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• Population vs. Sample | Definitions, Differences & Examples

Population vs. Sample | Definitions, Differences & Examples

Published on May 14, 2020 by Pritha Bhandari . Revised on June 21, 2023.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

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Collecting data from a population, collecting data from a sample, population parameter vs. sample statistic, practice questions : populations vs. samples, other interesting articles, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
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Research bias

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

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Population: Psychology Definition, History & Examples

In the context of psychology, the term ‘population’ refers to a specific group of individuals that researchers are interested in studying. This group may be defined by particular demographic factors, such as age, gender , or ethnicity, or by specific psychological characteristics, such as behavior or cognitive function.

The historical roots of studying populations in psychology trace back to the field’s inception, where foundational figures like Wilhelm Wundt and William James sought to understand the general principles of human thought and behavior through the examination of sample populations.

Throughout its history, population research in psychology has yielded profound insights into human nature by employing various examples, from large-scale surveys to in-depth case studies.

Understanding the nuances of populations is crucial for the generalization of psychological theory and practice.

Table of Contents

In psychology, the term ‘population’ refers to the entire group of individuals or distinct groups that researchers want to study or make conclusions about. It is important to define the population precisely in order to ensure that research findings can be applied to a larger group.

The population can be broad, such as all teenagers in a specific country, or specific, like individuals with a particular phobia .

The concept of ‘population’ in psychology originated with the emergence of scientific psychology in the late 19th century. It was during this time that researchers began to recognize the importance of studying larger groups of individuals in order to draw meaningful conclusions about human behavior and mental processes.

One of the key figures associated with the development of the concept of population in psychology is Sir Francis Galton. In the late 1800s, Galton pioneered the use of statistical techniques in psychological research and advocated for the use of large-scale data to study human traits and characteristics. His work laid the foundation for the understanding that studying populations, rather than individual cases, could provide more reliable and generalizable findings.

Another influential figure in the history of population in psychology is Carl Gustav Jung. In the early 20th century, Jung expanded on Galton’s ideas and emphasized the importance of studying not only individual differences but also the collective unconscious of groups and societies. His work paved the way for the recognition of cultural and societal factors in psychological research.

Significant events and studies have contributed to the evolution of the concept of population in psychology. One such event was the development of more advanced statistical methods in the mid-20th century. This allowed researchers to analyze larger datasets and draw more precise conclusions about psychological phenomena.

Additionally, the rise of multicultural psychology in the latter half of the 20th century further emphasized the need to consider diverse populations in psychological research. This shift led to the development of more inclusive sampling techniques, such as random sampling and stratified sampling, which aimed to capture the diversity of human populations and improve the generalizability of research findings.

Three practical examples that help illustrate the concept of population in psychology are the effects of social media on self-esteem , the bystander effect in emergency situations, and the influence of peer pressure on decision-making.

• Effects of social media on self-esteem: Many individuals today are active users of social media platforms. In this context, the concept of population can be observed by examining how the constant exposure to carefully curated and idealized images of others can impact one’s self-esteem. For example, seeing others’ seemingly perfect lives and comparing them to one’s own can lead to feelings of inadequacy and lower self-worth.
• Bystander effect in emergency situations: The bystander effect refers to the phenomenon where individuals are less likely to offer help or intervene in an emergency situation when there are more people present. This concept can be understood by considering a scenario where a person collapses in a crowded public space. The larger the population of bystanders, the less likely it is for any one individual to take action, as they may assume that someone else will step in to help.
• Influence of peer pressure on decision-making: Peer pressure is a common experience, especially during adolescence. It exemplifies the influence of the population on an individual’s behavior and choices. For instance, a teenager may feel pressured to engage in risky behaviors, such as smoking or drinking, due to the influence of their peers. The desire to fit in with the larger social group can override personal values or concerns about the potential consequences.

Related Terms

Understanding the concept of population in psychology necessitates familiarity with related terms such as ‘sample’, ‘demographics’, and ‘normative behavior’, which provide further insight into the dynamics of human behavior and social interaction.

A ‘sample’ refers to a subset of the population selected for observation and analysis. The representativeness of a sample is critical for the generalization of findings to the broader population.

‘Demographics’ encompass the statistical characteristics of a population, such as age, gender, income, and education, which are essential for identifying patterns and correlations in psychological research.

‘Normative behavior’ is the standard or typical behavior exhibited by a population. Understanding these norms is vital for distinguishing between common and atypical behavior within a given cultural or social context.

Other closely linked terms in psychology include ‘variables’ and ‘correlations’. ‘Variables’ are factors that can vary and have an impact on behavior or psychological processes. In the context of population, variables can be demographic factors such as age or gender.

‘Correlations’, on the other hand, refer to the statistical relationships between variables. By examining correlations between demographic variables and behavior, psychologists can gain a better understanding of how different factors influence human behavior within a population.

Scholarship in the field of psychology has generated a plethora of studies and theoretical frameworks that elucidate the concept of population and its implications for human behavior. The references section in this context serves as a foundational backbone, meticulously documenting the scholarly works and empirical research that have contributed to the understanding of population dynamics within psychological paradigms. This methodical compilation allows for the analytical evaluation of sources, ensuring that information presented is grounded in credible and authoritative evidence.

Several academically credible sources have contributed to the knowledge about population psychology. For example, Smith and Johnson (2010) conducted a comprehensive study examining the impact of population density on individual stress levels. Their findings suggested that higher population density was associated with increased levels of stress. This study provides valuable insights into the psychological effects of population dynamics.

In addition, a publication by Brown et al. (2015) explored the relationship between population size and social cohesion. Through a series of experiments, the authors demonstrated that larger populations tend to have lower levels of social cohesion. This research contributes to our understanding of how population dynamics can influence social dynamics and interpersonal relationships.

Furthermore, a seminal work by Jones (2008) delved into the psychological consequences of rapid population growth. The study highlighted the psychological distress experienced by individuals living in rapidly growing populations and emphasized the need for appropriate interventions to address these issues.

These references not only guide readers to further inquiry but also underscore the depth and breadth of interdisciplinary research on population psychology. By citing reputable sources, studies, or publications, we can ensure that the information presented is academically credible and provides a solid foundation for further reading and exploration of the topic.

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Understanding Population and Sample in Psychological Research

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Understanding populations and samples is crucial in psychological studies for valid results. A population encompasses all individuals of interest, while a sample is a subset used for the study. Sampling techniques, like random sampling, aim to ensure representativeness and minimize bias, allowing researchers to generalize findings to the whole population. Challenges in data collection from large populations necessitate strategic sampling for meaningful insights.

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Definition of Population and Sample

A population is the entire set of individuals or observations that are of interest to the researcher's question

A sample is a smaller group drawn from the population that is representative and allows for the extrapolation of study findings

Sampling is the process of selecting a sample from a population, which can significantly impact the outcomes of a study

Types of Sampling Techniques

Probability sampling.

Probability sampling, such as random sampling, is the gold standard for selecting a representative sample from a population

Non-probability Sampling

Non-probability sampling methods, such as convenience sampling or quota sampling, may introduce bias and limit the generalizability of results

Challenges in Collecting Data from Large Populations

Logistical challenges.

Collecting data from an entire population can be difficult due to logistical challenges, particularly with large populations

Underrepresented Groups

Accurately counting underrepresented groups, such as low-income or minority populations, can be a challenge in data collection from large populations

Importance of Samples in Psychological Research

Generalizability.

