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How to Write the Scope of the Study

• By DiscoverPhDs
• August 26, 2020

What is the Scope of the Study?

The scope of the study refers to the boundaries within which your research project will be performed; this is sometimes also called the scope of research. To define the scope of the study is to define all aspects that will be considered in your research project. It is also just as important to make clear what aspects will not be covered; i.e. what is outside of the scope of the study.

Why is the Scope of the Study Important?

The scope of the study is always considered and agreed upon in the early stages of the project, before any data collection or experimental work has started. This is important because it focuses the work of the proposed study down to what is practically achievable within a given timeframe.

A well-defined research or study scope enables a researcher to give clarity to the study outcomes that are to be investigated. It makes clear why specific data points have been collected whilst others have been excluded.

Without this, it is difficult to define an end point for a research project since no limits have been defined on the work that could take place. Similarly, it can also make the approach to answering a research question too open ended.

How do you Write the Scope of the Study?

In order to write the scope of the study that you plan to perform, you must be clear on the research parameters that you will and won’t consider. These parameters usually consist of the sample size, the duration, inclusion and exclusion criteria, the methodology and any geographical or monetary constraints.

Each of these parameters will have limits placed on them so that the study can practically be performed, and the results interpreted relative to the limitations that have been defined. These parameters will also help to shape the direction of each research question you consider.

The term limitations’ is often used together with the scope of the study to describe the constraints of any parameters that are considered and also to clarify which parameters have not been considered at all. Make sure you get the balance right here between not making the scope too broad and unachievable, and it not being too restrictive, resulting in a lack of useful data.

The sample size is a commonly used parameter in the definition of the research scope. For example, a research project involving human participants may define at the start of the study that 100 participants will be recruited. This number will be determined based on an understanding of the difficulty in recruiting participants to studies and an agreement of an acceptable period of time in which to recruit this number.

Any results that are obtained by the research group can then be interpreted by others with the knowledge that the study was capped to 100 participants and an acceptance of this as a limitation of the study. In other words, it is acknowledged that recruiting 100 rather than 1,000 participants has limited the amount of data that could be collected, however this is an acceptable limitation due to the known difficulties in recruiting so many participants (e.g. the significant period of time it would take and the costs associated with this).

Example of a Scope of the Study

The follow is a (hypothetical) example of the definition of the scope of the study, with the research question investigating the impact of the COVID-19 pandemic on mental health.

Whilst the immediate negative health problems related to the COVID-19 pandemic have been well documented, the impact of the virus on the mental health (MH) of young adults (age 18-24 years) is poorly understood. The aim of this study is to report on MH changes in population group due to the pandemic.

The scope of the study is limited to recruiting 100 volunteers between the ages of 18 and 24 who will be contacted using their university email accounts. This recruitment period will last for a maximum of 2 months and will end when either 100 volunteers have been recruited or 2 months have passed. Each volunteer to the study will be asked to complete a short questionnaire in order to evaluate any changes in their MH.

From this example we can immediately see that the scope of the study has placed a constraint on the sample size to be used and/or the time frame for recruitment of volunteers. It has also introduced a limitation by only opening recruitment to people that have university emails; i.e. anyone that does not attend university will be excluded from this study.

This may be an important factor when interpreting the results of this study; the comparison of MH during the pandemic between those that do and do not attend university, is therefore outside the scope of the study here. We are also told that the methodology used to assess any changes in MH are via a questionnaire. This is a clear definition of how the outcome measure will be investigated and any other methods are not within the scope of research and their exclusion may be a limitation of the study.

The scope of the study is important to define as it enables a researcher to focus their research to within achievable parameters.

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Frequently asked questions

How do i determine scope of research.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.
• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

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

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

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

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

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.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

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

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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

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

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

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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How to Write the Scope of Study Outlining its Salient Features.

To Write the Scope of study of a research work, certain salient points must feature and guide you to provide your readers adequate information on what basis and limits your research work covers.

The first thing to mention in any scope of study is to state categorically the periods the study covers. Most times, Researchers combine scope of study with the limitations of the study. They are somewhat interwoven. The difference is that limitation further covers others limitations like Combining lectures with your research, monetary constraints if necessary and Un-cooperation of targeted audience.

In addition, in writing the scope of the study, your research methods need to be stated which includes listing specific aspects of the data, such as sample size, geographic location and variables. The academic theories applied to the data also need to be listed so the reader knows the lens of analysis the researcher is using.

Since it is impossible to collect all data on a subject and explore every facet of a subject, all research is narrow in scope and subject to limitations. By acknowledging how research is restricted, it becomes more credible.

Practically, If you are writing on the topic ‘The role of Mass Media in educational development in Nigeria from 2010-2015’, Your scope of study is going to include its several roles within the time frame stated. It should also state Mass Media types used in the analysis of the study including locations and sample size used.

The scope of the study is limited to the role of Mass Media in educational development from 2010 to 2015. The scope of mass media equipments used were the television, radio and other electronic sets which are meant to give out information objectively through their effective usage to educate the poor masses. NTA 6 Enugu and the educational programmes they air were used as a major case study. 300 questionnaires were adequately filled and returned by the target audience to ascertain some variables.

Any other constraint and limitation can be stated under limitations of the study.

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How to write the scope of the study?

The scope of the study refers to the elements that will be covered in a research project. It defines the boundaries of the research. The scope is always decided in the preliminary stages of a study. Deciding it in the later stages creates a lot of ambiguity regarding the research goals. The main purpose of the scope of the study is that explains the extent to which the research area will be explored and thus specifies the parameters that will be observed within the study. In other words, it enables the researcher to define what the study will cover and the elements that it will not. Defining the scope helps the researcher acquire a high level of research and writing capability.

Goals of establishing the scope of the study

The following steps can help the researcher to effectively define the goals of establishing a scope of the study.

Identification of the project or research needs

The first step is to identify the research needs. This helps them set a benchmark from the first step. Identification of the ‘what’ and ‘why’ enables the researcher to clearly set the research goals and objectives and the manner in which they will be performed.

Confirmation of the goals and objectives of the research

The goals and objectives defined in the project scope should be aligned with the SMART (Specific, Measurable, Achievable, Realistic and Timeframe) guidelines, which are:

• Specific- this involves a clear specification of what the researcher wants to achieve. It involves specifying what, why and how things will be done. This reduces the chances of ambiguities and any misunderstanding in the future.
• Measurable- Goals should be measurable and dynamic so that constant feedback can be generated for improvement.
• Achievable- Research goals should be achievable with the resources that are available.
• Realistic- Goals should be easier to deliver so that complications that can hamper the quality of the research can be avoided. Other considerations to be kept in mind are the budget and timeline.  
• Time frame- lastly, the researcher should estimate whether the set goals can be achieved within the given time frame or not.

Expectations and Acceptance

The researcher should take into account the expectations of the research and how well the findings of the researcher will be accepted by the reader. For instance, will the findings of your study help in policymaking or not?

Identification of the constraints

there are always certain roadblocks in conducting research, such as environmental conditions, technological inefficiency and lack of resources. Identifying these limitations and their possible solutions in advance help achieve goals better.

Identifying the necessary changes

After the preliminary goals are set, the researcher must carry out some part of the research so that necessary changes that lead to waste of time and resources at later stages are reduced. For example, while conducting an interview, if the researcher believes that the sample size decided is too large or too small according to the scope of the study, then the researcher can make the necessary changes in that order to avoid wastage of time and resources.

Guidelines for writing the scope of the study

The major things that the researcher should keep in mind while writing the scope of the study are as follows.

• Time period: While writing the scope of the study the researcher should first mention or state categorically the time periods the study will cover. Generally, the researchers combine the scope of the study with the limitation of the study. These things are quite interwoven. The main difference between the two is that limitations further cover the points like monetary constraints or non-cooperation from the side of the target audience.
• Geography: In addition to this another major point that the researcher should keep in mind is that the scope of the study should state the specific aspect of the data that needs to be collected like the geographic locations and the variables.
• Research population: Another major aspect that should be involved while writing the scope of the study is the sample size or the population that the researcher has selected for the study. The sampling plan must clearly indicate the sample universe, target population, profile and sample size with justification.
• Theories: The researcher should state the academic theories that are being applied to the data collected so that the reader better knows the lens of the analysis. This is presented in the ‘theoretical framework’ section.
• Purpose: The scope of the study must indicate the purpose behind it. It must briefly define the larger picture, i.e. the overall goal the researcher is trying to achieve.  
• Limitations: It is impossible to avoid roadblocks in research. Every research is restricted in scope and is subjected to certain limitations. By acknowledging these limitations and how they are restricting the study makes its findings even more credible.

Consider the topic ‘Analysis of the role of social media on the educational development in India from 2000-2015’. The scope of the study for this research topic should include several roles within the mentioned time period. Further, it should also cover the mass media types that have been used in the analysis of the study also including the location and the sample size as well.

Scope of the study

With the increase in the number of social media users and its use in everyday communication at the individual and organizational levels, there has been a corresponding increase in its incorporation in educational development and especially in a country like India. In view of this situation, the present study analyzes the role of social media on the educational development of students. To this end, the study will also cover the changes in the usage of social media in the educational field over the time period ranging from 2000-2015. The scope of the study is restricted to select social media platforms, specifically Facebook, Twitter and YouTube. The empirical study in this research is restricted to five universities located across India, wherein the opinions of 30 teachers were studied in interview sessions. Further, the study also involves an analysis of students’ perspectives on the role of social media in education from the same university. Therefore the scope of this study is limited to India, and more specifically to those offering Arts and Science-related courses.

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Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

• Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
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• Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations  from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).

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Sample Scope and Delimitation

What is Scope of Study Section?

The Scope of study in the thesis or research paper is contains the explanation of what information or subject is being analyzed. It is followed by an explanation of the limitation of the research. Research usually limited in scope by sample size, time and geographic area.  While the delimitation of study is the description of the scope of study. It will explain why definite aspects of a subject were chosen and why others were excluded. It also mention the research method used as well as the certain theories that applied to the data.

Sample Scope and Delimitations The main focus of this project was the design of an efficient Energy Recovery System of Seawater Reverse Osmosis Plant.  The system will be using pressure technology by application of pressure exchanger as an energy recovery device.  Pressure exchanger transfer pressure from a high pressure stream to slow pressure stream in a ceramic motor. The proposed system is limited only in reducing high power consumption of the high pressure pump. The project can be used in all existing Seawater Reverse Osmosis Plant in the Philippines.  Some calculations, assumptions, and selections were made as a consideration of a proper and realistic design.

