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Primary Data – Types, Methods and Examples

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Primary Data

Definition:

Primary Data refers to data that is collected firsthand by a researcher or a team of researchers for a specific research project or purpose. It is original information that has not been previously published or analyzed, and it is gathered directly from the source or through the use of data collection methods such as surveys, interviews, observations, and experiments.

Types of Primary Data

Types of Primary Data are as follows:

Surveys are one of the most common types of primary data collection methods. They involve asking a set of standardized questions to a sample of individuals or organizations, usually through a questionnaire or an online form.

Interviews involve asking open-ended or structured questions to a sample of individuals or groups in person, over the phone, or through video conferencing. They can be conducted in a one-on-one setting or in a focus group.

Observations

Observations involve systematically recording the behavior or activities of individuals or groups in a natural or controlled setting. This type of data collection is often used in fields such as anthropology, sociology, and psychology.

Experiments

Experiments involve manipulating one or more variables and observing the effects on an outcome of interest. They are commonly used in scientific research to establish cause-and-effect relationships.

Case studies

Case studies involve in-depth analysis of a particular individual, group, or organization. They typically involve collecting a variety of data, including interviews, observations, and documents.

Action research

Action research involves collecting data to improve a specific practice or process within an organization or community. It often involves collaboration between researchers and practitioners.

Formats of Primary Data

Some common formats for primary data collection include:

• Textual data : This includes written responses to surveys or interviews, as well as written notes from observations.
• Numeric data: Numeric data includes data collected through structured surveys or experiments, such as ratings, rankings, or test scores.
• Audio data : Audio data includes recordings of interviews, focus groups, or other discussions.
• Visual data: Visual data includes photographs or videos of events, behaviors, or phenomena being studied.
• Sensor data: Sensor data includes data collected through electronic sensors, such as temperature readings, GPS data, or motion data.
• Biological data : Biological data includes data collected through biological samples, such as blood, urine, or tissue samples.

Primary Data Analysis Methods

There are several methods that can be used to analyze primary data collected from research, including:

• Descriptive statistics: Descriptive statistics involve summarizing and describing the characteristics of the data collected, such as mean, median, mode, and standard deviation.
• Inferential statistics: Inferential statistics involve making inferences about a population based on a sample of data. This can include techniques such as hypothesis testing and confidence intervals.
• Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as textual data from interviews or observations, to identify themes, patterns, or trends.
• Content analysis: Content analysis involves analyzing textual data to identify and categorize specific words or phrases, allowing researchers to identify themes or patterns in the data.
• Coding : Coding involves categorizing data into specific categories or themes, allowing researchers to identify patterns and relationships in the data.
• Data visualization : Data visualization involves creating graphs, charts, and other visual representations of data to help researchers identify patterns and relationships in the data.

Primary Data Gathering Guide

Here are some general steps to guide you in gathering primary data:

• Define your research question or problem: Clearly define the purpose of your research and the specific questions you want to answer.
• Determine the data collection method : Decide which primary data collection method(s) will be most appropriate to answer your research question or problem.
• Develop a data collection instrument : If you are using surveys or interviews, create a structured questionnaire or interview guide to ensure that you ask the same questions of all participants.
• Identify your target population : Identify the group of individuals or organizations that will provide the data you need to answer your research question or problem.
• Recruit participants: Use various methods to recruit participants, such as email, social media, or advertising.
• Collect the data : Conduct your survey, interview, observation, or experiment, ensuring that you follow your data collection instrument.
• Verify the data : Check the data for completeness, accuracy, and consistency. Resolve any missing data or errors.
• Analyze the data: Use appropriate statistical or qualitative analysis techniques to interpret the data.
• Draw conclusions: Use the results of your analysis to answer your research question or problem.
• Communicate your findings : Share your results through a written report, presentation, or publication.

Examples of Primary Data

Some real-time examples of primary data are:

• Customer surveys: When a company collects data through surveys or questionnaires, they are gathering primary data. For example, a restaurant might ask customers to rate their dining experience.
• Market research : Companies may conduct primary research to understand consumer trends or market demand. For instance, a company might conduct interviews or focus groups to gather information about consumer preferences.
• Scientific experiments: Scientists may gather primary data through experiments, such as observing the behavior of animals or testing new drugs on human subjects.
• Traffic counts: Traffic engineers might collect primary data by monitoring the flow of cars on a particular road to determine how to improve traffic flow.
• Consumer behavior : Companies may use primary data to track consumer behavior, such as how customers use a product or interact with a website.
• Social media analytics : Companies can collect primary data by analyzing social media metrics such as likes, comments, and shares to understand how their customers are engaging with their brand.

Applications of Primary Data

Primary data is useful in a wide range of applications, including research, business, and government. Here are some specific applications of primary data:

• Research : Primary data is essential for conducting scientific research, such as in fields like psychology, sociology, and biology. Researchers collect primary data through experiments, surveys, and observations.
• Marketing : Companies use primary data to understand customer needs and preferences, track consumer behavior, and develop marketing strategies. This data is typically collected through surveys, focus groups, and other market research methods.
• Business planning : Primary data can inform business decisions such as product development, pricing strategies, and expansion plans. For example, a company may gather primary data on the buying habits of its customers to decide what products to offer and how to price them.
• Public policy: Primary data is used by government agencies to develop and evaluate public policies. For example, a city government might use primary data on traffic patterns to decide where to build new roads or improve public transportation.
• Education : Primary data is used in education to evaluate student performance, identify areas of need, and develop curriculum. Teachers may gather primary data through assessments, observations, and surveys to improve their teaching methods and help students succeed.
• Healthcare : Primary data is used by healthcare professionals to diagnose and treat illnesses, track patient outcomes, and develop new treatments. Doctors and researchers collect primary data through medical tests, clinical trials, and patient surveys.
• Environmental management: Primary data is used to monitor and manage natural resources and the environment. For example, scientists and environmental managers collect primary data on water quality, air quality, and biodiversity to develop policies and programs aimed at protecting the environment.
• Product testing: Companies use primary data to test new products before they are released to the market. This data is collected through surveys, focus groups, and product testing sessions to evaluate the effectiveness and appeal of the product.
• Crime prevention : Primary data is used by law enforcement agencies to identify crime hotspots, track criminal activity, and develop crime prevention strategies. Police departments may collect primary data through crime reports, surveys, and community meetings to better understand the needs and concerns of the community.
• Disaster response: Primary data is used by emergency responders and disaster management agencies to assess the impact of disasters and develop response plans. This data is collected through surveys, interviews, and observations to identify the needs of affected populations and allocate resources accordingly.

Purpose of Primary Data

The purpose of primary data is to gather information directly from the source, without relying on secondary sources or pre-existing data. This data is collected through research methods such as surveys, interviews, experiments, and observations. Primary data is valuable because it is tailored to the specific research question or problem at hand and is collected with a specific purpose in mind. Some of the main purposes of primary data include:

• To answer research questions: Researchers use primary data to answer specific research questions, such as understanding consumer preferences, evaluating the effectiveness of a program, or testing a hypothesis.
• To gather original information : Primary data provides new and original information that is not available from other sources. This data can be used to make informed decisions, develop new products, or design new programs.
• To tailor research methods: Primary data collection methods can be customized to fit the research question or problem. This allows researchers to gather the most relevant and accurate information possible.
• To control the quality of data: Researchers have greater control over the quality of primary data, as they can design and implement the data collection methods themselves. This reduces the risk of errors or biases that may be present in secondary data sources.
• To address specific populations : Primary data can be collected from specific populations, such as customers, patients, or students. This allows researchers to gather data that is directly relevant to their research question or problem.

When to use Primary Data

Primary data should be used when the specific information required for a research question or problem cannot be obtained from existing data sources. Here are some situations where primary data would be appropriate to use:

• When no secondary data is available: Primary data should be collected when there is no existing data available that addresses the research question or problem.
• When the available secondary data is not relevant: Existing secondary data may not be specific or relevant enough to address the research question or problem at hand.
• When the research requires specific information : Primary data collection allows researchers to gather information that is tailored to their specific research question or problem.
• When the research requires a specific population: Primary data can be collected from specific populations, such as customers, patients, or employees, to provide more targeted and relevant information.
• When the research requires control over the data collection process: Primary data allows researchers to have greater control over the data collection process, which can ensure the data is of high quality and relevant to the research question or problem.
• When the research requires current or up-to-date information: Primary data collection can provide more current and up-to-date information than existing secondary data sources.

Characteristics of Primary Data

Primary data has several characteristics that make it unique and valuable for research purposes. These characteristics include:

• Originality : Primary data is collected for a specific research question or problem and is not previously published or available in any other source.
• Relevance : Primary data is collected to directly address the research question or problem at hand and is therefore highly relevant to the research.
• Accuracy : Primary data collection methods can be designed to ensure the data is accurate and reliable, reducing the risk of errors or biases.
• Timeliness: Primary data is collected in real-time or near real-time, providing current and up-to-date information for the research.
• Specificity : Primary data can be collected from specific populations, such as customers, patients, or employees, providing targeted and relevant information.
• Control : Researchers have greater control over the data collection process, allowing them to ensure the data is collected in a way that is most relevant to the research question or problem.
• Cost : Primary data collection can be more expensive than using existing secondary data sources, as it requires resources such as personnel, equipment, and materials.

Advantages of Primary Data

There are several advantages of using primary data in research. These include:

• Specificity : Primary data collection can be tailored to the specific research question or problem, allowing researchers to gather the most relevant and targeted information possible.
• Control : Researchers have greater control over the data collection process, which can ensure the data is of high quality and relevant to the research question or problem.
• Timeliness : Primary data is collected in real-time or near real-time, providing current and up-to-date information for the research.
• Flexibility : Primary data collection methods can be adjusted or modified during the research process to ensure the most relevant and useful data is collected.
• Greater depth : Primary data collection methods, such as interviews or focus groups, can provide more in-depth and detailed information than existing secondary data sources.
• Potential for new insights : Primary data collection can provide new and unexpected insights into a research question or problem, which may not have been possible using existing secondary data sources.

Limitations of Primary Data

While primary data has several advantages, it also has some limitations that researchers need to be aware of. These limitations include:

• Time-consuming: Primary data collection can be time-consuming, especially if the research requires collecting data from a large sample or a specific population.
• Limited generalizability: Primary data is collected from a specific population, and therefore its generalizability to other populations may be limited.
• Potential bias: Primary data collection methods can be subject to biases, such as social desirability bias or interviewer bias, which can affect the accuracy and reliability of the data.
• Potential for errors: Primary data collection methods can be prone to errors, such as data entry errors or measurement errors, which can affect the accuracy and reliability of the data.
• Ethical concerns: Primary data collection methods, such as interviews or surveys, may raise ethical concerns related to confidentiality, privacy, and informed consent.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is Primary Research and How do I get Started?

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Primary research is any type of research that you collect yourself. Examples include surveys, interviews, observations, and ethnographic research. A good researcher knows how to use both primary and secondary sources in their writing and to integrate them in a cohesive fashion.

Conducting primary research is a useful skill to acquire as it can greatly supplement your research in secondary sources, such as journals, magazines, or books. You can also use it as the focus of your writing project. Primary research is an excellent skill to learn as it can be useful in a variety of settings including business, personal, and academic.

But I’m not an expert!

With some careful planning, primary research can be done by anyone, even students new to writing at the university level. The information provided on this page will help you get started.

What types of projects or activities benefit from primary research?

When you are working on a local problem that may not have been addressed before and little research is there to back it up.

When you are working on writing about a specific group of people or a specific person.

When you are working on a topic that is relatively new or original and few publications exist on the subject.

You can also use primary research to confirm or dispute national results with local trends.

What types of primary research can be done?

Many types of primary research exist. This guide is designed to provide you with an overview of primary research that is often done in writing classes.

Interviews: Interviews are one-on-one or small group question and answer sessions. Interviews will provide a lot of information from a small number of people and are useful when you want to get an expert or knowledgeable opinion on a subject.

Surveys: Surveys are a form of questioning that is more rigid than interviews and that involve larger groups of people. Surveys will provide a limited amount of information from a large group of people and are useful when you want to learn what a larger population thinks.

Observations: Observations involve taking organized notes about occurrences in the world. Observations provide you insight about specific people, events, or locales and are useful when you want to learn more about an event without the biased viewpoint of an interview.

Analysis: Analysis involves collecting data and organizing it in some fashion based on criteria you develop. They are useful when you want to find some trend or pattern. A type of analysis would be to record commercials on three major television networks and analyze gender roles.

Where do I start?

Consider the following questions when beginning to think about conducting primary research:

• What do I want to discover?
• How do I plan on discovering it? (This is called your research methods or methodology)
• Who am I going to talk to/observe/survey? (These people are called your subjects or participants)
• How am I going to be able to gain access to these groups or individuals?
• What are my biases about this topic?
• How can I make sure my biases are not reflected in my research methods?
• What do I expect to discover?

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• Afr J Emerg Med
• v.10(Suppl 2); 2020

Acquiring data in medical research: A research primer for low- and middle-income countries

Vicken totten.

a Kaweah Delta Health Care District (KDHCD), KDHCD Department of Emergency Medicine, Visalia, CA, USA

Erin L. Simon

b Cleveland Clinic Akron General, Department of Emergency Medicine, Akron, OH, USA

Mohammad Jalili

c Department of Emergency Medicine, Tehran University of Medical Sciences, Tehran, Iran

Hendry R. Sawe

d Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania

Without data, there is no new knowledge generated. There may be interesting speculation, new paradigms or theories, but without data gathered from the universe, as representative of the truth in the universe as possible, there will be no new knowledge. Therefore, it is important to become excellent at collecting, collating and correctly interpreting data. Pre-existing and new data sources are discussed; variables are discussed, and sampling methods are covered. The importance of a detailed protocol and research manual are emphasized. Data collectors and data collection forms, both electronic and paper-based are discussed. Ensuring subject privacy while also ensuring appropriate data retention must be balanced.

African relevance

• • To get good quality information you first need good quality data
• • Data collection systematically and reproducibly gathers and measures variables to answer research questions.
• • Good data is a result of a well thought out study protocol

The International Federation for Emergency Medicine global health research primer

This paper forms part 9 of a series of ‘how to’ papers, commissioned by the International Federation for Emergency Medicine. It describes data sources, variables, sampling methods, data collection and the value of a clear data protocol. We have also included additional tips and pitfalls that are relevant to emergency medicine researchers.

Data collection is the process of systematically and reproducibly gathering and measuring variables in order to answer research questions, test hypotheses, or evaluate outcomes.

Data is not information. To get good quality information you first need good quality data, then you must curate, analyse and interpret it. Data is comprised of variables. Data collection begins with determining which variables are required, followed by the selection of a sample from a certain population. After that, a data collection tool is used to collect the variables from the selected sample, which is then converted into a data spreadsheet or database. The analysis is done on the database.

Sometimes you gather data yourself. Sometimes you analyse data others collected for different purposes. Ideally, you collect a universal sample, that is, 100%. In real life, you get a limited sample. Preferably, it will be a truly random sample with enough power to answer your question. Unfortunately, you may have to settle for consecutive or convenience sampling. Ideally, your data collectors would be blinded to the outcome of interest, to prevent bias. However, real life is full of biases. Imperfect data may be better than no data; you can often get useful information from imperfect data. Remember the enemy of good is perfect.

Why is good data important?

Acquiring data is the most important step in a research study. The best design with bad data is useless. Bad design produces bad data. The most sophisticated analysis cannot be performed without data; analysing bad data produces erroneous results. Analysis can never be better than the quality of the data on which it was run. Good data has integrity. Data integrity is paramount to learning “Truth in the Universe”. Good data is as complete and as clean, as you can reasonably make it. Clean data ‘has integrity’ when the variables access as much relevant information as possible, and in the same way for each subject.