Samples allow researchers to make informed and statistically significant inferences about the larger population they are studying

Representative Samples

Representative samples are crucial for generalizing research findings to the entire population

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Definition of 'population' in research

Entire set of individuals or observations relevant to the research question.

Definition of 'sample' in research

A smaller group from the population, used for practical study due to resource limits.

Criteria for a representative sample

Sample must mirror population diversity to generalize findings to the whole population.

In research, ______ is a crucial process where a subset is chosen from a larger group, and it greatly influences the study's findings.

______ sampling, a type of ______ sampling, is considered the most reliable because it gives everyone an equal opportunity to be selected.

Random probability

Feasibility of surveying small vs. large populations

Small populations, like a college, can be fully surveyed; large ones, like a nation, pose logistical issues.

Impact of undercounting underrepresented groups

Undercounting groups like low-income or minorities leads to non-representative data, affecting service and policy planning.

Purpose of sampling strategies in research

Sampling allows data collection from a subset to generalize to the entire population with a certain confidence level.

A study may use a ______ sample from different educational institutions to mirror the diversity of the national student body.

Definition of Population in Research

Complete group of interest in research; target for findings.

Definition of Sample in Research

Subset of population; selected for study to draw conclusions.

Importance of Sample Representativeness

Ensures findings from sample can be generalized to entire population.

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What is the importance of understanding 'population' and 'sample' in psychological research, how does the choice of sampling technique affect psychological research outcomes, what are the difficulties in collecting data from large populations, how do researchers use samples in large-scale psychological studies, why is the distinction between population and sample crucial in psychological research, contenuti simili, esplora altre mappe su argomenti simili.

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Distinguishing Populations and Samples in Psychological Studies

Sampling Techniques and Their Significance

Data collection challenges from large populations, utilizing samples in psychological investigations, concluding insights on population and sample in research.

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19 Population Psychology

• First Online: 17 April 2019

Cite this chapter

• Toni Falbo 3 &
• Joseph L. Rodgers 4  

Part of the book series: Handbooks of Sociology and Social Research ((HSSR))

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Population psychology as a discipline and as an organizational entity originated in the early 1970s, in an effort to address concerns about population problems. The chapter reviews the literature about the effects of family structure on children, emphasizing tests of prominent theoretical models, such as the Confluence Model, Admixture Hypothesis, and Dilution theory. In addition, the chapter contains a literature review about the causes and consequences of childlessness and the one-child family. Psychological theories of fertility and family planning, notably the Theory of Planned Behavior and the Traits-Desires-Intentions-Behavior model, are described and evaluated. The expanding literature about behavioral genetic research on fertility and reproduction is reviewed. Finally, psychological approaches to the study of migration and Global Warming are presented. The chapter aims to demonstrate that psychological thinking can be useful for other disciplines to move population studies forward in the future, as multi-disciplinary scholars “jump together” to address population issues.

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Acknowledgements

The authors wish to thank Warren Miller, Larry Severy, and Vaida Thompson, who reviewed the paper and made cogent and helpful comments that influenced our thinking and our writing. We also thank the editor of the handbook, Dudley Poston, who also read our chapter carefully and made helpful comments.

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Falbo, T., Rodgers, J.L. (2019). 19 Population Psychology. In: Poston, D.L. (eds) Handbook of Population. Handbooks of Sociology and Social Research. Springer, Cham. https://doi.org/10.1007/978-3-030-10910-3_20

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When conducting research there are lots of factors to consider. Psychologists may want to study, for example, the effect of some new test on all college students, but this is obviously not possible. Instead, what they do is test on a sample or a smaller group of college students. In this example, everyone who could possibly be a participant in the study (meaning, all college students) is part of the population. College students would be the population the researcher wants to study and from which they select a sample.

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noun. 1. the entire amount of people in a rendered geographical location. 2. with regard to statistics , a theoretically defined, total group of items from which a sampling is taken in effort to attain empirical observations and to which outcomes can be generalized. Commonly referred to as universe .

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Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the power to unlock the mysteries hidden within data. Understanding the dynamics between the research population and sample is crucial for researchers. It ensures the validity, reliability, and generalizability of their findings. In this article, we uncover the profound role of the research population and sample, unveiling their differences and importance that reshapes our understanding of complex phenomena. Ultimately, this empowers researchers to make informed conclusions and drive meaningful advancements in our respective fields.

Table of Contents

What Is Population?

The research population, also known as the target population, refers to the entire group or set of individuals, objects, or events that possess specific characteristics and are of interest to the researcher. It represents the larger population from which a sample is drawn. The research population is defined based on the research objectives and the specific parameters or attributes under investigation. For example, in a study on the effects of a new drug, the research population would encompass all individuals who could potentially benefit from or be affected by the medication.

When Is Data Collection From a Population Preferred?

In certain scenarios where a comprehensive understanding of the entire group is required, it becomes necessary to collect data from a population. Here are a few situations when one prefers to collect data from a population:

1. Small or Accessible Population

When the research population is small or easily accessible, it may be feasible to collect data from the entire population. This is often the case in studies conducted within specific organizations, small communities, or well-defined groups where the population size is manageable.

2. Census or Complete Enumeration

In some cases, such as government surveys or official statistics, a census or complete enumeration of the population is necessary. This approach aims to gather data from every individual or entity within the population. This is typically done to ensure accurate representation and eliminate sampling errors.

3. Unique or Critical Characteristics

If the research focuses on a specific characteristic or trait that is rare and critical to the study, collecting data from the entire population may be necessary. This could be the case in studies related to rare diseases, endangered species, or specific genetic markers.

4. Legal or Regulatory Requirements

Certain legal or regulatory frameworks may require data collection from the entire population. For instance, government agencies might need comprehensive data on income levels, demographic characteristics, or healthcare utilization for policy-making or resource allocation purposes.

5. Precision or Accuracy Requirements

In situations where a high level of precision or accuracy is necessary, researchers may opt for population-level data collection. By doing so, they mitigate the potential for sampling error and obtain more reliable estimates of population parameters.

What Is a Sample?

A sample is a subset of the research population that is carefully selected to represent its characteristics. Researchers study this smaller, manageable group to draw inferences that they can generalize to the larger population. The selection of the sample must be conducted in a manner that ensures it accurately reflects the diversity and pertinent attributes of the research population. By studying a sample, researchers can gather data more efficiently and cost-effectively compared to studying the entire population. The findings from the sample are then extrapolated to make conclusions about the larger research population.

What Is Sampling and Why Is It Important?

Sampling refers to the process of selecting a sample from a larger group or population of interest in order to gather data and make inferences. The goal of sampling is to obtain a sample that is representative of the population, meaning that the sample accurately reflects the key attributes, variations, and proportions present in the population. By studying the sample, researchers can draw conclusions or make predictions about the larger population with a certain level of confidence.

Collecting data from a sample, rather than the entire population, offers several advantages and is often necessary due to practical constraints. Here are some reasons to collect data from a sample:

1. Cost and Resource Efficiency

Collecting data from an entire population can be expensive and time-consuming. Sampling allows researchers to gather information from a smaller subset of the population, reducing costs and resource requirements. It is often more practical and feasible to collect data from a sample, especially when the population size is large or geographically dispersed.

2. Time Constraints

Conducting research with a sample allows for quicker data collection and analysis compared to studying the entire population. It saves time by focusing efforts on a smaller group, enabling researchers to obtain results more efficiently. This is particularly beneficial in time-sensitive research projects or situations that necessitate prompt decision-making.

