methods of data collection for quantitative research

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5 Methods of Data Collection for Quantitative Research

In this blog, read up on five different data collection techniques for quantitative research studies. 

Quantitative research forms the basis for many business decisions. But what is quantitative data collection, why is it important, and which data collection methods are used in quantitative research? 

Table of Contents: 

• What is quantitative data collection?
• The importance of quantitative data collection
• Methods used for quantitative data collection
• Example of a survey showing quantitative data
• Strengths and weaknesses of quantitative data

What is quantitative data collection? 

Quantitative data collection is the gathering of numeric data that puts consumer insights into a quantifiable context. It typically involves a large number of respondents - large enough to extract statistically reliable findings that can be extrapolated to a larger population.

The actual data collection process for quantitative findings is typically done using a quantitative online questionnaire that asks respondents yes/no questions, ranking scales, rating matrices, and other quantitative question types. With these results, researchers can generate data charts to summarize the quantitative findings and generate easily digestible key takeaways. 

Back to Table of Contents

The importance of quantitative data collection 

Quantitative data collection can confirm or deny a brand's hypothesis, guide product development, tailor marketing materials, and much more. It provides brands with reliable information to make decisions off of (i.e. 86% like lemon-lime flavor or just 12% are interested in a cinnamon-scented hand soap). 

Compared to qualitative data collection, quantitative data allows for comparison between insights given higher base sizes which leads to the ability to have statistical significance. Brands can cut and analyze their dataset in a variety of ways, looking at their findings among different demographic groups, behavioral groups, and other ways of interest. It's also generally easier and quicker to collect quantitative data than it is to gather qualitative feedback, making it an important data collection tool for brands that need quick, reliable, concrete insights. 

In order to make justified business decisions from quantitative data, brands need to recruit a high-quality sample that's reflective of their true target market (one that's comprised of all ages/genders rather than an isolated group). For example, a study into usage and attitudes around orange juice might include consumers who buy and/or drink orange juice at a certain frequency or who buy a variety of orange juice brands from different outlets. 

Methods used for quantitative data collection 

So knowing what quantitative data collection is and why it's important , how does one go about researching a large, high-quality, representative sample ?

Below are five examples of how to conduct your study through various data collection methods : 

Online quantitative surveys 

Online surveys are a common and effective way of collecting data from a large number of people. They tend to be made up of closed-ended questions so that responses across the sample are comparable; however, a small number of open-ended questions can be included as well (i.e. questions that require a written response rather than a selection of answers in a close-ended list). Open-ended questions are helpful to gather actual language used by respondents on a certain issue or to collect feedback on a view that might not be shown in a set list of responses).

Online surveys are quick and easy to send out, typically done so through survey panels. They can also appear in pop-ups on websites or via a link embedded in social media. From the participant’s point of view, online surveys are convenient to complete and submit, using whichever device they prefer (mobile phone, tablet, or computer). Anonymity is also viewed as a positive: online survey software ensures respondents’ identities are kept completely confidential.

To gather respondents for online surveys, researchers have several options. Probability sampling is one route, where respondents are selected using a random selection method. As such, everyone within the population has an equal chance of getting selected to participate. 

There are four common types of probability sampling . 

• Simple random sampling is the most straightforward approach, which involves randomly selecting individuals from the population without any specific criteria or grouping. 
• Stratified random sampling  divides the population into subgroups (strata) and selects a random sample from each stratum. This is useful when a population includes subgroups that you want to be sure you cover in your research. 
• Cluster sampling  divides the population into clusters and then randomly selects some of the clusters to sample in their entirety. This is useful when a population is geographically dispersed and it would be impossible to include everyone.
• Systematic sampling  begins with a random starting point and then selects every nth member of the population after that point (i.e. every 15th respondent). 

Learn how to leverage AI to help generate your online quantitative survey inputs:

While online surveys are by far the most common way to collect quantitative data in today’s modern age, there are still some harder-to-reach respondents where other mediums can be beneficial; for example, those who aren’t tech-savvy or who don’t have a stable internet connection. For these audiences, offline surveys   may be needed.

Offline quantitative surveys

Offline surveys (though much rarer to come across these days) are a way of gathering respondent feedback without digital means. This could be something like postal questionnaires that are sent out to a sample population and asked to return the questionnaire by mail (like the Census) or telephone surveys where questions are asked of respondents over the phone. 

Offline surveys certainly take longer to collect data than online surveys and they can become expensive if the population is difficult to reach (requiring a higher incentive). As with online surveys, anonymity is protected, assuming the mail is not intercepted or lost.

Despite the major difference in data collection to an online survey approach, offline survey data is still reported on in an aggregated, numeric fashion. 

In-person interviews are another popular way of researching or polling a population. They can be thought of as a survey but in a verbal, in-person, or virtual face-to-face format. The online format of interviews is becoming more popular nowadays, as it is cheaper and logistically easier to organize than in-person face-to-face interviews, yet still allows the interviewer to see and hear from the respondent in their own words. 

Though many interviews are collected for qualitative research, interviews can also be leveraged quantitatively; like a phone survey, an interviewer runs through a survey with the respondent, asking mainly closed-ended questions (yes/no, multiple choice questions, or questions with rating scales that ask how strongly the respondent agrees with statements). The advantage of structured interviews is that the interviewer can pace the survey, making sure the respondent gives enough consideration to each question. It also adds a human touch, which can be more engaging for some respondents. On the other hand, for more sensitive issues, respondents may feel more inclined to complete a survey online for a greater sense of anonymity - so it all depends on your research questions, the survey topic, and the audience you're researching.

Observations

Observation studies in quantitative research are similar in nature to a qualitative ethnographic study (in which a researcher also observes consumers in their natural habitats), yet observation studies for quant research remain focused on the numbers - how many people do an action, how much of a product consumer pick up, etc.

For quantitative observations, researchers will record the number and types of people who do a certain action - such as choosing a specific product from a grocery shelf, speaking to a company representative at an event, or how many people pass through a certain area within a given timeframe. Observation studies are generally structured, with the observer asked to note behavior using set parameters. Structured observation means that the observer has to hone in on very specific behaviors, which can be quite nuanced. This requires the observer to use his/her own judgment about what type of behavior is being exhibited (e.g. reading labels on products before selecting them; considering different items before making the final choice; making a selection based on price).

Document reviews and secondary data sources

A fifth method of data collection for quantitative research is known as secondary research : reviewing existing research to see how it can contribute to understanding a new issue in question. This is in contrast to the primary research methods above, which is research that is specially commissioned and carried out for a research project. 

There are numerous secondary data sources that researchers can analyze such as  public records, government research, company databases, existing reports, paid-for research publications, magazines, journals, case studies, websites, books, and more.

Aside from using secondary research alone, secondary research documents can also be used in anticipation of primary research, to understand which knowledge gaps need to be filled and to nail down the issues that might be important to explore further in a primary research study. Back to Table of Contents

Example of a survey showing quantitative data 

The below study shows what quantitative data might look like in a final study dashboard, taken from quantilope's Sneaker category insights study . 

The study includes a variety of usage and attitude metrics around sneaker wear, sneaker purchases, seasonality of sneakers, and more. Check out some of the data charts below showing these quantitative data findings - the first of which even cuts the quantitative data findings by demographics. 

Beyond these basic usage and attitude (or, descriptive) data metrics, quantitative data also includes advanced methods - such as implicit association testing. See what these quantitative data charts look like from the same sneaker study below:

These are just a few examples of how a researcher or insights team might show their quantitative data findings. However, there are many ways to visualize quantitative data in an insights study, from bar charts, column charts, pie charts, donut charts, spider charts, and more, depending on what best suits the story your data is telling. Back to Table of Contents

Strengths and weaknesses of quantitative data collection

quantitative data is a great way to capture informative insights about your brand, product, category, or competitors. It's relatively quick, depending on your sample audience, and more affordable than other data collection methods such as qualitative focus groups. With quantitative panels, it's easy to access nearly any audience you might need - from something as general as the US population to something as specific as cannabis users . There are many ways to visualize quantitative findings, making it a customizable form of insights - whether you want to show the data in a bar chart, pie chart, etc. 

For those looking for quick, affordable, actionable insights, quantitative studies are the way to go.  

quantitative data collection, despite the many benefits outlined above, might also not be the right fit for your exact needs. For example, you often don't get as detailed and in-depth answers quantitatively as you would with an in-person interview, focus group, or ethnographic observation (all forms of qualitative research). When running a quantitative survey, it’s best practice to review your data for quality measures to ensure all respondents are ones you want to keep in your data set. Fortunately, there are a lot of precautions research providers can take to navigate these obstacles - such as automated data cleaners and data flags. Of course, the first step to ensuring high-quality results is to use a trusted panel provider.  Back to Table of Contents

Quantitative research typically needs to undergo statistical analysis for it to be useful and actionable to any business. It is therefore crucial that the method of data collection, sample size, and sample criteria are considered in light of the research questions asked.

quantilope’s online platform is ideal for quantitative research studies. The online format means a large sample can be reached easily and quickly through connected respondent panels that effectively reach the desired target audience. Response rates are high, as respondents can take their survey from anywhere, using any device with internet access.

