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How to Write an APA Methods Section | With Examples

Published on February 5, 2021 by Pritha Bhandari . Revised on October 17, 2022.

The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods .

In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample , measures, and procedures used.

Table of contents

Structuring an apa methods section.


Example of an APA methods section

Frequently asked questions about writing an apa methods section.

The main heading of “Methods” should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles .

To structure your methods section, you can use the subheadings of “Participants,” “Materials,” and “Procedures.” These headings are not mandatory—aim to organize your methods section using subheadings that make sense for your specific study.

Note that not all of these topics will necessarily be relevant for your study. For example, if you didn’t need to consider outlier removal or ways of assigning participants to different conditions, you don’t have to report these steps.

The APA also provides specific reporting guidelines for different types of research design. These tell you exactly what you need to report for longitudinal designs , replication studies, experimental designs , and so on. If your study uses a combination design, consult APA guidelines for mixed methods studies.

Detailed descriptions of procedures that don’t fit into your main text can be placed in supplemental materials (for example, the exact instructions and tasks given to participants, the full analytical strategy including software code, or additional figures and tables).

Begin the methods section by reporting sample characteristics, sampling procedures, and the sample size.

Participant or subject characteristics

When discussing people who participate in research, descriptive terms like “participants,” “subjects” and “respondents” can be used. For non-human animal research, “subjects” is more appropriate.

Specify all relevant demographic characteristics of your participants. This may include their age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Depending on your study topic, other characteristics like educational or immigration status or language preference may also be relevant.

Be sure to report these characteristics as precisely as possible. This helps the reader understand how far your results may be generalized to other people.

The APA guidelines emphasize writing about participants using bias-free language , so it’s necessary to use inclusive and appropriate terms.

Sampling procedures

Outline how the participants were selected and all inclusion and exclusion criteria applied. Appropriately identify the sampling procedure used. For example, you should only label a sample as random  if you had access to every member of the relevant population.

Of all the people invited to participate in your study, note the percentage that actually did (if you have this data). Additionally, report whether participants were self-selected, either by themselves or by their institutions (e.g., schools may submit student data for research purposes).

Identify any compensation (e.g., course credits or money) that was provided to participants, and mention any institutional review board approvals and ethical standards followed.

Sample size and power

Detail the sample size (per condition) and statistical power that you hoped to achieve, as well as any analyses you performed to determine these numbers.

It’s important to show that your study had enough statistical power to find effects if there were any to be found.

Additionally, state whether your final sample differed from the intended sample. Your interpretations of the study outcomes should be based only on your final sample rather than your intended sample.

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Write up the tools and techniques that you used to measure relevant variables. Be as thorough as possible for a complete picture of your techniques.

Primary and secondary measures

Define the primary and secondary outcome measures that will help you answer your primary and secondary research questions.

Specify all instruments used in gathering these measurements and the construct that they measure. These instruments may include hardware, software, or tests, scales, and inventories.

Make sure to report the settings of (e.g., screen resolution) any specialized apparatus used.

For each instrument used, report measures of the following:

Giving an example item or two for tests, questionnaires , and interviews is also helpful.

Describe any covariates—these are any additional variables that may explain or predict the outcomes.

Quality of measurements

Review all methods you used to assure the quality of your measurements.

These may include:

For data that’s subjectively coded (for example, classifying open-ended responses), report interrater reliability scores. This tells the reader how similarly each response was rated by multiple raters.

Report all of the procedures applied for administering the study, processing the data, and for planned data analyses.

Data collection methods and research design

Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how you collected the necessary data.

Describe all procedures you applied in administering surveys, tests, physical recordings, or imaging devices, with enough detail so that someone else can replicate your techniques. If your procedures are very complicated and require long descriptions (e.g., in neuroimaging studies), place these details in supplementary materials.

To report research design, note your overall framework for data collection and analysis. State whether you used an experimental, quasi-experimental, descriptive (observational), correlational, and/or longitudinal design. Also note whether a between-subjects or a within-subjects design was used.

For multi-group studies, report the following design and procedural details as well:

Describe whether any masking was used to hide the condition assignment (e.g., placebo or medication condition) from participants or research administrators. Using masking in a multi-group study ensures internal validity by reducing research bias . Explain how this masking was applied and whether its effectiveness was assessed.

Participants were randomly assigned to a control or experimental condition. The survey was administered using Qualtrics ( To begin, all participants were given the AAI and a demographics questionnaire to complete, followed by an unrelated filler task. In the control condition , participants completed a short general knowledge test immediately after the filler task. In the experimental condition, participants were asked to visualize themselves taking the test for 3 minutes before they actually did. For more details on the exact instructions and tasks given, see supplementary materials.

Data diagnostics

Outline all steps taken to scrutinize or process the data after collection.

This includes the following:

To ensure high validity, you should provide enough detail for your reader to understand how and why you processed or transformed your raw data in these specific ways.

Analytic strategies

The methods section is also where you describe your statistical analysis procedures, but not their outcomes. Their outcomes are reported in the results section.

These procedures should be stated for all primary, secondary, and exploratory hypotheses. While primary and secondary hypotheses are based on a theoretical framework or past studies, exploratory hypotheses are guided by the data you’ve just collected.

This annotated example reports methods for a descriptive correlational survey on the relationship between religiosity and trust in science in the US. Hover over each part for explanation of what is included.

The sample included 879 adults aged between 18 and 28. More than half of the participants were women (56%), and all participants had completed at least 12 years of education. Ethics approval was obtained from the university board before recruitment began. Participants were recruited online through Amazon Mechanical Turk (MTurk; We selected for a geographically diverse sample within the Midwest of the US through an initial screening survey. Participants were paid USD $5 upon completion of the study.

A sample size of at least 783 was deemed necessary for detecting a correlation coefficient of ±.1, with a power level of 80% and a significance level of .05, using a sample size calculator (

The primary outcome measures were the levels of religiosity and trust in science. Religiosity refers to involvement and belief in religious traditions, while trust in science represents confidence in scientists and scientific research outcomes. The secondary outcome measures were gender and parental education levels of participants and whether these characteristics predicted religiosity levels.


Religiosity was measured using the Centrality of Religiosity scale (Huber, 2003). The Likert scale is made up of 15 questions with five subscales of ideology, experience, intellect, public practice, and private practice. An example item is “How often do you experience situations in which you have the feeling that God or something divine intervenes in your life?” Participants were asked to indicate frequency of occurrence by selecting a response ranging from 1 (very often) to 5 (never). The internal consistency of the instrument is .83 (Huber & Huber, 2012).

Trust in Science

Trust in science was assessed using the General Trust in Science index (McCright, Dentzman, Charters & Dietz, 2013). Four Likert scale items were assessed on a scale from 1 (completely distrust) to 5 (completely trust). An example question asks “How much do you distrust or trust scientists to create knowledge that is unbiased and accurate?” Internal consistency was .8.

Potential participants were invited to participate in the survey online using Qualtrics ( The survey consisted of multiple choice questions regarding demographic characteristics, the Centrality of Religiosity scale, an unrelated filler anagram task, and finally the General Trust in Science index. The filler task was included to avoid priming or demand characteristics, and an attention check was embedded within the religiosity scale. For full instructions and details of tasks, see supplementary materials.

For this correlational study , we assessed our primary hypothesis of a relationship between religiosity and trust in science using Pearson moment correlation coefficient. The statistical significance of the correlation coefficient was assessed using a t test. To test our secondary hypothesis of parental education levels and gender as predictors of religiosity, multiple linear regression analysis was used.

In your APA methods section , you should report detailed information on the participants, materials, and procedures used.

You should report methods using the past tense , even if you haven’t completed your study at the time of writing. That’s because the methods section is intended to describe completed actions or research.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

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:

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, 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 :

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.

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

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

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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 .


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.

Operationalisation 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 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 time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise 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 categorise observations.

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 organise and store your data.

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 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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Scientific Research and Methodology : An introduction to quantitative research and statistics

10 procedures for collecting data.

So far, you have learnt to ask a RQ, identify study types, and design the study. In this chapter , you will learn how to collect the data needed to answer the RQ. You will learn to:

data collection procedure example in research paper

10.1 Protocols

If the RQ is well-constructed, terms are clearly defined, and the research design is clear and well explained, then collecting the data should be reasonably easy to implement. However, data collection may still be time-consuming, tedious and expensive, so getting the data collection correct first time is important.

Before collecting the data, a plan should be established and documented that explains exactly how the data will be obtained. This plan is a protocol . Unforeseen complications are not unusual, so often a pilot study (or a practice run ) is conducted before the real data collection, to see if the planned procedure is practical and optimal. The pilot study may suggest changes to the protocol.

Definition 10.1 (Protocol) A protocol is a procedure documenting the details of the design and implementation of studies, and for data collection.

Definition 10.2 (Pilot study) A pilot study is a small test run of the study protocol, used to check that the protocol seems appropriate and practical, and to identify possible problems with the design or protocol.

A pilot study allows the researcher to:

data collection procedure example in research paper

After the pilot study, the planned protocol may need to be refined. Once the protocol has been finalised, then the data can be collected. Protocols ensure studies can be repeated so others can confirm or compare results, and others can understand exactly what was done, and how. Protocols should clearly indicate how design aspects (such as blinding the individuals, random allocation of treatments, etc.) will happen.

This final record is the final protocol , and at least parts of the protocol should appear in the final report. Someone else should be able to read the protocol and approximately repeat the study (this is ethical research practice: Sect. 4 ). Diagrams can be useful to aid explanations. All studies should have a well-established protocol for describing how the study was done.

Example 10.1 (Protocol) A study ( Wojcik et al. 1999 ) examined the forward-leaning angle from which people could recover and not fall, to determine if this angle was different (on average) for younger and older people. The paper goes into great detail to explain the protocol (almost 1.5 pages, plus a diagram).

Example 10.2 (Protocol) Consider this partial protocol, which shows ethics and honesty in describing a protocol:

Fresh cow dung was obtained from free-ranging, grass fed, and antibiotic-free Milking Shorthorn cows ( Bos taurus ) in the Tilden Regional Park in Berkeley, CA. Resting cows were approached with caution and startled by loud shouting, whereupon the cows rapidly stood up, defecated, and moved away from the source of the annoyance. Dung was collected in ZipLoc bags (1 gallon), snap-frozen and stored at \(-80\) C. --- Hare et al. ( 2008 ) , p. 10

A study ( Stensballe et al. 2005 ) examined three different types of male catheters, to compare 'withdrawal friction force'. The paper goes into great detail to explain the protocols (almost a whole page, plus a (painful-looking) diagram).

A protocol usually has at least three specific components:

Example 10.3 (Protocol) In a study to increase the nutritional value of cookies, chocolate bar cookies were created using pureed green peas in place of margarine as a fat-ingredient. The researchers wanted to assess the acceptance of these cookies to college students.

