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What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

In descriptive research , you simply seek an overall summary of your study variables.
In correlational research , you investigate relationships between your study variables.
In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

Chi square goodness of fit test
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
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 .

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014). The SAGE encyclopedia of action research (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017). The SAGE encyclopedia of communication research methods (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design? What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used. You will need to know how to decipher which design type is used. The following video will help you determine the quantitative design type.

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What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

In descriptive research , you simply seek an overall summary of your study variables.
In correlational research , you investigate relationships between your study variables.
In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

Superficiality

Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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.

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.

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.

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Bhandari, P. (2022, October 10). What Is Quantitative Research? | Definition & Methods. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-quantitative-research/

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Quantitative research methods

a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time. The goal of gathering this quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Quantitative research techniques include experiments and surveys.

SAGE Research Methods Videos

What are the strengths of quantitative research.

Professor Norma T. Mertz briefly discusses qualitative research and how it has changed since she entered the field. She emphasizes the importance of defining a research question before choosing a theoretical approach to research.

This is just one segment in a series about quantitative methods. You can find additional videos in our SAGE database, Research Methods:

Videos covering research methods and statistics

To login from SAGE, click Institution, then Access via Your Institution, then find and select City University of Seattle

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 .

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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.

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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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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

The data is usually gathered using structured research instruments.
The results are based on larger sample sizes that are representative of the population.
The research study can usually be replicated or repeated, given its high reliability.
Researcher has a clearly defined research question to which objective answers are sought.
All aspects of the study are carefully designed before data is collected.
Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

Things to keep in mind when reporting the results of a study using quantitative methods :

Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
Explain the techniques you used to "clean" your data set.
Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
Always tell the reader what to look for in tables and figures .

NOTE: When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
You can summarize vast sources of information and make comparisons across categories and over time; and,
Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
Uses a static and rigid approach and so employs an inflexible process of discovery;
The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
Results provide less detail on behavior, attitudes, and motivation;
Researcher may collect a much narrower and sometimes superficial dataset;
Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Quantitative research

Affiliation.

1 Faculty of Health and Social Care, University of Hull, Hull, England.
PMID: 25828021
DOI: 10.7748/ns.29.31.44.e8681

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Keywords: Experiments; measurement; nursing research; quantitative research; reliability; surveys; validity.

Biomedical Research / methods*
Double-Blind Method
Evaluation Studies as Topic
Longitudinal Studies
Randomized Controlled Trials as Topic
United Kingdom

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Frequently asked questions

Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history

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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Statistically

Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

definition of quantitative research by authors

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video.

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course .

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions.

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions.

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX .

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

Literature on Quantitative Research

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Learn more about Quantitative Research

Take a deep dive into Quantitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

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Definition | Quantitative Research

In the most basic terms, quantitative research methods are concerned with collecting and analysing data that is structured and can be represented numerically. One of the central goals is to build accurate and reliable measurements that allow for statistical analysis.

Because quantitative research focuses on data that can be measured, it is very effective at answering the "what" or "how" of a given situation. Questions are direct, quantifiable, and often contain phrases such as what percentage? what proportion? to what extent? how many? how much? See Goertzen (2017)

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What is Quantitative Research According to Authors?

By Med Kharbach, PhD | Published: May 9, 2023 | Updated: March 21, 2024

In this post, we will discuss the concept of quantitative research as viewed through the lens of various esteemed authors. The aim is to provide a holistic view of this research method, focusing particularly on guiding beginner researchers and graduate students towards seminal works that offer invaluable insights into the field.

Quantitative research is a pivotal aspect of academic inquiry, and understanding its fundamentals is crucial for anyone venturing into the realm of research. We’ll explore the definitions and perspectives of quantitative research according to John Creswell, along with other notable scholars in the field. These insights are not only foundational for grasping the essence of quantitative research but also serve as a beacon for those navigating the often-complex landscape of academic research methodologies.

Related: 12 Good Books on How to Write and Publish Research Papers

Here are some key definitions of quantitative research according to different scholars:

1.Quantitative Research According to John Creswell

Creswell (2014) defines quantitative research as :

an inquiry into a social or human problem, based on testing a theory composed of variables, measured with numbers, and analyzed with statistical procedures, in order to determine whether the predictive generalizations of the theory hold true. The final written report has a set structure consisting of introduction, literature and theory, methods, results, and discussion. Like qualitative researchers, those who engage in this form of inquiry have assumptions about testing theories deductively, building in protections against bias, controlling for alternative or counterfactual explanations, and being able to generalize and replicate the findings. (p. 4) Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: SAGE Publications.

To elaborate, Creswell’s definition highlights key aspects of quantitative research, emphasizing its focus on testing objective theories by examining relationships among variables. In this approach, variables are measurable and quantifiable, allowing researchers to gather numerical data that can be systematically analyzed using statistical methods.

Quantitative research is grounded in a positivist paradigm, which assumes that there is an objective reality that can be measured and understood through empirical observation. By employing standardized and structured instruments, such as surveys and experiments, researchers seek to minimize subjective biases and ensure the reliability and validity of their findings.

The process typically involves the formulation of specific hypotheses derived from existing theories, which are then tested through the analysis of data. This deductive approach enables researchers to confirm, refute, or refine their theoretical assumptions based on empirical evidence.

Statistical procedures play a crucial role in quantitative research, as they help identify patterns, trends, and relationships among variables. Descriptive statistics provide an overview of the data, while inferential statistics allow researchers to make generalizations from their sample to the broader population.

In summary, Creswell’s definition of quantitative research emphasizes its objective nature, the examination of relationships among measurable variables, and the use of statistical procedures for data analysis. This approach is instrumental in generating evidence-based insights, informing decision-making processes, and advancing knowledge across various fields.

For more, check out this detailed post titled What is Quantitative Research According to Creswell?

Quantitative Research According to Punch

Punch (1998) contrasts quantitative research with qualitative research stating that the earlier represents “empirical research where the data are in the form of numbers” and the latter represents an “empirical research where the data are not in the form of numbers” (p. 4).

As you can see, Punch’s definition of quantitative and qualitative research provides a straightforward distinction between the two methodologies based on the type of data collected.

Quantitative research, as Punch defines it, relies on numerical data. This approach allows for precise measurements, statistical analysis, and the identification of patterns, trends, and relationships among variables.

Quantitative research, as I stated earlier, is often grounded in the positivist paradigm, which assumes an objective reality that can be studied and understood through empirical observation. Examples of quantitative research methods include surveys, experiments, and structured observations.

On the other hand, qualitative research focuses on non-numerical data, such as words, images, or actions. This approach aims to capture the complexity and richness of human experiences and social phenomena.

Qualitative research is often rooted in the interpretivist or constructivist paradigm, which acknowledges that reality is subjective and co-constructed by individuals through their experiences and interpretations. Examples of qualitative research methods include interviews, focus groups, ethnography, and content analysis.

In summary, Punch distinguishes quantitative and qualitative research based on the nature of the data collected, with the former involving numerical data and the latter focusing on non-numerical data. This distinction reflects the different epistemological assumptions, research methods, and analytical approaches employed in each methodology.

3.Quantitative Research According to Leavy Patricia

According to Leavy Patricia (2022), Quantitative research :

“values breadth, statistical descriptions, and generalizability. Quantitative approaches to research center on achieving objectivity, control, and precise measurement. Methodological, these approaches rely on deductive designs aimed at refuting or building evidence in favor of specific theories and hypotheses. Marianne Fallon (2016) refers to quantitative research as a ‘top down process’ (p. 3). Quantitative approaches are most commonly used in explanatory research investigating causal relationships, associations, and correlations.” (p. 99) Patricia, L. (2022). Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches. Guilford Publications.

In this excerpt, Leavy (2022) characterizes quantitative research as an approach that values breadth, statistical descriptions, and generalizability. The focus of quantitative research is on achieving objectivity, control, and precise measurement, which is achieved through the use of structured and standardized methods. This approach is grounded in a deductive research design, which starts with theories and hypotheses that are then tested and validated or refuted based on empirical evidence.

Fallon (2016, cited by Leavy) describes quantitative research as a “top-down process” (p. 3), which emphasizes the importance of established theories and prior research in guiding the formulation of new hypotheses. This approach allows researchers to build upon existing knowledge and refine theoretical frameworks.

Quantitative research according to authors

Quantitative research is particularly well-suited for explanatory research, as it seeks to uncover causal relationships, associations, and correlations among variables. By employing rigorous sampling techniques and statistical analyses, quantitative researchers can identify patterns and relationships in the data, which can then be generalized to the broader population.

In conclusion, Leavy (2022) highlights the key aspects of quantitative research, emphasizing its focus on breadth, statistical descriptions, generalizability, objectivity, control, precise measurement, and explanatory power. This approach provides valuable insights into causal relationships and associations, contributing to the advancement of knowledge across various fields.

4.Quantitative Research According to Kothari

Let me share with you this lengthy passage by Kothari (2004) explaining quantitative research. According to Kothari (2004), quantitative research:

involves the generation of data in quantitative form which can be subjected to rigorous quantitative analysis in a formal and rigid fashion. This approach can be further sub-classified into inferential, experimental and simulation approaches to research. The purpose of inferential approach to research is to form a database from which to infer characteristics or relationships of population. This usually means survey research where a sample of population is studied (questioned or observed) to determine its characteristics, and it is then inferred that the population has the same characteristics. Experimental approach is characterised by much greater control over the research environment and in this case some variables are manipulated to observe their effect on other variables. Simulation approach involves the construction of an artificial environment within which relevant information and data can be generated. This permits an observation of the dynamic behaviour of a system (or its sub-system) under controlled conditions. The term ‘simulation’ in the context of business and social sciences applications refers to “‘the operation of a numerical model that represents the structure of a dynamic process. Given the values of initial conditions, parameters and exogenous variables, a simulation is run to represent the behaviour of the process over time.” Simulation approach can also be useful in building models for understanding future conditions. (p. 5) Kothari, C. R. (2004). Research Methodology: Methods & Techniques. New Age International.

Kothari (2004) provides a comprehensive overview of quantitative research, emphasizing its focus on generating data that can be subjected to rigorous quantitative analysis in a formal and rigid manner. The author further categorizes quantitative research into three sub-approaches: inferential, experimental, and simulation.

1. Inferential approach: This approach is commonly used in survey research, where a sample of the population is studied to determine its characteristics. Researchers then infer that the larger population shares these characteristics. The goal is to understand the population’s characteristics or relationships based on the analyzed data from the sample.

2. Experimental approach: This approach is characterized by greater control over the research environment, where variables are manipulated to observe their effects on other variables. Experimental research is used to establish cause-and-effect relationships and often involves controlled settings and random assignment of participants to different conditions.

3. Simulation approach: This approach entails creating an artificial environment to generate relevant data and observe the dynamic behavior of a system or its sub-systems under controlled conditions. In the context of business and social sciences, simulation refers to the operation of a numerical model representing the structure of a dynamic process. This approach helps in building models for understanding future conditions and predicting potential outcomes.