Surveys are easy to build with quantilope’s online survey builder. Simply choose questions to include from pre-designed survey templates or build your own questions using the platform’s drag & drop functionality (of which both options are fully customizable). Once the survey is live, findings update in real-time so that brands can get an idea of consumer attitudes long before the survey is complete. In addition to basic usage and attitude questions, quantilope’s suite of advanced research methodologies provides an AI-driven approach to many types of research questions. These range from exploring the features of products that drive purchase through a Key Driver Analysis , compiling the ideal portfolio of products using a TURF , or identifying the optimal price point for a product or service using a Price Sensitivity Meter (PSM) .

Depending on the type of data sought it might be worth considering a mixed-method approach, including both qual and quant in a single research study. Alongside quantitative online surveys, quantilope’s video research solution - inColor , offers qualitative research in the form of videoed responses to survey questions. inColor’s qualitative data analysis includes an AI-drive read on respondent sentiment, keyword trends, and facial expressions.

To find out more about how quantilope can help with any aspect of your research design and to start conducting high-quality, quantitative research, get in touch below:

Get in touch to learn more about quantitative research studies!

Related posts, how can brands build, measure, and manage brand equity, how to use a brand insights tool to improve your branding strategy, quantilope's 5th consecutive year as a 'fastest growing tech company', automated survey setup: how to utilize ai-generated question inputs.

Quantitative Data Collection Methods

Quantitative research methods describe and measure the level of occurrences on the basis of numbers and calculations. Moreover, the questions of “how many?” and “how often?” are often asked in quantitative studies. Accordingly, quantitative data collection methods are based on numbers and mathematical calculations.

Quantitative research can be described as ‘entailing the collection of numerical data and exhibiting the view of relationship between theory and research as deductive, a predilection for natural science approach, and as having an objectivist conception of social reality’ [1] . In other words, quantitative studies mainly examine relationships between numerically measured variables with the application of statistical techniques.

Quantitative data collection methods are based on random sampling and structured data collection instruments. Findings of quantitative studies are usually easy to present, summarize, compare and generalize.

Qualitative studies , on the contrary, are usually based on non-random sampling methods and use non-quantifiable data such as words, feelings, emotions ect. Table below illustrates the main differences between qualitative and quantitative data collection and research methods:

Main differences between quantitative and qualitative methods

The most popular quantitative data collection methods include the following:

• Face-to-face interviews;
• Telephone interviews;
• Computer-Assisted Personal Interviewing (CAPI).
• Internet-based questionnaire;
• Mail questionnaire;
• Face-to-face survey.
• Observations . The type of observation that can be used to collect quantitative data is systematic, where the researcher counts the number of occurrences of phenomenon.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

[1] Bryman, A. & Bell, E. (2015) “Business Research Methods” 4 th edition,  p.160

Part I: Sampling, Data Collection, & Analysis in Quantitative Research

In this module, we will focus on how quantitative research collects and analyzes data, as well as methods for obtaining sample population.

• Levels of Measurement
• Reliability and Validity
• Population and Samples
• Common Data Collection Methods
• Data Analysis
• Statistical Significance versus Clinical Significance

Objectives:

• Describe levels of measurement
• Describe reliability and validity as applied to critical appraisal of research
• Differentiate methods of obtaining samples for population generalizability
• Describe common data collection methods in quantitative research
• Describe various data analysis methods in quantitative research
• Differentiate statistical significance versus clinical significance

Levels of measurement

Once researchers have collected their data (we will talk about data collection later in this module), they need methods to organize the data before they even start to think about statistical analyses. Statistical operations depend on a variable’s level of measurement. Think about this similarly to shuffling all of your bills in some type of organization before you pay them. With levels of measurement, we are precisely recording variables in a method to help organize them.

There are four levels of measurement:

Nominal:  The data can only be categorized

Ordinal:  The data can be categorized and ranked

Interval:   The data can be categorized, ranked, and evenly spaced

Ratio:   The data can be categorized, ranked, even spaced, and has a natural zero

Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.

• A variable is nominal  if the values could be interchanged (e.g. 1 = male, 2 = female OR 1 = female, 2 = male).
• A variable is ordinal  if there is a quantitative ordering of values AND if there are a small number of values (e.g. excellent, good, fair, poor).
• A variable is usually considered interval  if it is measured with a composite scale or test.
• A variable is ratio level if it makes sense to say that one value is twice as much as another (e.g. 100 mg is twice as much as 50 mg) (Polit & Beck, 2021).

Reliability and Validity as Applied to Critical Appraisal of Research

Reliability measures the ability of a measure to consistently measure the same way. Validity measures what it is supposed to  measure. Do we have the need for both in research? Yes! If a variable is measured inaccurately, the data is useless. Let’s talk about why.

For example, let’s set out to measure blood glucose for our study. The validity  is how well the measure can determine the blood glucose. If we used a blood pressure cuff to measure blood glucose, this would not be a valid measure. If we used a blood glucose meter, it would be a more valid measure. It does not stop there, however. What about the meter itself? Has it been calibrated? Are the correct sticks for the meter available? Are they expired? Does the meter have fresh batteries? Are the patient’s hands clean?

Reliability  wants to know: Is the blood glucose meter measuring the same way, every time?

Validity   is asking, “Does the meter measure what it is supposed to measure?” Construct validity: Does the test measure the concept that it’s intended to measure? Content validity: Is the test fully representative of what it aims to measure? Face validity: Does the content of the test appear to be suitable to its aims?

Leibold, 2020

Obtaining Samples for Population Generalizability

In quantitative research, a population is the entire group that the researcher wants to draw conclusions about.

A sample is the specific group that the researcher will actually collect data from. A sample is always a much smaller group of people than the total size of the population. For example, if we wanted to investigate heart failure, there would be no possible way to measure every single human with heart failure. Therefore, researchers will attempt to select a sample of that large population which would most likely reflect (AKA: be a representative sample) the larger population of those with heart failure. Remember, in quantitative research, the results should be generalizable to the population studied.

A researcher will specify population characteristics through eligibility criteria. This means that they consider which characteristics to include ( inclusion criteria ) and which characteristics to exclude ( exclusion criteria ).

For example, if we were studying chemotherapy in breast cancer subjects, we might specify:

• Inclusion Criteria: Postmenopausal women between the ages of 45 and 75 who have been diagnosed with Stage II breast cancer.
• Exclusion Criteria: Abnormal renal function tests since we are studying a combination of drugs that may be nephrotoxic. Renal function tests are to be performed to evaluate renal function and the threshold values that would disqualify the prospective subject is serum creatinine above 1.9 mg/dl.

Sampling Designs:

There are two broad classes of sampling in quantitative research: Probability and nonprobability sampling.

Probability sampling : As the name implies, probability sampling means that each eligible individual has a random chance (same probability) of being selected to participate in the study.

There are three types of probability sampling:

Simple random sampling :  Every eligible participant is randomly selected (e.g. drawing from a hat).

Stratified random sampling : Eligible population is first divided into two or more strata (categories) from which randomization occurs (e.g. pollution levels selected from restaurants, bars with ordinances of state laws, and bars with no ordinances).

Systematic sampling : Involves the selection of every __ th eligible participant from a list (e.g. every 9 th  person).

Nonprobability sampling : In nonprobability sampling, eligible participants are selected using a subjective (non-random) method.

There are four types of nonprobability sampling:

Convenience sampling : Participants are selected for inclusion in the sample because they are the easiest for the researcher to access. This can be due to geographical proximity, availability at a given time, or willingness to participate in the research.

Quota sampling : Participants are from a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, the researcher could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota.

Consecutive sampling : A sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Consecutive sampling is defined as a nonprobability technique where samples are picked at the ease of a researcher more like convenience sampling, only with a slight variation. Here, the researcher selects a sample or group of people, conducts research over a period, collects results, and then moves on to another sample.

Purposive sampling : A group of non-probability sampling techniques in which units are selected because they have characteristics that the researcher needs in their sample. In other words, units are selected “on purpose” in purposive sampling.

Common Data Collection Methods in Quantitative Research

There are various methods that researchers use to collect data for their studies. For nurse researchers, existing records are an important data source. Researchers need to decide if they will collect new data or use existing data. There is also a wealth of clinical data that can be used for non-research purposed to help answer clinical questions.

Let’s look at some general data collection methods and data sources in quantitative research.

Existing data  could include medical records, school records, corporate diaries, letters, meeting minutes, and photographs. These are easy to obtain do not require participation from those being studied.

Collecting new data:

Let’s go over a few methods in which researcher can collect new data. These usually requires participation from those being studied.

Self-reports can be obtained via interviews or questionnaires . Closed-ended questions can be asked (“Within the past 6 months, were you ever a member of a fitness gym?” Yes/No) or open-ended questions such as “Why did you decide to join a fitness gym?” Important to remember (this sometimes throws students off) is that conducting interviews and questionnaires does not mean it is qualitative in nature! Do not let that throw you off in assessing whether a published article is quantitative or qualitative. The nature of the questions, however, may help to determine the type of research (quantitative or qualitative), as qualitative questions deal with ascertaining a very organic collection of people’s experiences in open-ended questions. 

Advantages of questionnaires (compared to interviews):

• Questionnaires are less costly and are advantageous for geographically dispersed samples.
• Questionnaires offer the possibility of anonymity, which may be crucial in obtaining information about certain opinions or traits.