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Organizing Your Social Sciences Research Paper

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The title summarizes the main idea or ideas of your study. A good title contains the fewest possible words needed to adequately describe the content and/or purpose of your research paper.

Importance of Choosing a Good Title

The title is the part of a paper that is read the most, and it is usually read first . It is, therefore, the most important element that defines the research study. With this in mind, avoid the following when creating a title:

• If the title is too long, this usually indicates there are too many unnecessary words. Avoid language, such as, "A Study to Investigate the...," or "An Examination of the...." These phrases are obvious and generally superfluous unless they are necessary to covey the scope, intent, or type of a study.
• On the other hand, a title which is too short often uses words which are too broad and, thus, does not tell the reader what is being studied. For example, a paper with the title, "African Politics" is so non-specific the title could be the title of a book and so ambiguous that it could refer to anything associated with politics in Africa. A good title should provide information about the focus and/or scope of your research study.
• In academic writing, catchy phrases or non-specific language may be used, but only if it's within the context of the study [e.g., "Fair and Impartial Jury--Catch as Catch Can"]. However, in most cases, you should avoid including words or phrases that do not help the reader understand the purpose of your paper.
• Academic writing is a serious and deliberate endeavor. Avoid using humorous or clever journalistic styles of phrasing when creating the title to your paper. Journalistic headlines often use emotional adjectives [e.g., incredible, amazing, effortless] to highlight a problem experienced by the reader or use "trigger words" or interrogative words like how, what, when, or why to persuade people to read the article or click on a link. These approaches are viewed as counter-productive in academic writing. A reader does not need clever or humorous titles to catch their attention because the act of reading research is assumed to be deliberate based on a desire to learn and improve understanding of the problem. In addition, a humorous title can merely detract from the seriousness and authority of your research. 
• Unlike everywhere else in a college-level social sciences research paper [except when using direct quotes in the text], titles do not have to adhere to rigid grammatical or stylistic standards. For example, it could be appropriate to begin a title with a coordinating conjunction [i.e., and, but, or, nor, for, so, yet] if it makes sense to do so and does not detract from the purpose of the study [e.g., "Yet Another Look at Mutual Fund Tournaments"] or beginning the title with an inflected form of a verb such as those ending in -ing [e.g., "Assessing the Political Landscape: Structure, Cognition, and Power in Organizations"].

Appiah, Kingsley Richard et al. “Structural Organisation of Research Article Titles: A Comparative Study of Titles of Business, Gynaecology and Law.” Advances in Language and Literary Studies 10 (2019); Hartley James. “To Attract or to Inform: What are Titles for?” Journal of Technical Writing and Communication 35 (2005): 203-213; Jaakkola, Maarit. “Journalistic Writing and Style.” In Oxford Research Encyclopedia of Communication . Jon F. Nussbaum, editor. (New York: Oxford University Press, 2018): https://oxfordre.com/communication.

Structure and Writing Style

The following parameters can be used to help you formulate a suitable research paper title:

• The purpose of the research
• The scope of the research
• The narrative tone of the paper [typically defined by the type of the research]
• The methods used to study the problem

The initial aim of a title is to capture the reader’s attention and to highlight the research problem under investigation.

Create a Working Title Typically, the final title you submit to your professor is created after the research is complete so that the title accurately captures what has been done . The working title should be developed early in the research process because it can help anchor the focus of the study in much the same way the research problem does. Referring back to the working title can help you reorient yourself back to the main purpose of the study if you find yourself drifting off on a tangent while writing. The Final Title Effective titles in research papers have several characteristics that reflect general principles of academic writing.

• Indicate accurately the subject and scope of the study,
• Rarely use abbreviations or acronyms unless they are commonly known,
• Use words that create a positive impression and stimulate reader interest,
• Use current nomenclature from the field of study,
• Identify key variables, both dependent and independent,
• Reveal how the paper will be organized,
• Suggest a relationship between variables which supports the major hypothesis,
• Is limited to 5 to 15 substantive words,
• Does not include redundant phrasing, such as, "A Study of," "An Analysis of" or similar constructions,
• Takes the form of a question or declarative statement,
• If you use a quote as part of the title, the source of the quote is cited [usually using an asterisk and footnote],
• Use correct grammar and capitalization with all first words and last words capitalized, including the first word of a subtitle. All nouns, pronouns, verbs, adjectives, and adverbs that appear between the first and last words of the title are also capitalized, and
• Rarely uses an exclamation mark at the end of the title.

The Subtitle Subtitles are frequently used in social sciences research papers because it helps the reader understand the scope of the study in relation to how it was designed to address the research problem. Think about what type of subtitle listed below reflects the overall approach to your study and whether you believe a subtitle is needed to emphasize the investigative parameters of your research.

1.  Explains or provides additional context , e.g., "Linguistic Ethnography and the Study of Welfare Institutions as a Flow of Social Practices: The Case of Residential Child Care Institutions as Paradoxical Institutions." [Palomares, Manuel and David Poveda.  Text & Talk: An Interdisciplinary Journal of Language, Discourse and Communication Studies 30 (January 2010): 193-212]

2.  Adds substance to a literary, provocative, or imaginative title or quote , e.g., "Listen to What I Say, Not How I Vote": Congressional Support for the President in Washington and at Home." [Grose, Christian R. and Keesha M. Middlemass. Social Science Quarterly 91 (March 2010): 143-167]

3.  Qualifies the geographic scope of the research , e.g., "The Geopolitics of the Eastern Border of the European Union: The Case of Romania-Moldova-Ukraine." [Marcu, Silvia. Geopolitics 14 (August 2009): 409-432]

4.  Qualifies the temporal scope of the research , e.g., "A Comparison of the Progressive Era and the Depression Years: Societal Influences on Predictions of the Future of the Library, 1895-1940." [Grossman, Hal B. Libraries & the Cultural Record 46 (2011): 102-128]

5.  Focuses on investigating the ideas, theories, or work of a particular individual , e.g., "A Deliberative Conception of Politics: How Francesco Saverio Merlino Related Anarchy and Democracy." [La Torre, Massimo. Sociologia del Diritto 28 (January 2001): 75 - 98]

6.  Identifies the methodology used , e.g. "Student Activism of the 1960s Revisited: A Multivariate Analysis Research Note." [Aron, William S. Social Forces 52 (March 1974): 408-414]

7.  Defines the overarching technique for analyzing the research problem , e.g., "Explaining Territorial Change in Federal Democracies: A Comparative Historical Institutionalist Approach." [ Tillin, Louise. Political Studies 63 (August 2015): 626-641.

With these examples in mind, think about what type of subtitle reflects the overall approach to your study. This will help the reader understand the scope of the study in relation to how it was designed to address the research problem.

Anstey, A. “Writing Style: What's in a Title?” British Journal of Dermatology 170 (May 2014): 1003-1004; Balch, Tucker. How to Compose a Title for Your Research Paper. Augmented Trader blog. School of Interactive Computing, Georgia Tech University; Bavdekar, Sandeep B. “Formulating the Right Title for a Research Article.” Journal of Association of Physicians of India 64 (February 2016); Choosing the Proper Research Paper Titles. AplusReports.com, 2007-2012; Eva, Kevin W. “Titles, Abstracts, and Authors.” In How to Write a Paper . George M. Hall, editor. 5th edition. (Oxford: John Wiley and Sons, 2013), pp. 33-41; Hartley James. “To Attract or to Inform: What are Titles for?” Journal of Technical Writing and Communication 35 (2005): 203-213; General Format. The Writing Lab and The OWL. Purdue University; Kerkut G.A. “Choosing a Title for a Paper.” Comparative Biochemistry and Physiology Part A: Physiology 74 (1983): 1; “Tempting Titles.” In Stylish Academic Writing . Helen Sword, editor. (Cambridge, MA: Harvard University Press, 2012), pp. 63-75; Nundy, Samiran, et al. “How to Choose a Title?” In How to Practice Academic Medicine and Publish from Developing Countries? A Practical Guide . Edited by Samiran Nundy, Atul Kakar, and Zulfiqar A. Bhutta. (Springer Singapore, 2022), pp. 185-192.

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Social Psychology: Definition, Theories, Scope, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

Social psychology is the scientific study of how people’s thoughts, feelings, beliefs, intentions, and goals are constructed within a social context by the actual or imagined interactions with others.

It, therefore, looks at human behavior as influenced by other people and the conditions under which social behavior and feelings occur.

Baron, Byrne, and Suls (1989) define social psychology as “the scientific field that seeks to understand the nature and causes of individual behavior in social situations” (p. 6).

Topics examined in social psychology include the self-concept , social cognition, attribution theory , social influence, group processes, prejudice and discrimination , interpersonal processes, aggression, attitudes , and stereotypes .

Social psychology operates on several foundational assumptions. These fundamental beliefs provide a framework for theories, research, and interpretations.

• Individual and Society Interplay : Social psychologists assume an interplay exists between individual minds and the broader social context. An individual’s thoughts, feelings, and behaviors are continuously shaped by social interactions, and in turn, individuals influence the societies they are a part of.
• Behavior is Contextual : One core assumption is that behavior can vary significantly based on the situation or context. While personal traits and dispositions matter, the circumstances or social environment often play a decisive role in determining behavior.
• Objective Reality is Difficult to Attain : Our perceptions of reality are influenced by personal beliefs, societal norms, and past experiences. Therefore, our understanding of “reality” is subjective and can be biased or distorted.
• Social Reality is Constructed : Social psychologists believe that individuals actively construct their social world . Through processes like social categorization, attribution, and cognitive biases, people create their understanding of others and societal norms.
• People are Social Beings with a Need to Belong : A fundamental assumption is the inherent social nature of humans. People have an innate need to connect with others, form relationships, and belong to groups. This need influences a wide range of behaviors and emotions.
• Attitudes Influence Behavior : While this might seem straightforward, it’s a foundational belief that our attitudes (combinations of beliefs and feelings) can and often do drive our actions. However, it’s also understood that this relationship can be complex and bidirectional.
• People Desire Cognitive Consistency : This is the belief that people are motivated to maintain consistency in their beliefs, attitudes, and behaviors. Cognitive dissonance theory , which posits that people feel discomfort when holding conflicting beliefs and are motivated to resolve this, is based on this assumption.
• People are Motivated to See Themselves in a Positive Light : The self plays a central role in social psychology. It’s assumed that individuals are generally motivated to maintain and enhance a positive self-view.
• Behavior Can be Predicted and Understood : An underlying assumption of any science, including social psychology, is that phenomena (in this case, human behavior in social contexts) can be studied, understood, predicted, and potentially influenced.
• Cultural and Biological Factors are Integral : Though earlier social psychology might have been criticized for neglecting these factors, contemporary social psychology acknowledges the roles of both biology (genes, hormones, brain processes) and culture (norms, values, traditions) in shaping social behavior.