The article described how the individuals were chosen:

One hundred and three untrained volunteers were recruited through advertisement across campus from students attending a university in the southeastern United States. --- Romanchik-Cerpovicz, Jeffords, and Onyenwoke ( 2018 ) , p. 4

That is, the sample was a voluntary response sample from a single university. However, there is no reason to suspect that these students would have a different opinion of cookies made with pureed green peas than students elsewhere, so the sample may be somewhat representative. However, the researchers report that:

The consumer panel consisted of women (80.6%) and men (19.4%) with a mean age of \(22.1\pm 6.4\) years. --- Romanchik-Cerpovicz, Jeffords, and Onyenwoke ( 2018 ) , p. 4

That is, the sample has a higher percentage of women that in the general population, or the college population. (Other extraneous variables were also recorded.)

Exclusion criteria were also applied:

Prior to assessing the products, panelists signed forms and indicated any food allergies or sensitivities they had. Any panelist with an allergy or sensitivity to an ingredient used in the preparation of the cookies was excluded from the study. --- Romanchik-Cerpovicz, Jeffords, and Onyenwoke ( 2018 ) , p. 5

To obtain the data from the individuals:

During the testing session, panelists were seated at individual tables. Each cookie was presented one at a time on a disposable white plate. Samples were previously coded and randomized. The presentation order for all samples was 25%, 0%, 50%, 100% and 75% substitution of fat with puree of canned green peas. To maintain standard procedures for sensory analysis [...], panelists cleansed their palates between cookie samples with distilled water (25 \(\circ\) C). Similarly, to be consistent with other studies of consumer acceptability of food products, a 9-point hedonic scale in which 9 = like extremely, 5 = neutral, and 1 = dislike extremely, was used to analyze characteristics of color, smell, moistness, flavor, aftertaste, and overall acceptability, for each sample of cookie. --- Romanchik-Cerpovicz, Jeffords, and Onyenwoke ( 2018 ) , p. 5

In this description, internal validity is managed using random allocation, blinding the individuals, and washouts.

Details are also given of how the cookies were prepared, and how objective measurements (such as moisture content) were determined. The statistical methods were listed, and the software used was "InStat Instant Biostatistics" (p. 6), though the version was not provided.

10.2 Collecting data using questionnaires

Data may be collected in many ways (laboratory experiments, taking measurements, field observations, etc.). For both observational and experimental studies, though, collecting data using questionnaires is common. Questionnaires are very difficult to do well: question wording is crucial, and surprisingly difficult to get right ( Fink 1995 ) .

Definition 10.3 (Questionnaire) A questionnaire is a set of questions for respondents to answer.

Questions in a questionnaire may be open-ended (respondents can write their own answers) or closed (respondents select from a small number of possible answers, as in multiple-choice questions). Open and closed questions both have advantages and disadvantages. Answers to open questions usually lend themselves to qualitative analysis.

This section only briefly examines questionnaires:

10.2.1 Asking questions

Some of the issues to keep in mind when framing questionnaire questions are:

Example 10.4 (Leading question) This question is a leading question , because the expected response is obvious:

Because bottles from bottled water create enormous amounts of non-biodegradable landfill and hence pose a threat to sensitive native wildlife, do you support a ban on bottled water in Australia?

Example 10.5 (Question wording) Question wording can be important. These two questions would produce different percentages of respondents agreeing:

Example 10.6 (Leading question) Consider this question:

Do you like this new orthotic?

Although not obvious, this question may incite respondents to please, since liking is the only option presented. Better would be to ask:

Do you like or dislike this new orthotic?

Even better (but more difficult to implement) is to ask the second question above, but randomly chose the order of the 'like' and 'dislike'; that is, ask some respondents if they 'like or dislike' the new orthotic, and others if they 'dislike or like' the new orthotic.

Example 10.7 (Ambiguous question) Consider this question:

Do children run faster now?

This question is ambiguous: Faster now compared to what or when ?

Example 10.8 (Asking the uninformed) Consider this question:

Is the use of fibre composites for waterside recreational purposes likely to cause the material to swell?

Only people involved in the industry are likely to be able to properly answer this question. Nonetheless, many people will still give an opinion, even if they are uninformed. This data will be effectively useless (response bias), but the researcher may not realise this.

Example 10.9 (Unclear wording) Consider this question:

I don't go out of my way to purchase low-fat food unless they are also low in calories but not necessarily salt. Do you agree or disagree?

It is not clear what a 'yes' answer means.

Example 10.10 (Double-barrelled question) Consider this question:

Do you jog and swim for exercise?

This question would be better asked as two separate questions: one asking about jogging, and one about swimming.

Example 10.11 (Confidentiality) Consider this question:

Do you have a water tank that has been installed illegally, without council permission?

Respondents are unlikely to admit to breaking rules.

Consider this question:

Consider this book that you are currently reading. How useful do you think this book would be for students and young professionals in the field?

What is the biggest problem with this question?

There are two questions; it is double-barrelled .

Asking the two questions separately is better: one about students , and one about young professionals . This separates the two components of the original question.

Example 10.12 (Mutually exclusive options) In a study to determine the time doctors spent on patients (from Chan et al. ( 2008 ) ), doctors were given the options:

This is a poor question, because a respondent does not know which option to select for an answer of "5 minutes".

The following (humourous) video shows how questions can be manipulated by those not wanting to be ethical:

10.2.2 Online and paper questionnaires

Questionnaires may be paper-based or online; both have advantages and disadvantages ( Porter 2004 ) .

Paper-based questionnaires require the information to be manually entered into the computer for later analysis, which is time consuming and expensive, and prone to data-entry errors.

Paper-based questionnaires can also be costly to prepare, especially if physical mailing and photocopying is necessary. However, people may be more likely to complete paper-based questionnaires if they are presented with a questionnaire face-to-face and someone waits to collect the completed questionnaires.

Online questionnaires make data collection and data entry easier: data are entered directly onto a computer. This means less manual handling and less chance of data entry errors. Online questionnaires are also easier to share with a geographically-diverse group of people (for example, through email or social media), but only if the relevant contact details are available.

However, online questionnaires may have a lower response rate, as respondents may be reluctant to click on links in emails (especially from unknown sources), may ignore emails, or the emails may be flagged a spam.

10.3 Summary

Having a detailed procedure for collecting the data (the protocol ) is important.

Using a pilot study to trial the protocol an often reveal unexpected changes necessary for a good protocol.

Sometimes, data can be collected using questionnaires, either on paper or online . However, creating good questionnaires questions is difficult.

10.4 Quick review questions

What is the biggest problem with this question: 'Do you have bromodosis?' (Possible answers are: Yes/No)

What is the biggest problem with this question: Do you spend too much time connected to the internet? (Possible answers are: Yes/No)

What is the biggest problem with this question: 'Do you eat fruits and vegetables?' (Possible answers are: Yes/No)

Which of these is a purpose of producing a well-defined protocol?

Are the following survey questions likely to be leading questions?

10.5 Exercises

Selected answers are available in Sect. D.10 .

Exercise 10.1 What is the problem with this question?

What is your age? (Select one option) Under 18 Over 18

Exercise 10.2 Which of these questionnaire questions is better, and why?

Exercise 10.3 In a study of sunscreen use ( Falk and Anderson 2013 ) , participants were asked questions that included these:

Exercise 10.4 Before the 2019 State of the Union address, American president Donald Trump distributed an online questionnaire to gather information. Some of the questions are given below. Critique these questions:

Do you agree that President Trump is taking our country in the RIGHT DIRECTION?

Do you agree with President Trump's unwavering commitment to, and respect for, our incredible veterans and TROOPS?

Are you satisfied with President Trump's efforts to revitalize American manufacturing?

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Data analysis section of a research paper

what is the data analysis section of a research paper

When writing a data analysis research paper or just a data analysis section of a research paper, most students face the issue of how to go about it. Data analysis is the most important part of research papers. The researcher summarizes the data collected during the research and provides statistical evidence that can be used to support the findings of the paper. Data analysis can be done in different ways, but most students choose to do it using Excel or SPSS.

There are many different ways to approach data analysis, but students should always start by carefully reviewing their data and making sure that it is accurate. Once the data has been verified, they can then begin to analyze it using the methods that they are most comfortable with.

In this guide, we will review the process of data analysis and how data analysis section of a research paper for college and graduate school students.

How to write preface for project report

Content analysis

Research paper title page.

What is data analysis?

Data analysis is the process of transforming data into information. This process involves the identification of patterns and trends in the data, as well as the formulation of hypotheses about the relationships between the variables.

The goal of data analysis is to understand the meaning of the data and to use this understanding to make decisions or predictions. Data analysis can be used to improve business processes, make better decisions, and understand the behavior of customers.

Data analysis in research is a process that can be divided into four steps:

There are a variety of software programs that can be used for data analysis. Some of the most popular programs are Excel, SPSS, and SAS. These programs allow you to perform a variety of data analysis operations, including:

When performing data analysis, it is important to use the right tool for the job. Each tool has its strengths and weaknesses, so it is important to select the tool that will give you the best results.

What is the data analysis section of a research paper?

The data analysis section of a research paper is where the researcher presents their findings and interpret the data they have collected. This is usually done through statistical methods, but can also include qualitative data analysis. In this section, the researcher will present their results clearly and concisely, making sure to discuss any limitations to their study. They will also make connections between their findings and the existing body of research on the topic.

How to analyze data for a research paper

Here is how to write data analysis in a research paper or a data analysis report ::

1. Collect the data.

This can be done through surveys, interviews, observations, or secondary sources. Depending on the type of data you need to collect, there are a variety of methods you can use. You should also prepare the data for analysis. This step involves cleaning the data and transforming it into a format that can be analyzed.

2. Organize and enter the data into a statistical software program.

The next step is organizing the data, selecting the right statistical software, and entering the data into the program.

Some students prefer to use Excel to analyze their data, while others prefer to use SPSS. Both of these software programs have their strengths and weaknesses, so it is important to choose the one that is best suited for the type of data that you are working with.

Excel is a good choice for data analysis if you are familiar with it and feel comfortable using it. However, Excel has its limitations and can be difficult to use for complex data sets. If you are not familiar with Excel, or if you are working with a large data set, you may want to consider using SPSS instead.

SPSS is a statistical software program that is designed for more complex data analysis. It is not as user-friendly as Excel, but it is much better suited for analyzing large data sets.

Once you have chosen the software program that you will use for data analysis, you need to decide how you will go about analyzing the data. Many different statistical methods can be used for data analysis, and each has its strengths and weaknesses. You should choose the method that is best suited for the type of data that you are working with.

