercises have been linked to improved cardiovascular health, musculoskeletal strength, and metabolic efficiency (Han et al. 2021 ; Peel et al. 2021 ; Šabić et al. 2020 ). These physical activities have been scientifically linked to lowering the risk factors associated with numerous chronic ailments, potentially delaying the onset of age-related diseases, and even playing a role in life extension (Lackey et al. 2021 ). Furthermore, a regular schedule of various physical leisure activities fosters improved agility and balance, which is essential for fall prevention in older persons. Furthermore, such activities boost total functional fitness, providing retirees with the physical capability and autonomy required to tackle the numerous obstacles of everyday living with greater ease and self-sufficiency (Fancourt and Steptoe 2021 ).

Emotional benefits

Leisure activities go beyond ordinary recreation to become significant emotional repair and wellness conduits. These activities provide a break from the unrelenting pace of daily life, raising spirits and acting as bulwarks against the tides of stress, worry, and sadness (Chen et al. 2022 ). The creative process, whether represented in the deft strokes of a painting, the fluidity of dance, or the painstaking building of a handcrafted product, is a type of emotional release (Eskiler et al. 2019 ). Such activities assist people in negotiating their emotions, allowing them to relieve internal tensions and cultivate a state of conscious presence (Wheatley and Bickerton 2022 ). The calm gained from leisure activities such as gardening, or yoga has a contemplative character, grounding the individual in the present and reducing concerns. More energetic occupations, such as team sports or the performing arts, on the other hand, can induce a state of “flow”, a profound immersion in which the outer world recedes, leaving a potent sensation of fulfillment and elation (Brymer et al. 2021 ). These leisure activities, taken together, function as wellsprings of emotional fortitude, replenishing emotional reserves, cultivating resilience, and instilling a profound sense of inner tranquillity and balance (Mansfield et al. 2020 ).

Social benefits

Leisure activities are not only diversions from a social standpoint; they are fundamental to the fabric of community dynamics, interweaving individuals in a network of social cohesion and shared enjoyment (Li et al. 2021 ). These activities go beyond ordinary pastimes for retirees, serving as vital channels for connection and active social engagement. They promote meaningful contact with peers, encourage involvement in community events, and provide the framework for developing long-lasting friendships (Poscia et al. 2018 ). Structured group activities, such as intellectually challenging book clubs, harmonic dance classes, and collaborative gardening projects, provide common areas for interchange and connection. These connections can exchange wisdom, share life stories, and form profound bonds (Brajša-Žganec et al. 2011 ). The effects of these events frequently extend far beyond the activities themselves, spawning subsequent social meetings such as coffee conversations, cultural trips, and various communal participation. Such lively exchanges do more than enliven retirees’ social lives; they also strengthen their sense of belonging and identification within the more extraordinary communal fabric (Adams et al. 2010 ; Lindsay Smith et al. 2017 ). Finally, leisure activities work as a catalyst for weaving a tapestry of social connections, promoting a sense of communal belonging essential to a fulfilling and full retirement life.

Self-fulfillment interests

In the golden years of retirement, leisure activities emerge as powerful catalysts for self-discovery and personal growth. These endeavors allow retirees to delve deeply into long-held hobbies or start on new adventurous excursions, creating a sense of competence and self-assurance (Stebbins 2017 ). Mastering a musical instrument, for example, is cognitively engaging and a source of tremendous personal joy. Painting and writing, for example, become channels for introspection and creativity, providing a sense of success and personal pride (Li et al. 2021 ). Similarly, learning a new language improves communication abilities and broadens one’s perspective by embracing various cultures and beliefs. Such interactions frequently induce a state of “flow”, an immersive experience in which profound focus and satisfaction coincide, providing great intrinsic pleasure (Stebbins 2013 ). Beyond personal fulfillment, these activities pave the path for social appreciation and active participation, enhancing retirees’ self-worth and maintaining their identity. Finally, leisure interests in the fabric of retirement go beyond mere hobbies; they are critical in filling life with significance, vigor, and an enduring enthusiasm for learning and self-development (Shutenko 2015 ).

Primarily, the domain of leisure activities is a complete cornerstone underpinning retirement well-being. These hobbies, which span life’s physical, cognitive, emotional, and social components, create a tapestry that enriches and elevates the whole fabric of retired living (Adams et al. 2010 ). With a more nuanced understanding of leisure’s various benefits, the emphasis shifts to the subtle interplay between cultural and demographic influences and leisure pastimes. The following part will explore the complexity of how cultural contexts and demographic nuances impact the character and outcomes of leisure activities, influencing retiree well-being.

Cultural and demographic influences on leisure and well-being

Leisure preferences are delicately woven into the tapestry of a person’s cultural milieu. The complex web of cultural conventions, values, and collective ideas defines the leisure landscape, sketching what is acceptable or desirable. Hofstede’s foundational work on cultural factors, according to Roy ( 2020 ), elucidates the significant influence of conceptions such as individualism and collectivism on leisure behaviors. Collectivist societies often highly value leisure activities that strengthen community solidarity and familial bonds. On the other hand, leisure activities tend to emphasize personal expression and the attainment of individual accomplishments in societies that trend toward individualism (Newman et al. 2014 ). Cultural heritage threads are similarly important, weaving traditional pleasures and customs into leisure participation (Brajša-Žganec et al. 2011 ).

Demographic factors intimately intertwine within the sphere of leisure, with each strand considerably impacting the mosaic of activities that people participate in. Age is a significant predictor, significantly impacting leisure preferences (Adams et al. 2010 ). Younger retirees may seek out physically exciting pursuits, but their elderly counterparts may seek refuge in milder yet equally gratifying activities (Agahi et al. 2011 ). Gender, a critical demographic factor, also impacts leisure decisions, frequently repeating deeply ingrained societal roles and expectations. As a result, the leisure sector reveals distinct gender-based patterns shaped by past access inequities and established norms (Fernandez 2023 ).

Socioeconomic position is a double-edged sword in leisure, enabling and restricting. Affluence opens up many leisure possibilities; however, financial constraints can narrow the range of available activities (Beenackers et al. 2012 ). Furthermore, educational background has a subtle but considerable influence on leisure participation. Higher education degrees extend one’s perspectives, encouraging interest in intellectually enriching or culturally sophisticated leisure activities. Thus, educational achievements gently pervade leisure preferences, shaping them to reflect an individual’s intellectual breadth and cultural depth (Stalsbergm and Pedersen 2010 ).

The leisure world is painted with the twin hues of cherished traditions and emerging modernity within Saudi Arabia’s vivid cultural mosaic (Amin et al. 2012 ). This juxtaposition creates a captivating story of continuity and change, uniquely molding the leisure domain. At its core, the Kingdom’s rich cultural past casts a long shadow on retiree leisure habits. Time-honored practices like falconry, woven into the fabric of history and social prestige, continue to maintain weight, as do poetic gatherings that connect with the nation’s historic literary past, anchoring retirees to their cultural roots (Al-Otaibi 2013 ). At the same time, the Kingdom is on the verge of revolution, pushed by the ambitious Vision 2030. This roadmap for socioeconomic transformation is altering the leisure paradigm by presenting a diverse range of contemporary leisure expressions (Alkhalaf and Orams 2021 ). As Saudi Arabia moves closer to its vision of the future, retirees are navigating a rising leisure revolution. New types of leisure are gaining traction, ranging from digital entertainment and adventure sports to the appeal of international travel. This fusion of the ancestral and the avant-garde creates a new language of leisure in the Saudi narrative, blurring the distinctions between tradition and innovation (Al-Otaibi 2013 ).