Advances of interviews (compared to questionnaires):

• Higher response rates
• Some people cannot fill out a questionnaire.
• Opportunities to clarify questions or to determine comprehension
• Opportunity to collect supplementary data through observation

Psychosocial scales are often utilized within questionnaires or interviews. These can help to obtain attitudes, perceptions, and psychological traits. 

Likert Scales :

• Consist of several declarative statements ( items ) expressing viewpoints
• Responses are on an agree/disagree continuum (usually five or seven response options).
• Responses to items are summed to compute a total scale score.

Visual Analog Scale:

• Used to measure subjective experiences (e.g., pain, nausea)
• Measurements are on a straight line measuring 100 mm.
• End points labeled as extreme limits of sensation

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

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

Biophysiologic Measures are defined as ‘those physiological and physical variables that require specialized technical instruments and equipment for their measurement’. Biophysiological measures are the most common instruments for collecting data in medical science studies. To collect valid and reliable data, it is critical to apply these measures appropriately.

• In vivo  refers to when research or work is done with or within an entire, living organism. Examples can include studies in animal models or human clinical trials.
• In vitro is used to describe work that’s performed outside of a living organism. This usually involves isolated tissues, organs, or cells.

Let’s watch a video about Sampling and Data Collection that I made a couple of years ago.

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.

Prevent plagiarism, run a free check.

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: Sources & Examples

Data is a collection of facts, figures, objects, symbols, and events gathered from different sources. Organizations collect data using various data collection methods to make better decisions. Without data, it would be difficult for organizations to make appropriate decisions, so data is collected from different audiences at various points in time.

For example, an organization must collect data on product demand, customer preferences, and competitors before launching a new product. If data is not collected beforehand, the organization’s newly launched product may fail for many reasons, such as less demand and inability to meet customer needs. 

Although data is a valuable asset for every organization, it does not serve any purpose until it is analyzed or processed to achieve the desired results.

What are Data Collection Methods?

Data collection methods are techniques and procedures for gathering information for research purposes. They can range from simple self-reported surveys to more complex experiments and can involve either quantitative or qualitative approaches.

Some common data collection methods include surveys, interviews, observations, focus groups, experiments, and secondary data analysis . The data collected through these methods can then be analyzed and used to support or refute research hypotheses and draw conclusions about the study’s subject matter.

LEARN ABOUT: Self-Selection Bias

Understanding Data Collection Methods

Data collection methods encompass a variety of techniques and tools for gathering both quantitative and qualitative data. These methods are integral to the data collection process, ensuring accurate and comprehensive data acquisition. 

Quantitative data collection methods involve systematic approaches to collecting data, like numerical data, such as surveys, polls, and statistical analysis, aimed at quantifying phenomena and trends. 

Conversely, qualitative data collection methods focus on capturing non-numerical information, such as interviews, focus groups, and observations, to delve deeper into understanding attitudes, behaviors, and motivations. 

Employing a combination of quantitative and qualitative data collection techniques can enrich organizations’ datasets and gain comprehensive insights into complex phenomena.

Effective utilization of accurate data collection tools and techniques enhances the accuracy and reliability of collected data, facilitating informed decision-making and strategic planning.

Importance of Data Collection Methods

Data collection methods play a crucial role in the research process as they determine the quality and accuracy of the data collected. Here are some major importance of data collection methods.

• Quality and Accuracy: The choice of data collection method directly impacts the quality and accuracy of the data obtained. Properly designed methods help ensure that the data collected is relevant to the research questions and free from errors.
• Relevance, Validity, and Reliability: Effective data collection methods help ensure that the data collected is relevant to the research objectives, valid (measuring what it intends to measure), and reliable (consistent and reproducible).
• Bias Reduction and Representativeness: Carefully chosen data collection methods can help minimize biases inherent in the research process, such as sampling bias or response bias. They also aid in achieving a representative sample, enhancing the findings’ generalizability.
• Informed Decision Making: Accurate and reliable data collected through appropriate methods provide a solid foundation for making informed decisions based on research findings. This is crucial for both academic research and practical applications in various fields.
• Achievement of Research Objectives: Data collection methods should align with the research objectives to ensure that the collected data effectively addresses the research questions or hypotheses. Properly collected data facilitates the attainment of these objectives.
• Support for Validity and Reliability: Validity and reliability are essential aspects of research validity. The choice of data collection methods can either enhance or detract from the validity and reliability of research findings. Therefore, selecting appropriate methods is critical for ensuring the credibility of the research.

The importance of data collection methods cannot be overstated, as they play a key role in the research study’s overall success and internal validity .

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Types of Data Collection Methods

The choice of data collection method depends on the research question being addressed, the type of data needed, and the resources and time available. Data collection methods can be categorized into primary and secondary methods.

1. Primary Data Collection Methods

Primary data is collected from first-hand experience and is not used in the past. The data gathered by primary data collection methods are highly accurate and specific to the research’s motive.

Primary data collection methods can be divided into two categories: quantitative methods and qualitative methods .

Quantitative Methods:

Quantitative techniques for market research and demand forecasting usually use statistical tools. In these techniques, demand is forecasted based on historical data. These methods of primary data collection are generally used to make long-term forecasts. Statistical analysis methods are highly reliable as subjectivity is minimal.

• Time Series Analysis: A time series refers to a sequential order of values of a variable, known as a trend, at equal time intervals. Using patterns, an organization can predict the demand for its products and services over a projected time period. 
• Smoothing Techniques: Smoothing techniques can be used in cases where the time series lacks significant trends. They eliminate random variation from the historical demand, helping identify patterns and demand levels to estimate future demand.  The most common methods used in smoothing demand forecasting are the simple moving average and weighted moving average methods. 
• Barometric Method: Also known as the leading indicators approach, researchers use this method to speculate future trends based on current developments. When past events are considered to predict future events, they act as leading indicators.

Qualitative Methods:

Qualitative data collection methods are especially useful when historical data is unavailable or when numbers or mathematical calculations are unnecessary.

Qualitative research is closely associated with words, sounds, feelings, emotions, colors, and non-quantifiable elements. These techniques are based on experience, judgment, intuition, conjecture, emotion, etc.

Quantitative methods do not provide the motive behind participants’ responses, often don’t reach underrepresented populations, and require long periods of time to collect the data. Hence, it is best to combine quantitative methods with qualitative methods.

1. Surveys: Surveys collect data from the target audience and gather insights into their preferences, opinions, choices, and feedback related to their products and services. Most survey software offers a wide range of question types.

You can also use a ready-made survey template to save time and effort. Online surveys can be customized to match the business’s brand by changing the theme, logo, etc. They can be distributed through several channels, such as email, website, offline app, QR code, social media, etc. 

You can select the channel based on your audience’s type and source. Once the data is collected, survey software can generate various reports and run analytics algorithms to discover hidden insights. 

A survey dashboard can give you statistics related to response rate, completion rate, demographics-based filters, export and sharing options, etc. Integrating survey builders with third-party apps can maximize the effort spent on online real-time data collection . 

Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

2. Polls: Polls comprise one single or multiple-choice question . They are useful when you need to get a quick pulse of the audience’s sentiments. Because they are short, it is easier to get responses from people.

Like surveys, online polls can be embedded into various platforms. Once the respondents answer the question, they can also be shown how they compare to others’ responses.

Interviews: In this method, the interviewer asks the respondents face-to-face or by telephone. 

3. Interviews: In face-to-face interviews, the interviewer asks a series of questions to the interviewee in person and notes down responses. If it is not feasible to meet the person, the interviewer can go for a telephone interview. 

This form of data collection is suitable for only a few respondents. It is too time-consuming and tedious to repeat the same process if there are many participants.

4. Delphi Technique: In the Delphi method, market experts are provided with the estimates and assumptions of other industry experts’ forecasts. Experts may reconsider and revise their estimates and assumptions based on this information. The consensus of all experts on demand forecasts constitutes the final demand forecast.

5. Focus Groups: Focus groups are one example of qualitative data in education . In a focus group, a small group of people, around 8-10 members, discuss the common areas of the research problem. Each individual provides his or her insights on the issue concerned. 

A moderator regulates the discussion among the group members. At the end of the discussion, the group reaches a consensus.

6. Questionnaire: A questionnaire is a printed set of open-ended or closed-ended questions that respondents must answer based on their knowledge and experience with the issue. The questionnaire is part of the survey, whereas the questionnaire’s end goal may or may not be a survey.

Secondary Data Collection Methods

Secondary data is data that has been used in the past. The researcher can obtain data from the data sources , both internal and external, to the organizational data . 

Internal sources of secondary data:

• Organization’s health and safety records
• Mission and vision statements
• Financial Statements
• Sales Report
• CRM Software
• Executive summaries

External sources of secondary data:

• Government reports
• Press releases
• Business journals

Secondary data collection methods can also involve quantitative and qualitative techniques. Secondary data is easily available, less time-consuming, and expensive than primary data. However, the authenticity of the data gathered cannot be verified using these methods.

Secondary data collection methods can also involve quantitative and qualitative observation techniques. Secondary data is easily available, less time-consuming, and more expensive than primary data. 

However, the authenticity of the data gathered cannot be verified using these methods.

Regardless of the data collection method of your choice, there must be direct communication with decision-makers so that they understand and commit to acting according to the results.

For this reason, we must pay special attention to the analysis and presentation of the information obtained. Remember that these data must be useful and functional to us, so the data collection method used has much to do with it.

