data collection methods for quantitative research pdf

Quantitative Methods

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• Juwel Rana 2 , 3 , 4 ,
• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Quantitative analysis ; Quantitative research methods ; Study design

Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations. It allows us to quantify effect sizes, determine the strength of associations, rank priorities, and weigh the strength of evidence of effectiveness.

Introduction

This entry aims to introduce the most common ways to use numbers and statistics to describe variables, establish relationships among variables, and build numerical understanding of a topic. In general, the quantitative research process uses a deductive approach (Neuman 2014 ; Leavy 2017 ), extrapolating from a particular case to the general situation (Babones 2016 ).

In practical ways, quantitative methods are an approach to studying a research topic. In research, the...

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia

John C. Oldroyd

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Correspondence to Juwel Rana .

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Florida Atlantic University, Boca Raton, FL, USA

Ali Farazmand

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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DOI : https://doi.org/10.1007/978-3-319-31816-5_460-1

Received : 31 January 2021

Accepted : 14 February 2021

Published : 11 June 2021

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Online ISBN : 978-3-319-31816-5

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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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Data Collection Methods in Quantitative Research

Sadan, Vathsala M.Sc (N)., Ph.D (N) *

* Professor, College of Nursing, CMC, Vellore

The information provided by the study participants on specific area of research called the data are very important that enable accurate information on the research work done by nurse researchers. Data collection methods are used to collect data in a systematic way. The researchers choose and use various data collection methods. They are broadly classified as self -reports, observation, and biophysiologic measures. This article highlights on the sources of data and on the various data collection techniques which include interviews, questionnaires, scales, category system and check lists, rating scales, and biophysiologic measures. It also analyses the advantages and disadvantages of each of these methods. Emphasis should be given on choosing appropriate method to collect accurate information which will lead to good quality research outcomes.

Introduction

In quantitative research process, data collection is a very important step. Quality data collection methods improve the accuracy or validity of study outcomes or findings. Nurse researchers have emphasized on the use of valid and reliable instruments to measure a variety of phenomena of interest in nursing. We need to be aware of the various measurement methods which are of importance to generate evidences needed for nursing practice. Researchers must choose appropriate data collection methods and approaches. An ideal data collection procedure captures a construct that is accurate, truthful, and sensitive (Polit & Beck, 2017). Quantitative data are collected in a more structured manner as compared to the qualitative data which are unstructured or semi-structured.

Data Collection and Data Resources

Data collection is a real challenge for researchers and it requires much time and effort. Nurse researchers should first identify the type of data to be collected and the sources from where they can be collected. The data sources can be either the existing data or the new data. Existing data such as from the existing records and documents can be of great value in some of the research studies. For answering the research question, if existing data are unavailable, researchers need to collect new data. The type of data also can be classified as primary data and secondary data. Primary data collection involves data collected directly from the study participants by the researcher or a trained data collector which includes surveys, questionnaires, interviews, observations, or biophysiologic measures. Secondary data collection is the use of data that were collected for another purpose such as patient's records, government data bases (Houser, 2011). After identifying what data need to be collected, the nurse researchers must choose the data collection methods and develop a data collection plan. The decision about choosing the data collection methods also should be based on the ethical guidelines, the cost, the time constraints, population appropriateness as well as the availability of research assistants to collect data.

Data Collection Methods

The most commonly used data collection approaches by nurse researchers include self-reports, observation, and bio- physiological measures. Whatever approaches the researcher uses, the data collection method differ along the four important dimensions: structure, quantifiability, researcher obtrusiveness, and objectivity. The data collected in quantitative studies are based on a structured plan which guides the researcher to what data to be collected, how long, and how to collect them. The information gathered must be quantified by doing statistical analysis. There is a possibility that the study participants can change their responses or behavior under some circumstances which need to be taken in to consideration. The data collected should be objective wherein similar observations are made even if two researchers observe the same concepts of interest. Data collection is an important component in creating relevant research evidence and hence need to be carried out rigorously. The most common methods of data collection are discussed below (see Figure 1 ).