Some information is very hard to get. You may have to use proxy variables for what you really want to know. A proxy variable is a variable that is not in itself directly relevant, but that serves in place of an unobservable or immeasurable variable. In order for a variable to be a good proxy, it must have a close correlation, not necessarily linear, with the variable of interest. One example for the variable of a specific illness might be a medication list.

Consequences of bad data include an inability to answer the research question; inability to replicate or validate the study; distorted findings and wasted resources; compromised knowledge and even harm to subjects.

Ensure data quality

Good data is a result of a well-thought-out study protocol, which is the written plan for the study. Good planning is the most cost-effective way to ensure data integrity. Good planning is documented by a thorough and detailed protocol, with a comprehensive procedures manual. Poorly written manuals risk incomplete or inconsistent collection of data, in other words, ‘bad data’. The manual should include rigorous, step-by-step instructions on how to administer tests or collect the data. It should cover the ‘who’ (the subject and the researcher); the ‘when’ (the timing), the ‘how’ (methods), and the ‘what’ (a complete listing of variables to be collected). There should also be an identified mechanism to document any changes in procedures that may evolve over the course of the investigation. The study design should be reproducible: so that the protocol can be followed by any other researcher. All data needs to be gathered in the same way. Test (trial-run) your manual before you start your study. If data is collected by several people, make sure there is a sufficient degree of inter-rater reliability.

To get good data, your sample needs to be representative of the population. For others to apply your results, you need to characterize your population, so others can decide if your conclusions are relevant to their population (see Sampling section, below).

Data integrity demands you supervise your study, making sure it is complete and accurate. You may wish to do interim analyses. Keep copies! Keep both the raw data and the data sheets, for the length of time required by law or by Good Research Practice in your country. This will protect you from accusations of falsification of data.

In real life, you may have to deal with any number of sampling and data collection biases. Some of these biases can be measured statistically. Regardless, all the limitations you can think of should be written in your limitations section. The best design you can practically use gives you the best data you can reasonably get. Remember, “you cannot fix with statistics what you fouled up by design.”

Before you acquire your first datum, consider: Do you have a developed protocol and a research manual? Have you sought Ethics Board approval? Do you have an informed consent? Do you have a plan to protect the subject's confidentiality? Do you have a plan for data analysis? Where will you safely store and protect the data? If you have collaborators, have you established, in writing, who owns the data, and who has the right to analyse and publish it?

Types of data: qualitative vs. quantitative data

Numerical data is generally called quantitative; if in words or sentences, it is qualitative. Medical research historically has focused on quantitative methods. Generally, quantitative research is cheaper, easier to gather and easier to analyse. For purposes of this chapter, we will focus on quantitative research.

Qualitative research is about words, sentences, sounds, feeling, emotions, colours and other elements that are non-quantifiable. It requires human intellect to extract themes from the sentences, evaluate the fit of the data to the themes, and to draw the implications of the themes. Primary sources for qualitative data include open ended surveys, interviews, and public meetings. Qualitative research is more common in politics and the social sciences, and will not be further discussed here, except to refer you to other sources.

Quantitative research can include questionnaires with closed-ended questions (open ended questions belong in qualitative research). The data is transformed into numbers and will be analysed with parametric and non-parametric statistical tests. In general, you will derive a mean, mode and median; you will calculate probabilities, make correlation and regressions in order to draw conclusions.

Sources of data: primary vs secondary data

To answer a research question, there are many potential sources of data. Two main categories are primary data and secondary data. Primary data is newly collected data; it can be gathered directly from people's responses (surveys), or from their biometrics (blood pressure, weight, blood tests, etc.). It is still considered primary data if you gather data that was collected for other (medical) purposes by extracting the data from medical records. Medical records can be a rich source of data, but data extraction by hand takes a lot of time.

Secondary data already exists; it has already been published or complied. There are extant local, regional, national and international databases such as Trauma Registries, Disease-specific Registries, Public Health Data, government statistics, and World Health Organization data. Locally, your hospital or clinic may already keep statistics on any number of topics. Combining information from disparate databases may sometimes yield interesting results. For example, in the US, the Centers for Disease Control and Prevention keeps databases of reportable diseases, accidents, causes of death and much more. The US Geographic Survey reports the average elevation of American cities. Combining the two databases revealed that, even when gun ownership, drug and alcohol use were statistically controlled for, there was a linear correlation between altitude and suicide rates [ 2 ]. Reno et al., reviewed the existing medical literature (also secondary data), and confirmed the correlation and concluded that the mechanisms have yet to be elucidated [ 3 ].

Collecting good data is often the hardest part of research. Ideally, you would want to collect 100% of the data (universal sampling to reflect target population). One example would be ‘all elderly persons with gout’. In real life, you have access to only a subset of the target population (the accessible population). Further, in your study you will be limited to a subset of the accessible population (the study population). Again, in the ideal world, that limited sample would be truly random, and have enough power to answer your question. You can find free random number generators online. In real life, you may have to settle for consecutive or convenience sampling. Of the two, consecutive sampling has less bias. Sometimes it is important to balance your groups. You may have 2 or 3 treatments (or interventions) and want to have an equal number of each kind. So, you create blocks — of a few times the number of treatments. You randomized within the block. Each time a block is filled, you are assured that you have the right balance of subjects. Blocks are often in groups of six, eight or 12. This is called balanced allocation .

If you must get only a convenience sample – for example because you only have a single data gatherer and can get data only when that person is available – you should, at a minimum, try to get some simple demographics from times when the data gatherer is not available, to see if subjects at that other time are systematically different. For example, if you are looking at injuries, people who are injured when drinking on a Friday night might be systematically different from people who are injured on their way to work on a Monday morning. If you can only collect injury data in the morning, your results will be biased.

Variables are the bits of data you collect. They change from subject to subject and describe the subject numerically. Age (or year of birth); gender; ethnic group or tribe; and geographic location are commonly called simple demographic variables and should be collected and reported for most populations.

Continuous variables are quantified on a continuous scale, such as body weight. Discrete variables use a scale whose units are limited to integers (such as the number of cigarettes smoked per day). Discrete variables have a number of possible values and can resemble continuous variables in statistical analysis and be equivalent for the purpose of designing measurements. A good general rule is to prefer continuous variables because the information they contain provides additional information and improves statistical efficiency (more study power and smaller sample size).

Categorical variables are those not suitable for quantification. They are often measured by classifying them into categories. If there are two possible values (dead or alive), they are dichotomous. If there are more than two categories, they can be classified according to the type of information they provide (polytomous).

Research variables are either predictor (independent) or outcome (dependent) variables. The predictor variables might include such things as “Diabetes, Yes/No”, “Age over 65 — Yes/No”, and “diagnosis of hypertension” (again, Yes/No). The respective outcome might be “lower limb amputation” or “death within 10 years”. Your question might have been, “How much additional risk of amputation does a diagnosis of hypertension add in a person with diabetes?”

Before analysis, variables are coded into numbers and entered into a database. Your Research Manual should describe how to code all the data. When the variables are binary, (male/female; alive/dead) coding them into “0” and “1” makes analysing the data much easier (“1” versus “2” makes it harder). The easiest variables for computers to analyse are binary. In other words, “0” or “1”. Such variables are Yes/No; True/False; Male/Female; 65 or over / under 65, etc. The next easiest are ordinal integers: 1, 2, 3, etc. You might create ordinal numbers from categories (0–9; 10–19; 20–29 years of age, etc.), but in order to be ordinal, they require an obvious sequence. Categorical variables do not have an intrinsic order. “Green” “Brown” and “Orange” are non-ordinal, categorical variables. It is possible to transform categorical variables into binary variables, by making columns where only one of the answers is marked with a “1” (if that variable is present) and all the others are marked “0”. The form of the variables and their distribution will determine the type of statistical analysis possible. Data which must be transformed or cleaned is more prone to error in the cleaning or transformation process.

There are alternative ways to get similar information. For example, if you wanted to know the HIV status of each of your subjects, you could either test each one, or you could ask them. The tests cost more, however; they are less likely to give biased results. How you gather each variable will depend on your resources and will inform the limitations of your study.

Precision of a variable is the degree to which it is reproducible with nearly the same value each time it is measured. Precision has a very important influence on the power of a study. The more precise a measurement, the greater the statistical power of a given sample size to estimate mean values and test your hypotheses. In order to minimize random error in your data, and increase the precision of measurements, you should standardize your measurement methods; train your observers; refine any instruments you may use (such as calibrating instruments); automate instruments when possible (automated blood pressure cuff instead of manual); and repeat your measurements.

Accuracy of the variable is the degree to which it actually represents what it is intended to (Truth in the Universe). This influences the validity of the study. Accuracy is impacted by systemic error (bias). The greater the error, the less accurate the variable. Three common biases are: observer bias (how the measurement is reported); instrument bias (faulty function of an instrument); and subject bias (bad reporting or recall of the measurement by the study subject).

Validity is the degree to which a measurement represents the phenomenon of interest. When validating an abstract concept, search the literature or consult with experts so you can find an already validated data collection instrument (such as a questionnaire). This allows your results to be comparable to prior studies in the same area and strengthens your study methods.

Research manual

Simple research with limited resources does not need a research manual, just a protocol. Nor is there much need if the primary investigator is the only data gatherer and analyser. However, if several persons gather data, it is important that the data be gathered the same way each time.

Prevention is the most cost-effective activity that will ensure the integrity of data collection. A detailed and comprehensive research manual will standardize data collection. Poorly written manuals are vague and ambiguous.

The research manual is based off your protocol. The manual should spell out every step of the data collection process. It should include the name of each variable and specific details about how each variable should be collected. Contingents should be written. For example: “If the patient does not have a left arm, the blood pressure may be taken on the right arm. If the patient has no arms, leg blood pressures may be recorded, but put an ‘*’ beside the reading.” The manual should also include every step of the coding process. The coding manual should describe the name of each variable, and how it should be coded. Both the coder and the statistician will want to refer to that section. The coding section should describe how each variable will be entered into the database. Test the manual to make sure everyone understands it the same way.

Think about various ways a plan can go wrong. Write them down, with preferred solutions. There will always be unexpected changes. They should be added into the manual on a continuing basis. An on-going section where questions, problems and their solutions are all recorded will increase the integrity of your research.

Data collection methods

Before you start data collection, you need to ask yourself what data you are going to collect and how you are going to collect them. Which data, and the amount of data to be collected needs to be defined clearly. Different people (including several data collectors) should have a similar understanding of each variable and how it is measured. Otherwise, the data cannot be relied on. Furthermore, the decision to collect a piece of data needs to be justified. The amount of data collected for the study should be sufficient. A common mistake is to collect too much data without actually knowing what will be done with it. Researchers should identify essential data elements and eliminate those that may seem interesting but are not central to the study hypothesis. Collection of the latter type of data places an unnecessary burden on both the study participants and data collectors.

Different data collection approaches which are commonly used in the conduct of clinical research include questionnaire surveys, patient self-reported data, proxy/informant information, hospital and ambulatory medical records, as well as the collection and analysis of biologic samples. Each of these methods has its own advantages and disadvantages.

Surveys are conducted through administration of standardized or home-grown questionnaires, where participants are asked to respond to a set of questions as yes/no, or perhaps on a Likert type scale. Sometimes open-ended responses are elicited.

Medical records can be important sources of high-quality data and may be used either as the only source of data, or as a complement to information collected through other instruments. Unfortunately, due to the non-standardized nature of data collection, information contained in the medical records may be conflicting or of questionable accuracy. Moreover, the extent of documentation by different providers can vary significantly. These issues can make the construction or use of key study variables very difficult.

Collection of biological materials, as well as various imaging modalities, from the study participants are increasingly being used in clinical research. They need to be performed under standardized conditions, and ethical implications should be considered.

Data collection tool

You may need to collect information on paper. If you do, it is useful to have the actual code which should be entered into the computerized database written on the forms themselves (as well as in the manual). If you have access to an electronic database such as REDcap [a web-based application developed by Vanderbilt University to capture data for clinical research and create databases and projects [ 4 ], you can enter the data directly as you get them ( male ; female ) and the database will automatically convert the data into code. This reduces transcribing errors. Another common electronic database is Excel, which can also be used to manipulate the data. In spite of the advantages of recording data electronically, such as directly into REDcap or Excel, there are advantages to collecting and keeping the original data on paper. Paper data collection forms can be saved for audit or quality control. Furthermore, paper records cannot be remotely hacked. Moreover, if the anonymous electronic database is compromised or corrupted, you can re-create your database.

Data collectors

Good data collectors are worth gold. If they are thorough and ethical, you will get great data. If not, your data may be unusable. Make sure they understand research ethics, the need for protection of human subjects, and the privacy of data. Ideally, your data collectors would be blinded to the outcome of interest, to prevent bias. It is ok to blind data collectors to the research question, but they need to understand that collecting every variable the same way for each subject is essential to data integrity.

Data gatherers should be trained in advance of collecting any data. They need to understand informed consent and have the time to explain a study to the satisfaction of the subjects. The importance of conducting a dry run in an attempt to anticipate and address issues that can arise during data collection cannot be over-stated. It would even be worthwhile to pilot the research manual, to learn if everyone understands it the same way.

Data storage

Data collection, done right, protects the confidentiality of the subject as well as the data. Data must also be properly stored safely and securely. It is reasonable to back up your data in a different, secure, location. You do not want to go to all the trouble of creating a protocol, collecting your data, only to lose it, or have no way to analyse it!

There are many reasons to keep your data safe and secure. Obviously, you do not want to lose your data. You may wish to use the data again. For example, you may wish to combine it with other data for a different study. An additional reason is that you do not want your subjects to risk a ‘loss of privacy’. Still another reason is that institutions and governments may require you to store data for a specified number of years. Know how long you must keep your data. Keep it in a locked cabinet in a secure room, or behind an institutional firewall.

Furthermore, if you keep a cipher , that is, a connector between a subject and their study number, keep that cipher separate from the research data. That way, even if someone learns that subject 302 has an embarrassing condition, they will not know who subject 302 really is.

These days, almost everyone has access to computers and programs, locally or ‘in the cloud’. For statistical analysis, you will need to have your data in electronic form. If you started with paper, consider double entry (two data extractors for each record, then compare the two) for greater accuracy.

Tips on this topic and pitfalls to avoid

Hazard: no research manual.

• • No identified mechanism to document changes in procedures that may evolve over the course of the investigation.
• • Vague description of data collection instruments to be used in lieu of rigorous step-by-step instructions on administering tests
• • Only a partial listing of variables to be collected
• • Forgetting to put instructions on the data collection sheet about how to code the data when transferring to an electronic medium.

Hazard: no assistant training

• • Failure to adequately train data collectors
• • Failure to do a Dry Run/Failure to try enrolling a mock subject
• • Uncertainty about when, how and who should review gathered data.

Hazard: failure to understand data management

• • Data should be easy to understand, and the protocol good enough that another researcher can repeat the study.
• • Data audit: keep raw data and collected data
• • Failure to keep backups

Annotated bibliography

• 1. RCR Data Acquisition and Management. This online book is pretty comprehensive. http://ccnmtl.columbia.edu/projects/rcr/rcr_data/foundation/ (Accessed 2019 June 23)
• 2. Qualitative research – Wikipedia: en.wikipedia.org/wiki/Qualitative_research (Accessed 2019 June 23) – this is a good overview with references so you can delve deeper if you wish.
• 3. Qualitative Research: Definition, Types, Methods and Examples: https://www.questionpro.com/blog/qualitative-research-methods/ (Accessed 2019 June 23) – this is a good overview with references so you can delve deeper if you wish.
• 4. Qualitative Research Methods: A Data Collector's Field Guide: https://course.ccs.neu.edu/is4800sp12/resources/qualmethods.pdf (Accessed 2019 June 23) – another on-line resource about data collection.