In summary, Kothari (2004) delineates quantitative research as a method that generates and analyzes data in a systematic, rigorous manner, further sub-dividing it into inferential, experimental, and simulation approaches. Each sub-approach offers unique insights and techniques for understanding various aspects of the phenomena under investigation.

5. Quantitative Research According to Williams, Malcolm, et al.

Williams et al. (2022) define quantitative research as:

investigations in which the data that are collected and coded are expressible as numbers. By contrast, studies in which data are collected and coded as words would be instances of qualitative research. Weightier distinctions have also been important in discussions of research methods – distinctions bordering on epistemologies, worldviews and ontologies, to name a few… Quantitative research is grounded in the scientific tradition, so description and inference with the potential to lead to causal explanation and prediction are its core business. Its methods are those of the experiment, the social survey or the analysis of official statistics or naturally occurring data. It can take many forms from a local neighbourhood survey to large-scale population surveys with several thousand people taking part. It may be a carefully controlled experiment in a laboratory, or it might be ‘big-data’ analysis of millions of Twitter feeds. (p. 3) Williams et al. (2022). Beginning Quantitative Research. SAGE Publications, Limited.

In this passage, Williams et al. (2022) provide a rule-of-thumb definition of quantitative research as investigations where the collected and coded data can be expressed as numbers, while qualitative research deals with data collected and coded as words. The authors acknowledge that more profound distinctions exist, touching upon epistemologies, worldviews, and ontologies.

Quantitative research is rooted in the scientific tradition, focusing on description and inference, with the potential to lead to causal explanation and prediction. The methods employed in quantitative research include experiments, social surveys, and the analysis of official statistics or naturally occurring data.

The scope of quantitative research can vary widely, from small-scale neighborhood surveys to large-scale population studies involving thousands of participants. It can also encompass controlled experiments in laboratories or the analysis of vast amounts of data, such as millions of Twitter feeds, commonly referred to as “big data.”

In summary, Williams et al. (2022) highlight the numerical nature of quantitative research and its grounding in the scientific tradition. This approach aims to describe, infer, and potentially explain causal relationships and make predictions using various methods, ranging from small-scale surveys to large-scale big data analysis.

After examining the various definitions of quantitative research provided by different scholars, we can conclude that quantitative research is a systematic and empirical approach to investigating phenomena, which is grounded in the scientific tradition and positivist paradigm. The key aspects of quantitative research include:

1. The collection and analysis of numerical data, often obtained through structured and standardized methods, such as surveys, experiments, or analyzing naturally occurring data.

2. A focus on objectivity, control, precision, generalizability, and the establishment of cause-and-effect relationships, associations, or correlations.

3. The use of deductive reasoning, where research begins with theories and hypotheses that are then tested and validated or refuted based on empirical evidence.

4. The employment of statistical procedures to analyze data, identify patterns, trends, and relationships, and make inferences or predictions about the broader population.

Quantitative research plays a vital role in advancing knowledge across various fields by providing evidence-based insights, informing decision-making processes, and building upon existing theories.

While the definitions and perspectives provided by different scholars may emphasize specific aspects of quantitative research, they all converge on its core characteristics, including the systematic collection and analysis of numerical data, the pursuit of objectivity and generalizability, and the reliance on statistical procedures for data interpretation.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: SAGE Publications.
Kothari, C. R. (2004). Research Methodology: Methods & Techniques . New Age International.
Patricia, L. (2022). Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches . Guilford Publications.
Punch, K. F. (1998). I ntroduction to social research: Quantitative and qualitative approaches . Thousand Oaks, CA: SAGE Publications.
Williams, et al. (2022). Beginning Quantitative Research . SAGE Publications, Limited.

Two other interesting works to consider are:

Tashakkori, A. & Teddlie, C. (2009). Integrating Qualitative and Quantitative Approaches to Research. In Bickman,l. & Debra J. Rog. (Eds.). T he SAGE Handbook of Applied Social Research Methods . SAGE Publications, Inc.
O’Leary, Z. (2009) The Essential Guide to Doing Your Research Project. London: Sage
8 Good Books on Quantitative Research , Selected Reads

what is quantitative research according to Creswell

Meet Med Kharbach, PhD

Dr. Med Kharbach is an influential voice in the global educational landscape, with an extensive background in educational studies and a decade-long experience as a K-12 teacher. Holding a Ph.D. from Mount Saint Vincent University in Halifax, Canada, he brings a unique perspective to the educational world by integrating his profound academic knowledge with his hands-on teaching experience. Dr. Kharbach's academic pursuits encompass curriculum studies, discourse analysis, language learning/teaching, language and identity, emerging literacies, educational technology, and research methodologies. His work has been presented at numerous national and international conferences and published in various esteemed academic journals.

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Open access
Published: 26 April 2022

Definition and conceptualization of the patient-centered care pathway, a proposed integrative framework for consensus: a Concept analysis and systematic review

Jean-Baptiste Gartner 1 , 2 , 3 , 4 , 5 ,
Kassim Said Abasse 1 , 2 , 3 , 5 ,
Frédéric Bergeron 6 ,
Paolo Landa 3 , 7 ,
Célia Lemaire 8 &
André Côté 1 , 2 , 3 , 4 , 5

BMC Health Services Research volume 22 , Article number: 558 ( 2022 ) Cite this article

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Confusion exists over the definition of the care pathway concept and existing conceptual frameworks contain various inadequacies which have led to implementation difficulties. In the current global context of rapidly changing health care systems, there is great need for a standardized definition and integrative framework that can guide implementation. This study aims to propose an accurate and up-to-date definition of care pathway and an integrative conceptual framework.

An innovative hybrid method combining systematic review, concept analysis and bibliometric analysis was undertaken to summarize qualitative, quantitative, and mixed-method studies. Databases searched were PubMed, Embase and ABI/Inform. Methodological quality of included studies was then assessed.

Forty-four studies met the inclusion criteria. Using concept analysis, we developed a fine-grained understanding, an integrative conceptual framework, and an up-to-date definition of patient-centered care pathway by proposing 28 subcategories grouped into seven attributes. This conceptual framework considers both operational and social realities and supports the improvement and sustainable transformation of clinical, administrative, and organizational practices for the benefit of patients and caregivers, while considering professional experience, organizational constraints, and social dynamics. The proposed attributes of a fluid and effective pathway are (i) the centricity of patients and caregivers, (ii) the positioning of professional actors involved in the care pathway, (iii) the operation management through the care delivery process, (iv) the particularities of coordination structures, (v) the structural context of the system and organizations, (vi) the role of the information system and data management and (vii) the advent of the learning system. Antecedents are presented as key success factors of pathway implementation. By using the consequences and empirical referents, such as outcomes and evidence of care pathway interventions, we went beyond the single theoretical aim, proposing the application of the conceptual framework to healthcare management.

Conclusions

This study has developed an up-to-date definition of patient-centered care pathway and an integrative conceptual framework. Our framework encompasses 28 subcategories grouped into seven attributes that should be considered in complex care pathway intervention. The formulation of these attributes, antecedents as success factors and consequences as potential outcomes, allows the operationalization of this model for any pathway in any context.

Peer Review reports

While having a performant healthcare system is a crucial issue for every country, the health sector operates in silos that need to be challenged. Indeed, many authors have pointed to fragmented care processes as a cause of breakdowns in the continuity of healthcare services [ 1 ], unnecessary waiting times [ 2 , 3 ], flaws in the flow of information between the different episodes [ 4 ] and the realization of exams that may be superfluous [ 5 ]. This fragmentation results in a sub-optimal use of material and financial resources and unsatisfactory team management [ 4 ]. Based on this observation, several repeated calls to improve the quality and performance of healthcare services have been made since 2001 by national and international institutions such as the Institute of Medicine of America (IOM) in 2001 [ 6 ] and 2013 [ 7 ], the National Academies of Sciences, Engineering, Medicine in 2018 [ 8 ] and the World Health Organization (WHO) in 2016 [ 9 ] and 2020 [ 10 ]. These calls have progressively shifted from an injunction to improve quality based on criteria to provide safe, effective, efficient, timely, equitable and patient-centered care [ 6 ], to the development of models for the organization of health care and services that meet the current challenges of effectiveness and efficiency in healthcare systems. The WHO urges member countries to base their quality improvement policies on the entire continuum of care, taking into account at least the criteria of effectiveness, safety, equity, efficiency, integrated care and timeliness [ 11 ]. These calls also emphasize the need to improve care pathways by focusing on outcomes that matter to the patient from a clinical, quality of life and health system experience perspective [ 12 , 13 , 14 , 15 ], rather than on the needs of the production units. This change of perspective leads to the study of the redesign of performance evaluation models by focusing on the needs and expectations of the patient [ 16 , 17 ]. The problem is that there is confusion about the definition and characterization of a care and health service pathway. Indeed, Bergin et al. [ 2 ] identified 37 different definitions of the term care pathway based on a review of the literature. Definitions and characteristics vary across countries and include multiple phases ranging from prevention or screening to cure or palliative care. This confusion has led to wide variability in the outcomes of these interventions, resulting in underutilization of care pathway improvement programs [ 2 ]. Furthermore, such confusion leads to great variability in the analysis and modeling of care pathways. For example, in their scoping review, Khan et al. [ 18 ] showed the great variability that exists among studies of oncology care pathways in both the phases of care represented, and their characteristics. The lack of a common definition and clearly defined criteria leads to a lack of standardization, resulting in an inability to conduct reliable comparative studies of care pathway programs internationally [ 19 ].

The Oxford Concise Medical Dictionary 10th ed. [ 20 ] and the Oxford Dictionary of Nursing 8th ed. [ 21 ] define, in a concise way, care pathway as “a multidisciplinary plan for delivering health and social care to patients with a specific condition or set of symptoms. Such plans are often used for the management of common conditions and are intended to improve patient care by reducing unnecessary deviation from best practice”. The concept of a care pathway is one originally used in the field of Health Operations Management, whose definition was proposed by Vissers and Beech [ 22 ]. However, these definitions seem to be too imprecise and address neither the aim nor the social reality of implementing such pathways. The European Pathway Association (EPA) adopts the more precise definition from the 2007 thesis of Vanhaecht [ 23 ]. However this has not yet led to an international consensus, as confusion over the concepts remains high. Moreover, this definition does not clearly define the antecedents or factors favoring the success of such interventions, the means by which to implement them or the best practices through which to support them; nor does it sufficiently take into account the importance of the patient-centered care and patient-centered services approach. Similarly, the proposed implementation models largely neglected the social reality and the social dynamic of organizations [ 24 ], resulting in major implementation difficulties, as care pathways still being considered as complex interventions [ 25 , 26 ].