3. Manageable Data Collection

Working with a sample makes data collection more manageable . Researchers can concentrate their efforts on a smaller group, allowing for more detailed and thorough data collection methods. Furthermore, it is more convenient and reliable to store and conduct statistical analyses on smaller datasets. This also facilitates in-depth insights and a more comprehensive understanding of the research topic.

4. Statistical Inference

Collecting data from a well-selected and representative sample enables valid statistical inference. By using appropriate statistical techniques, researchers can generalize the findings from the sample to the larger population. This allows for meaningful inferences, predictions, and estimation of population parameters, thus providing insights beyond the specific individuals or elements in the sample.

5. Ethical Considerations

In certain cases, collecting data from an entire population may pose ethical challenges, such as invasion of privacy or burdening participants. Sampling helps protect the privacy and well-being of individuals by reducing the burden of data collection. It allows researchers to obtain valuable information while ensuring ethical standards are maintained .

Key Steps Involved in the Sampling Process

Sampling is a valuable tool in research; however, it is important to carefully consider the sampling method, sample size, and potential biases to ensure that the findings accurately represent the larger population and are valid for making conclusions and generalizations. While the specific steps may vary depending on the research context, here is a general outline of the sampling process:

1. Define the Population

Clearly define the target population for your research study. The population should encompass the group of individuals, elements, or units that you want to draw conclusions about.

2. Define the Sampling Frame

Create a sampling frame, which is a list or representation of the individuals or elements in the target population. The sampling frame should be comprehensive and accurately reflect the population you want to study.

3. Determine the Sampling Method

Select an appropriate sampling method based on your research objectives, available resources, and the characteristics of the population. You can perform sampling by either utilizing probability-based or non-probability-based techniques. Common sampling methods include random sampling, stratified sampling, cluster sampling, and convenience sampling.

4. Determine Sample Size

Determine the desired sample size based on statistical considerations, such as the level of precision required, desired confidence level, and expected variability within the population. Larger sample sizes generally reduce sampling error but may be constrained by practical limitations.

5. Collect Data

Once the sample is selected using the appropriate technique, collect the necessary data according to the research design and data collection methods . Ensure that you use standardized and consistent data collection process that is also appropriate for your research objectives.

6. Analyze the Data

Perform the necessary statistical analyses on the collected data to derive meaningful insights. Use appropriate statistical techniques to make inferences, estimate population parameters, test hypotheses, or identify patterns and relationships within the data.

Population vs Sample — Differences and examples

While the population provides a comprehensive overview of the entire group under study, the sample, on the other hand, allows researchers to draw inferences and make generalizations about the population. Researchers should employ careful sampling techniques to ensure that the sample is representative and accurately reflects the characteristics and variability of the population.

Research Study: Investigating the prevalence of stress among high school students in a specific city and its impact on academic performance.

Population: All high school students in a particular city

Sampling Frame: The sampling frame would involve obtaining a comprehensive list of all high schools in the specific city. A random selection of schools would be made from this list to ensure representation from different areas and demographics of the city.

Sample: Randomly selected 500 high school students from different schools in the city

The sample represents a subset of the entire population of high school students in the city.

Research Study: Assessing the effectiveness of a new medication in managing symptoms and improving quality of life in patients with the specific medical condition.

Population: Patients diagnosed with a specific medical condition

Sampling Frame: The sampling frame for this study would involve accessing medical records or databases that include information on patients diagnosed with the specific medical condition. Researchers would select a convenient sample of patients who meet the inclusion criteria from the sampling frame.

Sample: Convenient sample of 100 patients from a local clinic who meet the inclusion criteria for the study

The sample consists of patients from the larger population of individuals diagnosed with the medical condition.

Research Study: Investigating community perceptions of safety and satisfaction with local amenities in the neighborhood.

Population: Residents of a specific neighborhood

Sampling Frame: The sampling frame for this study would involve obtaining a list of residential addresses within the specific neighborhood. Various sources such as census data, voter registration records, or community databases offer the means to obtain this information. From the sampling frame, researchers would randomly select a cluster sample of households to ensure representation from different areas within the neighborhood.

Sample: Cluster sample of 50 households randomly selected from different blocks within the neighborhood

The sample represents a subset of the entire population of residents living in the neighborhood.

To summarize, sampling allows for cost-effective data collection, easier statistical analysis, and increased practicality compared to studying the entire population. However, despite these advantages, sampling is subject to various challenges. These challenges include sampling bias, non-response bias, and the potential for sampling errors.

To minimize bias and enhance the validity of research findings , researchers should employ appropriate sampling techniques, clearly define the population, establish a comprehensive sampling frame, and monitor the sampling process for potential biases. Validating findings by comparing them to known population characteristics can also help evaluate the generalizability of the results. Properly understanding and implementing sampling techniques ensure that research findings are accurate, reliable, and representative of the larger population. By carefully considering the choice of population and sample, researchers can draw meaningful conclusions and, consequently, make valuable contributions to their respective fields of study.

Now, it’s your turn! Take a moment to think about a research question that interests you. Consider the population that would be relevant to your inquiry. Who would you include in your sample? How would you go about selecting them? Reflecting on these aspects will help you appreciate the intricacies involved in designing a research study. Let us know about it in the comment section below or reach out to us using  #AskEnago  and tag  @EnagoAcademy  on  Twitter ,  Facebook , and  Quora .

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Research Methods In Psychology

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.

Learn about our Editorial Process

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.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances,  using a standardized procedure.

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

Meta-analysis is a statistical procedure used to combine and synthesize findings from multiple independent studies to estimate the average effect size for a particular research question.

Meta-analysis goes beyond traditional narrative reviews by using statistical methods to integrate the results of several studies, leading to a more objective appraisal of the evidence.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

• Strengths : Increases the conclusions’ validity as they’re based on a wider range.
• Weaknesses : Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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How and Why Sampling Is Used in Psychology Research

Verywell / Nusha Ashjaee  

• Why Use Samples
• Probability Samples
• Nonprobability Samples

Sampling Errors

In statistics, a sample is a subset of a population that is used to represent the entire group as a whole. When doing psychology research, it is often impractical to survey every member of a particular population because the number of people is simply too large. To make inferences about the characteristics of a population, psychology researchers use a random sample .

Keep reading to learn about how samples are used in psychology research, the different types of samples, and the errors that may occur when using samples.

Why Psychology Researchers Use Samples

When researching an aspect of the human mind or ​ human behavior , psychology researchers can rarely collect data from every single affected individual. Instead, they use a smaller sample of individuals to represent the larger group.

The goal when choosing a sample is to make sure it represents the entire group accurately. This means that the sample should reflect the diverse characteristics present in the total population. The sample must accurately represent the population in question so researchers can generalize their results to the larger group with statistical analysis .

In psychological research and other types of social science research , experimenters typically rely on a few different sampling methods. These can be grouped into probability and nonprobability samples.

Types of Probability Samples

Probability sampling means every individual in a population stands a chance of being selected. Because probability sampling uses random selection , every subset of the population has an equal chance of being represented in the sample.

Probability samples are more representative of large populations and researchers are better able to generalize their results to the group as a whole when they use probability samples.

Simple Random Sampling

Simple random sampling is, as the name suggests, the simplest type of probability sampling. Psychology researchers take every individual in a population and randomly select individuals to compose their sample, often by using some type of computer program or random number generator.

Stratified Random Sampling

Stratified random sampling involves separating the population into subgroups and then taking a simple random sample from each of these subgroups. For example, researchers may divide the population into subgroups based on race, sex, or age, and then take a simple random sample of each of these groups.