Early Influences

Aristotle believed that humans were naturally sociable, a necessity that allows us to live together (an individual-centered approach), whilst Plato felt that the state controlled the individual and encouraged social responsibility through social context (a socio-centered approach).

Hegel (1770–1831) introduced the concept that society has inevitable links with the development of the social mind. This led to the idea of a group mind, which is important in the study of social psychology.

Lazarus & Steinthal wrote about Anglo-European influences in 1860. “Volkerpsychologie” emerged, which focused on the idea of a collective mind.

It emphasized the notion that personality develops because of cultural and community influences, especially through language, which is both a social product of the community as well as a means of encouraging particular social thought in the individual. Therefore Wundt (1900–1920) encouraged the methodological study of language and its influence on the social being.

Early Texts

Texts focusing on social psychology first emerged in the 20th century. McDougall published the first notable book in English in 1908 (An Introduction to Social Psychology), which included chapters on emotion and sentiment, morality, character, and religion, quite different from those incorporated in the field today.

He believed social behavior was innate/instinctive and, therefore, individual, hence his choice of topics.  This belief is not the principle upheld in modern social psychology, however.

Allport’s work (1924) underpins current thinking to a greater degree, as he acknowledged that social behavior results from interactions between people.

He also took a methodological approach, discussing actual research and emphasizing that the field was a “science … which studies the behavior of the individual in so far as his behavior stimulates other individuals, or is itself a reaction to this behavior” (1942: p. 12).

His book also dealt with topics still evident today, such as emotion, conformity, and the effects of an audience on others.

Murchison (1935) published The first handbook on social psychology was published by Murchison in 1935.  Murphy & Murphy (1931/37) produced a book summarizing the findings of 1,000 studies in social psychology.  A text by Klineberg (1940) looked at the interaction between social context and personality development. By the 1950s, several texts were available on the subject.

Journal Development

• 1950s – Journal of Abnormal and Social Psychology

• 1963 – Journal of Personality, British Journal of Social and Clinical Psychology

• 1965 – Journal of Personality and Social Psychology, Journal of Experimental Social Psychology

• 1971 – Journal of Applied Social Psychology, European Journal of Social Psychology

• 1975 – Social Psychology Quarterly, Personality and Social Psychology Bulletin

• 1982 – Social Cognition

• 1984 – Journal of Social and Personal Relationships

Early Experiments

There is some disagreement about the first true experiment, but the following are certainly among some of the most important.

Triplett (1898) applied the experimental method to investigate the performance of cyclists and schoolchildren on how the presence of others influences overall performance – thus, how individuals are affected and behave in the social context.

By 1935, the study of social norms had developed, looking at how individuals behave according to the rules of society. This was conducted by Sherif (1935).

Lewin et al. then began experimental research into leadership and group processes by 1939, looking at effective work ethics under different leadership styles.

Later Developments

Much of the key research in social psychology developed following World War II, when people became interested in the behavior of individuals when grouped together and in social situations. Key studies were carried out in several areas.

Some studies focused on how attitudes are formed, changed by the social context, and measured to ascertain whether a change has occurred.

Amongst some of the most famous works in social psychology is that on obedience conducted by Milgram in his “electric shock” study, which looked at the role an authority figure plays in shaping behavior.  Similarly,  Zimbardo’s prison simulation notably demonstrated conformity to given roles in the social world.

Wider topics then began to emerge, such as social perception, aggression, relationships, decision-making, pro-social behavior, and attribution, many of which are central to today’s topics and will be discussed throughout this website.

Thus, the growth years of social psychology occurred during the decades following the 1940s.

The scope of social psychology is vast, reflecting the myriad ways social factors intertwine with individual cognition and behavior.

Its principles and findings resonate in virtually every area of human interaction, making it a vital field for understanding and improving the human experience.

• Interpersonal Relationships : This covers attraction, love, jealousy, friendship, and group dynamics. Understanding how and why relationships form and the factors that contribute to their maintenance or dissolution is central to this domain.
• Attitude Formation and Change : How do individuals form opinions and attitudes? What methods can effectively change them? This scope includes the study of persuasion, propaganda, and cognitive dissonance.
• Social Cognition : This examines how people process, store, and apply information about others. Areas include social perception, heuristics, stereotypes, and attribution theories.
• Social Influence : The study of conformity, compliance, obedience, and the myriad ways individuals influence one another falls within this domain.
• Group Dynamics : This entails studying group behavior, intergroup relations, group decision-making processes, leadership, and more. Concepts like groupthink and group polarization emerge from this area.
• Prejudice and Discrimination : Understanding the roots of bias, racism, sexism, and other forms of prejudice, as well as exploring interventions to reduce them, is a significant focus.
• Self and Identity : Investigating self-concept, self-esteem, self-presentation, and the social construction of identity are all part of this realm.
• Prosocial Behavior and Altruism : Why do individuals sometimes help others, even at a cost to themselves? This area delves into the motivations and conditions that foster cooperative and altruistic behavior.
• Aggression : From understanding the underlying causes of aggressive behavior to studying societal factors that exacerbate or mitigate aggression, this topic seeks to dissect the nature of hostile actions.
• Cultural and Cross-cultural Dimensions : As societies become more interconnected, understanding cultural influences on behavior, cognition, and emotion is crucial. This area compares and contrasts behaviors across different cultures and societal groups.
• Environmental and Applied Settings : Social psychology principles find application in health psychology, environmental behavior, organizational behavior, consumer behavior, and more.
• Social Issues : Social psychologists might study the impact of societal structures on individual behavior, exploring topics like poverty, urban stress, and crime.
• Education : Principles of social psychology enhance teaching methods, address issues of classroom dynamics, and promote effective learning.
• Media and Technology : In the digital age, understanding the effects of media consumption, the dynamics of online communication, and the formation of online communities is increasingly relevant.
• Law : Insights from social psychology inform areas such as jury decision-making, eyewitness testimony, and legal procedures.
• Health : Concepts from social psychology are employed to promote health behaviors, understand doctor-patient dynamics, and tackle issues like addiction.

Example Theories

Allport (1920) – social facilitation.

Allport introduced the notion that the presence of others (the social group) can facilitate certain behavior.

It was found that an audience would improve an actor’s performance in well-learned/easy tasks but leads to a decrease in performance on newly learned/difficult tasks due to social inhibition.

Bandura (1963) Social Learning Theory

Bandura introduced the notion that behavior in the social world could be modeled. Three groups of children watched a video where an adult was aggressive towards a ‘bobo doll,’ and the adult was either just seen to be doing this, was rewarded by another adult for their behavior, or was punished for it.

Children who had seen the adult rewarded were found to be more likely to copy such behavior.

Festinger (1950) –  Cognitive Dissonance

Festinger, Schacter, and Black brought up the idea that when we hold beliefs, attitudes, or cognitions which are different, then we experience dissonance – this is an inconsistency that causes discomfort.

We are motivated to reduce this by either changing one of our thoughts, beliefs, or attitudes or selectively attending to information that supports one of our beliefs and ignores the other (selective exposure hypothesis).

Dissonance occurs when there are difficult choices or decisions or when people participate in behavior that is contrary to their attitude. Dissonance is thus brought about by effort justification (when aiming to reach a modest goal), induced compliance (when people are forced to comply contrary to their attitude), and free choice (when weighing up decisions).

Tajfel (1971) –  Social Identity Theory

When divided into artificial (minimal) groups, prejudice results simply from the awareness that there is an “out-group” (the other group).

When the boys were asked to allocate points to others (which might be converted into rewards) who were either part of their own group or the out-group, they displayed a strong in-group preference. That is, they allocated more points on the set task to boys who they believed to be in the same group as themselves.

This can be accounted for by Tajfel & Turner’s social identity theory, which states that individuals need to maintain a positive sense of personal and social identity: this is partly achieved by emphasizing the desirability of one’s own group, focusing on distinctions between other “lesser” groups.

Weiner (1986) – Attribution Theory

Weiner was interested in the attributions made for experiences of success and failure and introduced the idea that we look for explanations of behavior in the social world.

He believed that these were made based on three areas: locus, which could be internal or external; stability, which is whether the cause is stable or changes over time: and controllability.

Milgram (1963) – Shock Experiment

Participants were told that they were taking part in a study on learning but always acted as the teacher when they were then responsible for going over paired associate learning tasks.

When the learner (a stooge) got the answer wrong, they were told by a scientist that they had to deliver an electric shock. This did not actually happen, although the participant was unaware of this as they had themselves a sample (real!) shock at the start of the experiment.

They were encouraged to increase the voltage given after each incorrect answer up to a maximum voltage, and it was found that all participants gave shocks up to 300v, with 65 percent reaching the highest level of 450v.

It seems that obedience is most likely to occur in an unfamiliar environment and in the presence of an authority figure, especially when covert pressure is put upon people to obey. It is also possible that it occurs because the participant felt that someone other than themselves was responsible for their actions.

Haney, Banks, Zimbardo (1973) – Stanford Prison Experiment

Volunteers took part in a simulation where they were randomly assigned the role of a prisoner or guard and taken to a converted university basement resembling a prison environment. There was some basic loss of rights for the prisoners, who were unexpectedly arrested, and given a uniform and an identification number (they were therefore deindividuated).

The study showed that conformity to social roles occurred as part of the social interaction, as both groups displayed more negative emotions, and hostility and dehumanization became apparent.

Prisoners became passive, whilst the guards assumed an active, brutal, and dominant role. Although normative and informational social influence played a role here, deindividuation/the loss of a sense of identity seemed most likely to lead to conformity.

Both this and Milgram’s study introduced the notion of social influence and the ways in which this could be observed/tested.

Provides Clear Predictions

As a scientific discipline, social psychology prioritizes formulating clear and testable hypotheses. This clarity facilitates empirical testing, ensuring the field’s findings are based on observable and quantifiable phenomena.