1. Self - reports

Structured self- reports are the most commonly used data collection method among the nurse researchers (Polit & Beck, 2017). The self-report instruments are interview schedule, questionnaires, and scales. In an interview schedule, data are collected by asking questions orally either face-to face or through telephone. In a questionnaire or Self- Administered Questionnaire (SAQ), the study participants complete answering questions themselves, either on a paper or onto computer (Polit & Beck, 2017). Scales are also a form of self-report in which the phenomena of interest are measured (Grove, Burns, & Gray, 2013).

1.1. Interviews

Interviews can be used in descriptive studies and qualitative studies. They can be unstructured in which the content is controlled by the study participant or structured in which the content is similar to that of a questionnaire, with the possible responses to questions that are carefully designed by the researcher (Creswell, 2014). The questions can be closed, open ended, or probing. The interview questions are developed before the researcher begins data collection and are arranged in a logical sequence. The questions are asked orally to the participants, and explained if clarification is required by the participants. The order of questions arranged should be from broad to specific and topic wise. The sensitive nature of questions should be asked at the end. The vocabulary and sentence structuring of the question should be at the reading and understanding level of participants. After the interview questions are developed, the instrument is validated by experts for content. It has to be field tested through pilot study in order to identify problems in the design of questions, sequencing of questions, or procedure for recording responses. Piloting also provides an opportunity to establish the reliability and validity of the interview instrument (Grove et al., 2013).

The researcher or the data collector need to be skillful in obtaining interview data. They need to be familiar or trained with the content of the interview. The questions should be asked in a clear and unambiguous manner. The verbal and nonverbal communication must be unbiased. Sometimes the interviewer may have to repeat questions or explain questions, or probe to get more information. Probing has to be done carefully to avoid biased responses. The data can be written or recorded responses. It has to be done without distracting the interviewee. Prior permission should be obtained from Institutional Review Board (IRB) as well as from the study participants before the data collection procedure can be initiated.

In the present scenario, telephone interviews are used widely and it has been a convenient method to collect data. The interviewer has to be more tactful while collecting data through telephone interviews. They are faster and less expensive. However, the data can be collected only during a limited duration of time. The challenge is to ensure the true identification of the respondent, and can also cause bias and trigger unwanted responses since the researcher is not present physically. Focus group interviews, another method of data collection help to get aggregate perception of people, their feelings as well their thinking (Murugan, 2015). The researcher or the data collector who conducts the interview should get adequate training to ask questions in a logical sequence, and how to handle unanticipated questions or answers that might arise during the interview process.

Advantages and disadvantages

Through face to-face interview in-depth information can be obtained and it is more often a very flexible technique. In fact, the questions are restructured during the interview. It is an opportunity to obtain personal information and responses to all questions can be obtained. It provides an opportunity for probing and complex answers can be obtained. The response rate is higher and interviews provide a more representative sample. Data can also be collected from sick individuals, and from those who have problems with reading, writing, or difficulty in expressing. However, interview has its own disadvantages. It requires more time and it is expensive. As it needs more time, the sample size is usually minimized. Subject bias is always a threat to validity of the findings and its consistency in data collection from one subject to another (Grove et al., 2013). Interviewing children and lack of language skills are the other challenges faced while using interview schedule as a data collection technique.

1.2 Questionnaires

The most common instrument used for data collection is questionnaires. The participants fill in their responses themselves on a paper pencil instrument or on computer directly. Questionnaires can be structured or unstructured. In structured questionnaires, both the questions and the responses/answers are provided and the study participants need to pick up the correct responses. In unstructured questionnaires, the participants are required to give their own responses to the predetermined questions. Structured questionnaires can consist of either open ended or closed ended questions. In open ended questions, the participants provide their own answers in narrative form whereas in closed ended questions, there are fixed answers to the questions and the participants need to choose the correct/best response (s).