3. Analyze the data.

Once you have chosen the software program that you will use for data analysis, the method that you will use to analyze your data, and the type of data that you are working with, you are ready to begin your data analysis. Be sure to take your time and analyze the data carefully. The results of your data analysis will be used to support the findings of your research paper, so it is important to make sure that you do a thorough job.

After the data is entered into the software program, it is time to analyze it. This step involves identifying patterns and trends in the data and formulating hypotheses about the relationships between the variables.

It is important to note that data analysis is not a one-size-fits-all process. The methods used will vary depending on the type of data being analyzed. For quantitative data, the researcher may use descriptive statistics, inferential statistics, or regression analyses. For qualitative data, the researcher may use content analysis, thematic analysis, or narrative analysis.

4. Interpret the data.

After the data has been analyzed, it is time to interpret it. This step involves using the results of the data analysis to make decisions or predictions.

5. Present the data/results.

Once the data has been analyzed and interpreted, it is time to present it in a research paper. This step involves writing a clear and concise paper that discusses the findings of the study. The paper should also discuss any limitations to the study and make connections between the findings and the existing body of research on the topic.

The data analysis section of a research paper is an important part of the paper. It is where the researcher presents their findings and interpret the data they have collected. This data analysis section of a research paper should be clear and concise, and it should discuss any limitations to the study. The researcher should also make connections between the findings and the existing body of research on the topic.

While the data analysis section of a research paper is important, it is also one of the most challenging sections to write. By following these guidelines, you can ensure that your data analysis section is clear, concise, and informative.

How to write data analysis in a research paper

The data analysis section of a research paper is where you present the results of your statistical analyses. This section can be divided into two parts: descriptive statistics and inferential statistics.

In the descriptive statistics section, you will describe the basic characteristics of the data. This includes the mean, median, mode, and standard deviation. You may also want to include a graph or table to visually represent the data.

In the inferential statistics section, you will interpret the results of your statistical analyses. This includes discussing whether or not the results are statistically significant. You will also discuss the implications of your results and how they contribute to our understanding of the research question.

What is a data analysis research paper?

A data analysis research paper is a type of scientific paper that is written to analyze data collected from a study. The purpose of this type of paper is to present the data in a clear and organized manner and to discuss any patterns or trends that were observed in the data. Data analysis papers can be used to inform future research projects, or to help policymakers make informed decisions.

When writing a data analysis research paper, it is important to be clear and concise in your writing. You should also make sure to include all of the relevant information, including the methods that were used to collect the data, as well as any statistics or graphs that were used to analyze it. It is also important to discuss any limitations of your data, as this can help to improve the quality of future studies. Finally, you should also provide a conclusion that summarizes your findings and discusses their implications.

When writing a data analysis research paper, it is important to:

Example of data analysis in research paper

The following is an example of data analysis from a research paper on the effects of stress on academic performance.

Descriptive Statistics:

To describe the basic characteristics of the data, the mean, median, mode, and standard deviation were calculated. The results are shown in the table below.

As can be seen from the table, the mean and median scores were both 3. The mode was 2, which occurred twice as often as any other score. The standard deviation was 1.2.

Inferential Statistics:

To determine whether or not the results were statistically significant, a t-test was conducted. The results are shown in the table below.

As can be seen from the table, the results of the t-test were statistically significant, with a p-value of 0.05. This means that there is a significant difference between the stress levels of the two groups.


The data from this study suggest that stress has a significant impact on academic performance. This finding has important implications for students, as well as for educators and policymakers.


There are a few limitations to this study that should be noted. First, the sample size was relatively small, which may have affected the results. Second, the data were self-reported, which means that they may not be accurate. Finally, this was a cross-sectional study, which means that cause and effect cannot be established.

Future Research:

This study provides a starting point for future research on the effects of stress on academic performance. Future studies should aim to replicate these findings with larger sample size. Additionally, longitudinal studies would be beneficial to establish causality. Finally, qualitative research could be used to explore the experiences of students who are struggling with stress.

Research paper on mass shootings in america

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Data Collection Procedure

Related terms:.

Data Gathering, Analysis and Protection of Privacy Through Randomized Response Techniques: Qualitative and Quantitative Human Traits

M. Rueda , ... R. Arnab , in Handbook of Statistics , 2016

1 Introduction

Warner (1965) developed a data collection procedure , the randomized response (RR) technique that allows researchers to obtain sensitive information while guaranteeing privacy to respondents. This method encourages greater cooperation from respondents and reduces their motivation to falsely report their attitudes. The most important claim made for RR is that it yields more valid point estimates of sensitive behavior.

Warner's study generated a rapidly expanding body of research literature on alternative techniques for eliciting suitable RR schemes in order to estimate a population proportion (see Arnab, 2002, 2004; Arnab and Singh, 2010; Bouza, 2009; Chang et al., 2004; Christofides, 2003; Fox and Wyrick, 2008; Singh and Tarray, 2015; Tracy and Mangat, 1996; Van den Hout et al., 2010 ). A good revision of RR techniques can be seen in Bouza et al. (2010) , in Chaudhuri (2011) , or in Chaudhuri and Christofides (2013) . Half a century after the original RRT model was introduced, these models continue to be used in a variety of disciplines.

There have been many reports that RR provides more accurate estimates of the prevalence of socially undesirable behavior than does asking the sensitive question directly. Numerous empirical studies have shown that RR obtains higher estimates of sensitive characteristics than are produced by direct questioning ( Lara et al., 2006; van der Heijden et al., 2000 ). However, using RR incurs extra costs, and the advantage of using RR will only outweigh these extra costs if the estimates are substantially better than those derived from straightforward question-and-answer designs ( Lensvelt-Mulders et al., 2006 ).

Other disadvantage of randomized response techniques is the cost for the need to use randomization device. Randomization devices must be provided to the responding party without producing mistrust in the interviewee. The device suggested by Warner was a spinner. Other devices are coins, cards, box with poker cards. For continuous variables the randomization device is even more complex. Eichhorn and Hayre (1983) used the multiplicative approach to produce a randomize response device. In this approach we need a random variable that is independent of the main variable “y,” with known mean and variance.

In this chapter we will see some software that the interviewee can used to generate his random response easily and not mistrust of the scrambling system.

Currently, for the estimation procedures there are many programs and programming languages for working with complex surveys, but there are few that have implemented modules to work with randomized response. We will discuss both programs specifically designed to analyze data obtained from randomized response techniques, as codes available in the literature that implement certain specific estimation techniques with randomized data. We will emphasize language R for being the free software that is most commonly used in the scientific community nowadays.

Software Reliability

Claes Wohlin , ... Anders Wesslén , in Encyclopedia of Physical Science and Technology (Third Edition) , 2003

III.A Purpose

The data collection provides the basis for reliability estimations. Thus, a good data collection procedure is crucial to ensure that the reliability estimate is trustworthy. A prediction is never better than the data on which it is based. Thus, it is important to ensure the quality of the data collection. Quality of data collection involves:

Collection consistency. Data shall be collected and reported in the same way all the time, for example, the time for failure occurrence has to be reported with enough accuracy.

Completeness. All data has to be collected, for example, even failures for which the tester corrects the causing fault.

Measurement system consistency. The measurement system itself must as a whole be consistent, for example, faults shall not be counted as failures, since they are different attributes.

Preparing to Testify

Dale Liu , in Cisco Router and Switch Forensics , 2009

Applicability to Procedures

When performing forensic investigations on Cisco routers and switches; you should base your checklists and procedures on the policies the organization has in place. Knowing the rules of evidence and data collection procedures and your expert testimony guidelines will make your job both as a forensic investigator and as an expert witness more successful.

It is also important to match your procedures and checklists not only to the relevant federal and state laws, but also to the policies of your organization. Also keep in mind the laws regarding admissibility, and laws such as the Sarbanes-Oxley Act of 2002 (SOX) and the Health Insurance Portability and Accountability Act of 1996 (HIPPA), as well as any international laws relevant to your situation.

27th European Symposium on Computer Aided Process Engineering

Ahmed Shokry , ... Antonio Espuña , in Computer Aided Chemical Engineering , 2017

6 Conclusions

The work presents a methodology for the Data-Based Dynamic Modeling of complex nonlinear batch processes operated under different Initial Conditions, and involving significantly different sampling rates. The method is based on the combination of NAR models with a data collection procedure and an imputation step for the missing data. Compared to their direct/classical application, the constructed NAR models show promising enhancements in terms of accuracy and ability to predict the QIV dynamic behavior along different batch runs. Thus, they can be better used as dynamic soft sensors for online prediction, and also to be integrated into a process supervision system, allowing better chances to monitor process behavior when it is difficult to follow it through FPMs. The consideration of the imputed data allows higher modeling degrees of freedom (i.e. possibilities of trying different model lags) when very limited number of samples measured over wide time intervals along each batch run are available, in contrast to the direct application of the NAR models.

Archiving: Ethical Aspects

J.J. Card , in International Encyclopedia of the Social & Behavioral Sciences , 2001

2.5 Assignment of Due Credit to Both Original Producer and Archivist

Data sets and program materials typically are received by an archive in a format that data developers and their colleagues found workable, but one not yet suitable for public use. The archivist contributes significant additional value in preparing the database for public use. For example, with the approval of the data donor, inconsistencies in the database are eliminated, or at least documented. The documentation is augmented, both at the study level (describing study goals, sampling and data collection procedures ) and at the variable level (assigning names and labels for each variable; documenting algorithms for constructed scale variables). Occasionally, the variable and scale documentation is done using the syntax of a popular statistical analysis package such as SPSS or SAS, facilitating future data analysis. Archivists who prepare intervention program packages for public use make analogous contributions. Program materials are edited and ‘prettified’ for public use. User's Guides and Facilitator's Manuals are created so that the package is replication-ready in the absence of the original developer. In short, the archiving process is best viewed and executed as collaboration between original developer and archivist. Care must be taken to give due credit for the final product to both individuals, teams, and institutions.


Agnieszka (Aga) Bojko , ... Sokol Zace , in Handbook of Global User Research , 2010

3.6 Developing the Moderator's Guide

A good moderator's guide for a global study should be easy to translate into other languages. Therefore, abbreviations, unnecessarily rare terms and phrases, and idioms should be avoided as much as possible to reduce ambiguity. The guide should also be explicit and detailed to ensure correct and consistent data collection procedures across locations. Besides questions and task instructions that the moderator has to say to the participants, the guide should include other information that will help local moderators understand the purpose of the tasks and questions, task priority (e.g., which tasks can be skipped if there is not enough time for all), required depth of probing, and allowed latitude of probing.

The information for the moderator can be inserted in appropriate places in the guide in a way that is easily distinguishable from main content of the script. This information should also include stimulus-related instructions, such as on which Web page each task should begin or how to reset the test devices prior to each session. Figure 3.5 shows a sample page from a moderator's guide. The black text indicates what needs to be said to the participant. The grey italics are instructions for the moderator.

data collection procedure example in research paper

Figure 3.5 . Sample page from a moderator's guide.

