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Chapter 3. Introduction to Quantitative Research and Data

T he foundation of any e-book analysis framework rests on knowledge of the general e-book landscape and the existing information needs of a local user community. From this starting point, quantitative methods, such as cost analysis, can provide evidence for collection development initiatives and demonstrate how they align with patrons’ needs and the overarching goals of library administrators or funding agencies.

Essentially, “data stands in place of reality we wish to study. We cannot simply know a phenomenon, but we can attempt to capture it as data which represents the reality we have experienced . . . and are trying to explain.” 1 The data collected through quantitative investigations provides a baseline for future evaluation, evidence for when and how patrons make use of electronic collections, and promotes data-driven decisions throughout collection development departments. To get the most mileage out of the time and resources invested into quantitative investigations, it is essential to first understand what quantitative research is and what types of questions it can answer.

What Is Quantitative Research?

In the most basic terms, quantitative research methods are concerned with collecting and analyzing data that is structured and can be represented numerically. 2 One of the central goals is to build accurate and reliable measurements that allow for statistical analysis.

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

Quantitative research allows librarians to learn more about the demographics of a population, measure how many patrons use a service or product, examine attitudes and behaviors, document trends, or explain what is known anecdotally. Measurements like frequencies (i.e., counts), percentages, proportions, and relationships provide means to quantify and provide evidence for the variables listed above.

Findings generated from quantitative research uncover behaviors and trends. However, it is important to note that they do not provide insight into why people think, feel, or act in certain ways. In other words, quantitative research highlights trends across data sets or study groups, but not the motivation behind observed behaviors. To fill in these knowledge gaps, qualitative studies like focus groups, interviews, or open-ended survey questions are effective.

Whenever I sit down to a new quantitative research project and begin to think about my goals and objectives, I like to keep a small cheat sheet on my desk to remind me of the trends quantitative data can uncover and the stories that I can tell with study conclusions. This serves as one quick strategy that keeps my thoughts focused and prevents scope creep as I discuss project plans with various stakeholders.

Quantitative Research Cheat Sheet

Six key characteristics of quantitative research:

  • It deals with numbers to assess information.
  • Data can be measured and quantified.
  • It aims to be objective.
  • Findings can be evaluated using statistical analysis.
  • It represents complex problems through variables.
  • Results can be summarized, compared, or generalized.

Quantitative findings can provide evidence or answers in the following areas:

  • Demonstrate to what extent services and collection are used and accessed.
  • Back up claims about use and impact.
  • Provide evidence for how the budget is spent and whether adjustments should be made.
  • Demonstrate return on investment when presenting budget figures.
  • Inform decisions regarding packages and subscriptions that are or are not worth pursuing.
  • Demonstrate evidence for trends and prove or discount what is known anecdotally.
  • Provide a method to make information accessible to audiences.
  • Provide evidence of success and highlight areas where unmet information needs exist.

Main advantages of quantitative research:

  • Findings can be generalized to a specific population.
  • Data sets are large, and findings are representative of a population.
  • Documentation regarding the research framework and methods can be shared and replicated.
  • Standardized approaches permit the study to be replicated over time.

Main limitations of quantitative research:

  • Data does not provide evidence for why populations think, feel, or act in certain ways.
  • Specific demographic groups, particularly vulnerable or disadvantaged groups, may be difficult to reach.
  • Studies can be time consuming and require data collection over long periods of time. 3

Quantitative Research in Information Management Environments

In the current information landscape, a wealth of quantitative data sources is available to librarians. One of the challenges surrounding quantitative research in the information management profession is “how to make sense of all these data sources and use them in a way that supports effective decision-making.” 4

Most libraries pay for and receive materials through multiple routes. As a result, a quantitative research framework for e-book collections often consist of two central components: an examination of resource allocations and expenditures from funds, endowments, or gifts; and an examination of titles received through firm orders, subscriptions, packages, and large aggregated databases. 5 In many cases, examining funds and titles according to subject areas adds an extra layer of knowledge that can provide evidence for teaching, learning, or research activities in a specific field or justify requests for budget increases. 6

Many of the quantitative research projects that I have conducted over the past four years are in direct response to an inquiry from library administrators. In most cases, I have been asked to provide evidence for collection development activities that support expressed information needs, justify expenditures, or project annual increases in preparation for a new fiscal year. Study results are often expected to describe or weigh several courses of action in the short and long term. Essentially, my work is categorized into three basic concepts related to library management:

