quantitative research definition journal article

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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.

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Methodology

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).

Quantitative research methods
Research method	How to use	Example
	Control or manipulate an to measure its effect on a dependent variable.	To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
	Ask questions of a group of people in-person, over-the-phone or online.	You distribute with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observation	Identify a behavior or occurrence of interest and monitor it in its natural setting.	To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary research	Collect data that has been gathered for other purposes e.g., national surveys or historical records.	To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available .

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.

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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Knowledge Base
Methodology
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.

Quantitative research methods
Research method	How to use	Example
	Control or manipulate an to measure its effect on a dependent variable.	To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
	Ask questions of a group of people in-person, over-the-phone or online.	You distribute with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observation	Identify a behavior or occurrence of interest and monitor it in its natural setting.	To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary research	Collect data that has been gathered for other purposes e.g., national surveys or historical records.	To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available .

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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.

Cite this Scribbr article

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Bhandari, P. (2022, October 10). What Is Quantitative Research? | Definition & Methods. Scribbr. Retrieved 18 June 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-quantitative-research/

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Pritha Bhandari

Writing Quantitative Research Studies

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First Online: 13 January 2019
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quantitative research definition journal article

Ankur Singh 2 ,
Adyya Gupta 3 &
Karen G. Peres 4

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Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to maintain the credibility and integrity of quantitative studies is outlined. Commonly used terminologies in empirical studies are defined and discussed with suitable examples. Key elements that build consistency between different sections (background, methods, results, and the discussion) of a research study using quantitative methods in a journal article are explicated. Specifically, recommended standard guidelines for randomized controlled trials and observational studies for reporting and discussion of findings from quantitative studies are elaborated. Key aspects of methodology that include describing the study population, sampling strategy, data collection methods, measurements/variables, and statistical analysis which informs the quality of a study from the reviewer’s perspective are described. Effective use of references in the methods section to strengthen the rationale behind specific statistical techniques and choice of measures has been highlighted with examples. Identifying ways in which data can be most succinctly and effectively summarized in tables and graphs according to their suitability and purpose of information is also detailed in this chapter. Strategies to present and discuss the quantitative findings in a structured discussion section are also provided. Overall, the chapter provides the readers with a comprehensive set of tools to identify key strategies to be considered when reporting quantitative research.

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Bhaumik S, Arora M, Singh A, Sargent JD. Impact of entertainment media smoking on adolescent smoking behaviours. Cochrane Database Syst Rev. 2015;6:1–12. https://doi.org/10.1002/14651858.CD011720 .

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Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia

Ankur Singh

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Adyya Gupta

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Karen G. Peres

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Singh, A., Gupta, A., Peres, K.G. (2019). Writing Quantitative Research Studies. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_117

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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns. 

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.

Here are two quantitative research examples:

Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).
A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:

Theory : Define the problem area or area of interest and create a research question.
Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.
Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
Data collection : This process could be extensive based on your research objective and sample size.
Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.
Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.

Quantitative research characteristics 4

Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .
Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.
Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.
Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.
Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.
Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.

The following data collection methods are commonly used in primary quantitative research :

Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.
Interviews : These are commonly telephonic or face-to-face.
Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .
Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:

Cross-tabulation —Uses a tabular format to draw inferences among collected data
MaxDiff analysis —Gauges the preferences of the respondents
TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business
Gap analysis —Identify gaps in attaining the desired results
SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization
Text analysis —Used for interpreting unstructured data

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.

The main sources of secondary data are:

The Internet
Government and non-government sources
Public libraries
Educational institutions
Commercial information sources such as newspapers, journals, radio, TV

When to use quantitative research 6

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:

recommend a final course of action
find whether a consensus exists regarding a particular subject
generalize results to a larger population
determine a cause-and-effect relationship between variables
describe characteristics of specific groups of people
test hypotheses and examine specific relationships
identify and establish size of market segments

A research case study to understand when to use quantitative research 7

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?

Method: The researchers obtained quantitative data from three sources:

Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.
Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.
Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.

Results of quantitative research : The following observations were made based on quantitative data analysis:

The move to the new design did not result in a significant change in the proportion of time spent on different activities.
Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.
A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.
Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.
Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.

Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.
This type of research uses numeric data so analysis is relatively easier .
In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.
The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.
Higher levels of control can be applied to the research so the chances of bias can be reduced.
Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research.

Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.
Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.
Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.
Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.
Large sample sizes are needed for more accurate and generalizable analysis .
Quantitative research cannot be used to address complex issues.

Frequently asked questions on  quantitative research  

Q:  What is the difference between quantitative research and qualitative research? 1

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.


Purpose and design
Research question
Sample size	Large	Small
Data
Data collection method	Experiments, controlled observations, questionnaires and surveys with a rating scale or close-ended questions. The methods can be experimental, quasi-experimental, descriptive, or correlational.	Semi-structured interviews/surveys with open-ended questions, document study/literature reviews, focus groups, case study research, ethnography
Data analysis

Q:  What is the difference between reliability and validity? 8,9  

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.

The following table gives the key differences between reliability and validity.