The relationship between cultural context and demographic characteristics is complex and multi-layered, altering the landscape of leisure and its impact on well-being significantly. The mosaic of leisure is a reflection not just of cultural heritage but also of the several demographic threads—age, gender, socioeconomic level—that intertwine to produce a great diversity of leisure experiences (Iwasaki et al. 2014 ). This dynamic is fundamental in the Kingdom of Saudi Arabia, as the country is undergoing tremendous socioeconomic and cultural developments. The developing societal fabric, supported by programs such as Vision 2030, sheds a dynamic light on the intersectionality of culture and demographics, substantially altering leisure interests and their implications for well-being (Bashatah et al. 2023 ).

Finally, the interaction of cultural and demographic variables significantly impacts leisure pursuits and, as a result, the well-being of retirees. This interdependence is remarkable in the Saudi environment, characterized by rapid and dramatic transitions. The following section outlines the methodological framework used to investigate and understand these factors in the Saudi context.

Previous studies

The relationship between leisure activities and well-being has been the focus of scholarly investigation across various cultural terrains. A strong relationship between leisure activity and improved well-being in the retired population stands out in the corpus of Western academic studies. Amin et al. ( 2012 ) conducted a seminal study highlighting the significant importance of leisure activities to increase life satisfaction and psychological well-being among the elderly. This idea is supported by Poscia et al.’s ( 2018 ) research, which emphasizes the crucial function of leisure in catalyzing social connections and alleviating feelings of loneliness among the elderly. To support these findings, Li et al. ( 2021 ) outline the various benefits of leisure activities, which include physical health, cognitive agility, emotional stability, and social connectedness.

Stebbins ( 2017 ) investigated the idea of severe leisure and its implications for self-fulfillment and identity reformation in the retirement phase, broadening the investigational purview of the European environment. Zwart and Hines’ ( 2022 ) study emphasizes the social benefits of outdoor adventure recreation, which reflect the broader social and communal benefits of leisure activities. Their findings, which emphasize shared experiences and social participation, are consistent with the function that leisure plays in building social bonds and well-being, highlighting the importance of leisure in fostering community and interpersonal connections.

Regarding the Middle Eastern story, particularly within Saudi Arabia, the scholarly terrain looks relatively unexplored. Human resources, societal culture, facilities, financing, instruments, programs, and policies, according to Sayyd and Abuhassna ( 2023 ), are the seven significant variables promoting leisure-time physical activity among Saudi male university students. The study highlights the value of integrated sports facilities, financial incentives, and customized programs in boosting public health through leisure-time physical exercise. According to a study by Bashatah et al. ( 2023 ), many adults in Riyadh, Saudi Arabia, maintain sedentary lifestyles with little involvement in leisure and exercise activities. Nearly half reported exercising only 1–2 days per week, with a sizable proportion never exercising. Despite being aware of the health concerns, sedentary behavior during leisure time persists. The study implies that governmental measures are needed to encourage more active leisure lifestyles among Saudi citizens. These recent studies critically investigated evolving leisure preferences against Saudi Arabia’s socioeconomic transformations, as spurred by projects like Vision 2030. Although these studies shed light on the general leisure habits of the Kingdom, they fall short of offering a granular insight into the retired demographic’s unique experiences.

A significant study vacuum concerning the retired community in Saudi Arabia exists in gerontology and leisure studies. The Kingdom’s distinct socio-cultural and economic fabric, particularly in the middle of revolutionary programs such as Vision 2030, warrants an in-depth investigation of how leisure activities influence retirees’ well-being in this specific setting. Existing studies primarily employed either quantitative or qualitative approaches, frequently delving into isolated aspects of well-being and ignoring seniors’ multidimensional experiences. However, this study uses a mixed-methods approach to overcome this gap, capturing the intricate interplay between health, emotional well-being, social integration, and self-fulfillment among Saudi retirees. The research attempts to improve worldwide understanding of leisure and well-being through this holistic lens, customizing its discoveries to the particular Saudi cultural and socioeconomic environment.

Research method

Research design.

This study adopts a mixed-method research design, combining quantitative and qualitative methodologies to capitalize on each offer’s distinct advantages. Grounded in the comprehensive framework proposed by Guetterman et al. ( 2019 ), this methodological synergy is strategically chosen to encompass the breadth and depth required to explore the intricate dynamics between leisure activities and well-being among the retired population.

Quantitative methodologies are employed to systematically quantify the correlations between leisure engagements and well-being metrics, facilitating an empirical assessment of prevalent trends and patterns within this demographic. This aspect is informed by prior empirical research that has elucidated quantifiable aspects of retiree well-being, thus providing a solid benchmark for evaluating the impact of leisure activities (Bashatah et al. 2023 ; Li et al. 2021 ; Zwart and Hines 2022 ).

Conversely, the qualitative facet of this research delves into retirees’ narratives, perceptions, and lived experiences, offering a rich, contextual understanding of the statistical patterns observed. This approach is inspired by foundational qualitative studies in the field (Amin et al. 2012 ; Fancourt et al. 2021 ; Stebbins 2017 ; Verma 2017 ), highlighting the critical importance of capturing the subjective interpretations and meanings that retirees attribute to their leisure activities and their consequent influence on their well-being.

This dual approach ensures that the findings are statistically validated and deeply rooted in the authentic experiences of the study population, providing a comprehensive and nuanced view of the leisure-well-being nexus among retirees in Saudi Arabia. The selection of a mixed-methods design is a deliberate strategic choice aimed at producing findings that are empirically robust, contextually rich, and practically relevant for enhancing the well-being of retirees.

Participants

The study engaged 545 retirees for its quantitative analysis, comprising 268 women and 277 men. These individuals, now distanced from their roles in various public and private sector jobs, were selected through a snowball sampling method to ensure a diverse representation across socioeconomic, educational, and cultural backgrounds. Snowball sampling was employed due to its capacity to penetrate diverse retiree networks, ensuring a varied representation across socioeconomic and cultural strata, which is vital for examining the study’s questions. This technique complements our theoretical framework, enabling a comprehensive analysis of the relationship between leisure activities and well-being (Parker et al. 2019 ). The age range of these participants spanned from 55 years to those above 65 years of age, aiming to capture a broad spectrum of post-retirement experiences. In the subsequent qualitative phase of the research, a focused group of 23 retirees was strategically chosen from the initial quantitative pool. This selection was tailored to gain deeper insights and reflect the larger group’s heterogeneity. The qualitative interviews aimed to elaborate on themes and patterns that emerged from the quantitative data (Roulston and Choi 2018 ). Detailed demographic and background information for both segments of the study population are methodically outlined in Fig. 1 .

