experiment method in data collection

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Chapter 1: Understanding Statistics

Back to chapter, data collection by experiments, previous video 1.8: data collection by observations, next video 1.10: data collection by survey.

There are several ways to collect data from a sample and extrapolate the data to the entire population. 

The experimental study is a common method of data collection. Here, the samples are manipulated by applying some form of treatment before collecting data. 

Suppose a researcher wants to know the effect of sunlight on plant growth. 

In this experiment, one group of plants is exposed to sunlight, and another group is kept in the dark. After a month, the heights of the plants are recorded, and an inference– whether sunlight is required for plant growth–is drawn. Thus, in an experiment, the samples are manipulated before collecting the data. 

Clinical trials are typical examples of data collection by experiments. Before a drug or treatment method is released for public use, its efficacy is tested on a small number of randomly selected groups of volunteers.

Here, one group of subjects is treated with specific doses of drugs or treatment methods, and a control group may be given a placebo. Then, the effects on disease symptoms are evaluated.

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”

An example of the experimental method is a public clinical trial of a drug. For instance, to test the efficacy of a new drug effective in treating blood pressure, one needs to perform an experimental data collection. The new drug is given to a small number of randomly selected volunteers who suffer from chronic high blood pressure. One group of subjects is treated with specific doses of drugs or treatment methods, and a control group may be given a placebo. The subjects are monitored for a few weeks. The symptoms of disease treatment and after-effects of the drug are observed, and the data is collected. As this process involves modifying the subjects, it is categorized under the experimental method.

Another example is studying the effect of a particular fertilizer on the plant's growth. For this purpose, a few plants are taken and subjected to treatment with the new fertilizer. The growth of the plants is monitored daily for a few weeks, and the data is collected.

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Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

Operationalization

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

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

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

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

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

Standardizing procedures

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

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

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

Creating a data management plan

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

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

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

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

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

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

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

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

Research bias

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

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What are the purpose and uses of experimental research design?

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Design: Selection of Data Collection Methods

Associated data.

Editor's Note: The online version of this article contains resources for further reading and a table of strengths and limitations of qualitative data collection methods.

The Challenge

Imagine that residents in your program have been less than complimentary about interprofessional rounds (IPRs). The program director asks you to determine what residents are learning about in collaboration with other health professionals during IPRs. If you construct a survey asking Likert-type questions such as “How much are you learning?” you likely will not gather the information you need to answer this question. You understand that qualitative data deal with words rather than numbers and could provide the needed answers. How do you collect “good” words? Should you use open-ended questions in a survey format? Should you conduct interviews, focus groups, or conduct direct observation? What should you consider when making these decisions?

Introduction

Qualitative research is often employed when there is a problem and no clear solutions exist, as in the case above that elicits the following questions: Why are residents complaining about rounds? How could we make rounds better? In this context, collecting “good” information or words (qualitative data) is intended to produce information that helps you to answer your research questions, capture the phenomenon of interest, and account for context and the rich texture of the human experience. You may also aim to challenge previous thinking and invite further inquiry.

Coherence or alignment between all aspects of the research project is essential. In this Rip Out we focus on data collection, but in qualitative research, the entire project must be considered. 1 , 2 Careful design of the data collection phase requires the following: deciding who will do what, where, when, and how at the different stages of the research process; acknowledging the role of the researcher as an instrument of data collection; and carefully considering the context studied and the participants and informants involved in the research.

Types of Data Collection Methods

Data collection methods are important, because how the information collected is used and what explanations it can generate are determined by the methodology and analytical approach applied by the researcher. 1 , 2 Five key data collection methods are presented here, with their strengths and limitations described in the online supplemental material.

• 1 Questions added to surveys to obtain qualitative data typically are open-ended with a free-text format. Surveys are ideal for documenting perceptions, attitudes, beliefs, or knowledge within a clear, predetermined sample of individuals. “Good” open-ended questions should be specific enough to yield coherent responses across respondents, yet broad enough to invite a spectrum of answers. Examples for this scenario include: What is the function of IPRs? What is the educational value of IPRs, according to residents? Qualitative survey data can be analyzed using a range of techniques.
• 2 Interviews are used to gather information from individuals 1-on-1, using a series of predetermined questions or a set of interest areas. Interviews are often recorded and transcribed. They can be structured or unstructured; they can either follow a tightly written script that mimics a survey or be inspired by a loose set of questions that invite interviewees to express themselves more freely. Interviewers need to actively listen and question, probe, and prompt further to collect richer data. Interviews are ideal when used to document participants' accounts, perceptions of, or stories about attitudes toward and responses to certain situations or phenomena. Interview data are often used to generate themes , theories , and models . Many research questions that can be answered with surveys can also be answered through interviews, but interviews will generally yield richer, more in-depth data than surveys. Interviews do, however, require more time and resources to conduct and analyze. Importantly, because interviewers are the instruments of data collection, interviewers should be trained to collect comparable data. The number of interviews required depends on the research question and the overarching methodology used. Examples of these questions include: How do residents experience IPRs? What do residents' stories about IPRs tell us about interprofessional care hierarchies?
• 3 Focus groups are used to gather information in a group setting, either through predetermined interview questions that the moderator asks of participants in turn or through a script to stimulate group conversations. Ideally, they are used when the sum of a group of people's experiences may offer more than a single individual's experiences in understanding social phenomena. Focus groups also allow researchers to capture participants' reactions to the comments and perspectives shared by other participants, and are thus a way to capture similarities and differences in viewpoints. The number of focus groups required will vary based on the questions asked and the number of different stakeholders involved, such as residents, nurses, social workers, pharmacists, and patients. The optimal number of participants per focus group, to generate rich discussion while enabling all members to speak, is 8 to 10 people. 3 Examples of questions include: How would residents, nurses, and pharmacists redesign or improve IPRs to maximize engagement, participation, and use of time? How do suggestions compare across professional groups?
• 4 Observations are used to gather information in situ using the senses: vision, hearing, touch, and smell. Observations allow us to investigate and document what people do —their everyday behavior—and to try to understand why they do it, rather than focus on their own perceptions or recollections. Observations are ideal when used to document, explore, and understand, as they occur, activities, actions, relationships, culture, or taken-for-granted ways of doing things. As with the previous methods, the number of observations required will depend on the research question and overarching research approach used. Examples of research questions include: How do residents use their time during IPRs? How do they relate to other health care providers? What kind of language and body language are used to describe patients and their families during IPRs?
• 5 Textual or content analysis is ideal when used to investigate changes in official, institutional, or organizational views on a specific topic or area to document the context of certain practices or to investigate the experiences and perspectives of a group of individuals who have, for example, engaged in written reflection. Textual analysis can be used as the main method in a research project or to contextualize findings from another method. The choice and number of documents has to be guided by the research question, but can include newspaper or research articles, governmental reports, organization policies and protocols, letters, records, films, photographs, art, meeting notes, or checklists. The development of a coding grid or scheme for analysis will be guided by the research question and will be iteratively applied to selected documents. Examples of research questions include: How do our local policies and protocols for IPRs reflect or contrast with the broader discourses of interprofessional collaboration? What are the perceived successful features of IPRs in the literature? What are the key features of residents' reflections on their interprofessional experiences during IPRs?