Good closed ended questions are more difficult to develop than open ended questions but are easy to analyze. Open ended questions can yield rich information, provided the participants are expressive and cooperative. While constructing structured questionnaires, the researchers must be careful with the wording of questions for clarity, sensitive to participant's psychological state, ensure absence of bias, and consider the reading level of participants (Polit & Beck, 2015).

Closed ended questions are of various types which are given below (Houser, 2011; Polit & Beck, 2017).

• Dichotomous questions: The participants decide between two choices of answers/responses such as yes/no and these type of questions are useful in collecting facts and provide only limited information.
• Multiple choice questions: There will be four to seven alternative responses to each of the questions, and the study participant chooses the responses as their answers. These type of questions are useful in collecting people's opinion and views.
• Rank order questions: The participants choose their answers along a continuum such as most to least important. When we use these type of questions clear instructions should be provided.
• Forced-choice questions: In this type of questions, participants are required to choose between two related statements.
• Rating questions: Here, the participants need to evaluate something on a given ordered continuum of responses. The rating questions can be from 0 to 10
• Check lists: In this type of instrument, a series of questions are listed and arranged vertically and the responses are also listed along with the other.
• Visual Analogue Scale (VAS): The subjective experiences such as pain are measured using VAS. It is a straight line and the end of the line are labelled as the extreme limits of the participant's experiences or feelings.

When developing a questionnaire, the researcher has to first identify the information that need to be collected. A blue print has to be made including the different aspects of the topic. A literature review on the pertinent topic will guide the researcher in the development of questionnaires. Each question should be carefully designed and clearly expressed according to the level of the participants. They should not be ambiguous or vague. Long question can threaten the validity of the instrument (Grove et al., 2013). The instrument should have proper instructions on how to fill the responses. The validity and the reliability of the developed questionnaires should be established and the instrument must be pilot tested before it is used in the study.

Self-reported questionnaires are administered either in person or through e-mails. Questionnaires usually appear easy to develop, but it requires much time and effort. They are less expensive, involves less time and less energy to administer as compared to interview schedule. Electronically mailed questionnaire is faster and cheaper too. A larger number of samples can be included in the study, and it provides an opportunity for complete anonymity in data collection. There is less possibilities for interviewer bias. However, information may be incomplete leading to missing data. The response rate especially when data are collected through posted mails and e mails is less.

1.3. Scales

Scales are a form of self-report and are a more precise form of measuring a phenomena than questionnaires (Grove et al., 2013). Scales measure the characteristics or traits of human beings in which more emphasis is placed on verification of reliability and validity. They are also called as psychometric instrumentation (Houser, 2014). There are existing scales available. If located, the researcher should get the psychometric properties of theses scales and document them. Psychosocial variables such as pain, nausea etc., are commonly measured using scales. Scores are given to each of ? the items to be measured in the scale. The types of scales commonly used in nursing studies are rating scale, Likert scale, Sementic Differential scale (SD) and Visual Analogue Scale (VAS) (Grove, Gray & Burns, 2015), which are discussed below (see Figure 3 ).

• Rating scale: In a rating scale, an ordered series of categories of a phenomena being studied is listed. A numerical value is assigned to each of the categories and the distinction between the categories vary. On this scale, the participants choose the best catergory that fits their experience. They are easy to construct but should be careful with extreme statements. e.g., Faces Pain Scale
• Likert scale: It is the most commonly used scale which contains many declarative statements that express the view point on a topic. The study participants are expected to indicate the degree to which they agree or disagree with the view point expressed in the statement. Likert scale usually consists of 10 to 20 items, each addressing an element of the concept which is being measured. The scale has both positively and negatively worded statements. The responses provided by the participants are scored and the total scores are summated
• Semantic Differential Scales: Psychological characteristics of people are measured by semantic differential scales. It is used to measure the variation in the views of a phenomena of interest. It is a 7 point scale and one end is the most positive and the other margin is the most negative. Each line is considered as one scale and the scores are summed up.
• Visual Analogue Scale (VAS): VAS is used to measure magnitude, strength, and intensity of people's feelings, sensations, or attitude about symptoms or situations. In VAS, there is a vertical or horizontal line with descriptors at both ends, as well as range of possible feelings of participants along the line. The participants need to place a mark on the line to indicate the intensity of their sensation or feeling, e.g., Visual Analogue Pain Scale. It is commonly used in health research.