Additional reading about statistical variables

• 1. Types of Variables in Statistics and Research: A List of Common and Uncommon Types of Variables. https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/types-of-variables/
• 2. Research Variables: Dependent, Independent, Control, Extraneous & Moderator. https://study.com/academy/lesson/research-variables-dependent-independent-control-extraneous-moderator.html
• 3. Knatterud GL. Rockhold FW. George SL. Barton FB. Davis CE. Fairweather WR. Honohan, T. Mowery R. O'Neill R. (1998). Guidelines for quality assurance in multicenter trials: a position paper. Controlled Clinical Trials, 19:477–493.
• 4. Whitney CW. Lind BK. Wahl PW. (1998). Quality assurance and quality control in longitudinal studies. Epidemiologic Reviews, 20 [ 1 ]: 71–80.

Additional relevant information to consider

Consider who owns the data before and after collection (this brings up questions of consent, privacy, sponsorship and data-sharing, most of which are beyond the scope of this paper).

Authors' contribution

Authors contributed as follow to the conception or design of the work; the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content: ES contributed 70%; VT, MJ and HS contributed 10% each. All authors approved the version to be published and agreed to be accountable for all aspects of the work.

Declaration of competing interest

The authors declared no conflicts of interest.

Primary Data: Definition, Examples & Collection Methods

Introduction

What is meant by primary data, what is the difference between primary and secondary data, what are examples of primary data, primary data collection methods, advantages of primary data collection, disadvantages of primary data collection, ethical considerations for primary data.

Understanding the type of data being analyzed is crucial for drawing accurate conclusions in qualitative research. Collecting primary data directly from the source offers unique insights that can benefit researchers in various fields.

This article provides a comprehensive guide on primary data, illustrating its definition, how it stands apart from secondary data , pertinent examples, and the common methods employed in the primary data collection process. Additionally, we will explore the advantages and disadvantages associated with primary data acquisition.

Primary data refers to information that is collected firsthand by the researcher for a specific research purpose. Unlike secondary data, which is already available and has been collected for some other objective, primary data is raw and unprocessed, offering fresh insights directly related to the research question at hand. This type of data is gathered through various methods such as surveys , interviews , experiments, and observations , allowing researchers to obtain tailored and precise information.

The main characteristic of primary data is its relevancy to the specific study. Since it is collected with the research objectives and questions in mind, it directly addresses the issues or hypotheses under investigation. This direct connection enhances the validity and accuracy of the research findings, as the data is not diluted or missing important information relevant to the research question.

Moreover, primary data provides the most current information available, making it especially valuable in fast-changing fields or situations where timely data is crucial. By analyzing primary data, researchers can draw unique conclusions and develop original insights that contribute significantly to their field of study.

Understanding the distinction between primary and secondary data is fundamental in the realm of research, as it influences the research design , methodology , and analysis . Primary data is information collected firsthand for a specific research purpose. It is original and unprocessed, providing new insights directly relevant to the researcher's questions or objectives. Common methods of collecting data from primary sources include observations , surveys , interviews , and experiments, each allowing the researcher to gather specific, targeted information.

Conversely, secondary data refers to information that was collected by someone else for a different purpose and is subsequently used by a researcher for a new study. This data can come from a primary source such as an academic journal, a government report, a set of historical records, or a previous research study. While secondary data is invaluable for providing context, background, and supporting evidence, it may not be as precisely tailored to the specific research questions as primary data.

The key differences between these two types of data also extend to their advantages and disadvantages concerning accessibility, cost, and time. Primary data is typically more time-consuming and expensive to collect but offers specificity and relevance that is unmatched by secondary data. On the other hand, secondary data is usually more accessible and less costly, as it leverages existing information, although it may not align perfectly with the current research needs and might be outdated or less specific.

In terms of accuracy and reliability, primary data allows for greater control over the quality and methodology of the data collected, reflecting the current scenario accurately. However, secondary data's reliability depends on the original data collection's accuracy and the context in which it was gathered, which might not be fully verifiable by the new researcher.

Synthesizing primary and secondary data

While primary and secondary data each have distinct roles in research, synthesizing both types can provide a more comprehensive understanding of the research topic . Integrating primary data with secondary data allows researchers to contextualize their firsthand findings within the broader literature and existing knowledge.

This approach can enhance the depth and relevance of the research, providing a more nuanced analysis that leverages the detailed, current insights of primary data alongside the extensive, contextual background of secondary data.

For example, primary data might offer detailed consumer behavior insights, which researchers can then compare with broader market trends or historical data from secondary sources. This synthesis can reveal patterns, corroborate findings, or identify anomalies, enriching the research's analytical value and implications.

Ultimately, combining primary and secondary data helps build a robust research framework, enabling a more informed and comprehensive exploration of the research question .

Primary data collection is a cornerstone of research in the social sciences, providing firsthand insights that are crucial for understanding complex human behaviors and societal structures. This direct approach to data gathering allows researchers to uncover rich, context-specific information.

The following subsections highlight examples of primary data across various social science disciplines, showcasing the versatility and depth of these research methods.

Economic behaviors in market research

Market research within economics often relies on primary data to understand consumer preferences, spending habits, and decision-making processes. For instance, a study may collect primary data through surveys or interviews to gauge consumer reactions to a new product or service.

This information can reveal economic behaviors, such as price sensitivity and brand loyalty, offering valuable insights for businesses and policymakers.

Voting patterns in political science

In political science, researchers collect primary data to analyze voting patterns and political engagement. Through exit polls and surveys conducted during elections, researchers can obtain firsthand accounts of voter preferences and motivations.

This data is pivotal in understanding the dynamics of electoral politics, voter turnout, and the influence of campaign strategies on public opinion.

Cultural practices in anthropology

Anthropologists gather primary data to explore cultural practices and beliefs, often through ethnographic studies . By immersing themselves in a community, researchers can directly observe rituals, social interactions, and traditions.

For example, a study might focus on marriage ceremonies, food customs, or religious practices within a particular culture, providing in-depth insights into the community's way of life.

Social interactions in sociology

Sociologists utilize primary data to investigate the intricacies of social interactions and societal structures. Observational studies , for instance, can reveal how individuals behave in group settings, how social norms are enforced, and how social hierarchies influence behavior.

By analyzing these interactions within settings like schools, workplaces, or public spaces, sociologists can uncover patterns and dynamics that shape social life.

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Primary data collection is an integral aspect of research, enabling investigators to gather fresh, relevant data directly related to their study objectives. This direct engagement provides rich, nuanced insights that are critical for in-depth analysis. Selecting the appropriate data collection method is pivotal, as it influences the study's overall design, data quality, and conclusiveness.

Below are some of the different types of primary data utilized across various research disciplines, each offering unique benefits and suited to different research needs.

In-person and online surveys collect data from a large audience efficiently. By utilizing structured questionnaires, researchers can gather data on a wide range of topics, such as attitudes, preferences, behaviors, or factual information.

Surveys can be distributed through various channels, including online platforms, phone, mail, or in-person, allowing for flexibility in reaching diverse populations.

Interviews provide an in-depth look into the respondents' perspectives, experiences, or opinions. They can range from highly structured formats to open-ended, conversational styles, depending on the research goals.

Interviews are particularly valuable for exploring complex issues, understanding personal narratives, and gaining detailed insights that are not easily captured through other methods.

Focus groups

Focus groups involve guided discussions with a small group of participants, allowing researchers to explore collective views, uncover trends in perceptions, and stimulate debate on a specific topic.

This method is particularly useful for generating rich qualitative data, understanding group dynamics, and identifying variations in opinions across different demographic groups.

Observations

Observational research involves systematically watching and recording behaviors and interactions in their natural context. It can be conducted in various settings, such as schools, workplaces, or public areas, providing authentic insights into real-world behaviors.

The observation method can be either participant, where the observer is involved in the activities, or non-participant, where the researcher observes without interaction.

Experiments

Experiments are a fundamental method in scientific research, allowing researchers to control variables and measure effects accurately.

By manipulating certain factors and observing the outcomes, experiments can establish causal relationships, providing a robust basis for testing hypotheses and drawing conclusions.

Case studies

Case studies offer an in-depth examination of a particular instance or phenomenon, often involving a comprehensive analysis of individuals, organizations, events, or other entities.

This method is particularly suited to exploring new or complex issues, providing detailed contextual analysis, and uncovering underlying mechanisms or principles.

Ethnography

As a key method in anthropology, ethnography involves extended observation of a community or culture, often through fieldwork. Researchers immerse themselves in the environment, participating in and observing daily life to gain a deep understanding of social practices, norms, and values.

Ethnography is invaluable for exploring cultural phenomena, understanding community dynamics, and providing nuanced interpretations of social behavior.

Primary data collection is a fundamental aspect of research, offering distinct advantages that enhance the quality and relevance of study findings. By gathering high-quality primary data firsthand, a research project can obtain specific, up-to-date information that directly addresses their research questions or hypotheses. This section explores four key advantages of primary data collection, highlighting how it contributes to robust and insightful research outcomes.

Specificity

One of the most significant advantages of primary data collection is its specificity. Data gathered firsthand is tailored specifically to the research question or hypothesis, ensuring that the information is directly relevant and applicable to the study's objectives. This level of specificity enhances the precision of the research, allowing for a more targeted analysis and reducing the likelihood of extraneous variables influencing the results.

Primary data collection offers the advantage of currency, providing the most recent information available. This is particularly crucial in fields where data rapidly change, such as market trends, technological advancements, or social dynamics. By accessing current data, researchers can draw conclusions that are timely and reflective of the present context, adding significant value and relevance to their findings.

Control over data quality

When collecting primary data, researchers have direct control over the data quality. They can design the data collection process, choose the sample, and implement quality assurance measures to ensure valid and reliable data. This direct involvement allows researchers to address potential biases, minimize errors, and adjust methodologies as needed, ensuring that the data is accurate and representative of the population under study.

Exclusive insights

Gathering primary data provides exclusive insights that might not be available through secondary sources. By collecting unique data sets, researchers can explore uncharted territories, generate new theories, and contribute original findings to their field. This exclusivity not only advances academic knowledge but also offers competitive advantages in applied settings, such as business or policy development, where novel insights can lead to innovative solutions and strategic advancements.

While primary data collection offers numerous benefits, it also comes with distinct disadvantages that researchers must consider. These drawbacks can impact the feasibility, reliability, and overall outcome of a study. Understanding these limitations is crucial for researchers to design effective and comprehensive research methodologies . Below, we explore four significant disadvantages of primary data collection.

Time-consuming process

Primary data collection often requires a significant investment of time. From designing the data collection tools and protocols to actually gathering the data and analyzing results, each step can take a long time to carry out. For instance, conducting in-depth interviews , surveys , or extensive observations demands considerable time for both preparation and execution. This extended timeline can be a significant hurdle, especially in fields where timely data is crucial.

The financial implications of primary data collection can be substantial. Resources are needed for various stages of the process, including material creation, data gathering, personnel, and data analysis . For example, organizing focus groups or conducting large-scale surveys involves logistical expenses, compensation for participants, and possibly travel costs. Such financial requirements can limit the scope of the research or even render it unfeasible for underfunded projects.

Limited scope

Primary data collection is typically focused on a specific research question or context, which may limit the breadth of the data. While this specificity provides detailed insights into the chosen area of study, it may not offer a comprehensive overview of the subject. For example, a case study provides in-depth data about a particular case, but its findings may not be generalizable to other contexts or populations, limiting the scope of the research conclusions.

Potential for data collection bias

The process of collecting primary data is susceptible to various biases , which can compromise the data's accuracy and reliability. Researcher bias, selection bias, or response bias can skew results, leading to misleading conclusions. For instance, the presence of an observer might influence participants' behavior, or poorly designed survey questions might lead to ambiguous or skewed responses. Mitigating these biases requires meticulous planning and execution, but some level of bias is often inevitable.

Ethical considerations are paramount in the realm of primary data collection , ensuring the respect and dignity of participants are maintained while preserving the integrity of the research process. Researchers are obligated to adhere to ethical standards that promote trust, accountability, and scientific excellence. This section delves into key ethical principles that must be considered when collecting primary data.

Informed consent

Informed consent is the cornerstone of ethical research. Participants must be fully informed about the study's purpose, procedures, potential risks, and benefits, as well as their right to withdraw at any time without penalty. This information should be communicated in a clear, understandable manner, ensuring participants can make an informed decision about their involvement. Documented consent, whether written or verbal, is essential to demonstrate that participants have agreed to partake in the study voluntarily, understanding all its aspects.

Confidentiality and privacy

Protecting participants' confidentiality and privacy is crucial to uphold their rights and the data's integrity. Researchers must implement measures to ensure that personal information is securely stored and only accessible to authorized team members. Data should be anonymized or de-identified to prevent the identification of individual participants in reports or publications. Researchers must also be transparent about any data sharing plans and obtain consent for such activities, ensuring participants are aware of who might access their information and for what purposes.

Data integrity and reporting

Maintaining data integrity is fundamental to ethical research practices. Researchers are responsible for collecting, analyzing, and presenting data accurately and transparently, without fabrication, falsification, or inappropriate data manipulation. Reporting should be honest and comprehensive, reflecting all relevant findings, including any that contradict the research hypotheses. Researchers should also disclose any conflicts of interest that might influence the study's outcomes, maintaining transparency throughout the research process.

Minimizing harm

Research should be designed and conducted in a way that minimizes any potential harm to participants. This includes considering physical, psychological, emotional, and social risks. Researchers must take steps to reduce any discomfort or adverse effects, providing support or referrals if participants experience distress. Ethical research also involves selecting appropriate methodologies that align with the study's objectives while safeguarding participants' well-being, ensuring that the research's potential benefits justify any risks involved.

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Primary Research: What It Is, Purpose & Methods + Examples

As we continue exploring the exciting research world, we’ll come across two primary and secondary data approaches. This article will focus on primary research – what it is, how it’s done, and why it’s essential. 

We’ll discuss the methods used to gather first-hand data and examples of how it’s applied in various fields. Get ready to discover how this research can be used to solve research problems , answer questions, and drive innovation.

What is Primary Research: Definition

Primary research is a methodology researchers use to collect data directly rather than depending on data collected from previously done research. Technically, they “own” the data. Primary research is solely carried out to address a certain problem, which requires in-depth analysis .

There are two forms of research:

• Primary Research
• Secondary Research

Businesses or organizations can conduct primary research or employ a third party to conduct research. One major advantage of primary research is this type of research is “pinpointed.” Research only focuses on a specific issue or problem and on obtaining related solutions.

For example, a brand is about to launch a new mobile phone model and wants to research the looks and features they will soon introduce. 

Organizations can select a qualified sample of respondents closely resembling the population and conduct primary research with them to know their opinions. Based on this research, the brand can now think of probable solutions to make necessary changes in the looks and features of the mobile phone.

Primary Research Methods with Examples

In this technology-driven world, meaningful data is more valuable than gold. Organizations or businesses need highly validated data to make informed decisions. This is the very reason why many companies are proactive in gathering their own data so that the authenticity of data is maintained and they get first-hand data without any alterations.