How You Can Start TODAY

• • Review medical education journals to find qualitative research in your area of interest and focus on the methods used as well as the findings.
• • When you have chosen a method, read several different sources on it.
• • From your readings, identify potential colleagues with expertise in your choice of qualitative method as well as others in your discipline who would like to learn more and organize potential working groups to discuss challenges that arise in your work.

What You Can Do LONG TERM

• • Either locally or nationally, build a community of like-minded scholars to expand your qualitative expertise.
• • Use a range of methods to develop a broad program of qualitative research.

Supplementary Material

Methods of data collection: experiments and focus groups

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• Sotirios Sarantakos 2  

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The successful completion of a sampling procedure connects the research with the respondents and specifies the kind and number of respondents who will be involved. The investigator knows at this stage not only what be studied, but also who to approach to collect the required information. The information will be available, provided that the right ‘connection’ between the researcher and the respondents is made. This connection is made through the methods of data collection.

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Sarantakos, S. (1998). Methods of data collection: experiments and focus groups. In: Social Research. Palgrave, London. https://doi.org/10.1007/978-1-349-14884-4_7

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

Table of Contents

Data Collection

Definition:

Data collection is the process of gathering and collecting information from various sources to analyze and make informed decisions based on the data collected. This can involve various methods, such as surveys, interviews, experiments, and observation.

In order for data collection to be effective, it is important to have a clear understanding of what data is needed and what the purpose of the data collection is. This can involve identifying the population or sample being studied, determining the variables to be measured, and selecting appropriate methods for collecting and recording data.

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

Primary data collection is the process of gathering original and firsthand information directly from the source or target population. This type of data collection involves collecting data that has not been previously gathered, recorded, or published. Primary data can be collected through various methods such as surveys, interviews, observations, experiments, and focus groups. The data collected is usually specific to the research question or objective and can provide valuable insights that cannot be obtained from secondary data sources. Primary data collection is often used in market research, social research, and scientific research.

Secondary Data Collection

Secondary data collection is the process of gathering information from existing sources that have already been collected and analyzed by someone else, rather than conducting new research to collect primary data. Secondary data can be collected from various sources, such as published reports, books, journals, newspapers, websites, government publications, and other documents.

Qualitative Data Collection

Qualitative data collection is used to gather non-numerical data such as opinions, experiences, perceptions, and feelings, through techniques such as interviews, focus groups, observations, and document analysis. It seeks to understand the deeper meaning and context of a phenomenon or situation and is often used in social sciences, psychology, and humanities. Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences.

Quantitative Data Collection

Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods. This data is typically collected through surveys, experiments, and other structured data collection methods. Quantitative data collection seeks to quantify and measure variables, such as behaviors, attitudes, and opinions, in a systematic and objective way. This data is often used to test hypotheses, identify patterns, and establish correlations between variables. Quantitative data collection methods allow for precise measurement and generalization of findings to a larger population. It is commonly used in fields such as economics, psychology, and natural sciences.

Data Collection Methods

Data Collection Methods are as follows:

Surveys involve asking questions to a sample of individuals or organizations to collect data. Surveys can be conducted in person, over the phone, or online.

Interviews involve a one-on-one conversation between the interviewer and the respondent. Interviews can be structured or unstructured and can be conducted in person or over the phone.

Focus Groups

Focus groups are group discussions that are moderated by a facilitator. Focus groups are used to collect qualitative data on a specific topic.

Observation

Observation involves watching and recording the behavior of people, objects, or events in their natural setting. Observation can be done overtly or covertly, depending on the research question.

Experiments

Experiments involve manipulating one or more variables and observing the effect on another variable. Experiments are commonly used in scientific research.

Case Studies

Case studies involve in-depth analysis of a single individual, organization, or event. Case studies are used to gain detailed information about a specific phenomenon.

Secondary Data Analysis

Secondary data analysis involves using existing data that was collected for another purpose. Secondary data can come from various sources, such as government agencies, academic institutions, or private companies.

How to Collect Data

The following are some steps to consider when collecting data:

• Define the objective : Before you start collecting data, you need to define the objective of the study. This will help you determine what data you need to collect and how to collect it.
• Identify the data sources : Identify the sources of data that will help you achieve your objective. These sources can be primary sources, such as surveys, interviews, and observations, or secondary sources, such as books, articles, and databases.
• Determine the data collection method : Once you have identified the data sources, you need to determine the data collection method. This could be through online surveys, phone interviews, or face-to-face meetings.
• Develop a data collection plan : Develop a plan that outlines the steps you will take to collect the data. This plan should include the timeline, the tools and equipment needed, and the personnel involved.
• Test the data collection process: Before you start collecting data, test the data collection process to ensure that it is effective and efficient.
• Collect the data: Collect the data according to the plan you developed in step 4. Make sure you record the data accurately and consistently.
• Analyze the data: Once you have collected the data, analyze it to draw conclusions and make recommendations.
• Report the findings: Report the findings of your data analysis to the relevant stakeholders. This could be in the form of a report, a presentation, or a publication.
• Monitor and evaluate the data collection process: After the data collection process is complete, monitor and evaluate the process to identify areas for improvement in future data collection efforts.
• Ensure data quality: Ensure that the collected data is of high quality and free from errors. This can be achieved by validating the data for accuracy, completeness, and consistency.
• Maintain data security: Ensure that the collected data is secure and protected from unauthorized access or disclosure. This can be achieved by implementing data security protocols and using secure storage and transmission methods.
• Follow ethical considerations: Follow ethical considerations when collecting data, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring that the research does not cause harm to participants.
• Use appropriate data analysis methods : Use appropriate data analysis methods based on the type of data collected and the research objectives. This could include statistical analysis, qualitative analysis, or a combination of both.
• Record and store data properly: Record and store the collected data properly, in a structured and organized format. This will make it easier to retrieve and use the data in future research or analysis.
• Collaborate with other stakeholders : Collaborate with other stakeholders, such as colleagues, experts, or community members, to ensure that the data collected is relevant and useful for the intended purpose.