2. Observational Methods

Observation technique is used to record the specific behaviors, actions of people and events. Observational measurement can be unstructured or structured. Unstructured observations are done spontaneously and recorded as what is seen in words. Whereas, in structured observations, the researcher should carefully decide what to observe, how to observe, how long, and how to record the observed data. Observational measurement are usually more subjective than other methods of data collection. However, in some situations, observation may be the only way to collect information. In structured observations the specific behaviors of study subjects or the events to be observed or studied should be carefully defined.

In observation measurement, the observer plays an important role. A participant observer plays an active role and take part in the activity or event being observed. The nonparticipant observer adopts a passive role while observing the phenomena of interest. If observations are done by more than one data collector, establishing interrater reliability is vital. The most commonly used observation methods are discussed below (Polit & Beck, 2017):

2.1 Category systems and check lists

Behaviors, events or attributes of the subjects to be studied are grouped into categories and the categories are observed and recorded. The categories must by explained clearly. The maximum number of categories for effective observation is 15 to 20. The observer makes inference from the recorded observation from the category (Grove et al., 2013).

Behavior of participants are observed to see whether the behavior occurred or not and is recorded as tally marks in various categories. In observational check list, single category is selected for observation. Check lists are designed using category system. For example, while measuring the behavioral indicator related to pain, it can be cry and facial expressions which are called as categories. The facial expressions are measured by checking whether brow bulge, eye squeeze, and naso- labial furrow occurred or not

2.2 Rating scales

Rating scales permit the observers to rate the behavior of the participant or the event on a scale at specified time intervals and then it is quantified. However, rating scales provide more information than check lists. If they are combined with category system and check lists, the data collected will be much useful in studying the phenomena. Rating scales can be used for observation as well as for self - reporting.

3. Physiologic measurement

Many of the nursing studies have included physiological measures to assess the outcomes of nursing care. Today, nurse researchers use different kinds of bio-physiologic measures in research. For example, Yeo (2009) examined the effects of a walking versus stretching exercise on preeclampsia risk factors such as heart rate, and blood pressure in sedentary pregnant women as cited in Polit and Beck (2017). Biophysical and bio-chemical measures are the two categories included in the physiological measurement. Use of stethoscope and sphygmomanometer to check the blood pressure is an example of bio-physical measurement and laboratory value of blood sugar is an example of bio-chemical measure. Physiological measurements are either direct (body temperature) or indirect (blood sugar). Physiological measures can be obtained through self- reports, observation, laboratory tests, and electronic monitoring. Example: irregular heartbeats can be self reported by subjects, observed by the nurse as well as monitored electronically (Grove et al., 2015).

In this method of data collection, use of specialized equipment are needed to measure the study variables. The two types of biophysiological methods used for data collection include in vivo and in vitro. In vivo measurements are done directly in or on living organism, whereas, in vitro measurements are performed outside the body as in case of checking the blood sugar level (Polit & Beck, 2017). In many studies, nurse researchers link physiological variables with psychological and social variables such as linking stress with blood pressure measures over a period of time.

The data obtained through laboratory tests and electronic monitoring provide precise and accurate data and are direct measures of many physiological variables. Bio-physiologic measures are more objective. Since the data collection setting is hospitals, the cost involved in collecting bio-physiologic information may be low. However, there are few disadvantages of using these measurements. The measuring instrument itself can affect the study variables. There are possibilities of related risks while applying energy and instruments. Special care must be taken in selecting appropriate instruments in relation to practical, ethical, medical, and technical considerations.