The Asch conformity experiments hypothesized that individuals would conform to a group’s incorrect judgment.

The clear prediction allowed for controlled experimentation to determine the extent and conditions of such conformity.

Emphasizes Objective Measurement

Social psychology leans heavily on empirical methods, emphasizing objectivity. This means that results are less influenced by biases or subjective interpretations.

Double-blind procedures , controlled settings, and standardized measures in many social psychology experiments ensure that results are replicable and less prone to experimenter bias.

Empirical Evidence

Over the years, a multitude of experiments in social psychology have bolstered the credibility of its theories. This experimental validation lends weight to its findings and claims.

The robust body of experimental evidence supporting cognitive dissonance theory, from Festinger’s initial studies to more recent replications, showcases the theory’s enduring strength and relevance.

Limitations

Underestimates individual differences.

While social psychology often looks at broad trends and general behaviors, it can sometimes gloss over individual differences.

Not everyone conforms, obeys, or reacts in the same way, and these nuanced differences can be critical.

While Milgram’s obedience experiments showcased a startling rate of compliance to authority, there were still participants who resisted, and their reasons and characteristics are equally important to understand.

Ignores Biology

While social psychology focuses on the social environment’s impact on behavior, early theories sometimes neglect the biological underpinnings that play a role.

Hormones, genetics, and neurological factors can influence behavior and might intersect with social factors in complex ways.

The role of testosterone in aggressive behavior is a clear instance where biology intersects with the social. Ignoring such biological components can lead to an incomplete understanding.

Superficial Snapshots of Social Processes

Social psychology sometimes offers a narrow view, capturing only a momentary slice of a broader, evolving process. This might mean that the field fails to capture the depth, evolution, or intricacies of social processes over time.

A study might capture attitudes towards a social issue at a single point in time, but not account for the historical evolution, future shifts, or deeper societal underpinnings of those attitudes.

Allport, F. H. (1920). The influence of the group upon association and thought. Journal of Experimental Psychology , 3(3), 159.

Allport, F. H. (1924). Response to social stimulation in the group. Social psychology , 260-291.

Allport, F. H. (1942). Methods in the study of collective action phenomena. The Journal of Social Psychology , 15(1), 165-185.

Bandura, A., Ross, D., & Ross, S. A. (1963). Vicarious reinforcement and imitative learning. The Journal of Abnormal and Social Psychology , 67(6), 601.

Baron, R. A., Byrne, D., & Suls, J. (1989). Attitudes: Evaluating the social world. Baron et al, Social Psychology . 3rd edn. MA: Allyn and Bacon, 79-101.

Festinger, L., Schachter, S., & Back, K. (1950). Social processes in informal groups .

Haney, C., Banks, W. C., & Zimbardo, P. G. (1973). Study of prisoners and guards in a simulated prison. Naval Research Reviews , 9(1-17).

Klineberg, O. (1940). The problem of personality .

Krewer, B., & Jahoda, G. (1860). On the scope of Lazarus and Steinthals “Völkerpsychologie” as reflected in the. Zeitschrift für Völkerpsychologie und Sprachwissenschaft, 1890, 4-12.

Lewin, K., Lippitt, R., & White, R. K. (1939). Patterns of aggressive behavior in experimentally created “social climates”. The Journal of Social Psychology , 10(2), 269-299.

Mcdougall, W. (1908). An introduction to social psychology . Londres: Methuen.

Milgram, S. (1963). Behavioral study of obedience. The Journal of Abnormal and Social Psychology , 67(4), 371.

Murchison, C. (1935). A handbook of social psychology .

Murphy, G., & Murphy, L. B. (1931). Experimental social psychology .

Sherif, M. (1935). A study of some social factors in perception. Archives of Psychology (Columbia University).

Tajfel, H., Billig, M. G., Bundy, R. P., & Flament, C. (1971). Social categorization and intergroup behavior. European journal of social psychology , 1(2), 149-178.

Triplett, N. (1898). The dynamogenic factors in pacemaking and competition. American journal of Psychology , 9(4), 507-533.

Weiner, B. (1986). An attributional theory of motivation and emotion . New York: Springer-Verlag.

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Risky Driving

• Distracted Driving

Drowsy Driving

• Drug-Impaired Driving
• Drunk Driving

Drowsy driving kills — but is preventable. Learn about three factors commonly associated with drowsy-driving crashes and pick up some helpful tips to avoid falling asleep at the wheel. In this section, you’ll also find several resources and learn what NHTSA is doing to help eliminate this risky behavior.

Scope of the Problem

• Crash Factors

Tips to Drive Alert

• NHTSA In Action

Attitudes About Drowsy Driving Need to Change

Fatigue has costly effects on the safety, health, and quality of life of the American public. Whether fatigue is caused by sleep restriction due to a new baby waking every couple of hours, a late or long shift at work, hanging out late with friends, or a long and monotonous drive for the holidays – the negative outcomes can be the same. These include impaired cognition and performance, motor vehicle crashes, workplace accidents, and health consequences.

Tackling these issues can be difficult when our lifestyle does not align with avoiding drowsy driving. In a 24/7 society, with an emphasis on work, longer commutes, and exponential advancement of technology, many people do not get the sleep they need. Effectively dealing with the drowsy-driving problem requires fundamental changes to societal norms and especially attitudes about drowsy driving.

The terms drowsy, sleepy, and fatigue are used interchangeably although there are differences in the way these terms are used and understood.

Precise Numbers of Drowsy-Driving Crashes, Injuries, and Fatalities Are Hard to Nail Down

Unfortunately, determining a precise number of drowsy-driving crashes, injuries, and fatalities is not yet possible. Crash investigators can look for clues that drowsiness contributed to a crash, but these clues are not always identifiable or conclusive.

NHTSA’s census of fatal crashes and estimate of traffic-related crashes and injuries rely on police and hospital reports to determine the incidence of drowsy-driving crashes. NHTSA estimates that in 2017, 91,000 police-reported crashes involved drowsy drivers. These crashes led to an estimated 50,000 people injured and nearly 800 deaths. But there is broad agreement across the traffic safety, sleep science, and public health communities that this is an underestimate of the impact of drowsy driving. 

Crashes and Fatalities

Sleepiness can result in crashes any time of the day or night, but three factors are most commonly associated with drowsy-driving crashes.

Drowsy-driving crashes:

• Occur most frequently between midnight and 6 a.m., or in the late afternoon. At both times of the day, people experience dips in their circadian rhythm—the human body’s internal clock that regulates sleep;
• Often involve only a single driver (and no passengers) running off the road at a high rate of speed with no evidence of braking; and
• Frequently occur on rural roads and highways.

How To Avoid Driving Drowsy

• Getting adequate sleep on a daily basis is the only true way to protect yourself against the risks of driving when you’re drowsy. Experts urge consumers to make it a priority to get seven to eight hours of sleep per night. For more information on healthy sleep, see In Brief: Your Guide to Healthy Sleep  (PDF, 1.81 MB) at the National Heart, Lung, and Blood Institute website .
• Before the start of a long family car trip, get a good night’s sleep, or you could put your entire family and others at risk.
• Many teens do not get enough sleep at a stage in life when their biological need for sleep increases, which makes them vulnerable to the risk of drowsy-driving crashes, especially on longer trips. Advise your teens to delay driving until they’re well-rested.
• Avoid drinking any alcohol before driving. Consumption of alcohol interacts with sleepiness to increase drowsiness and impairment.
• Always check your prescription and over-the-counter medication labels to see if drowsiness could result from their use.
• If you take medications that could cause drowsiness as a side effect, use public transportation when possible.
• If you drive, avoid driving during the peak sleepiness periods (midnight – 6 a.m. and late afternoon). If you must drive during the peak sleepiness periods, stay vigilant for signs of drowsiness, such as crossing over roadway lines or hitting a rumble strip, especially if you’re driving alone.

SHORT-TERM INTERVENTIONS

• Drinking coffee or energy drinks alone is not always enough. They might help you feel more alert, but the effects last only a short time, and you might not be as alert as you think you are. If you drink coffee and are seriously sleep-deprived, you still may have “micro sleeps” or brief losses of consciousness that can last for four or five seconds. This means that at 55 miles per hour, you’ve traveled more than 100 yards down the road while asleep. That’s plenty of time to cause a crash.
• If you start to get sleepy while you’re driving, drink one to two cups of coffee and pull over for a short 20-minute nap in a safe place, such as a lighted, designated rest stop. This has been shown to increase alertness in scientific studies, but only for short time periods.

NHTSA is dedicated to eliminating risky behaviors on our nation’s roads

NHTSA demonstrates its commitment to eliminating drowsy driving on our nation’s roads by working with the Centers for Disease Control and Prevention and the National Institutes of Health to expand our understanding of drowsy driving so we can reduce related deaths and injuries and help people avoid becoming a drowsy-driving statistic.

Other efforts include:

• In 2016, NHTSA released a strategic plan, Drowsy Driving and Research Program Plan (PDF, 613 KB), addressing six broad focus areas: Measurement and Problem Identification, Public Awareness and Education, Policy Development, High-Risk Populations, Vehicle Technology, and Infrastructure.
• In 2015, NHTSA convened the forum Asleep at the Wheel: A Nation of Drowsy Drivers (PDF, 1.66 MB) during the National Sleep Foundation’s National Drowsy Driving Prevention Week. This meeting included more than 100 participants from many diverse organizations, setting the stage for a national coordinated effort by bringing together motor vehicle and highway safety experts with sleep/circadian science experts and the sleep medicine community.

More on Drowsy Driving

Centers for Disease Control & Prevention (CDC)

• Dangers of Drowsy Driving
• Morbidity and Mortality Weekly Report (January 4, 2013) Drowsy Driving – 19 States and the District of Columbia 2009-2010  
• Morbidity and Mortality Weekly Report (July 4, 2014) Drowsy Driving and Risk Behaviors 

Food & Drug Administration (FDA)

• Some Medicines and Driving Don’t Mix
• Driving When You Are Taking Medications

Federal Highway Administration (FHWA)

• Rumble Strips: A Wake-Up Call for Drowsy Drivers 
• State DOT Report: Utah Department of Transportation Research & Development Division, A Safety Analysis of Fatigue and Drowsy Driving in the State of Utah

Federal Motor Carrier Safety Administration (FMCSA)

• Drowsy Driving Quiz: Are you at risk for falling asleep behind the wheel?  
• CMV Driving Tips

National Institutes of Health (NIH) National Center on Sleep Disorders Research and Office of Prevention, Education, and Control

• Educating Youth About Sleep and Drowsy Driving  (PDF, 981 KB)

National Institute for Occupational Safety and Health (NIOSH)

• Quick Sleep Tips for Truck Drivers  (PDF, 1.9 MB)

National Transportation Safety Board (NTSB)

• 2014 Forum: Awake, Alert, Alive: Overcoming the Dangers of Drowsy Driving  

Drowsy Driving Prevention Week

• Each November, the National Sleep Foundation conducts Drowsy Driving Prevention Week in an effort to reduce the number of drowsy-driving crashes.