How QuestionPro Can Help in Data Collection Methods

QuestionPro is a comprehensive online survey software platform that can greatly assist in various data collection methods. Here’s how it can help:

• Survey Creation: QuestionPro offers a user-friendly interface for creating surveys with various question types, including multiple-choice, open-ended, Likert scale, and more. Researchers can customize surveys to fit their specific research needs and objectives.
• Diverse Distribution Channels: The platform provides multiple channels for distributing surveys, including email, web links, social media, and embedding surveys on websites. This enables researchers to reach a wide audience and collect data efficiently.
• Panel Management: QuestionPro offers panel management features, allowing researchers to create and manage panels of respondents for targeted data collection. This is particularly useful for longitudinal studies or when targeting specific demographics.
• Data Analysis Tools: The platform includes robust data analysis tools that enable researchers to analyze survey responses in real time. Researchers can generate customizable reports, visualize data through charts and graphs, and identify trends and patterns within the data.
• Data Security and Compliance: QuestionPro prioritizes data security and compliance with regulations such as GDPR and HIPAA. The platform offers features such as SSL encryption, data masking, and secure data storage to ensure the confidentiality and integrity of collected data.
• Mobile Compatibility: With the increasing use of mobile devices, QuestionPro ensures that surveys are mobile-responsive, allowing respondents to participate in surveys conveniently from their smartphones or tablets.
• Integration Capabilities: QuestionPro integrates with various third-party tools and platforms, including CRMs, email marketing software, and analytics tools. This allows researchers to streamline their data collection processes and incorporate survey data into their existing workflows.
• Customization and Branding: Researchers can customize surveys with their branding elements, such as logos, colors, and themes, enhancing the professional appearance of surveys and increasing respondent engagement.

The conclusion you obtain from your investigation will set the course of the company’s decision-making, so present your report clearly, and list the steps you followed to obtain those results.

Make sure that whoever will take the corresponding actions understands the importance of the information collected and that it gives them the solutions they expect.

QuestionPro offers a comprehensive suite of features and tools that can significantly streamline the data collection process, from survey creation to analysis, while ensuring data security and compliance. Remember that at QuestionPro, we can help you collect data easily and efficiently. Request a demo and learn about all the tools we have for you.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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3.3 Methods of Quantitative Data Collection

Data collection is the process of gathering information for research purposes. Data collection methods in quantitative research refer to the techniques or tools used to collect data from participants or units in a study. Data are the most important asset for any researcher because they provide the researcher with the knowledge necessary to confirm or refute their research hypothesis. 2 The choice of data collection method will depend on the research question, the study design, the type of data to be collected, and the available resources. There are two main types of data which are primary data and secondary data. 34 These data types and their examples are discussed below.

Data Sources

Secondary data

Secondary data is data that is already in existence and was collected for other purposes and not for the sole purpose of a researcher’s project. 34 These pre-existing data include data from surveys, administrative records, medical records, or other sources (databases, internet). Examples of these data sources include census data, vital registration (birth and death), registries of notifiable diseases, hospital data and health-related data such as the national health survey data and national drug strategy household survey. 2 While secondary data are population-based, quicker to access, and cheaper to collect than primary data, there are some drawbacks to this data source. Potential disadvantages include accuracy of the data, completeness, and appropriateness of the data, given that the data was collected for an alternative purpose. 2 

Primary data

Primary data is collected directly from the study participants and used expressly for research purposes. 34 The data collected is specifically targeted at the research question, hypothesis and aims. Examples of primary data include observations and surveys (questionnaires). 34

• Observations: In quantitative research, observations entail systematically watching and recording the events or behaviours of interest. Observations can be used to collect information on variables that may be difficult to quantify through self-reported methods. Observations, for example, can be used to obtain clinical measurements involving the use of standardised instruments or tools to measure physical, cognitive, or other variables of interest. Other examples include experimental or laboratory studies that necessitate the collection of physiological data such as blood pressure, heart rate, urine, e.t.c. 2
• Surveys:  While observations are useful data collection methods, surveys are more commonly used data collection methods in healthcare research. 2, 34 Surveys or questionnaires are designed to seek specific information such as knowledge, beliefs, attitudes and behaviour from respondents. 2, 34 Surveys can be employed as a single research tool (as in a cross-sectional survey) or as part of clinical trials or epidemiological studies. 2, 34   They can be administered face-to-face, via telephone, paper-based, computer-based or a combination of the different methods. 2 Figure 3.7 outlines some advantages and disadvantages of questionnaires/surveys.

Designing a survey/questionnaire

A questionnaire is a research tool that consists of questions that are designed to collect information and generate statistical data from a specified group of people (target population). There are two main considerations in relation to design principles, and these are (1) content and (2) layout and sequence. 36 In terms of content, it is important to review the literature for related validated survey tools, as this saves time and allows for the comparison of results. Additionally, researchers need to minimise complexity by using simple direct language, including only relevant and accurate questions, with no jargon. 36 Concerning layout and sequence, there should be a logical flow of questions from general and easier to more sensitive ones, and the questionnaire should be as short as possible and NOT overcrowded. 36 The following steps can be used to develop a survey/ questionnaire.

Question Formats

Open and closed-ended questions are the two main types of question formats. 2   Open-ended questions allow respondents to express their thoughts without being constrained by the available options. 2, 38 Open-ended questions are chosen if the options are many and the range of answers is unknown. 38

On the other hand, closed-ended questions provide respondents with alternatives and require that they select one or more options from a list. 38 The question type is favoured if the choices are few and the range of responses is well-known. 38 However, other question formats may be used when assessing things on a continuum, like attitudes and behaviour. These variables can be considered using rating scales like visual analogue scales, adjectival scales and Likert scales. 2 Figure 3.8 presents a visual representation of some question types, including open-ended, closed-ended, likert rating scales, symbols, and visual Analogue Scales.

It is important to carefully craft survey questions to ensure that they are clear, unbiased and accurately capture the information researchers seek to gather. Clearly written questions with consistency in wording increase the likelihood of obtaining accurate and reliable data. Poorly crafted questions, on the other hand, may sway respondents to answer in a particular way which can undermine the validity of the survey. The following are some general guidelines for question wording. 39

Be concise and clear: Ask succinct and precise questions, and do not use ambiguous and vague words. For example, do not ask a patient, “ how was your clinic experience ? What do you mean by clinic experience? Are you referring to their interactions with the nurses, doctors or physiotherapists?

Instead, consider using a better-phrased question such as “ please rate your experience with the doctor during your visit today ”.

Avoid double-barrelled questions. Some questions may have dual questions, for example: Do you think you should eat less and exercise more?

Instead, ask:

• Do you think you should eat less?
• Do you think you should exercise more?

Steer clear of questions that involve negatives: Negatively worded questions can be confusing. For example, I find it difficult to fall asleep unless I take sleeping pills .

A better phrase is, “sleeping pills make it easy for me to fall asleep.”

Ask for specific answers. It is better to ask for more precise information. For example, “what is your age in years?________ Is preferable to -Which age category do you belong to?

☐  <18 years

☐ 18 – 25 years

☐ 25 – 35 years

☐ > 35 years

The options above will give more room for errors because the options are not mutually exclusive (there are overlaps) and not exhaustive (there are older age groups above 35 years).

Avoid leading questions. Leading questions reduces objectivity and make respondents answer in a particular way. Questions related to values and beliefs should be neutrally phrased. For example, the question below is worded in a leading way – Conducting research is challenging. Does research training help to prepare you for your research project?

An appropriate alternative: Research training prepares me for my research project.

Strongly agree           Agree                    Disagree              Strongly disagree

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

• Data Science

Caltech Bootcamp / Blog / /

Data Collection Methods: A Comprehensive View

• Written by John Terra
• Updated on February 21, 2024

Companies that want to be competitive in today’s digital economy enjoy the benefit of countless reams of data available for market research. In fact, thanks to the advent of big data, there’s a veritable tidal wave of information ready to be put to good use, helping businesses make intelligent decisions and thrive.

But before that data can be used, it must be processed. But before it can be processed, it must be collected, and that’s what we’re here for. This article explores the subject of data collection. We will learn about the types of data collection methods and why they are essential.

We will detail primary and secondary data collection methods and discuss data collection procedures. We’ll also share how you can learn practical skills through online data science training.

But first, let’s get the definition out of the way. What is data collection?

What is Data Collection?

Data collection is the act of collecting, measuring and analyzing different kinds of information using a set of validated standard procedures and techniques. The primary objective of data collection procedures is to gather reliable, information-rich data and analyze it to make critical business decisions. Once the desired data is collected, it undergoes a process of data cleaning and processing to make the information actionable and valuable for businesses.

Your choice of data collection method (or alternately called a data gathering procedure) depends on the research questions you’re working on, the type of data required, and the available time and resources and time. You can categorize data-gathering procedures into two main methods:

• Primary data collection . Primary data is collected via first-hand experiences and does not reference or use the past. The data obtained by primary data collection methods is exceptionally accurate and geared to the research’s motive. They are divided into two categories: quantitative and qualitative. We’ll explore the specifics later.
• Secondary data collection. Secondary data is the information that’s been used in the past. The researcher can obtain data from internal and external sources, including organizational data.

Let’s take a closer look at specific examples of both data collection methods.