This figure presents a composite bar and line graph detailing the sample population’s demographics. The bars show the distribution of individuals across various economic statuses (low income to high income) and education levels (primary to tertiary education), while the line graph indicates the age distribution within the sample. Social status categories are separated into divorced, separated, married, and single. The final bars compare the number of males to females. Each bar is labeled with the number of respondents in that category.

In the demographic section of the survey, the income categories were delineated as follows: Earnings below 5000 Saudi Riyals are classified as “low income”; those between 5000 and 7999 Riyals are categorized as “middle income”; incomes ranging from 8000 to 10,999 Riyals are defined as “upper-middle income”; and a monthly income of 11,000 Riyals or more is designated as “high income”. This stratification facilitates a nuanced understanding of respondents’ economic statuses.

Instruments

To collect the quantitative data, the “Leisure Benefits Scale” by Li et al. ( 2021 ) was used to measure this research, assessing the multifaceted advantages of leisure pursuits among retirees. This scale has been widely used by several studies (Ertekin 2021 ; Geng et al. 2023 ; Li et al. 2021 ), underscoring its validity and reliability in measuring the constructs of interest across diverse contexts. It consists of four constructs, each focusing on the dimensions of leisure benefits. The scale includes four items dedicated to evaluating the health benefits and assessing the impact of leisure activities on physical well-being. Another four items explore the emotional benefits, identifying the psychological and emotional uplift that leisure can provide. The social benefits construct, also with four items, measures the extent of social engagement and community connection derived from leisure activities. Lastly, the construct of self-fulfillment interests comprises five items, which delve into the personal development and sense of achievement that retirees gain from their leisure pursuits. Each construct collectively contributes to a comprehensive assessment of the positive effects of leisure activities on retirees’ overall quality of life. Each item was evaluated using a five-point Likert scale, ranging from 1, denoting “strongly disagree”, to 5, signifying “strongly agree”. This scaling method was chosen to provide a nuanced measure of the respondents’ levels of agreement or disagreement with each statement. The selection of health, emotional, social, and self-fulfillment benefits constructs, as well as the utilization of a five-point Likert scale, are informed by prior literature emphasizing their relevance to retirees’ well-being (e.g., Mansfield et al. 2020 ; Geng et al. 2023 ; Li et al. 2021 ). This approach ensures the study’s methodological design is grounded in empirically validated concepts, offering a detailed examination of how leisure activities enhance life quality.

To gather the qualitative data, a set of three semi-structured, open-ended interview questions was formulated. These questions were strategically designed to prompt detailed responses and discussions, enabling participants to articulate their experiences and perspectives on how leisure activities have influenced their well-being. The flexibility of the semi-structured format allowed for a conversational depth that facilitated the emergence of rich, narrative data, capturing the complex and personal dimensions of leisure that quantitative measures might overlook (Roulston and Choi 2018 ). This approach complements the empirical data, providing a more textured understanding of the retirees’ engagement with leisure activities.

Instrumentation’s validity

A two-pronged approach was adopted to ensure the validity of the questionnaire employed in this study, encompassing both expert evaluation and empirical testing for internal consistency. Initially, the questionnaire underwent rigorous assessment through the jury panel method. The expert panel method was chosen for its effectiveness in leveraging specialized knowledge to enhance the validity and reliability of the assessment process (Almanasreh et al. 2019 ). The assessment process involved a thorough evaluation by a panel of eight esteemed experts, each with specialized recreation and leisure management expertise. Their insightful feedback and recommendations were incorporated to enhance the questionnaire’s relevance and accuracy. Subsequently, to further reinforce the questionnaire’s validity, a pilot study was conducted involving 41 participants. This research phase involved a quantitative evaluation of the questionnaire’s internal consistency. Advanced statistical methods were employed to calculate the correlation coefficients for each item concerning the total score of its respective dimension. This comprehensive process ensured that the questionnaire was theoretically sound, as vetted by experts, and empirically robust, meeting the standards of internal consistency. Table 1 provides a detailed depiction of these correlation coefficients, showcasing the relationship between each item’s score and the overall score of its corresponding dimension within the questionnaire.

Table 1 presents a comprehensive analysis of correlation coefficients across four critical dimensions in a survey: Health Benefits, Emotional Benefits, Social Benefits, and Self-fulfillment Interests. Each dimension undergoes a thorough evaluation for internal consistency, as indicated by the Dimension Correlation, and its congruence with the overarching framework of the questionnaire, denoted by the Overall Correlation. The coefficients, consistently marked with double asterisks to denote statistical significance, exhibit a spectrum from moderate to high across these dimensions. This trend evidences a robust internal coherence within each dimension and underscores their substantial congruity with the global objectives of the questionnaire. The persistent presence of the double asterisks (**) underscores the statistical robustness of these correlations, bolstering the reliability of the findings. These outcomes collectively attest to the questionnaire’s efficacy in precisely capturing the targeted constructs within each dimension, thereby ensuring that each query contributes effectively to the broader research objectives.

Factorial validity

Exploratory factor analysis (EFA) was conducted using the principal components method, where orthogonal rotation via the Varimax technique was applied. This was done to extract factors by selecting the items most heavily loaded on each factor after rotation, as demonstrated in Table 2 .

An examination of Table 2 reveals the presence of four distinct factors, onto which a total of seventeen items are substantially loaded. Cumulatively, these factors account for 66.387% of the total variance. In detail, the first factor encompasses four items and has an eigenvalue of 3.075, contributing to 18.09% of the total variance. Similarly, the second factor includes four items and possesses an eigenvalue of 2.809, explaining 16.524% of the total variance. The third factor, comprising four items, has an eigenvalue of 2.725, accounting for 16.029% of the total variance. The fourth factor, consisting of five items, has an eigenvalue of 2.676, representing 15.743% of the total variance. To corroborate the proposed item-factor alignments, Confirmatory Factor Analysis (CFA) was employed using the Maximum Likelihood Method, facilitated by the LISREL software. Figure 2 depicts the resultant factor structure, confirming the factor structure hypothesized for the scale.

This figure illustrates the confirmatory factor analysis of the scale’s factor structure. The diagram presents the path coefficients for the scale items, which range from 0.52 to 0.89, signifying statistical significance at p  ≤ 0.01. The Chi-square statistic ( χ 2 ) is 561.18 with 168 degrees of freedom, corresponding to a χ 2 /df ratio of 3.34, indicating a good model fit. Goodness-of-fit indices including RMSEA, GFI, AGFI, and NFI are within optimal ranges, thereby affirming the factorial validity of the scale and its suitability for this study.

The analysis revealed that the path coefficients for the items on the scale ranged from 0.52 to 0.89, all achieving statistical significance at a threshold of p  ≤ 0.01. The Chi-square ( χ 2 ) statistic was calculated to be 561.18, with 168 degrees of freedom and a highly significant level of p  ≤ 0.001. This resulted in a (χ 2 /df) ratio of 3.34, indicative of a favorable model fit to the data. Moreover, the indices assessing the model’s goodness-of-fit, including RMSEA, GFI, AGFI, and NFI, all fell within their respective optimal ranges. These findings collectively underscore the factorial validity of the scale, confirming its robustness and appropriateness for the study.