Applications of Data Collection

Data collection methods are widely used in different fields, including social sciences, healthcare, business, education, and more. Here are some examples of how data collection methods are used in different fields:

• Social sciences : Social scientists often use surveys, questionnaires, and interviews to collect data from individuals or groups. They may also use observation to collect data on social behaviors and interactions. This data is often used to study topics such as human behavior, attitudes, and beliefs.
• Healthcare : Data collection methods are used in healthcare to monitor patient health and track treatment outcomes. Electronic health records and medical charts are commonly used to collect data on patients’ medical history, diagnoses, and treatments. Researchers may also use clinical trials and surveys to collect data on the effectiveness of different treatments.
• Business : Businesses use data collection methods to gather information on consumer behavior, market trends, and competitor activity. They may collect data through customer surveys, sales reports, and market research studies. This data is used to inform business decisions, develop marketing strategies, and improve products and services.
• Education : In education, data collection methods are used to assess student performance and measure the effectiveness of teaching methods. Standardized tests, quizzes, and exams are commonly used to collect data on student learning outcomes. Teachers may also use classroom observation and student feedback to gather data on teaching effectiveness.
• Agriculture : Farmers use data collection methods to monitor crop growth and health. Sensors and remote sensing technology can be used to collect data on soil moisture, temperature, and nutrient levels. This data is used to optimize crop yields and minimize waste.
• Environmental sciences : Environmental scientists use data collection methods to monitor air and water quality, track climate patterns, and measure the impact of human activity on the environment. They may use sensors, satellite imagery, and laboratory analysis to collect data on environmental factors.
• Transportation : Transportation companies use data collection methods to track vehicle performance, optimize routes, and improve safety. GPS systems, on-board sensors, and other tracking technologies are used to collect data on vehicle speed, fuel consumption, and driver behavior.

Examples of Data Collection

Examples of Data Collection are as follows:

• Traffic Monitoring: Cities collect real-time data on traffic patterns and congestion through sensors on roads and cameras at intersections. This information can be used to optimize traffic flow and improve safety.
• Social Media Monitoring : Companies can collect real-time data on social media platforms such as Twitter and Facebook to monitor their brand reputation, track customer sentiment, and respond to customer inquiries and complaints in real-time.
• Weather Monitoring: Weather agencies collect real-time data on temperature, humidity, air pressure, and precipitation through weather stations and satellites. This information is used to provide accurate weather forecasts and warnings.
• Stock Market Monitoring : Financial institutions collect real-time data on stock prices, trading volumes, and other market indicators to make informed investment decisions and respond to market fluctuations in real-time.
• Health Monitoring : Medical devices such as wearable fitness trackers and smartwatches can collect real-time data on a person’s heart rate, blood pressure, and other vital signs. This information can be used to monitor health conditions and detect early warning signs of health issues.

Purpose of Data Collection

The purpose of data collection can vary depending on the context and goals of the study, but generally, it serves to:

• Provide information: Data collection provides information about a particular phenomenon or behavior that can be used to better understand it.
• Measure progress : Data collection can be used to measure the effectiveness of interventions or programs designed to address a particular issue or problem.
• Support decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions.
• Identify trends : Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Monitor and evaluate : Data collection can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.

When to use Data Collection

Data collection is used when there is a need to gather information or data on a specific topic or phenomenon. It is typically used in research, evaluation, and monitoring and is important for making informed decisions and improving outcomes.

Data collection is particularly useful in the following scenarios:

• Research : When conducting research, data collection is used to gather information on variables of interest to answer research questions and test hypotheses.
• Evaluation : Data collection is used in program evaluation to assess the effectiveness of programs or interventions, and to identify areas for improvement.
• Monitoring : Data collection is used in monitoring to track progress towards achieving goals or targets, and to identify any areas that require attention.
• Decision-making: Data collection is used to provide decision-makers with information that can be used to inform policies, strategies, and actions.
• Quality improvement : Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Characteristics of Data Collection

Data collection can be characterized by several important characteristics that help to ensure the quality and accuracy of the data gathered. These characteristics include:

• Validity : Validity refers to the accuracy and relevance of the data collected in relation to the research question or objective.
• Reliability : Reliability refers to the consistency and stability of the data collection process, ensuring that the results obtained are consistent over time and across different contexts.
• Objectivity : Objectivity refers to the impartiality of the data collection process, ensuring that the data collected is not influenced by the biases or personal opinions of the data collector.
• Precision : Precision refers to the degree of accuracy and detail in the data collected, ensuring that the data is specific and accurate enough to answer the research question or objective.
• Timeliness : Timeliness refers to the efficiency and speed with which the data is collected, ensuring that the data is collected in a timely manner to meet the needs of the research or evaluation.
• Ethical considerations : Ethical considerations refer to the ethical principles that must be followed when collecting data, such as ensuring confidentiality and obtaining informed consent from participants.

Advantages of Data Collection

There are several advantages of data collection that make it an important process in research, evaluation, and monitoring. These advantages include:

• Better decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions, leading to better decision-making.
• Improved understanding: Data collection helps to improve our understanding of a particular phenomenon or behavior by providing empirical evidence that can be analyzed and interpreted.
• Evaluation of interventions: Data collection is essential in evaluating the effectiveness of interventions or programs designed to address a particular issue or problem.
• Identifying trends and patterns: Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Increased accountability: Data collection increases accountability by providing evidence that can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.
• Validation of theories: Data collection can be used to test hypotheses and validate theories, leading to a better understanding of the phenomenon being studied.
• Improved quality: Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Limitations of Data Collection

While data collection has several advantages, it also has some limitations that must be considered. These limitations include:

• Bias : Data collection can be influenced by the biases and personal opinions of the data collector, which can lead to inaccurate or misleading results.
• Sampling bias : Data collection may not be representative of the entire population, resulting in sampling bias and inaccurate results.
• Cost : Data collection can be expensive and time-consuming, particularly for large-scale studies.
• Limited scope: Data collection is limited to the variables being measured, which may not capture the entire picture or context of the phenomenon being studied.
• Ethical considerations : Data collection must follow ethical principles to protect the rights and confidentiality of the participants, which can limit the type of data that can be collected.
• Data quality issues: Data collection may result in data quality issues such as missing or incomplete data, measurement errors, and inconsistencies.
• Limited generalizability : Data collection may not be generalizable to other contexts or populations, limiting the generalizability of the findings.

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”

An example of the experimental method is a public clinical trial of a drug. For instance, to test the efficacy of a new drug effective in treating blood pressure, one needs to perform an experimental data collection. The new drug is given to a small number of randomly selected volunteers who suffer from chronic high blood pressure.One group of subjects is treated with specific doses of drugs or treatment methods, and a control group may be given a placebo. The subjects are monitored for a few weeks. The symptoms of disease treatment and after-effects of the drug are observed, and the data is collected. As this process involves modifying the subjects, it is categorized under the experimental method.