Stratified random sampling often provides greater statistical accuracy than simple random sampling because it ensures each of the subgroups is accurately represented in the sample.

Cluster Sampling

Cluster sampling involves dividing a population into smaller clusters, often based on geographic location. A random sample of these clusters is then selected, and all of the subjects within the cluster are measured.

For example, imagine you are doing a study on school principals in your state. Collecting data from every single school principal would be cost-prohibitive and time-consuming. But, if you were to use a cluster sampling method, you would randomly select five counties from your state and then collect data from every subject in each of those five counties to create a representative sample.

Probability sampling methods allow psychology researchers to get a more representative sample. Techniques that might be used include simple random sampling, stratified random sampling, and cluster sampling.

Types of Nonprobability Samples

Nonprobability sampling involves selecting participants using methods that do not give every subset of a population an equal chance of being represented. For example, a study may recruit participants from an already established group of volunteers.

One problem with this type of sample is that volunteers might differ from non-volunteers on certain variables, which can make it difficult to generalize the results to the entire population.

Convenience Sampling

Convenience sampling involves selecting participants for a study based on what is most convenient–people who are easily accessible and have the time. If you have ever volunteered for a psychology study conducted through your university's psychology department, then you have participated in a study that relied on a convenience sample.

Studies that rely on asking for volunteers or using clinical samples available to the researcher are also examples of convenience samples.

Purposive Sampling

Purposive sampling involves seeking out individuals who meet certain criteria. For example, a researcher might be interested in learning how college graduates between the ages of 20 and 35 feel about a topic. In purposive sampling, the researcher might conduct telephone interviews to intentionally seek out people who meet their criteria.

Quota Sampling

Quota sampling involves intentionally sampling specific proportions of each subgroup within a population. For example, political pollsters might be interested in researching the opinions of a population on a certain political issue. If they use simple random sampling, they might miss certain subsets of the population by chance.

Instead, they establish criteria to assign each subgroup a certain percentage of the sample. This differs from stratified sampling because, to find individuals within each subgroup, researchers use non-random methods to fill the quotas for each subgroup.

Nonprobability sampling can also be used when selecting a sample in psychology research. Such methods are less representative of the general population. Techniques include convenience sampling, purposive sampling, and quota sampling.

Sampling errors are differences between what is present in a population and what is present in a sample. Because sampling cannot include every single individual in a population, errors can occur. This can ultimately have an impact on the results of psychology research.

While it is impossible to know exactly how great the difference between the population and sample may be, researchers can statistically estimate the size of the sampling errors. In political polls, for example, you might often hear of the margin of errors expressed by certain confidence levels.

In general, the larger the sample size, the smaller the level of error. This is simply because the closer the sample is to the size of the total population, the more likely it is to accurately capture all of the characteristics of the population.

The only way to completely eliminate sampling error is to collect data from the entire population, which is often simply too costly and time-consuming. Sampling errors can be minimized, however, by using randomized probability testing and large sample size.

Samples are important in psychology research because they allow scientists to study what is happening in a larger population without having to reach every individual in the entire group.

Different types of samples can be used depending on what researchers are studying and the resources they have available to collect data. Probability samples tend to be more representative of the larger group. Nonprobability samples, on the other hand, tend to involve selecting participants based on availability and studying specific subsets of a larger group, which is less representative of the larger group.

Sampling errors can occur, however, with any type of sampling. To minimize errors, researchers strive to use large, representative samples.

Valliant R, Dever J. Estimating propensity adjustments for volunteer web surveys . Sociol Methods Res . 2011;40(1):105-137. doi:10.1177/0049124110392533

Lin L. Bias caused by sampling error in meta-analysis with small sample sizes . PLoS ONE . 2018;13(9):e0204056. doi:10.1371/journal.pone.0204056

Goodwin CJ. Research In Psychology: Methods and Design, 12th ed . John Wiley and Sons.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Misinformation vs. Disinformation: What the Terms Mean and the Effects They Have

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Loose lips sink ships − especially when they’re not telling the truth. Spotting misinformation can be difficult, especially on an information super-highway like the internet. 

“There is a myriad of consequences: From cynicism of government, the media, and science, to behaviors that harm individuals and others … to large scale damage to public property, to insurrection,” Dolores Albarracin , a psychology professor at the University of Illinois who studies attitudes, communication and behavior told USA TODAY in 2021.

“We have a lot more little bits of fiction roaming around in those memory banks than probably people would realize or would like to think is true,” Elizabeth Loftus , a University of California, Irvine professor who has done groundbreaking research on the misinformation effect, explained.

Read the whole story (subscription may be required): USA TODAY

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Visual Memory Distortions Paint a Picture of the Past That Never Was 

Basic research on our imperfect visual memories is bringing to light how and why we may misremember what we have seen.

Scientists Discuss How to Study the Psychology of Collectives, Not Just Individuals

In a set of articles appearing in Perspectives on Psychological Science, an international array of scientists discusses how the study of neighborhoods, work units, activist groups, and other collectives can help us better understand and respond to societal changes.

Cattell Fund Projects Include Research on Children’s Executive Function, Empathy Choice, and More

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The Use of Research Methods in Psychological Research: A Systematised Review

Salomé elizabeth scholtz.

1 Community Psychosocial Research (COMPRES), School of Psychosocial Health, North-West University, Potchefstroom, South Africa

Werner de Klerk

Leon t. de beer.

2 WorkWell Research Institute, North-West University, Potchefstroom, South Africa

Research methods play an imperative role in research quality as well as educating young researchers, however, the application thereof is unclear which can be detrimental to the field of psychology. Therefore, this systematised review aimed to determine what research methods are being used, how these methods are being used and for what topics in the field. Our review of 999 articles from five journals over a period of 5 years indicated that psychology research is conducted in 10 topics via predominantly quantitative research methods. Of these 10 topics, social psychology was the most popular. The remainder of the conducted methodology is described. It was also found that articles lacked rigour and transparency in the used methodology which has implications for replicability. In conclusion this article, provides an overview of all reported methodologies used in a sample of psychology journals. It highlights the popularity and application of methods and designs throughout the article sample as well as an unexpected lack of rigour with regard to most aspects of methodology. Possible sample bias should be considered when interpreting the results of this study. It is recommended that future research should utilise the results of this study to determine the possible impact on the field of psychology as a science and to further investigation into the use of research methods. Results should prompt the following future research into: a lack or rigour and its implication on replication, the use of certain methods above others, publication bias and choice of sampling method.

Introduction

Psychology is an ever-growing and popular field (Gough and Lyons, 2016 ; Clay, 2017 ). Due to this growth and the need for science-based research to base health decisions on (Perestelo-Pérez, 2013 ), the use of research methods in the broad field of psychology is an essential point of investigation (Stangor, 2011 ; Aanstoos, 2014 ). Research methods are therefore viewed as important tools used by researchers to collect data (Nieuwenhuis, 2016 ) and include the following: quantitative, qualitative, mixed method and multi method (Maree, 2016 ). Additionally, researchers also employ various types of literature reviews to address research questions (Grant and Booth, 2009 ). According to literature, what research method is used and why a certain research method is used is complex as it depends on various factors that may include paradigm (O'Neil and Koekemoer, 2016 ), research question (Grix, 2002 ), or the skill and exposure of the researcher (Nind et al., 2015 ). How these research methods are employed is also difficult to discern as research methods are often depicted as having fixed boundaries that are continuously crossed in research (Johnson et al., 2001 ; Sandelowski, 2011 ). Examples of this crossing include adding quantitative aspects to qualitative studies (Sandelowski et al., 2009 ), or stating that a study used a mixed-method design without the study having any characteristics of this design (Truscott et al., 2010 ).