To finalize the moderator's guide, the lead team should conduct one or more pilot tests. Pilot testing will help refine the wording, order, and priority of tasks and questions; determine proper time management strategy; and make sure that the guide is in perfect alignment with the tested artifact(s).

Case studies

Jonathan Lazar , ... Harry Hochheiser , in Research Methods in Human Computer Interaction (Second Edition) , 2017

7.8.2 Collecting Data

Once you have identified your data sources, you need to develop protocols for how you will use each of them to collect data. For interviews, this will include the type of interview, questions, and an interview guide (see Chapter 8 ). Similar approaches can be used for examination of artifacts. Observations require you to specify the structure of the tasks that will be performed and the questions that will be asked. Each data source, in effect, becomes a mini-experiment within the larger case study, all tied to the common goals of the study as a whole.

You should also develop a protocol for the case study as a whole. In addition to the specific data sources and the procedures that you will use in examining each of these sources, the protocol includes important details that are needed to conduct the case study from start to finish. The case study protocol should start with an introduction, including the questions and hypotheses. It should continue with details of data collection procedures , including criteria for choosing cases, contact information for relevant individuals; and logistical plans for each case, including time requirements, materials, and other necessary preparations. Specific questions and methods for each of the data sources should be included in the protocol. Finally, the protocol should include an outline of the report that will be one of the products of the case study ( Yin, 2014 ).

Although this may seem like an excessive amount of overhead, effort spent on careful development of a protocol is rarely wasted. The process of developing a clear and explicit explanation of your research plan will help clarify your thinking, leading to a better understanding of possible shortcomings and challenges that may arise during the study. Any problems that you identify can stimulate reconsideration and redesign, leading to a stronger research plan.

A draft outline of your report serves a similar purpose. Constructing a report before you collect any data may seem strange, but it's actually quite constructive. Many of the sections of your report are easy to enumerate: your report will always contain an introduction to the problem, a description of your questions and hypotheses; an explanation of your design and how it addresses those questions; informative presentations of data and analysis; and discussions of results. Within each of these components there is substantial room for adaptation to meet the needs of each project. An outline that is as specific as possible—even down to the level of describing charts, tables, and figures to be used for presentation of data and analysis—will help guide your design of the questions and methods that you will use to generate the necessary data.

A case study protocol can be a powerful tool for establishing reliability ( Yin, 2014 ). If your protocol is sufficiently detailed, you should be able to use it to conduct directly comparable investigations of multiple cases—the protocol guarantees that differences in procedures are not the cause of differences in your observations or results. Ideally, a research protocol will be clear enough that it can be used by other researchers to replicate your results.

Consider running a pilot case study. Pilot tests will help you debug your research protocols, identifying questions that you may have initially omitted while potentially exposing flaws in your analysis plans. For some studies, a pilot may not be possible or desirable. If you have a unique case, this may not be possible. If your study is exploratory, you may find that a single case will provide you with sufficient data to generate an informative analysis.

Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design

Alberto Benavides-Serrano , ... Carl Laird , in Computer Aided Chemical Engineering , 2014

4 Numerical Results

Based on an analysis following Modarres et al.(2010) , and using real detector reliability data from the Offshore REliability DAta (OREDA) database ( SINTEF, 2002 ), gas detectors in facilities with proper maintenance and repair systems can be expected to have time-averaged unavailabilities below 0.05 (the upper bound of the 90% confidence interval for an operating time of 2 years is 0.042). Using this information, we examine the difference in solutions achieved when the number of backup detection levels considered is reduced. Four different data sets were considered. Data set A was previously employed by Legg et al. (2012a,b, 2013) and Benavides-Serrano et al.(2014) . Data sets B, C, and D were previously employed by Benavides-Serrano et al. (2013) . For a complete discussion regarding the data sets, and the data generation and data collection procedures , please refer to Benavides-Serrano et al. (2013) . The damage coefficients, d a,i , correspond to the length of time between the initiation of leak scenario a and its detection at a given location i, that is, the objective function to minimize corresponds to the expected time to detection across the set of gas leak scenarios. A gas concentration equal to or greater than 10% of the Lower Flammability Limit (LFL) value was required before considering a scenario detected at a given location. The same likelihood of occurrence, α a   =   1/M, was assumed for each of the leak scenarios. The dummy damage coefficient (d max ) was set to be 10 seconds greater than the largest damage coefficient for the given data set.

The ideal case in which the maximum number of detector levels is equal to the number of allowed detectors (p) was used as the base case. To compare this with results produced with reduced detection levels, three metrics were considered. The first metric shows the number of sensor locations that are the same as the base case. The second metric corresponds to the percent difference in the expected time to detection (objective) between the solution produced with reduced detection levels and the base case. The third metric is the minimum total Euclidean distance required to match detector locations in the two cases.

A highly conservative uniform time-averaged unavailability value, q ¯ =0.1, was assumed for all detectors. Under this assumption, the original SP-U formulation applies, enabling us to obtain sensitivity results for real size data sets, a task otherwise unachievable since it would have required the solution of the full MINLP. The problems were all formulated in Pyomo ( Hart et al., 2011 , 2012 ) and solved using CPLEX 12.2.

Figure 1 presents the effect of reducing available detection levels, testing C=1 and C=2 for q ¯ =0.1. Initially, for a low number of detectors, both formulations strive to cover the primary detection level. As the number of allowed detectors is increased, the the focus changes to the second backup level, and so on. With each successive change of focus the neglected detection levels become more important, and the differences increase. However, even with a high number of allowed detectors (up to 100), for C=1 the percent difference in objective between the solutions is less than 1.4% for the entire range of allowed detectors presented. Adding additional backup levels brings the gap to less than 0.05% and 0.001% for the C=2 and C=3 cases, respectively. Furthermore, with C=5 the base case and the truncated objective formulation will yield the same results over the full range explored in these figures. Due to space constraints, we present results for dataset A only.

data collection procedure example in research paper

Figure 1 . Results for data set A with 1 and 2 backup levels (C=1,2). The number of matching locations is shown in Figures 1a and 1c . Minimum total distance (●) and expected time to detection percent difference (o) results are presented in Figures 1b and 1d .

24th European Symposium on Computer Aided Process Engineering

Hande Bozkurt , ... Gürkan Sin , in Computer Aided Chemical Engineering , 2014

3 Case Study: Benchmark Wastewater Treatment Plant

3.1 step 1: problem definition and formulation.

The problem is defined as the design of a WWTP for the treatment of an average dry weather wastewater composition ( Copp, 2002 ) in compliance with the emission limits defined by the EU Urban Wastewater Treatment Directive. The objective of the design is the minimization of the total annualized cost (TAC) ( Eq.1 ). The superstructure developed for the case study problem is presented in Figure 2 and the treatment alternatives placed in between the source (WW) and sink intervals (Water and Sludge) are defined in Table 1 . The systematic data collection procedure is followed to design the treatment technologies prior to placing them in the process intervals.

data collection procedure example in research paper

Figure 2 . Case study superstructure

Table 1 . Process interval descriptions

3.2 Step 2: Uncertainty characterization

The uncertainty characterization is done under two scenarios: The first scenario deals with uncertainty with respect to cost parameters and effluent total nitrogen limitation; the second scenario deals with the effect of uncertainty in influent wastewater characterization. The parameters that are considered uncertain for the first and second scenarios and their probability distributions together with mean, minimum and maximum values are given in Table 2 . The alpha, beta and fouling factor parameters in Table 2 are correction factors when the standard oxygen transfer rate in tap water (SOTR) is converted to actual oxygen transfer rate (AOTR) by taking into account the effects of salinity-surface tension, temperature, elevation etc. The relation between AOTR and SOTR is given in Eq. (10) ( Tchobanoglous et al., 2003 ). This affects the electricity consumption needed to supply the oxygen demand to the WWTP. The electricity price is taken as the end-user energy price for industrial consumers in Denmark and a variation of 20 % is assumed over the average price given. Landfill cost, given for Denmark by the Confederation of European Waste-to-energy Plants as a range, is used in the study. Lastly, the effluent total nitrogen limitation is assumed to change between its current value 15 and 10   mg N/L. For the second scenario on the other hand, the possible change in the COD fractionation is taken into account together with the change in influent ammonium nitrogen (SNH) concentration. Four different COD fractions (Si, S S , Xi and X BH ) were sampled and the resulting X S concentration was calculated assuming that the total COD in the influent wastewater is constant.

Table 2 . Probability distribution of uncertain data

3.3 Step 3: MI(N)LP formulation and deterministic solution

The deterministic problem was formulated as an MiLP problem and solved by using GAMS using CPLEX as the solver. The resulting process flow diagram and cost summary and performance evaluation are shown in Figure 3 and Table 3 (all values given in unit cost), respectively.

data collection procedure example in research paper

Figure 3 . Resulting WWTP process flow diagram shown by bold line and units with grey shading

Table 3 . Cost summary and performance evaluation

3.4 Step 4:Uncertainty mapping and analysis

LHS was used to generate 100 samples, and for each of them the optimization problem was solved resulting in 100 different solutions. The results are presented in Table 4 and Figure 4 . The value of the objective function changes from 818 to 1,530 (unit cost) for scenario 1 with the realization of 3 different selected configurations; whereas in scenario 2 the objective function value differs between 978 and 1,402 (unit cost) with 2 possible process flow diagram configurations. Figure 4 shows the probability (y-axis) that the value of TAC will be lower than or equal to the value represented in the x-axis. It can be inferred that the value of TAC changes with the changing network configuration (3 main parts in the graph on the left and 2 main parts in the graph on the right) and also a large variability can be observed within the selected network for different future scenarios.

Table 4 . Uncertainty mapping results

data collection procedure example in research paper

Figure 4 . Uncertainty mapping results for scenario 1 (left) and scenario 2 (right) Table 5 . Summary of SAA results

3.5 Step 5: Decision making under uncertainty

In this step, the optimization problem is formulated and solved using sample average approximation (SAA), and the results presented in Table 5 are obtained.

Table 5 . Summary of SAA results

Response Bias

Timothy R. Graeff , in Encyclopedia of Social Measurement , 2005

Reducing Response Bias

As listed above, there are many sources of response bias. By being aware of these, researchers can plan their survey procedures to minimize the likelihood that these sources of response bias will have significant effects on their survey results. Strategies for reducing these types of response bias include:

Assure respondents of anonymity of responses and privacy with respect to data related to their individual responses (to reduce social desirability bias and threat bias).

Whenever possible, use anonymous survey data collection procedures and consider procedures that do not require an interviewer (to reduce social desirability bias, prestige bias, and interviewer bias).