Data collection methods play a vital role in generating evidence through research. Each of the measurement approaches have their own advantages and disadvantages. Researchers must identify the type of data that need to be collected. Importance should be given to make sure that the data collection techniques are carefully chosen, applied, and properly managed to provide accurate information which can support the quality of research work done by the nurse researchers. Nurse researchers should be aware of the different data collection approaches and need to get familiarized with them.

Conflicts of Interest: The author has declared no conflicts of interest.

data; quantitative; interview; questionnaire; observation; biophysiologic

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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.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

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• Vague sentences
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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.

Prevent plagiarism. Run a free check.

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
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• Chi square tests
• Confidence interval
• Cluster sampling
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• Reproducibility vs Replicability
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Research bias

• Implicit bias
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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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• Methods for recording and reporting of epidemiological data on injury and illness in sport: ReFORM synthesis of the International Olympic Committee consensus statement
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• http://orcid.org/0000-0003-1969-3612 Pascal Edouard 1 , 2 ,
• http://orcid.org/0000-0002-7937-9863 Camille Tooth 3 , 4 , 5
• 1 Inter-university Laboratory of Human Movement Biology (EA 7424) , University Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc , Saint-Etienne , France
• 2 Department of Clinical and Exercise Physiology, Sports Medicine Unit , University Hospital of Saint-Etienne , Saint-Etienne , France
• 3 Department of Physical Medicine, Rehabilitation and Sports Traumatology, SportS², FIFA Medical Centre of Excellence, FIMS Collaborative Centre of Sports Medicine , CHU de Liège , Liège , Belgium
• 4 Department of Physical Activity and Rehabilitation Sciences , University of Liège , Liège , Belgium
• 5 ReFORM IOC Research Centre for Prevention of Injury and Protection of Athlete Health , Liège , Belgium
• Correspondence to Prof Pascal Edouard, University Hospital of Saint-Etienne, SAINT-ETIENNE, France; Pascal.Edouard{at}univ-st-etienne.fr

https://doi.org/10.1136/bjsports-2024-108516

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• Sporting injuries
• Epidemiology
• Preventive Medicine

Introduction

Epidemiological studies through injury and illness surveillance and data collection are fundamental to protect athlete health. To encourage consistency in definitions and methodology, and to allow for data comparison between studies, consensus statements have been published in various sports. The aim of the International Olympic Committee (IOC) consensus by Bahr et al 1 on methods for collecting and reporting epidemiological data on injuries and illnesses in sport was (1) to further strengthen consistency in data collection, injury definitions and research reporting through an updated set of recommendations for studying injuries and illnesses in sports, and (2) to provide practical guidance to researchers and clinicians on how to plan, conduct data collection and communicate findings. The ReFORM IOC Research Centre 2 first summarised and translated the consensus statement into a shorter synopsis in French to facilitate broader use by French-speaking researchers. 3 4 In this Editorial, we provide this summary to highlight key areas from the IOC consensus on methods for collecting and reporting epidemiological data on injuries and illnesses in sport 1 (figure 1).

Infographic of the ReFORM synthesis of the International Olympic Committee consensus statement on methods for recording and reporting of epidemiological data on injury and illness in sport.

Definitions and classifications of health problems

X @https://x.com/PascalEdouard42, @https://x.com/ToothCamille

Contributors PE and CT wrote the synthesis. All authors understand that they are accountable for all aspects of the work and ensure the accuracy or integrity of this manuscript. PE is the guarantor of the manuscript.

Funding This work has been financially supported by the International Olympic Committee Medical and Scientific Commission programme for Prevention of injury and protection of athlete health (IOC Research Centres).

Competing interests None declared.

PE is an Associate Editor for the British Journal of Sports Medicine.

Provenance and peer review Not commissioned; internally peer reviewed.