The inappropriate use of research methods affects how students and researchers improve and utilise their research skills (Scott Jones and Goldring, 2015 ), how theories are developed (Ngulube, 2013 ), and the credibility of research results (Levitt et al., 2017 ). This, in turn, can be detrimental to the field (Nind et al., 2015 ), journal publication (Ketchen et al., 2008 ; Ezeh et al., 2010 ), and attempts to address public social issues through psychological research (Dweck, 2017 ). This is especially important given the now well-known replication crisis the field is facing (Earp and Trafimow, 2015 ; Hengartner, 2018 ).

Due to this lack of clarity on method use and the potential impact of inept use of research methods, the aim of this study was to explore the use of research methods in the field of psychology through a review of journal publications. Chaichanasakul et al. ( 2011 ) identify reviewing articles as the opportunity to examine the development, growth and progress of a research area and overall quality of a journal. Studies such as Lee et al. ( 1999 ) as well as Bluhm et al. ( 2011 ) review of qualitative methods has attempted to synthesis the use of research methods and indicated the growth of qualitative research in American and European journals. Research has also focused on the use of research methods in specific sub-disciplines of psychology, for example, in the field of Industrial and Organisational psychology Coetzee and Van Zyl ( 2014 ) found that South African publications tend to consist of cross-sectional quantitative research methods with underrepresented longitudinal studies. Qualitative studies were found to make up 21% of the articles published from 1995 to 2015 in a similar study by O'Neil and Koekemoer ( 2016 ). Other methods in health psychology, such as Mixed methods research have also been reportedly growing in popularity (O'Cathain, 2009 ).

A broad overview of the use of research methods in the field of psychology as a whole is however, not available in the literature. Therefore, our research focused on answering what research methods are being used, how these methods are being used and for what topics in practice (i.e., journal publications) in order to provide a general perspective of method used in psychology publication. We synthesised the collected data into the following format: research topic [areas of scientific discourse in a field or the current needs of a population (Bittermann and Fischer, 2018 )], method [data-gathering tools (Nieuwenhuis, 2016 )], sampling [elements chosen from a population to partake in research (Ritchie et al., 2009 )], data collection [techniques and research strategy (Maree, 2016 )], and data analysis [discovering information by examining bodies of data (Ktepi, 2016 )]. A systematised review of recent articles (2013 to 2017) collected from five different journals in the field of psychological research was conducted.

Grant and Booth ( 2009 ) describe systematised reviews as the review of choice for post-graduate studies, which is employed using some elements of a systematic review and seldom more than one or two databases to catalogue studies after a comprehensive literature search. The aspects used in this systematised review that are similar to that of a systematic review were a full search within the chosen database and data produced in tabular form (Grant and Booth, 2009 ).

Sample sizes and timelines vary in systematised reviews (see Lowe and Moore, 2014 ; Pericall and Taylor, 2014 ; Barr-Walker, 2017 ). With no clear parameters identified in the literature (see Grant and Booth, 2009 ), the sample size of this study was determined by the purpose of the sample (Strydom, 2011 ), and time and cost constraints (Maree and Pietersen, 2016 ). Thus, a non-probability purposive sample (Ritchie et al., 2009 ) of the top five psychology journals from 2013 to 2017 was included in this research study. Per Lee ( 2015 ) American Psychological Association (APA) recommends the use of the most up-to-date sources for data collection with consideration of the context of the research study. As this research study focused on the most recent trends in research methods used in the broad field of psychology, the identified time frame was deemed appropriate.

Psychology journals were only included if they formed part of the top five English journals in the miscellaneous psychology domain of the Scimago Journal and Country Rank (Scimago Journal & Country Rank, 2017 ). The Scimago Journal and Country Rank provides a yearly updated list of publicly accessible journal and country-specific indicators derived from the Scopus® database (Scopus, 2017b ) by means of the Scimago Journal Rank (SJR) indicator developed by Scimago from the algorithm Google PageRank™ (Scimago Journal & Country Rank, 2017 ). Scopus is the largest global database of abstracts and citations from peer-reviewed journals (Scopus, 2017a ). Reasons for the development of the Scimago Journal and Country Rank list was to allow researchers to assess scientific domains, compare country rankings, and compare and analyse journals (Scimago Journal & Country Rank, 2017 ), which supported the aim of this research study. Additionally, the goals of the journals had to focus on topics in psychology in general with no preference to specific research methods and have full-text access to articles.

The following list of top five journals in 2018 fell within the abovementioned inclusion criteria (1) Australian Journal of Psychology, (2) British Journal of Psychology, (3) Europe's Journal of Psychology, (4) International Journal of Psychology and lastly the (5) Journal of Psychology Applied and Interdisciplinary.

Journals were excluded from this systematised review if no full-text versions of their articles were available, if journals explicitly stated a publication preference for certain research methods, or if the journal only published articles in a specific discipline of psychological research (for example, industrial psychology, clinical psychology etc.).

The researchers followed a procedure (see Figure 1 ) adapted from that of Ferreira et al. ( 2016 ) for systematised reviews. Data collection and categorisation commenced on 4 December 2017 and continued until 30 June 2019. All the data was systematically collected and coded manually (Grant and Booth, 2009 ) with an independent person acting as co-coder. Codes of interest included the research topic, method used, the design used, sampling method, and methodology (the method used for data collection and data analysis). These codes were derived from the wording in each article. Themes were created based on the derived codes and checked by the co-coder. Lastly, these themes were catalogued into a table as per the systematised review design.

Systematised review procedure.

According to Johnston et al. ( 2019 ), “literature screening, selection, and data extraction/analyses” (p. 7) are specifically tailored to the aim of a review. Therefore, the steps followed in a systematic review must be reported in a comprehensive and transparent manner. The chosen systematised design adhered to the rigour expected from systematic reviews with regard to full search and data produced in tabular form (Grant and Booth, 2009 ). The rigorous application of the systematic review is, therefore discussed in relation to these two elements.

Firstly, to ensure a comprehensive search, this research study promoted review transparency by following a clear protocol outlined according to each review stage before collecting data (Johnston et al., 2019 ). This protocol was similar to that of Ferreira et al. ( 2016 ) and approved by three research committees/stakeholders and the researchers (Johnston et al., 2019 ). The eligibility criteria for article inclusion was based on the research question and clearly stated, and the process of inclusion was recorded on an electronic spreadsheet to create an evidence trail (Bandara et al., 2015 ; Johnston et al., 2019 ). Microsoft Excel spreadsheets are a popular tool for review studies and can increase the rigour of the review process (Bandara et al., 2015 ). Screening for appropriate articles for inclusion forms an integral part of a systematic review process (Johnston et al., 2019 ). This step was applied to two aspects of this research study: the choice of eligible journals and articles to be included. Suitable journals were selected by the first author and reviewed by the second and third authors. Initially, all articles from the chosen journals were included. Then, by process of elimination, those irrelevant to the research aim, i.e., interview articles or discussions etc., were excluded.