Importance	Refers to the consistency of a measure	Refers to the accuracy of a measure
Ease of achieving	Easier, yields results faster	Involves more analysis, more difficult to achieve
Assessment method	By examining the consistency of outcomes over time, between various observers, and within the test	By comparing the accuracy of the results with accepted theories and other measurements of the same idea
Relationship	Unreliable measurements typically cannot be valid	Valid measurements are also reliable
Types	Test-retest reliability, internal consistency, inter-rater reliability	Content validity, criterion validity, face validity, construct validity

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.

References

Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research
Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/
The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/
What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/
Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research
Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/
Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/
McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html
Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/
Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr

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What is Research? Definition, Types, Methods, and Examples

Language and Cultural Barriers in Research: How to Bridge the Gap

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

Description : To provide a detailed and accurate description of a particular phenomenon or population.
Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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v.21(6); 2018 Dec

Public and patient involvement in quantitative health research: A statistical perspective

Ailish hannigan.

1 Public and Patient Involvement Research Unit, Graduate Entry Medical School, University of Limerick, Limerick, Ireland

2 Health Research Institute, University of Limerick, Limerick, Ireland

The majority of studies included in recent reviews of impact for public and patient involvement (PPI) in health research had a qualitative design. PPI in solely quantitative designs is underexplored, particularly its impact on statistical analysis. Statisticians in practice have a long history of working in both consultative (indirect) and collaborative (direct) roles in health research, yet their perspective on PPI in quantitative health research has never been explicitly examined.

To explore the potential and challenges of PPI from a statistical perspective at distinct stages of quantitative research, that is sampling, measurement and statistical analysis, distinguishing between indirect and direct PPI.

Conclusions

Statistical analysis is underpinned by having a representative sample, and a collaborative or direct approach to PPI may help achieve that by supporting access to and increasing participation of under‐represented groups in the population. Acknowledging and valuing the role of lay knowledge of the context in statistical analysis and in deciding what variables to measure may support collective learning and advance scientific understanding, as evidenced by the use of participatory modelling in other disciplines. A recurring issue for quantitative researchers, which reflects quantitative sampling methods, is the selection and required number of PPI contributors, and this requires further methodological development. Direct approaches to PPI in quantitative health research may potentially increase its impact, but the facilitation and partnership skills required may require further training for all stakeholders, including statisticians.

1. BACKGROUND

Public and patient involvement (PPI) in health research has been defined as research being carried out “with” or “by” members of the public rather than “to,” “about” or “for” them. 1 PPI covers a diverse range of approaches from “one off” information gathering to sustained partnerships. Tritter's conceptual framework for PPI distinguished between indirect involvement where information is gathered from patients and the public, but they do not have the power to make final decisions and direct involvement where patients and the public take part in the decision‐making. 2

A bibliometric review of the literature reported strong growth in the number of published empirical health research studies with public involvement. 3 In a systematic review of the impact of PPI on health and social care research, Brett et al 4 reported positive impacts at all stages of research from planning and undertaking the study to analysis, dissemination and implementation. The design of the majority of empirical research studies included in both reviews was qualitative (70% of studies in Brett. et al 4 and 73% in Boote et al 3 ). More significant tensions have been reported in community‐academic partnerships that use quantitative methods rather than solely qualitative methods, for example tensions with the community about having and recruiting to a “no intervention” comparison group. 5 Particular challenges for PPI have been reported in the most structured and regulated of quantitative designs, that is a randomized controlled trial (RCT), where there is little opportunity for flexibility once the trial has started 6 and Boote et al 3 concluded that researchers may find it easier to involve the public in qualitative rather than quantitative research.

If the full potential of PPI for health research is to be realized, its potential and challenges in quantitative research require more exploration, particularly the features of quantitative research which are different from qualitative research, for example, sampling, measurement and statistical analysis. Statisticians in practice have a long history of working with a variety of stakeholders in health research and have examined the difference between an indirect or consulting role for the statistician and a more direct, collaborative role, 7 yet their perspective has never been explicitly explored in health research with PPI. The objective of this study therefore was to critically reflect on the potential and challenges for PPI at distinct stages of quantitative research from a statistical perspective, distinguishing between direct and indirect approaches to PPI. 2

2. SAMPLE SIZE AND SELECTION

Quantitative research usually aims to provide precise, unbiased estimates of parameters of interest for the entire population which requires a large, randomly selected sample. Brett et al 4 reported a positive impact of PPI on recruitment in studies, but the representativeness of the sample is as important in quantitative research as sample size. Studies have shown that even when accrual targets have been met, the sample may not be fully representative of the population of interest. In cancer clinical trials, for example, those with health insurance and from higher socio‐economic backgrounds can be over‐represented, while older patients, ethnic minorities and so‐called hard‐to‐reach groups (often with higher cancer mortality rates) are under‐represented. 8 This limits the ability to generalize the results of the trials to all those with cancer. There is evidence that a direct approach to PPI with sustained partnerships between community leaders, primary care providers and clinical trial researchers can be effective in increasing awareness and participation of under‐represented groups in cancer clinical trials 9 , 10 and therefore help to achieve the goal of a population‐representative sample.