Avoid revealing the purpose of the research, the sponsor of the research, or the source of the survey (to reduce acquiescence bias and year-saying bias).

Make the survey short, interesting, and easy to complete. Try to get respondents committed to completing the entire survey. Use prompters to help respondents work their way through the survey, such as the next section will be easier; thank you for your help with those questions, please answer a few more questions; or there are only a few more questions remaining to answer (to reduce hostility bias and apathy bias).

Carefully consider the order of the survey questions and the possible response categories. Try to ask more general questions earlier in the survey, and ask questions about more specific issues, people, events, places, or ideas later in the survey (to reduce question order bias and extremity bias).

Reduce the amount of time between a respondent's experience of an event and their responses to questions about that event (to reduce memory bias).

Consider using reverse scored items on the survey. Most survey questions are phrased positively. However, some researchers purposely reverse the phrasing of some items so that they are phrased negatively to increase the chance that respondents will read all of the questions, decreasing the likelihood of acquiescence bias, apathy bias, and straight line (column) responding (e.g., apathetically circling a column of Strongly Answer answers). For example, questions one, two, and four below are phrased positively, and question three is phrased negatively. If respondents answer strongly agree (SA) to all four questions, this indicates that they did not carefully read all four questions. They might have assumed that all four questions were phrased positively—leading to a straight line (column) of Strongly Agree answers (see Table II ).

Table II . Adding a Reverse Scored Item to a Survey

A respondent's answer to a reverse scored question must be converted by subtracting the answer's scale value ( X ) from the total number of scale values plus one. In this example, if SD were coded as 1, and SA were coded as 5, a respondent's answer to question 3 would be converted as (6   −   X) to place all four scales in the same direction.

However, using reverse scored (reverse worded) items is not without its own limitations. Recent research has demonstrated that the mixture of positive and negative phrased items can lessen a scale's internal consistency. Negative worded items often show lower reliability and weaker item-to-total correlations than positive worded items. When a scale is subjected to factor analysis, reverse scored items often load on a separate factor, thus eliminating the unidimensionality of a scale designed to measure a single construct. Research has also demonstrated that such problems often arise when researching respondents from subcultures, such as ethnic and racial minorities. Differences in cultures and traditions can lead to varying patterns of responses to negatively worded questions, especially when survey questions are translated into languages that employ different methods of representing negatives and contradictions. Thus, including reverse scored items can be particularly problematic for cross-cultural research and surveys conducted in foreign cultures.

Make respondents aware that they can answer any question with Don't Know or No Opinion . Include questions that measure respondents' level of knowledge about a topic in addition to their attitudes and opinions about a topic (to identify and reduce uninformed response bias).

Tips for writing your data collection procedures

Your data collection plan is a crucial key to developing a sound study. The plan indicates how you will access and gather information from your participants. A clear data collection plan at the proposal stage can alleviate stress and ensure that future researchers can replicate your study. Additionally, a clear data collection plan will help ensure that you obtain the information you need to answer your research questions. Below are some suggestions for creating a solid data collection plan.

First, it may be helpful to outline your steps. This allows you to see where your data collection procedures must begin and end. This should include all of the steps that you will take from the time that you obtain Institutional Review Board (IRB) approval to the time that your data is collected and ready for analysis. A simple bulleted list of the steps you plan to conduct will suffice for this step.

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From there, cross reference this list with your research questions and the variables in each research question. Make sure you have an instrument to measure each variable and you have included each of these instruments in your outline. Once you have developed an outline that includes all of the necessary instruments, you can move on to writing a full detailed draft of your data collection procedures. However, before you do that, you may want to take some time to have an accountability partner review your work. This should be a person who can be a sounding board and who can provide basic feedback on your work. Describe the purpose of your study, the research questions, and the data you will need to access to address your research questions. Let them review your outline and double check to ensure that all necessary data collection steps are presented.

Now you are ready to turn your outline into the data collection draft. Observe the appropriate tone and wording as you turn your outline into a doctoral level narrative. Imagine this as a recipe that your dissertation committee, IRB, and future researchers can use to understand and replicate your study. The draft should be succinct, clear, and comprehensive.

Once you have completed the narrative, you can compare it to the outline to make sure everything is addressed. You should also review your school’s template or guidelines for the data collection section to ensure that all the required points have been addressed.

Research Method

Home » Data Collection – Methods and Examples

Data Collection – Methods and Examples

Table of Contents

Data collection

Data Collection

Data collection is the process of gathering data from various sources and then analyzing it to find trends and patterns. Data collection is a crucial step in any research project . Without data, it would be impossible to know what trends are happening in the world around us. Data helps us to understand the past, present, and future. It allows us to make predictions about what might happen next and to plan for how to best respond to change.

Data Collection Methods

The Data Collection methods can be decided into two categories.

Both methods have further types to gather the data.

Primary Data Collection

Primary data collection is a process of gathering data from first-hand sources. There are many ways to collect primary data for research. Some common methods include:

Focus groups


Surveys are a type of data collection method that involves asking questions of a sample of individuals. Surveys can be administered in person, by phone, or online. Survey questions can be open-ended or closed-ended.

Open-ended survey questions allow respondents to answer in their own words. Closed-ended survey questions require respondents to choose from a set of pre-determined answers. Surveys are an efficient way to collect data from a large number of people.

interviews are one of the most popular methods for collecting data. This is because they provide rich, detailed information that can be used to understand a wide range of topics.

There are several different types of interviews that can be used in data collection, including face-to-face interviews, telephone interviews, and online surveys. Each type has its own advantages and disadvantages, so it’s important to choose the right method for your research project.

Focus groups are a powerful method for collecting data. By bringing together a group of people with similar experiences or backgrounds, you can get rich, detailed information that would be difficult to obtain through other methods.

Observation is a data collection method in which researchers closely observe and document the behavior of individuals or groups. It is a systematic and objective process that can be used to collect detailed information about people, objects, or events.

Secondary Data Collection

Secondary data collection is the process of gathering data that has already been collected by another source. This data can be gathered from a variety of sources including the followings:

Government agencies

Research studies, online databases.

The United States government has a long history of collecting data on its citizens. This began in earnest with the census, which has been conducted every ten years since 1790. Today, there are dozens of government agencies that collect data on everything from our income and education levels to our shopping habits and travel patterns.

This data collection is essential to the functioning of our government. It allows policymakers to make informed decisions about how to allocate resources and target programs. It also helps us track social trends and identify potential problems early on.

However, some people are concerned about the government having too much information on its citizens. They worry that this data could be used to unfairly target certain groups or individuals. Others believe that the government should not be collecting data at all, arguing that it is a violation of our privacy rights.

There are many research studies in data collection that help organizations and businesses make decisions. The most common type of research study is the observational study, which simply observes and records what is happening. This type of study is useful for studying human behavior, but has its limitations.

Online databases are a great resource for data collection because they provide a centralized location for data that can be accessed by anyone with an internet connection. There are many different online databases available, each with its own unique features and benefits.

One of the most popular online databases is Google Sheets. Google Sheets is a free, web-based spreadsheet application that allows users to create and edit spreadsheets online. It offers features such as formulas, charts, and pivot tables that make data analysis easy. Another popular option is Microsoft Excel, which is a paid application that offers more advanced features than Google Sheets.

There are also several specialized online databases that cater to specific industries or fields of study.

Books are a great source of data. They can provide information on everything from history to the latest trends. When it comes to data collection, books can be an invaluable resource.

In the past, newspapers were the primary source of information and data. However, with the advent of the internet, newspapers are no longer the only source of data. There are now many online sources that provide data and information. While newspapers can still be useful in data collection, they are no longer the only source.

Also see Research Questions

About the author

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Muhammad Hassan

I am Muhammad Hassan, a Researcher, Academic Writer, Web Developer, and Android App Developer. I have worked in various industries and have gained a wealth of knowledge and experience. In my spare time, I enjoy writing blog posts and articles on a variety of Academic topics. I also like to stay up-to-date with the latest trends in the IT industry to share my knowledge with others through my writing.

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Data Collection: Best Methods + Practical Examples

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Data is an extremely important factor when it comes to gaining insights about a specific topic, study, research, or even people. This is why it is regarded as a vital component of all of the systems that make up our world today. 

In fact, data offers a broad range of applications and uses in the modern age. So whether or not you’re considering digital transformation, data collection is an aspect that you should never brush off, especially if you want to get insights, make forecasts, and manage your operations in a way that creates significant value. 

However, many people still gravitate towards confusion when they come to terms with the idea of data collection. 

In this article, we will help you understand:

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What is Data Collection?

Data collection is defined as a systematic method of obtaining, observing, measuring, and analyzing accurate information to support research conducted by groups of professionals regardless of the field where they belong. 

While techniques and goals may vary per field, the general data collection methods used in the process are essentially the same. In other words, there are specific standards that need to be strictly followed and implemented to make sure that data is collected accurately.

Not to mention, if the appropriate procedures are not given importance, a variety of problems might arise and impact the study or research being conducted.

The most common risk is the inability to identify answers and draw correct conclusions for the study, as well as failure to validate if the results are correct. These risks may also result in questionable research, which can greatly affect your credibility.

So before you start collecting data, you have to rethink and review all of your research goals. Start by creating a checklist of your objectives. Here are some important questions to take into account:

Take note that bad data can never be useful. This is why you have to ensure that you only collect high-quality ones. But to help you gain more confidence when it comes to collecting the data you need for your research, let’s go through each question presented above.

What is the Goal of your Research?

Identifying exactly what you want to achieve in your research can significantly help you collect the most relevant data you need. Besides, clear goals always provide clarity to what you are trying to accomplish. With clear objectives, you can easily identify what you need and determine what’s most useful to your research.

What Type of Data are you Collecting?

Data can be divided into two major categories: qualitative data and quantitative data. Qualitative data is the classification given to a set of data that refers to immeasurable attributes. Quantitative data, on the other hand, can be measured using numbers. Based on the goal of your research, you can either collect qualitative data or quantitative data; or a combination of both.

What Data Collection Methods will you use?

There are specific types of data collection methods that can be used to acquire, store, and process the data. If you’re not familiar with any of these methods, keep reading as we will tackle each of them in the latter part of this article. But to give you a quick overview, here are some of the most common data collection methods that you can utilize:

Note : We will discuss these methods more in the Data Collection Methods + Examples section of this article.

Benefits of Collecting Data

Regardless of the field, data collection offers heaps of benefits. To help you become attuned to these advantages, we’ve listed some of the most notable ones below:

These are just a few of the many benefits of data collection in general. In fact, there are still a lot of advantages when it comes to collecting consumer data that you can benefit from.

Data Collection Methods + Examples

As mentioned earlier, there are specific types of data collection methods that you can utilize when gathering data for your research. These data collection methods involve conventional, straightforward, and more advanced data gathering and analysis techniques.