However, care pathway programs have recently demonstrated encouraging results in terms of reduced variation in care, improved accessibility, quality, sustainability, and cost effectiveness of care [ 2 ]. The definition we aim to develop through this research is significant and timely, in that it has the potential to guide the ongoing development, implementation, monitoring and evaluation of care pathway programs within the rapidly changing service and system contexts that we are experiencing. For example, the following initial barriers to the systemic and holistic implementation of care pathways have recently been removed. Firstly, limited access to valid and reliable data from multiple organizations [ 27 ] has been offset by a massive investment in Electronic Medical Records [ 28 ]. Secondly, the main difficulties in highlighting the complexity of the referral trajectory [ 29 ], frequently resulting from the clinicians’ perspective, have been overcome by proposing new approaches such as data mining or qualitative methods, focusing on the real care trajectory and the qualitative part of the patients’ experience [ 16 , 17 , 30 ]. Therefore, the evolution of knowledge and information technology and the investment of health systems in data-sharing infrastructure, as well as a definition of the levers of patient engagement and the advent of patient-centered-care and patient-centered services, make it possible to define a powerful model for improving them by placing the patient’s needs and expectations at the center of the care pathway. It is therefore the right time to define a recognized definition and an integrative conceptual framework that meets the demand for sharing knowledge internationally regarding the development, implementation, and evaluation of care pathways.

The concept of patient-centered care is defined as “care provision that is consistent with the values, needs, and desires of patients and is achieved when clinicians involve patients in healthcare discussions and decisions” [ 31 ]. This approach is known to provide benefits by improving health outcomes, patient satisfaction, but also to reducing health costs [ 32 ].

A preliminary search for existing reviews was conducted in Cochrane Database, JBI Database of Systematic Reviews and Implementation Reports and PROSPERO. Care pathways have been the subject of few reviews, but these were limited to a single pathology such as cancer in general [ 33 ], blunt thoracic injury [ 34 ], cardiovascular disease [ 35 ], adolescent idiopathic scoliosis [ 36 ] or for particular pathway phases [ 37 ]. In the end, focusing on a single condition is not entirely consistent with a patient-centered approach to care insofar as patients often have comorbidities. The only review that did not focus on one specific pathology was made in 2006 [ 38 ] and was interested in the concept of clinical pathway. Authors reviewed literature published within 3 years using only one bibliographic database. Therefore, the aim of this article is to propose an accurate and up-to-date definition of care pathway and to develop an integrative conceptual framework for the patient-centered care pathway concept in a holistic operational approach of the concept.

Combining systematic review, concept analysis and bibliometric analysis

To achieve a fine-grained understanding of the concept, we have chosen a hybrid method combining the systematic review, the concept analysis and the bibliometric analysis methodologies. We followed the latest PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement for conducting and reporting a systematic review [ 39 ]. However, the systematic review methodology presents some limitations on the qualitative analysis of literature, hence derives our interest to use Concept analysis. Concept analysis [ 40 ] aims specifically to clarify a specific concept including a semantic field linked to a specific theoretical framework. This approach is based on eight steps allowing to: (1) select the concept, (2) determine the aims or purposes of the analysis, (3) identify all uses of the concept, (4) determine the defining attributes, (5) identify a model case, (6) identify additional cases, (7) identify antecedents and consequences and (8) define empirical referents. However, this method does not provide a systematic and rigorous procedure for identifying and selecting relevant literature. Therefore, we decided to combine the strengths of both methods to overcome the limitations of each. In order to make our analysis more robust and to base our inferences, specifically in the comparative analysis of the related concepts, we performed a bibliometric analysis allowing us to link the attributes of each of the concepts to make a comparison.

Information sources and search strategy

We developed a search strategy, in collaboration with a Health Sciences Librarian who specializes in systematic literature review in healthcare, to identify relevant peer-reviewed studies. An initial limited search of MEDLINE and CINAHL was conducted, followed by analysis of the text words containing title and abstract and index terms used to describe the article. This informed the development of a search strategy that was tailored toward each information source. The search strategy was applied to the following databases: PubMed, Embase and ABI/Inform. The complete search strategy is provided in Additional file 1 .

Eligibility criteria

This review considers studies that focus on quantitative and/or qualitative data, with no limitation in terms of methodology. Our search focused on peer-reviewed scientific articles. Therefore, books, doctoral or master’s theses were excluded due to time and resource limitations. In order to guide the selection, we chose the Population, Context, Concept (PCC) mnemonic criteria [ 41 ]. The population considers all types of patients managed by healthcare delivery systems. The context studied is composed of healthcare providers in any geographic area, including all providers of primary, secondary, tertiary, and quaternary care. For the concept, this review focuses on theoretical and empirical studies that contribute to the definition and conceptualization of the different related concepts of care processes at the organizational or system level, such as care pathway, clinical pathway, patient journey and care processes. Quantitative, qualitative and mixed method studies involving a single episode of care limited in time (a one-time treatment) or space (a single hospital service/department) were excluded to the extent that care pathway involves multiple points of interaction over time [ 13 , 42 ] and multiple organizational structures or intra-organizational entities along the care continuum [ 43 ]. In addition, studies with no theoretical or conceptual input were excluded. Finally, there was no language or geographic restrictions applied to the search, and the study period was limited from 1995 to 2020.

These studies were imported into the Covidence® software (version 2020). The team developed screening questions and forms for levels 1 (abstract) and 2 (full text) screening based on the inclusion and exclusion criteria. Two reviewers independently screened the titles and abstracts. In case of disagreement, two senior reviewers decided after analysis and discussion. Review author pairs then screened the full-text articles against inclusion and exclusion criteria. In case of disagreement, the same process as for the title and abstract selection was implemented. Reasons for excluding studies were recorded.

Assessment of methodological quality

Because of the heterogeneity of the methods used in the selected articles, we decided to use a separate appraisal tool for each study type. The following appraisal tools were selected for their clarity, relevance, and because their items covered the most common assessment criteria comparing to other tools:

For qualitative studies: the JBI Qualitative Assessment Research Instrument (QARI) [ 41 ]

For surveys: the Center for Evidence Based Management (CEBMa) Appraisal Questions for a Survey [ 44 ]

For descriptive cross-sectional studies: the Institute for Public Health Sciences 11 questions to help you make sense of descriptive/cross-sectional studies [ 45 ]

For mixed-method: the scoring system for appraising mixed methods research [ 46 ]

No articles were excluded from this systematic review due to the weaknesses of their methodological quality, so as not to exclude valuable information [ 47 ].

Data extraction and analysis

Descriptive numerical summary analysis followed the systematic review guidelines, and the following items were systematically extracted: Reference, Title, First Author country, Case country, Year of publication, Type of publication, Target patient population, Phases of the pathway included, People involved in the modeling process, Study parameters and level of analysis.

Qualitative data were extracted using MaxQDA® software (version 2020) by two independent analysts. The data extraction followed the concept analysis guideline [ 40 ] and the following items were systematically extracted: Variant concept studied, Concept uses, Concept definition, Concept attributes, Antecedents, Consequences and empirical referents. In order to develop a detailed analysis and arrive at a robust theoretical framework, we relied on general inductive analysis [ 48 ], consisting of coding, categorization, linking, integration and modeling. Each step has been validated by at least two senior authors.

A bibliometric analysis was performed with the complete texts of the 44 selected studies using Vosviewer® software (version 2020).

The systematic review was reported following the latest PRISMA statement for conducting and reporting a systematic review [ 39 ] and mobilized the PRISMA 2020 checklist (see Additional file 2 ).

The interrogation of the three databases resulted in 15,281 articles. Figure 1 details the selection process following the PRISMA 2020 statement [ 39 ]. After deleting the duplicates, 15,072 records were reviewed but only 44 publications ultimately met the inclusion and exclusion criteria.

PRISMA 2020 flow diagram of the systematic review process

Description and methodological quality appraisal of studies

A summary table containing a brief description of selected studies and their evaluation results for methodological quality is presented in Table 1 . Quality appraisal of selected studies is presented in Additional file 3 .

Published articles, describing care pathways as multiple points, in time and space, of patient interaction appeared in the early 2000s. However, most of this work has been published since 2010, with a progressive and growing interest, whatever the theoretical position, to reach 22 articles in the last 3 years (see Fig. 2 ).

Frequency of selected publications over time

The countries of the first authors interested in this concept are predominantly anglophone such as the United Kingdom (k = 9), Australia (k = 5), the United States (k = 4), and Canada (k = 3). Researchers from other countries are less represented.

Three types of publications were found; 34 were original research studies, eight were literature reviews and two were perspective studies. In the original research studies, 23 used a qualitative approach to study either the implementation of a care pathway program or patient experience of a care pathway, four used a descriptive cross-sectional approach, four used a mix-method approach and three used a survey.

Since the definition of the concept is still unclear and terminology is important, the studies meeting the selection criteria reported several terminologies. The most frequently used terms in the selected studies were the patient journey (k = 14) and the care pathway (k = 13) with their some country-specific modifications namely integrated care pathway mainly in the United Kingdom [ 73 , 74 ], optimal care pathway in Australia [ 2 ] and standardized care pathway in Sweden [ 15 ]. The other terms used were clinical pathway (k = 8), patient-centered care (k = 4), care process (k = 3), disease pathway management (k = 1) and value-based integrated care (k = 1).

Studies focused mainly on the care of chronic conditions (k = 24), followed by acute diseases (k = 11). Of those with a chronic care focus, cancer was by far the most studied disease (k = 10), followed by stroke, hearing impairment and mental disease. Acute care studies covered, articular pathologies of the hip and knee, and pregnancy.

Concerning the level of the study, most addressed the systemic (k = 31) rather than the organizational (k = 13) level. Most authors, in their approach to the concept, largely focused on the treatment phase (k = 39), but some included, more or less, pretreatment and subsequent phases. Only seven articles took a global approach starting from the prevention phase and screening to survivorship or palliative care phase.

Concept analysis results

The conceptual analysis followed an automatic data extraction method in the proposed main categories and then, after several iterations, resulted in a coding of subcategories grouped into main themes. The detailed results of the coding are presented in Additional file 4 .

Concept uses

Uses of the concepts of care pathway have evolved in the literature over time with a strong tendency to focus on the care pathway at the systemic level. Main objectives have been improving quality and safety (k = 26), improving efficiency in the delivery of care (k = 24), optimizing the delivery process through an operation management point of view (k = 22) and integrating best practices through guidelines and evidence-based medicine (k = 17). These objectives were widely shared and present throughout the period. However, interest emerged in 2009 and quickly grew, in improving the patient experience through the analysis of the patient journey (k = 17). To a lesser extent, the goals of developing patient-centered care (k = 13), improving patient outcomes (k = 13), improving coordination of service delivery (k = 13), and standardizing care delivery (k = 12) were also present. Beyond standardization, reduced variation in care practices (k = 9) was not well addressed, nor was continuous performance assessment (k = 8). The aim of meeting the patient’s needs (k = 6) has been addressed more frequently in recent years, since its first appearance in 2011 [ 71 ], and is considered of crucial importance by some authors. Other concept uses were proposed, such as to improve interprofessional collaboration (k = 5), support changes (k = 5), support clinical decision making (k = 4), improve communication (k = 3), consider needs of healthcare workers, improve referral system, define shared purposes and meaningful objectives (k = 2), monitor staff compliance, support the knowledge management, improve patient and family member access to information, adopt a system approach and understanding power dynamics and relational factors (k = 1). As described previously, these concept uses came mainly from the chronic disease care context, although acute care was also represented.