Collecting representative health data for some groups in the population may only be possible with their involvement. Marin et al 11 reports on the challenges of identifying an appropriate sampling frame for a health survey of Aboriginal adults in Southern Australia. Access to information identifying Aboriginal dwellings was not publically available, making it difficult to randomly select participants for large population household surveys. Trying to overcome this challenge involved reaching agreement on the process of research for Aboriginal adults with their local communities. An 8‐month consultation process was undertaken with representatives from multiple locations including Aboriginal owned lands in one region; however, it was ultimately agreed that it was culturally inappropriate for the research team to survey this region. The study demonstrated the opportunities for PPI in quantitative research with a representative sample of randomly chosen Aboriginal adults (excluding those resident in one region) ultimately achieved but also the challenges for PPI. The direct approach to involvement in this study, after a lengthy consultation process, resulted in a decision not to carry out the planned sampling and data collection in one region with implications for generalization of results and overall sample size.

Of course, given the importance of representativeness in quantitative research, there may be particular challenges for statisticians and quantitative researchers in accepting the term patient or public representative with some suggesting PPI contributor as a more appropriate term. 6 PPI representative may suggest to a quantitative researcher that an individual patient or member of the public is typical of an often diverse population, yet there is evidence that the opportunities and capacity to be involved as PPI contributors vary by level of education, income, cognitive skills and cultural background. 12 Dudley et al carried out a qualitative study of the impact of PPI in RCTs with patients and researchers from a cohort of RCTs. 6 The types of roles of PPI contributors described by researchers involved in the RCTs were grouped into oversight, managerial and responsive roles. Responsive PPI was described as informal and impromptu with researchers approaching multiple “responsive” PPI contributors as difficulties arose, for example advising on patient information sheets and follow‐up of patients. It was reported that contributions from responsive roles may carry more weight with the researchers in RCTs because it allowed access to a more diverse range of contributors who researchers saw as more “representative” of the target population.

3. MEASUREMENT

Measurement of quantitative data involves decisions about what to measure, how to measure it and how often to measure it with these decisions typically made by the research team. Without the involvement of patients and the public, however, important outcomes for people living with a condition have been missed or overlooked, for example fatigue for people with rheumatoid arthritis 13 or the long‐term effects of therapy for children with asthma. 14

Core outcome sets (COS) are a minimum set of agreed important outcomes to be measured in research on particular illnesses, conditions or treatments to ensure important outcomes are consistently reported and allow the results from multiple studies to be easily combined and compared. Young reported on workshops to explore what principles, methods and strategies that COS developers may need to consider when seeking patient input into the development of a COS. 15 The importance of distinguishing between an indirect role for patients in COS development where patients respond to a consensus survey or a direct role where patients are partners in planning, running and disseminating a COS study was highlighted by delegates in the workshops. While all delegates agreed that participation by patients should be meaningful and on an equal footing with other stakeholders, there was considerable uncertainty on how to achieve this, for example how many patients are needed in the COS development process or what proportion of patients relative to other stakeholders should be included. This raises the issue again of the number and selection of PPI contributors for quantitative researchers, and it was concluded that methodological work was needed to understand the COS development process from the perspective of patients and how the process may be improved for them.

Important considerations in longitudinal research are the number and timing of repeated measurements. From a statistical perspective, measurements on the same subject at different times are almost always correlated, with measurements taken close together in time being more highly correlated than measurements taken far apart in time. Unequal spacing of observation times may be more computationally challenging in statistical analysis of repeated measurements and missing data within subjects over time can be particularly challenging depending on the amount, cause and pattern of missing data. 16 There are therefore important statistical considerations to be taken into account in the design of longitudinal studies but these have to be balanced with input from PPI contributors on appropriate timing and frequency of data collection for potential participants.

Lucas et al reported on how European birth cohorts are engaging and consulting with young birth cohort members. 17 Of the 84 individual cohorts identified, only eight had a mechanism for consulting with parents and three a mechanism for consulting with young people themselves (usually “one off” consultations). Very varied follow‐up rates were reported from 13% to 84% more than 10 years after enrolment for individual data rounds of the birth cohorts. 17 Being motivated to continue to participate may be influenced by whether a participant believes the study is interesting, important, or relevant to them. 18 One of the key strategies for retention in the Australian Aboriginal Birth Cohort study was partnerships with community members with local knowledge who were involved in all phases of the follow‐up. 19 Retention rates of 86% at 11‐year follow‐up and 72% at 18‐year follow‐up were reported which demonstrates the potential of a direct approach to PPI. Ethical approval for the study involved an Aboriginal Ethical Sub‐committee which had the power of veto and a staged consent was used where participants had the right to refuse individual procedures at each wave. As with all missing data, this has implications for the statistical analysis yet only 10% of participants in this study chose to opt out of different assessments at follow‐up.