The Specific Types of Data Collection Methods

As mentioned, primary data collection methods are split into quantitative and qualitative. We will examine each method’s data collection tools separately. Then, we will discuss secondary data collection methods.

Quantitative Methods

Quantitative techniques for demand forecasting and market research typically use statistical tools. When using these techniques, historical data is used to forecast demand. These primary data-gathering procedures are most often used to make long-term forecasts. Statistical analysis methods are highly reliable because they carry minimal subjectivity.

• Barometric Method. Also called the leading indicators approach, data analysts and researchers employ this method to speculate on future trends based on current developments. When past events are used to predict future events, they are considered leading indicators.
• Smoothing Techniques. Smoothing techniques can be used in cases where the time series lacks significant trends. These techniques eliminate random variation from historical demand and help identify demand levels and patterns to estimate future demand. The most popular methods used in these techniques are the simple moving average and the weighted moving average methods.
• Time Series Analysis. The term “time series” refers to the sequential order of values in a variable, also known as a trend, at equal time intervals. Using patterns, organizations can predict customer demand for their products and services during the projected time.

Qualitative Methods

Qualitative data collection methods are instrumental when no historical information is available, or numbers and mathematical calculations aren’t required. Qualitative research is closely linked to words, emotions, sounds, feelings, colors, and other non-quantifiable elements. These techniques rely on experience, conjecture, intuition, judgment, emotion, etc. Quantitative methods do not provide motives behind the participants’ responses. Additionally, they often don’t reach underrepresented populations and usually involve long data collection periods. Therefore, you get the best results using quantitative and qualitative methods together.

• Questionnaires . Questionnaires are a printed set of either open-ended or closed-ended questions. Respondents must answer based on their experience and knowledge of the issue. A questionnaire is a part of a survey, while the questionnaire’s end goal doesn’t necessarily have to be a survey.
• Surveys. Surveys collect data from target audiences, gathering insights into their opinions, preferences, choices, and feedback on the organization’s goods and services. Most survey software has a wide range of question types, or you can also use a ready-made survey template that saves time and effort. Surveys can be distributed via different channels such as e-mail, offline apps, websites, social media, QR codes, etc.

Once researchers collect the data, survey software generates reports and runs analytics algorithms to uncover hidden insights. Survey dashboards give you statistics relating to completion rates, response rates, filters based on demographics, export and sharing options, etc. Practical business intelligence depends on the synergy between analytics and reporting. Analytics uncovers valuable insights while reporting communicates these findings to the stakeholders.

• Polls. Polls consist of one or more multiple-choice questions. Marketers can turn to polls when they want to take a quick snapshot of the audience’s sentiments. Since polls tend to be short, getting people to respond is more manageable. Like surveys, online polls can be embedded into various media and platforms. Once the respondents answer the question(s), they can be shown how they stand concerning other people’s responses.
• Delphi Technique. The name is a callback to the Oracle of Delphi, a priestess at Apollo’s temple in ancient Greece, renowned for her prophecies. In this method, marketing experts are given the forecast estimates and assumptions made by other industry experts. The first batch of experts may then use the information provided by the other experts to revise and reconsider their estimates and assumptions. The total expert consensus on the demand forecasts creates the final demand forecast.
• Interviews. In this method, interviewers talk to the respondents either face-to-face or by telephone. In the first case, the interviewer asks the interviewee a series of questions in person and notes the responses. The interviewer can opt for a telephone interview if the parties cannot meet in person. This data collection form is practical for use with only a few respondents; repeating the same process with a considerably larger group takes longer.
• Focus Groups. Focus groups are one of the primary examples of qualitative data in education. In focus groups, small groups of people, usually around 8-10 members, discuss the research problem’s common aspects. Each person provides their insights on the issue, and a moderator regulates the discussion. When the discussion ends, the group reaches a consensus.

Secondary Data Collection Methods

Secondary data is the information that’s been used in past situations. Secondary data collection methods can include quantitative and qualitative techniques. In addition, secondary data is easily available, so it’s less time-consuming and expensive than using primary data. However, the authenticity of data gathered with secondary data collection tools cannot be verified.

Internal secondary data sources:

• CRM Software
• Executive summaries
• Financial Statements
• Mission and vision statements
• Organization’s health and safety records
• Sales Reports

External secondary data sources:

• Business journals
• Government reports
• Press releases

The Importance of Data Collection Methods

Data collection methods play a critical part in the research process as they determine the accuracy and quality and accuracy of the collected data. Here’s a sample of some reasons why data collection procedures are so important:

• They determine the quality and accuracy of collected data
• They ensure the data and the research findings are valid, relevant and reliable
• They help reduce bias and increase the sample’s representation
• They are crucial for making informed decisions and arriving at accurate conclusions
• They provide accurate data, which facilitates the achievement of research objectives

So, What’s the Difference Between Data Collecting and Data Processing?

Data collection is the first step in the data processing process. Data collection involves gathering information (raw data) from various sources such as interviews, surveys, questionnaires, etc. Data processing describes the steps taken to organize, manipulate and transform the collected data into a useful and meaningful resource. This process may include tasks such as cleaning and validating data, analyzing and summarizing data, and creating visualizations or reports.

So, data collection is just one step in the overall data processing chain of events.

Do You Want to Become a Data Scientist?

If this discussion about data collection and the professionals who conduct it has sparked your enthusiasm for a new career, why not check out this online data science program ?

The Glassdoor.com jobs website shows that data scientists in the United States typically make an average yearly salary of $129,127 plus additional bonuses and cash incentives. So, if you’re interested in a new career or are already in the field but want to upskill or refresh your current skill set, sign up for this bootcamp and prepare to tackle the challenges of today’s big data.

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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Knowledge Base

Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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.

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

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

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

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.

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

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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

Published on 4.4.2024 in Vol 26 (2024)

Impacts of an Acute Care Telenursing Program on Discharge, Patient Experience, and Nursing Experience: Retrospective Cohort Comparison Study

Authors of this article:

Original Paper

• Courtenay R Bruce, MA, JD   ; 
• Steve Klahn, RN, MBA   ; 
• Lindsay Randle, MBA   ; 
• Xin Li, BS   ; 
• Kelkar Sayali, BS   ; 
• Barbara Johnson, BSN, MBA, DNP   ; 
• Melissa Gomez, MBA   ; 
• Meagan Howard, MHA   ; 
• Roberta Schwartz, PhD   ; 
• Farzan Sasangohar, PhD  

Houston Methodist, Houston, TX, United States

Corresponding Author:

Courtenay R Bruce, MA, JD

Houston Methodist

8100 Greenbriar Drive

Houston, TX, 77030

United States

Phone: 1 281 620 9040

Email: [email protected]

Background: Despite widespread growth of televisits and telemedicine, it is unclear how telenursing could be applied to augment nurse labor and support nursing.

Objective: This study evaluated a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients.

Methods: A retrospective, observational cohort comparison was performed in a large academic hospital system (approximately 2100 beds) in Houston, Texas, comparing patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, with patients who did not participate (nontelenursing cohort) in the same units and timeframe. We used a case mix index analysis to confirm comparable patient cases between groups. The outcomes investigated were patient experience, measured using the Hospital Consumer Assessment of Health Care Providers and Systems (HCAHCPS) survey; nursing experience, measured by a web-based questionnaire with quantitative multiple-choice and qualitative open-ended questions; time of discharge during the day (from electronic health record data); and duration of discharge education processes.

Results: Case mix index analysis found no significant case differences between cohorts ( P =.75). For the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHCPS domains. Scores for “communication with doctors” and “would recommend hospital” were improved significantly ( P =.03 and P =.04, respectively) in 1 unit in phase 1. The impact of telenursing in phases 2 and 3 was mixed. However, “communication with doctors” was significantly improved in 2 units ( P =.049 and P =.002), and the overall rating of the hospital and the ”would recommend hospital” scores were significantly improved in 1 unit ( P =.02 and P =04, respectively). Of 289 nurses who were invited to participate in the survey, 106 completed the nursing experience survey (response rate 106/289, 36.7%). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was very helpful or somewhat helpful to them as bedside nurses. The only noticeable difference between the telenursing and nontelenursing cohorts for the time of day discharge was a shift in the volume of patients discharged before 2 PM compared to those discharged after 2 PM at a hospital-wide level. The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice (nontelenursing cohort) of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation time study.

Conclusions: This study shows that ACTN programs are feasible and associated with improved outcomes for patient and nursing experience and reducing time allocated to admission and discharge education.

Introduction

Telemedicine, particularly video televisits, has greatly expanded in the wake of the COVID-19 pandemic [ 1 , 2 ]. Televisits have shown promise as a robust, practical, efficacious, and scalable alternative to in-person office visits that could ameliorate labor supply shortages [ 3 , 4 ]. The published evidence suggests a generally positive attitude toward televisit appointments for chronic care, focused on addressing financial and transportation barriers and improving patients’ access to care [ 5 - 7 ]. Despite the promise shown by televisits, limited attention has been paid to applying this method in the acute care setting and, in particular, on how this promising technology can be leveraged to support nurses.

Estimates suggest that approximately 200,000 open nursing positions will become available each year between 2021 and 2031 [ 8 ]. Telenursing can augment nursing labor supply, decrease nursing workload, maintain patient and nurse safety, and positively impact nursing and patient experiences [ 9 ]. However, the impact of telenursing on outcomes in acute care settings remains a research gap.