Instrumentation’s reliability

The questionnaire’s reliability was assessed by the computation of Cronbach’s alpha ( α ) coefficient. The outcomes of this assessment are systematically presented in Table 3 .

The analysis of Table 3 reveals that the questionnaire possesses a robust overall reliability coefficient, quantified at 0.85. The dimension-specific reliability indices, evaluated using Cronbach’s alpha for assessing internal consistency, vary between 0.75 and 0.81. Each of these indices surpasses the established minimum reliability threshold of 0.6, indicating a commendable level of consistency across the questionnaire’s dimensions. These findings collectively affirm the questionnaire’s high reliability, validating its appropriateness for implementation within the study’s sample.

Data collection

Data collection was an integral component of this research, aiming to capture a comprehensive snapshot of the leisure pursuits among retirees. The data collection process for this study was conducted methodically over a 3-month period, from the beginning of May to the end of July 2023, allowing for extensive participation and detailed data accumulation. The process began with identifying retiree groups active on social media platforms like WhatsApp, capitalizing on the widespread use of such networks for enhanced reach and engagement. Subsequent efforts to expand the participant base employed a snowball sampling technique, which effectively utilized existing study participants to recommend additional retirees, thus facilitating the inclusion of diverse individuals across varying demographics. The survey distribution was substantial, reaching out to 1243 retirees, a number designed to ensure a wide berth of data for robust analysis. This outreach garnered a substantial response, with 545 retirees contributing their insights.

To enrich the quantitative findings with qualitative depth, the study extended invitations to 35 retirees from the initial pool of 545 respondents for personal interviews. The selection criteria prioritized diversity in leisure activities and engagement levels with the preliminary survey. This targeted approach was designed to capture a broad spectrum of experiences, ensuring that the qualitative interviews offer a detailed and representative view of the leisure phenomenon among retirees. Of those invited, 23 retirees accepted, demonstrating interest and readiness to provide more granular insights into their leisure pursuits. This positive response underscored the value retirees placed on discussing their leisure experiences and the potential impact on their well-being. The interviews were set to provide a layered understanding of leisure activities, bringing to light the individual stories behind the data and offering a comprehensive view of the retirees’ lived experiences.

Data analysis

To analyze the quantitative data, a comprehensive statistical approach was adopted to ascertain the psychometric robustness of the study instrument. Pearson correlation coefficients and Cronbach’s alpha formula were employed to validate the tool’s reliability and consistency. Furthermore, the study incorporated both exploratory and confirmatory factor analysis methodologies to assess the structure of the data. Quantitative measures such as frequencies, percentages, arithmetic means, and standard deviations were calculated for detailed data analysis. Comparative statistical techniques, including the Independent sample T -test for contrasting two independent means and one-way ANOVA for variance analysis, were utilized. These were augmented with Scheffe’s method for conducting multiple comparison tests. The statistical analyses were methodically executed using advanced statistical software, namely SPSS and LISREL, ensuring precision and reliability in the results.

To analyze the qualitative phase of data from semi-structured interviews, thematic analysis was utilized, enabling the identification and interpretation of emergent themes within participant narratives. Thematic analysis stands out for its ability to delve into human experiences’ particulars, offering invaluable depth to qualitative investigations. Identifying and analyzing themes significantly enriches the understanding of complex phenomena, aligning closely with the nuanced objectives of behavioral and psychological research (Castleberry and Nolen 2018 ). This approach was selected for its proficiency in capturing the experiences of retirees engaging in leisure activities. Supplementing the quantitative findings, this method facilitates a detailed exploration of how leisure activities influence well-being, with themes meticulously correlated to the research aims.

Ethical considerations

The study was designed with transparent communication of its purpose and objectives. On the first page of the questionnaire, a clear statement outlined the study’s primary goal, which served as an implicit agreement and permission from participants who proceeded with the questionnaire. This upfront disclosure ensured that participants were fully informed about the nature and intentions of the research before contributing their data. This practice not only adhered to ethical standards of informed consent but also fostered trust between the researchers and participants, which is fundamental to the integrity of the research process.

The findings section of this study is structured into two segments: an initial analysis of the quantitative data, followed by a detailed examination of the qualitative data, providing a comprehensive view of the research outcomes.

Quantitative findings

To effectively respond to the research question regarding the overall level of well-being enhancement attributed to leisure activity participation among Saudi retirees, an analytical approach was employed. This involved calculating the arithmetic means and standard deviations for each item on the questionnaire. The results of these calculations are systematically presented in Table 4 . The focus of this analysis is to ascertain the aggregate scores on the Leisure Benefits Scale, thereby providing a quantified measure of the impact of leisure activities on the well-being of the retired population in Saudi Arabia.

The table analysis reveals that the perceived impact of leisure activities on the well-being of the Saudi retiree community is moderately significant. This observation is supported by an arithmetic mean of 2.96 and a notably low standard deviation of 0.28, under one. Such a minimal standard deviation indicates a remarkable consistency in the perceptions of the study’s participants regarding the contribution of leisure activities to the well-being of retirees in Saudi Arabia. This uniformity underscores a collective agreement among the sample population on the moderate yet noteworthy role of leisure activities in enhancing retirees’ quality of life.

To answer the second question, which aims to identify which among health, emotional well-being, social integration, and self-fulfillment are most substantially enhanced by leisure activities among Saudi Arabia’s retired population, a methodical approach was taken. This involved calculating the arithmetic means and standard deviations for each dimension within the questionnaire. The results of these calculations, essential for providing a clear understanding of the relative impact of leisure activities in these specific domains, are methodically presented in the subsequent Table 5 . This detailed analysis is instrumental in discerning the most positively affected domain among the retired demographic in the context of leisure activities.

The table’s analysis provides a clear ranking of the impact of leisure activities on various well-being dimensions among retirees in Saudi Arabia. It shows that Health Benefits is the most positively impacted domain, with a high average mean score of 3.50. Afterwards, Emotional Benefits rank second with a moderate average mean of 3.10. Social Benefits are also moderately enhanced, coming in third with an average score of 3.01. Lastly, despite being beneficial, Self-fulfillment Interests are less influenced by leisure activities compared to the other domains, as indicated by its lower average mean of 2.38, placing it in the fourth position. This hierarchy reflects the varying degrees to which different aspects of well-being are affected by leisure activities among the retired population.

In addressing the third research question, which aims to uncover potential disparities in how leisure activities are perceived to benefit various demographic groups (including gender, age, socio-economic status, and educational level) within Saudi Arabia’s retired community, the initial focus is on the gender demographic. This phase of the analysis entailed a comprehensive computation of arithmetic means and standard deviations for the responses of the study’s sample, segregated by gender. The independent sample T -test was applied to ascertain the statistical significance of the observed differences in these means. The findings of this rigorous analysis, which are systematically outlined in Table 6 , provide critical insights into the existence and extent of gender-specific differences in the perception of the benefits of leisure activities. This strategic approach offers a refined understanding of the influence of gender on the valuation of leisure benefits among the Saudi retired populace.