Search for more resources

Explore other topics in risky driving.

MENTORSHIP/SUPPORT

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• (234)-701-114-7037
• [email protected]
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SCOPE OF THE STUDY IN RESEARCH

• Latest Blog

WRITING YOUR SCOPE OF THE STUDY

Every research work must be delimited to a particular scope. Your scope of study is your coverage area, you need to limit your study to a catchment you can sufficiently handle, it must be within your capacity. The idea of wanting to cover a whole country is not admissible, before choosing a scope of study, ask yourself the following questions:

• Do I have enough fund to handle the area?
• Do I have a means of collecting data from respondents across the selected area?
• How does my study relate to this area?
• Does my population of study fall within this area?
• Do I have enough time to visit the area for personal observation or interview?
• Can I freely access the location?
• How does my research problem affect the area?

These (above items) should be put into consideration. It is recommended that you identify areas you can reach within the shortest time possible, within your budget and very accessible.

This is because you don’t want to throw in questionnaire in locations that would take you 7hours of travel, what then happens after the questionnaires have been filled, would you still travel another 7hours to collect completed questionnaires. Why not pick one or two Local Government Area, a city or at most a state especially where you reside or very familiar with.

Even if your topic deals with problems affecting the nation (e.g., case study research), why not select a region, a few states (if you have to compare) or a few locations from two or three states and do proper research on them. The truth be told, most research whose case study covers the entirety of a country find it difficult collecting accurate data (with use of questionnaires), they end up copying from already researched papers (plagiarism).

In addition, the scope of the study defines the upper and lower bounds of the study and it is meant to explain the keywords in the topic or title either in terms of the subject matter, location, or in terms of time dimension. The main goal is to limit the level of responsibility of researchers to whatever queries may arise during the execution of the study. Defining the scope of the study exonerates the researchers from other issues that are relevant to the study but not covered. It is important for the researcher to know that the scope of the study should be specific, measurable, identifiable, time-bound, and cost-effective. If the scope is too wide, it will be very expensive to cover.

Furthermore, it is important to set limits on how much one tries to cover in a single study. The issues (or factors or variables) investigated are the study’s independent variables. It is possible for the dependent variable in the problem to relate to many factors. When this is the case, delimit the study to a few of the independent factors. If the study applies to a subject in a wide geographical coverage, it is necessary to also delimit the study to a reasonable coverage.

In general, the scope of a study in research must cover the following;

• The geographical area under study
• Materials/data on the subject area, population, components, or element to be covered.  
• Introduction of time factor or period to be studied.

Example 1 of the simple scope of a study

The study is on the effect of Government quarters monetization policy on housing development in Enugu urban; therefore, to effectively do justice to this subject, Secretariat Quarters Ogui, Ologo Quarters, Agbani Road, CBN Quarters Trans Ekulu and Artisan Quarters are adopted as a case study. This includes the buildings, infrastructural facilities, environment, and the general components of the various quarters in Enugu urban.

Example 2 of the simple scope of a study

The study is on the examination of problems associated with using real estate as collateral for loan facilities in selected banks in FCT – Abuja between 2013 to 2022. The study was further limited to fourteen commercial bank branches in FCT – Abuja comprising Zenith Bank Plc., First Bank of Nigeria; Fidelity Bank, Union Bank plc, First City Monument Bank, Sterling Bank.

Example of a slightly detailed scope of a study

The study is on the problems associated with using real estate as collateral for loan facilities in selected banks in Enugu. There are various motives for loan applications in Enugu, notwithstanding; for the purpose of this study; emphasis is laid on loan applications for real estate investments and business transactions; the study was limited to the following commercial banks in Enugu urban; Zenith Bank, Independence layout; First Bank of Nigeria, New Haven Branch; Fidelity Bank, Okpara Avenue; Sterling Bank, Market Road and Access Bank, Ogui Road.

Example of detailed scope of a study in research

The scope of this study covers the Ketu area; it is bordered by a major highway in Lagos Metropolis, the Lagos Ikorodu Road which serves as a link from Jibowu to Ikorodu. The area to be studied comprises of Demurin and Akintan Corridor in Kosofe Local Government Area in Lagos metropolis. It requires a piece of comprehensive information consisting of variables on the socio-economic features of residents as affecting housing quality in the subject area; including the population, age and sex distribution, level of education, and marital status, the study also intends to investigate the physical features of available housing in the area under study and demographic variables of the households representing the housing characteristics such as costs, rents and quality of housing in the metropolis.

For in-depth study, this work will assess the condition of housing, available facilities in the subject neighborhood, the number of floors, size of the habitable rooms, the occupancy ratio, housing quality indicators the likes of structural adequacy, neighborhood quality, residents’ perception on neighborhood safety, level of public services allowed, access to work and other social amenities, room density and affordability of housing. The study also considers the contributing factors to housing problems over a period of 10 years (2013 to 2023).

Your scope should have a jurisdiction, a limit…a start point and an end point.

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• Data governance
• Alexander S. Gillis, Technical Writer and Editor
• Jacqueline Biscobing, Senior Managing Editor, News

What is compliance?

Compliance is the state of being in accordance with established guidelines or specifications, or the process of becoming so. Software, for example, may be developed in compliance with specifications created by a standards body, and then deployed by user organizations in compliance with a vendor's licensing agreement. The definition of compliance can also encompass efforts to ensure that organizations are abiding by both industry regulations and government legislation.

Compliance is a prevalent business concern, partly because of an ever-increasing number of regulations that require companies to be vigilant about maintaining a full understanding of their regulatory requirements for compliance. To adhere to compliance standards, an organization must follow requirements or regulations imposed by either itself or government legislation.

Regulatory compliance examples

Some prominent regulations, standards and legislation that organizations may need to be compliant with include the following:

• Sarbanes-Oxley Act of 2002. The Sarbanes-Oxley Act was enacted in response to the high-profile Enron and WorldCom financial scandals to protect shareholders and the general public from accounting errors and fraudulent practices. Among other provisions, the law sets rules on storing and retaining business records in IT systems.
• Can Spam Act of 2003. The Can Spam Act requires businesses to label commercial emails as advertising, use legitimate return email addresses, provide recipients with opt-out options and process opt-out requests within 10 business days.
• Health Insurance Portability and Accountability Act ( HIPAA ) of 1996. HIPAA Title II includes an administrative simplification section that mandates standardization of electronic health records systems and includes security mechanisms designed to protect data privacy and patient confidentiality.
• Dodd-Frank Act . Enacted in 2010, this act aims to reduce federal dependence on banks by subjecting them to regulations that enforce transparency and accountability to protect customers.
• Payment Card Industry Data Security Standard ( PCI DSS ). PCI DSS is a set of policies and procedures created in 2004 by Visa, MasterCard, Discover and American Express to ensure the security of credit, debit and cash card transactions.
• Federal Information Security Management Act ( FISMA ). Signed into law in 2002, FISMA requires federal agencies to conduct annual reviews of information security programs. This is done to keep risks to data at or below specified acceptable levels.
• Occupational Safety and Health Administration ( OSHA ). The OSHA requirements were introduced by the U.S. Congress in 1971 to protect worker health and safety in the U.S.
• General Data Protection Regulation ( GDPR ). GDPR is legislation that went into effect in the European Union in 2018 that updated and unified data privacy laws. The purpose of GDPR is to protect individuals and the data that describes them and to ensure organizations that collect this data do so in a responsible manner.

IT compliance guidelines vary by country; Sarbanes-Oxley Act, for example, is U.S. legislation. Similar legislation in other countries includes Germany's Deutscher Corporate Governance Kodex and Australia's Corporate Law Economic Reform Program Act 2004. As a result, multinational organizations must be cognizant of the regulatory compliance requirements of each country they operate within. For example, GDPR applies to all organizations that are based outside the European Union, as long as they also operate in the EU.

Regulatory compliance vs. corporate compliance

There are two main types of compliance that denote where the framework is coming from: corporate and regulatory. Both corporate and regulatory compliance consist of a framework of rules, regulations and practices to follow.

• Corporate compliance applies to the rules, regulations and practices an organization puts into place for compliance -- according to both external regulations and internal policies.
• Regulatory compliance applies to the rules, regulations and practices an organization puts into place for compliance -- according to external regulations.

Corporate and regulatory compliance are very similar, with their main difference being whether their policies come from internal or external regulations.

Chief compliance officer and other compliance roles

As regulations and other guidelines have increasingly become a concern for corporate management, companies are turning more frequently to specialized compliance software and IT compliance consultancies. Many organizations have even added compliance jobs, such as the role of chief compliance officer (CCO).

The main responsibilities of a CCO include ensuring the organization is able to both manage compliance risk and pass a compliance audit . The exact nature of a compliance audit will vary, depending on factors such as the organization's industry, whether it is a public or private company, and the nature of the data it creates, collects and stores. Other responsibilities of a CCO include identifying the potential risks an organization faces, assessing the effectiveness of any risk-prevention processes and resolving any compliance issues.

Other possible compliance roles include the following:

• Compliance analysts. Compliance analysts help organizations remain compliant with regulations and prepare them for audits.
• Compliance services associates. This role focuses on identifying, prioritizing and resolving issues for clients.
• Compliance coordinator. This role focuses on preparing and completing regulatory and compliance documents, as well as making sure they adhere to federal, state and government requirements.
• Compliance director. This role focuses on ensuring organizations conform to all rules, regulations and laws placed upon them. They are also responsible for managing and correcting any violations that occur.