Defining attributes

Definitional attributes are features commonly encountered in definitions of the concept or frequently used to describe it [ 40 ]. Twenty-eight attributes were inductively extracted and categorized into seven main themes, ordered by level of empirical importance: (1) The centricity of patients and caregivers; (2) the positioning of professional actors involved in the care pathway; (3) the operation management through the care delivery process; (4) the particularities of coordination structures; (5) the structural context of the system and organizations; (6) the special role of the information system and data management; and (7) the advent of the learning system (k = 3).

Attribute theme 1: The centricity of patients and caregivers

Firstly, there has been a growing interest in the patient experience (k = 15), mainly through the concept of the patient journey [ 5 , 13 , 14 , 15 , 24 , 30 , 42 , 51 , 52 , 58 ], which has progressively emerged as the third pillar of quality in healthcare with clinical effectiveness and patient quality and safety [ 30 ]. It is formed by all the interactions at the meeting point, or point of contact, between health services and patient [ 14 , 30 , 42 , 51 ]. However, taking the patient experience into account is complex insofar as it requires a detailed understanding of what influences it. Therefore, some authors have defined the dimensions that can influence the patient experience as the temporal dimension, meaning that accessibility and short waiting times are valued [ 13 , 15 , 30 , 42 , 51 ], the spatial dimension [ 30 ], and the geographical position of the services [ 42 ], the emotional dimension [ 13 , 30 , 42 ] and the social and cognitive dimensions [ 13 , 42 ]. All these dimensions can be the source of both positive outcomes [ 13 , 30 ] and negative outcomes [ 15 ] or for socio-political authors, a feeling of considerable disempowerment [ 53 ]. Although authors are increasingly interested in it, the patient experience is still sometimes overlooked [ 14 ].

Patient information and education (k = 15) were addressed in numerous studies. Patient information contributes to the quality of the patient experience [ 3 , 15 , 36 , 42 , 53 , 64 , 71 , 75 ]. Beyond the simple satisfaction, the provision of information, at an appropriate health literacy level, increases patient awareness [ 36 , 51 ] and thus increases patient education. This results in a better detection of the symptoms at an early stage by the patient [ 3 , 36 ], the development of the “expert patient” [ 51 , 57 , 58 , 71 ], which aids adherence to treatment, supports shared decision-making [ 57 ] and improves self-management [ 51 , 58 ]. However, many empirical studies showed there to be a lack of patient information throughout patient journeys [ 5 , 14 , 15 , 42 , 51 , 53 , 64 ].

Patient engagement (k = 15) was an important attribute of this theme in the more recent literature. The management by the patient of his or her care treatment plan has become increasingly important [ 24 , 50 , 51 , 53 , 67 ]. This translates into shared decision-making on care and treatment [ 3 , 14 , 24 , 35 , 51 , 53 , 55 , 54 , 55 , 58 , 64 , 65 ]. According to Devi et al. [ 51 ], this process can only be viable if supported by good information about treatment possibilities and possible outcomes. However, socio-political authors see this as a major issue of patient empowerment, which is “seen as a solution to many of the most pressing problems facing modern healthcare” [ 53 ].

Proposed only since 2014, and strongly present in the last 3 years, relationship as the basic need (k = 9) is also a subject of interest. Part of the patient experience, the relational quality reflects how patients perceive their interactions [ 13 , 42 ]. Some empirical studies have shown that a poor relationship can negatively affect other processes and tasks [ 3 , 5 ]. Therefore, quality of the relationship seems a fundamental prerequisite [ 14 , 64 ]. For this reason, some authors have placed the notion of trust as essential to the quality of interactions and to the patient’s follow-up through the care pathway [ 3 , 12 , 58 ].

Patient and Public Involvement (k = 9) is part of these new topics. Its importance in the design and improvement of the care pathway is supported by some international organizations [ 9 ]. The objective is to improve the quality of care provided by assessing patients’ perceptions [ 12 , 13 ]. In this way, the design of care delivery can be based on the real needs and expectations of patients [ 12 , 13 , 51 , 56 , 62 ]. However, some models have been criticized as tokenistic rather than being viable solution for balancing power between patients and health care providers [ 53 ].

Although the stated goal of care pathways incorporates an approach aimed at standardizing care practices, several authors have raised the need for individualized care (k = 8). Joosten et al. [ 74 ] saw a potential conflict between standardization and the demand for a personalized approach to healthcare. However, several authors have subsequently agreed that there is still room for individualization of care beyond the standardization [ 55 ], in particular through the definition of personalized treatment goals [ 51 ], or even maintaining flexibility in the interaction to better adapt to the patient’s specific needs [ 64 , 65 ].

Developed only since 2016, the importance of psychosocial support (k = 8) has increased rapidly. Although the need has been clearly identified and documented [ 5 , 15 , 42 , 58 ] and many international guidelines have integrated it, it seems that its translation within the care pathway is still complex [ 62 ] and no obvious answer was provided.

The inclusion of family and caregiver (k = 8) is also a new topic of the last 5 years which highlights the potential of family or caregivers involvement in decision-making [ 50 , 51 , 57 , 65 ]; notably by supporting both the integration of information and personal decision-making [ 14 , 15 ].

Attribute theme 2: The positioning of professional actors involved in the care pathway

Firstly, most authors consider the care pathway as a tool to develop patient-centered care (k = 18). The patient-centered care approach has a disease-specific orientation [ 25 ] and considers the patient as a real partner [ 51 , 25 ]. In doing so, this approach recognizes an individual’s specific health needs and preferences as the driving force in all healthcare decisions [ 13 , 51 , 65 , 67 ]. Thus, professional actors emphasize their accessibility and their attitudes and behaviors towards patients [ 13 ]. In addition, this approach considers the importance of integrating family and caregivers and is recognized as a necessary attribute of healthcare quality [ 65 ]. Finally, its implementation seems to improve patient satisfaction by moving toward an individualized therapy approach and personalized treatment goals [ 51 ].

Not surprisingly, multidisciplinary team-working (k = 17), and attribute which is consistent with previous definitions, is supported by several authors. The enrollment of all professional categories involved directly or indirectly in the care pathway at all steps is valued [ 2 , 50 , 75 ]. The multidisciplinary teamwork allows tackling the complexity of patient care across the pathway and developing a shared understanding supported by knowledge sharing among professionals [ 53 , 72 ]. In addition, it allows outlining the optimal sequence and timing of interventions [ 38 , 59 ] and to focus only on patient needs and engagement rather than on problems of a particular profession [ 56 ]. From an operational view, multidisciplinary care teams make it possible to share formal screening between disciplines [ 62 ]. Recently, multidisciplinary engagement was identified as a mandatory prerequisite for successful care pathway programs [ 24 , 50 ].

Staff skills (k = 10) could be considered equally important for care pathways. However, they were not addressed in this literature before 2014. Authors gave little attention to technical skills, except to point out possible deficiencies, particularly in diagnosis [ 3 , 13 ], but also in training [ 3 ]. Rather, authors focused almost exclusively on interpersonal skills [ 3 , 12 , 13 , 15 , 51 , 64 ], which were considered critical, both in the relations between professionals [ 12 , 15 , 51 , 56 , 64 ] as well as those with patients and their caregivers [ 15 , 51 , 64 ]. Interpersonal skills could be seen as facilitators or barriers to the patient experience [ 64 ]. Some authors have recently suggested that peer cooperation was critical [ 5 , 50 , 56 ] and that creating a culture of mutual respect among both medical and administrative colleagues can ultimately improve the fluidity of care [ 3 , 5 ].

Few authors have highlighted that the implementation of a care pathway leads professionals to examine their roles and responsibilities (k = 6). The need to define each step in the care process requires professionals to describe precisely the tasks and roles of professional actors [ 25 ]. In doing so, it creates a rare opportunity to step back from daily tasks and reassess competences, roles and responsibilities [ 12 , 51 , 73 ].

Finally, very recently, authors have been interested in the experience of staff (k = 2) in care pathway programs. These authors have demonstrated the link between staff experiences and their individual performance [ 24 , 53 ]. They therefore support the idea that staff well-being is directly related to engagement and performance and, thus, a negative staff experience can influence patient, clinician, and organizational outcomes.

Attribute theme 3: The operation management through the care delivery process

This analysis has shown, unsurprisingly, that the process approach to care delivery (k = 23) was the core of the care pathway approach across the literature to date. From an engineering perspective, as define by the International Organization for Standardization, a process is “a set of interrelated or interacting activities that transforms inputs into outputs” (ISO 9000:2000 clause 3.4.1). Through this approach, the care process can be defined as an arrangement of tasks or actions sequenced in time resulting in a time matrix [ 24 , 30 , 38 , 52 , 60 , 68 , 25 , 73 ]. What distinguishes the different process approaches to care delivery are the tasks and actions included with them. Some authors tend to focus on operational planning by treating tasks, actions and their timing through business processes [ 43 , 49 , 54 , 60 , 69 ], while other authors consider both the context of action through the physical and organizational environment [ 24 , 30 ] and social dynamic through the experience of actors [ 24 , 52 , 53 ]. Through this approach to care processes, some authors focus on patients and caregivers [ 52 ] and other authors focus on human actors, both patients and caregivers and the professional actors involved in the care pathway [ 24 ]. In 2018, Ponsignon et al. [ 13 ] proposed to differentiate the direct, indirect and independent interactions (those disconnected from the delivery system), in care processes. Direct interactions constitute the points of contact between patients and the system, and so are responsible, along with indirect interactions, for the patient version of the pathway that some authors call the patient journey [ 5 , 13 , 30 , 51 , 53 ]. More recently, the complexity of the care process has led some authors to consider that the care pathway should involve pathway rules which control the process [ 70 ]. Thus, decision-making becomes a central element in the smooth running of the care pathway [ 60 ]. In addition, many authors consider that healthcare decisions and care pathways are intertwined so that it becomes imperative to co-design both care pathways and the decision-making activities [ 60 ].

The issue of process management for the delivery of care naturally raises the question of process modeling methods (k = 18). In the empirical articles, the use of the Business Process Modeling Notation (BPMN) developed by the Object Management Group seems to be progressively imposed, sometimes improved by decision modeling [ 4 , 43 , 54 , 60 , 68 , 69 ]. The use of process mapping or flowcharts with sometimes less formal rules seems to be favored for global approaches to processes, especially for the patient journey, although some authors such as Combi et al. [ 60 ], have demonstrated that BPMN modeling was quite compatible with the systemic approach.