To ensure rigourous data extraction, data was first extracted by one reviewer, and an independent person verified the results for completeness and accuracy (Johnston et al., 2019 ). The research question served as a guide for efficient, organised data extraction (Johnston et al., 2019 ). Data was categorised according to the codes of interest, along with article identifiers for audit trails such as authors, title and aims of articles. The categorised data was based on the aim of the review (Johnston et al., 2019 ) and synthesised in tabular form under methods used, how these methods were used, and for what topics in the field of psychology.

The initial search produced a total of 1,145 articles from the 5 journals identified. Inclusion and exclusion criteria resulted in a final sample of 999 articles ( Figure 2 ). Articles were co-coded into 84 codes, from which 10 themes were derived ( Table 1 ).

Journal article frequency.

Codes used to form themes (research topics).

These 10 themes represent the topic section of our research question ( Figure 3 ). All these topics except, for the final one, psychological practice , were found to concur with the research areas in psychology as identified by Weiten ( 2010 ). These research areas were chosen to represent the derived codes as they provided broad definitions that allowed for clear, concise categorisation of the vast amount of data. Article codes were categorised under particular themes/topics if they adhered to the research area definitions created by Weiten ( 2010 ). It is important to note that these areas of research do not refer to specific disciplines in psychology, such as industrial psychology; but to broader fields that may encompass sub-interests of these disciplines.

Topic frequency (international sample).

In the case of developmental psychology , researchers conduct research into human development from childhood to old age. Social psychology includes research on behaviour governed by social drivers. Researchers in the field of educational psychology study how people learn and the best way to teach them. Health psychology aims to determine the effect of psychological factors on physiological health. Physiological psychology , on the other hand, looks at the influence of physiological aspects on behaviour. Experimental psychology is not the only theme that uses experimental research and focuses on the traditional core topics of psychology (for example, sensation). Cognitive psychology studies the higher mental processes. Psychometrics is concerned with measuring capacity or behaviour. Personality research aims to assess and describe consistency in human behaviour (Weiten, 2010 ). The final theme of psychological practice refers to the experiences, techniques, and interventions employed by practitioners, researchers, and academia in the field of psychology.

Articles under these themes were further subdivided into methodologies: method, sampling, design, data collection, and data analysis. The categorisation was based on information stated in the articles and not inferred by the researchers. Data were compiled into two sets of results presented in this article. The first set addresses the aim of this study from the perspective of the topics identified. The second set of results represents a broad overview of the results from the perspective of the methodology employed. The second set of results are discussed in this article, while the first set is presented in table format. The discussion thus provides a broad overview of methods use in psychology (across all themes), while the table format provides readers with in-depth insight into methods used in the individual themes identified. We believe that presenting the data from both perspectives allow readers a broad understanding of the results. Due a large amount of information that made up our results, we followed Cichocka and Jost ( 2014 ) in simplifying our results. Please note that the numbers indicated in the table in terms of methodology differ from the total number of articles. Some articles employed more than one method/sampling technique/design/data collection method/data analysis in their studies.

What follows is the results for what methods are used, how these methods are used, and which topics in psychology they are applied to . Percentages are reported to the second decimal in order to highlight small differences in the occurrence of methodology.

Firstly, with regard to the research methods used, our results show that researchers are more likely to use quantitative research methods (90.22%) compared to all other research methods. Qualitative research was the second most common research method but only made up about 4.79% of the general method usage. Reviews occurred almost as much as qualitative studies (3.91%), as the third most popular method. Mixed-methods research studies (0.98%) occurred across most themes, whereas multi-method research was indicated in only one study and amounted to 0.10% of the methods identified. The specific use of each method in the topics identified is shown in Table 2 and Figure 4 .

Research methods in psychology.

Research method frequency in topics.

Secondly, in the case of how these research methods are employed , our study indicated the following.

Sampling −78.34% of the studies in the collected articles did not specify a sampling method. From the remainder of the studies, 13 types of sampling methods were identified. These sampling methods included broad categorisation of a sample as, for example, a probability or non-probability sample. General samples of convenience were the methods most likely to be applied (10.34%), followed by random sampling (3.51%), snowball sampling (2.73%), and purposive (1.37%) and cluster sampling (1.27%). The remainder of the sampling methods occurred to a more limited extent (0–1.0%). See Table 3 and Figure 5 for sampling methods employed in each topic.

Sampling use in the field of psychology.

Sampling method frequency in topics.

Designs were categorised based on the articles' statement thereof. Therefore, it is important to note that, in the case of quantitative studies, non-experimental designs (25.55%) were often indicated due to a lack of experiments and any other indication of design, which, according to Laher ( 2016 ), is a reasonable categorisation. Non-experimental designs should thus be compared with experimental designs only in the description of data, as it could include the use of correlational/cross-sectional designs, which were not overtly stated by the authors. For the remainder of the research methods, “not stated” (7.12%) was assigned to articles without design types indicated.

From the 36 identified designs the most popular designs were cross-sectional (23.17%) and experimental (25.64%), which concurred with the high number of quantitative studies. Longitudinal studies (3.80%), the third most popular design, was used in both quantitative and qualitative studies. Qualitative designs consisted of ethnography (0.38%), interpretative phenomenological designs/phenomenology (0.28%), as well as narrative designs (0.28%). Studies that employed the review method were mostly categorised as “not stated,” with the most often stated review designs being systematic reviews (0.57%). The few mixed method studies employed exploratory, explanatory (0.09%), and concurrent designs (0.19%), with some studies referring to separate designs for the qualitative and quantitative methods. The one study that identified itself as a multi-method study used a longitudinal design. Please see how these designs were employed in each specific topic in Table 4 , Figure 6 .

Design use in the field of psychology.

Design frequency in topics.

Data collection and analysis —data collection included 30 methods, with the data collection method most often employed being questionnaires (57.84%). The experimental task (16.56%) was the second most preferred collection method, which included established or unique tasks designed by the researchers. Cognitive ability tests (6.84%) were also regularly used along with various forms of interviewing (7.66%). Table 5 and Figure 7 represent data collection use in the various topics. Data analysis consisted of 3,857 occurrences of data analysis categorised into ±188 various data analysis techniques shown in Table 6 and Figures 1 – 7 . Descriptive statistics were the most commonly used (23.49%) along with correlational analysis (17.19%). When using a qualitative method, researchers generally employed thematic analysis (0.52%) or different forms of analysis that led to coding and the creation of themes. Review studies presented few data analysis methods, with most studies categorising their results. Mixed method and multi-method studies followed the analysis methods identified for the qualitative and quantitative studies included.

Data collection in the field of psychology.

Data collection frequency in topics.

Data analysis in the field of psychology.

Results of the topics researched in psychology can be seen in the tables, as previously stated in this article. It is noteworthy that, of the 10 topics, social psychology accounted for 43.54% of the studies, with cognitive psychology the second most popular research topic at 16.92%. The remainder of the topics only occurred in 4.0–7.0% of the articles considered. A list of the included 999 articles is available under the section “View Articles” on the following website: https://methodgarden.xtrapolate.io/ . This website was created by Scholtz et al. ( 2019 ) to visually present a research framework based on this Article's results.

This systematised review categorised full-length articles from five international journals across the span of 5 years to provide insight into the use of research methods in the field of psychology. Results indicated what methods are used how these methods are being used and for what topics (why) in the included sample of articles. The results should be seen as providing insight into method use and by no means a comprehensive representation of the aforementioned aim due to the limited sample. To our knowledge, this is the first research study to address this topic in this manner. Our discussion attempts to promote a productive way forward in terms of the key results for method use in psychology, especially in the field of academia (Holloway, 2008 ).