Furthermore, it is important to remember that the data collection method being used will depend on the type of business you’re running. Therefore, not all types of data collection methods are appropriate for the study or research that you are conducting for your business. That is why being mindful of these methods can definitely help you find the best one for your needs.

Here are the top 5 data collection methods and examples that we’ve summarized for you:

1. Surveys and Questionnaires

Surveys and questionnaires, in their most foundational sense, are a means of obtaining data from targeted respondents with the goal of generalizing the results to a broader public. Almost everyone involved in data collection, especially in the business and academic sector relies on surveys and questionnaires to obtain credible data and insights from their target audience.

Here are several key points to remember when utilizing this data collection method:

Here is an example of an online survey/questionnaire:


2. Interviews

An interview is accurately defined as a formal meeting between two individuals in which the interviewer asks the interviewee questions in order to gather information. An interview not only collects personal information from the interviewees, but it is also a way to acquire insights into people’s other skills.

Here is the summary of advantages you can gain from this data collection method:

Should you want to take advantage of this data collection method, you can refer to the table below for guidance:

Types of Interviews

data collection procedure example in research paper

3. Observations

The observation method of data collection involves seeing people in a certain setting or place at a specific time and day. Essentially, researchers study the behavior of the individuals or surroundings in which they are analyzing. This can be controlled, spontaneous, or participant-based research.

Here are the advantages of Observation as a data collection method:

When a researcher utilizes a defined procedure for observing individuals or the environment, this is known as structured observation . When individuals are observed in their natural environment, this is known as naturalistic observation .  In participant observation , the researcher immerses himself or herself in the environment and becomes a member of the group being observed.

Here are relevant case studies and citations from PRESSBOOKS that provide in-depth examples of Observational research.

Structured Observation

“Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider.“

Naturalistic Observation

“Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr. Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being that.” 

Participant Observation

“Another example of participant observation comes from a study by sociologist Amy Wilkins (published in Social Psychology Quarterly) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008). Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.”

4. Records and Documents

This data collection method involves analyzing an organization’s existing records and documents to track or project substantial changes over a specific time period. The data may include the following:

Here are the significant advantages of using records and documents as a data collection method for your business:

Examples of Records and Documents:

Customer Database

data collection procedure example in research paper

5. Focus Groups

A focus group is a group interview of six to twelve persons with comparable qualities or shared interests. A moderator leads the group through a series of planned topics. The moderator creates an atmosphere that encourages people to discuss their thoughts and opinions. Focus groups are a type of qualitative data collection in which the information is descriptive and cannot be quantified statistically.

Here are the advantages of Focus Groups as a data collection method:

Since Focus Groups are commonly carried out in person, there are no tangible examples to refer to. Moreover, here’s a diagram from QuestionPro to show how it works:

data collection procedure example in research paper

Quantitative Data vs. Qualitative Data

Data collection is comprehensive, analytical, and in some cases, extremely difficult. But when you categorize the data into the two categories we’ve mentioned earlier in this article, it becomes easy to deal with. To provide you with a brief understanding of qualitative data collection methods and quantitative data collection methods, we’ve outlined each of them below:

Quantitative Data

Quantitative data is numerical and is generally organized which means that it is more precise and definite. And because this method of data collection is measured in terms of numbers and values, it is a better choice for statistical analysis.

Here are some of the most popular quantitative data collection methods you can use to obtain concrete results:

Quantitative data examples:

data collection procedure example in research paper

Qualitative Data

Unlike quantitative data, qualitative data is composed of non-statistical information that is commonly structured or unstructured. Qualitative data isn’t also measured based on concrete statistics that are used to create graphs and charts. They are classified according to characteristics, features, identities, and other categorizations.

Qualitative data is also exploratory in nature and is frequently left wide open until more study has been completed. Theorizations, assessments, hypotheses, and presumptions are all based on qualitative research data.

Here are some of the most commonly known qualitative data collection methods you can use to generate non-statistical results:

Qualitative data examples:

data collection procedure example in research paper


Operationalization is the process of turning theoretical data into measurable observations. With the help of operationalization, you can effectively gather data on concepts that can’t be easily measured. This method converts a hypothetical, abstract variable into a collection of specific processes or procedures that determine the variable’s meaning in a given research. In a nutshell, operationalization serves as a link between hypothetically grounded ideas and the procedures employed to validate them.

Operationalization is a crucial element of empirically grounded research because it allows researchers to describe how a notion is analyzed or generated in a given study. There are three key phases in the operationalization process:

To provide you with a clear guide on how operationalization works, let’s illustrate how the process is carried out based on the three key phases.

Please refer to the following:

1. Determine which of the major ideas or concepts you want to learn more about.  

For example, the two main ideas you want to learn more about are the following:

From the chosen concepts, formulate a question that will lead you to realize your research goal. Is there are correlation between marketing and business performance?

2. Each idea should be represented by a different variable.

Here is an illustration of the second phase of the operationalization process:

data collection procedure example in research paper

Take note that in order to find the alternate and null hypothesis of the following variables, utilizing the right data collection method is extremely important.

3. For each of your variables, choose indicators.

Your indicators will help you collect the necessary data that you need in order to arrive at the most credible conclusions.

data collection procedure example in research paper

Data Collection Tools

There are heaps of data collection tools that you can utilize to gather good data online. Some of these tools have already been discussed above such as interviews, surveys, focus groups, etc.

While most of the aforementioned methods of data collection are effective, there are other data collection tools that offer convenience to business researchers. Here are some of them:

Data Scraping

Data scraping is the process of collecting data from a website and saving it as a local file on a computer. It’s among the most effective data collection tools that you can use to gather information from the web.

Some of the most popular data scraping utilization includes the following:

You may customize your scraping criteria or parameters to selectively target a specific attribute, especially with the proper data scraping tool. You can easily collect qualitative and quantitative data in a manner that can be readily implemented into your study or business procedures.

Information Management Systems

Although these management systems are generally meant to manage and monitor your database, they may also assist you in collecting data, particularly internal data generated by your business. Some of the information management systems used by various businesses that you can collect data from can be found in the following areas or categories:

Data Collection Software

There is plenty of data collection software that can be used to acquire information from the internet. One of the best examples is Google Forms. It allows you to develop specific forms like job application forms, making it simple to collect information from applicants.

Here is some data collection software you can use:

Data collection has become a crucial strategy for many professionals and businesses. While it might be a difficult task for tenderfoot researchers or business owners, understanding its methods can be contributory to collecting data in the most accurate way.

Well illustrated, clear and understandable. Thank you so much.

Very clear and understandable, Thank you so much.

Very clear and understandable Thank you so much

Thank you now have a picture of what i want to do

Thank you so much. very understandable 😊

data collection methods explained in an easy way. thanks

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Why You Should Read a Data Gathering Procedure Example

Patricia Stones

The data gathering procedure you employ in your paper determines if you receive a piece that is trustworthy or not. Therefore, it is crucial to employ the best procedure to get the perfect results. It improves the quality of the paper and makes you sound scholarly.

Most people struggle when need to gather data. While some do not know the data collection methodologies to follow, the majority do not have the experience in data handling. Eventually, they prepare papers that only earn them low grades. What is the remedy in such cases? Have a look at a perfect data gathering procedure example to be well-versed with the procedure that can work for your situation. In the process, you can make your work easier and improve the general quality of the papers you can prepare.

The best remedy for those without the sills in data gathering is to hire experts who are proficient in this field. Fortunately, we stand out as the company that can assist you with such issues. We have worked on a variety of papers that require verifiable data and understand what can work perfectly for you. With our assistance, you do not strain with data collection and handling. You follow every stage to ensure what you receive is perfect.

Table of Contents

What Is the Definition of Data Gathering Procedure?

Dissertation writing involves the handling of statistical data. Therefore, you need to know the best data to use in your paper. The definition of data gathering procedure is that it is the technique used to obtain the information used in a dissertation to substantiate the claims made by a writer. To get the perfect outcome, you should use the best procedure. If you are unsure of how to obtain your data, it is advisable to hire experts in this field to offer assistance. We have data experts who can help with these tasks.

What are the data collection methods that you can use? They are explained below:

The method is mainly effective for those who need qualitative data to use in their academic documents. In surveys, open-ended questions are used. What kind of information can be collected using this method? They include the perception people have on a product, attitudes towards government policy, the beliefs people hold, or the knowledge people have on a given issue, among other information types. For the exact information needed, the questions should not be leading and should cover the exact areas needed by the researchers. The data is later analyzed to obtain the conclusions needed.

This quantitative research data gathering procedure is used to obtain from people on a one-on-one basis. In this case, the researcher should have several predetermined questions. The interview questions can be close-ended, like in the case where the interviewees are expected to provide the ‘YES’ or ‘NO’ type of responses. It can also have open-ended questions in which the respondent has the freedom to provide a response they are comfortable with. To ensure the data collected is rich in the content required, the interviewer should ensure there are follow-up questions for areas where the respondent may provide ambiguous information.

There are different ways the interviews can be conducted. The first way is to do it face-to-face. As the respondent provides the answers, the interviewee can record them by writing or tape-recording. The data collected is later sorted and written in the paper. The other method is through phone conversations. Your respondent should provide the answers required as you keep a clean record that you can use later to write the paper needed.

In this case, the interviewee can take a group and get the information from them. There is a set of predetermined questions that are inquired from the respondents in turns. The method is effective when different people hold varied opinions on the same issue. Focus groups differ depending on the type of responses required in the probe. To get the most reliable results from this method, the number of people in the group should be between 5 and 10 people.

The data gathering procedure for qualitative research applies the sensory organs such as the eyes to see what is going on, ears to hear the things going on, and the ears to smell. The method helps the researcher to avoid bias in what people say.

The researcher uses data that is already available and supports their point of view. Different documents can be used in this case, including newspapers with reputation, research articles from known experts, approved government reports, and other online data sources that can be of help in this case. For the reliability of the data, different sources should be used for research.

It is you to determine the methodology that can work for your case when it comes to data collection. Choosing a wrong procedure may mean that you obtain unreliable or irrelevant data. You do not want to face the frustrations of presenting data that is unrelated to your topic. Therefore, it is advisable to hire an expert who understands how things work as far as data is concerned. We come in handy in such situations. Do not use faulty data gathering procedures when we can assist you in collecting the best data using our proven collection techniques.

What Determines the Sample of Data Gathering Procedure

Not all the procedures are effective for your paper. What applies to one paper may not be recommended for another. What are the factors in assessing to settle on the best procedure? Get answers:

The Course and Topic of Study Handled

Different courses require varying procedures when it comes to the collection and handling of data. While there are those courses where secondary information sources can work, others need data that one obtains first first-hand. For example, the type of data that is acceptable for those handling engineering courses is not the same as what works for those pursuing psychology. The same applies to the topic. The data needs for different subjects vary. Therefore, you must analyze the needs of your course and topic before selecting a procedure for data gathering.