Another example is studying the effect of a particular fertilizer on the plant's growth. For this purpose, a few plants are taken and subjected to treatment with the new fertilizer. The growth of the plants is monitored daily for a few weeks, and the data is collected.

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Data Collection Methods

Data collection is a process of collecting information from all the relevant sources to find answers to the research problem, test the hypothesis (if you are following deductive approach ) and evaluate the outcomes. Data collection methods can be divided into two categories: secondary methods of data collection and primary methods of data collection.

Secondary Data Collection Methods

Secondary data is a type of data that has already been published in books, newspapers, magazines, journals, online portals etc.  There is an abundance of data available in these sources about your research area in business studies, almost regardless of the nature of the research area. Therefore, application of appropriate set of criteria to select secondary data to be used in the study plays an important role in terms of increasing the levels of research validity and reliability.

These criteria include, but not limited to date of publication, credential of the author, reliability of the source, quality of discussions, depth of analyses, the extent of contribution of the text to the development of the research area etc. Secondary data collection is discussed in greater depth in Literature Review chapter.

Secondary data collection methods offer a range of advantages such as saving time, effort and expenses. However they have a major disadvantage. Specifically, secondary research does not make contribution to the expansion of the literature by producing fresh (new) data.

Primary Data Collection Methods

Primary data is the type of data that has not been around before. Primary data is unique findings of your research. Primary data collection and analysis typically requires more time and effort to conduct compared to the secondary data research. Primary data collection methods can be divided into two groups: quantitative and qualitative.

Quantitative data collection methods are based on mathematical calculations in various formats. Methods of quantitative data collection and analysis include questionnaires with closed-ended questions, methods of correlation and regression, mean, mode and median and others.

Quantitative methods are cheaper to apply and they can be applied within shorter duration of time compared to qualitative methods. Moreover, due to a high level of standardisation of quantitative methods, it is easy to make comparisons of findings.

Qualitative research methods , on the contrary, do not involve numbers or mathematical calculations. Qualitative research is closely associated with words, sounds, feeling, emotions, colours and other elements that are non-quantifiable.

Qualitative studies aim to ensure greater level of depth of understanding and qualitative data collection methods include interviews, questionnaires with open-ended questions, focus groups, observation, game or role-playing, case studies etc.

Your choice between quantitative or qualitative methods of data collection depends on the area of your research and the nature of research aims and objectives.

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

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:

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

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

Table of contents

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

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

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

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

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

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

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

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

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

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

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

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

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

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

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

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

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 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:

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

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:

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

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

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:

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

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

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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AP® Statistics

Data collection methods: what to know for ap® statistics.

• The Albert Team
• Last Updated On: March 1, 2022

Introduction

When faced with a research problem, you need to collect, analyze and interpret data to answer your research questions. Examples of research questions that could require you to gather data include how many people will vote for a candidate, what is the best product mix to use and how useful is a drug in curing a disease. The research problem you explore informs the type of data you’ll collect and the data collection method you’ll use. In this article, we will explore various types of data, methods of data collection and advantages and disadvantages of each. After reading our review, you will have an excellent understanding of when to use each of the data collection methods we discuss.

Types of Data

Quantitative Data

Data that is expressed in numbers and summarized using statistics to give meaningful information is referred to as quantitative data . Examples of quantitative data we could collect are heights, weights, or ages of students. If we obtain the mean of each set of measurements, we have meaningful information about the average value for each of those student characteristics.

Qualitative Data

When we use data for description without measurement, we call it qualitative data . Examples of qualitative data are student attitudes towards school, attitudes towards exam cheating and friendliness of students to teachers. Such data cannot be easily summarized using statistics.

Primary Data

When we obtain data directly from individuals, objects or processes, we refer to it as primary data . Quantitative or qualitative data can be collected using this approach. Such data is usually collected solely for the research problem to you will study. Primary data has several advantages. First, we tailor it to our specific research question, so there are no customizations needed to make the data usable. Second, primary data is reliable because you control how the data is collected and can monitor its quality. Third, by collecting primary data, you spend your resources in collecting only required data. Finally, primary data is proprietary, so you enjoy advantages over those who cannot access the data.

Despite its advantages, primary data also has disadvantages of which you need to be aware. The first problem with primary data is that it is costlier to acquire as compared to secondary data. Obtaining primary data also requires more time as compared to gathering secondary data.

Secondary Data

When you collect data after another researcher or agency that initially gathered it makes it available, you are gathering secondary data . Examples of secondary data are census data published by the US Census Bureau, stock prices data published by CNN and salaries data published by the Bureau of Labor Statistics.

One advantage to using secondary data is that it will save you time and money, although some data sets require you to pay for access. A second advantage is the relative ease with which you can obtain it. You can easily access secondary data from publications, government agencies, data aggregation websites and blogs. A third advantage is that it eliminates effort duplication since you can identify existing data that matches your needs instead of gather new data.

Despite the benefits it offers, secondary data has its shortcomings. One limitation is that secondary data may not be complete. For it to meet your research needs, you may need to enrich it with data from other sources. A second shortcoming is that you cannot verify the accuracy of secondary data, or the data may be outdated. A third challenge you face when using secondary data is that documentation may be incomplete or missing. Therefore, you may not be aware of any problems that happened in data collection which would otherwise influence its interpretation. Another challenge you may face when you decide to use secondary data is that there may be copyright restrictions.

Now that we’ve explained the various types of data you can collect when conducting research, we will proceed to look at methods used to collect primary and secondary data.

Methods Employed in Primary Data Collection

When you decide to conduct original research, the data you gather can be quantitative or qualitative. Generally, you collect quantitative data through sample surveys, experiments and observational studies. You obtain qualitative data through focus groups, in-depth interviews and case studies. We will discuss each of these data collection methods below and examine their advantages and disadvantages.

Sample Surveys

A survey is a data collection method where you select a sample of respondents from a large population in order to gather information about that population. The process of identifying individuals from the population who you will interview is known as sampling .

To gather data through a survey, you construct a questionnaire to prompt information from selected respondents. When creating a questionnaire, you should keep in mind several key considerations. First, make sure the questions and choices are unambiguous. Second, make sure the questionnaire will be completed within a reasonable amount of time. Finally, make sure there are no typographical errors. To check if there are any problems with your questionnaire, use it to interview a few people before administering it to all respondents in your sample. We refer to this process as pretesting.

Using a survey to collect data offers you several advantages. The main benefit is time and cost savings because you only interview a sample, not the large population. Another benefit is that when you select your sample correctly, you will obtain information of acceptable accuracy. Additionally, surveys are adaptable and can be used to collect data for governments, health care institutions, businesses and any other environment where data is needed.