3.1. Statistical analysis

A report on the impact of PPI found that it had a positive impact at all stages of qualitative research including data analysis but that there was little evidence of its impact on quantitative data analysis. 20 It was concluded this lack of evidence may reflect a lack of involvement rather than an evidence gap. Booth et al 3 also suggested that the public may be more comfortable with interpreting interview and focus group data compared with numeric data. Low levels of numerical and statistical literacy in the general population may contribute to this.

Statistical analysis involves describing the data using appropriate graphical and numerical summaries (descriptive statistics) and using more advanced statistical methods to draw inferences about the population using the data from a sample (statistical inference). Choosing appropriate methods for statistical inference, testing the underlying assumptions and checking the adequacy of the models produced requires advanced statistical training and implementing them typically involves the use of statistical software or programming. Statisticians bring this expertise to quantitative health research and while it is important that the chosen methods are adequately communicated to all stakeholders, replicating this type of expertise in PPI contributors seems like an inefficient use of resources for PPI.

Quantitative data are, however, “not just numbers, they are numbers with a context” 21 and most practising statisticians agree that knowledge of the context is needed to carry out even a purely technical role effectively. 22 While many associate statistical analysis with objectivity, in practice, statisticians routinely use “subjective” external information to guide, for example the decision on what is a meaningful effect size; whether an outlier is an error in data entry or represents an unusual but meaningful observation; and potential issues with measurement of variables and confounding. 23 Gelman and Hennin argue that we should move beyond the discussion of objectivity and subjectivity in statistics and “replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence.” 23 This debate within statistics is relevant for PPI where the perceived objectivity and standardization of statistical analysis can be used as a reason for lack of involvement.

External information and context are particularly important in statistical modelling where statisticians are often faced with many potential predictors of an outcome. The “best” way of selecting a multivariable model is still unresolved from a statistical perspective, and it is generally agreed that subject matter knowledge, when available, should guide model building. 24 Even when the potential predictors are known, understanding the causal pathways of exposure on an outcome is challenging where the effect of a variable on the outcome can be direct or indirect. Christiaens et al 25 used a causal diagram to visualize the relationship between pain acceptance and personal control of women in labour and the use of pain medication during labour. Their analysis accounted for the maternal care context of the country where the women were giving birth and other characteristics such as age of the woman and duration of labour. The choice of these characteristics was underpinned by a literature review but women who have given birth also have expert knowledge on why they use pain relief and how other variables such as their personal beliefs and social context might influence that decision. 26

Collaborative or participatory modelling is an approach to scientific modelling in areas such as natural resource management which involves all stakeholders in the model building process. Participants can suggest characteristics for inclusion in the model and how they may impact on the outcome. Causal diagrams are then used to create a shared view across stakeholders. 27 Rockman et al 28 concluded, in the context of marine policy, that “participatory modelling has the potential to facilitate and structure discussions between scientists and stakeholders about uncertainties and the quality of the knowledge base. It can also contribute to collective learning, increase legitimacy and advance scientific understanding.”

There is emerging evidence that the importance of PPI in the development and application of modelling in health research is being recognized. Van Voorn 29 discussed the benefits and risks of PPI in health economic modelling of cost‐effectiveness of new drugs and treatment strategies, with public and patients described as the missing stakeholder group in the modelling process. The potential benefits included the expertise that patients could bring to the process, a greater understanding and possible acceptance by patients of the results of the models and improved model validation. The risks included potential patient bias and the increased resources required for training. The number and selection of patients to contribute to the process was also discussed with a suggestion to include patients “who were able to take a neutral view” and “at least five patients that differ significantly in their background,” again highlighting the focus of quantitative researchers on bias and sample size. The role for this type of participatory modelling in informing debate on public health problems is increasingly being recognized, drawing on the experience of its use in other areas where optimal use of limited resources is required to address complex problems in society. 30

4. CONCLUSIONS

Statistical analysis of quantitative data is underpinned by having a representative sample, and there is evidence that a direct approach to PPI can help achieve that by supporting access to and increasing participation of under‐represented groups in the population. The direct approach has also demonstrated its potential in the retention of those recruited over time, thus reducing bias caused by missing data in longitudinal studies. At all stages of statistical analysis, a statistician continuously refers back to the context of the data collected. 22 Lay knowledge of PPI contributors has an important role in providing this context, and there is evidence from other disciplines of the benefits of including this knowledge in analysis to support collective learning and advance scientific understanding.

The direct approach to PPI where patients and the public have the power to make decisions also brings challenges and the statistician needs to be able to clearly communicate the impact of each decision on the scientific rigour and validity of sampling, measurement and analysis to all stakeholders. Decisions made on participation impact on generalizability. Participatory modelling requires facilitation and partnership skills which may require further training for all stakeholders, including statisticians.