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Collecting Quantitative Data

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Manajemen Program Komunitas Belajar Sekolah untuk Peningkatan Kompetensi Pedagogik Guru

• Joko Arifin Pascasarjana Program Studi Manajemen Pendidikan Islam UIN SAIZU Purwokerto, Banyumas, Jawa Tengah,  Indonesia
• Muh Hanif UIN SAIZU Purwokerto, Banyumas, Jawa Tengah,  Indonesia

The low pedagogical competence of teachers is the basis for this research. Efforts to improve this use the school learning community program. Research objectives: (1) describe the management of the school learning community program, (2) determine the relationship and influence of the school learning community program on pedagogical competence, (3) determine the magnitude of the increase in pedagogical competence, and (4) describe the achievement categories of the school learning community and pedagogical competence . The research was carried out from 20 to 27 April 2024 at SMP Negeri 2 Pengadegan. Qualitative and quantitative research methods were used in this research. Data collection techniques: questionnaires, observations, interviews, and documentation. Qualitative data was analyzed using the Miles and Huberman Model. Quantitative data was analyzed using tests: normality, homogeneity, product moment correlation and linear regression. Research conclusions: (1) the learning community program has been well managed through activities a) planning: team formation, goal setting, commitment making, and socialization, b) organizing: issuing a decision letter, c) implementation: delivering material and concrete PMM actions, and d) supervision: questionnaires, Google forms, and direct reflection, (2) there is a strong relationship and significant influence between the school learning community and pedagogical competence, with a correlation coefficient of 0.7274 and a determination coefficient of 52.92%, (3) Teacher pedagogical competence increased from 69.69 to 88.22, and (4) Achievement of the school learning community was in the low to very high category, while pedagogical competence was in the low to high category .

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Affandi, L. H., Saputra, H. H., Husniati, H., & Ermiana, I. (2020). Workshop Penyusunan Rencana Pengembangan Komunitas Belajar Profesional Guru DI SDN 10 Mataram Dan SDN 30 Mataram. Jurnal Warta Desa (JWD), 1(3). https://doi.org/10.29303/jwd.v1i3.75

Arifa, F. N., & Prayitno, U. S. (2019). Peningkatan Kualitas Pendidikan: Program Pendidikan Profesi Guru Prajabatan dalam Pemenuhan Kebutuhan Guru Profesional di Indonesia. Aspirasi: Jurnal Masalah-Masalah Sosial, 10(1). https://doi.org/10.46807/aspirasi.v10i1.1229

Fatimah, S., Manuardi, A. R., & Meilani, R. (2021). Tingkat Efikasi Diri Performa Akademik Mahasiswa Ditinjau Dari Perspektif Dimensi Bandura. Prophetic : Professional, Empathy, Islamic Counseling Journal, 4(1). https://doi.org/10.24235/prophetic.v4i1.8753

Ferayanti, M., Nissa, H., Kurnianingsih, S., Irfan, R., & Patria, H. (2023). Panduan Optimalisasi Komunitas Belajar (Tim Implementasi Kurikulum Merdeka, Ed.; 1st ed.). Kemendikbudristek.

Giyanto, B., Kurnia, P., Julizar, K., Sari, D. K., & Hartono, D. (2023). Implementasi Kebijakan Komunitas Belajar dalam Kurikulum Merdeka Belajar di Indonesia. Jurnal Pembangunan Dan Administrasi Publik, 5(2).

Hading, H., & Purnamawati. (2023). Pengaruh Kompetensi Pedagogik Dan Kompetensi Profesional Guru Terhadap Motivasi Belajar Peserta Didik SMK Negeri 3 Sidrap. Seminar Nasional Dies Natalis 62, 1, 487–495. https://doi.org/10.59562/semnasdies.v1i1.1041

Hasanah, M. (2019). Pemilihan Jumlah Kategori Terbaik Pada Model Rough-Regresi Berdasarkan Mean Square Error (Studi Kasus: Tiga Variabel Bebas Numerik). Tugas Akhir, tidak dipublikasikan. Universitas Islam Sultan Syarif Kasium Riau.