A major shortcoming of surveys occurs when you fail to select a sample correctly; without an appropriate sample, the results will not accurately generalize the population.

Ways of Interviewing Respondents

Once you have selected your sample and developed your questionnaire, there are several ways you can interview participants. Each approach has its advantages and disadvantages.

In-person Interviewing

When you use this method, you meet with the respondents face to face and ask questions. In-person interviewing offers several advantages. This technique has excellent response rates and enables you to conduct interviews that take a longer amount of time. Another benefit is you can ask follow-up questions to responses that are not clear.

In-person interviews do have disadvantages of which you need to be aware. First, this method is expensive and takes more time because of interviewer training, transport, and remuneration. A second disadvantage is that some areas of a population, such as neighborhoods prone to crime, cannot be accessed which may result in bias.

Telephone Interviewing

Using this technique, you call respondents over the phone and interview them. This method offers the advantage of quickly collecting data, especially when used with computer-assisted telephone interviewing. Another advantage is that collecting data via telephone is cheaper than in-person interviewing.

One of the main limitations with telephone interviewing it’s hard to gain the trust of respondents. Due to this reason, you may not get responses or may introduce bias. Since phone interviews are generally kept short to reduce the possibility of upsetting respondents, this method may also limit the amount of data you can collect.

Online Interviewing

With online interviewing, you send an email inviting respondents to participate in an online survey. This technique is used widely because it is a low-cost way of interviewing many respondents. Another benefit is anonymity; you can get sensitive responses that participants would not feel comfortable providing with in-person interviewing.

When you use online interviewing, you face the disadvantage of not getting a representative sample. You also cannot seek clarification on responses that are unclear.

Mailed Questionnaire

When you use this interviewing method, you send a printed questionnaire to the postal address of the respondent. The participants fill in the questionnaire and mail it back. This interviewing method gives you the advantage of obtaining information that respondents may be unwilling to give when interviewing in person.

The main limitation with mailed questionnaires is you are likely to get a low response rate. Keep in mind that inaccuracy in mailing address, delays or loss of mail could also affect the response rate. Additionally, mailed questionnaires cannot be used to interview respondents with low literacy, and you cannot seek clarifications on responses.

Focus Groups

When you use a focus group as a data collection method, you identify a group of 6 to 10 people with similar characteristics. A moderator then guides a discussion to identify attitudes and experiences of the group. The responses are captured by video recording, voice recording or writing—this is the data you will analyze to answer your research questions. Focus groups have the advantage of requiring fewer resources and time as compared to interviewing individuals. Another advantage is that you can request clarifications to unclear responses.

One disadvantage you face when using focus groups is that the sample selected may not represent the population accurately. Furthermore, dominant participants can influence the responses of others.

Observational Data Collection Methods

In an observational data collection method, you acquire data by observing any relationships that may be present in the phenomenon you are studying. There are four types of observational methods that are available to you as a researcher: cross-sectional, case-control, cohort and ecological.

In a cross-sectional study, you only collect data on observed relationships once. This method has the advantage of being cheaper and taking less time as compared to case-control and cohort. However, cross-sectional studies can miss relationships that may arise over time.

Using a case-control method, you create cases and controls and then observe them. A case has been exposed to a phenomenon of interest while a control has not. After identifying the cases and controls, you move back in time to observe how your event of interest occurs in the two groups. This is why case-control studies are referred to as retrospective. For example, suppose a medical researcher suspects a certain type of cosmetic is causing skin cancer. You recruit people who have used a cosmetic, the cases, and those who have not used the cosmetic, the controls. You request participants to remember the type of cosmetic and the frequency of its use. This method is cheaper and requires less time as compared to the cohort method. However, this approach has limitations when individuals you are observing cannot accurately recall information. We refer to this as recall bias because you rely on the ability of participants to remember information. In the cosmetic example, recall bias would occur if participants cannot accurately remember the type of cosmetic and number of times used.

In a cohort method, you follow people with similar characteristics over a period. This method is advantageous when you are collecting data on occurrences that happen over a long period. It has the disadvantage of being costly and requiring more time. It is also not suitable for occurrences that happen rarely.

The three methods we have discussed previously collect data on individuals. When you are interested in studying a population instead of individuals, you use an ecological method. For example, say you are interested in lung cancer rates in Iowa and North Dakota. You obtain number of cancer cases per 1000 people for each state from the National Cancer Institute and compare them. You can then hypothesize possible causes of differences between the two states. When you use the ecological method, you save time and money because data is already available. However the data collected may lead you to infer population relationships that do not exist.

Experiments

An experiment is a data collection method where you as a researcher change some variables and observe their effect on other variables. The variables that you manipulate are referred to as independent while the variables that change as a result of manipulation are dependent variables. Imagine a manufacturer is testing the effect of drug strength on number of bacteria in the body. The company decides to test drug strength at 10mg, 20mg and 40mg. In this example, drug strength is the independent variable while number of bacteria is the dependent variable. The drug administered is the treatment, while 10mg, 20mg and 40mg are the levels of the treatment.

The greatest advantage of using an experiment is that you can explore causal relationships that an observational study cannot. Additionally, experimental research can be adapted to different fields like medical research, agriculture, sociology, and psychology. Nevertheless, experiments have the disadvantage of being expensive and requiring a lot of time.

This article introduced you to the various types of data you can collect for research purposes. We discussed quantitative, qualitative, primary and secondary data and identified the advantages and disadvantages of each data type. We also reviewed various data collection methods and examined their benefits and drawbacks. Having read this article, you should be able to select the data collection method most appropriate for your research question. Data is the evidence that you use to solve your research problem. When you use the correct data collection method, you get the right data to solve your problem.

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12 thoughts on “data collection methods: what to know for ap® statistics”.

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Research methods for social sciences.

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Introduction

As part of your research plan and design, you will select a data collection method to address your research problems. This page provides information on quantitative, qualitative, and combined methods.

If you are planning to use an exisiting dataset from other researcher or organization, visit the Finding Datasets guide for information of public datasets, data platforms available through the UNT Libraries, and analytic tools available to use directly from certain data providers.

Quantitative Data Collection

Quantitative methods to collect data involve measures and numerical information that can be further tested and analyzed with statistical methods. The most common forms of quantitative data collection methods are:

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Qualitative data collection focuses on collecting information based on experience, thoughts, and feelings from your subjects or representations from artifacts in your discipline. The most common ways to collect qualitative data are:

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Below are some resources from the UNT Libraries that provide guidance on qualitative data collection methods and sampling techniques commonly used in social science research.