Best practices and strategies for corporate compliance

To ensure an organization follows compliance laws or regulations, they should follow these best practices:

• Determine compliance goals. Focus on the areas of compliance the organization needs to improve the most, such as a specific regulation, law or a violation that is costing the organization money.
• Know the regulatory environment. Laws and regulations may change over time, so having staff members -- either as a part of a compliance department or otherwise -- who keep up to date on new regulations relevant to the organization's industry is a good idea.
• Implement compliance tools. Compliance tools can automatically track data, aiding in compliance risk management.
• Hold compliance audits. An in-depth review of regulatory compliance areas ensures an organization is following compliance regulations correctly and can help identify areas an organization needs to improve.
• Review compliance regulations regularly. A regular review helps find weak points and gives an organization a chance to improve and keep its compliance efforts up to date.
• Train employees for compliance policy. If employees cannot follow compliance policies, then the organization cannot fully adhere to the policies. Employees should be trained and made aware of relevant policies and be held accountable when policies are not followed.

Learn more about compliance and its related security concerns in this article.

Continue Reading About compliance

• How compliance provides stakeholders evidence of success
• How can a compliance strategy improve customer trust?
• Data protection compliance costs less than noncompliance
• Top five threats to compliance during the pandemic
• Binance CEO says 'compliance is a journey' as world's largest crypto exchange faces growing crackdown

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This paper is in the following e-collection/theme issue:

Published on 10.6.2024 in Vol 26 (2024)

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

Authors of this article:

Original Paper

• Caroline Stellmach 1 , MSc   ; 
• Sina Marie Hopff 2 , Dr med   ; 
• Thomas Jaenisch 3 , Dr med, PhD   ; 
• Susana Marina Nunes de Miranda 2 , Dr rer nat   ; 
• Eugenia Rinaldi 1 , MSc   ; 
• The NAPKON, LEOSS, ORCHESTRA, and ReCoDID Working Groups 4

1 Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany

2 Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Department I of Internal Medicine, University Hospital Cologne and Faculty of Medicine, University of Cologne, Cologne, Germany

3 Heidelberg Institut für Global Health, Universitätsklinikum Heidelberg, Heidelberg, Germany

4 See Acknowledgments

Corresponding Author:

Caroline Stellmach, MSc

Berlin Institute of Health

Charité - Universitätsmedizin Berlin

Anna-Louisa-Karsch-Str 2

Berlin, 10178

Phone: 49 15752614677

Email: [email protected]

Background: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.

Objective: This study’s objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.

Methods: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.

Results: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.

Conclusions: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element’s (variable’s) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by “wrapping” them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium’s Clinical Data Acquisition Standards Harmonization Model.

Introduction

In response to the spread of SARS-CoV-2 starting in late 2019, large-scale observational studies as well as clinical trials have been launched worldwide to gain insights into disease patterns, treatment options, prevention measures, severity, and outcomes [ 1 ]. New findings related to the diagnosis, prevention, and treatment of many infectious diseases, including COVID-19, heavily rely on data generated by diagnostic tools and laboratory analysis of the pathogen and host response [ 2 ].

Immunological testing has become a cost- and time-efficient way to monitor infections [ 3 ]. Hence, a growing number of clinical studies include biosample information as part of their data collection targets, particularly results of analytical tests performed on blood samples [ 4 ].

Data from patients enrolled in a study are commonly collected using a case report form (CRF) [ 5 ]. The International Conference on Harmonization Guidelines for Good Clinical Practice defines a CRF as a “printed, optical or electronic document designed to record all of the protocol-required information to be reported to the sponsor on each trial subject” [ 6 ]. Since the design of a CRF can affect study outcomes, time and resources need to be invested to maximize the quality of the data collected and ensure that good clinical practice guidelines are being followed [ 7 ].

The identification of common data elements (CDEs), each comprising 1 or more questions and respective answer value sets, is an approach to standardize data collection instruments (ie, CRFs) across studies [ 8 ]. A CDE may also contain standardized ontology concepts directly or include a link to the unique identifier for an appropriate ontology concept [ 9 ].

We have previously described [ 10 ] how incorporating standard codes into clinical trials metadata can increase their findability, accessibility, interoperability, and reusability (FAIR)ness [ 11 ]. The FAIR principles are recognized internationally as important guides to conducting research [ 12 ]. Interoperability, in particular, is defined as the ability of several systems to exchange information, as well as read and use the received information without requiring further preprocessing [ 13 ]. Although there are several levels of interoperability [ 14 ], the focus of this study in the context of health care data was on semantic (use of standard terminologies and classifications) and syntactic (implementation of a standard exchange format) interoperability.

The use of data standards when designing CRFs can serve multiple purposes: in addition to supporting data quality, it facilitates the merging and exchange of data from multiple sources, as well as subsequent analysis [ 5 ]. International standards development organizations (SDOs), such as Health Level Seven (HL7) or Integrating the Healthcare Enterprise (IHE), promote and coordinate the use of these standards [ 15 ]. HL7 has developed the exchange standard Fast Healthcare Interoperability Resource (FHIR), which allows for the exchange of health-related information based on packaging it into so-called resources. The FHIR can represent a wide range of data, particularly those generated in care settings [ 16 ]. In comparison, the Clinical Data Interchange Standards Consortium (CDISC) has published standards for the representation of CRF data used in clinical trials [ 17 ].

By mapping study data elements to international semantic standard codes, the included concepts receive an unambiguous definition that is tied to an identifier that makes it machine-readable [ 18 ]. Among the widely used terminologies and classifications for health care concepts are the Logical Observation Identifiers Names and Codes (LOINC) and the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT). The National Cancer Institute Thesaurus (NCIt) is also available as a reference terminology focused, among others, on translational research and clinical care information [ 19 ]. LOINC provides standard codes (each comprising a set of an identifier, a name, and a code) for laboratory observations, documents, and questionnaires [ 20 ]. SNOMED CT covers a broad range of health care information, and each of its concepts has a unique identifier and is defined by a description and 1 or more relationships [ 21 ].

In this study, we set out to analyze CRF variables from 6 study protocols capturing information about diagnostic testing with the purpose of identifying CDEs specific to infectious diseases. The selected studies investigated 3 different infectious diseases in humans: COVID-19 [ 1 ], monkeypox (mpox) [ 22 ], and Zika [ 23 ]. The CRF variables we included originate from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 Core CRF [ 24 ], as well as from 3 of the many international research projects focused on gaining new insights into SARS-CoV-2: the ORCHESTRA project [ 25 ], the Intersectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON SUEP) study [ 26 ], and the Lean European Open Survey on SARS-CoV‑2 (LEOSS) [ 27 ] study. Additionally, we analyzed the World Health Organization (WHO) CRF on the mpox infection [ 28 ] and the Zika CRFs of ZIKAlliance [ 29 ].

Our goal of proposing standardized paninfectious disease CRF variables for diagnostic testing information for use in CRFs was broken down into 3 subtasks: (1) identification of interstudy CDEs, (2) creation of a preliminary map of the CDEs to semantic standard codes, and (3) development of a proposed mapping of the CDEs to the FHIR syntax standards [ 30 ] and the CDISC’s standards for data collection [ 17 ].

Ethical Considerations

Since only CRF metadata (meaning definitions of questions and answers used to comprise CRFs) were used and no actual patient data were reviewed in this study, ethics approval was not required.

Study Design

Figure 1 provides a graphical overview of the steps we followed to create a standardized set of variables for use in data collection instruments in infectious disease studies focusing on diagnostic testing.

We examined 6 CRFs provided to us by 4 research consortia, and we downloaded the publicly available CRFs from the ISARIC and WHO websites [ 24 ]. We proceeded to extract diagnostic testing variables from each CRF and organized them for analysis and comparison in a Microsoft Excel sheet.

The following CRFs were included:

• ORCHESTRA work package 6 CRF [ 31 ]
• Cross-sectoral platform (SUEP) CRF of NAPKON [ 32 ]
• LEOSS study [ 27 , 33 ] electronic case report form (eCRF)
• ISARIC-WHO COVID-19 core CRF [ 24 ]
• Zika study CRF
• Mpox study CRF [ 28 ]

We translated the variables from the NAPKON SUEP study from German into English to harmonize it with the language of the other selected studies (English). The study manager verified the translation.

Common Data Elements

In the first step of analyzing the study metadata, we reviewed all CRF variables (questions and answers). Adopting the National Institutes of Health’s methodology to derive CDEs [ 34 ], we created common categories to group variables based on the key information they contained. We then reviewed the newly organized variables to determine which categories were present in at least 2 (33%) of the 6 CRFs. These common variables then formed the basis as newly identified CDEs for infectious diseases.

For each of these preliminary CDEs, the extensive value set (sum of all unique answers) across all reviewed CRFs was determined. If necessary, we created value set subsets based on informational content and pathogen type.

Mapping to Standards

Each CDE (question and value set) was then mapped to the appropriate semantic standard code(s) and FHIR element(s). We searched for available terminology codes using the NCIt browser (version 23.02d, release date February 27, 2023), the SearchLOINC tool (v2.26), and the SNOMED CT browser (version 2023-03-31). If no semantic standard code was found, we prepared a submission to request the creation of a new code, depending on the informational domain, with NCIt, SNOMED CT, or LOINC.

CRF Analysis

The analysis of the CRFs used in 6 infectious disease studies led to the identification of 216 variables focusing on diagnostic testing, which were in the scope of further analysis: 103 (47.7%) from ORCHESTRA, 51 (23.6%) from NAPKON SUEP, 27 (12.5%) from the Zika study, 16 (7.4%) from the ISARIC CRF, 13 (6%) from the LEOSS survey, and 6 (2.8%) from the mpox study (Table S1 in Multimedia Appendix 1 [ 25 , 28 , 32 , 35 - 42 ]). These diagnostic testing variables could be grouped into 22 newly defined categories, which are shown in Table S2 in Multimedia Appendix 1 .

Based on the analysis of the 6 CRFs, we identified 11 CDEs, each of which was present in at least 2 (33%) of the 6 reviewed data collection instruments and reflected diagnostic testing information applicable to infectious disease studies. We mapped these CDEs to semantic standard codes and FHIR resources (illustrated in Figure 2 ), as well as to the CDISC ( Multimedia Appendix 2 ).

Viral Lineage/Clade

The first CDE was defined as “viral lineage” or “viral clade.” Depending on the virus investigated, its value sets would vary to reflect the applicable clade and lineage details, as exemplified in Figure 3 .

Genetic diversity, as described in a phylogenetic tree, is classified by clades. A clade, also called genotype or subtype, comprises a set of lineages that are all descended from only 1 ancestor, common to them [ 43 ].