Hoesny, M. U., & Darmayanti, R. (2021). Permasalahan dan Solusi untuk Meningkatkan Kompetensi dan Kualitas Guru : Sebuah Kajian Pustaka. Scholaria : Jurnal Pendidikan Dan Kebudayaan, 11(2).

Jannati, P., Ramadhan, F. A., & Rohimawan, M. A. (2023). Peran Guru Penggerak Dalam Implementasi Kurikulum Merdeka Di Sekolah Dasar. Al-Madrasah: Jurnal Pendidikan Madrasah Ibtidaiyah, 7(1). https://doi.org/10.35931/am.v7i1.1714

Kemendikbudristek. (2022a). Buku Saku Penggerak Komunitas Belajar. Kemendikbudristek.

Kemendikbudristek. (2022b). Petunjuk Awal Membangun Komunitas Belajar Dalam Sekolah (1st ed.). Kemendikbudristek.

Khusna, R., & Priyanti, N. (2023). Pengaruh Komunitas Belajar Terhadap Kemampuan Pedagogik Guru Di Ikatan NSIN TK Bekasi. Jurnal Ilmiah Potensia, 8(2).

Mardhatillah, O., & Surjanti, J. (2023). Peningkatan Kompetensi Pedagogik dan Profesionalitas Guru di Indonesia Melalui Pendidikan Profesi Guru (PPG). Jurnal Pendidikan Ekonomi Undiksha, 15(1). https://doi.org/10.23887/jjpe.v15i1.65200

Mufidah, E. F., Pravesti, C. A., Ardika, D., & Farid, M. (2022). Urgensi Efikasi Diri: Tinjauan Teori Bandura. Prosiding Seminar & Lokakarya Nasional Bimbingan Dan Konseling.

Mulyati, M. (2022). Kurangnya Kompetensi Pendidik Menjadi Masalah di Indonesia. Tugas Mata Kuliah Mahasiswa, 1(1).

Nurhalimah, N., Baisa, H., & Asmahasanah, S. (2020). Pengaruh Kompetensi Pedagogik Guru Terhadap Motivasi Belajar Siswa di MI I’anatusshibyan. JPG: Jurnal Pendidikan Guru, 1(1). https://doi.org/10.32832/jpg.v1i1.2865

Nuryuanita, S. (2019). Pengaruh Kelompok Kerja Guru (KKG) Terhadap Profesional Guru Dalam Mengajar di MIN 1 Rejang Lebong. Skripsi, tidak dipublikasikan. Instutut Agama Islam Negeri (IAIN) Curup.

Palettei, A. D., & Sulfemi, W. B. (2019). Pengaruh Kelompok Kerja Guru (KKG) Terhadap Peningkatan Kompetensi Pedagogik dan Kemampuan Menulis Karya Ilmiah. JPDI (Jurnal Pendidikan Dasar Indonesia), 4(2). https://doi.org/10.26737/jpdi.v4i2.1522

Pramesti, A. S., Apriyagung, Sujaya, D., Putra, F. M., Martono, Heryati, N., Afriliana, N., Wibowo, S., aramitasari, S. V. P., Rizki, Widiarti, W., Maysari, V., & Fany, Y. G. (2023). Buku Pintar Pendidikan Profesi Guru Prajabatan (D. Handoko, Ed.; 1st ed.). Kemendikbudristek.

Pratama, S. H., Ananda, D. N., Aji, F. M., & ... (2020). Pengaruh Kompetensi Pedagogik Guru terhadap Hasil Belajar Matematika Siswa Kelas IV SD. … Ilmu Pendidikan Dan …, 3(9).