  • Distinguish between recurrent and capital expenditure and projects, and between past, present, and future states.
  • Accommodate priorities and determine how resources are spread across collections.
  • Indicate the ways of allocating resources at input, monitor performance, and assess performance at output. 7

To assist in my prep work for a quantitative research project, I put together a file of background information about my library system and local user community to ensure that the project supports institutional goals and aligns with the general direction of programs and services on campus. Below are seven categories of information that I have on file at all times:

  • the institutional identity of the library
  • the stakeholder groups to be served
  • collection resources
  • financial resources
  • library personnel
  • facilities and equipment
  • the various programs and services related to the quantitative investigation 8

Typically, I take a day or two at the beginning of each fiscal year to update this information and ensure that it accurately reflects the landscape of collections and services available at CUL. From this starting point, it is simple to look at new project descriptions and think about the data required to support high-level decisions regarding the allocation of resources, to assess the effectiveness of collections and services, or to measure the value and impact of collections.

A wealth of local and external data sources is available to librarians, and each one can be used to tell a story about collection size, value, and impact. All that is required is an understanding of what the data measures and how different sources can be combined to tell a story about a user community.

Definitions of Local and External Data Sources

The remaining sections of this issue of Library Technology Reports discuss how I use quantitative data, what evidence I have uncovered to support e-book collection decisions, and how I apply quantitative findings in practical library settings. For the purposes of these discussions, I will use the following terminology:

Bibliographic record: A library catalog record that represents a specific title or resource.

Catalog clickthroughs: Counts of patron use of the catalog to access electronic full texts.

Citation analysis: Measurement of the impact of an article based on the number of times it has been cited.

Consortia reports: Consolidated usage reports for consortia. Often used to view usage linked to each individual consortia member.

COUNTER (Counting Online Usage of Networked Electronic Resources): An international initiative to improve the reliability of online usage statistics by providing a Code of Practice that standardizes the collection of usage data. It works to ensure vendor usage data is credible and comparable.

Cost data: Factual information concerning the cost of library materials, annual budget allocations, and general acquisitions budget.

FTE (full-time equivalent): The number of full-time faculty and students working or studying at a specific institution.

IP (Internet Protocol) address: A numerical label usually assigned to a library router or firewall that provides access to a private network (e.g., school or library network).

Link resolver statistics: Information regarding the pathways users take to access electronic resources.

Overlap data: Measurement of the degree of duplication across a collection.

Publication analysis: Measurement of impact by counting the research output of an author. Metrics include the number of peer-reviewed articles, coauthor collaborations, publication patterns, and extent of interdisciplinary research.

Title lists: Lists of e-book titles available in subscriptions, databases, or packages. These lists are generated and maintained by vendors and publishers.

Turnaway statistics: The number of patrons denied access to a specific title.

Vendor use data: Electronic use statistics provided by vendors.

Indicators and Performance Measures That Support Quantitative Research

I regularly use several indicators and performance measures to analyze e-book collections. Local and external data sources (listed in the section above) inform these investigations and provide the necessary “ingredients” to conduct cost analysis, examine return on investment, or measure the value of e-book collections to the community at CUL. Below is a breakdown of how I classify data and relate it to different indicators. 9

Input Cost Measures

Data source: Cost data pulled from Voyager reports (or your institution’s ILS system).

In general, cost data demonstrates how funds are allocated across a budget. Analysis can identify areas where additional resources are required, monitor cost changes over time, and flag collection areas where funds can be pulled (e.g., overbudgeted funds, subject areas that no longer support the curriculum, etc.) and “reinvested” in the collection to support current information needs.

Each of the investigations described in the following chapter began with a review of cost data. I relied on a basic knowledge of how e-book acquisition budgets are distributed across subject areas or pooled to purchase interdisciplinary materials. Essentially, these investigations involved the identification of fund codes linked to subject areas, expenditures across set date ranges (e.g., calendar years, fiscal years, academic years), and bulk versus long-tail purchases.