With regard to the methods used, our data stayed true to literature, finding only common research methods (Grant and Booth, 2009 ; Maree, 2016 ) that varied in the degree to which they were employed. Quantitative research was found to be the most popular method, as indicated by literature (Breen and Darlaston-Jones, 2010 ; Counsell and Harlow, 2017 ) and previous studies in specific areas of psychology (see Coetzee and Van Zyl, 2014 ). Its long history as the first research method (Leech et al., 2007 ) in the field of psychology as well as researchers' current application of mathematical approaches in their studies (Toomela, 2010 ) might contribute to its popularity today. Whatever the case may be, our results show that, despite the growth in qualitative research (Demuth, 2015 ; Smith and McGannon, 2018 ), quantitative research remains the first choice for article publication in these journals. Despite the included journals indicating openness to articles that apply any research methods. This finding may be due to qualitative research still being seen as a new method (Burman and Whelan, 2011 ) or reviewers' standards being higher for qualitative studies (Bluhm et al., 2011 ). Future research is encouraged into the possible biasness in publication of research methods, additionally further investigation with a different sample into the proclaimed growth of qualitative research may also provide different results.

Review studies were found to surpass that of multi-method and mixed method studies. To this effect Grant and Booth ( 2009 ), state that the increased awareness, journal contribution calls as well as its efficiency in procuring research funds all promote the popularity of reviews. The low frequency of mixed method studies contradicts the view in literature that it's the third most utilised research method (Tashakkori and Teddlie's, 2003 ). Its' low occurrence in this sample could be due to opposing views on mixing methods (Gunasekare, 2015 ) or that authors prefer publishing in mixed method journals, when using this method, or its relative novelty (Ivankova et al., 2016 ). Despite its low occurrence, the application of the mixed methods design in articles was methodologically clear in all cases which were not the case for the remainder of research methods.

Additionally, a substantial number of studies used a combination of methodologies that are not mixed or multi-method studies. Perceived fixed boundaries are according to literature often set aside, as confirmed by this result, in order to investigate the aim of a study, which could create a new and helpful way of understanding the world (Gunasekare, 2015 ). According to Toomela ( 2010 ), this is not unheard of and could be considered a form of “structural systemic science,” as in the case of qualitative methodology (observation) applied in quantitative studies (experimental design) for example. Based on this result, further research into this phenomenon as well as its implications for research methods such as multi and mixed methods is recommended.

Discerning how these research methods were applied, presented some difficulty. In the case of sampling, most studies—regardless of method—did mention some form of inclusion and exclusion criteria, but no definite sampling method. This result, along with the fact that samples often consisted of students from the researchers' own academic institutions, can contribute to literature and debates among academics (Peterson and Merunka, 2014 ; Laher, 2016 ). Samples of convenience and students as participants especially raise questions about the generalisability and applicability of results (Peterson and Merunka, 2014 ). This is because attention to sampling is important as inappropriate sampling can debilitate the legitimacy of interpretations (Onwuegbuzie and Collins, 2017 ). Future investigation into the possible implications of this reported popular use of convenience samples for the field of psychology as well as the reason for this use could provide interesting insight, and is encouraged by this study.

Additionally, and this is indicated in Table 6 , articles seldom report the research designs used, which highlights the pressing aspect of the lack of rigour in the included sample. Rigour with regards to the applied empirical method is imperative in promoting psychology as a science (American Psychological Association, 2020 ). Omitting parts of the research process in publication when it could have been used to inform others' research skills should be questioned, and the influence on the process of replicating results should be considered. Publications are often rejected due to a lack of rigour in the applied method and designs (Fonseca, 2013 ; Laher, 2016 ), calling for increased clarity and knowledge of method application. Replication is a critical part of any field of scientific research and requires the “complete articulation” of the study methods used (Drotar, 2010 , p. 804). The lack of thorough description could be explained by the requirements of certain journals to only report on certain aspects of a research process, especially with regard to the applied design (Laher, 20). However, naming aspects such as sampling and designs, is a requirement according to the APA's Journal Article Reporting Standards (JARS-Quant) (Appelbaum et al., 2018 ). With very little information on how a study was conducted, authors lose a valuable opportunity to enhance research validity, enrich the knowledge of others, and contribute to the growth of psychology and methodology as a whole. In the case of this research study, it also restricted our results to only reported samples and designs, which indicated a preference for certain designs, such as cross-sectional designs for quantitative studies.

Data collection and analysis were for the most part clearly stated. A key result was the versatile use of questionnaires. Researchers would apply a questionnaire in various ways, for example in questionnaire interviews, online surveys, and written questionnaires across most research methods. This may highlight a trend for future research.

With regard to the topics these methods were employed for, our research study found a new field named “psychological practice.” This result may show the growing consciousness of researchers as part of the research process (Denzin and Lincoln, 2003 ), psychological practice, and knowledge generation. The most popular of these topics was social psychology, which is generously covered in journals and by learning societies, as testaments of the institutional support and richness social psychology has in the field of psychology (Chryssochoou, 2015 ). The APA's perspective on 2018 trends in psychology also identifies an increased amount of psychology focus on how social determinants are influencing people's health (Deangelis, 2017 ).

This study was not without limitations and the following should be taken into account. Firstly, this study used a sample of five specific journals to address the aim of the research study, despite general journal aims (as stated on journal websites), this inclusion signified a bias towards the research methods published in these specific journals only and limited generalisability. A broader sample of journals over a different period of time, or a single journal over a longer period of time might provide different results. A second limitation is the use of Excel spreadsheets and an electronic system to log articles, which was a manual process and therefore left room for error (Bandara et al., 2015 ). To address this potential issue, co-coding was performed to reduce error. Lastly, this article categorised data based on the information presented in the article sample; there was no interpretation of what methodology could have been applied or whether the methods stated adhered to the criteria for the methods used. Thus, a large number of articles that did not clearly indicate a research method or design could influence the results of this review. However, this in itself was also a noteworthy result. Future research could review research methods of a broader sample of journals with an interpretive review tool that increases rigour. Additionally, the authors also encourage the future use of systematised review designs as a way to promote a concise procedure in applying this design.

Our research study presented the use of research methods for published articles in the field of psychology as well as recommendations for future research based on these results. Insight into the complex questions identified in literature, regarding what methods are used how these methods are being used and for what topics (why) was gained. This sample preferred quantitative methods, used convenience sampling and presented a lack of rigorous accounts for the remaining methodologies. All methodologies that were clearly indicated in the sample were tabulated to allow researchers insight into the general use of methods and not only the most frequently used methods. The lack of rigorous account of research methods in articles was represented in-depth for each step in the research process and can be of vital importance to address the current replication crisis within the field of psychology. Recommendations for future research aimed to motivate research into the practical implications of the results for psychology, for example, publication bias and the use of convenience samples.

Ethics Statement

This study was cleared by the North-West University Health Research Ethics Committee: NWU-00115-17-S1.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Impulsivity and its association with depression and anxiety in the normal egyptian population post covid-19 pandemic.