Analysis of the data presented in the table indicates the presence of statistically significant disparities, noted at the α  ≤ 0.05 significance level, in the study participants’ average responses when segmented by gender. Notably, these differences are in favor of female respondents. This finding highlights a gender-specific variation in the perception or experience of the studied phenomena, underscoring the nuanced impact of gender on the research outcomes.

Moving on to examine another critical demographic variable, Table 7 provides the outcomes of the one-way ANOVA test. This analysis was conducted to discern any statistically significant differences in the average responses of the study’s participants, explicitly focusing on the age variable. The results are systematically organized and presented to offer a clear understanding of how age influences the perceptions within the study’s scope.

The statistical analysis of age-based variations in the perception of leisure benefits among Saudi retirees, as reflected in Table 7 , reveals a tendency toward differences across age cohorts. However, these differences did not achieve statistical significance ( F  = 2.794, p  = 0.053). This outcome implies that, although there might be slight variations in the valuation of leisure activities among different age groups within the retired population, such differences are not markedly significant within this study’s dataset. Consequently, this analysis suggests a uniform perception of the benefits of leisure activities across the age spectrum among retirees in Saudi Arabia.

Table 8 presents the findings from the one-way analysis of variance (ANOVA) test, which was executed to ascertain any statistical disparities in the mean responses of the study’s sample population based on the variable of Social Status.

The analysis of Table 8 , detailing Social Status-based differences in leisure benefits among Saudi retirees, reveals significant statistical disparities ( F  = 11.642, p  = 0.000). This significant variation indicates that retirees’ social status notably influences their perception of leisure benefits, suggesting that access and attitudes toward leisure activities vary across different social strata within the retired population in Saudi Arabia. To determine the direction of the differences, the post hoc Scheffe test was used; Table 9 below illustrates the direction of these differences.

Table 9 , focusing on social status, reveals differences in how leisure activities’ benefits are perceived among Saudi retirees. Married individuals show a significant variation in their perception compared to divorced retirees, as indicated by the mean scores. This suggests that marital status, including being single, married, divorced, or separated, plays a role in shaping perceptions of leisure’s impact on well-being, with marital status particularly influencing these viewpoints.

Table 10 presents the outcomes from a one-way analysis of variance (ANOVA) conducted to ascertain the presence of statistical differences in the average responses of participants in the study. This analysis specifically focuses on understanding how perceptions of the role of leisure activities in enhancing well-being in Saudi’s retired community vary concerning the economic status of the respondents.

Table 10 , which focuses on the differences based on economic status, reveals pronounced statistical variations in how leisure activities are perceived to enhance well-being among Saudi retirees. The considerable F -statistic of 206.038 and a p value of 0.000 robustly indicate marked disparities in perceptions across varying economic groups. This significant variance underscores the pivotal influence of economic status in molding retirees’ perspectives regarding the benefits of leisure activities, highlighting economic factors as critical determinants in shaping the perceived efficacy of leisure in enhancing well-being. To determine the direction of these differences, the Scheffe post hoc test was utilized; Table 11 illustrates the direction of these differences.

Table 11 reveals notable disparities in the perception of leisure benefits across different economic statuses among Saudi retirees. High-income individuals perceive significantly more benefits from leisure activities than upper-middle, middle, and low-income groups. These findings suggest a clear relationship between economic status and the perceived impact of leisure on well-being, with higher income correlating with a greater appreciation of leisure benefits. This highlights the influence of economic factors on the perceived quality and effectiveness of leisure activities in enhancing well-being.

Table 12 features the one-way ANOVA results, evaluating statistical differences in how participants, segmented by educational background, perceive leisure activities’ impact on well-being among retired individuals in Saudi Arabia.

Table 12 demonstrates statistically significant variances in the perceptions of leisure activities’ role in enhancing well-being correlated with the educational levels of Saudi retirees. The pronounced F -value of 10.289 and the definitive p value of 0.000 prove that a retiree’s educational background markedly influences their perception of leisure benefits. This finding implies that the educational level is a crucial factor in forming retirees’ perspectives and experiences related to leisure, significantly affecting its perceived value and impact on overall well-being during retirement. To determine the direction of the differences, the Scheffe post hoc test was utilized. Table 13 illustrates the direction of these differences.

Table 13 , showcasing the results from the Scheffe post hoc analysis, indicates significant differences in perceptions of leisure benefits among Saudi retirees according to their educational levels. It highlights that retirees with tertiary education have markedly different perceptions than those with primary or secondary education, as evident in the distinct mean scores. This finding suggests a correlation between higher educational attainment and a unique understanding of leisure activities’ contribution to well-being in retirement.

Qualitative findings

This section outlines the qualitative findings, where the thematic analysis identified three main themes that reflect the retirees’ experiences and perspectives on leisure activities and their impact on well-being.

Holistic health and emotional well-being

The retirees’ narratives reveal a strong link between leisure activities and improved physical health, a finding particularly relevant in the Saudi context, where traditional lifestyles may have been more sedentary. “Since I began regular evening walks around our neighbourhood parks, I have noticed my blood pressure stabilizing,” one retiree shared, indicating the health benefits of accessible, low-impact physical activities. Another retiree’s experience with a more culturally traditional activity, such as falconry, underscores this, “Falconry is not just a sport for me; it is an engaging way to stay active, which I find beneficial for my health.”

The emotional benefits retirees associate with leisure activities are profound. “In our culture, family and social gatherings are significant. Organizing and participating in these brings me immense joy and a sense of belonging,” a participant noted, reflecting the social aspect of leisure that resonates strongly in the collectivist Saudi culture. Another retiree mentioned, “ Pursuing calligraphy, a cherished art form in our culture, has been incredibly soothing for me.”

The analysis shows that Saudi retirees’ leisure activities are often closely tied to their cultural heritage, enhancing their sense of identity and continuity. Activities such as gardening, calligraphy, or participating in community events offer physical and emotional benefits and help maintain a connection with their cultural roots.

In summary, the qualitative responses from Saudi retirees highlight that leisure activities contribute significantly to their well-being, with the benefits of these activities being amplified by their alignment with cultural practices and values. The findings suggest that integrating culturally relevant leisure activities into the daily routines of retirees could be vital to enhancing their overall health and emotional well-being.

Social integration and self-fulfillment

The qualitative data revealed that leisure activities enhance social integration among Saudi retirees. Participants frequently emphasized how these activities catalyzed social interaction and community bonding. A retiree articulated, “My involvement in a local walking group transcends physical health benefits; it has been instrumental in forging new connections with neighbours and creating a supportive community network.” The significance of participating in culturally resonant events was also noted, with another retiree stating, “My engagement in mosque gatherings and community volunteerism has deepened my sense of belonging and unity within my community.” These narratives highlight the intrinsic value of leisure activities as vehicles for social engagement, which is crucial in a societal context that places high importance on communal relationships and solidarity.

In the domain of self-fulfillment, retirees voiced how leisure pursuits were instrumental in providing a sense of personal achievement and identity. One participant reflected, “ Retirement has allowed me to immerse myself in painting, transforming it from a hobby to an integral part of my identity.” Another retiree’s experience acquiring new skills, such as digital photography, was cited as a source of personal accomplishment and cognitive stimulation. These insights underscore the significant role of leisure activities in promoting self-worth and ongoing personal development, particularly during retirement, which often prompts a reevaluation of personal identity and aspirations.