The Specific Faculty Guidelines on Data Gathering

Your department has its instructions when it comes to the sample of data gathering procedure. Failure to adhere to what is specified may mean you miss important marks because your paper may not be as good as what is expected from you. Therefore, it is crucial to be well-versed with your faculty guidelines. Where the rules seem too strict for you, it is advisable to get experts who are comfortable with the specifications. We are the best company when it comes to adherence to the rules. The professionals assess all the guidelines you submit to ensure the data obtained meet the specifications you submit.

Personal Preferences in Data Gathering

The convenience encountered in data gathering varies from one person to the next. What one person considers to be hard may be easy for another. On a personal level, you should opt for a procedure that you are comfortable with. It is you who decide on the topic, settle on the data, analyze and come up with the conclusion. Therefore, selecting a procedure you are sure can work for you is fundamental. A convenient information gathering procedure saves you from stress.

What Should You Do Before Data Gathering?

You should not embark on the data gathering if you are unsure of what is required. The first step is to analyze and understand the topic you have. The keywords encountered determine whether you need a quantitative or qualitative type of data. Where you are expected to settle on your own topic, take something you are sure you can easily obtain data to defend.

The next procedure is to study the guidelines that are provided for doing the paper and collection of the data. For example, some professors insist that a student should use a given method of data collection. Your grade depends on whether you adhere to that specification or not.

Prepare adequately before you begin the gathering. For instance, you have to settle on a given method and determine the tools you need for data gathering. You can read an approved data gathering procedure pdf to understand what to do.

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The underlying need for Data collection is to capture quality evidence that seeks to answer all the questions that have been posed. Through data collection businesses or management can deduce quality information that is a prerequisite for making informed decisions.

To improve the quality of information, it is expedient that data is collected so that you can draw inferences and make informed decisions on what is considered factual.

At the end of this article, you would understand why picking the best data collection method is necessary for achieving your set objective. 

Sign up on Formplus Builder to create your preferred online surveys or questionnaire for data collection. You don’t need to be tech-savvy! Start creating quality questionnaires with Formplus.

What is Data Collection?

Data collection is a methodical process of gathering and analyzing specific information to proffer solutions to relevant questions and evaluate the results. It focuses on finding out all there is to a particular subject matter. Data is collected to be further subjected to hypothesis testing which seeks to explain a phenomenon.

Hypothesis testing eliminates assumptions while making a proposition from the basis of reason.

data collection procedure example in research paper

For collectors of data, there is a range of outcomes for which the data is collected. But the key purpose for which data is collected is to put a researcher in a vantage position to make predictions about future probabilities and trends.

The core forms in which data can be collected are primary and secondary data. While the former is collected by a researcher through first-hand sources, the latter is collected by an individual other than the user. 

Types of Data Collection 

Before broaching the subject of the various types of data collection. It is pertinent to note that data collection in itself falls under two broad categories; Primary data collection and secondary data collection.

Primary Data Collection

Primary data collection by definition is the gathering of raw data collected at the source. It is a process of collecting the original data collected by a researcher for a specific research purpose. It could be further analyzed into two segments; qualitative research and quantitative data collection methods. 

The qualitative research methods of data collection do not involve the collection of data that involves numbers or a need to be deduced through a mathematical calculation, rather it is based on the non-quantifiable elements like the feeling or emotion of the researcher. An example of such a method is an open-ended questionnaire.

data collection procedure example in research paper

Quantitative methods are presented in numbers and require a mathematical calculation to deduce. An example would be the use of a questionnaire with close-ended questions to arrive at figures to be calculated Mathematically. Also, methods of correlation and regression, mean, mode and median.

data collection procedure example in research paper

Read Also: 15 Reasons to Choose Quantitative over Qualitative Research

Secondary Data Collection

Secondary data collection, on the other hand, is referred to as the gathering of second-hand data collected by an individual who is not the original user. It is the process of collecting data that is already existing, be it already published books, journals, and/or online portals. In terms of ease, it is much less expensive and easier to collect.

Your choice between Primary data collection and secondary data collection depends on the nature, scope, and area of your research as well as its aims and objectives. 


There are a bunch of underlying reasons for collecting data, especially for a researcher. Walking you through them, here are a few reasons; 

A key reason for collecting data, be it through quantitative or qualitative methods is to ensure that the integrity of the research question is indeed maintained.

The correct use of appropriate data collection of methods reduces the likelihood of errors consistent with the results. 

To minimize the risk of errors in decision-making, it is important that accurate data is collected so that the researcher doesn’t make uninformed decisions. 

Data collection saves the researcher time and funds that would otherwise be misspent without a deeper understanding of the topic or subject matter.

To prove the need for a change in the norm or the introduction of new information that will be widely accepted, it is important to collect data as evidence to support these claims.

What is a Data Collection Tool?

Data collection tools refer to the devices/instruments used to collect data, such as a paper questionnaire or computer-assisted interviewing system. Case Studies, Checklists, Interviews, Observation sometimes, and Surveys or Questionnaires are all tools used to collect data.

It is important to decide the tools for data collection because research is carried out in different ways and for different purposes. The objective behind data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the posed questions.

The objective behind data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed – Click to Tweet

The Formplus’ online data collection tool is perfect for gathering primary data, i.e. raw data collected from the source. You can easily get data with at least three data collection methods with our online and offline data gathering tool. I.e Online Questionnaires , Focus Groups, and Reporting. 

In our previous articles, we’ve explained why quantitative research methods are more effective than qualitative methods . However, with the Formplus data collection tool, you can gather all types of primary data for academic, opinion or product research.

Here are 7 top data collection methods and tools for Academic, Opinion or Product Research

The following are the top 7 data collection methods for Academic, Opinion-based or product research. Also discussed in detail is the nature, pros and cons of each one. At the end of this segment, you will be best informed about which method best suits your research. 

An interview is a face-to-face conversation between two individuals with the sole purpose of collecting relevant information to satisfy a research purpose. Interviews are of different types namely; Structured, Semi-structured , and unstructured with each having a slight variation from the other.

Use this interview consent form template to let an interviewee give you consent to use data gotten from your interviews for investigative research purposes.

What are the best Data Collection Tools for Interviews? 

For collecting data through interviews, here are a few tools you can use to easily collect data.

An audio recorder is used for recording sound on disc, tape, or film. Audio information can meet the needs of a wide range of people, as well as provide alternatives to print data collection tools.

An advantage of a digital camera is that it can be used for transmitting those images to a monitor screen when the need arises.

A camcorder is used for collecting data through interviews. It provides a combination of both an audio recorder and a video camera. The data provided is qualitative in nature and allows the respondents to answer questions asked exhaustively. If you need to collect sensitive information during an interview, a camcorder might not work for you as you would need to maintain your subject’s privacy.

Want to conduct an interview for qualitative data research or special report? Use this online interview consent form template to allow the interviewee to give their consent before you use the interview data for research or report. With premium features like e-signature, upload fields, form security, etc., Formplus Builder is the perfect tool to create your preferred online consent forms without coding experience. 


This is the process of collecting data through an instrument consisting of a series of questions and prompts to receive a response from individuals it is administered to. Questionnaires are designed to collect data from a group. 

For clarity, it is important to note that a questionnaire isn’t a survey, rather it forms a part of it. A survey is a process of data gathering involving a variety of data collection methods, including a questionnaire.

On a questionnaire, there are three kinds of questions used. They are; fixed-alternative, scale, and open-ended. With each of the questions tailored to the nature and scope of the research.

What are the best Data Collection Tools for Questionnaire? 

Formplus lets you create powerful forms to help you collect the information you need. Formplus helps you create the online forms that you like. The Formplus online questionnaire form template to get actionable trends and measurable responses. Conduct research, optimize knowledge of your brand or just get to know an audience with this form template. The form template is fast, free and fully customizable.

A paper questionnaire is a data collection tool consisting of a series of questions and/or prompts for the purpose of gathering information from respondents. Mostly designed for statistical analysis of the responses, they can also be used as a form of data collection.

By definition, data reporting is the process of gathering and submitting data to be further subjected to analysis. The key aspect of data reporting is reporting accurate data because of inaccurate data reporting leads to uninformed decision making.

What are the best Data Collection Tools for Reporting?

Reporting tools enable you to extract and present data in charts, tables, and other visualizations so users can find useful information. You could source data for reporting from Non-Governmental Organizations (NGO) reports, newspapers, website articles, hospital records.

Contained in NGO reports is an in-depth and comprehensive report on the activities carried out by the NGO, covering areas such as business and human rights. The information contained in these reports is research-specific and forms an acceptable academic base for collecting data. NGOs often focus on development projects which are organized to promote particular causes.

Newspaper data are relatively easy to collect and are sometimes the only continuously available source of event data. Even though there is a problem of bias in newspaper data, it is still a valid tool in collecting data for Reporting.

Gathering and using data contained in website articles is also another tool for data collection. Collecting data from web articles is a quicker and less expensive data collection Two major disadvantages of using this data reporting method are biases inherent in the data collection process and possible security/confidentiality concerns.

Health care involves a diverse set of public and private data collection systems, including health surveys, administrative enrollment and billing records, and medical records, used by various entities, including hospitals, CHCs, physicians, and health plans. The data provided is clear, unbiased and accurate, but must be obtained under legal means as medical data is kept with the strictest regulations.


This is the introduction of new investigative questions in addition to/other than the ones originally used when the data was initially gathered. It involves adding measurement to a study or research. An example would be sourcing data from an archive.

What are the Best Data Collection Tools for Existing Data?

The concept of Existing data means that data is collected from existing sources to investigate research questions other than those for which the data were originally gathered. Tools to collect existing data include: 


This is a data collection method by which information on a phenomenon is gathered through observation. The nature of the observation could be accomplished either as a complete observer, an observer as a participant, a participant as an observer, or as a complete participant. This method is a key base for formulating a hypothesis.

What are the best Data Collection Tools for Observation?

Observation involves the active acquisition of information from a primary source. Observation can also involve the perception and recording of data via the use of scientific instruments. The best tools for Observation are:


The opposite of quantitative research which involves numerical-based data, this data collection method focuses more on qualitative research. It falls under the primary category for data based on the feelings and opinions of the respondents. This research involves asking open-ended questions to a group of individuals usually ranging from 6-10 people, to provide feedback.

What are the best Data Collection Tools for Focus Groups?

A focus group is a data collection method that is tightly facilitated and structured around a set of questions. The purpose of the meeting is to extract from the participants’ detailed responses to these questions. The best tools for tackling Focus groups are: 


This method of data collection encompasses the use of innovative methods to enhance participation to both individuals and groups. Also under the primary category, it is a combination of Interviews and Focus Groups while collecting qualitative data . This method is key when addressing sensitive subjects. 