Here are some of the primary research methods organizations or businesses use to collect data:

1. Interviews (telephonic or face-to-face)

Conducting interviews is a qualitative research method to collect data and has been a popular method for ages. These interviews can be conducted in person (face-to-face) or over the telephone. Interviews are an open-ended method that involves dialogues or interaction between the interviewer (researcher) and the interviewee (respondent).

Conducting a face-to-face interview method is said to generate a better response from respondents as it is a more personal approach. However, the success of face-to-face interviews depends heavily on the researcher’s ability to ask questions and his/her experience related to conducting such interviews in the past. The types of questions that are used in this type of research are mostly open-ended questions . These questions help to gain in-depth insights into the opinions and perceptions of respondents.

Personal interviews usually last up to 30 minutes or even longer, depending on the subject of research. If a researcher is running short of time conducting telephonic interviews can also be helpful to collect data.

2. Online surveys

Once conducted with pen and paper, surveys have come a long way since then. Today, most researchers use online surveys to send to respondents to gather information from them. Online surveys are convenient and can be sent by email or can be filled out online. These can be accessed on handheld devices like smartphones, tablets, iPads, and similar devices.

Once a survey is deployed, a certain amount of stipulated time is given to respondents to answer survey questions and send them back to the researcher. In order to get maximum information from respondents, surveys should have a good mix of open-ended questions and close-ended questions . The survey should not be lengthy. Respondents lose interest and tend to leave it half-done.

It is a good practice to reward respondents for successfully filling out surveys for their time and efforts and valuable information. Most organizations or businesses usually give away gift cards from reputed brands that respondents can redeem later.

3. Focus groups

This popular research technique is used to collect data from a small group of people, usually restricted to 6-10. Focus group brings together people who are experts in the subject matter for which research is being conducted.

Focus group has a moderator who stimulates discussions among the members to get greater insights. Organizations and businesses can make use of this method, especially to identify niche markets to learn about a specific group of consumers.

4. Observations

In this primary research method, there is no direct interaction between the researcher and the person/consumer being observed. The researcher observes the reactions of a subject and makes notes.

Trained observers or cameras are used to record reactions. Observations are noted in a predetermined situation. For example, a bakery brand wants to know how people react to its new biscuits, observes notes on consumers’ first reactions, and evaluates collective data to draw inferences .

Primary Research vs Secondary Research – The Differences

Primary and secondary research are two distinct approaches to gathering information, each with its own characteristics and advantages. 

While primary research involves conducting surveys to gather firsthand data from potential customers, secondary market research is utilized to analyze existing industry reports and competitor data, providing valuable context and benchmarks for the survey findings.

Find out more details about the differences: 

1. Definition

• Primary Research: Involves the direct collection of original data specifically for the research project at hand. Examples include surveys, interviews, observations, and experiments.
• Secondary Research: Involves analyzing and interpreting existing data, literature, or information. This can include sources like books, articles, databases, and reports.

2. Data Source

• Primary Research: Data is collected directly from individuals, experiments, or observations.
• Secondary Research: Data is gathered from already existing sources.

3. Time and Cost

• Primary Research: Often time-consuming and can be costly due to the need for designing and implementing research instruments and collecting new data.
• Secondary Research: Generally more time and cost-effective, as it relies on readily available data.

4. Customization

• Primary Research: Provides tailored and specific information, allowing researchers to address unique research questions.
• Secondary Research: Offers information that is pre-existing and may not be as customized to the specific needs of the researcher.
• Primary Research: Researchers have control over the research process, including study design, data collection methods , and participant selection.
• Secondary Research: Limited control, as researchers rely on data collected by others.

6. Originality

• Primary Research: Generates original data that hasn’t been analyzed before.
• Secondary Research: Involves the analysis of data that has been previously collected and analyzed.

7. Relevance and Timeliness

• Primary Research: Often provides more up-to-date and relevant data or information.
• Secondary Research: This may involve data that is outdated, but it can still be valuable for historical context or broad trends.

Advantages of Primary Research

Primary research has several advantages over other research methods, making it an indispensable tool for anyone seeking to understand their target market, improve their products or services, and stay ahead of the competition. So let’s dive in and explore the many benefits of primary research.

• One of the most important advantages is data collected is first-hand and accurate. In other words, there is no dilution of data. Also, this research method can be customized to suit organizations’ or businesses’ personal requirements and needs .
• I t focuses mainly on the problem at hand, which means entire attention is directed to finding probable solutions to a pinpointed subject matter. Primary research allows researchers to go in-depth about a matter and study all foreseeable options.
• Data collected can be controlled. I T gives a means to control how data is collected and used. It’s up to the discretion of businesses or organizations who are collecting data how to best make use of data to get meaningful research insights.
• I t is a time-tested method, therefore, one can rely on the results that are obtained from conducting this type of research.

Disadvantages of Primary Research

While primary research is a powerful tool for gathering unique and firsthand data, it also has its limitations. As we explore the drawbacks, we’ll gain a deeper understanding of when primary research may not be the best option and how to work around its challenges.

• One of the major disadvantages of primary research is it can be quite expensive to conduct. One may be required to spend a huge sum of money depending on the setup or primary research method used. Not all businesses or organizations may be able to spend a considerable amount of money.
• This type of research can be time-consuming. Conducting interviews and sending and receiving online surveys can be quite an exhaustive process and require investing time and patience for the process to work. Moreover, evaluating results and applying the findings to improve a product or service will need additional time.
• Sometimes, just using one primary research method may not be enough. In such cases, the use of more than one method is required, and this might increase both the time required to conduct research and the cost associated with it.

Every research is conducted with a purpose. Primary research is conducted by organizations or businesses to stay informed of the ever-changing market conditions and consumer perception. Excellent customer satisfaction (CSAT) has become a key goal and objective of many organizations.

A customer-centric organization knows the importance of providing exceptional products and services to its customers to increase customer loyalty and decrease customer churn. Organizations collect data and analyze it by conducting primary research to draw highly evaluated results and conclusions. Using this information, organizations are able to make informed decisions based on real data-oriented insights.

QuestionPro is a comprehensive survey platform that can be used to conduct primary research. Users can create custom surveys and distribute them to their target audience , whether it be through email, social media, or a website.

QuestionPro also offers advanced features such as skip logic, branching, and data analysis tools, making collecting and analyzing data easier. With QuestionPro, you can gather valuable insights and make informed decisions based on the results of your primary research. Start today for free!

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Everything you need to know about primary research

Last updated

28 February 2023

Reviewed by

Miroslav Damyanov

They might search existing research to find the data they need—a technique known as secondary research .

Alternatively, they might prefer to seek out the data they need independently. This is known as primary research.

Analyze your primary research

Bring your primary research together inside Dovetail and uncover actionable insights

• What is primary research?

During primary research, the researcher collects the information and data for a specific sample directly.

Types of primary research

Primary research can take several forms, depending on the type of information studied. Here are the four main types of primary research:

Observations

Focus groups

When conducting primary research, you can collect qualitative or quantitative data (or both).

Qualitative primary data collection provides a vast array of feedback or information about products and services. However, it may need to be interpreted before it is used to make important business decisions.

Quantitative primary data collection , on the other hand, involves looking at the numbers related to a specific product or service.

• What types of projects can benefit from primary research?

Data obtained from primary research may be more accurate than if it were obtained from previous data samples.

Primary research may be used for

Salary guides

Industry benchmarks

Government reports

Any information based on the current state of the target, including statistics related to current information

Scientific studies

Current market research

Crafting user-friendly products

Primary research can also be used to capture any type of sentiment that cannot be represented statistically, verbally, or through transcription. This may include tone of voice, for example. The researcher might want to find out if the subject sounds hesitant, uncertain, or unhappy.

• Methods for conducting primary research

Your methods for conducting primary research may vary based on the information you’re looking for and how you prefer to interact with your target market.

Surveys are a method to obtain direct information and feedback from the target audience. Depending on the target market’s specific needs, they can be conducted over the phone, online, or face-to-face.

Observation

In some cases, primary research will involve watching the behaviors of consumers or members of the target audience.

Communication with members of the target audience who can share direct information and feedback about products and services.

Test marketing

Explore customer response to a product or marketing campaign before a wider release.

Competitor visits

Competitor visits allow you to check out what competitors have to offer to get a better feel for how they interact with their target markets. This approach can help you better understand what the market might be looking for.

This involves bringing a group of people together to discuss a specific product or need within the industry. This approach could help provide essential insights into the needs of that market.

Usability testing

Usability testing allows you to evaluate a product’s usability when you launch a live prototype. You might recruit representative users to perform tasks while you observe, ask questions, and take notes on how they use your product.

• When to conduct primary research

Primary research is needed when you want first-hand information about your product, service, or target market. There are several circumstances where primary research may be the best strategy for getting the information you need.

You might use it to:

Understand pricing information, including what price points customers are likely to purchase at. 

Get insight into your sales process. For example, you might look at screenshots of a sales demo, listen to audio recordings of the sales process, or evaluate key details and descriptions. 

Learn about problems your consumers might be having and how your business can solve them.

Gauge how a company feels about its competitors. For example, you might want to ask an e-tailer if they plan to offer free shipping to compete with Amazon, Walmart, and other major retailers.

• How to get started with primary research

Step one: Define the problem you’re trying to answer. Clearly identify what you want to know and why it’s important. Does the customer want you to perform the “usual?” This is often the case if they are new, inexperienced, or simply too busy and want to have the task taken care of.

Step two: Determine the best method for getting those answers. Do you need quantitative data , which can be measured in multiple-choice surveys? Or do you need more detailed qualitative data , which may require focus groups or interviews?

Step three: Select your target. Where will you conduct your primary research? You may already have a focus group available; for example, a social media group where people already gather to discuss your brand.

Step four: Compile your questions or define your method. Clearly set out what information you need and how you plan to gather it.

Step five: Research!

• Advantages of primary research

Primary research offers a number of potential advantages. Most importantly, it offers you information that you can’t get elsewhere.

It provides you with direct information from consumers who are already members of your target market or using your products.

You are able to get feedback directly from your target audience, which can allow you to immediately improve products or services and provide better support to your target market.

Primary data is current. Secondary sources may contain outdated data.

Primary data is reliable. You will know what methods you used and how the data relates to your research because you collected it yourself.

• Disadvantages of primary research

You might decide primary research isn’t the best option for your research project when you consider the disadvantages.

Primary research can be time-consuming. You will have to put in the time to collect data yourself, meaning the research may take longer to complete.

Primary research may be more expensive to conduct if it involves face-to-face interactions with your target audience, subscriptions for insight platforms, or participant remuneration.

The people you engage with for your research may feel disrupted by information-gathering methods, so you may not be able to use the same focus group every time you conduct that research.

It can be difficult to gather accurate information from a small group of people, especially if you deliberately select a focus group made up of existing customers. 

You may have a hard time accessing people who are not already members of your customer base.

Biased surveys can be a challenge. Researchers may, for example, inadvertently structure questions to encourage participants to respond in a particular way. Questions may also be too confusing or complex for participants to answer accurately.

Despite the researcher’s best efforts, participants don’t always take studies seriously. They may provide inaccurate or irrelevant answers to survey questions, significantly impacting any conclusions you reach. Therefore, researchers must take extra caution when examining results.

Conducting primary research can help you get a closer look at what is really going on with your target market and how they are using your product. That research can then inform your efforts to improve your services and products.

What is primary research, and why is it important?

Primary research is a research method that allows researchers to directly collect information for their use. It can provide more accurate insights into the target audience and market information companies really need.

What are primary research sources?

Primary research sources may include surveys, interviews, visits to competitors, or focus groups.

What is the best method of primary research?

The best method of primary research depends on the type of information you are gathering. If you need qualitative information, you may want to hold focus groups or interviews. On the other hand, if you need quantitative data, you may benefit from conducting surveys with your target audience.

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Introduction to Primary Research: Observations, Surveys, and Interviews

Primary Research: Definitions and Overview

   How research is defined varies widely from field to field, and as you progress through your college career, your coursework will teach you much more about what it means to be a researcher within your field.* For example, engineers, who focus on applying scientific knowledge to develop designs, processes, and objects, conduct research using simulations, mathematical models, and a variety of tests to see how well their designs work. Sociologists conduct research using surveys, interviews, observations, and statistical analysis to better understand people, societies, and cultures. Graphic designers conduct research through locating images for reference for their artwork and engaging in background research on clients and companies to best serve their needs. Historians conduct research by examining archival materials—newspapers, journals, letters, and other surviving texts—and through conducting oral history interviews. Research is not limited to what has already been written or found at the library, also known as secondary research. Rather, individuals conducting research are producing the articles and reports found in a library database or in a book. Primary research, the focus of this essay, is research that is collected firsthand rather than found in a book, database, or journal.

   Primary research is often based on principles of the scientific method, a theory of investigation first developed by John Stuart Mill in the nineteenth century in his book Philosophy of the Scientific Method .  Although the application of the scientific method varies from field to field, the general principles of the scientific method allow researchers to learn more about the world and observable phenomena. Using the scientific method, researchers develop research questions or hypotheses and collect data on events, objects, or people that is measurable, observable, and replicable. The ultimate goal in conducting primary research is to learn about something new that can be confirmed by others and to eliminate our own biases in the process.

Essay Overview and Student Examples

     The essay begins by providing an overview of ethical considerations when conducting primary research, and then covers the stages that you will go through in your primary research: planning, collecting, analyzing, and writing. After the four stages comes an introduction to three common ways of conducting primary research in first year writing classes:

Observations . Observing and measuring the world around you, including observations of people and other measurable events.

Interviews . Asking participants questions in a one-on-one or small group setting.

Surveys . Asking participants about their opinions and behaviors through a short questionnaire.

In addition, we will be examining two student projects that used substantial portions of primary research:

    Derek Laan, a nutrition major at Purdue University, wanted to learn more about student eating habits on campus. His primary re-search included observations of the campus food courts, student behavior while in the food courts, and a survey of students’ daily food intake. His secondary research included looking at national student eating trends on college campuses, information from the United States Food and Drug Administration, and books on healthy eating.

    Jared Schwab, an agricultural and biological engineering major at Purdue, was interested in learning more about how writing and communication took place in his field. His primary research included interviewing a professional engineer and a student who was a senior majoring in engineering. His secondary research included examining journals, books, professional organizations, and writing guides within the field of engineering.

Ethics of Primary Research

   Both projects listed above included primary research on human participants; therefore, Derek and Jared both had to consider research ethics throughout their primary research process. As Earl Babbie writes in The Practice of Social Research , throughout the early and middle parts of the twentieth century researchers took advantage of participants and treated them unethically. During World War II, Nazi doctors performed heinous experiments on prisoners without their consent, while in the U.S., a number of medical and psychological experiments on caused patients undue mental and physical trauma and, in some cases, death. Because of these and other similar events, many nations have established ethical laws and guidelines for researchers who work with human participants. In the United States, the guidelines for the ethical treatment of human research participants are described in The Belmont Report , released in 1979. Today, universities have Institutional Review Boards (or IRBs) that oversee research. Students conducting research as part of a class may not need permission from the university’s IRB, although they still need to ensure that they follow ethical guidelines in research. The following provides a brief overview of ethical considerations:

• Voluntary participation . The Belmont Report suggests that, in most cases, you need to get permission from people before you involve them in any primary research you are conducting. If you are doing a survey or interview, your participants must first agree to fill out your survey or to be interviewed. Consent for observations can be more complicated, and is dis-cussed later in the essay.

Confidentiality and anonymity . Your participants may reveal embarrassing or potentially damaging information such as racist comments or unconventional behavior. In these cases, you should keep your participants’ identities anonymous when writing your results. An easy way to do this is to create a “pseudonym” (or false name) for them so that their identity is protected.