For healthcare service designers, the methods for building care pathways are important considerations. Several methods exist, but all involve the discovery of a different path, thus change is inevitable and change management a necessity. The initial method came mainly from the expertise of professionals through interviews, focus groups or Delphi methods [ 49 , 59 ]. The advantage of collaboration with staff and experts is that more information can be gathered about certain decisions and possible variances from the pathway [ 49 ]. However, this method did not consider the real trajectory or the ideal pathway but rather the one integrating the constraints of the professionals. Since these early efforts, data driven approaches has developed considerably [ 43 , 49 ]. Their advantage is that they inform pathway development from data derived factually and objectively from actual occurrences of the pathway [ 49 ]. Moreover, data on the perspectives of patients through experience mapping, interviews, focus groups or observations [ 5 , 13 , 30 ], and patient shadowing [ 53 ] can be integrated to better reflect the real trajectory and to define the ideal pathway according to the needs and expectations of patients and caregivers. However, this approach does not allow for the integration of context and organizational constraints. Finally, few authors adopt an approach that consists of comparing the experience of professionals and patients, making it possible to define the lived experience, the patient’s journey, and its confrontation with operational realities and constraints through the experience of professionals [ 1 , 3 , 4 , 15 , 65 , 71 ].

Regarding the process of care delivery, the management of operations aims to integrate the organization of the delivery process with its ongoing improvement (k = 11) by focusing as much on analyzing the variations as on eliminating the wastes [ 74 ]. Process improvement tools serve as much to redesign the processes as define a workflow management system to monitor the care pathway [ 4 ]. The information generated [ 60 , 61 , 63 ] can be used for process re-engineering, objective reassessment or supporting non-clinical decision-making [ 60 ], such as the identification of bottlenecks [ 61 , 67 ] or highlighting interfacing problems between organizations [ 61 ]. The output generated by the analysis of the process-related data allows defining standardized expedited diagnostic processes [ 4 , 60 ]. Finally, the data obtained allows the use of simulation and optimization models. On this subject, Aspland et al.’s literature review [ 49 ] provides an exhaustive review of available methods.

Attribute theme 4: The particularities of coordination structures

In line with most of the definitions, the integration of the clinical practice guidelines, based on evidenced-based medicine, into the care pathway (k = 24) has been accepted since the beginning of such programs. The clinical decisions directly affect the flow of the care delivery process and thus the process performance and the quality of outcomes [ 60 ]. Therefore, the adherence to clinical practice guidelines must support decision-making [ 70 , 73 ] and aid diagnosis and treatment in order to improve patient outcomes [ 50 , 51 , 58 ]. In 2010, Vanhaecht et al. [ 25 ] expressed concern about a lack of evidence-based key interventions within care pathways. The care pathway can be an effective method to integrate and guarantee the appropriate use of evidence-based interventions and clinical practice guidelines [ 55 ] and may help to overcome two limitations of clinical practice guideline use, which are emerging as key issues [ 60 , 66 ]. Firstly, that they should not be followed blindly as they represent only explicit medical knowledge [ 67 ], but rather require integration of the contextual knowledge of healthcare professionals for appropriate use [ 72 ]. Secondly, it has been shown that physicians can be unaware of updates and changes to clinical guidelines [ 3 ], and so, integrating them into care pathway maps may improve guideline use and adherence. Finally, collectively integrating and discussing clinical practice guidelines appears to improve interprofessional collaboration and clarify roles [ 36 ], but also could benefit the involvement of patients in the co-design of the care pathway [ 35 ].

Some authors consider information continuity (k = 13) as a key factor. Not only because sharing information must support decision-making [ 60 , 75 ] and facilitate communication [ 2 , 12 , 38 ], but more broadly because the disruption of the information flow can lead to coordination problems and easily avoidable costs linked to the repetition of examinations [ 5 , 56 , 59 ]. Therefore, the continuity of information must be supported to ensure sustainable health improvements [ 51 , 70 ]. Some authors insist on the importance of defining an information medium throughout the pathway which is as accessible to care professionals as it is to patients and caregivers [ 65 ].

Recently, some authors have dealt with the subject of leadership of the care pathway (k = 9). The importance of defining a leader for each step of the care pathway was noted [ 25 ]. The lack of coordination without a responsible actor has been shown, especially when the care pathway includes actors in several contexts such as primary care [ 3 ]. Thus, new roles have been defined, such as case managers, joint program or nurse coordinators [ 4 , 15 , 42 , 65 ], roles that enhance coordination among providers through the improvement of the continuity and quality of the information as well as communication [ 15 ].

More recently, the integration of services (k = 9) has been addressed. Because the care pathway approach can involve multiple partnerships between organizations and primary care, it is essential to integrate all stakeholders. The integration needs to be both organizational, at the macro and meso-level through shared purpose and priorities [ 4 , 57 , 25 ] and shared governance mechanisms [ 4 , 12 , 14 , 59 ], and functional at the micro level through communication mechanisms and tools [ 4 , 12 , 14 ]. The unifying element is discussed between the shared interest for the patient [ 56 , 57 ] or the outcomes [ 12 ] to align strategic goals. For Louis et al. [ 56 ], achieving shared purpose is part of the structural context.

Finally, the care pathway is seen as a means of health knowledge management (k = 7) that optimizes quality, efficiency, and organization [ 68 , 70 , 72 ]. But this topic, although strongly addressed between 2011 and 2012, did not seem to be unanimously agreed upon because it was not very well addressed afterwards. However, particular attention can be paid to the elicitation and integration of the contextual knowledge of the various actors involved throughout the care pathway into daily healthcare routine [ 3 , 70 , 72 ].

Attribute theme 5: The structural context of the system and organizations

Firstly, the local physical context (k = 10), topical in the recent literature, includes both the number of units and their positions [ 12 , 67 ], but also the variety of services offered [ 13 ], and can be either an asset in terms of choice and accessibility or a constraint becoming a source of delay [ 14 ]. These barriers are important as the pathway crosses several formal healthcare organizations or informal care settings [ 24 ]. Therefore, the challenge of service integration has become essential [ 51 ].

Secondly, the availability of resources (k = 10) (human, material and financial) has a direct impact on the care pathway and the ability to meet the needs of the population [ 2 , 62 , 25 ]. A lack of adequate resources is an obvious obstacle to care pathways [ 50 ]. A lack of material and human resources, such as the availability of time at each service point [ 52 , 53 ], or the lack of an electronic medical record [ 5 ], meant the unnecessary repetition of history taking, examinations and full investigations. From a financial point of view, the financial and personal resources that people have, are also key to determinants of the care pathways followed by patients [ 51 ].

Thirdly, the social context (k = 7) is less addressed in the current literature but has shown rapid growth in recent years. Social structure includes material and social resources including roles, rules, norms, and values [ 3 , 24 , 53 , 68 ]. Some authors consider the social context as regularities of perception, behavior, belief and value that are expressed as customs, habits, patterns of behavior and other cultural artifacts [ 68 ]. Other authors consider that social structures shape people’s actions and that through people’s interactions they can then reproduce or change these social structures [ 53 ]. While others consider, for their part, that social and physical contexts can be at the origin of boundaries that mitigate against collaboration, adding to the complexity of shared clinical practices in this field [ 3 , 24 ].

Attribute theme 6: The special role of the information system and data management

Data management (k = 14) plays an increasingly important role in the analysis and improvement of care pathways. The implementation of a care flow management system aligned to clinical workflows [ 67 , 69 ], allows real-world data to be used [ 51 ], and visualized through performance dashboards to generate timely corrective action [ 4 ]. It also enables the analysis and monitoring of the variance in time and space within care pathways [ 43 ]. It is considered responsible for the rise of accountability [ 12 , 75 ].

The Electronic Health Record system is a support tool (k = 13) in several aspects. Numerous authors consider that it supports the patient-centered approach [ 51 , 67 ]. In particular, it has the capacity to support communication between health professionals, and between them and the patient [ 5 , 12 , 65 , 67 , 73 , 75 ], but also to support healthcare knowledge learning [ 67 , 73 ], and integrate clinical decision support into IT applications and clinical workflows [ 70 ]. This support throughout the care pathway can improve the quality of care and health outcomes by reducing medication errors and unnecessary investigations [ 5 ]. As stated by Fung-Kee-Fung et al. [ 4 ], the information system provides the fundamental connectivity across silos and professional groups to support the creation of care pathways and sustainable change at the system level.

The issue of digitalization (k = 5) has been treated very recently. It raises the issue of system integration throughout the care pathway. Despite the technological advances and the support of international organizations such as the guidelines on evidence-based digital health interventions for health system strengthening released by the WHO [ 76 ], there are still inefficiencies associated with trying to integrate EHRs across organizations [ 56 ]. These are frequently due to the use of different technological solutions by different stakeholders [ 30 ]. The challenge is therefore to propose a model for integrating information systems throughout the care pathway that are accessible to all stakeholders including patients themselves [ 4 , 50 , 51 , 65 ].

Attribute theme 7: The advent of the learning system

Although it was not frequently addressed, some authors have developed, very recently, the importance of setting up a learning system (k = 3) to support the care pathway. Resulting from the work of Quinn [ 77 ] and Senge [ 78 ], it consists of the development of a system to learn from itself and its past experience and improve the effectiveness, efficiency, safety, and patient and family/caregiver experiences [ 65 ] through a feedback loop [ 24 ]. Data on outcomes can be used as feedback to identify improvement opportunities at various stages of the process or at specific interfaces between stakeholders. The learning system promotes “individual competence, systems thinking, cohesive vision, team learning, and integrating different perspectives” [ 4 ].

Related concepts

The related concepts are confusingly close or even integrated with the main concept studied [ 40 ]. Given the complexity of the use of concepts, we have relied, in addition to definitions found on an analysis of a bibliometric network by integrating all 44 articles, excluding abstracts and bibliographies, into the Vosviewer® software (version 2020). The results help us to refine our understanding of the concepts which define the links between the different keywords. The care pathway bibliometric links are provided as a comparator (see Fig. 3 ).

Care pathway bibliometric links

Clinical pathway (Fig. 4 ) was initially defined by De Bleser et al. [ 38 ]. It is a multidisciplinary intervention that aims to integrate the guidelines into daily routine and manage medical activities in order to improve the quality of service and optimize the use of resources [ 70 ]. It integrates a process of care approach [ 72 ] and aims at standardize care on a procedure or an episode of care [ 38 , 49 , 68 ], integrating decision-making supported by knowledge. What differentiates it from the care pathway is that it is restrained in time and is anchored in an organization [ 25 ], or even a service, and does not deal with the patient experience in any way. Clinical pathways are thus integrated in care pathways at the local level and focus on a single phase of care.

Clinical pathway bibliometric links

Patient journey (Fig. 5 ) consisted of sequential steps in the clinical process of the patient through their experience. It can be defined as “the spatiotemporal distribution of patients’ interactions with multiple care settings over time” [ 24 ]. By analyzing and mapping the patient experience from their perspective [ 5 , 14 , 57 , 58 , 71 ], the objective is to improve the quality of the service provided [ 14 , 52 ]. In this approach, the patient journey is an integral part, and an essential component, of the care pathway. Although it also integrates the process approach, it is not linked to decision-making or knowledge management and does not consider structural constraints or the perception of the providers.