Tip: When working with cost data and examining input cost measures, I have found it helpful to categorize data by fund type. E-book collections at CUL are often built with general income (GI) funds, endowments, and gifts. Policies and procedures regarding how funds can be transferred and what materials can be purchased impact how resources are allocated to build e-book collections. Before beginning a cost analysis project at your institution, it may be helpful to review the policies in place and determine how they relate to overarching institutional goals and collection priorities.

Collection Output Measures

Data sources: Cost data, title lists, overlap data, bibliographic records (particularly subject headings).

Collection output measures are related to the quantity and quality of output. Examples include the number of e-book titles included in a subscription or package deal acquired by a library, the number of e-book records acquired over a given period of time, the number of publishers and unique subject areas represented in an e-book collection, the currency of information (e.g., publication year), and the percentage of title overlap, or duplication, within a collection.

At this stage in my cost analysis projects, it is often necessary to combine data to create a snapshot of how funds flow in and out of subject areas to acquire research and teaching materials. For example, many of our large e-book packages are interdisciplinary. By pulling cost data, I can determine how the total cost was split across subject divisions based on fund code counts. Then, I break title lists apart by subject to determine what percentage of total content relates to each library division. By comparing the cost breakdown and title list breakdown, it is possible to determine what percentage of total content each library division receives and if it is on par with the division’s financial contribution.

Effectiveness Measures and Indicators

Data sources: Cost data, title lists, COUNTER reports, vendor reports, consortia reports, resolver statistics, turnaway statistics, Google Analytics.

Examining input and output measures is an effective way of determining how budgets are allocated and the quantity and quality of materials available to patrons. To develop a quantitative baseline for the general value of e-book collections, measures like rate of use, cost per use, and turnaway rates can be very effective.

Again, this form of analysis relies on data from multiple sources. The ability to combine cost data, title lists, and COUNTER data (or vendor data) has yielded actionable results at my library. For instance, I combine data from these three sources to measure the value of databases. By pulling cost data covering three fiscal years and matching title lists against COUNTER reports, I have been able to examine trends in annual increase rates, examine overlap between subscriptions in the same subject area, and calculate cost per use to determine what percentage of the user community makes use of subscriptions.

Finally, by looking at turnaway statistics (also found in COUNTER data), it is possible to determine if sufficient access is provided to users. For instance, I look at turnaway statistics to evaluate if e-books listed on course reading lists provide sufficient access to a class of students over a semester. In cases where access is limited to a single user, I may look at the budget to find areas where funds can be shifted to purchase simultaneous usage instead.

Together, the data sets mentioned above provide evidence for how funds are invested, if they are invested in materials that are heavily used by patrons, and if access models are suited to the needs of the local user community.

In some cases, particularly when dealing with foreign language materials, I have encountered challenges because COUNTER data is not provided, and in some cases, it is difficult to obtain vendor reports as well. In the absence of usage data, I have experimented with link resolver statistics to determine what information they provide about user activities and the value of e-book materials.

Link resolver statistics provide information about the pathways users take to access electronic resources. 10 Resolver statistics show that a patron made a “request” via the link resolver and started the process of trying to view a full text. If the patron successfully accesses the full text, this is counted as a “clickthrough.”

It is important to note that link resolver statistics and usage statistics (like COUNTER) are not comparable because they measure different activities. Link resolvers measure attempts to connect while usage data measures usage activity. However, comparing sets of link resolver statistics against each other may provide insight into which resources patrons attempt to access most frequently. This can provide a ballpark idea of resource value in cases where usage statistics are not available.

Domain Measures

Data sources: FTE (full-time equivalent), IP address, demographic information.

Domain measures relate to the user community served by a library. They include total population, demographic information, attributes (e.g., undergraduate level, graduate level), and information needs.

In my work, domain measures impact subscription or package costs because campus-wide access is often priced according to FTE. Due to the size of CUL’s student body, access to essential collections can become extremely expensive and fall outside of the budget range. When this occurs, examining patron access by IP address has opened the door to negotiation, particularly when dealing with content that is discipline-specific. For instance, when negotiating subscription prices for science materials, IP data provided evidence that usage is concentrated at the library router located in the Science and Engineering Library. This allowed science selectors to negotiate pricing models based around the FTE of natural science programs as opposed to the campus community as a whole.

Cost-Effectiveness Indicators

Data sources: COUNTER reports, vendor reports, turnaway statistics, citation analysis, publication analysis.