The direct and indirect role for PPI contributors mirrors what happens for statisticians in practice. Statisticians can have a consultative role, that is answering a specific statistical question or a collaborative role where a statistician works with others as equal partners to create new knowledge, with professional organizations for statisticians providing guidance and mentorship on moving from consulting to collaboration to leadership roles. 7 , 31 Statisticians therefore bring very relevant experience and understanding for PPI contributors on the ladder of participation in health research. Further exploration is required on the impact of direct compared to indirect involvement in quantitative research, drawing on the evidence base for community‐based participatory research in quantitative designs 9 and the framework for participatory health research and epidemiology. 32 , 33

CONFLICT OF INTERESTS

No conflict of interests.

ACKNOWLEDGEMENTS

Prof. Anne MacFarlane, Public and Patient Involvement Research Unit, University of Limerick, for discussion of ideas and comments on drafts.

Hannigan A. Public and patient involvement in quantitative health research: A statistical perspective . Health Expect . 2018; 21 :939–943. 10.1111/hex.12800 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

NURP 410: Advanced Research Methods: Qualitative and Quantitative Articles

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Here you will find descriptions, criteria, and examples of qualitative and quantitative literature. Once you understand the differences between qualitative and quantitative research articles, see the Database Search Tips page in this guide for help with finding the articles you need.

Qualitative vs. Quantitative


	Research that seeks to provide understanding of human experience, perceptions, motivations, intentions, and behaviours based on description and observation and utilizing a naturalistic interpretative approach to a subject and its contextual setting.	Research based on traditional scientific methods, which generates numerical data and usually seeks to establish causal relationships between two or more variables, using statistical methods to test the strength and significance of the relationships.
	Observations in	Observations in
	A situation the researcher can observe	A
	Participants are comfortable with the researcher. They are honest and forthcoming, so that the researcher can make robust observations.	Others can repeat the findings of the study Variables are defined and correlations between them are studied
	If the researcher is biased, or is expecting to find certain results, it can be difficult to make completely objective observations	Researchers may be so careful about measurement methods that they do not make connections to a greater context
	Open-ended interviews Focus groups Observation Participant observation	Close-ended interviews Surveys Clinical Trials Laboratory Experiments

From A Dictionary of Nursing

About Qualitative Studies

Qualitative research includes all modes of inquiry that do not rely on numbers or statistical methods.

Naturalistic [qualitative] approaches comprise a wide array of research traditions, most often in the categories of ethnography, grounded theory, and phenomenology, but they also include ethnology, ethnomethodology, hermeneutics, oral and life histories, discourse analysis, case study methods, and critical, philosophical, and historical approaches to inquiry.

Learn more! Encyclopedia of Nursing Research

Finding Qualitative Articles

Finding qualitative studies can be slightly more challenging because this type of methodology is less commonly used in nursing research.

Try adding one of the following keywords to your search:

qualitative studies (also a subject term)
focus group
grounded theory
ethnographic
phenomenological

Look at the following qualitative article example for more search ideas:

Qualitative Research Example

Evaluating Qualitative Articles

Consider using one of the following when examining qualitative research:

Critical Review Form: Qualitative Studies
Critical Appraisal Checklist for an Article on Qualitative Research

About Quantitative Studies

Quantitative research consists of the collection, tabulation, summarization, and analysis of numerical data for the purpose of answering research questions or hypotheses.

Quantitative research uses statistical methodology at every stage in the research process. At the inception of a research project, when the research questions are formulated, thought must be given to how the research variables are to be quantified, defined, measured, and analyzed.

Learn more! Dictionary of Nursing Theory and Research

Finding Quantitative Articles

According to the Encyclopedia of Nursing Research, "The vast majority of all nursing studies can be classified as quantitative."

As a result, you'll likely find quantitative research articles when you search for your topic.

You can also try adding one of the following keywords to your search:

quantitative studies (also a subject term)
statistics OR statistical
clinical trial
randomized controlled trial

Look at the following quantitative article example for more search ideas.

Quantitative Research Example

Evaluating Quantitative Articles

Consider using one of the following when examining quantitative research:

Critical Review Form: Quantitative Studies
Critical Appraisal Checklist for an Article on Quantitative Research

Is it qualitative or quantitative research?

If you're still wondering if the article you have is qualitative or quantitative, below you'll find a table that highlights some of the key differences in qualitative versus quantitative research methods.

Image from the Oak Ridge Institute for Science and Education .

Mixed Methods Research

Mixed methods research combines quantitative and qualitative research methods in a single study. The use of mixed methods research is increasingly popular in nursing and health sciences research. This growth in popularity has been driven by the increasing complexity of research problems relating to human health and wellbeing.

Mixed Methods Research for Nursing and Health Sciences

<< Previous: Research and Review Articles
Next: Types of Review Articles >>
Last Updated: May 13, 2024 10:11 AM
URL: https://simmons.libguides.com/nurs410_online

Organizing Academic Research Papers: Quantitative Methods

Purpose of Guide
Design Flaws to Avoid
Glossary of Research Terms
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
Executive Summary
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tertiary Sources
What Is Scholarly vs. Popular?
Qualitative Methods
Quantitative Methods
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Annotated Bibliography
Dealing with Nervousness
Using Visual Aids
Grading Someone Else's Paper
How to Manage Group Projects
Multiple Book Review Essay
Reviewing Collected Essays
About Informed Consent
Writing Field Notes
Writing a Policy Memo
Writing a Research Proposal
Acknowledgements

Quantitative methods emphasise on objective measurements and numerical analysis of data collected through polls, questionnaires or surveys. Quantitative research focuses on gathering numerical data and generalizing it across groups of people.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010.