Patient journey bibliometric links

Finally, the care process (Fig. 6 ) is involved across the care continuum to standardize and streamline end-to-end care using management tools [ 4 ]. It is directly linked to the care pathway, the clinical pathway and the patient journey. However, although it supports coordination through decision-making and knowledge management, it does not consider the patient experience, the social relationships and the social dynamics. So, the care process is an integral part of the care pathway but does not consider all the characteristics of the latter.

Care process bibliometric links

Antecedents of the concept

Antecedents are events occurring or in place before the concept can emerge [ 40 ]. Our analysis has highlighted several prerequisites for care pathway implementation (see Additional file 4 ).

Firstly, several authors have stressed the importance of the availability of managerial skills (k = 10). They recommend the creation of a change management team [ 49 , 55 ] consisting of a multidisciplinary team integrating not only knowledge about care pathways [ 60 , 70 ], but also knowledge about operations research, information systems and industrial engineering [ 49 , 55 ]. In addition, some authors advocate the presence of key change leaders in the group included clinicians, administrators, IT leaders, process experts, data analysts, nurses, and patient and family members [ 4 , 24 ]. The project leaders must be available on a long-term basis [ 50 , 75 ], have the ability to understand system interdependencies [ 24 ] and have the ability to create a safe learning environment in which openness is encouraged and everyone’s opinion is valued [ 3 , 50 ]. This could be achieved by using consensus-driven approaches that could address institutional process barriers, resistance to change, and conflicting targets and priorities [ 4 ].

Secondly, care pathway projects should have a priori the adequate resources (k = 4), but their availability must be verified [ 62 , 75 ]. The presence of an EHR is necessary to have access to reliable data at the pre-analysis phase and during the implementation phase to identify the relationships between the context, the mechanisms and the results obtained [ 2 , 73 ].

Finally, other key success factors emerged from the literature (k = 10). Some authors noted that rules of co-involvement and a bottom-up strategy was needed [ 55 ]. Other authors emphasized that the selection of areas where there were clearly established deficiencies was essential given the cost of such projects, but also that the identification of any subgroups for whom its use may not be appropriate, was also required [ 73 ]. They highlighted the importance of following guidelines to achieve professional adherence [ 2 , 50 , 62 , 72 , 73 ], while maintaining flexibility in the approach to implementing a care pathway improvement program [ 62 ]. They also pointed to the importance of communicating on the progress of the project [ 50 ] and of monitoring the applicability of daily work tasks [ 73 ]. Finally, they consider it essential to embed the pathway into policy and strategy [ 2 , 50 , 72 , 75 ]. While others, for their part, highlighted the importance of defining an iterative feedback loop for individuals and aggregated operational and clinical data [ 4 , 24 ].

Consequences (outcomes) and identification of empirical referents

Consequences are events that are the results of the mobilization of the concept [ 40 ] and empirical referents, for their part, consist of observable phenomena by which defining attributes are recognized [ 40 ] (see Additional file 4 ). In a larger sense, this could be the Key Performance Indicators (KPIs) by which one can recognize the defining attributes and their outcomes.

Although the terms of quality and safety, efficiency and process improvement were the first themes in terms of aims, the most frequently occurring theme in the findings pertained to effects on the patient experience (k = 16). These were measured in different ways, including the impact of waiting times (k = 10), patient satisfaction (k = 7) and the patient quality of life (QALYs) (k = 4). There were also attempts to analyze the patient experience more broadly (k = 5), and to integrate patient needs into the redesign of the care pathway [ 5 , 13 , 56 ].

Efficiency of care (k = 15) was strongly supported by some authors as a desired outcome in care pathways. This outcome was first seen, as an objective, through the costs and cost effectiveness of programs [ 49 , 55 , 61 , 70 ], however, more recently it has been considered a consequence of process improvements, rather than a program objective. It has been clearly defined as the reduction of costs through the reduction of the use of healthcare services [ 57 ]. Moreover, reduction in time spent in care, such as the length of stay or cycle time [ 2 , 55 ], is commonly the consequence of process improvements.

Quality of care (k = 11) was addressed but much less frequently than expected. In the global approach, time to diagnostic is a good empirical referent to analyze the capacity of the first steps of the care pathway [ 4 , 69 ]. Other referents such as reduction of unnecessary investigations and medication errors are also addressed but the number and types of complaints were addressed only by socio-political authors [ 53 ].

Health outcomes (k = 11) were also proposed but only since 2009 [ 73 ]. Clinical outcomes and mortality rates are empirical referents that are unanimously accepted. Recovery time and readmission rates were less frequently considered. Single disease index evaluation was proposed by very few authors [ 49 , 70 ].

Process metrics and patient flow (k = 11) was addressed but only the execution time was unanimously accepted as an empirical referent. Apart from the process variance which is shared, only few authors have developed other KPIs such as the percentage of pathway completion [ 70 ], and evaluation for the reasons of pathway failure [ 70 ].

The variance of practices (k = 9) was not frequently addressed as an empirical referent; however, this is one of the objectives of the care pathway addressed in the literature. The introduction of guidelines [ 2 ] aims to decrease the variation within or between practices (k = 3).

Continuity of care (k = 6) was poorly addressed, even though we might assume that this is one of the primary objectives of the care pathway. This may be due to the difficulty of providing tangible results given the duration of such interventions.

Some authors noted an improvement in documentation and data collection (k = 5), measured by rate of documentation [ 54 ], the ability to better understand resource adequacy (k = 3) and a better comprehension of the links between decision outcomes and process performance (k = 2).

Not defined as an outcome, the Human Resources metrics are proposed by some authors and notably diagnostic quality and referral appropriateness, professional competences and staffing levels. Only Carayon et al. [ 24 ] proposed to integrate the quality of working life as an indicator, based on the principle that well-being at work has a direct impact on individual performance and on the results of the care pathway.

Moreover, not present in the empirical references, the measure of the team relationship and coordination (k = 4) has been proposed by some authors, however, the type of indicator has not been clearly explained.

An integrative definition and conceptual framework of patient-centered care pathways

Given the results of our systematic review and concept analysis and our main objective of defining an integrative framework, we suggest the following definition:

“A patient-centered care pathway is a long-term and complex managerial intervention adopting a systemic approach, for a well-defined group of patients who journey across the entire continuum of care, from prevention and screening to recovery or palliative care. This intervention:

prioritizes the centricity of patients and caregivers by analyzing the patient experience through their needs and expectations, taking into account the need for information, education, engagement and involvement and integrates the patient relationships as a fundamental need.

supports the roles of professional actors involved in the care pathway by developing adherence to the patient-centered care approach; working on interdisciplinarity through the development of skills, both technical and above all relational; the clarification of roles and responsibilities; and by taking into account the experience of professionals both in understanding the organizational constraints and their well-being at work.

integrates a process of care approach through the modeling and improvement of the care pathway by continuously integrating the latest knowledge and information to support clinical decision-making and by defining feedback loops to continuously improve clinical and non-clinical process supported by operation management contained within process improvement methodology approaches;

embeds coordination structures through: the implementation of best practices and the translation of guidelines into daily practice; the support of informational continuity through the integration of services at the systemic level; the implementation of knowledge management along the care continuum; and the identification of leaders at each step of the care pathway;

adapts to the contexts of both the physical and social structures by integrating the human, material, economic and financial resource constraints, as well as the social dynamics of power and trust relationships;

is supported by information systems and data management, enabled by digitalization, which ensure the flow of information within the right context at the right time and place, and allows the continuous integration of the latest knowledge into the care flow and the management of accessible data in real time to monitor and evaluate variances in practices and outcomes;

promotes the development of a learning health system to support the care pathway.

The aim and shared goal of a care pathway is to meet the needs and expectations of patients through continuous improvement of patient experience, patient outcomes, quality and safety while taking into account operational and social realities of the system.”

We know that this definition is important but feel that there is a great need for clarification of this concept and how these interventions can be successful given the costs involved. Furthermore, we consider that the proper sequencing of the care pathway should be defined according to the following eight phases: (1) Prevention and screening; (2) Signs and symptoms; (3) Early detection; (4) Diagnostic; (5) Referral systems; (6) Treatment; (7) Follow-ups; (8) Reeducation or Palliative care. In this way, the development of recognized KPIs enabling international comparisons of care pathways should finally make it possible to share knowledge and improve care pathways.

According to this definition and based on the literature review, we propose the following integrative conceptual framework illustrated in Fig. 7 .

Integrative conceptual framework of care pathway

Using systematic review, concept analysis and bibliometric analysis, it was possible to develop a detailed understanding of the care pathway concept enabling us to propose an integrative conceptual framework and definition to try to meet the need for an international consensus and thus enabling international comparisons and improvement of care pathways.

The results of our work have highlighted the evolution and advances of the various uses of care pathways. Initially focused more on an organizational approach, there is growing support in the literature for a holistic approach that addresses the entire care across the continuum at the system level [ 4 , 24 , 42 , 60 ]. Thus, patient centeredness has become the primary focus as more and more authors focus on the patient experience as the unit of quality analysis. In doing so, they have given greater importance to social relationships and especially to the relationship as a basic need and highlighted the need to design the service line structures mirroring patients’ needs [ 56 ]. They therefore approach the patient, not only as the individual who follows the pathway, but as a social being who has needs and expectations to fulfill, making meeting the needs and expectations of the patient and caregivers the core of the care pathway [ 24 , 50 , 51 , 57 ]. However, the evaluation of the quality of healthcare services by the patient still raises several methodological questions to finally go beyond the simple consideration of satisfaction. Finally, patient and public involvement and patient engagement are also important issues to the point that some authors see a real power struggle between patients and clinicians [ 53 ] that can lead to tokenistic involvement.

The professional actors involved in the care pathway are naturally essential players, both because of their professional competencies and their ability to orient themselves towards the needs of the patient. However, they are also often part of a neglected factor. Some authors have shown one of the key criteria for the potential failure of care pathways is a failure to take into account the prevailing social dynamics and the importance of the buy-in of all stakeholders [ 65 ]. Moreover, some authors insist on the importance of the actors involved in the pathway to both integrate the social dynamics and confront the patient’s needs with operational realities and organizational constraints [ 24 ].

The operation management of process approach to care delivery also raises many challenges. Thus, some authors have developed tools for modeling and improving care processes by applying them in a systemic approach to incorporate clinical decision support into the modeling method [ 60 ]. This issue of continuous integration of updated guidelines into care pathways is indeed a major challenge given the rapid evolution of knowledge and the limited capacity of professionals to continuously integrate new knowledge. In addition, data simulation and data analysis methods coupled with process improvement methods are undeniable contributions to improve the issue of fluidity of processes and therefore the overall performance [ 49 ]. However, one of the pitfalls of staying focused on the process would be a failure to consider the social dimension, particularly the prevailing social dynamics.

Coordination structures are one of the points of improvement in the systemic approach. Ensuring the continuity of information along the care pathway, as well as having a formal leader for each portion of the pathway, would solve many of the problems of path breaks or unnecessary repetition of exams that cause unnecessary costs [ 5 , 56 , 59 ]. This begins with the implementation of a single information system and the integration of IT infrastructures across the entire care pathway at the system level and accessible to care professionals as well as patients and caregivers [ 4 , 50 , 51 , 65 ].