To address this gap, this study aimed to evaluate the outcomes associated with a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients compared to patients who did not undergo the ACTN program intervention. Admission and discharge are 2 substantive and time-consuming acute care nursing tasks that involve tedious documentation in the electronic health record (EHR) and extensive interaction with patients and families to gather history and provide patient education [ 10 , 11 ]. We aimed to develop an ACTN program to augment nursing care by conducting admission and discharge processes through telenursing in a large health system. Subsequently, we discuss the impacts on 4 end points: patient experience, nursing experience, time of discharge during the day, and length of time for discharge education processes. We hypothesized that the ACTN program would be associated with higher patient experience scores and improved nursing experience compared to standard admission and discharge practices.

This study was conducted in a large academic hospital system (approximately 2100 beds) in Houston, Texas. The preimplementation methods are reported more extensively in the studies by Hehman et al [ 12 ] and Schwartz et al [ 13 ]. Program implementation was first informed by nursing time and workload surveys and pilot implementation in 4 comparatively understaffed units. The chief innovation officer, along with nursing leaders and ACTN program administrators, met with the bedside nursing staff of these 4 understaffed units to solicit their input on where and how ACTN would add value to their workflow. Bedside nursing staff provided critical input on admission processes that could be delegated to individuals working remotely with no perceived negative impact on patient experience. We conducted participatory workflow design sessions with bedside nursing staff on the ACTN program to cocreate workflow integration points where the remote team could assist [ 13 ].

Pilot Implementation and Procedures

Before implementation, the ACTN administrators trained bedside nurses in pilot units by demonstrating the use of technology during shift huddles. Then, the trainers presented slides on contact information and available support and provided a role demarcation process map, showing what the remote telenurse staff would be doing compared to what the bedside nurses needed to do to launch and conduct discharge education. Furthermore, the trainers invited the nursing staff to observe several discharges to learn how to conduct them. A software with Health Insurance Portability and Accountability Act compliance was uploaded to iPads (Apple Inc) and stored on each unit. Handheld iPads were available, and roaming iPads were made available for patients who could not hold an iPad.

The pilot implementation was staggered in a phased rollout, consisting of 3 sequenced phases, as shown in Figure 1 . Upon admission, the acute care bedside nurse contextualized the ACTN program with patients and families by handing the patient an iPad with a preloaded remote program app (Caregility) and then pressing a soft key to allow the ACTN to enter the patient’s room via the iPad screen. The ACTN introduced themselves, completed the nursing admission profile in the EHR, placed a request for a consultation, and notified the bedside nurse that the admission was completed using secure SMS text messaging [ 13 ]. A similar process was followed for discharge workflow processes, where the ACTN completed patient education on discharge instructions, confirmed the patient’s pharmacy details, confirmed discharge transportation, and arranged for departure.

Bedside nurses used their discretion regarding which patients would be appropriate for the ACTN program. They based this determination principally on whether documentation was needed and whether the patient could benefit from the undivided attention the ACTN program could afford. Furthermore, they excluded patients from the ACTN program if the patients expressed discomfort using an iPad. After the initial rollout, patients’ input was sought on their experience with the ACTN program to identify where and how improvements could be made, and this feedback was incorporated into iterative revisions in subsequent rollouts.

Pilot Outcomes Monitoring

A retrospective, observational cohort comparison was performed, in which all patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, were compared with all patients who did not participate (nontelenursing cohort) in the same units in the same timeframe.

Our primary outcomes were patient experience and nursing experience. Patient experience scope was any process observable by patients [ 14 ]. We compared patient experiences in the telenursing and nontelenursing cohorts by evaluating patients’ responses to the widely used Hospital Consumer Assessment of Health Care Providers and Systems (HCAHPS) survey [ 15 ], which represented 8 aspects (called dimensions) of patient satisfaction. Each dimension was measured using a continuous variable (0 to 100 points).

For the telenursing cohort, we analyzed bedside nurses’ collective responses using a Forms (Microsoft Corp) survey conducted in April 2023. The survey consisted of 5 questions, asking them to indicate whether the ACTN program was helpful using a Likert scale with 5 items (very helpful to very unhelpful). Nurses were asked to provide open-ended comments to explain the reasons for their evaluation. At the end of the survey, we included 2 open-ended fields for nurses to describe opportunities for improvement in future rollouts and provide any additional comments.

Furthermore, we explored the time at which discharge occurred using the EHR admission, discharge, and transfer date and time. We compared the hour of the day the patient was discharged in the telenursing cohort with the hour of the day the patient was discharged in the nontelenursing cohort, hypothesizing a priori that patients might be discharged earlier in the day in the telenursing cohort. Finally, we analyzed the duration of discharge education for both cohorts, measured in minutes.

Data Analysis

The patient demographic data were available for all patients. To confirm that the telenursing cohort had similar patient demographics as the nontelenursing cohort (and therefore to confirm that nurse biases in patient selection for the ACTN program were unlikely), we conducted a case mix index (CMI) evaluation. We first isolated the population of both cohorts into adults (aged ≥18 y). We compared only those patients who were discharged home and excluded those who were on extracorporeal membrane oxygenation or those who underwent a tracheostomy. The remaining population was evaluated to determine whether there was a difference in patient acuity and severity. After confirming that patient acuity and severity were of no significant difference, we included the inpatient and observation populations to evaluate the intervention results.

For the patient experience data, independent sample t tests (2-tailed) were used to compare the telenursing and nontelenursing cohorts across different HCAHPS dimensions and units. Analysis was conducted using R software (R Foundation for Statistical Computing). For the nursing experience survey data, we used Excel (Microsoft Corp) to analyze the responses to multiple-choice, discrete questions and thematic analysis to evaluate the open-text fields. Thematic analysis allows for eliciting key themes that emerge based on recurring statements [ 16 ]. The analysis followed an inductive approach. This approach uses open-ended questions, allowing themes to emerge with a few previously articulated assumptions on responses. Given the limited content, CRB served as the primary coder. Coding labels were used for data contextualizing, allowing for new themes to emerge throughout the coding process, using a codebook [ 16 , 17 ]. We stored emergent patterns and themes in an electronic format.

Ethical Considerations

The hospital’s review board determined that the ACTN pilot would not be considered regulated human subjects research. All data reported in this study were aggregated and deidentified.

The demographics of the telenursing and nontelenursing cohorts were relatively similar. Both cohorts had an average age of 60 years with an SD of 16.91; had a similar distribution in race and ethnicity (approximately 92/2319, 3.96% Asian; 525/2319, 22.64% Black; 425/2319, 18.33% Hispanic; 70/2319, 3.02% Native American, declined to identify, or other categories; and 1202/2319, 51.83% White); and had a similar distribution in female participants versus male participants (1249/2319, 53.86% vs 1070/2319, 46.14%). To further understand the population, the CMI analysis for acuity and severity showed that the CMI was slightly higher in the telenursing cohort than in the nontelenursing cohort, but the difference was not statistically significant ( P =.75).

Patient Experience

Among the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHPS domains (Table S1 in Multimedia Appendix 1 ). On average, 6 out of 8 HCAHPS domains were improved for patients in the telenursing cohort. All 4 units experienced improvements in the “overall rating” domain, and 3 of the 4 units experienced improvements in “likelihood to recommend” domain for patients in the telenursing cohort compared to those in the nontelenursing cohort within the same units. The improvement scores ranged from 1.4% for the neurosurgery unit (36 beds) to 11.6% for the medical unit (37 beds). Furthermore, all 4 units in the first phase of roll out experienced improved scores in the “responsiveness” domain by >4 points (ranging from 5% to 10.1%). A total of 2 out of the 4 units also experienced improvements in the “communication with nurses” (ranging from 1.7% to 3%) and “communication about medicines” (ranging from 3.3% to 11.7%) domains. The 2 units that did not experience improvement in the communication domains were the combined medical and surgery neurology and neurosurgical units (36 beds). Only the neurosurgical unit showed statistically significant improvements in 2 dimensions: “communication with doctors” ( P =.03) and “would recommend hospital” ( P =.04).

For the 7 units that rolled out during phase 2, only 1 orthopedic surgery unit (28 beds) experienced improvements in every domain (ranging from 0.9% to 12.5%). Medical observation unit 1 also improved in 5 areas. However, only improvements in “communication with doctors” ( P =.002), “overall rating of hospital” ( P =.02), and “would recommend hospital” ( P =.04) were statistically significant . The remaining units experienced improvements in some domains for the telenursing cohort compared to the nontelenursing cohort, with no improvement in other domains. However, the scores for “communication with nurses” and “communication with doctors” domains were improved for most of the units that rolled out in phase 2 (Table S2 in Multimedia Appendix 1 ).

For the 2 units that rolled out in phase 3, both of which were surgical cardiac units with 36 beds, 1 unit experienced improvement in every domain except “responsiveness” (ranging from 1% to 12%). The other unit only experienced improvement in the “communication with doctors” (4.9%) and “care transitions” domains (1.1%). However, none of these improvements were statistically significant (Table S3 in Multimedia Appendix 1 ).

Nursing Experience

Of the 289 nurses who were invited to participate in the survey, 106 completed the survey (36.7% response rate). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was “very helpful” or “somewhat helpful” to them as bedside nurses.