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• Mobile application strategy

What is geolocation? Explaining how geolocation data works

• Cameron Hashemi-Pour, Site Editor

Geolocation technology identifies physical locations of devices and individuals based on information such as geographic coordinates and Internet Protocol (IP) addresses. The term isn't used a lot, but it has become more commonplace. Most tech users rely on geolocation technology even if they don't realize it.

Devices connected to the internet use geolocation data to provide smarter, more personalized navigation. The technology comes with drawbacks, including privacy issues and the potential misuse of personal information. Nevertheless, it has pragmatic and practical uses in various industries and for consumers, making it nearly ubiquitous.

Geolocation data is typically collected by various entities, including internet service providers, mobile app developers, governments, law enforcement and advertisers. With accurate geolocation data, a brand can target advertising to specific customers, and app developers can build better navigational apps for mobile devices.

How geolocation works

Internet-connected devices, such as smartphones, laptops, tablets, smart cars, internet of things (IoT) sensors and smart watches, are conducive to sending geolocation data to entities that require it. These devices are assigned IP addresses for identification purposes. They can also have built-in Global Positioning System ( GPS ) receivers so satellite technology can track their physical locations.

An internet connection isn't needed for GPS-enabled devices because they use satellite signals to determine location. However, all other geolocation methods require an internet connection, such through Wi-Fi or a cellular network.

The process involved in geolocation data collection involves these key steps:

• Collection. A device collects raw data about its location from multiple sources. It does this by sending GPS signals, Wi-Fi pings and cellular data exchanges and then seeing how long it takes to get a response back.
• Transmission. The raw data is transmitted to the central server or cloud infrastructure that operates a geolocation app or service through different communications protocols, such as GPS or cell networks.
• Triangulation. Using this raw data, the device determines its location by measuring its distance from multiple cell towers, Wi-Fi access points or satellites.
• Processing. Algorithms process the raw data to produce accurate location data.

Geolocation data collection methods

The most common geolocation methods for accessing data sources include the following:

• GPS. GPS pinpoints a device's location based on latitude and longitude coordinates. It only works for devices with built-in GPS sensors.
• Network connection. Cell phones and other internet-connected devices can be located wirelessly through a connection to a cell tower or Wi-Fi network.
• Combined GPS and cell towers. If a device has both a built-in GPS sensor and a cell connection, a combined approach to the location of a device is more reliable because the cellular connection can take over in geographical locations when the GPS signal is weak.
• Bluetooth beacons. Bluetooth -enabled beacons are small pieces of hardware capable of transmitting data via radio waves. They are placed in different locations so devices can determine their distance from them. However, this is proximity-based and doesn't work over long distances.
• IP addresses. These long numerical sequences are used as unique identifiers. Since no two IP addresses are the same, IP geolocation is a convenient way to identify the location of specific devices.

Why should businesses care about geolocation data?

Geolocation isn't limited to navigation on mobile phones. It has many practical uses across various industries. At the enterprise level, geolocation data assists in many different tasks:

• Sales and marketing efforts. Business analytics applications use location data about customers and website or social media visitors to reach them with offers and sales pitches that consider where they physically are.
• Fraud detection. Geolocation data is a useful component in fraud prevention. A user can pair a device, such as a smartphone, with a specific form of payment, such as a credit card. If a malicious actor attempts to use this card with a different device, geolocation data is used to determine that the device doesn't belong to the user.
• Increasing efficiency. Geolocation data lets logistics and delivery companies do real-time tracking. It's one of multiple factors involved in optimizing routes to deliver goods and location-based services faster and more efficiently to customers.
• Real-time fleet and vehicle safety. Geolocation data not only tracks individual vehicles and fleets of vehicles but also assists with real-time incident management to enhance their safety. A cargo ship experiencing a malfunction at sea, for instance, can provide GPS coordinates to a repair team, allowing them to offer help much more quickly.
• Connected devices. IoT and industrial IoT devices can be implemented in various industries. Depending on how far assets are placed from each other, these devices can rely on geolocation data to transmit important data, such as performance data.

Geolocation use cases and examples

Industries where geolocation plays an important role include the following:

• Finance. Device data coupled with payment methods facilitates fraud detection in finance and banking. Users can be immediately notified when unauthorized devices from different locations use their payment methods.
• Logistics. Route optimization and incident management in supply chains require precise geolocation data. With this information, any impediments to delivering goods and services can be addressed faster and more easily. For example, a trucking company might have vehicles break down, creating supply chain issues. With geolocation data, dispatchers can quickly locate a truck and send a repair team to fix it.
• Retail. Geolocation lets businesses use the geographic locations of their customers to tailor their marketing strategies. This is especially important in e-commerce where customers are scattered nationally or even globally. An online retailer, such as Amazon, relies on this data for clues about and insight into a customer's preferences.
• Health services. Health providers can use geolocation data to improve their emergency services and reach patients faster. For example, accurate location data lets emergency medical teams reach patients with critical health issues faster.
• Architecture and construction. Geolocation data makes it easier to identify building sites and equipment. While radio frequency identification tags are useful for tracking nearby assets used in construction, geolocation provides a broader scope of location information when sites, personnel and materials required are in different locations covering long distances. For instance, a construction firm contracted to build multiple locations of a business in a municipal or regional area can use geolocation data to better coordinate the movement of equipment needed at different sites.

Geolocation accuracy and limitations

Multiple factors determine the accuracy of a device's geolocation capabilities. These include the following:

• Device type. The device being used can affect geolocation accuracy. For example, mobile devices typically provide more accurate geolocation readings than stationary ones. This is because mobile devices usually have built-in GPS sensors and more constant cellular or Wi-Fi connectivity. Desktops, on the other hand, have Wi-Fi on less frequently and lack triangulation.
• Data collection method. The various data collection methods aren't equal in accuracy. For instance, IP geolocation is typically less accurate than network connections.
• Surrounding environment. Urban environments typically have many more nearby cell towers and Wi-Fi access points than rural areas, therefore enhancing accuracy. With GPS signals, physical buildings, obstructions and weather conditions can be a problem. GPS is often combined with cell technology to counterbalance weak GPS signals. GPS can also be combined with Wi-Fi, cellular and Bluetooth to enhance location accuracy.

Geolocation privacy issues

Geolocation data is often considered sensitive personal information that, in the wrong hands, could put a person or property at risk. Organizations handling this data must use security software tools and methods to safeguard it.

Misuse isn't limited to malicious actors. The organizations that collect this data can also handle it improperly or fail to be transparent about how they plan to use the data. Transparency issues arise when organizations that collect and process geolocation data don't tell customers or clients upfront how they plan to use the data. Various data privacy regulations, such as the European Union's General Data Protection Regulation, require transparency and consent.