Prawira, Y. A., & Nugraha, F. (2021). Peningkatan Kompetensi Pedagogik Guru Madrasah Melalui Pelatihan Partisipatif Secara Daring Berbasis Heuristik. Aksara: Jurnal Ilmu Pendidikan Nonformal, 7(2). https://doi.org/10.37905/aksara.7.2.307-316.2021

Pribadi, R. A., Anisah, R. W., & Intan, R. N. (2020). Dinamika Komunitas Guru Dalam Meningkatkan Kualitas Pembelajaran. GENTA MULIA: Jurnal Ilmiah Pendidikan.

Rahmadani, D. (2020). Pengaruh Kompetensi Pedagogik Guru Terhadap Hasil Belajar Siswa SMK Ibnu Taimiyah Pekanbaru. Skripsi, tidak dipublikasikan. Universitas Islam Riau Pekanbaru.

Rasyid, M. N., & Nurqalbiani, A. (2020). Implementasi Pendidikan Dan Pelatihan Dalam Meningkatkan Kompetensi Guru (Studi Kasus Pada UPT SMP Negeri 2 Duampanua Kabupaten Pinrang). Jurnal Ilmu Manajemen Profitability, 4(1). https://doi.org/10.26618/profitability.v4i1.3048

Rizkasari, E., Rahman, I. H., & Aji, P. T. (2022). Upaya Meningkatkan Kompetensi Pedagogik Guru Sekolah Dasar Dalam Menghadapi Tantangan Pembelajaran Abad 21. Jurnal Pendidikan Dan Konseling, 4(3), 694–699.

Rosyada, A., & Hudaida. (2020). Relevansi Persepsi Pendidikan KH. Hasyim Asyari dan Dekadensi Moral. Jurnal Humanitas, 7(1), 45–56.

Sa’diah, N. (2021). Peningkatan Kinerja Guru Dalam Mendesain Pembelajaran Melalui Program Pembinaan Kepala Sekolah di SDN 1 Natai Raya Tahun 2019. Anterior Jurnal, 20(3). https://doi.org/10.33084/anterior.v20i3.2652

Sekar, R. Y., & Kamarubiani, N. (2023). Komunitas Belajar Sebagai Sarana Belajar Dan Pengembangan Diri. Indonesian Journal of Adult and Community Education, 2(1). https://doi.org/10.17509/ijace.v2i1.28285

Setyadi, T. E., Solihin, L., Fujianita, S., Rakhmah, D. N., & Claudia, S. (2024). Optimalisasi Komunitas Belajar di Sekolah Guna Menyukseskan Implementasi Kurikulum Merdeka. https://pskp.kemdikbud.go.id/

Sugiyono. (2019). Metode Penelitian Kuantitatif, Kualitatif dan R&D. In Alfabeta,cv (1st ed.). Alfabeta.

Syahputra, R. D., & Aslami, N. (2023). Prinsip-Prinsp Utama Manajemen George R. Terry . Manajemen Kreatif Jurnal (MAKREJU), 1(3).

Wahyuni, N., & Fitriani, W. (2022). Relevansi Teori Belajar Sosial Albert Bandura dan Metode Pendidikan Keluarga dalam Islam. Qalam: Jurnal Ilmu Kependidikan, 11(2).

Wardiana. (2023). Efektivitas Program Kelompok Kerja Guru (KKG) Terhadap Kompetensi Pedagogik Guru PAI Wilayah IV Indrajaya Kabupaten Pidie. Tesis, tidak dipublikasikan. Universitas Islam Negeri (UIN) Ar-Raniry Banda Aceh.

Wijaya, C., Suhardi, & Amiruddin. (2023). Manajemen Pengembangan Kompetensi Guru (N. S. Chaniago, Ed.; 1st ed.). Umsu Press.

Yohanitas, W. A. (2021). Inovasi Literasi Teknologi Komunitas Pembelajar Berbasis Teknologi Informasi Melalui Sistem Informasi Rumah Cerdas Widyaiswara. Konferensi Nasional Ilmu Administrasi Ke-5 (KNIA 5.0) “Inovasi Pelayanan Dan Kepemimpinan Publik Menghadapi Era Society 5.0,” 148–158.

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