Quantitative Findings

The main reasons nurses gave for the program’s helpfulness included that it saved them time (94/106, 88.7%), allowed them to focus on more urgent clinical needs (90/106, 84.9%), allowed them to focus on activities they felt were more in line with their skill level (55/106, 51.9%), and allowed patients to have undivided attention for their discharge education (52/106, 49.1%). Among the 5 nurses who indicated that the ACTN program was somewhat unhelpful or very unhelpful, 3 (60%) indicated that workflows were not clear or needed further refinement or clarification. Furthermore, the nurse respondents shared several barriers and provided opportunities for improvement, with 91 (85.8%) out of 106 nurses offering suggestions.

Qualitative Findings

For the free-text explanation fields, all but 3 nurses (103/106, 97.2%) provided additional comments on the ACTN program helpfulness. Three themes emerged from the qualitative analysis of the free-text comments: (1) most of the nurses’ comments reflected that telenurses help bedside nurses save time, (2) respondents indicated that extra hands provided emotional and physical support in providing patient care, and (3) respondents perceived an improvement in patient safety by having a telenurse who could “catch missed” issues.

Time Saving

One of the perceived benefits of the telenursing program was saving time. One nurse said the following:

... Just putting in home medications alone takes up so much time. This new telenurse service helps [save time]

Several nurses highlighted that admission and discharge processes are so complex and time-consuming that shifting this work to the ACTN program freed nurses to perform other activities, as reflected by this nurse:

The tele RN is able to spend as much time possible sufficiently educating an admission or discharge while allowing me time to respond to the needs of my other patients saving me time on one patient especially charting.

Emotional and Physical Support

For the second theme, several responses focused less on time management and perceived efficiencies and instead centered more on the emotional appeal and support in having an extra hand, as one nurse mentioned:

Being in such a fast-paced unit, it can be a bit stressful with so many discharges and admissions. Having a helpful hand is beneficial.

Improved Patient Safety

Finally, the third theme was perceived improvement in patient safety by having a telenurse who could “catch missed” issues (eg, an incorrectly identified pharmacy details), simultaneously allowing the primary bedside nurse to focus more intensely on other needs, essentially creating a 2-fold safety promotion. Some nurses noted that they could begin carrying out orders while the telenurses began completing the admission, facilitating quicker treatment and resolution of care needs, thereby improving the safety and quality of care. One nurse mentioned the following:

Allows [telenurses] to take on thorough and accurate admissions, while also preventing any rushing the patient might experience from the primary RN.

When asked for areas of improvement, the most recurring theme was having 24 hours of support during the weekend and during the week. The second theme for improvement was the reduced time to connect to a telenurse. The third theme was the availability of iPads. Nurses mentioned that iPads could sometimes be unavailable in patients’ rooms or they may not be fully charged.

Time of Discharge

The time of day distribution is presented in Figure 2 . The only noticeable difference between the telenursing and nontelenursing cohorts was a shift in the volume of patients discharged before 2 PM compared with those discharged after 2 PM at a hospital-wide level ( Table 1 ). At an individual unit level, these results were not consistent and could be further explored by patient population and their needs to discharge. The variation was further illustrated when reviewing the length of stay of patients in the telenursing and nontelenursing cohorts. Only 5 out of the 12 units showed a decrease in the average inpatient length of stay.

Discharge Length

The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation nursing time study.

Principal Findings

Our results suggest that the ACTN program was associated with positive nursing experiences because it saved time. Furthermore, the ACTN program was associated with higher HCAHPS scores in several domains but only in the first series of units that piloted the intervention. In phase 1, the improvement in “communication with doctors” and “would recommend hospital” scores in 1 unit was statistically significant. In phase 2, the improvement in “communication with doctors” score was significant in 2 units and that in “overall rating of hospital” and “would recommend hospital” scores were significant in 1 unit. The time of day discharge was nearly the same in both the telenursing and nontelenursing cohorts. The duration for discharge processes was less than half in the ACTN cohort compared to the nonintervention cohort.

At the time of writing this paper, the United States was experiencing a critical nursing shortage that will likely reach an epidemic level in the next few decades [ 8 ]. Despite the promise shown by telenursing, to our knowledge, only 1 existing paper documents the impact of ACTN programs on HCAHPS-measured patient satisfaction using a small cohort of patients in a single, time-limited pre- and posttelenursing analysis [ 18 ]. A study by Schuelke et al [ 18 ] revealed a 6.2% increase in “communication with meds” and 12.7% increase in “communication with nursing” domain scores; other HCAHPS domains were not evaluated. This research builds upon the promising work of Schuelke et al [ 18 ], evaluating the impact of an ACTN program on several units with a much larger cohort of patients using a staggered rollout and comparing all HCAHPS domains between telenursing and nontelenursing cohorts within the same time frame and in the same units.

By conducting granular HCAHPS analyses, we identified what we believed to be a time sequence variability in that units that rolled out in phase 1 performed considerably stronger in HCAHPS impacts than units that rolled out in later phases. An explanation for this sequence effect might be that some later adopters had less potential for high effect size, given that the first 4 units of the rollout were specifically chosen for their staffing problems compared to later units. ACTN support might have augmented the staffing support to such a degree that allowed the impacts of the program to be more salient. An alternative explanation is that the early adopters and promoters tend to have greater diffusion uptake, greater saturation and adoptability, and greater impacts compared to late adopters or those resistant to adoption [ 19 , 20 ]. Our anecdotal evidence suggests that early adopters might have wanted the telenursing program to succeed; therefore, they applied consistent implementation practices to ensure success. Adopters in later stages were more aware of barriers and potential downsides and might have been more ambivalent about telenursing and, therefore, less likely to modify their behaviors to promote the telenursing program’s success.

Another interesting finding was that the ACTN program seemed to be effective for both medical and surgical units of all specialties. Phase 1 was a mix of medical and surgical units; however, all units experienced increases in scores. Phases 2 and 3 experienced mixed results, without a clear lead for one specialty over the other. This may suggest that ACTN programs are broadly applicable across acute settings and that success depends most crucially on the need and desire of unit leaders.

Our time of day discharge findings showed only a few quantitative positive efficiencies. However, our discharge duration analysis and nursing experience survey results showed that ACTN has major time-saving benefits for nurses, suggesting a discrepancy between perceived and actual time savings versus time-of-day discharge savings. One explanation for this discrepancy may be that many factors beyond nursing impact the time of the day a patient is discharged; therefore, while the bedside nurses’ time is saved, the remaining discharge processes beyond nurses remain unaffected. Specifically, there are 3 segments of time during discharge processes: (1) the time for the discharge order and medication reconciliation [ 21 ] to the time the after-visit summary (AVS) is populated and printed [ 22 ]; (2) the time the AVS is completed and printed to the time the discharge instructions are provided; and (3) the time from providing the discharge instructions to the actual discharge ( Figure 3 ). Notably, telenurses’ involvement is currently limited to only the second segment of time. Specifically, telenurses’ involvement is not initiated until the AVS is printed by the nurse, which means that telenurses cannot positively impact any discharge activity that occurs between the time the discharge order is written and the time the AVS is printed. However, there are inefficiencies and bottlenecks in discharge processes that occur well before the AVS is printed [ 23 , 24 ]. For instance, the discharging physician may write a conditional discharge order early in the morning, listing conditions that cannot be fulfilled within a few hours or it may take bedside nursing longer than anticipated time to print the AVS.

To create a wider cascade effect for positively impacting the discharge processes for all segments of time, we are currently trying to obtain greater transparency through EHR reporting in what occurs for segments 1 and 3. For instance, at present, we know that at least 2 hospitals in our 8-hospital system have high incidence rates of conditional discharge orders that should be reduced. One hospital anecdotally reports that the discharging physician identifies incorrect pharmacies, which requires a nurse to send the scripts back to the discharging pharmacist to reconcile before discharge education can occur [ 25 ]; however, the prevalence and location of these issues remain speculative. Segment 3 is a black box of time [ 26 ]—the time it takes for hospital transport or an ambulance to arrive and move the patient to their destination and the time it takes for the family to pick up the patient. All these factors impact the discharge processes and need to be fully elucidated, explored, and streamlined. Furthermore, we hope to facilitate processes that enable telenurses to print the AVS, to remove the dependency on bedside nurses to begin the discharge education process.

Limitations

This study has several noteworthy limitations. First, the study was conducted in 1 health system and the results may not be generalizable to other settings with different patient populations, processes, and implementation strategies [ 27 ]. Second, in this study, we did not control for other factors that could impact patient and provider satisfaction as well as discharge times; telenursing can only improve upon one component in a complex set of factors limiting discharge efficiency and satisfaction outcomes. Finally, participating nurses were aware of the ongoing study, and this knowledge might have affected their behavior [ 28 ].

Future Directions

After the completion of this pilot study, the ACTN admission and discharge program has been rolled out to pilot medical units and all surgical and observation units. Our rationale for expansion rested on the premise that nursing experience is important to maintain and strengthen, particularly at a time when turnover is high in the health care industry in general. It is important to reduce staff inefficiencies in workload as a means of preserving or strengthening organizational morale and cost saving. Because our nursing experience findings for the ACTN program heavily supported the program, this served as the primary motivation for expansion. The nursing experience findings, coupled with the findings related to time-savings in discharge education and modest improvement, though not negative, in the HCAHPS findings for the ACTN program compared to the nontelenursing cohort, further supported expansion.