ORCHESTRA and the human mpox study contained 3 (1.4%) variables providing monkeypox virus (MPXV) and SARS-CoV-2 clade details. In addition, viral lineage information was collected from ORCHESTRA, the ISARIC CRF, and NAPKON SUEP across 4 (1.9%) variables.

There is no uniform convention for naming viral clades and lineages. In the case of SARS-CoV-2, the most widely used nomenclatures for subtypes are provided by the Global Initiative on Sharing All Influenza Data [ 44 ], Rambault et al [ 43 ], and Nextstrain [ 45 ], which differ in the position at which clades are differentiated from one another.

Specimen Identifier, Specimen Collection Date, and Specimen Type

Our analysis led to the identification of “specimen identifier,” “specimen collection date,” and “specimen type” as additional CDEs across the 6 studies. ORCHESTRA and the Zika and mpox studies included 6 (2.8%) variables that were grouped as “specimen identifiers” and had a free-text format. Any biological specimen (ie, blood, urine, cerebrospinal fluid, feces) used for laboratory analysis must be uniquely identified so that the resulting findings are associated with the right patient. Identifiers might contain a patient’s first and last names, birth date, medical facility number, or a unique, randomly generated code [ 46 ]. In addition to this internal laboratory-based specimen identifier, a particular specimen might have a second, external identifier that is assigned when results based on the analysis of said specimen are uploaded to a public/restricted databases or to a biobank [ 47 ].

Furthermore, 8 (3.7%) variables across the Zika study, NAPKON SUEP, ORCHESTRA, and ISARIC CRFs constituted the data element “specimen collection date,” requiring the input of a date format (mm/dd/yyyy). The specimen collection date marks the date on which a specimen was collected from a patient and placed in a specimen container for ensuing processing and analysis.

Details about the kind of specimen collected and used for analysis are provided by the coded “specimen type” CDE. All 6 reviewed studies included the data element “specimen type” in their variables. Our analysis led to the finding that there tended to be 2 axes involved in the value set elements of the specimen type, which covered information about the method used to collect the specimen (ie, swab) and the site of origin (ie, skin lesion). Examples are shown in Multimedia Appendix 3 .

Test Date and Test Performer

The CDEs “test date” and “test performer” included variables from the ORCHESTRA and Zika study CRFs and the ORCHESTRA and NAPKON SUEP CRFs, respectively. The test date refers to the calendar date on which a particular laboratory diagnostic test (specified by the CDE “test type”) was conducted. The “test performer” CDE captures the full name of the individual(s) executing this diagnostic test in free-text format.

Test Type, Target Gene, and Test Manufacturer

The coded CDE “test type” captures a specific laboratory test, which in this context would fall into 3 main categories: serology, sequencing, and polymerase chain reaction (PCR) analysis. All 6 reviewed CRFs included variables providing details about diagnostic tests. For serology tests, the test type in the analyzed SARS-CoV-2 studies provided details on the method, along with the analyzed target, whereas in the Zika study, only the target was given ( Multimedia Appendix 4 ).

In the context of COVID-19 research, lateral flow testing, immunofluorescence assay (IFA), enzyme-linked immunosorbent assay (ELISA), and chemiluminescence immunoassay (CLIA) are frequently used methods for the diagnosis of infections [ 48 ]. The detection of Zika and mpox infections is usually also based on serology, specifically ELISA-based antibody measurements [ 23 ].

The coded CDE “target gene” grouped 8 (3.7%) variables across the NAPKON SUEP, LEOSS, and ORCHESTRA CRFs. It refers to the target of a genome-focused diagnostic test, such as PCR or a sequencing method. Using primers that contain bases that are complementary to a conserved sequence within the target gene of a particular virus, this sequence, if present in the biological sample, is amplified and can be detected through PCR [ 49 ].

In total, 13 (6%) variables used across the Zika, NAPKON SUEP, and ORCHESTRA CRFs were grouped into the coded CDE “test manufacturer.” This data element provides information about the manufacturer of the diagnostic test (ie, kit or testing system). For example, the following PCR systems (manufacturers) were mentioned in a study variable in the NAPKON SUEP CRF: Seegene (Allplex) [ 50 ], altona Diagnostics (RealStar) [ 51 ], and Roche Deutschland Holding (cobas) [ 52 ].

Qualitative and Quantitative Results

All reported results of diagnostic testing covered by variables in the 6 CRFs we reviewed could be clustered into either qualitative or quantitative results, and thus, they formed the last 2 (18%) of 11 coded CDEs that we identified. A qualitative result details the findings about the presence or absence of a measured observable, such as virus-specific antibody or gene material. In contrast, a quantitative result constitutes numeric measurements (see Table S3 in Multimedia Appendix 1 ). In the studies that we analyzed, those numeric values were given for the titer, cycle threshold, and concentration of the same observables mentioned before.

Semantic Standards

To facilitate semantic interoperability of the proposed diagnostic testing CDEs, we suggested mapping each CDE and respective value set to the terminology standards SNOMED CT, LOINC, and NCIt. For each CDE, we created a suggested mapping that covers the variable itself and a nonexclusive list of possible value set elements (Table S4 in Multimedia Appendix 1 ).

The CDEs “viral lineage” and “viral clade” could be mapped to the following NCIt codes (code and description are shown), respectively: “C60792 Lineage” and “C179767 Clade.” Depending on the analyzed virus, the value sets (answers) could differ and be represented through mapping to either NCIt or LOINC codes. For example, in the case of detection of the SARS-CoV-2 variant B.1.1.7, the NCIt code “C179573 SARS Coronavirus 2 B.1.1.7” or the LOINC code “LA31705-9 SARS-CoV-2 B.1.1.7 lineage” is available.

The CDE “specimen identifier” could be represented in a standardized way using SNOMED CT, LOINC, and NCIt terms, as shown in Table S4 in Multimedia Appendix 1 . Likewise, codes from all 3 standards were available to represent the free-text CDEs “specimen collection date” and “specimen type.”

There are semantic standard concepts available to describe the “test date” and “test performer” CDEs. Using SNOMED CT codes from the “procedure” hierarchy or using NCIt terms, diagnostic test types, such as serology assays, sequencing, and PCR, can be described in a standardized manner. Incidentally, there are a few standard codes available to represent the value sets for “target gene” (for the envelope gene in SNOMED CT and a few in the NCIt), although not necessarily specifically meant to map viral pathogens’ genes (exception in the NCIt: “C19108 Viral Envelope Gene”). Thus, we prepared a submission to the NCIt for the creation of concepts that cover the prominently analyzed SARS-CoV-2 [ 53 ] and Zika virus (ZIKV) genes [ 54 ]. We submitted 33 concepts for code creation to the SDOs LOINC and NCIt (Table S5 in Multimedia Appendix 1 ).

No SNOMED CT codes were available to describe the value set elements for the “test manufacturer” CDE. However, both the NCIt and LOINC provide terms for this purpose; the NCIt has created concepts for specific COVID-19 diagnostic kits, detailing the manufacturer, analytical target, and method. Likewise, LOINC has created codes that bundle several kits into a single term, such as “94558-4 SARS-CoV-2 (COVID-19) Ag [Presence] in Respiratory specimen by Rapid immunoassay,” which represents 4 commercially available kits [ 55 ].

There are generic semantic terms from SNOMED CT and the NCIt to describe the “quantitative result” and “qualitative result” CDEs in a standardized manner, which can be used across viral pathogen studies, such as “Laboratory Test Result” or just “Result.” However, this would omit the distinction between “qualitative” and “quantitative.”

LOINC provides a comprehensive list of terms to describe qualitative results of laboratory diagnostic tests for SARS-CoV-2 and antibody measurements specific to ZIKV.

The use of the SNOMED CT terminology requires a country (or institutional) license. SNOMED International has, however, been releasing its Global Patient Set containing currently around 24,000 concepts, which can be used free of charge [ 56 ]. Of the 90 SNOMED CT codes, 33 (37%) that we included in the exemplary value set mappings for our proposed infectious disease diagnostic CDEs are covered by the Global Patient Set.

Syntax Standard

We proposed a preliminary mapping of the diagnostic testing CDEs to FHIR (version R4) elements as a first step toward establishing syntactical interoperability ( Figure 2 , right). Of the 11 CDEs that we identified, 8 (72.7%) were mapped to the Observation resource and the remaining 3 (27.3%) to the Specimen resource.

Additionally, we provided a preliminary suggested mapping of the FHIR elements to the CDISC according to the FHIR to CDISC Joint Mapping Implementation Guide v1.0 [ 57 ] (see Multimedia Appendix 2 ).

Principal Findings

Resulting from the review of 6 CRFs, we identified 11 panstudy CDEs that capture key diagnostic testing information commonly collected across the reviewed infectious disease studies. These CDEs were purposefully kept generic to enhance the probability that they could be adopted by researchers and integrated into data collection instruments of other infectious disease studies, even if a different pathogen was studied. The pathogen under investigation in a given study would determine the value set elements of CDEs of the coded data.

The CDEs “viral lineage” and “viral clade” provide the means to describe genetic relatedness of viruses, which is critical to pathogen surveillance and relies on the availability of well-defined nomenclature [ 58 ]. Currently, no panvirus approach to naming viral clades and lineages exists. The International Committee on Taxonomy of Viruses, founded in 1966, has the goal to develop a taxonomy for viruses and establish names for viral taxa based on international agreement. However, the International Committee on Taxonomy of Viruses does not address the naming of viral clades and lineages [ 59 ]. In the context of ensuring that diagnostic testing results are linked to the right sample (specimen) and patient, the CDEs “specimen identifier,” “specimen collection date,” and “specimen type” are important parameters. Regarding the diagnostic test itself, documentation of the CDEs “test date” and “test performer” can help identify quality problems retrospectively. Diagnostic testing results can be split into the CDEs “qualitative result” and “quantitative result,” which would confirm the presence/absence of signs of a pathogen or numeric values of measured observables, such as antibody titers. The CDEs “test type,” “target gene,” and “test manufacturer” provide all complementary details to the diagnostic tests conducted. Along with the increasing inclusion of molecular testing variables in the study of infectious diseases, we expect that this number of recurring elements (which would be candidate CDEs) that describe diagnostic tests across different studies will continue to grow.