Researcher bias . There is little point in collecting data and learning about something if you already think you know the answer! Bias might be present in the way you ask questions, the way you take notes, or the conclusions you draw from the data you collect.

   The above are only three of many considerations when involving human participants in your primary research. For a complete under-standing of ethical considerations please refer to The Belmont Report .

   Now that we have considered the ethical implications of research, we will examine how to formulate research questions and plan your primary research project.

Planning Your Primary Research Project

   The primary research process is quite similar to the writing process, and you can draw upon your knowledge of the writing process to understand the steps involved in a primary research project. Just like in the writing process, a successful primary research project begins with careful planning and background research. This section first describes how to create a research timeline to help plan your research. It then walks you through the planning stages by examining when primary research is useful or appropriate for your first year composition course, narrowing down a topic, and developing research questions.

The Research Timeline

   When you begin to conduct any kind of primary research, creating a timeline will help keep you on task. Because students conducting primary research usually focus on the collection of data itself, they often overlook the equally important areas of planning (invention), analyzing data, and writing. To help manage your time, you should create a research timeline, such as the sample timeline presented here.

When Primary Research Is Useful or Appropriate

   In Evaluating Scientific Research: Separating Fact from Fiction , Fred Leavitt explains that primary research is useful for questions that can be answered through asking others and direct observation. For first year writing courses, primary research is particularly useful when you want to learn about a problem that does not have a wealth of published information. This may be because the problem is a recent event or it is something not commonly studied. For example, if you are writing a paper on a new political issue, such as changes in tax laws or healthcare, you might not be able to find a wealth of peer-reviewed research because the issue is only several weeks old. You may find it necessary to collect some of your own data on the issue to supplement what you found at the library. Primary research is also useful when you are studying a local problem or learning how a larger issue plays out at the local level. Although you might be able to find information on national statistics for healthy eating, whether or not those statistics are representative of your college campus is something that you can learn through primary research.

   However, not all research questions and topics are appropriate for primary research. As Fred Leavitt writes, questions of an ethical, philosophical, or metaphysical nature are not appropriate because these questions are not testable or observable. For example, the question “Does an afterlife exist?” is not a question that can be answered with primary research. However, the question “How many people in my community believe in an afterlife?” is something that primary research can answer.

Narrowing Your Topic

   Just like the writing process, you should start your primary research process with secondary (library) research to learn more about what is already known and what gaps you need to fill with your own data. As you learn more about the topic, you can narrow down your interest area and eventually develop a research question or hypothesis, just as you would with a secondary research paper.

Developing Research Questions or Hypotheses

   As John Stuart Mill describes, primary research can use both inductive and deductive approaches, and the type approach is usually based on the field of inquiry. Some fields use deductive reasoning , where researchers start with a hypothesis or general conclusion and then collect specific data to support or refute their hypothesis. Other fields use inductive reasoning , where researchers start with a question and collect information that eventually leads to a conclusion.

   Once you have spent some time reviewing the secondary research on your topic, you are ready to write a primary research question or hypothesis. A research question or hypothesis should be something that is specific, narrow, and discoverable through primary research methods. Just like a thesis statement for a paper, if your research question or hypothesis is too broad, your research will be unfocused and your data will be difficult to analyze and write about. Here is a set of sample research questions:

Poor Research Question : What do college students think of politics and the economy?

Revised Research Question : What do students at Purdue University believe about the current economic crisis in terms of economic recoverability?

   The poor research question is unspecific as to what group of students the researcher is interested in—i.e. students in the U.S.? In a particular state? At their university? The poor research question was also too broad; terms like “politics” and the “economy” cover too much ground for a single project. The revised question narrows down the topic to students at a particular university and focuses on a specific issue related to the economy: economic recoverability. The research question could also be rephrased as a testable hypothesis using deductive reasoning: “Purdue University college students are well informed about economic recoverability plans.” Because they were approaching their projects in an exploratory, inductive manner, both Derek and Jared chose to ask research questions:

Derek: Are students’ eating habits at Purdue University healthy or unhealthy? What are the causes of students’ eating behavior?

Jared: What are the major features of writing and communication in agricultural and biological engineering? What are the major controversies? 

   A final step in working with a research question or hypothesis is determining what key terms you are using and how you will define them. Before conducting his research, Derek had to define the terms “healthy” and “unhealthy”; for this, he used the USDA’s Food Pyramid as a guide. Similarly, part of what Jared focused on in his interviews was learning more about how agricultural and biological engineers defined terms like “writing” and “communication.” Derek and Jared thought carefully about the terms within their research questions and how these terms might be measured. 

Choosing a Data Collection Method 

    Once you have formulated a research question or hypothesis, you will need to make decisions about what kind of data you can collect that will best address your research topic. Derek chose to examine eating habits by observing both what students ate at lunch and surveying students about eating behavior. Jared decided that in-depth interviews with experienced individuals in his field would provide him with the best information.

   To choose a data collection method for your research question, read through the next sections on observations, interviews, and surveys.

Observations

   Observations have lead to some of the most important scientific discoveries in human history. Charles Darwin used observations of the animal and marine life at the Galapagos Islands to help him formulate his theory of evolution that he describes in On the Origin of Species . Today, social scientists, natural scientists, engineers, computer scientists, educational researchers, and many others use observations as a primary research method.

   Observations can be conducted on nearly any subject matter, and the kinds of observations you will do depend on your research question. You might observe traffic or parking patterns on campus to get a sense of what improvements could be made. You might observe clouds, plants, or other natural phenomena. If you choose to observe people, you will have several additional considerations including the manner in which you will observe them and gain their consent.

   If you are observing people, you can choose between two common ways to observe: participant observation and unobtrusive observation. Participant observation is a common method within ethnographic research in sociology and anthropology. In this kind of observation, a researcher may interact with participants and become part of their community. Margaret Mead, a famous anthropologist, spent extended periods of time living in, and interacting with, communities that she studied. Conversely, in unobtrusive observation, you do not interact with participants but rather simply record their behavior. Although in most circumstances people must volunteer to be participants in research, in some cases it is acceptable to not let participants know you are observing them. In places that people perceive as public, such as a campus food court or a shopping mall, people do not expect privacy, and so it is generally acceptable to observe without participant consent. In places that people perceive as private, which can include a church, home, classroom, or even an intimate conversation at a restaurant, participant consent should be sought. 

   The second issue about participant consent in terms of unobtrusive observation is whether or not getting consent is feasible for the study. If you are observing people in a busy airport, bus station, or campus food court, getting participant consent may be next to impossible. In Derek’s study of student eating habits on campus, he went to the campus food courts during meal times and observed students purchasing food. Obtaining participant consent for his observations would have been next to impossible because hundreds of students were coming through the food court during meal times. Since Derek’s research was in a place that participants would perceive as public, it was not practical to get their consent, and since his data was anonymous, he did not violate their privacy.

Eliminating Bias in Your Observation Notes

The ethical concern of being unbiased is important in recording your observations. You need to be aware of the difference between an observation (recording exactly what you see) and an interpretation (making assumptions and judgments about what you see). When you observe, you should focus first on only the events that are directly observable. Consider the following two example entries in an observation log:

• The student sitting in the dining hall enjoys his greasy, oil-soaked pizza. He is clearly oblivious of the calorie content and damage it may do to his body.
• The student sits in the dining hall. As he eats his piece of pizza, which drips oil, he says to a friend, “This pizza is good.”

The first entry is biased and demonstrates judgment about the event. First, the observer makes assumptions about the internal state of the student when she writes “enjoys” and “clearly oblivious to the calorie content.” From an observer’s standpoint, there is no way of ascertaining what the student may or may not know about pizza’s nutritional value nor how much the student enjoys the pizza. The second entry provides only the details and facts that are observable.

   To avoid bias in your observations, you can use something called a “double-entry notebook.” This is a type of observation log that encourages you to separate your observations (the facts) from your feelings and judgments about the facts.

• Observations Thoughts
• The student sits in the dining hall. As he eats his piece of pizza, which drips oil, he says to a friend, "this pizza is good."  It seems like the student really enjoys the high-calorie-content pizza. 
• I observed cash register #1 for 15 minutes. During that time, 22 students paid for meals. Of those 22 students, 15 grabbed a candy bar or granola bar. 3 of the 22 students had a piece of fruit on their plate Fruit is less accessible than candy bars (it is further back in the dining court). Is this why more students are reaching for candy bars?

Figure 3: Two sample entries from a double-entry notebook.

   Observations are only one strategy in collecting primary research. You may also want to ask people directly about their behaviors, beliefs, or attitudes—and for this you will need to use surveys or interviews.

Surveys and Interviews: Question Creation

Sometimes it is very difficult for a researcher to gain all of the necessary information through observations alone. Along with his observations of the dining halls, Derek wanted to know what students ate in a typical day, and so he used a survey to have them keep track of their eating habits. Likewise, Jared wanted to learn about writing and communication in engineering and decided to draw upon expert knowledge by asking experienced individuals within the field.

   Interviews and surveys are two ways that you can gather information about people’s beliefs or behaviors. With these methods, the information you collect is not first-hand (like an observation) but rather “self-reported” data, or data collected in an indirect manner. William Shadish, Thomas Cook, and Donald Campbell argued that people are inherently biased about how they see the world and may report their own actions in a more favorable way than they may actually behave. Despite the issues in self-reported data, surveys and interviews are an excellent way to gather data for your primary research project.

Survey or Interview? 

How do you choose between conducting a survey or an interview? It depends on what kind of information you are looking for. You should use surveys if you want to learn about a general trend in people’s opinions, experiences, and behavior. Surveys are particularly useful to find small amounts of information from a wider selection of people in the hopes of making a general claim. Interviews are best used when you want to learn detailed information from a few specific people. Interviews are also particularly useful if you want to interview experts about their opinions, as Jared did. In sum, use interviews to gain de-tails from a few people, and surveys to learn general patterns from many people.

Writing Good Questions

One of the greatest challenges in conducting surveys and interviews is writing good questions. As a researcher, you are always trying to eliminate bias, and the questions you ask need to be unbiased and clear. Here are some suggestions on writing good questions:

Ask about One Thing at a Time

A poorly written question can contain multiple questions, which can confuse participants or lead them to answer only part of the question you are asking. This is called a “double-barreled question” in journalism. The following questions are taken from Jared’s research:

Poor question: What kinds of problems are being faced in the field today and where do you see the search for solutions to these problems going?

Revised question #1: What kinds of problems are being faced in the field today?

Revised question #2: Where do you see the search for solutions to these problems going?

Avoid Leading Questions

A leading question is one where you prompt the participant to respond in a particular way, which can create bias in the answers given:

Leading question: The economy is clearly in a crisis, wouldn’t you agree?

Revised question: Do you believe the economy is currently in a crisis? Why or why not?

Understand When to Use Open and Closed Questions

Closed questions, or questions that have yes/no or other limited responses, should be used in surveys. However, avoid these kinds of questions in interviews because they discourage the interviewee from going into depth. The question sample above, “Do you believe the economy currently is in a crisis?” could be answered with a simple yes or no, which could keep a participant from talking more about the issue. The “why or why not?” portion of the question asks the participant to elaborate. On a survey, the question “Do you believe the economy currently is in a crisis?” is a useful question because you can easily count the number of yes and no answers and make a general claim about participant responses.

Write Clear Questions

When you write questions, make sure they are clear, concise, and to the point. Questions that are too long, use unfamiliar vocabulary, or are unclear may confuse participants and you will not get quality responses.

Now that question creation has been addressed, we will next examine specific considerations for interviews and surveys.

Interviews, or question and answer sessions with one or more people, are an excellent way to learn in-depth information from a person for your primary research project. This section presents information on how to conduct a successful interview, including choosing the right person, ways of interviewing, recording your interview, interview locations, and transcribing your interview.

Choosing the Right Person

One of the keys to a successful interview is choosing the right person to interview. Think about whom you would like to interview and whom you might know. Do not be afraid to ask people you do not know for interviews. When asking, simply tell them what the interview will be about, what the interview is for, and how much time it will take. Jared used his Purdue University connection to locate both of the individuals that he ended up interviewing—an advanced Purdue student and a Purdue alum working in an Engineering firm.

Face-to-Face and Virtual Interviews

When interviewing, you have a choice of conducting a traditional, face-to-face interview or an interview using technology over the Internet. Face-to-face interviews have the strength that you can ask follow-up questions and use non-verbal communication to your advantage. Individuals are able to say much more in a face-to-face interview than in an email, so you will get more information from a face-to-face interview. However, the Internet provides a host of new possibilities when it comes to interviewing people at a distance. You may choose to do an email interview, where you send questions and ask the person to respond. You may also choose to use a video or audio conferencing program to talk with the person virtually. If you are choosing any Internet-based option, make sure you have a way of recording the interview. You may also use a chat or instant messaging program to interview your participant—the benefit of this is that you can ask follow-up questions during the interview and the interview is already transcribed for you. Because one of his interviewees lived several hours away, Jared chose to interview the Purdue student face-to-face and the Purdue alum via email.

Finding a Suitable Location

If you are conducting an in-person interview, it is essential that you find a quiet place for your interview. Many universities have quiet study rooms that can be reserved (often found in the university library). Do not try to interview someone in a coffee shop, dining hall, or other loud area, as it is difficult to focus and get a clear recording.

Recording Interviews

One way of eliminating bias in your research is to record your interviews rather than rely on your memory. Recording interviews allows you to directly quote the individual and re-read the interview when you are writing. It is recommended that you have two recording devices for the interview in case one recording device fails. Most computers, MP3 players, and even cell phones come with recording equipment built in. Many universities also offer equipment that students can check out and use, including computers and recorders. Before you record any interview, be sure that you have permission from your participant.

Transcribing Your Interview

Once your interview is over, you will need to transcribe your interview to prepare it for analysis. The term transcribing means creating a written record that is exactly what was said—i.e. typing up your interviews. If you have conducted an email or chat interview, you already have a transcription and can move on to your analysis stage.

Other than the fact that they both involve asking people questions, interviews and surveys are quite different data collection methods. Creating a survey may seem easy at first, but developing a quality survey can be quite challenging. When conducting a survey, you need to focus on the following areas: survey creation, survey testing, survey sampling, and distributing your survey.

Survey Creation: Length and Types of Questions

One of the keys to creating a successful survey is to keep your survey short and focused. Participants are unlikely to fill out a survey that is lengthy, and you’ll have a more difficult time during your analysis if your survey contains too many questions. In most cases, you want your survey to be something that can be filled out within a few minutes. The target length of the survey also depends on how you will distribute the survey. If you are giving your survey to other students in your dorm or classes, they will have more time to complete the survey. Therefore, five to ten minutes to complete the survey is reasonable. If you are asking students as they are walking to class to fill out your survey, keep it limited to several questions that can be answered in thirty seconds or less. Derek’s survey took about ten minutes and asked students to describe what they ate for a day, along with some demographic information like class level and gender.

   Use closed questions to your advantage when creating your survey. A closed question is any set of questions that gives a limited amount of choices (yes/no, a 1–5 scale, choose the statement that best describes you). When creating closed questions, be sure that you are accounting for all reasonable answers in your question creation. For example, asking someone “Do you believe you eat healthy?” and providing them only “yes” and “no” options means that a “neutral” or “undecided” option does not exist, even though the survey respondent may not feel strongly either way. Therefore, on closed questions you may find it helpful to include an “other” category where participants can fill in an answer. It is also a good idea to have a few open-ended questions where participants can elaborate on certain points or earlier responses. How-ever, open-ended questions take much longer to fill out than closed questions. 