Within the Saudi cultural framework, where family and community relationships are central to social life, the dual aspects of social integration and self-fulfillment acquire heightened significance. Leisure activities provide a harmonious blend of traditional communal engagement and individual self-expression, reinforcing personal identities in the post-retirement phase.

In summary, the qualitative findings indicate that leisure activities in Saudi Arabia are far more than mere pastimes. They are integral to fostering meaningful social connections and achieving personal fulfillment. This dual role is especially pertinent in the Saudi context, where leisure bridges traditional communal values and the pursuit of individual interests, thereby enhancing retirees’ overall quality of life.

Cultural influence and barriers to leisure

The qualitative findings underscored the significant role of cultural ethos in shaping leisure activities among Saudi retirees. Participants frequently discussed how entrenched cultural norms and traditions underpin their approach to leisure. One retiree illustrated this: “Our leisure practices are deeply rooted in cultural values; they extend beyond personal amusement to embrace family traditions and communal gatherings.” Another participant elaborated, “Leisure choices in our Saudi society are invariably intertwined with our cultural and religious principles, guiding us towards socially appropriate and personally enriching activities.” These reflections reveal that in the Saudi context, leisure is not solely an individual pursuit but is inextricably linked to cultural identity, often reflecting collective values and societal expectations.

Several impediments to leisure engagement for Saudi retirees were reported. Recurring discussions centered on the inadequacy of leisure infrastructure and opportunities that cater to older adults’ needs and preferences. “Navigating the limited options for age-appropriate and accessible leisure activities presents a significant challenge,” expressed a respondent. Economic factors also emerged as substantial barriers, with a participant noting, “Economic constraints at times limit our participation in diverse leisure pursuits, particularly those requiring financial investment or travel.” Also, prevalent societal perceptions of ageing and activity levels were highlighted as deterrents. A retiree voiced, “There exists a societal notion that retirement should be a period of reduced activity, which can be disheartening for those of us eager to explore and engage more actively.” These barriers underscore the necessity for developing more inclusive leisure strategies and advocating a paradigm shift in societal attitudes toward aging to optimize the role of leisure in enhancing retirees’ well-being and life satisfaction.

To address the first research question concerning the extent to which leisure activity engagement enhances well-being among retirees in Saudi Arabia, this study revealed a moderate level of enhancement in well-being, as quantified by the scores on the Leisure Benefits Scale. The observed moderate enhancement in retirees’ well-being can be understood through unique Saudi cultural and socio-economic contexts. Predominantly, cultural norms in Saudi society, which prioritize family and communal activities over individual leisure pursuits, might have tempered the impact of leisure on individual well-being. Moreover, this cultural inclination toward communal leisure could suggest a detailed pathway through which leisure activities contribute to well-being, potentially emphasizing the value of social cohesion and familial bonds. Furthermore, the accessibility and diversity of leisure activities available to retirees are likely influenced by the country’s ongoing socio-economic changes, particularly those associated with the Vision 2030 reform plan. These reforms, aiming to diversify entertainment and leisure opportunities, may gradually alter the landscape of leisure activities available to retirees, influencing future well-being outcomes.

The study’s outcomes resonate with the observations made by Adams et al. ( 2010 ), who acknowledged the complexities inherent in correlating leisure activities with well-being enhancement in later life, particularly under varying contextual influences. Similarly, Agahi et al. ( 2011 ) underscore the importance of sustained engagement in leisure activities for augmented well-being, suggesting that the intensity and nature of leisure activities pursued by Saudi retirees might be pivotal in understanding the observed moderate enhancement. This emphasis on sustained engagement aligns with the notion that not just any leisure activity but those consistently engaged in and meaningful to the individual are likely to have the most significant impact on well-being.

Conversely, the findings slightly contradict Brajša-Žganec et al. ( 2011 ), who reported a more pronounced link between leisure activities and subjective well-being. This discrepancy might stem from the distinct socio-cultural milieu of Saudi Arabia, which potentially modulates retirees’ perceptions and participation in leisure activities. The cultural specificity of Saudi Arabia, including its values and norms, plays a critical role in shaping how leisure activities are perceived and engaged in, thereby influencing their impact on well-being. Furthermore, this research aligns with Li et al. ( 2021 ), who discuss the differential impacts of leisure activities across various dimensions of well-being in older adults. Therefore, the moderate enhancement observed in the Saudi context might reflect a variation in how leisure activities align with the retirees’ specific well-being needs and expectations. This suggests that the leisure activities most beneficial for well-being in Saudi Arabia may differ from those in cultures with different norms and values regarding leisure and retirement. The study reinforces that, as recognized globally, leisure activities are vital in enhancing retirees’ well-being. However, the specific context of Saudi Arabia, marked by its unique cultural and socio-economic landscape, significantly shapes the extent of this enhancement, highlighting the importance of contextual factors in designing and implementing policies and programs aimed at promoting retiree well-being through leisure.

Concerning the second research question regarding the domains most significantly enhanced by leisure activities among the retired population in Saudi Arabia, the study pinpointed health benefits as the foremost area of improvement. This prioritization of physical health mirrors broader global trends underscoring the critical role of physical well-being in later life. Within Saudi Arabia, such emphasis likely reflects a cultural and societal evolution toward valuing physical health during retirement, perhaps spurred by recent public health initiatives and a movement toward more active living.

The study also found moderate emotional and social well-being enhancements through leisure activities. These enhancements, while noteworthy, highlight a differential impact of leisure across well-being domains, with physical health seeing the most substantial benefits. This could be attributed to the cultural norms in Saudi Arabia, where leisure may often be experienced in communal settings, intertwining emotional and social benefits. Indeed, the communal nature of leisure in Saudi society may inherently bolster emotional and social well-being, yet distinguishing these effects from the broader context of health benefits presents an intriguing area for further investigation. Such a pattern indicates that emotional well-being and social integration through leisure are fostered within community and family-oriented activities, a characteristic feature of Saudi society. However, the area of self-fulfillment through leisure showed relatively lower enhancement. This observation suggests a potential area for policy intervention, aiming to broaden the scope and perception of leisure’s role in supporting comprehensive well-being, including personal growth.

Comparing these findings with existing literature, the emphasis on health benefits is consistent with the research of Lee et al. ( 2023 ), who highlighted the relationship between leisure activities and physical well-being in older adults. The moderate impact on emotional well-being parallels Chen et al. ( 2022 ), indicating that the influence of leisure on emotional health is subject to individual and contextual factors. For social benefits, the study’s findings align with Lindsay Smith et al. ( 2017 ), though the impact in Saudi Arabia may be nuanced by the society’s existing solid communal ties. In contrast, the lower emphasis on self-fulfillment aligns less with the observations of Ertekin ( 2021 ), who found a significant impact of leisure on personal growth among younger demographics, underscoring a potential generational divide in leisure’s contributions to well-being. Overall, the study affirms the beneficial role of leisure in enhancing various aspects of well-being among Saudi retirees. However, the variations in the degree of enhancement across different domains highlight the importance of cultural and societal factors in shaping retirees’ leisure experiences and their perceived benefits, pointing to the nuanced role leisure plays in the fabric of retirees’ lives, influenced by both individual preferences and the prevailing cultural ethos.