The power of research findings can be expanded through combining data from several clinical studies for analysis in an effort to create a larger data set. Without considering privacy or legal considerations, the basis for merging data from different sources is that the correct information (ie, data variables) is linked together to ensure accuracy and avoid misinterpretation. Defining standardized CDEs that serve as a common language across clinical studies is one way to approach this challenge [ 9 ]. Lin et al [ 5 ] described a similar approach of how CRF design can be optimized for data harmonization by creating a pool of reusable CDEs. There are numerous examples for the creation of CDEs for specific medical specialties and use cases, such as stroke trials [ 60 ], pregnancy pharmacovigilance [ 61 ], and COVID-19 [ 62 ]. This includes a set of CDEs on the quality of life in neurological disorders, as well as the PhenX Toolkit to capture key information on phenotypes [ 8 ].

To facilitate interoperability of study data in particular, we proposed a mapping of the identified CDEs to semantic and syntactic standards. We also created a table with practical examples of available standard codes to identify value set concepts ambiguously for variables contained in CRFs from studies focused on SARS-CoV-2, ZIKV, and MPXV (see Table S4 in Multimedia Appendix 1 ).

In the past, we have described how semantic interoperability standard codes can be integrated directly into the study metadata to facilitate merging, sharing, and analysis of patient data that are being collected across several clinical studies and cohort types, where several methods for data storage and collection have been used [ 10 ]. Kush et al [ 9 ] and Kersloot et al [ 11 ], among others, have discussed the advantage of introducing interoperability standards prior to data collection rather than retrospectively with the aim to save time and other resources.

An important aspect of mapping study data to semantic standard concepts is choosing appropriate terminology. Although there is no universal guidance for this process, we can draw instructive conclusions from our attempt to propose a mapping for the CDEs we identified for which we searched within the LOINC, SNOMED CT and NCIt, terminologies.

The selection of semantic standards to represent CDEs and their value sets depends on the way the CDEs (and underlying CRF variables) are phrased with regard to the level of detail and the kind of information that are described. The category of information covered by a CRF variable is the first “filter” for finding the appropriate terminology. The NCIt, which is managed by the National Cancer Institute, focuses on providing a vocabulary for the cancer domain [ 63 ]; hence, it comprises many (gen)omics-related terms. Each NCIt term is represented by a code and a name and has several annotations [ 64 ].

In contrast, the LOINC coding system, which is published by the Regenstrief Institute, is used by numerous large laboratories and government agencies, such as the Centers for Disease Control and Prevention, to describe laboratory and clinical findings, as well as documents [ 65 ]. Although LOINC has a clear focus on representing laboratory terms, SNOMED CT terms have a broader coverage of information and are commonly used to represent clinical information in electronic health records [ 63 ]. SNOMED CT and th eNCIt both provide concepts that are suitable to describe variables and value sets if they are kept more generic in their wording. LOINC terms, in contrast, are specific and should only be used to represent questions, not value sets. Contrary to the NCIt, SNOMED CT comprises a limited set of concepts to describe genomic methods and results.

Unlike the use of LOINC and the NCIt, embedding SNOMED CT concepts into the metadata of research data requires a license. In recent years, many countries have purchased a SNOMED CT affiliate license or become a SNOMED CT member, including Germany, Spain, and Portugal [ 66 ].

The LOINC coding system includes suitable codes for several of the CDEs we defined. For example, we chose the concept “95609-4 SARS-CoV-2 (COVID-19) S gene [Presence] in Respiratory specimen by Sequencing” as 1 of the available standard terms for coding the “qualitative result” CDE. However, it also covers the “target gene” (S gene), “specimen type” (respiratory specimen), and “test type” (sequencing) CDEs. Another aspect that should be kept in mind, especially concerning selecting standard terms for the “quantitative result” CDE when used in a CRF, is that the units of the result should be clearly defined and match those of the standard term. Although each LOINC term has a defined unit, SNOMED CT concepts do not necessarily implicitly or explicitly define units. The concept “1240461000000109 Measurement of severe acute respiratory syndrome coronavirus 2 antibody (observable entity)” has no unit of measure attached and hence can be used if a CRF variable can be measured using several different units. A standard way to describe units is offered by the Unified Code for Units of Measure [ 67 ].

Regarding finding the appropriate standard code for viral lineage, the more general-purpose terminology of SNOMED CT does not include the required level of detail for this CDE, which is captured in the NCIt. However, the list of microorganisms defined as concepts by SNOMED CT under the hierarchy “organism” is detailed and can be used to describe a pathogen. The hierarchical organization of SNOMED CT, which also includes sublevels of concepts, provides a clear idea of the positioning of any microorganism within the complex classification of organisms overall.

As knowledge rapidly evolves in health care, missing concepts are regularly added to ontologies. The process involves concept creation requests from the public, which are submitted to the SDOs. Zheng et al [ 63 ] describe an approach of using formal concept analysis to identify missing concepts in the NCIt and SNOMED CT.

We also proposed a mapping of the 11 diagnostic testing CDEs to the corresponding FHIR (version R4) element. This provides data with a standardized exchange format, which can incorporate standard terminologies. Elements in the Specimen [ 68 ] and Observation [ 69 ] (and for the test manufacturer, also Device [ 70 ]) resources can be used to represent all 11 CDEs.

Limitations

The identified CDEs focus on diagnostic tests used in infectious disease studies. Additional CDEs that would fall into other informational categories (eg, therapeutics or comorbidities) were not considered as they were out of the scope of our study. Furthermore, since the reviewed ORCHESTRA variables include CRF variables from several COVID-19 studies, the selection of protocols might appear unbalanced.

The need to investigate COVID-19 quickly and extensively has made the pool of available variables describing diagnostic tests particularly abundant. Kush et al [ 9 ] point out that although the name “CDE” implies that these elements are common, they are not so commonly used. This is due to a lack of mandatory requirements for their use [ 9 ]. A necessary step to increase the adoption and value of CDEs would be that funding bodies (eg, the National Institutes of Health or the European Commission) in collaboration with SDOs create and impose mandatory requirements for the implementation of existent CDEs on recipients of project funding.

Acknowledgments

This material contains content from Logical Observation Identifiers Names and Codes (LOINC) [ 71 ]. LOINC is the copyright of Regenstrief Institute, Inc, and the LOINC Committee and is available at no cost under the license [ 72 ]. LOINC is a registered United States trademark of Regenstrief Institute, Inc.

This material also includes content from the National Cancer Institute Thesaurus, published by the National Cancer Institute [ 19 ].

The Systemized Nomenclature of Medicine - Clinical Terms (SNOMED CT) was used by the permission of SNOMED International. SNOMED CT was originally created by the College of American Pathologists. “SNOMED,” “SNOMED CT,” and “SNOMED Clinical Terms” are registered trademarks of SNOMED International [ 73 ].

We would like to thank all 3 SDOs for their collaboration and support with new term submissions and SNOMED International, in particular, for granting us permission to display and share the suggested terminology bindings.

The ORCHESTRA project received funding from the European Union’s Horizon 2020 Research and Innovation Program (grant agreement 101016167). The ZIKAlliance project received funding from the European Union’s Horizon 2020 Research and Innovation Program (grant agreement 734548). The ReCODID project received funding from the European Union’s Horizon 2020 Research and Innovation Program (grant agreement 825746). The Lean European Open Survey on SARS-CoV‑2 (LEOSS) registry was supported by the German Centre for Infection Research (DZIF) and the Willy Robert Pitzer Foundation. The National Pandemic Cohort Network (NAPKON) is part of the Network University Medicine and was funded by the German Federal Ministry of Education and Research (FKZ: 01KX2021). Parts of the infrastructure of the Würzburg study site were supported by the Bavarian Ministry of Research and Art to support coronavirus research projects. Parts of the NAPKON project suite and study protocols of the cross-sectoral cohort platform are based on projects funded by the DZIF.

The members of the working groups are as follows: NAPKON Working Group: Gabriele Anton, Katharina Appel, Sabine Blaschke, Isabel Bröhl, Johanna Erber, Karin Fiedler, Ramsia Geisler, Peter U. Heuschmann, Thomas Illig, Monika Kraus, Dagmar Krefting, Jens-Peter Reese, Margarete Scherer, Jörg Janne Vehreschild, Maria J.G.T. Vehreschild, and Luise Wolf. ORCHESTRA Working Group: Chiara Dellacasa, Miroslav Puskaric, Thomas Osmo, Elisa Rossi, and Anna Gorska. LEOSS Working Group: Jörg Janne Vehreschild, Carolin E. M. Koll, Margarete Scherer, and Maria J.G.T. Vehreschild. ReCoDID Working Group: Lauren Maxwell, Heather Hufstedler, and Frank Tobian.

Data Availability

The analyzed case report forms are stored and available in Excel format for review on the project’s online data repository [ 74 ].

Authors' Contributions

CS and ER created the first draft of the manuscript, together with SMH, SMNdM, and TJ. All authors reviewed the draft, commented on it, and provided revisions, and they have approved the final version of the manuscript.

Conflicts of Interest

None declared.

Supplementary tables.

Map between the proposed FHIR representation (FHIR resources) and the corresponding CDISC elements for the 11 identified diagnostic testing CDEs for infectious disease studies. CDE: common data element; CDISC: Clinical Data Interchange Standards Consortium; FHIR: Fast Healthcare Interoperability Resources.

Example specimen types listed within CRF variables in the analyzed infectious disease studies. CRF: case report form.

Example variables (question and value set) from 2 of the reviewed CRFs. One came from the ZIKV study and the other from the NAPKON SUEP study. CLIA: chemiluminescence immunoassay; CRF: case report form; ELISA: enzyme-linked immunosorbent assay; IFA: immunofluorescence assay; IgA: immunoglobulin A; IgG: immunoglobulin G; IgM: immunoglobulin M; LFT: lateral flow immunoassay; PCR: polymerase chain reaction; SGTF: S gene target failure; VNTR: variable number of tandem repeats; WES: whole exome sequencing; WGS: whole genome sequencing; ZIKV: Zika virus.

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Abbreviations

Edited by A Mavragani; submitted 19.06.23; peer-reviewed by K Ndlovu, S Hume; comments to author 19.09.23; revised version received 10.10.23; accepted 18.01.24; published 10.06.24.

©Caroline Stellmach, Sina Marie Hopff, Thomas Jaenisch, Susana Marina Nunes de Miranda, Eugenia Rinaldi, The NAPKON, LEOSS, ORCHESTRA, and ReCoDID Working Groups. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

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• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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