Characteristics of Quantitative Research

In quantitative research, your goal is to determine the relationship between one thing (an independent variable) and another (a dependent or outcome variable) in 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 experiment establishes causality.

Quantitative research deals in numbers, logic and the objective, focusing on logic, numbers, and unchanging static data and detailed, convergent reasoning rather than divergent reasoning.

Its main characteristics are :

The data is usually gathered using more 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.
Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
Researcher uses tools, such as questionnaires or equipment 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.
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 .

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 from the third person point of view and covers the following information:

Identify the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
Review 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 those gaps.
Describe 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 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 that the reader can make an informed assessment of the method being used to obtain results associated with the research problem.

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; Note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methodology.
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.

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 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.

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 highlight 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 you believe them to be 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 learned that you did not know before conducting your study.
Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations.
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.

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 Compostion 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 study, 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 researchers by keeping a 'distance' from participating subjects and employing subjects unknown to them.

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 Quantiative 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 preconceived hypothesis.
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Next: 7. The Results >>
Last Updated: Jul 18, 2023 11:58 AM
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Literacy and signing deaf students: a multi-national scoping review

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Hannah Dostal, Jessica Scott, Ana Gediel, Shirley Vilhalva, Literacy and signing deaf students: a multi-national scoping review, The Journal of Deaf Studies and Deaf Education , 2024;, enae023, https://doi.org/10.1093/jdsade/enae023

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Many literature reviews or other types of reviews (e.g., meta-analyses, scoping reviews) in deaf education research are focused upon primarily or exclusively research that is performed in U.S. contexts or English-speaking contexts only. However, research that is conducted in non-English-speaking, non-U.S. settings that may be more likely to be multilingual, has value for our understanding of how deaf students using multiple languages may develop literacy skills. The objective of this review was to explore the literature on literacy development with deaf learners conducted outside of English-speaking contexts that has been published in English, Portuguese, or Spanish. We identified 13 English-language articles, 9 Portuguese-language articles, and 0 Spanish articles that met inclusion criteria. From these articles, we glean important insights into the reading process, including the teaching of subskills of reading, writing instruction, early literacy experiences, and the potential relationship between signed languages and literacy. We also note the need for multiple, converging sources of evidence and the value of an asset-driven approach to understanding deaf learners.

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Volume 30, Number 7—July 2024

Evidence of Orientia spp. Endemicity among Severe Infectious Disease Cohorts, Uganda

Conclusions

We identified Orientia seroconversion among 4 participants hospitalized with severe infection in sub-Saharan Africa. We demonstrated that Orientia seropositivity was common among patients admitted for severe infection at 3 hospitals in Uganda. Our findings of highly prevalent seropositivity at 3 sites, identification of seroconversion, and molecular confirmation of a case with otherwise negative broad microbiologic testing support Orientia circulation and raise suspicion for infections extending to East Africa.

Prior clinical evidence of suspected scrub typhus in Africa relied on case reports of returning travelers with Orientia seroconversion ( 5 ). In addition to seroconversion identified in this study, seroconversions were observed in a pediatric cohort in Kenya (3.6%; n = 10) ( 10 ), and in 1 case among 49 abattoir workers in Djibouti ( 11 ). Our well-characterized multisite results supplement the limited literature suggesting Orientia spp. infections in sub-Saharan Africa.

In addition to prior suggestive evidence, our results build on a shift in understanding of worldwide Orientia spp. clinical infections. SFGR and TGR test results were negative in our cohorts, decreasing the likelihood of cross-reactivity. Despite IFA being the preferred method for rickettsial diagnosis, intrinsic interobserver variability limitations exist ( 12 ); we aimed to reduce those limitations through our reading approach and seroconversion criteria. Although we were able to confirm an infection by using real-time RT-PCR, sequence results were limited to a small fragment of the abundant 16S rRNA. The clinical relevance requires further confirmation with Orientia culture growth and extended genome sequencing. Because we relied on convalescent serology, we might have missed early fatal cases, which could skew our results toward less severe illness. Research efforts are needed to characterize the circulating species, incidence, pathogenic potential, and clinical relevance of Orientia infections in East Africa.

In summary, our findings suggest Orientia spp. circulation within the human–environment interface in Uganda and suggest novel Orientia infections within severe infection cohorts in Uganda. After excluding common causes of infections, our findings provide evidence of locally acquired Orientia infections among adults in sub-Saharan Africa.

Dr. Blair is an infectious diseases physician-scientist at Uniformed Services University, Bethesda, Maryland, USA. His research interests include molecular and imaging approaches to clinically detect emerging infectious diseases.