Privacy issues also occur when organizations share geolocation data with third parties to improve geolocation services or for other purposes. For example, a person might give their bank permission to use real-time geolocation data to help locate nearby ATMs or bank branches on their cell phone as they move around a city. However, if the bank then sends that information to a third party, that vendor might use it for marketing and advertising in ways the customer finds intrusive and wrong.

Many people don't consider the collection of macro information on the country, state or even city they're located in as raising privacy issues. Problems happen when street-level data and home addresses are collected. This can be deemed sensitive location information depending on the jurisdiction. In the U.S., different states determine what is acceptable to track when individuals use mobile devices.

How to mitigate risks associated with geolocation

There are various ways to mitigate the security and privacy risks of geolocation data collection technology. These methods can also empower executives and employees alike to be cognizant of geolocation risks in their personal lives outside work. They include the following:

• Develop internal policies. An organization can craft detailed policies around privacy and security that dictate the terms for allowing geolocation data and determine acceptable use. Those writing them must be aware of global, federal, state and local privacy laws that affect them. And compliance is key when implementing these policies.
• Implement controls. Appropriate controls should be mandated for all employees. These include setting and configuring apps to limit geolocational data and implementing security measures such as access control and encryption to protect an organization's private data.
• Data classification. To further protect sensitive information , an organization can develop a system for classifying geolocation data as either safe or threatening to privacy and security.
• Training employees. Training programs increase employees' awareness of how devices and apps administered through their organizations use geolocation data and pose possible risks.
• Transparency. An organization's policies should make clear to customers and clients exactly how geolocation data is collected, processed and used. Employees must also understand what data is collected and how it's used to ensure they comply with applicable laws and regulations.

Spatial analysis and geolocation have some commonalities. Spatial analysis is the use of data that references a specific geographical area or location. Explore how spatial analysis is used for deeper insights from location data .

Continue Reading About What is geolocation? Explaining how geolocation data works

• How to address security risks in GPS-enabled devices
• Geospatial analytics bolsters predictive capabilities
• Benefits of RFID in supply chain management and logistics
• Common social media privacy issues
• How satellite technology can combat the digital divide

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Research on acoustic signal identification mechanism and denoising methods of combine harvesting loss.

1. Introduction

2. materials and methods, 2.1. impact simulation between plate and throws, 2.2. signal acquisition and analysis, 2.2.1. design of the signal acquisition circuit, 2.2.2. acoustic monitoring test bench, 2.2.3. acoustic feature analysis, 2.3. research on loss detection methods, 2.3.1. signal denoising method, 2.3.2. recognition and counting method of grain signals, 4. discussion, 5. conclusions, author contributions, data availability statement, conflicts of interest.