In exploring the third research question, which investigates the presence of demographic differences in how leisure activities are perceived to benefit retirees in Saudi Arabia, the study unveils distinct variations across gender, age, socio-economic status, and educational level. The research found that female retirees perceive a more significant benefit from leisure activities compared to their male counterparts. This difference underlines the impact of societal roles and cultural expectations on leisure engagement and its perceived value, suggesting that leisure activities offer a unique avenue for social and emotional fulfillment for women. This could be attributed to gender-specific roles and cultural norms, where women might find more value or solace in leisure activities, particularly those that foster social interaction or creativity. Moreover, these findings align with the broader discourse on gender and leisure, highlighting the need to consider gender dynamics when designing and promoting leisure activities for retirees. This gender distinction in leisure perception may also reflect broader societal dynamics, where leisure serves different psychosocial functions for men and women.

While initial data suggested variations in leisure benefits perceptions across age groups, these differences were not statistically significant. This lack of significant variation highlights a universal appreciation for leisure across the retirement spectrum, suggesting that leisure’s role in enhancing well-being is broadly recognized among the elderly in Saudi Arabia. This implies a consistency in the value placed on leisure activities among the elderly, regardless of age. Such findings challenge the notion that leisure’s importance diminishes with advancing age, reinforcing that leisure activities remain a crucial component of well-being for retirees. It indicates that, despite physiological and lifestyle changes that come with different stages of ageing, the perceived importance and benefits of leisure remain relatively stable among Saudi retirees.

The analysis highlights socio-economic and marital status as critical determinants in shaping retirees’ perceptions of leisure benefits. It was observed that retirees belonging to higher income groups reported a heightened perception of the benefits derived from leisure activities. This association points to the critical role of financial stability in enhancing leisure experiences, where economic resources expand access to diverse and potentially more fulfilling leisure options. This trend suggests that more significant economic resources, which enable access to a broader spectrum of leisure opportunities, significantly contribute to an enhanced sense of well-being from these activities. Concurrently, marital status emerged as a pivotal factor, with married retirees consistently reporting more favorable perceptions of leisure benefits than their unmarried peers. This finding highlights the importance of companionship and shared experiences in magnifying the positive impacts of leisure on well-being, suggesting that social connections are integral to the leisure experience. This variation can likely be ascribed to the added social and emotional support provided by marital relationships, which may amplify the enjoyment and overall satisfaction derived from leisure pursuits. Furthermore, the significant influence of educational level on leisure perceptions emphasizes the link between education and leisure engagement, where higher education fosters an enriched understanding and valuation of leisure’s benefits. These findings underscore the multifaceted nature of leisure benefits and their dependency on individual socio-economic and relational contexts. Ultimately, these insights call for a nuanced approach to promoting leisure among retirees, considering how socioeconomic status, marital status, and education level influence leisure’s perceived value and impact on well-being.

These findings align with various strands of existing research. The gender-based differences affirm the nuanced view of leisure engagement across genders, with women potentially valuing leisure differently due to societal roles and cultural expectations. The gender-based differences resonate with Fernandez’s ( 2023 ) exploration of gender dynamics in leisure activities. In contrast, the age-related findings present a different narrative from studies like Nielsen et al. ( 2021 ), which observed more pronounced age-based variations in leisure engagement. This discrepancy suggests that cultural and societal contexts, such as those in Saudi Arabia, may be crucial in moderating the relationship between age and leisure engagement. The impact of socio-economic status on leisure perceptions finds support in Beenackers et al. ( 2012 ), highlighting the role of economic factors in leisure activity participation. This emphasizes the importance of accessibility and the ability to engage in preferred leisure activities as key factors influencing well-being. Lastly, the influence of education aligns with Li et al. ( 2021 ), emphasizing the role of educational background in shaping leisure experiences and benefits. Educational attainment not only influences the types of leisure activities pursued but also affects the depth of engagement and the derived psychological benefits. These findings underscore the need for a nuanced understanding of leisure engagement among retirees, considering the diverse backgrounds and life experiences within this demographic. Such insights are vital for developing targeted interventions and policies that cater to retirees’ specific needs and preferences, ensuring leisure activities are accessible and meaningful across different segments of the retired population.

Regarding the fourth research question dealing with how retirees in Saudi Arabia perceive the role of leisure activities in enhancing their well-being, particularly in terms of health, emotional balance, social integration, and self-fulfillment, post-retirement, the qualitative findings provide in-depth insights. Interviews revealed that retirees associate significant health benefits with regular engagement in leisure activities, highlighting a shift from potentially sedentary lifestyles to more active pursuits. The study reveals a profound link between leisure activities and physical health improvement among retirees. Participants frequently mentioned activities like evening walks and gardening alongside culturally specific pursuits such as falconry, underscoring their dual benefits for physical health and emotional enrichment.

Retirees emphasize the emotional upliftment of leisure activities, especially those involving family and social interactions. These activities contribute to emotional well-being and echo the collectivist values inherent in Saudi society, reinforcing familial and social bonds. These activities are credited with bringing joy and fostering a deep sense of belonging, reflecting the collectivist nature of Saudi culture. Moreover, the findings indicate that leisure activities are crucial in promoting social integration. Retirees highlighted the role of leisure in expanding their social networks and maintaining active social lives, which is particularly valuable in the context of Saudi Arabia’s communal culture. For many retirees, these activities are pivotal in building and maintaining social connections, contributing significantly to their sense of community involvement and social life. Furthermore, participants viewed retirement as a unique opportunity for exploring new hobbies and interests, signifying leisure’s critical role in personal development and self-discovery. Retirement is seen as an opportunity for personal growth and identity development. Leisure pursuits are instrumental in this phase, allowing retirees to explore new skills and hobbies, enhancing their self-worth and personal accomplishment.

These qualitative insights corroborate and expand upon existing literature, providing a nuanced understanding of the multifaceted benefits of leisure among Saudi retirees. The study’s insights align with existing research in gerontology and leisure studies. The focus on health and emotional well-being through leisure resonates with the findings of Lee et al. ( 2023 ), who identified a strong correlation between leisure activities and physical well-being in older adults. Similarly, the emphasis on social integration through leisure activities finds support in the research of Lindsay Smith et al. ( 2017 ), who highlighted the importance of social support in leisure activities among older adults. Moreover, the narrative around leisure as a means for self-enhancement and identity reconstruction enriches the discourse on leisure’s transformative potential in the post-retirement phase. Besides, the significance of leisure in facilitating personal development and identity reconstruction is consistent with the observations made by Ertekin ( 2021 ), emphasizing leisure’s role in personal growth.