Cost-effectiveness indicators are related to measures like cost per use and ultimately examine the general return on investment. They evaluate the financial resources invested in a product and determine if the investment brings added value to the existing collection.

In my work, I often combine cost data with usage data to calculate cost per use and also capture usage trends spanning at least three calendar years. The results provide a benchmark regarding whether the financial investment in the product is equivalent to its general “demand” within the user community. A recent project with colleagues at the science and medical science libraries has examined how to use citation and publication data to determine general impact of electronic resources.

Challenges Presented by Quantitative Research

One of the challenges surrounding quantitative research in library environments is a lack of standardization across data sets, particularly vendor reports. The general situation has improved in recent years due to widespread compliance with the COUNTER Code of Practice, but there is still work to be done. It is difficult to interpret the meaning of vendor usage data that is still not COUNTER-compliant because clear definitions of use do not exist. This can create significant roadblocks when running quantitative projects that examine multiple e-book collections to get a sense of comparative value.

Also, usage data is generated outside of libraries by publishers or aggregators and vendors. Factors like turnover, company mergers, or password changes result in significant time lags between when usage statistics are generated and when libraries receive them. Also, some vendors pull down usage statistics after a period of months. In most cases, librarians need statistics captured over two or three years to meet reporting requirements, and data dating back this far can be difficult to obtain. Finally, annual usage statistics are provided according to calendar year. However, librarians look at usage by fiscal year and academic year as well. In many cases, this means that multiple usage reports have to be stitched together in order to capture the appropriate timeframe for reporting purposes. This process is labor intensive and takes a considerable amount of time to complete.

These challenges emphasize an ongoing need to build positive working relationships with publishers, aggregators, and vendors to discuss challenges and develop solutions that benefit all stakeholders. It is important to note that libraries have valuable information that is not available to content providers, namely how e-books are discovered and used. Strong relationships allow for the transparent exchange of information between all parties, which ultimately benefits patrons by providing a seamless e-book experience.

Designing a Quantitative Research Framework

As mentioned earlier in this chapter, data stands in place of a reality we wish to study, quantify, and explain. In order to prevent scope creep and pull together bodies of data that add value to local work environments, it is essential to begin any quantitative research project with a set of clearly defined objectives, a strong understanding of the stakeholder group or audience, and knowledge of local information needs. These bits of information serve as markers to measure progress and ensure the project stays on track.

It is tempting to dive straight into a project and investigate if anecdotal information or assumptions are correct, but time spent developing a project outline is never wasted. The development of a successful plan requires “a clear idea of what it is to be achieved among the stakeholders. Clearly articulated objectives are the engine that drives the assessment process. This is one of the most difficult but most rewarding stages of the assessment process.” 11 Creating a roadmap for research projects can save countless hours down the line and ensures the correct quantitative method is selected. The plan also provides focus when the analysis phase of a project begins. Keep in mind that the data set you end up working with will be large; approaching it with stated goals and objectives saves significant amounts of time, which is especially important when working under a tight deadline!

Below is a checklist that I use at the beginning of any research project. It is based on recommendations made by Bakkalbasi, Sundre, and Fulcher. 12

While goals and objectives are closely related, they are not the same. Project goals should state exactly what you hope to learn or demonstrate through your research. Objectives state what you will assess or measure in order to achieve your overarching project goal.

Example of a project goal:

Example of project objectives:

  • To learn what activities local patrons engage in when using library facilities.
  • Consider how results may support improvement of collection development initiatives or lead to evaluation of existing workflows, policies, and procedures.
  • What questions and/or evidence are required by stakeholders?
  • What information do stakeholders require to make decisions?
  • How will results support the improvement of collection development initiatives?
  • How will results be made accessible to stakeholders?
  • Are the results intended for internal use, or will they be shared with the professional community?
  • Will findings be used to support grant or funding applications?
  • Is there a stated project deadline? What methods or resources will allow you to collect data, conduct analysis, and provide findings within the stated timeframe?
  • Does the project coincide with other activities that may require your attention (e.g., fiscal year, subscription renewal period)?
  • Are there colleagues at the library who may be able to provide assistance given the timeline of the project?
  • What data collected through the study cannot be shared with external stakeholders (e.g., cost data, FOIP compliance, etc.)?
  • Are there any permissions required before study results can be disseminated to external stakeholders?
  • Is clearance required to collect data from a user community?
  • What data sources are most valued and meaningful to your library?
  • What data sources will allow results to be applied at your library?
  • What data collection methods will be most effective?
  • What data collection methods will provide valid and reliable results?
  • Are there parameters such as specific fiscal years, calendar years, or academic years that you are required to report on?
  • How will data be summarized and described?
  • What features of the data set are most relevant to project objectives and goals?
  • What are the relationships between different data sets?
  • How is data evaluated?
  • How is data interpreted into meaningful results and conclusions?
  • What are the recommendations for action or improvements?
  • How will findings be communicated to stakeholders?