The structural context of the system and organizations cannot be neglected because it directly impacts the results of the implementation of the care pathway. Firstly, because some physical constraints such as distances between several organizational entities [ 12 , 14 ] can only be solved by major transformations in the infrastructures or in the initial process. Secondly, because failing to consider the dominant social dynamics could immediately call into question the entire care pathway intervention [ 3 , 24 ] by implementing only cosmetic changes and not transforming clinical, administrative and organizational practices in a sustainable manner.

The information system plays a special role in care pathway, not only because it is the support of the informational continuity, but also because it enables real-time data analysis to support decision-making within the care pathway in the form of feedback loops [ 4 , 24 , 51 ].

Finally, it seems clear that care pathway programs at the systemic level are one potential intervention which could benefit from the implementation of a learning system [ 4 ]. Care pathway outcome data can be used as feedback to identify improvement opportunities at various stages of the process or at specific interfaces between stakeholders. This approach makes it possible to support the continuous improvement of the care process.

Given the richness of the contributions of the last 20 years, we advocate an integrated approach resulting in a fine-grained and comprehensive understanding of care pathway. Our proposal is compatible with the definition of Vanhaecht et al. [ 25 ] currently used by the EPA, but in our opinion, enriches it. It allows users to specify the operational realities to which stakeholders should pay attention. Moreover, it insists on adaptation to the social realities and the changes that inevitably accompany it and directly impact the success or failure. However, we were surprised that the approach to managing organizational change and transformation of practices were little addressed. Only Van Citters et al. [ 65 ] had noted that change management approaches were critical for successful care transformation and that they had been largely neglected in care pathways. We share this point of view and believe that care pathway intervention leaders must develop communicative action skills to support practices transformation. Not mentioned in the selected literature, we propose to enrich our conceptual framework of communicative action proposed by Habermas [ 79 ]. From our point of view, this dimension could explain the failures of such interventions or at least the difficulty in developing sustainable transformations in practices.

In general, the concept analysis approach has raised several questions about the depth of concept analysis and its place in knowledge advancement [ 80 ]. However, we believe that the combination of systematic review rigor and concept analysis richness, was necessary to meet the aims of this study and produced an integrated conceptual framework which is ready for use. However, this research has some limitations. Although interest is growing, few studies offer comprehensive empirical results on the deployment of a care pathway and its outcomes in a global systemic approach over the entire continuum of care. Moreover, there are a few examples of in-depth analysis of car pathways over a long period of time. Together, this means that the literature still offers little insight into potential outcomes of care pathways. Lastly, our analysis was limited to peer-reviewed articles; including other contributions such as theses and dissertations as well as grey literature could have brought out other categories or themes.

This study has resulted in a fine-grained understanding of care pathways and in a clear definition relying on a powerful conceptual framework. It responds to a strong need for conceptual precision, as previous reviews have not addressed the care pathway on a systemic scale and in a holistic manner. In addition, our framework offers a holistic view of the pathway without being specific to a particular condition or context. Our framework encompasses 28 subcategories grouped into seven care pathway attributes that should be considered in complex care pathway intervention. It considers both operational and social realities and supporting the improvement and sustainable transformation of clinical, administrative, and organizational practices for the benefit of patients and caregivers, while taking into account professional experience, organizational constraints, and social dynamics. The formulation of these attributes, antecedents as success factors and consequences as potential outcomes, linked to their KPIs, allows the operationalization of this model for any pathway in any context. We believe that these results are of particular interest to policymakers, decision makers, managers and researchers alike, and that they could lead to an international consensus that would finally allow comparison of care pathway improvement programs. However, we consider that the development of a framework for analyzing the performance of such an intervention has yet to be developed in a more in-depth manner, such as by focusing on certain particularities of each phase so that managers and decision makers can rely on validated dashboards and KPIs. More empirical work needs to be done on the comprehensive approach, as defined in our proposed definition, to provide reliable results on the ability of these interventions to result in an overall improvement. In addition, the question of the understanding of social evaluation of the quality of care by the patient remains an open question, as the patient experience does not yet have conclusive KPIs as it is too often limited to patient satisfaction or QALYs.

Availability of data and materials

This systematic review is based on an analysis of 44 published papers which are all referenced within this manuscript. Data supporting our findings are included in the form of additional files.

Abbreviations

European Pathway Association

Institute of Medicine of America

Key Performance Indicator

Preferred Reporting Items for Systematic reviews and Meta-Analyses

Quality Adjusted Life Year

World Health Organization

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Quantitative research uses methods that seek to explain phenomena by collecting numerical data, which are then analysed mathematically, typically by statistics. With quantitative approaches, the data produced are always numerical; if there are no numbers, then the methods are not quantitative. Many phenomena lend themselves to quantitative methods because the relevant information is already available numerically. Qualitative methods provide a mechanism to provide answers based on the collection of non-numerical data ‘i.e words, actions, behaviours’. Both quantitative and qualitative methodologies are important in medical imaging and radiation therapy. In some instances, both quantitative and qualitative approaches can be combined into a mixed-methods approach. This chapter discusses all methodological approaches to research from both medical imaging and radiation therapy perspectives.

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England, A. (2021). Quantitative and Qualitative Research Methods. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_5

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Christina Reppas-Rindlisbacher 1 , 2 ,
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1 Department of Medicine , Temerty Faculty of Medicine, University of Toronto , Toronto , Ontario , Canada
2 Division of General Internal Medicine and Geriatrics , University Health Network and Sinai Health System , Toronto , Ontario , Canada
Correspondence to Dr Shail Rawal, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; shail.rawal{at}uhn.ca

https://doi.org/10.1136/bmjqs-2023-016873

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Communication
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When patients and clinicians do not speak the same language, the quality and safety concerns that can arise seem evident. However, the literature on the association between language and a host of health outcomes is vast and varied. In this issue of BMJQS , Chu et al share the results of their well-conducted systematic review and meta-analysis of the relationship between a patient’s spoken language and hospital readmissions and emergency department (ED) revisits. 1 They report that adult inpatients who prefer a non-dominant language are more likely to experience an unplanned hospital readmission or ED revisit after discharge. Moreover, they found that children whose parents spoke a non-dominant language had more ED revisits. The authors’ work is a thoughtful synthesis of a somewhat disparate literature and offers a starting point to consider key challenges in the broader area of research on linguistic inequities in healthcare.

Language as a variable

There are several challenges that arise when language is used as a quantitative variable in research. The first challenge is one of definition. Chu et al describe the heterogeneous approach to the measurement of language in the studies they reviewed as a limitation of their results. Some studies used dominant language proficiency, while others used preferred language, and yet others used primary language. Each measure assesses a different construct. And so, it becomes difficult to aggregate outcomes across studies when fundamentally different concepts are measured and compared.

Importantly, spoken language typically involves both speakers and listeners. However, the current literature almost exclusively examines patients’ language use and rarely reports or assesses that of clinicians. This makes the patient the focus of study rather than the clinician. 2 Future research must consider how to measure the languages used by clinicians and their concordance with a patient’s preferred language, especially given the ample evidence that language concordant care is associated with improved patient outcomes. 3–6

The role of dominant language speakers and institutions

Interactions between patients and clinicians occur within institutions that are embedded in a wider healthcare system and social context. A focus on the patient’s language use as the primary subject of study obscures the role of dominant language speakers, institutions and the health system in shaping linguistic equity. For example, do institutions offer meaningful access to interpretation services? Do they have policies that govern the use of interpretation? Do their hiring practices reflect the linguistic diversity of the communities they serve? What is the institutional culture as it relates to health equity? Understanding linguistic inequities requires that we move beyond measuring the patient’s language to assessing the degree to which institutions provide multilingual care.

Measuring the impact of interpretation services

Chu et al’s work underscores the challenges of understanding how interpretation services influence health outcomes such as readmission rates. In a stratified analysis by ‘access to or use of interpretation services’, the authors found higher odds of readmission from studies that did not describe interpretation services and no difference in readmission rates from studies that did describe interpretation services. Thus, the authors conclude that ‘interpretation access’ could be a mitigating factor in the association between language discordance and hospital readmissions. This finding should be interpreted with some caution due to the small sample size of the two studies analysed and the imprecise definition of interpretation access and use. One of the two studies verified patient-level interpretation and described the clinician who used the service but not the form of interpretation used (in-person, video, telephone). 7 While the other study described the form of interpretation used (telephone) but was unable to match an interpreted encounter to a specific patient or describe the clinical context of the interaction. 8

Thus, although the benefits of interpretation services are well established, 9 a significant barrier to better characterising the relationship between interpretation and health system outcomes, such as hospital readmission, remains the absence of patient-level data on whether interpretation was received by a patient, what form it took (telephone, in-person, video), and what type of communication it was used for (medication reconciliation, discharge counselling, etc). Institutions must create policies to collect such data at the level of the individual patient while also taking steps to protect personal health information. This would allow for a more nuanced understanding of the causative factors underlying the association between interpretation and health outcomes of interest. It would also allow institutions to identify areas for quality improvement and move from simply providing access to interpretation services to setting benchmarks for interpretation use.

The need for theory to inform the claims we make

Chu et al’s work offers an opportunity to reconsider how we account for the relationship between a patient’s spoken language and a given health outcome. Typically, poor communication between language-discordant patients and clinicians is theorised to be the primary mediator of poor patient outcomes. Professional interpretation is known to improve clinical care. 9 Thus, increasing the use of interpretation services is seen as the principal method for improving outcomes and remedying inequities for patients who speak non-dominant languages. However, the outcomes experienced by patients from language minority communities are likely shaped by more than poor communication alone, requiring an understanding of language as a social process.

Chu et al note that an important challenge in the analysis of research on linguistic inequities is the ‘complex relationship between language, race/ethnicity, migration status and socioeconomic status’. Language intersects with these characteristics in ways that are not well examined in conventional healthcare research. For example, it is unclear what proportion of the studies reviewed by the authors collected sociodemographic data from participants. Other studies that have explored these factors have found that patients who do not speak English in English-dominant countries are more likely to be racialised and live in poverty. 10 11 Thus, when considering Chu et al’s findings, non-dominant language speakers may have less access to home care and other social services that support a transition from hospital to home. Using a sociolinguistic lens, we might also consider how perceptions of a person’s language use are linked to racism or xenophobia. For example, a white French speaker in an English-dominant institution may be cared for in a different manner from a French speaker who is not white. 2 Similarly, language hierarchies position European languages above others, 12 and discourses related to migration, assimilation, and visible markers of cultural or religious difference may also shape the care of speakers who are deemed to be inadequately proficient in the dominant language.

Moving forward

At a time when global migration is reshaping population demographics, Chu et al’s study provides an opportunity to reflect on the status of research on linguistic inequities in healthcare. First, it brings to light the need for a standardised approach to defining language use in order to better synthesise findings across studies. This requires greater coordination among researchers and the development of a consensus view on how best to report the complex dimensions of language. Health systems must also be mandated to collect such data. Self-reported language preference is a useful starting point, 13 but we must develop the theory and methods needed to move beyond a binary understanding of language to one that considers the reality that patients and clinicians may speak multiple languages with varying proficiency and preference.