In summarizing this discourse, it becomes clear that this research not only addresses a significant gap but also advances our understanding of the impact of leisure activities on the well-being of retirees in Saudi Arabia. Through a nuanced examination of the effects across diverse demographic groups, this study unveils previously unrecognized aspects of how leisure contributes to post-retirement life. These insights challenge existing paradigms and enrich the academic dialogue surrounding gerontological well-being. The implications of this research extend beyond academic interest, providing a robust foundation for policy formulations that aim to cultivate a more inclusive and supportive leisure environment for retirees. This study’s contributions are mainly distinguished by its methodical analysis and culturally sensitive recommendations, which are pivotal for practitioners aiming to enhance retiree well-being through targeted leisure programs. This research offers a strategic roadmap for implementing culturally attuned leisure initiatives by explicitly detailing how these findings can influence policy and practice. Such contributions are vital for shaping future studies and encouraging the adoption of informed practices that optimize the quality of life for retirees in Saudi Arabia, marking a significant advancement in the field.

Implications

Drawing from this study’s key findings, significant theoretical and practical implications emerge, highlighting the multidimensional impact of leisure activities on retirees’ well-being.

Theoretical implications

This research substantially enriches the theoretical framework surrounding the impact of leisure activities on the well-being of retirees. It reaffirms the idea that leisure serves as a vital component in enhancing life quality, transcending the traditional view of it as a mere pastime, particularly in the context of retirement. This perspective aligns with the findings of Mansfield et al. ( 2020 ), who also emphasized leisure’s critical role in retirees’ quality of life. By doing so, this study extends beyond conventional leisure theories to highlight the multifaceted role of leisure in promoting comprehensive well-being among retirees, incorporating aspects such as physical health, emotional well-being, social integration, and self-fulfillment. A notable aspect of the study is its emphasis on the role of cultural context in shaping leisure experiences. This introduces a good perspective to leisure theories, suggesting that cultural dimensions are pivotal in influencing how leisure activities impact well-being, thereby calling for a broader, more culturally inclusive framework in future research, as supported by Poscia et al. ( 2018 ), who explored the cultural determinants of leisure activity. Furthermore, the study sheds light on the variations in perceived leisure benefits across different demographic segments, including gender, socio-economic status, and educational levels. These findings prompt reevaluating existing assumptions within the academic discourse on leisure and aging, advocating for a refined approach that better accommodates the diversity of retirees’ experiences and needs, echoing the work of Li et al. ( 2021 ), who highlighted demographic influences on leisure engagement.

Practical implications

Practically, the study offers critical insights for enhancing retirees’ well-being through leisure activities. Highlighting the importance of culturally sensitive and accessible leisure programs, it emphasizes the need for initiatives that cater to retirees’ physical capabilities and align with their cultural values and social preferences. This approach aligns with the findings of Adams et al. ( 2010 ), who demonstrated the positive impact of culturally tailored leisure programs on the well-being of diverse elderly populations. This calls for creating leisure opportunities that are not only physically accessible but also resonate deeply with the cultural and social fabric of the Saudi retired community. Moreover, identifying barriers to leisure engagement underscores the critical need for targeted interventions to remove obstacles related to economic constraints and infrastructural deficiencies. The importance of overcoming these barriers is echoed in the work of Beenackers et al. ( 2012 ), who explored the significant role of accessible leisure infrastructure in promoting active aging. The study also highlights the need to address prevalent barriers to leisure engagement, such as economic limitations and inadequate infrastructure. Therefore, policymakers and community developers are urged to prioritize the development of inclusive leisure environments that support the holistic well-being of the retired population. Lastly, the research points to the crucial role of societal perceptions regarding aging and retirement. It advocates for shifting societal narratives toward viewing retirement as a period of continued engagement and fulfillment, leveraging public campaigns and community programs to foster positive attitudes toward aging and active lifestyles among retirees. This recommendation aligns with the conclusions of Li et al. ( 2021 ), who highlighted the transformative potential of public narratives in shaping retirement experiences. Facilitating this societal change involves public awareness campaigns and community initiatives that support and celebrate the active participation of older individuals in diverse leisure and social activities, thereby fostering a more inclusive and supportive environment for retirees.

Limitations and recommendations for further research

This study identifies three primary limitations, each accompanied by a corresponding recommendation. First, the sample’s limited diversity may not fully capture the heterogeneity of Saudi Arabia’s retired population. To address this, future research should aim for a broader demographic reach, including more varied age groups, socio-economic statuses, and participants from urban and rural areas, ensuring inclusivity of various regions, socio-economic statuses, and cultural backgrounds. Second, the cultural specificity of the study, focused primarily on the Saudi context, limits the generalizability of findings to other cultural environments. It is recommended that subsequent studies incorporate comparative analyses with different cultural groups, such as retirees in Western, Asian, and other Middle Eastern contexts, to enhance the applicability of the research findings. Finally, the study’s cross-sectional nature constrains exploring longitudinal changes and causality in leisure activities’ impact on well-being. Future research should consider longitudinal designs to assess changes over time and identify causal relationships to observe how retirees’ perceptions and benefits of leisure evolve, providing a more dynamic understanding of the role of leisure in retirement life.

In summarizing this research, it is evident that the study significantly advances our understanding of how leisure activities contribute to the well-being of retirees in Saudi Arabia. It comprehensively delineates the multifaceted role of leisure—highlighting its profound influence on physical health, emotional well-being, social connectivity, and the pursuit of self-fulfillment among the elderly. The study elucidates that leisure activities, particularly those resonating with cultural traditions, transcend mere recreation to become pivotal in enhancing the quality of life for retirees. The investigation examines the impact of demographic variables, including gender, age, socio-economic status, and educational level, on how leisure benefits are perceived and experienced. It demonstrates that the positive effects of leisure on well-being are universally acknowledged yet vary in magnitude across different demographic segments. This variation underscores the necessity for customized leisure program designs that accommodate the diverse needs and preferences within the retired community. Beyond leisure and gerontology, the findings offer strategic insights for policymakers, urban planners, and healthcare professionals. The study advocates for establishing inclusive and culturally attuned leisure infrastructures and programs, ensuring broad accessibility for the entire spectrum of the retired population. Ultimately, this research contributes to our comprehension of leisure’s role in retirement, promoting its strategic integration into initiatives aimed at augmenting the life quality of the elderly. As Saudi Arabia navigates through ongoing demographic and cultural changes, the pertinence of such research becomes increasingly vital in informing policies and practices that foster a vibrant, engaged, and content retired community.

Data availability

The datasets generated and analyzed during this study are shared in an online Supplementary file with this published article.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2024-48-02”.

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Homoud Mohammed Nawi Alanazi

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As the sole author of this paper, I was responsible for all aspects of the research and manuscript preparation. This included interpreting the results, articulating the study’s implications, and drafting the manuscript’s key sections, including the “Introduction”, “Discussion”, and “Conclusion”. I also analyzed the statistical data, examining and interpreting the quantitative data collected. This all-encompassing role ensured a thorough and rigorous examination of the research topic.

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Alanazi, H.M.N. The role of leisure activities in enhancing well-being in Saudi’s retired community: a mixed methods study. Humanit Soc Sci Commun 11 , 604 (2024). https://doi.org/10.1057/s41599-024-03126-x

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Received : 15 December 2023

Accepted : 24 April 2024

Published : 10 May 2024

DOI : https://doi.org/10.1057/s41599-024-03126-x

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