Acknowledgments

Acute Febrile Illness and Sepsis in Uganda Study Team members: Nehkonti Adams, Rodgers R. Ayebare, Helen Badu, Melissa Gregory, Francis Kakooza, Mubaraka Kayiira, Willy Kayondo, Stacy M. Kemigisha Hannah Kibuuka, Abraham Khandathil, Prossy Naluyima, Edgar C. Ndawula, David F. Olebo, Matthew Robinson, Abdullah Wailagala, and Peter Waitt.

This study was conducted in compliance with the Declaration of Helsinki and Good Clinical Practice Guidelines. All participants signed written informed consent prior to study procedures. The investigators have adhered to the policies for protection of human subjects as prescribed in 45 CFR 46. Parent acute febrile illness cohort: The study and informed consent process were reviewed and approved by the Joint Clinical Research Centre (JCRC) Research Ethics Committee (JC1518) and the Uganda National Council for Science and Technology (UNCST), HS 371ES, and Johns Hopkins University School of Medicine Internal Review Board (IRB no. 00176961). Parent sepsis cohort: This protocol and informed consent were approved by the US Army Medical Research and Development Command Institutional Review (approval no. M-10573) and Makerere University School of Public Health (IRB no. 490). Secondary use protocol: this laboratory work was reviewed and received an exempt determination by Uniformed Services University (IRB no. DBS.2020.174).

Pathogen testing was supported by cooperative agreement with the Naval Medical Logistics Command (NMLC; agreement no. N626451920001) and by the Congressionally Directed Medical Research Program (agreement no. W81XWH-19-2-0057).

The opinions and assertions expressed herein are those of the authors and do not reflect the official policy or position of the Uniformed Services University of the Health Sciences, US Department of Defense, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., or any other government or agency. Mention of trade names, commercial products, or organizations does not imply endorsement by the US government. Some of the authors of this work are employees of the US government. This work was prepared as part of their official duties. Title 17 U.S.C. x105 provides that “Copyright protection under this title is not available for any work of the United States government.” Title 17 U.S.C. x101 defines a US government work as a work prepared by a military service member or employee of the US government as part of that person’s official duties.

Y.C.M. receives research funding from Becton Dickinson, Quanterix, and Hologic, and receives funding support from miDiagnostics to Johns Hopkins University. Y.C.M. receives research funding from Becton Dickinson, Quanterix, and Hologic, and receives funding support from miDiagnostics to Johns Hopkins University. M.L. receives research funding support from Pfizer Inc. to Infectious Diseases Institute.

Bonell A , Lubell Y , Newton PN , Crump JA , Paris DH . Estimating the burden of scrub typhus: A systematic review. PLoS Negl Trop Dis . 2017 ; 11 : e0005838 . DOI PubMed Google Scholar
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Masakhwe C , Linsuwanon P , Kimita G , Mutai B , Leepitakrat S , Yalwala S , et al. Identification and characterization of Orientia chuto in trombiculid chigger mites collected from wild rodents in Kenya. J Clin Microbiol . 2018 ; 56 : e01124 – 18 . DOI PubMed Google Scholar
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Reller ME , Dumler JS . Optimization and evaluation of a multiplex quantitative PCR assay for detection of nucleic acids in human blood samples from patients with spotted fever rickettsiosis, typhus rickettsiosis, scrub typhus, monocytic ehrlichiosis, and granulocytic anaplasmosis. J Clin Microbiol . 2020 ; 58 : e01802 – 19 . DOI PubMed Google Scholar
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Phetsouvanh R , Thojaikong T , Phoumin P , Sibounheuang B , Phommasone K , Chansamouth V , et al. Inter- and intra-operator variability in the reading of indirect immunofluorescence assays for the serological diagnosis of scrub typhus and murine typhus. Am J Trop Med Hyg . 2013 ; 88 : 932 – 6 . DOI PubMed Google Scholar
Figure 1 . Alluvial diagram of serology from acute serum samples used in a study of Orientia genus endemicity among severe infectious disease cohorts, Uganda. The diagram represents Orientia spp.–positive immunofluorescent assay...
Figure 2 . Phylogenetic tree (left) and aligned sequences (right) of Orientia spp. and locally endemic Rickettsia spp. in a study of Orientia genus endemicity among severe infectious disease cohorts, Uganda. We...
Table 1 . Rickettsia IgG results from participants with Orientia spp. seroconversion in a study of Orientia genus endemicity among severe infectious disease cohorts, Uganda
Table 2 . Clinical characteristics of participants with Orientia spp. seroconversion in a study of Orientia genus endemicity among severe infectious disease cohorts, Uganda

Suggested citation for this article : Blair PW, Kobba K, Okello S, Alharthi S, Naluyima P, Clemens E, et al.; Acute Febrile Illness and Sepsis in Uganda study teams. Evidence of Orientia spp. endemicity among severe infectious disease cohorts, Uganda. Emerg Infect Dis. 2024 Jul [ date cited ]. https://doi.org/10.3201/eid3007.231040