Second, the study reminds us to characterise the context in which communication occurs, so that the role of both speakers and listeners are examined. This requires that data on clinician language be included in health and administrative records so that language concordant care can be studied. It also underwscores the need for sociodemographic data be collected so that the scope of analysis can be expanded to include consideration of the effects of racism, xenophobia, religious discrimination and classism on health outcomes in non-dominant language speakers. Finally, the study highlights the need to develop measures and quality standards to assess how well institutions provide multilingual care. Such efforts must be grounded in the views of patients who speak non-dominant languages, and begin with collecting patient-level interpretation data.

Remedying linguistic inequities requires us to move from understanding language as a technical problem of poor communication, 14 to one that accounts for social context. It requires us to enrich our view of language and its intersections with race, migration status, religion and socioeconomic position, and to recognise that interventions to address linguistic inequities should occur in concert with broader efforts to improve health equity. Only then can we better understand the associations between the variables we seek to describe and move towards substantive action to address the inequities experienced by language minority communities.

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Bardach NS , et al
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McElhinny B

Contributors CR-R and SR contributed to the conception, drafting and critical review of this article.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests CR-R reported receiving the Vanier Canada Graduate Scholarship Award, the Eliot Phillipson Clinician-Scientist Training Program Award and the PSI Foundation Resident Research Grant outside the present manuscript. SR is supported by an award from the Mak Pak Chiu and Mak-Soo Lai Hing Chair in General Internal Medicine, University of Toronto.

Provenance and peer review Commissioned; internally peer reviewed.

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Systematic review Association between language discordance and unplanned hospital readmissions or emergency department revisits: a systematic review and meta-analysis Janet N Chu Jeanette Wong Naomi S Bardach Isabel Elaine Allen Jill Barr-Walker Maribel Sierra Urmimala Sarkar Elaine C Khoong BMJ Quality & Safety 2023; - Published Online First: 30 Dec 2023. doi: 10.1136/bmjqs-2023-016295

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Accumulating harm and waiting for crisis: Parents perspectives of accessing Child and Adolescent Mental Health Services for their autistic child experiencing mental health difficulties

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Background Autistic children and young people are at increased risk of mental health difficulties, but often face barriers when seeking help from Child and Adolescent Mental Health Services (CAMHS). There is limited literature exploring the accessibility of CAMHS for autistic young people, particularly from parents perspectives. The present study aimed to 1) explore the experiences of parents/carers seeking help from CAMHS for their autistic childs mental health difficulties, and 2) gain parents perceptions of the accessibility of CAMHS support for their child and understand what could be improved. Methods A mixed-methods survey design was used to learn from parents/carers. 300 parents/carers took part from across the UK between June and October 2023. Quantitative data were analysed using descriptive statistics, and qualitative data using qualitative content analysis. Results Findings demonstrated the ongoing struggles that parents/carers faced when seeking professional help from CAMHS for their child. Many were not referred to CAMHS or were rejected without an assessment, often due to issues relating to diagnostic overshadowing, a high threshold for assessment, or a lack of professional knowledge about autism and care pathways. Those who were referred reported a lack of reasonable adjustments and offers of ineffective or inappropriate therapies, leaving young people unable to engage, and thus not benefiting. Ultimately, parents felt their childs mental health difficulties either did not improve or declined to the point of crisis. However, there was a recognition that some professionals were kind and compassionate, and provided the validation that parents needed. Conclusions There is a need for a more neuro-inclusive and personalised approach in CAMHS, from the professionals themselves, in the adjustments that are offered, and in the therapies that are provided. Further research, funding, and training are urgently needed to ensure mental health support is accessible, timely, and effective for autistic CYP.

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The authors have declared no competing interest.

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This study did not receive any funding.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee of Liverpool John Moores University gave ethical approval for this work (reference: 23/PSY/046).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Quantitative Research: Definition, Methods, Types and Examples
quantitative research meaning, types and methods
(PDF) Quantitative Research Method
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Quantitative Research:Its definition, types , purposes and goals
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What Is Quantitative Research?
Revised on June 22, 2023. Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. Quantitative research is the opposite of qualitative research, which involves collecting and analyzing ...
What is Quantitative Research? Definition, Methods, Types, and Examples
Quantitative research is the process of collecting and analyzing numerical data to describe, predict, or control variables of interest. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations. The purpose of quantitative research is to test a predefined ...
PDF Introduction to quantitative research
Quantitative research is 'Explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particu-lar statistics)'. Let's go through this definition step by step. The first element is explaining phenomena. This is a key element of all research, be it quantitative or quali-tative.
Quantitative and Qualitative Research
Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population. Allen, M. (2017). The SAGE encyclopedia ...
Quantitative Methods
Definition. Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations.
Quantitative Research
Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. . High-quality quantitative research is ...
What Is Quantitative Research?
Revised on 10 October 2022. Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations. Quantitative research is the opposite of qualitative research, which involves collecting and ...
Quantitative research
Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.. Associated with the natural, applied, formal, and social sciences this research strategy promotes the objective empirical investigation of ...
Quantitative Research Methods
Quantitative Research for the Qualitative Researcher is a concise, supplemental text that provides qualitatively oriented students and researchers with the requisite skills for conducting quantitative research. Throughout the book, authors Laura M. O'Dwyer and James A. Bernauer provide ample support and guidance to prepare readers both ...
Quantitative and Qualitative Research: An Overview of Approaches
2.1.1 Types of Quantitative Research. Several authors (Creswell & Guetterman, 2019; Gray et al., 2017; Bhattacherjee, ... Furthermore, the steps must include the research design used and the choice of which will demand the following: definition of population and sample, selection of measurement tools, development of plan for data collection and ...
PDF Introduction to Quantitative Research
Controlled collection and analysis of information in order to understand a phenomenon. Originates with a question, a problem, a puzzling fact. Requires both theory and data. Previous theory helps us form an understanding of the data we see (no blank slate). Data lets us tests our hypotheses.
A Practical Guide to Writing Quantitative and Qualitative Research
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 ...
Quantitative Research
Introduction. Quantitative research, in contrast to qualitative research, deals with data that are numerical or that can be converted into numbers. The basic methods used to investigate numerical data are called 'statistics'. Statistical techniques are concerned with the organisation, analysis, interpretation and presentation of numerical data.
Quantitative Methods
Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
Quantitative research
This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys - the principal research designs in quantitative research - are described ...
What is Quantitative Research?
Quantitative research is the methodology which researchers use to test theories about people's attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.
Leigh A. Wilson, Quantitative Research
Abstract. Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey ...
What is Quantitative Research? Definition, Examples, Key ...
Quantitative research is a type of research that focuses on collecting and analyzing numerical data to answer research questions. There are two main methods used to conduct quantitative research: 1. Primary Method. There are several methods of primary quantitative research, each with its own strengths and limitations.
Quantitative Research
Definition | Quantitative Research. In the most basic terms, quantitative research methods are concerned with collecting and analysing data that is structured and can be represented numerically. One of the central goals is to build accurate and reliable measurements that allow for statistical analysis. Because quantitative research focuses on ...
What is Quantitative Research According to Authors?
Quantitative Research According to Williams, Malcolm, et al. Williams et al. (2022) define quantitative research as: investigations in which the data that are collected and coded are expressible as numbers. By contrast, studies in which data are collected and coded as words would be instances of qualitative research.
(PDF) Quantitative Research Method
2.0 Quantitative Research. Quantitative research is regarded as the organized inquiry about phenomenon through collection. of numer ical data and execution of statistical, mathematical or ...
(PDF) Quantitative Research Methods : A Synopsis Approach
Abstract. The aim of th is study i s to e xplicate the quanti tative methodology. The study established that. quantitative research de als with quantifying and analyzing variables in o rder to get ...
Definition and conceptualization of the patient-centered care pathway
Background Confusion exists over the definition of the care pathway concept and existing conceptual frameworks contain various inadequacies which have led to implementation difficulties. In the current global context of rapidly changing health care systems, there is great need for a standardized definition and integrative framework that can guide implementation. This study aims to propose an ...
Quantitative and Qualitative Research Methods
5.1 Quantitative Research Methods. Quantitative research uses methods that seek to explain phenomena by collecting numerical data, which are then analysed mathematically, typically by statistics. With quantitative approaches, the data produced are always numerical; if there are no numbers, then the methods are not quantitative.
(PDF) Quantitative methods for integrating climate adaptation
e authors declare tha t the research was conducted in the absence of any commercial or n ancial relationships that co uld be construed as a potential con ict of interest.
A framework for quality control in quantitative proteomics
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with ...
Understanding linguistic inequities in healthcare: moving from the
The authors' work is a thoughtful synthesis of a somewhat disparate literature and offers a starting point to consider key challenges in the broader area of research on linguistic inequities in healthcare. There are several challenges that arise when language is used as a quantitative variable in research. The first challenge is one of ...
(PDF) Quantitative Research Design
PDF | On Mar 25, 2024, Lena Ellitan published Quantitative Research Design | Find, read and cite all the research you need on ResearchGate
Accumulating harm and waiting for crisis: Parents perspectives of
Background Autistic children and young people are at increased risk of mental health difficulties, but often face barriers when seeking help from Child and Adolescent Mental Health Services (CAMHS). There is limited literature exploring the accessibility of CAMHS for autistic young people, particularly from parents perspectives. The present study aimed to 1) explore the experiences of parents ...
A quantitative tumor-wide analysis of morphological ...
Morphologic heterogeneity of colorectal adenocarcinoma (CRC) is poorly understood. Previously, we identified morphological patterns associated with CRC molecular subtypes, and showed that these patterns have distinct molecular motifs (Budinska et al., 2023). Here, we evaluated the heterogeneity of these patterns across CRC. Three pathologists evaluated dominant, secondary, and tertiary ...

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Further Reading

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

Glafera Janet Matanguihan

INTRODUCTION

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions in quantitative research

Hypotheses in quantitative research

Research questions in qualitative research

Hypotheses in qualitative research

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

Organizing Your Social Sciences Research Paper

Need Help Locating Statistics?

Characteristics of Quantitative Research

Basic Research Design for Quantitative Studies

Strengths of Using Quantitative Methods

Limitations of Using Quantitative Methods

Research Tip

Quantitative research

What is Quantitative Research?

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative Research Methods You Can Use to Guide Optimal Designs

Learn More about Quantitative Research

Questions related to Quantitative Research

Literature on Quantitative Research

Learn more about Quantitative Research

All open-source articles on Quantitative Research

Card Sorting

Understand the User’s Perspective through Research for Mobile UX

7 Simple Ways to Get Better Results From Ethnographic Research

Question Everything

Tree Testing

Adding Quality to Your Design Research with an SSQS Checklist

How to Fit Quantitative Research into the Project Lifecycle

Why and When to Use Surveys

Correlation in User Experience

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What to Test

Rating Scales in UX Research: The Ultimate Guide

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Quantitative Research