Survey Creation: Testing Your Survey

To make sure your survey is an appropriate length and that your questions are clear, you can “pilot test” your survey. Prior to administering your survey on a larger scale, ask several classmates or friends to fill it out and give you feedback on the survey. Keep track of how long the survey takes to complete. Ask them if the questions are clear and make sense. Look at their answers to see if the answers match what you wanted to learn. You can revise your survey questions and the length of your survey as necessary.

Sampling and Access to Survey Populations

“Sampling” is a term used within survey research to describe the subset of people that are included in your study. Derek’s first research question was: “Are students’ eating habits at Purdue University healthy or unhealthy?” Because it was impossible for Derek to survey all 38,000 students on Purdue’s campus, he had to choose a representative sample of students. Derek chose to survey students who lived in the dorms because of the wide variety of student class levels and majors in the dorms and his easy access to this group. By making this choice, however, he did not account for commuter students, graduate students, or those who live off campus. As Derek’s case demonstrates, it is very challenging to get a truly representative sample.

   Part of the reason that sampling is a challenge is that you may find difficulty in finding enough people to take your survey. In thinking about how get people to take your survey, consider both your everyday surroundings and also technological solutions. Derek had access to many students in the dorms, but he also considered surveying students in his classes in order to reach as many people as possible. Another possibility is to conduct an online survey. Online surveys greatly increase your access to different kinds of people from across the globe, but may decrease your chances of having a high survey response rate. An email or private message survey request is more likely to be ignored due to the impersonal quality and high volume of emails most people receive.

Analyzing and Writing About Primary Research

Once you collect primary research data, you will need to analyze what you have found so that you can write about it. The purpose of analyzing your data is to look at what you collected (survey responses, interview answers to questions, observations) and to create a cohesive, systematic interpretation to help answer your research question or examine the validity of your hypothesis.

   When you are analyzing and presenting your findings, remember to work to eliminate bias by being truthful and as accurate as possible about what you found, even if it differs from what you expected to find. You should see your data as sources of information, just like sources you find in the library, and you should work to represent them accurately.

The following are suggestions for analyzing different types of data.

If you’ve counted anything you were observing, you can simply add up what you counted and report the results. If you’ve collected descriptions using a double-entry notebook, you might work to write thick descriptions of what you observed into your writing. This could include descriptions of the scene, behaviors you observed, and your overall conclusions about events. Be sure that your readers are clear on what were your actual observations versus your thoughts or interpretations of those observations.

If you’ve interviewed one or two people, then you can use your summary, paraphrasing, and quotation skills to help you accurately describe what was said in the interview. Just like in secondary research when working with sources, you should introduce your interviewees and choose clear and relevant quotes from the interviews to use in your writing. An easy way to find the important information in an interview is to print out your transcription and take a highlighter and mark the important parts that you might use in your paper. If you have conducted a large number of interviews, it will be helpful for you to create a spreadsheet of responses to each question and compare the responses, choosing representative answers for each area you want to describe.

Surveys can contain quantitative (numerical) and qualitative (written answers/descriptions) data. Quantitative data can be analyzed using a spreadsheet program like Microsoft Excel to calculate the mean (average) answer or to calculate the percentage of people who responded in a certain way. You can display this information in a chart or a graph and also describe it in writing in your paper. If you have qualitative responses, you might choose to group them into categories and/or you may choose to quote several representative responses.

Writing about Primary Research

In formal research writing in a variety of fields, it is common for research to be presented in the following format: introduction/background; methods; results; discussions; conclusion. Not all first year writing classes will require such an organizational structure, although it is likely that you will be required to present many of these elements in your paper. Because of this, the next section examines each of these in depth.

Introduction (Review of Literature)

The purpose of an introduction and review of literature in a research paper is to provide readers with information that helps them under-stand the context, purpose, and relevancy of your research. The introduction is where you provide most of your background (library) research that you did earlier in the process. You can include articles, statistics, research studies, and quotes that are pertinent to the issues at hand. A second purpose in an introduction is to establish your own credibility (ethos) as a writer by showing that you have researched your topic thoroughly. This kind of background discussion is required in nearly every field of inquiry when presenting research in oral or written formats.

   Derek provided information from the Food and Drug Administration on healthy eating and national statistics about eating habits as part of his background information. He also made the case for healthy eating on campus to show relevancy:

Currently Americans are more overweight than ever. This is coming at a huge cost to the economy and government. If current trends in increasing rates of overweight and obesity continue it is likely that this generation will be the first one to live shorter lives than their parents did. Looking at the habits of university students is a good way to see how a new generation behaves when they are living out on their own for the first time.

Describing What You Did (Methods)

When writing, you need to provide enough information to your readers about your primary research process for them to understand what you collected and how you collected it. In formal research papers, this is often called a methods section. Providing information on your study methods also adds to your credibility as a writer. For surveys, your methods would include describing who you surveyed, how many surveys you collected, decisions you made about your survey sample, and relevant demographic information about your participants (age, class level, major). For interviews, introduce whom you interviewed and any other relevant information about interviewees such as their career or expertise area. For observations, list the locations and times you observed and how you recorded your observations (i.e. double-entry notebook). For all data types, you should describe how you analyzed your data.

The following is a sample from Jared about his participants:

In order to gain a better understanding of the discourse community in environmental and resource engineering, I interviewed Anne Dare, a senior in environmental and natural resource engineering, and Alyson Keaton an alumnus of Purdue University. Alyson is a current employee of the Natural Resource Conservation Service (NRCS), which is a division of the United States Department of Agriculture (USDA).

Here is a sample from Derek’s methods section:

I conducted a survey so that I could find out what students at Purdue actually eat on a typical day. I handed out surveys asking students to record what they ate for a day . . . I received 29 back and averaged the results based on average number of servings from each food group on the old food guide pyramid. The group included students from the freshman to the graduate level and had 8 women and 21 men respond.

Describing Your Study Findings (Results)

In a formal research paper, the results section is where you describe what you found. The results section can include charts, graphs, lists, direct quotes, and overviews of findings. Readers find it helpful if you are able to provide the information in different formats. For example, if you have any kind of numbers or percentages, you can talk about them in your written description and then present a graph or chart showing them visually. You should provide specific details as supporting evidence to back up your findings. These details can be in the form of direct quotations, numbers, or observations.

Jared describes some of his interview results:

Alyson also mentioned the need for phone conversation. She stated, “The phone is a large part of my job. I am communicating with other NRCS offices daily to find out the status of our jobs.” She needs to be in constant contact in order to insure that everything is running smoothly. This is common with those overseeing projects. In these cases, the wait for a response to an email or a memo can be too long to be effective.

Interpreting What You Learned (Discussion)

In formal research papers, the discussion section presents your own interpretation of your results. This may include what you think the results mean or how they are useful to your larger argument. If you are making a proposal for change or a call to action, this is where you make it. For example, in Derek’s project about healthy eating on campus, Derek used his primary research on students’ unhealthy eating and observations of the food courts to argue that the campus food courts needed serious changes. Derek writes, “Make healthy food options the most accessible in every dining hall while making unhealthy foods the least. Put nutrition facts for everything that is served in the dining halls near the food so that students can make more informed decisions on what to eat.”

   Jared used the individuals he interviewed as informants that helped him learn more about writing in agricultural and biological engineering. He integrated the interviews he conducted with secondary research to form a complete picture of writing and communication in agricultural and biological engineering. He concludes:

Writing takes so many forms, and it is important to know about all these forms in one way or another. The more forms of writing you can achieve, the more flexible you can be. This ability to be flexible can make all the difference in writing when you are dealing with a field as complex as engineering.

Primary Research and Works Cited or References Pages

The last part of presenting your primary research project is a works cited or references page. In general, since you are working with data you collected yourself, there is no source to cite an external source. Your methods section should describe in detail to the readers how and where the data presented was obtained. However, if you are working with interviews, you can cite these as “personal communication.” The MLA and APA handbooks both provide clear listings of how to cite personal communication in a works cited/references page.

This essay has presented an overview to three commonly used methods of primary research in first year writing courses: observations, interviews, and surveys. By using these methods, you can learn more about the world around you and craft meaningful written discussions of your findings.

• Primary research techniques show up in more places than just first year writing courses. Where else might interviews, surveys, or observations be used? Where have you seen them used?
• The chapter provides a brief discussion of the ethical considerations of research. Can you think of any additional ethical considerations when conducting primary research? Can you think of ethical considerations unique to your own research project?
• Primary research is most useful for first year writing students if it is based in your local community or campus. What are some current issues on your campus or in your community that could be investigated using primary research methods?
• In groups or as a class, make a list of potential primary research topics. After each topic on the list, consider what method of inquiry (observation, interview, or survey) you would use to study the topic and answer why that method is a good choice.

Suggested Resources

For more information on the primary methods of inquiry described here, please see the following sources:

Works Cited

This essay was written by Dana Lynn Driscoll and was published as a chapter in Writing Spaces: Readings on Writing , Volume 2, a peer-reviewed open textbook series for the writing classroom. This work is licensed under the Attribution-NonCommercial-ShareAlike 3.0 Unported License (CC BY-NC-SA 3.0) . Please keep this information on this material if you use, adapt, and/or share it.  

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6 How to Analyze Data in a Primary Research Study

Melody Denny and Lindsay Clark

This chapter introduces students to the idea of working with primary research data grounded in qualitative inquiry, closed-and open-ended methods, and research ethics (Driscoll; Mackey and Gass; Morse; Scott and Garner). [1] We know this can seem intimidating to students, so we will walk them through the process of analyzing primary research, using information from public datasets including the Pew Research Center. Using sample data on teen social media use, we share our processes for analyzing sample data to demonstrate different approaches for analyzing primary research data (Charmaz; Creswell; Merriam and Tisdale; Saldaña). We also include links to additional public data sets, chapter discussion prompts, and sample activities for students to apply these strategies.

At this point in your education, you are familiar with what is known as secondary research or what many students think of as library research. Secondary research makes use of sources most often found in the library or, these days, online (books, journal articles, magazines, and many others). There’s another kind of research that you may or may not be familiar with: primary research. The Purdue OWL defines primary research as “any type of research you collect yourself” and lists examples as interviews, observations, and surveys (“What is Primary Research”).

Primary research is typically divided into two main types—quantitative and qualitative research. These two methods (or a mix of these) are used by many fields of study, so providing a singular definition for these is a bit tricky. Sheard explains that “quantitative research…deals with data that are numerical or that can be converted into numbers. The basic methods used to investigate numerical data are called ‘statistics’” (429). Guest, et al. explain that qualitative research is “information that is difficult to obtain through more quantitatively-oriented methods of data collection” and is used more “to answer the whys and hows of human behavior, opinion, and experience” (1).

This chapter focuses on qualitative methods that explore peoples’ behaviors, interpretations, and opinions. Rather than being only a reader and reporter of research, primary research allows you to be creators of research. Primary research provides opportunities to collect information based on your specific research questions and generate new knowledge from those questions to share with others. Generally, primary research tends to follow these steps:

• Develop a research question. Secondary research often uses this as a starting point as well. With primary research, however, rather than using library research to answer your research question, you’ll also collect data yourself to answer the question you developed. Data, in this case, is the information you collect yourself through methods such as interviews, surveys, and observations.
• Decide on a research method. According to Scott and Garner, “A research method is a recognized way of collecting or producing [primary data], such as a survey, interview, or content analysis of documents” (8). In other words, the method is how you obtain the data.
• Collect data. Merriam and Tisdale clarify what it means to collect data: “data collection is about asking, watching, and reviewing” (105-106). Primary research might include asking questions via surveys or interviews, watching or observing interactions or events, and examining documents or other texts.
• Analyze data. Once data is collected, it must then be analyzed. “Data analysis is the process of making sense out of the data… Basically, data analysis is the process used to answer your research question(s)” (Merriam and Tisdale 202). It’s worth noting that many researchers collect data and analyze at the same time, so while these may seem like different steps in the process, they actually overlap.
• Report findings. Once the researcher has spent time understanding and interpreting the data, they are then ready to write about their research, often called “findings.” You may also see this referred to as “results.”

While the entire research process is discussed, this chapter focuses on the analysis stage of the process (step 4). Depending on where you are in the research process, you may need to spend more time on step 1, 2, or 3 and review Driscoll’s “Introduction to Primary Research” (Volume 2 of Writing Spaces ).

Primary research can seem daunting, and some students might think that they can’t do primary research, that this type of research is for professionals and scholars, but that’s simply not true. It’s true that primary research data can be difficult to collect and even more difficult to analyze, but the findings are typically very revealing. This chapter and the examples included break down this research process and demonstrate how general curiosity can lead to exciting chances to learn and share information that is relevant and interesting. The goal of this chapter is to provide you with some information about data analysis and walk you through some activities to prepare you for your own data analysis. The next section discusses analyzing data from closed-ended methods and open-ended methods.

Data from Primary Research

As stated above, this chapter doesn’t focus on methods, but before moving on to analysis, it’s important to clarify a few things related to methods as they are directly connected to analyzing data. As a quick reminder, a research method is how researchers collect their data such as surveys, interviews, or textual analysis. No matter which method used, researchers need to think about the types of questions to ask for answering their overall research question. Generally, there are two types of questions to consider: closed-ended and open-ended. The next section provides examples of the data you might receive from asking closed-ended and open-ended questions and options for analyzing and presenting that data.

Data from Closed-Ended Methods

The data that is generated by closed-ended questions on methods such as surveys and polls is often easier to organize. Because the way respondents could answer those questions is limited to specific answers (Yes/No, numbered scales, multiple choice), the data can be analyzed by each question or by looking at the responses individually or as a whole. Though there are several approaches to analyzing the data that comes from closed-ended questions, this section will introduce you to a few different ways to make sense of this kind of data.

Closed-ended questions are those that have limited answers, like multiple choice or check-all-that-apply questions. These questions mean that respondents can provide only the answers given or they may select an “other” option. An example of a closed-ended question could be “Do you use YouTube? Yes, No, Sometimes.” Closed-ended questions have their perks because they (mostly) keep participants from misinterpreting the question or providing unhelpful responses. They also make data analysis a bit easier.

If you were to ask the “Yes, No, Sometimes” question about YouTube to 20 of your closest friends, you may get responses like Yes = 18, No = 1, and Sometimes = 1. But, if you were to ask a more detailed question like “Which of the following social media platforms do you use?” and provide respondents with a check-all-that-apply option, like “Facebook, YouTube, Twitter, Instagram, Snapchat, Reddit, and Tumblr,” you would get a very different set of data. This data might look like Facebook = 17, YouTube = 18, Twitter = 12, Instagram = 20, Snapchat = 15, Reddit = 8, and Tumblr = 3. The big takeaway here is that how you ask the question determines the type of data you collect.

Analyzing Closed-Ended Data

Now that you have data, it’s time to think about analyzing and presenting that data. Luckily, the Pew Research Center conducted a similar study that can be used as an example. The Pew Research Center is a “nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research” (“About Pew Research Center”). The information provided below comes from their public dataset “Teens, Social Media, and Technology, 2018” (Anderson and Jiang). This example is used to show how you might analyze this type of data once collected and what that data might look like. “Teens, Social Media, and Technology 2018” reported responses to questions related to which online platforms teens use and which they use most often. In figure 1 below, Pew researchers show the final product of their analysis of the data:

Pew analyzed their data and organized the findings by percentages to show what they discovered. They had 743 teens who responded to these questions, so presenting their findings in percentages helps readers better “see” the data overall (rather than saying YouTube = 631 and Instagram = 535). However, results can be represented in different ways. When the Pew researchers were deciding how to present their data, they could have reported the frequency, or the number of people who said they used YouTube, Instagram, and Snapchat.