DOI: 10.3201/eid3007.231040

1 Members of the Acute Febrile Illness and Sepsis in Uganda study teams are listed at the end of this article.

Table of Contents – Volume 30, Number 7—July 2024

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Lecture 41: Quantitative Research
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Qualitative Research Reporting Standards: How are qualitative articles different from quantitative?
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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles
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 ...
(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 ...
(PDF) An Overview of Quantitative Research Methods
quantitative research are: Describing a problem statement by presenting the need for an explanation of a variable's relationship. Offering literature, a significant function by answering research ...
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 ...
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 ...
Synthesising quantitative and qualitative evidence to inform guidelines
Introduction. Recognition has grown that while quantitative methods remain vital, they are usually insufficient to address complex health systems related research questions. 1 Quantitative methods rely on an ability to anticipate what must be measured in advance. Introducing change into a complex health system gives rise to emergent reactions, which cannot be fully predicted in advance.
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 ...
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.
(PDF) Quantitative Research: A Successful Investigation in Natural and
Quantitative research explains phenomena by collecting numerical unchanging d etailed data t hat. are analyzed using mathematically based methods, in particular statistics that pose questions of ...
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 and Qualitative Approaches to Generalization and
Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory. ... Journal Dev. Educ. Glob. Learn. 7, 6 ...
Writing Quantitative Research Studies
Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to ...
PDF Quantitative Research Methods
Quantitative . Research Methods. T. his chapter focuses on research designs commonly used when conducting . quantitative research studies. The general purpose of quantitative research is to investigate a particular topic or activity through the measurement of variables in quantifiable terms. Quantitative approaches to conducting educational ...
Quantitative Research
Qualitative research is a method to explore and understand the meaning of individuals or groups regarding social or human problems ( Creswell, 2003 ), it" engage in naturalistic inquiry, studying real-world settings inductively to generate rich narrative descriptions and construct case studies.". ( Patton, 2005 ).
Critical Quantitative Literacy: An Educational Foundation for Critical
Quantitative research in the social sciences is undergoing a change. After years of scholarship on the oppressive history of quantitative methods, quantitative scholars are grappling with the ways that our preferred methodology reinforces social injustices (Zuberi, 2001).Among others, the emerging fields of CritQuant (critical quantitative studies) and QuantCrit (quantitative critical race ...
Quantitative Data Analysis—In the Graduate Curriculum
Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships of a study's contextual situation. Learning data analysis is not learning how to use statistical tests to crunch ...
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 ...
Quantitative Research
Quantitative research is a method of inquiry that uses numbers and mathematical operations to explore questions about reality. ... your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and ...
Public and patient involvement in quantitative health research: A
Public and patient involvement (PPI) in health research has been defined as research being carried out "with" or "by" members of the public rather than "to," "about" or "for" them. 1 PPI covers a diverse range of approaches from "one off" information gathering to sustained partnerships. Tritter's conceptual framework for ...
Qualitative and Quantitative Articles
Quantitative. Definition. Research that seeks to provide understanding of human experience, perceptions, motivations, intentions, and behaviours based on description and observation and utilizing a naturalistic interpretative approach to a subject and its contextual setting. ... Quantitative research consists of the collection, tabulation ...
Organizing Academic Research Papers: Quantitative Methods
An Overview of Quantitative Research in Compostion 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.
The Methodological Underdog: A Review of Quantitative Research in the
Differences in methodological strengths and weaknesses between quantitative and qualitative research are discussed, followed by a data mining exercise on 1,089 journal articles published in Adult Education Quarterly, Studies in Continuing Education, and International Journal of Lifelong Learning. A categorization of quantitative adult education ...
Literacy and signing deaf students: a multi-national scoping review
Among the 13 reviewed English-language studies that met our inclusion criteria, 10 of the articles reported using quantitative research methods (see Table 1), and three reported using qualitative research methods (see Table 2). Of the 10 articles that followed a quantitative methodology, 1 published in 2014, 3 in 2015, and 2 in each of the ...
(PDF) Quantitative Research Method
The research method used is a quantitative research method with secondary data types in the form of data series with a period of 12 years (2011-2022). The data analysis method used is path ...
Early Release
Because no prior estimates of Orientia seroprevalence were available for Uganda, we used stringent criteria to define probable cases (Appendix Figure 1).To evaluate the specificity of IFA results, we used a subset of high titer samples to corroborate evidence of antibody binding by using a dot blot, Western blot, and Gilliam strain IFA (Appendix Methods, Figure 2).

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What is Quantitative Research? Definition, Methods, Types, and Examples

What is quantitative research ? 1,2

Quantitative research characteristics 4

Quantitative research methods 5

Primary quantitative research method:

Secondary quantitative research methods :

When to use quantitative research 6

A research case study to understand when to use quantitative research 7

Advantages of quantitative research 1,2

Disadvantages of quantitative research 1,2

Frequently asked questions on quantitative research

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Muhammad Hassan

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Conclusions

1. BACKGROUND

2. SAMPLE SIZE AND SELECTION

3. MEASUREMENT

3.1. Statistical analysis

Frequently asked questions on  quantitative research  