• Gao, L.; Xu, S.; Li, Z.; Cheng, S.; Yu, W.; Zhang, Y.; Wu, C. Main grain crop postharvest losses and its reducing potential in China. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 2016 , 32 , 1–11. [ Google Scholar ]
• Liu, J.G.; Lundqvist, J.; Weinberg, J.; Gustafsson, J. Food Losses and Waste in China and Their Implication for Water and Land. Environ. Sci. Technol. 2013 , 47 , 10137–10144. [ Google Scholar ] [ CrossRef ]
• Nie, X.; Sun, Y.; Chen, X.; Wang, J. Based on DSP and Winform, the software research of online monitoring system of grain loss rate and the construction of cloud platform are studied. In Proceedings of the 2020 ASABE Annual International Meeting, Virtual, 13–15 July 2020. [ Google Scholar ] [ CrossRef ]
• Liang, Z.; Wada, M. Development of cleaning systems for combine harvesters: A review. Biosyst. Eng. 2023 , 236 , 79–102. [ Google Scholar ] [ CrossRef ]
• Jie, Z.; Liu, H.; Hou, F. Research advances and prospects of combine on precision agriculture in China. Trans. Chin. Soc. Agric. Eng. 2005 , 21 , 179–182. [ Google Scholar ]
• Zhuo, W.; Dong, C.; Xiaoping, B.; Hechun, H. Improvement and Experiment of Cleaning Loss Rate Monitoring Device for Corn Combine Harvester. Trans. Chin. Soc. Agric. Mach. 2018 , 49 , 100–108. [ Google Scholar ] [ CrossRef ]
• Wayne, M.; Thomas, G.; Robert, J.; James, N. Unthreshed Head Grain Loss Monitor. U.S. Patent US4825146A, 25 April 1989. Available online: https://patents.google.com/patent/US4825146A/en (accessed on 14 August 2024).
• Richard, K. Absolute Grain Loss Monitor. U.S. Patent US4360998A, 30 November 1982. Available online: https://patents.google.com/patent/US4360998A/en (accessed on 14 August 2024).
• Jahari, M.; Yamamoto, K.; Miyamoto, M.; Kondo, N.; Ogawa, Y.; Suzuki, T.; Ahmad, U. Double lighting machine vision system to monitor harvested paddy grain quality during head-feeding combine harvester operation. Machines 2015 , 3 , 352–363. [ Google Scholar ] [ CrossRef ]
• Chen, J.; Lian, Y.; Li, Y. Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm. Comput. Electron. Agric. 2020 , 175 , 105591. [ Google Scholar ] [ CrossRef ]
• Gao, J.; Zhang, G.; Yu, L.; Li, Y. Chaos detection of grain impact at combine cleaning loss sensor. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 2011 , 27 , 22–27. [ Google Scholar ] [ CrossRef ]
• Liang, Z. Selecting the proper material for a grain loss sensor based on DEM simulation and structure optimization to improve monitoring ability. Precis. Agric. 2021 , 22 , 1120–1133. [ Google Scholar ] [ CrossRef ]
• Liang, Z.; Li, Y.; Baerdemaeker, J.; Xu, L.; Saeys, W. Development and testing of a multi-duct cleaning device for tangential-longitudinal flow rice combine harvesters. Biosyst. Eng. 2019 , 182 , 95–106. [ Google Scholar ] [ CrossRef ]
• Sun, Y.; Yu, Y.; Tian, H.; Qian, X.; Wang, J. Design and modeling of grain impact sensor utilizing two crossed polyvinylidene fluoride films. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, DC, USA, 16–19 July 2017. [ Google Scholar ] [ CrossRef ]
• Xu, L.; Wei, C.; Liang, Z.; Chai, X.; Li, Y.; Liu, Q. Development of rapeseed cleaning loss monitoring system and experiments in a combine harvester. Biosyst. Eng. 2019 , 178 , 118–130. [ Google Scholar ] [ CrossRef ]
• Jin, M.; Zhao, Z.; Chen, S.; Chen, J. Improved piezoelectric grain cleaning loss sensor based on adaptive neuro-fuzzy inference system. Precis. Agric. 2022 , 23 , 1174–1188. [ Google Scholar ] [ CrossRef ]
• Wu, Y.H.; Li, X.Y.; Mao, E.R.; Du, Y.F.; Yang, F. Design and development of monitoring device for corn grain cleaning loss based on piezoelectric effect. Comput. Electron. Agric. 2020 , 179 , 11. [ Google Scholar ] [ CrossRef ]
• Baydar, N.; Ball, A. Detection of gear failures via vibration and acoustic signals using wavelet transform. Mech. Syst. Signal Process. 2003 , 17 , 787–804. [ Google Scholar ] [ CrossRef ]
• Park, S.W.; Lee, S.K. Development of a new sound metric for impact sound in a passenger car using the wavelet transform. Int. J. Automot. Technol. 2010 , 11 , 809–818. [ Google Scholar ] [ CrossRef ]
• Loss, L.H. Experiments with Acoustic Sensors for Grain Loss Measuring on Combines ; SAE International: Warrendale, PA, USA, 1992. [ Google Scholar ] [ CrossRef ]
• Batcheller, B.D.; Gelinske, J.N.; Nystuen, P.A.; Reich, A.A. System and Method for Determining Material Yield and/or Loss from a Harvesting Machine Using Acoustic Sensors. U.S. Patent US9474208B2, 25 October 2016. Available online: https://patents.google.com/patent/US9474208B2/en (accessed on 14 August 2024).
• Li, Q.; Jones, N. Shear and adiabatic shear failures in an impulsively loaded fully clamped beam. Int. J. Impact Eng. 1999 , 22 , 589–607. [ Google Scholar ] [ CrossRef ]
• Benson, D.J.; Stander, N.; Jensen, M.R.; Craig, K.J. On the application of LS-OPT to identify non-linear material models in LS-DYNA. In Proceedings of the 7th International LS-DYNA Users Conference, Dearborn, MI, USA, 6–8 May 2002; Available online: https://www.dynalook.com/conferences/international-conf-2002/Session_16-4.pdf (accessed on 14 August 2024).
• Carney, K.; Pereira, J.M.; Revilock, D.; Matheny, P. Jet engine fan blade containment using an alternate geometry. Int. J. Impact Eng. 2009 , 36 , 720–728. [ Google Scholar ] [ CrossRef ]
• Jorgensen, K.; Swan, V. Modeling of armour-piercing projectile perforation of thick aluminium plates. In Proceedings of the 13th International LS-DYNA Users Conference, Detroit, MI, USA, 8–10 June 2014. [ Google Scholar ]
• Ma, X.; Lei, D.; Zhao, S.; Zhao, Z. Study on the Mechanical-Rheological Properties of Soybean and Wheat Grain Grown in Northeast China. Trans. CSAE 1999 , 3 , 70–75. [ Google Scholar ]
• Liang, L.; Guo, Y. Relationship between stalk biomechanical properties and morphological traits of wheat at different growth stages. Trans. CSAE 2008 , 8 , 131–134. [ Google Scholar ]
• Liang, Z.; Li, Y.; Zhao, Z. Monitoring method and sensor for grain separation loss on axial flow combine harvester. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 2014 , 30 , 18–26. [ Google Scholar ] [ CrossRef ]
• Feldman, M. Hilbert transform in vibration analysis. Mech. Syst. Signal Process. 2011 , 25 , 735–802. [ Google Scholar ] [ CrossRef ]
• Shen, J.; Zhao, S.; Chen, J. The Processing Way of Pipeline Vibration Signal based on Wavelet Transform. In Proceedings of the International Conference on Advanced Design and Manufacturing Engineering (ADME 2011), Guangzhou, China, 16–18 September 2011. [ Google Scholar ] [ CrossRef ]
• Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 1998 , 454 , 903–995. [ Google Scholar ] [ CrossRef ]
• Boudraa, A.O.; Cexus, J.C. EMD-based signal filtering. IEEE Trans. Instrum. Meas. 2007 , 56 , 2196–2202. [ Google Scholar ] [ CrossRef ]
• Du, Y.; Zhang, L.; Mao, E.; Li, X.; Wang, H. Design and experiment of corn combine harvester grain loss monitoring sensor based on EMD. Trans. Chin. Soc. Agric. Mach. 2022 , 53 , 158–165. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Chen, T.; Xiaopeng, W.; Kunpeng, Y.; Chao, Z. Theoretical analysis and numerical simulation for impact noise due to impact of two cylinders. J. Vib. Shock 2014 , 33 , 162–166+173. [ Google Scholar ] [ CrossRef ]
• Sasaoka, N.; Hamahashi, N.; Itoh, Y. Speech Enhancement with Impact Noise Activity Detection Based on the Kurtosis of an Instantaneous Power Spectrum. Ieice Trans. Fundam. Electron. Commun. Comput. Sci. 2017 , E100A , 1942–1950. [ Google Scholar ] [ CrossRef ]
• Jiao, Y.; Shi, H.; Wang, X. Lifting Wavelet Denoising Algorithm For Acoustic Emission Signal. In Proceedings of the International Conference on Robots & Intelligent System (ICRIS), Zhangjiajie, China, 27–28 August 2016. [ Google Scholar ] [ CrossRef ]
• Shi, Y.; Zhang, J.; Jiao, J.; Zhao, R.; Cao, H. Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising. Sensors 2021 , 21 , 1231. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, Z.; Liu, X.; Liu, X. Wavelet and Empirical Mode Decomposition Denoising for GLAS Full Waveform Data. Laser Optoelectron. Prog. 2021 , 58 , 364–371. [ Google Scholar ]
• Liang, Z.; Li, Y.; Zhao, Z.; Xu, L.; Zhao, Z. Sensor for monitoring rice grain sieve losses in combine harvesters. Biosyst. Eng. 2016 , 147 , 51–66. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Shen, Y.; Gao, J.; Jin, Z. Research on Acoustic Signal Identification Mechanism and Denoising Methods of Combine Harvesting Loss. Agronomy 2024 , 14 , 1816. https://doi.org/10.3390/agronomy14081816

Shen Y, Gao J, Jin Z. Research on Acoustic Signal Identification Mechanism and Denoising Methods of Combine Harvesting Loss. Agronomy . 2024; 14(8):1816. https://doi.org/10.3390/agronomy14081816

Shen, Yuhao, Jianmin Gao, and Zhipeng Jin. 2024. "Research on Acoustic Signal Identification Mechanism and Denoising Methods of Combine Harvesting Loss" Agronomy 14, no. 8: 1816. https://doi.org/10.3390/agronomy14081816

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