What are the best Data Collection Tools for Combination Research? 

The Combination Research method involves two or more data collection methods, for instance, interviews as well as questionnaires or a combination of semi-structured telephone interviews and focus groups. The best tools for combination research are: 


With Formplus, you can create your unique survey form. With options to change themes, font colour, font, font type, layout, width, and more, you can create an attractive survey form. The builder also gives you as many features as possible to choose from and you do not need to be a graphic designer to create a form.

Form Analytics, a feature in formplus helps you view the number of respondents, unique visits, total visits, abandonment rate, and average time spent before submission. This tool eliminates the need for a manual calculation of the received data and/or responses as well as the conversion rate for your poll.

Copy the link to your form and embed as an iframe which will automatically load as your website loads, or as a popup which opens once the respondent clicks on the link. Embed the link on your Twitter page to give instant access to your followers.

data collection procedure example in research paper

The geolocation feature on Formplus lets you ascertain where individual responses are coming. It utilises Google Maps to pinpoint the longitude and latitude of the respondent, to the nearest accuracy, along with the responses.

This feature helps to conserve horizontal space as it allows you to put multiple options in one field. This translates to including more information on the survey form. 

Read Also: 10 Reasons to Use Formplus for Online Data Collection

Here’s how to use Formplus to collect online data in 7 simple steps. 

data collection procedure example in research paper

Formplus gives you a free plan with basic features you can use to collect online data. Pricing plans with vast features starts at $20 monthly, with reasonable discounts for Education and Non-Profit Organizations. 

2. Input your survey title and use the form builder choice options to start creating your surveys. 

Use the choice option fields like single select, multiple select, checkbox, radio, and image choices to create your preferred multi-choice surveys online.

data collection procedure example in research paper

3. Do you want customers to rate any of your products or services delivery? 

Use the rating to allow survey respondents rate your products or services. This is an ideal quantitative research method of collecting data. 

data collection procedure example in research paper

4. Beautify your online questionnaire with Formplus Customisation features.

data collection procedure example in research paper

5. Edit your survey questionnaire settings for your specific needs

Choose where you choose to store your files and responses. Select a submission deadline, choose a timezone, limit respondents responses, enable Captcha to prevent spam and collect location data of customers.

data collection procedure example in research paper

Set an introductory message to respondents before they begin the survey, toggle the “start button” post final submission message or redirect respondents to another page when they submit their questionnaires. 

Change the Email Notifications inventory and initiate an autoresponder message to all your survey questionnaire respondents. You can also transfer your forms to other users who can become form administrators.

6. Share links of your survey questionnaire page with customers.

There’s an option to copy and share the link as “Popup” or “Embed code” The data collection tool automatically creates a QR Code for Survey Questionnaire where you can download and share as appropriate. 

data collection procedure example in research paper

Congratulations if you’ve made it to this stage. You can start sharing your link to your survey questionnaire with your customers.

7. View your Responses to the Survey Questionnaire

Toggle with the presentation of your summary from the options. Whether as a single, table or cards.

data collection procedure example in research paper

8. Allow Formplus Analytics to interpret your Survey Questionnaire Data

data collection procedure example in research paper

  With online form builder analytics, a business can determine;

7 Tips to Create The Best Surveys For Data Collections

Try out Formplus today . You can start making your own surveys with the Formplus online survey builder. By applying these tips, you will definitely get the most out of your online surveys.

Top Survey Templates For Data Collection 

On the template, you can collect data to measure customer’s satisfaction over key areas like the commodity purchase and the level of service they received. It also gives insight as to which products the customer enjoyed, how often they buy such a product, and whether or not the customer is likely to recommend the product to a friend or acquaintance. 

With this template, you would be able to measure, with accuracy, the ratio of male to female, age range and a number of unemployed persons in a particular country as well as obtain their personal details such as names and addresses.

Respondents are also able to state their religious and political views about the country under review.

Contained in the template for the online feedback form is the details of a product and/or service used. Identifying this product or service and documenting how long the customer has used them.

The overall satisfaction is measured as well as the delivery of the services. The likelihood that the customer also recommends said product is also measured.

The online questionnaire template houses the respondent’s data as well as educational qualification to collect information to be used for academic research.

Respondents can also provide their gender, race, a field of study as well as present living conditions as prerequisite data for the research study.

The template is a data sheet containing all the relevant information of a student. The student’s name, home address, guardians name, a record of attendance as well as performance in school is well represented on this template. This is a perfect data collection method to deploy for a school or an education organizations.

Also included is a record for interaction with others as well as a space for a short comment on the overall performance and attitude of the student. 

This online interview consent form template allows interviewee sign off their consent to use the interview data for research or report for journalist. With premium features like short text fields, upload, e-signature, etc., Formplus Builder is the perfect tool to create your preferred online consent forms without coding experience.

What is best data collection method for qualitative data?

Ans: Combination Research

The best data collection method for a researcher for gathering qualitative data which generally is data relying on the feelings, opinions and beliefs of the respondents would be Combination Research.

The reason why combination research is the best fit is that it encompasses the attributes of Interviews and Focus Groups. It is also useful when gathering data that is sensitive in nature. It can be described as all-purpose quantitative data collection method.

Above all, combination research improves the richness of data collected when compared with other data collection methods for qualitative data.

data collection procedure example in research paper

What is best data collection method for quantitative research data?

Ans: Questionnaire

The best data collection method a researcher can employ in gathering quantitative data which takes into consideration data that can be represented in numbers and figures that can be deduced mathematically is the Questionnaire.

These can be administered to a large number of respondents, while saving cost. For quantitative data that may be bulky or voluminous in nature, the use of a Questionnaire makes such data easy to visualize and analyze.

Another key advantage of the Questionnaire is that it can be used to compare and contrast previous research work done to measure changes.

Sign up on Formplus Builder to create your preferred online surveys or questionnaire for data collection. You don’t need to be tech-savvy! 


Use Formplus online survey & form builder to collect data anywhere in the world


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data collection procedure example in research paper

Home Market Research Research Tools and Apps

Data processing in research: What is it, steps & examples

Data processing in research is the process of collecting research data and transforming it into information usable to multiple stakeholders.

Data processing is often misunderstood as manipulation or data analysis, but it is much more than that. Multiple decisions are taken based on the accurate processing of data, and brands and researchers rely on data to make actionable decisions. The processing of data in research is one of the most critical components of the research process and can be the difference between brands being successful or not.

While market research data can be processed in multiple ways, it all boils down to what sort of insights you elicit from the collected data and the impact it makes on your decision-making processes.  

What is data processing in research?

Data processing in research is the process of collecting research data and transforming it into information usable to multiple stakeholders. While data can be looked at in numerous ways and through various lenses, data processing aids in proving or disproving theories, helping make business decisions, or even advancing enhancements in products and services. Data processing is even used in research to understand pricing sentiments, consumer behavior and preferences, and competitive analysis.

Through this process, research stakeholders turn qualitative data and quantitative data from a research study into a readable format in the form of graphs, reports, or anything else that business stakeholders resonate with. The process also provides context to the data that has been collected and helps with strategic business decisions.  

While it is a critical aspect of a business, data processing is still an underutilized process in research. With the proliferation of data and the number of research studies conducted, processing and putting the information into knowledge management repositories like InsightsHub is critical. 

Data processing Steps in Research

The data processing cycle in research has six steps. Let’s look at these steps and why they are an imperative component of the research design . 

Collection of research data

Data collection is the primary stage in the research process. This process could be through various online and offline research techniques and could be a mix of primary and secondary research methods. The most commonly used form of data collection is research surveys. However, with a mature market research platform , you can collect qualitative data through focus groups, discussion modules, and more.

Preparing research data

The second step in research data management is preparing the data to eliminate inconsistencies, remove bad or incomplete survey data, and clean the data to maintain consensus. This step is critical since insufficient data could render research studies wholly useless and could be a waste of time and effort.

Inputting research data

The next step is putting the cleaned-up data into a digitally readable format consistent with organizational policies, research needs, and more. This step is critical since the data is then put into online systems compatible with managing research data.

Processing research data

Once the data is input into systems, it is critical to process this data to make sense of it. The information is processed basis on needs, types of data collected, time available to process data, and multiple other factors. This is one of the most critical components of the research process. 

Output of research data

This stage of research data processing is where it gets turned into insights. This stage allows business owners, stakeholders, and other personnel to look at data in graphs, charts, reports, and other easy-to-consume formats. 

Storage of the processed research data

The final stage of the steps of data processing is the storage. Keeping the data in a format that is indexable, searchable, and creates a single source of truth is essential. Knowledge management platforms are most commonly used for storage of processed research data.

Benefits of data processing in research

Data processing can differentiate between actionable insights and their lack of existince in the research process . However, there are some distinct benefits and advantages of processing research data. They are:

Streamlined processing and management:

When research data is processed, there is a high probability that this data is going to be used for multiple purposes at this moment and in the future. Accurate data processing helps streamline how research data is handled and managed.

Better decision making:

With accurate data processing, the probability of making sense of the data to get to decisions faster and better becomes possible. Decisions then are taken based on data that tells stories instead of on a whim.

Democratization of insights:

Processing data allows raw data to be turned into a format that works for multiple teams and personnel. Easy-to-consume data allows for the data democratization of insights.

Reduced costs and high ROI:

Data-backed decisions aid brands and organizations in making decisions based on evidence-backed data from credible sources. This helps to reduce costs since decisions are linked to data. The process also helps to maintain a very high ROI on business decisions. 

Easy to store, report and distribute:

Processed data is easier to store and manage since there is a structure to raw data. This data is then referenceable and accessible in the future and can be called upon when required. 

Data processing in research Examples 

Now that you know the finer nuances of data processing in research let us look at specific examples to help you make sense of its importance.

Example at a global SaaS brand

Software-as-a-service (Saas) brands have a global footprint and have a plethora of customers – multiple times both B2B and B2C customers. Each brand and customer has different problems they hope to solve using the SaaS platform and hence have distinct needs. While conducting consumer research , the SaaS brand can understand consumer expectations, purchase and buying behaviors, and more. This also helps to profile customers, align product or service enhancements, manage marketing spending, and more basis on the processed research data. 

Other examples of this data processing include retail brands with a global United States footprint with customers across various  demographics vehicle manufacturers and distributors with multiple dealerships, and more. Everyone conducting market research needs to leverage data processing to make sense of the data.  

Process your research data with QuestionPro

Collecting research data, including survey research and other qualitative data, is possible with an enterprise-grade research platform like QuestionPro . Due to the nature of the tool, there is also the distinct possibility of processing data and making decisions that matter. The platform also allows you to process and store data for easy access. Get started now!


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