The initial scope for expansion included a complete system-wide implementation for all admissions and discharges. Furthermore, we are planning to expand the ACTN program beyond admissions and discharges. Responsive to qualitative feedback reported earlier, the next phase of the ACTN program will add safeguards on high-risk medications by having telenurses conduct double-checks, skin assessments, hourly rounding assistance, and auditing of safety functions and educational activities. These activities were chosen because they are time-intensive for nursing staff on the patient floors. Additional support in these areas would be a staff morale booster in addition to improved efficiencies for bedside nursing. Conducting hourly rounding using the ACTN program will require more time and resources; however, conducting high-quality, uninterrupted hourly rounds is known to be effective at improving patient safety and patient experience outcomes [ 29 ]. Therefore, we suspect that the ACTN program will have some positive impacts if rounds are consistently conducted, even if conducted virtually.

In addition, the ACTNs have been motivating other specialties to adopt or consider a similar program as the ACTN program to support stretched staffing. These specialties include respiratory care, in which virtual support can quickly identify patients in need of intensive on-site support; pharmacy, in which direct communication with staff on medications and patient training can happen through virtual means; infection control, in which room environments can be reviewed through virtual audits, moving quickly from floor to floor; and guest relations and spiritual care, in which patients can be visited virtually upon patient request. Furthermore, physicians who wish to either virtually enter inpatient rooms during their clinic days or from home can quickly drop in to see patients using the virtual program. For these groups to further develop advanced inpatient telemedicine programs, additional technology will be required, including cameras that can zoom into various portions of the room and advanced sound capabilities. Future work could expand programs similar to ACTN to specialties such as respiratory therapy, pharmacy, infection prevention, and spiritual care.

Conclusions

This study provides preliminary evidence suggesting that telenursing may effectively address nursing shortages in acute care settings and positively impact patient and provider satisfaction as well as admission and discharge times. More work is needed to validate the findings in other settings, use other satisfaction metrics, and investigate the impact of telenursing on the quality of care and cost.

Acknowledgments

The authors would like to thank Jacob M Kolman, MA, ISMPP CMPP, senior scientific writer, Houston Methodist Academic Institute, for the critical review and for providing formatting feedback on this manuscript. The authors would also like to thank Amir Hossein Javid for his help with statistical analysis.

Data Availability

Data sharing is not applicable as no data sets were generated during this study.

Authors' Contributions

All authors were involved in the conceptualization, review and approval, and writing of the manuscript. LR, BJ, MG, RS, SK, and MH were extensively involved in the implementation of the project. BJ, MH, SK, and MG conducted the training. SK and XL conducted the analyses. CRB wrote and edited the manuscript, inserted and refined the citations, and provided critical feedback during implementation and analyses. CRB and FS were involved in all stages of writing and publication. All authors meaningfully contributed to the drafting, writing, brainstorming, executing, finalizing, and approving of the manuscript.

Conflicts of Interest

None declared.

Additional outcome information for Hospital Consumer Assessment of Health Care Providers and Systems, time of day discharges, and discharge education processes.

• Greiwe J. Telemedicine lessons learned during the COVID-19 pandemic. Curr Allergy Asthma Rep. Jan 21, 2022;22(1):1-5. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Doraiswamy S, Abraham A, Mamtani R, Cheema S. Use of telehealth during the COVID-19 pandemic: scoping review. J Med Internet Res. Dec 01, 2020;22(12):e24087. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform. Nov 2010;79(11):736-771. [ CrossRef ] [ Medline ]
• Nanda M, Sharma R. A review of patient satisfaction and experience with telemedicine: a virtual solution during and beyond COVID-19 pandemic. Telemed J E Health. Dec 01, 2021;27(12):1325-1331. [ CrossRef ] [ Medline ]
• Yu X, Bayram A. Managing capacity for virtual and office appointments in chronic care. Health Care Manag Sci. Dec 2021;24(4):742-767. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Laskowski ER, Johnson SE, Shelerud RA, Lee JA, Rabatin AE, Driscoll SW, et al. The telemedicine musculoskeletal examination. Mayo Clin Proc. Aug 2020;95(8):1715-1731. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hughes L, Petrella A, Phillips N, Taylor RM. Virtual care and the impact of COVID-19 on nursing: a single centre evaluation. J Adv Nurs. Feb 2022;78(2):498-509. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pulse on the nation’s nurses survey series: COVID-19 two-year impact assessment survey. American Nurses Foundation. Mar 1, 2022. URL: https:/​/www.​nursingworld.org/​~4a2260/​contentassets/​872ebb13c63f44f6b11a1bd0c74907c9/​covid-19-two-year-impact-assessment-written-report-final.​pdf [accessed 2023-04-14]
• Abraham C, Jensen C, Rossiter L, Dittman Hale D. Telenursing and remote patient monitoring in cardiovascular health. Telemed J E Health. Sep 08, 2023. (forthcoming). [ CrossRef ] [ Medline ]
• Rouleau G, Gagnon M, Côté J, Payne-Gagnon J, Hudson E, Dubois CA. Impact of information and communication technologies on nursing care: results of an overview of systematic reviews. J Med Internet Res. Apr 25, 2017;19(4):e122. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Moy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. Apr 23, 2021;28(5):998-1008. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hehman MC, Fontenot NM, Drake GK, Musgrove RS. Leveraging digital technology in nursing. Health Emerg Disaster Nurs. 2023;10:41-45. [ CrossRef ]
• Schwartz RL, Hamlin SK, Vozzella GM, Randle LN, Klahn S, Maris GJ, et al. Utilizing telenursing to supplement acute care nursing in an era of workforce shortages: a feasibility pilot. Comput Inform Nurs. Feb 01, 2024;42(2):151-157. [ CrossRef ] [ Medline ]
• Cleary PD. Evolving concepts of patient-centered care and the assessment of patient care experiences: optimism and opposition. J Health Polit Policy Law. Aug 28, 2016;41(4):675-696. [ CrossRef ] [ Medline ]
• CAHPS hospital survey. Agency for Healthcare Research and Quality. URL: https://www.ahrq.gov/cahps/surveys-guidance/hospital/index.html [accessed 2023-10-12]
• Strauss A, Corbin JM. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Thousand Oaks, CA. Sage Publications; 1990.
• Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are enough? Qual Health Res. Mar 2017;27(4):591-608. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schuelke S, Aurit S, Connot N, Denney S. Virtual nursing: the new reality in quality care. Nurs Adm Q. 2019;43(4):322-328. [ CrossRef ] [ Medline ]
• Rogers E. Diffusion of Innovations. New York, NY. Free Press of Glenco; 1962.
• Mohammadi MM, Poursaberi R, Salahshoor MR. Evaluating the adoption of evidence-based practice using Rogers's diffusion of innovation theory: a model testing study. Health Promot Perspect. 2018;8(1):25-32. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fernandes BD, Almeida PH, Foppa AA, Sousa CT, Ayres LR, Chemello C. Pharmacist-led medication reconciliation at patient discharge: a scoping review. Res Social Adm Pharm. May 2020;16(5):605-613. [ CrossRef ] [ Medline ]
• Federman A, Sarzynski E, Brach C, Francaviglia P, Jacques J, Jandorf L, et al. Challenges optimizing the after visit summary. Int J Med Inform. Dec 2018;120:14-19. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bristol AA, Elmore CE, Weiss ME, Barry LA, Iacob E, Johnson EP, et al. Mixed-methods study examining family carers' perceptions of the relationship between intrahospital transitions and patient readiness for discharge. BMJ Qual Saf. Aug 2023;32(8):447-456. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Piniella NR, Fuller TE, Smith L, Salmasian H, Yoon CS, Lipsitz SR, et al. Early expected discharge date accuracy during hospitalization: a multivariable analysis. J Med Syst. May 12, 2023;47(1):63. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schnipper JL, Reyes Nieva H, Yoon C, Mallouk M, Mixon AS, Rennke S, et al. What works in medication reconciliation: an on-treatment and site analysis of the MARQUIS2 study. BMJ Qual Saf. Aug 2023;32(8):457-469. [ CrossRef ] [ Medline ]
• Abu HO, Anatchkova MD, Erskine NA, Lewis J, McManus DD, Kiefe CI, et al. Are we "missing the big picture" in transitions of care? Perspectives of healthcare providers managing patients with unplanned hospitalization. Appl Nurs Res. Dec 2018;44:60-66. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Leung L. Validity, reliability, and generalizability in qualitative research. J Family Med Prim Care. 2015;4(3):324-327. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Merrett F. Reflections on the Hawthorne effect. Educ Psychol. Jan 2006;26(1):143-146. [ CrossRef ]
• Mitchell M, Lavenberg JG, Trotta RL, Umscheid CA. Hourly rounding to improve nursing responsiveness: a systematic review. J Nurs Adm. Sep 2014;44(9):462-472. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso, G Eysenbach; submitted 06.11.23; peer-reviewed by C Jensen; comments to author 08.12.23; revised version received 16.01.24; accepted 17.02.24; published 04.04.24.

©Courtenay R Bruce, Steve Klahn, Lindsay Randle, Xin Li, Kelkar Sayali, Barbara Johnson, Melissa Gomez, Meagan Howard, Roberta Schwartz, Farzan Sasangohar. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2024.

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