The data sets collected through quantitative methods are large and can easily be examined from a variety of perspectives. As the project develops, mentally frame emerging trends into a story that can be shared with stakeholders. This process determines how results will ultimately be applied to collection development initiatives. Background knowledge of the local patron community and institutional goals serves as a compass; use it to shape results that bring value to your library or the greater professional community.

From my experience, each quantitative project that I work on allows me to expand my skill sets and understand how I can structure my daily activities to support overarching institutional goals. During many projects, I have encountered unexpected challenges or had to improvise when quantitative methods did not yield expected results (e.g., low survey response rates). However, each challenge equipped me to take on larger projects, better understand how our budget is structured, or build stronger relationships with patrons and colleagues.

One skill that has been invaluable to my work is the ability to develop a quantitative research plan. I hope that by sharing this structure, along with performance measures and data sources that I use, readers have a behind-the-scenes view of my process and all of the moving parts that I work with to conduct e-book collection analysis. And of course, now to the fun part! It is time to get down to the nitty-gritty and demonstrate how I conduct analysis to inform budget decisions and collection development activities at CUL.

  • Bob Matthews and Liz Ross, Research Methods: A Practical Guide for the Social Sciences (Harlow, UK: Pearson Education, 2010), 45.
  • Ibid., 465.
  • Based on information provided by Stephen A. Roberts, Financial and Cost Management for Libraries and Information Management Services (London: Bowker-Saur, 1998), 140–41.
  • Darby Orcutt, Library Data: Empowering Practice and Persuasion (Santa Barbara, CA: Libraries Unlimited, 2009), 106.
  • Northwestern University Libraries, “DataBank: How to Interpret Your Data: Financial Support,” LibGuide, last updated December 8 2015, http://libguides.northwestern.edu/c.php?g=115065&p=748741 .
  • Roberts, Financial and Cost Management , 132.
  • For further information regarding indicators and performance measures, please see Roberts, Financial and Cost Management , 140–41.
  • Orcutt, Library Data , 107.
  • Nisa Bakkalbasi, Donna Sundre, and Kenton Fulcher, “Assessing Assessment: A Framework to Evaluate Assessment Practices and Progress for Library Collections and Services,” in Proceedings of the 2012 Library Assessment Conference: Building Effective, Sustainable, Practical Assessment, October 29–31, 2012, Charlottesville, VA , ed. Steve Hiller, Martha Kyrillidou, Angela Pappalardo, Jim Self, and Amy Yeager (Washington, DC: Association of Research Libraries, 2013), 538-545.
  • Based on information provided by Matthews and Ross, Research Methods , 345.
  • There are currently no refbacks.

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Handbook of Research Methods in Health Social Sciences pp 27–49 Cite as

Quantitative Research

  • Leigh A. Wilson 2 , 3  
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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 characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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  • Methodology
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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

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Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

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  • What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

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

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

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

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

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

Table of contents

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

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

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

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

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

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

Prevent plagiarism. Run a free check.

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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  • 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.

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Table of contents

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.

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).

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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 .

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 visualise your data and check for any trends or outliers.

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

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 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 .

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

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.

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.

Cite this Scribbr article

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Bhandari, P. (2022, October 10). What Is Quantitative Research? | Definition & Methods. Scribbr. Retrieved 22 February 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-quantitative-research/

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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 ]

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Graduate students need more quantitative methods support

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Graduate students in psychology need hands-on support to conduct research using quantitative techniques that exceed their curricular training. If supervisors are not willing or able to provide this support, student-led projects must be redesigned to leverage basic statistical skills learned in the classroom.

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