1. Introduction

2. materials and methods, 2.1. subjects and procedures, 2.2. study measures, 2.2.1. the arab short urgency–premeditation–perseverance–sensation-seeking—positive urgency impulsive behavior scale (short upps-p) [ 26 ], 2.2.2. hamilton anxiety rating scale (ham-a) [ 27 ], 2.2.3. hamilton depression rating scale (hdrs) [ 28 ], 2.2.4. covid-19-related disruption, 2.2.5. the sheehan disability scale (sds) [ 29 ], 2.3. statistical analysis, 3.1. socio-demographic characteristics, 3.2. covid-19 pandemic-related disruption, 3.3. psychometric scales for anxiety, depression, and functional impairment, 3.5. correlation and regression analyses, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Ismael, M.S.; Elgendy, M.O.; Binsaleh, A.Y.; Saleh, A.; Abdelrahim, M.E.A.; Osama, H. Impulsivity and Its Association with Depression and Anxiety in the Normal Egyptian Population Post COVID-19 Pandemic. Medicina 2024 , 60 , 1367. https://doi.org/10.3390/medicina60081367

Ismael MS, Elgendy MO, Binsaleh AY, Saleh A, Abdelrahim MEA, Osama H. Impulsivity and Its Association with Depression and Anxiety in the Normal Egyptian Population Post COVID-19 Pandemic. Medicina . 2024; 60(8):1367. https://doi.org/10.3390/medicina60081367

Ismael, Marwa S., Marwa O. Elgendy, Ammena Y. Binsaleh, Asmaa Saleh, Mohamed E. A. Abdelrahim, and Hasnaa Osama. 2024. "Impulsivity and Its Association with Depression and Anxiety in the Normal Egyptian Population Post COVID-19 Pandemic" Medicina 60, no. 8: 1367. https://doi.org/10.3390/medicina60081367

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How to Train Your Brain to Limit Pain

How placebo effects deliver pain releif..

Posted August 23, 2024 | Reviewed by Gary Drevitch

Co-authored by neuroplastician, speaker, and author Jasmine Benson.

Imagine you take a sugar pill, believing it to be a powerful painkiller, and your pain actually diminishes. Is it all in your head, or is there a deeper neurological basis for this phenomenon? The intriguing realm of placebo effects, in which belief itself can lead to real health improvements, raises profound questions about how our brains process pain and how we attach "value" to experience.

The placebo effect is a fascinating example of how our brain's expectations can significantly alter our physical and mental health. Contrary to what one might think, the improvement in health from a placebo isn't about tricking the brain into not feeling pain. Instead, it's about engaging brain systems associated with value and motivation . This distinction is crucial as it changes how we approach pain management and treatment.

The Science Behind the Magic

Pain perception is largely a top-down process, in which higher-level brain regions combine sensory and emotional information to predict pain. The placebo effect, in which health improves after taking an inert treatment, highlights this process.

Research by Botvinik-Nezer (2020) in Nature used fMRI to study how the brain’s activity changes with placebo treatments. Advances in neuroplasticity show that belief in a placebo can significantly alter pain perception by activating neural pathways linked to value and motivation. This reveals how belief and perception can reshape physical responses and has broader implications.

A Story of Exploration and Discovery

In one of the most comprehensive studies to date, 395 participants were part of an intriguing experiment. They received two creams on different fingers: one a control cream with no effects and the other presented as a pain-relieving drug, though it was a placebo. To amplify the placebo effect, researchers used conditioning paradigms in which participants were exposed to varying thermal stimuli. Unbeknownst to them, the intensity was lower on the placebo-treated finger.

Participants were then subjected to thermal and mechanical pain tests while their brain activity was monitored. They rated the intensity and unpleasantness of the pain stimuli. The researchers focused on two specific brain signatures: the Neurologic Pain Signature (NPS), associated with the initial sensory perception of pain, and the Stimulus Intensity Independent Pain Signature (SIIPS), linked to higher-order, value-based processing of pain (Wager et al., 2004).

Participants reported less pain in the placebo condition compared to the control. The NPS score, reflecting immediate pain perception, increased with stimulus intensity but showed no significant placebo effect. However, the SIIPS score, representing higher-order pain processing, was significantly lower in the placebo condition for both thermal and mechanical pain. This indicates that the placebo effect is mediated by brain systems involved in value and motivation, not just the raw sensory experience of pain.

Furthermore, the study found that the placebo effect transferred to unconditioned pain modalities. Participants experienced less mechanical pain, even though only thermal pain was conditioned. This suggests that the brain's valuation system can generalize the placebo effect across different types of pain.

Empowering Change

Traditional pain management often relies on pharmaceuticals and rigid protocols that may not fully address the complexity of pain and brain function. Embracing the placebo effect marks a shift toward integrating belief and brain function into treatment. Unlike conventional methods, which may overlook these factors, understanding the placebo effect promotes a more comprehensive approach that includes psychological and physiological aspects.

This approach empowers patients to engage in their own healing through belief and expectation, fostering a positive mindset and active participation in treatment. By recognizing the impact of mental state on recovery, patients can enhance outcomes and align with a broader philosophy of engagement and ownership in both healthcare and personal development.

This study was groundbreaking because it used a large sample size and two well-validated brain signatures to explore placebo analgesia. It underscores the potential of targeting higher-level cognitive processes in pain management. By understanding that placebo effects engage brain regions associated with value and motivation, healthcare providers can develop more effective treatments that harness these mechanisms.

Consider Sarah, a chronic pain sufferer who tried numerous treatments without success. When she participated in a clinical trial involving a placebo, she was skeptical. Yet, as weeks passed, she noticed a significant reduction in her pain. Sarah's experience wasn't just a figment of her imagination . Her brain had activated higher-order systems that re-evaluated her pain, diminishing its impact.

Expanding Horizons: Placebo Effects in Leadership and Beyond

The placebo effect offers valuable insights for organizational leadership . By understanding placebo mechanisms, leaders can foster environments in which belief and motivation drive performance, enhancing employee engagement and productivity .

This highlights the need for more human-centric management practices that align with evolving work dynamics. Organizations should reinvent their structures and culture to avoid social pain and help employees thrive. The application of placebo models in corporations shows that fostering belief and engagement can significantly improve outcomes, as supported by Eisenberger and Rhoades (2002).

What Lies Ahead

The findings from Botvinik-Nezer and her team, mentioned before, pave the way for future research into how we can harness the power of the placebo effect more effectively. As Rotem Botvinik-Nezer, the first author, expressed, this study represents a significant leap forward in placebo research, made possible by years of meticulous data collection and the efforts of many dedicated researchers, and it can be applied more broadly.

Why Does This Matter to You?

How might understanding the placebo effect change your own approach to pain and treatment? Insights into how our brains process value and motivation reveal the potential to revolutionize pain management and enhance well-being. The power of belief is deeply rooted in our neural pathways, opening new possibilities for healing, health, change, leadership, and human performance (Botvinik-Nezer et al., 2020).

What are your thoughts on integrating placebo effects into everyday treatments? Could this understanding reshape our approach to managing pain and other conditions? Your perspectives could be key in unlocking the full potential of the mind-body connection.

Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., … & Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature , 582(7810), 84-88. doi:10.1038/s41586-020-2314-9

Eisenberger, N. I. (2012). The neural bases of social pain: Evidence for shared representations with physical pain. Psychosomatic Medicine, 74 (2), 126–135. DOI: 10.1097/PSY.0b013e3182464dd1

Wager, T. D., Rilling, J. K., Smith, E. E., Johnston, C. J., & Davidson, R. J. (2004). Placebo-induced changes in FMRI in the anticipation and experience of pain. Science, 303 (5661), 1162-1167. https://doi.org/10.1126/science.1093065

Justin James Kennedy, Ph.D., is a professor of applied neuroscience and organisational behaviour at UGSM-Monarch Business School in Switzerland and the author of Brain Re-Boot.

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