In the scenario of polling 20 of your closest friends, you, too, would need to decide how to present your data: Facebook = 17, YouTube = 18, Twitter = 12, Instagram = 20, Snapchat = 15, Reddit = 8, and Tumblr = 3. In your case, you might want to present the frequency (number) of responses rather than the percentages of responses like Pew did. You could choose a bar graph like Pew or maybe a simple table to show your data.

Looking again at the Pew data, researchers could use this data to generate further insights or questions about user preferences. For example, one could highlight the fact that 85% of respondents reported using YouTube the most, while only 7% reported using Reddit. Why is that? What conclusions might you be able to make based on these data? Does the data make you wonder if any additional questions might be explored? If you want to learn more about your respondents’ opinions or preference, you might need to ask open-ended questions.

Data from Open-Ended Methods

Whereas closed-ended questions limit how respondents might answer, open-ended questions do not limit respondents’ answers and allow them to answer more freely. An example of an open-ended question, to build off the question above, could be “Why do you use social media? Explain.” This type of question gives respondents more space to fully explain their responses. Open-ended questions can make the data varied because each respondent may answer differently. These questions, which can provide fruitful responses, can also mean unexpected responses or responses that don’t help to answer the overall research question, which can sometimes make data analysis challenging.

In that same Pew Research Center data, respondents were likely limited in how they were able to answer by selecting social media platforms from a list. Pew also shares selected data (Appendix A), and based on these data, it can be assumed they also asked open-ended questions, something about the positive or negative effects of social media platforms. Because their research method included both closed-ended questions about which platforms teens use as well as open-ended questions that invited their thoughts about social media, Pew researchers were able to learn more about these participants’ thoughts and perceptions. To give us, the readers, a clearer idea of how they justified their presentation of the data, Pew offers 15 sample excerpts from those open-ended questions. They explain that these excerpts are what the researchers believe are representative of the larger data set. We explain below how we might analyze those excerpts.

Analyzing Open-Ended Data

As Driscoll reminds us, ethical considerations impact all stages of the research process, and researchers should act ethically throughout the entire research process. You already know a little something about research ethics. For example, you know that ethical writers cite sources used in research papers by giving credit to the person who created that information. When creating primary sources, you have a few different ethical considerations for analyzing data, which will be discussed below.

To demonstrate how to analyze data from open-ended methods, we explain how we (Melody and Lindsay) analyzed the 15 excerpts from the Pew data using open coding. Open coding means analyzing the data without any predetermined categories or themes; researchers are just seeing what emerges or seems significant (Charmaz). Creswell suggests four specific steps when coding qualitative data, though he also stresses that these steps are iterative, meaning that researchers may need to revisit a step anywhere throughout the process. We use these four steps to explain our analysis process, including how we ethically coded the data, interpreted what the coding process revealed, and worked together to identify and explain categories we saw in the data.

Step 1: Organizing and Preparing the Data

The first part of the analysis stage is organizing the data before examining it. When organizing data, researchers must be careful to work with primary data ethically because that data often represents actual peoples’ information and opinions. Therefore, researchers need to carefully organize the data in such a way as to not identify their participants or reveal who they are. This is a key component to The Belmont Report , guidelines published in 1979 meant to guide researchers and help protect participants. Using pseudonyms or assigning numbers or codes (in place of names) to the data is a recommended ethical step to maintain participants’ confidentiality in a study. Anonymizing data, or removing names, has the additional effect of eliminating researcher bias, which can occur when researchers are so familiar with their own data and participants that the researchers may begin to think they already know the answers or see connections prior to analysis (Driscoll). By assigning pseudonyms, researchers can also ensure that they take an objective look at each participant’s answers without being persuaded by participant identity.

The first part of coding is to make notations while reading through the data (Merriam and Tisdale). At this point, researchers are open to many possibilities regarding their data. This is also where researchers begin to construct categories. Offering a simple example to illustrate this decision-making process, Merriam and Tisdale ask us to imagine sorting and categorizing two hundred grocery store items (204). Some items could be sorted into more than one category; for example, ice cream could be categorized as “frozen” or as “dessert.” How you decide to sort that item depends on your research question and what you want to learn.

For this step, we, Melody and Lindsay, each created a separate document that included the 15 excerpts. Melody created a table for the quotes, leaving a column for her coding notes, and Lindsay added spaces between the excerpts for her notes. For our practice analysis, we analyzed the data independently, and then shared what we did to compare, verify, and refine our analysis. This brings a second, objective view to the analysis, reduces the effect of researcher bias, and ensures that your analysis can be verified and supported by the data. To support your analysis, you need to demonstrate how you developed the opinions and conclusions you have about your data. After all, when researchers share their analyses, readers often won’t see all of the raw data, so they need to be able to trust the analysis process.

Step 2: Reading through All the Data

Creswell suggests getting a general sense of the data to understand its overall meaning. As you start reading through your data, you might begin to recognize trends, patterns, or recurring features that give you ideas about how to both analyze and later present the data. When we read through the interview excerpts of these 15 participants’ opinions of social media, we both realized that there were two major types of comments: positive and negative. This might be similar to categorizing the items in the grocery store (mentioned above) into fresh/frozen foods and non-perishable items.

To better organize the data for further analysis, Melody marked each positive comment with a plus sign and each negative comment with a minus sign. Lindsay color-coded the comments (red for negative, indicated by boldface type below; green for positive, indicated by grey type below) and then organized them on the page by type. This approach is in line with Merriam and Tisdale’s explanation of coding: “assigning some sort of shorthand designation to various aspects of your data so that you can easily retrieve specific pieces of the data. The designations can be single words, letters, numbers, phrases, colors, or combinations of these” (199). While we took different approaches, as shown the two sections below, both allowed us to visually recognize the major sections of the data:

Lindsay’s Coding Round 1, which shows her color coding indicated by boldface type

“[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) “It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15) “[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17) “It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15) “I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14) “Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14) “We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

Melody’s Coding Round 1, showing her use of plus and minus signs to classify the comments as positive or negative, respectively

+ “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) – “It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15) – “[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17) + “It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15) + “I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14) – “Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14) + “We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

Step 3: Doing Detailed Coding Analysis of the Data

It’s important to mention that Creswell dedicates pages of description on coding data because there are various ways of approaching detailed analysis. To code our data, we added a descriptive word or phrase that “symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute” to a portion of data (Saldaña 3). From the grocery store example above, that could mean looking at the category of frozen foods and dividing them into entrees, side dishes, desserts, appetizers, etc. We both coded for topics or what the teens were generally talking about in their responses. For example, one excerpt reads “Social media allows us to communicate freely and see what everyone else is doing. It gives us a voice that can reach many people.” To code that piece of data, researchers might assign words like communication, voice, or connection to explain what the data is describing.

In this way, we created the codes from what the data said, describing what we read in those excerpts. Notice in the section below that, even though we coded independently, we described these pieces of data in similar ways using bolded keywords:

Melody’s Coding Round 2, with key words added to summarize the meanings of the different quotes

– “Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13) bullying – “It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15) fake + “Because a lot of things created or made can spread joy.” (Boy, age 17) reaching people + “I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15) connection + “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) reaching people

Lindsay’s Coding Round 2, with key words added in capital letters to summarize the meanings of the quotations

“Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13) OPPORTUNITIES TO COMMUNICATE NEGATIVELY/MORE EASILY “It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15) FAKE, NOT REALITY “Because a lot of things created or made can spread joy.” (Boy, age 17) SPREAD JOY “I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15) INTERACTION, LESS LONELY “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) COMMUNICATE, VOICE

Though there are methods that allow for researchers to use predetermined codes (like from previous studies), “the traditional approach…is to allow the codes to emerge during the data analysis” (Creswell 187).

Step 4: Using the Codes to Create a Description Using Categories, Themes, Settings, or People

Our individual coding happened in phases, as we developed keywords and descriptions that could then be defined and relabeled into concise coding categories (Saldaña 11). We shared our work from Steps 1-3 to further define categories and determine which themes were most prominent in the data. A few times, we interpreted something differently and had to discuss and come to an agreement about which category was best.

In our process, one excerpt comment was interpreted as negative by one of us and positive by the other. Together we discussed and confirmed which comments were positive or negative and identified themes that seemed to appear more than once, such as positive feelings towards the interactional element of social media use and the negative impact of social media use on social skills. When two coders compare their results, this allows for qualitative validity, which means “the researcher checks for the accuracy of the findings” (Creswell 190). This could also be referred to as intercoder reliability (Lavrakas). For intercoder reliability, researchers sometimes calculate how often they agree in a percentage. Like many other aspects of primary research, there is no consensus on how best to establish or calculate intercoder reliability, but generally speaking, it’s a good idea to have someone else check your work and ensure you are ethically analyzing and reporting your data.

Interpreting Coded Data

Once we agreed on the common categories and themes in this dataset, we worked together on the final analysis phase of interpreting the data, asking “what does it mean?” Data interpretation includes “trying to give sense to the data by creatively producing insights about it” (Gibson and Brown 6). Though we acknowledge that this sample of only 15 excerpts is small, and it might be difficult to make claims about teens and social media from just this data, we can share a few insights we had as part of this practice activity.

Overall, we could report the frequency counts and percentages that came from our analysis. For example, we counted 8 positive comments and 7 negative comments about social media. Presented differently, those 8 positive comments represent 53% of the responses, so slightly over half. If we focus on just the positive comments, we are able to identify two common themes among those 8 responses: Interaction and Expression. People who felt positively about social media use identified the ability to connect with people and voice their feelings and opinions as the main reasons. When analyzing only the 7 negative responses, we identified themes of Bullying and Social Skills as recurring reasons people are critical of social media use among teens. Identifying these topics and themes in the data allows us to begin thinking about what we can learn and share with others about this data.

How we represent what we have learned from our data can demonstrate our ethical approach to data analysis. In short, we only want to make claims we can support, and we want to make those claims ethically, being careful to not exaggerate or be misleading.

To better understand a few common ethical dilemmas regarding the presentation of data, think about this example: A few years ago, Lindsay taught a class that had only four students. On her course evaluations, those four students rated the class experience as “Excellent.” If she reports that 100% of her students answered “Excellent,” is she being truthful? Yes. Do you see any potential ethical considerations here? If she said that 4/4 gave that rating, does that change how her data might be perceived by others? While Lindsay could show the raw data to support her claims, important contextual information could be missing if she just says 100%. Perhaps others would assume this was a regular class of 20-30 students, which would make that claim seem more meaningful and impressive than it might be.

Another word for this is cherry picking. Cherry picking refers to making conclusions based on thin (or not enough) data or focusing on data that’s not necessarily representative of the larger dataset (Morse). For example, if Lindsay reported the comment that one of her students made about this being the “best class ever,” she would be telling the truth but really only focusing on the reported opinion of 25% of the class (1 out of 4). Ideally, researchers want to make claims about the data based on ideas that are prominent, trending, or repeated. Less prominent pieces of data, like the opinion of that one student, are known as outliers, or data that seem to “be atypical of the rest of the dataset” (Mackey and Gass 257). Focusing on those less-representative portions might misrepresent or overshadow the aspects of the data that are prominent or meaningful, which could create ethical problems for your study. With these ethical considerations in mind, the last step of conducting primary research would be to write about the analysis and interpretation to share your process with others.

This chapter has introduced you to ethically analyzing data within the primary research tradition by focusing on close-ended and open-ended data. We’ve provided you with examples of how data might be analyzed, interpreted, and presented to help you understand the process of making sense of your data. This is just one way to approach data analysis, but no matter your research method, having a systematic approach is recommended. Data analysis is a key component in the overall primary research process, and we hope that you are now excited and curious to participate in a primary research project.

Works Cited

“About Pew Research Center.” Pew Research Center, 2020. www.pewresearch.org/about/ . Accessed 28 Dec 2020. Anderson, Monica, and Jingjing Jiang.

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“My mom had to get a ride to the library to get what I have in my hand all the time. She reminds me of that a lot.” (Girl, age 14)

“Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13)

“It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15)

“Because a lot of things created or made can spread joy.” (Boy, age 17)

“I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15)

“[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15)

“It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15)

“[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17)

“It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15)

“I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14)

“Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14)

“We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

“It has given many kids my age an outlet to express their opinions and emotions, and connect with people who feel the same way.” (Girl, age 15)

“People can say whatever they want with anonymity and I think that has a negative impact.” (Boy, age 15)

“It has a negative impact on social (in-person) interactions.” (Boy, age 17)

Teacher Resources for How to Analyze Data in a Primary Research Study

Overview and teaching strategies.

This chapter is intended as an overview of analyzing qualitative research data and was written as a follow-up piece to Dana Lynn Driscoll’s “Introduction to Primary Research: Observations, Surveys, and Interviews” in Volume 2 of this collection. This chapter could work well for leading students through their own data analysis of a primary research project or for introducing students to the idea of primary research by using outside data sources, those in the chapter and provided in the activities below, or data you have access to.

From our experiences, students usually have limited experience with primary research methods outside of conducting a small survey for other courses, like sociology. We have found that few of our students have been formally introduced to primary research and analysis. Therefore, this chapter strives to briefly introduce students to primary research while focusing on analysis. We’ve presented analysis by categorizing data as open-ended and closed-ended without getting into too many details about qualitative versus quantitative. Our students tend to produce data collection tools with a mix of these types of questions, so we feel it’s important to cover the analysis of both.

In this chapter, we bring students real examples of primary data and lead them through analysis by showing examples. Any of these exercises and the activities below may be easily supplemented with additional outside data. One way that teachers can bring in outside data is through the use of public datasets.

Public Data Sets

There are many public data sets that teachers can use to acquaint their students with analyzing data. Be aware that some of these datasets are for experienced researchers and provide the data in CSV files or include metadata, all of which is probably too advanced for most of our students. But if you are comfortable converting this data, it could be valuable for a data analysis activity.

• In the chapter, we pulled from Pew Research, and their website contains many free and downloadable data sets (Pew Research Center).
• The site Data.gov provides searchable datasets, but you can also explore their data by clicking on “data” and seeing what kinds of reports they offer.
• The U.S. Census Bureau offers some datasets as well (Explore Census Data): Much of this data is presented in reports, but teachers could pull information from reports and have students analyze the data and compare their results to those in the report, much like we did with the Pew Research data in the chapter.
• Similarly, HealthData.gov offers research-based reports packed with data for students to analyze.
• In one of the activities below, we used Google Trends to look at searches over a period of time. There are some interesting data and visuals provided on the homepage to help students get started.
• If you’re looking for something a bit more academic, the Michigan Corpus of Academic Spoken English is a great database of transcripts from academic interactions and situations.
• Similarly, the Corpus of Contemporary American English allows users to search for words or word strings to see their frequency and in which genre and when these occur.

Before moving on to student activities, we’d like to offer one additional suggestion for teachers to consider.

Class Google Form

One thing that Melody does at the beginning of almost all of her research-based writing courses is ask students to complete a Google Form at the beginning of the semester. Sometimes, these forms are about their experiences with research. Other times, they revolve around a class topic (recently, she’s been interested in Generation Z or iGeneration and has asked students questions related to that). Then, when it’s time to start thinking about primary research, she uses that Google Form to help students understand more about the primary research process. Here are some ways that teachers can employ the data gathered from Google Form given to students.

• Ask students to look at the questions asked on the survey and deduce the overall research question.
• • Ask students to look at the types of questions asked (open- and closed-ended) and consider why they were constructed that way.
• Ask students to evaluate the wording of the questions asked.
• Ask students to examine the results of a few (or more) or the questions on the survey. This can be done in groups with each group looking at 1-3 questions, depending on the size of your Google Form.
• Ask students to think about how they might present that data in visual form. Yes, Google provides some visuals, but you can give them the raw data and see what they come up with.
• Ask students to come up with 1-3 major takeaways based on all the data.

This exercise allows students to work with real data and data that’s directly related to them and their classmates. It’s also completely within ethical boundaries because it’s data collected in the classroom, for educational purposes, and it stays within the classroom.

Below we offer some guiding questions to help move students through the chapter and the activities as well as some additional activities.

Discussion Questions

• In the opening of this chapter, we introduced you to primary research , or “any type of research you collect yourself” (“What is Primary Research”). Have you completed primary research before? How did you decide on your research method, based on your research question? If you have not worked on primary research before, brainstorm a potential research question for a topic you want to know more about. Discuss what research method you might use, including closed- or open-ended methods and why.
• Looking at the chart from the Pew Research dataset, “Teens, Social Media, and Technology 2018,” would you agree that the distributions among online platforms remain similar, or have trends changed?
• What do you make of the “none of the above” category on the Pew table? Do you think teens are using online platforms that aren’t listed, or do you think those respondents don’t use any online platforms?

• When analyzing data from open-ended questions, which step seems most challenging to you? Explain.

Activity #1: TurnItIn and Infographics

Infographics can be a great way to help you see and understand data, while also giving you a way to think about presenting your own data. Multiple infographics are available on TurnItIn, downloadable for free, that provide information about plagiarism.

Figure 3, titled “The Plagiarism Spectrum,” provides you with the “severity” and “frequency” based on survey findings of nearly 900 high school and college instructors from around the world. TurnItIn encourages educators to print this infographic and hang in their classroom:

This infographic provides some great data analysis examples: specific categories with definitions (and visual representation of their categories), frequency counts with bar graphs, and color gradient bars to show higher vs. lower numbers.

• Write a summary of how this infographic presents data.
• How do you think they analyzed the data based on this visual?

Activity #2: How Text Messages Change from Dating to Marriage

In Alice Zhao’s Huffington Post piece, she analyzes text messages that she collected during her relationship with her boyfriend, turned fiancé, turned husband to answer the question of how text messages (or communication) change over the course of a relationship. While Zhao offers some insight into her data, she also provides readers with some really cool graphics that you can use to practice your analysis skills.

These first graphics are word clouds. In figure 4, Zhao put her textual data into a program that creates these images based on the most frequently occurring words. Word clouds are another option for analyzing your data. If you have a lot of textual data and want to know what participants said the most, placing your data into a word cloud program is an easy way to “see” the data in a new way. This is usually one of the first steps of analysis, and additional analysis is almost always needed.

• What do you notice about the texts from 2008 to 2014?
• What do you notice between her texts (me) and his texts (him)?

Zhao also provided this graphic (figure 5), a comparative look at what she saw as the most frequently occurring words from the word clouds. This could be another step in your data analysis procedure: zooming in on a few key aspects and digging a bit deeper.

• What do you make of this data? Why might the word “hey” occur more frequently in the dating time frame and the word “ok” occur more frequently in the married time frame?

As part of her research, Zhao also looked at the time of day text messages were sent, shown below in figure 6:

Here, Zhao looked at messages sent a month after their first date, a month after their engagement, and a month after their wedding.

• She offers her own interpretation in her piece in figure 6, but what do you think of this?
• Also make note of this graphic. It’s a great way to look at the data another way. If your data may be time sensitive, this type of graphic may help you better analyze and understand your data.
• This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) and is subject to the Writing Spaces Terms of Use. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ , email [email protected] , or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. To view the Writing Spaces Terms of Use, visit http://writingspaces.org/terms-of-use . ↵

How to Analyze Data in a Primary Research Study Copyright © 2021 by Melody Denny and Lindsay Clark is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Primary and Secondary Data Collection to Conduct Researches, Write Thesis and Dissertation Amidst COVID-19 Pandemic: A Guidepost

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• Antonio S. Valdez 13 ,
• Tabassam Raza 13 , 14 ,
• Martha I. Farolan 15 ,
• Celso I. Mendoza 16 , 18 ,
• Leticia Q. Perez 17 , 19 ,
• Jose F. Peralta 13 ,
• Richelle I. Valencia 13 &
• Harold Anthony Martin P. Lim 13  

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 294))

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Research has always been regarded by many as tedious because of the difficulties and challenges associated with doing research such as having to forego certain habits like social life. Doing research became even more difficult, especially with regard to limitation on collecting applicable primary and secondary data due to the COVID-19 pandemic lockdowns. It is to be noted that substantive, thorough, sophisticated literature review and intensive pertinent primary data availability are ncessary for doing quality research relevant to the status quo. Various novel approaches have been adopted by scholars through their diverse academic spheres in conducting internationally acceptable research amidst the COVID-19 pandemic. This research aims to come up with a guidepost to facilitate researchers and other stakeholders with fundamental knowledge and skills in conducting substantive, thorough, sophisticated researches that are of international standards. A comparative and diagnostic analysis method is used for analyzing existing literature and policies developed by higher education institutions and schools for doing research in the advent of the COVID-19 pandemic. The output allowed authors to develop a guidepost with rules on using limited primary and extensive secondary data in doing research. The guidepost consists of various sections explaining on how to do research and write theses and dissertations. These sections include among others research title, statement of the problem, research objectives, theoretical and conceptual frameworks, review of related literature, research methodology, analysis and interpretation of data, and conclusion and recommendations. The guidepost is very significant in doing researches and aids researchers in conducting internationally accepted researches with limited primary data and extensive secondary data in the advent of the COVID-19 Pandemic. The guidepost is flexible and can easily be used by local and international institutions’ researchers through little modification in context of their research fields.

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Valdez, A.S. et al. (2023). Primary and Secondary Data Collection to Conduct Researches, Write Thesis and Dissertation Amidst COVID-19 Pandemic: A Guidepost. In: Pal, I., Kolathayar, S., Tawhidul Islam, S., Mukhopadhyay, A., Ahmed, I. (eds) Proceedings of the 2nd International Symposium on Disaster Resilience and Sustainable Development. Lecture Notes in Civil Engineering, vol 294. Springer, Singapore. https://doi.org/10.1007/978-981-19-6297-4_20

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What Is a Primary Source?

Glossary of Grammatical and Rhetorical Terms - Definition and Examples

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• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

In research and academics, a primary source refers to information collected from sources that witnessed or experienced an event firsthand. These can be historical documents , literary texts, artistic works, experiments, journal entries, surveys, and interviews. A primary source, which is very different from a secondary source , is also called primary data.

The Library of Congress defines primary sources as "the raw materials of history—original documents and objects which were created at the time under study," in contrast to secondary sources , which are "accounts or interpretations of events created by someone without firsthand experience," ("Using Primary Sources").

Secondary sources are often meant to describe or analyze a primary source and do not give firsthand accounts; primary sources tend to provide more accurate depictions of history but are much harder to come by.

Characteristics of Primary Sources

There are a couple of factors that can qualify an artifact as a primary source. The chief characteristics of a primary source, according to Natalie Sproull, are: "(1) [B]eing present during the experience, event or time and (2) consequently being close in time with the data. This does not mean that data from primary sources are always the best data."

Sproull then goes on to remind readers that primary sources are not always more reliable than secondary sources. "Data from human sources are subject to many types of distortion because of such factors as selective recall, selective perceptions, and purposeful or nonpurposeful omission or addition of information. Thus data from primary sources are not necessarily accurate data even though they come from firsthand sources," (Sproull 1988).

Original Sources

Primary sources are often called original sources, but this is not the most accurate description because you're not always going to be dealing with original copies of primary artifacts. For this reason, "primary sources" and "original sources" should be considered separate. Here's what the authors of "Undertaking Historical Research in Literacy," from Handbook of Reading Research , have to say about this:

"The distinction also needs to be made between primary and original sources . It is by no means always necessary, and all too often it is not possible, to deal only with original sources. Printed copies of original sources, provided they have been undertaken with scrupulous care (such as the published letters of the Founding Fathers), are usually an acceptable substitute for their handwritten originals." (E. J. Monaghan and D. K. Hartman, "Undertaking Historical Research in Literacy," in Handbook of Reading Research , ed. by P. D. Pearson et al. Erlbaum, 2000)

When to Use Primary Sources

Primary sources tend to be most useful toward the beginning of your research into a topic and at the end of a claim as evidence, as Wayne Booth et al. explain in the following passage. "[Primary sources] provide the 'raw data' that you use first to test the working hypothesis and then as evidence to support your claim . In history, for example, primary sources include documents from the period or person you are studying, objects, maps, even clothing; in literature or philosophy, your main primary source is usually the text you are studying, and your data are the words on the page. In such fields, you can rarely write a research paper  without using primary sources," (Booth et al. 2008).

When to Use Secondary Sources

There is certainly a time and place for secondary sources and many situations in which these point to relevant primary sources. Secondary sources are an excellent place to start. Alison Hoagland and Gray Fitzsimmons write: "By identifying basic facts, such as year of construction, secondary sources can point the researcher to the best primary sources , such as the right tax books. In addition, a careful reading of the bibliography in a secondary source can reveal important sources the researcher might otherwise have missed," (Hoagland and Fitzsimmons 2004).

Finding and Accessing Primary Sources

As you might expect, primary sources can prove difficult to find. To find the best ones, take advantage of resources such as libraries and historical societies. "This one is entirely dependent on the assignment given and your local resources; but when included, always emphasize quality. ... Keep in mind that there are many institutions such as the Library of Congress that make primary source material freely available on the Web," (Kitchens 2012).

Methods of Collecting Primary Data

Sometimes in your research, you'll run into the problem of not being able to track down primary sources at all. When this happens, you'll want to know how to collect your own primary data; Dan O'Hair et all tell you how: "If the information you need is unavailable or hasn't yet been gathered, you'll have to gather it yourself. Four basic methods of collecting primary data are field research, content analysis, survey research, and experiments. Other methods of gathering primary data include historical research, analysis of existing statistics, ... and various forms of direct observation," (O'Hair et al. 2001).

• Booth, Wayne C., et al. The Craft of Research . 3rd ed., University of Chicago Press, 2008.
• Hoagland, Alison, and Gray Fitzsimmons. "History."  Recording Historic Structures. 2nd. ed., John Wiley & Sons, 2004.
• Kitchens, Joel D. Librarians, Historians, and New Opportunities for Discourse: A Guide for Clio's Helpers . ABC-CLIO, 2012.
• Monaghan, E. Jennifer, and Douglas K. Hartman. "Undertaking Historical Research in Literacy." Handbook of Reading Research. Lawrence Erlbaum Associates, 2002.
• O'Hair, Dan, et al. Business Communication: A Framework for Success . South-Western College Pub., 2001.
• Sproull, Natalie L. Handbook of Research Methods: A Guide for Practitioners and Students in the Social Sciences. 2nd ed. Scarecrow Press, 1988.
• "Using Primary Sources." Library of Congress .
• Secondary Sources in Research
• Documentation in Reports and Research Papers
• Primary and Secondary Sources in History
• Definition and Examples of Primary Verbs in English
• What Is an Annotated Bibliography?
• Research in Essays and Reports
• An Introduction to Academic Writing
• Definition and Examples of Science Writing
• Definition and Examples of Interjections in English
• What Is Zimbabwean English
• Definition and Examples of Humorous Essays
• Definition and Examples of Target Domain in Conceptual Metaphors
• Definition and Examples of Explication (Analysis)
• Definition and Examples of Corpora in Linguistics
• What Is the Genitive Case?

Public Health Research Guide: Primary & Secondary Data Definitions

• COVID-19 Myths, misinformation

Primary & Secondary Data Definitions

• Primary Data Sources
• Secondary Data Sources
• Database Search Strategy
• Searching Database PUBMED
• Creating Infographics
• Sample Poster Presentations
• Writing for Peer-Reviewed Journals
• SPSS Tutorial
• Books and eBooks
• CORE Journals (Medical Library Association)

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Primary Data: Data that has been generated by the researcher himself/herself, surveys, interviews, experiments, specially designed for understanding and solving the research problem at hand.

Secondary Data:  Using existing data generated by large government Institutions, healthcare facilities etc. as part of organizational record keeping. The data is then extracted from more varied datafiles. 

Supplementary Data : A few years ago the Obama Administration judged that any research that is done using Federal Public funds should be available for free to the public. Moreover Data Management Plans should be in place to store and preserve the data for almost eternity. These data sets are published as Supplementary Materials in the journal lliterature, and data sets can downloaded and manipulated for research. 

NOTE: Even though the research is Primary source, the supplemental files downloaded by others becomes Secondary Source.

 Pros and Cons for each. 

Comparison Chart

Quantitative & Qualitative Research Methods

Quantitative Research Definition:  Data that can be measured, quantified. Basically Descriptive Statistics.

Read:  Introduction to Quantitative Methods

Qualitative Research Definition: Data collected that is not numerical, hence cannot be quantified. It measures other characteristics through interviews, observation and focused groups among a few methods. It can also be termed as  " Categorical Statistics ". 

Read:  Qualitative methods in public health

Mixed methods research. When quantitative and qualitative research methods are used.

Qualitative Research Methods:

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Peer Review and Primary Literature: An Introduction: Is it Primary Research? How Do I Know?

• Scholarly Journal vs. Magazine
• Peer Review: What is it?
• Finding Peer-Reviewed Articles
• Primary Journal Literature
• Is it Primary Research? How Do I Know?

Components of a Primary Research Study

As indicated on a previous page, Peer-Reviewed Journals also include non -primary content. Simply limiting your search results in a database to "peer-reviewed" will not retrieve a list of only primary research studies.

Learn to recognize the parts of a primary research study. Terminology will vary slightly from discipline to discipline and from journal to journal.  However, there are common components to most research studies.

When you run a search, find a promising article in your results list and then look at the record for that item (usually by clicking on the title). The full database record for an item usually includes an abstract or summary--sometimes prepared by the journal or database, but often written by the author(s) themselves. This will usually give a clear indication of whether the article is a primary study.  For example, here is a full database record from a search for family violence and support in SocINDEX with Full Text :

Although the abstract often tells the story, you will need to read the article to know for sure. Besides scanning the Abstract or Summary, look for the following components: (I am only capturing small article segments for illustration.)

Look for the words METHOD or METHODOLOGY . The authors should explain how they conducted their research.

NOTE: Different Journals and Disciplines will use different terms to mean similar things. If instead of " Method " or " Methodology " you see a heading that says " Research Design " or " Data Collection ," you have a similar indicator that the scholar-authors have done original research.

Look for the section called RESULTS . This details what the author(s) found out after conducting their research.

Charts , Tables , Graphs , Maps and other displays help to summarize and present the findings of the research.

A Discussion indicates the significance of findings, acknowledges limitations of the research study, and suggests further research.

References , a Bibliography or List of Works Cited indicates a literature review and shows other studies and works that were consulted. USE THIS PART OF THE STUDY! If you find one or two good recent studies, you can identify some important earlier studies simply by going through the bibliographies of those articles.

A FINAL NOTE:  If you are ever unclear about whether a particular article is appropriate to use in your paper, it is best to show that article to your professor and discuss it with them.  The professor is the final judge since they will be assigning your grade.

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Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

• If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
• You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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

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

Research bias

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

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 organizations.

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 standardize 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, labor-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.

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

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

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

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

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

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